Monthly Archives: December 2025

Obesity, Socioeconomic Transitions, and the Evolving Social Gradient of Non-Communicable Diseases in Low- and Middle-Income Countries

DOI: 10.31038/AWHC.2025835

 

Non-communicable diseases (NCDs) are now the leading cause of premature mortality in low- and middle-income countries (LMICs), accounting for more than 80% of early NCD deaths worldwide. As LMICs undergo rapid demographic, nutritional, and epidemiological transitions, obesity has emerged as a central driver of cardiometabolic risk—particularly among women. Although NCDs have long been characterised as “diseases of affiuence” in developing country settings, accumulating evidence suggests that this social gradient is weakening. New longitudinal evidence from India provides timely insights into how rising obesity may be reshaping the distribution of NCD risk across socioeconomic groups [1].

Using two waves of the nationally representative panel data from the India Human Development Survey (IHDS) 2004-05 & 2011- 12, which followed more than 24,000 women of reproductive age over seven years, Barik(2025) assessed the risk of developing non- communicable disease (NCDs) like hypertension, diabetes, or heart disease among the overweight/obese women. The study demonstrates that overweight and obesity significantly increase the likelihood of subsequent NCD onset, independent of age, education, caste, and household economic status. Crucially, the analysis shows that the rich–poor gap in NCD risk narrows sharply once women become overweight or obese, indicating that excess body weight acts as a powerful leveller of disease risk across socio-economic strata.

This finding resonates with emerging evidence from other LMICs. In Bangladesh, analyses of Demographic and Health Survey data have documented rapid increases in overweight and obesity among urban women across both wealthy and poorer households. While NCD prevalence remains higher among richer women, obesity- related metabolic risk factors—such as hypertension and raised blood glucose—are increasingly observed among women from lower wealth quintiles, particularly in urban settings [2]. Studies from Bangladesh suggest that once high BMI is established, socioeconomic advantage offers limited protection against cardiometabolic risk, mirroring the convergence observed in India.

Similar patterns are evident across sub-Saharan Africa, where obesity prevalence—especially among women—has risen sharply over the past two decades. In countries such as Ghana, South Africa, and Kenya, obesity is no longer confined to affiuent urban elites. Nationally representative surveys show that overweight and obese women from poorer households face risks of hypertension and diabetes comparable to those of wealthier women once BMI is accounted for. In several African settings, the association between socioeconomic status and hypertension weakens substantially after adjusting for adiposity, indicating that obesity increasingly mediates NCD risk across income groups [3].

Together, these findings point to a broader global shift: obesity is progressively eroding traditional socioeconomic gradients in NCDs across LMICs. While absolute disease burden often remains higher among wealthier populations—owing to better diagnosis and longer survival—the marginal effect of obesity on NCD risk appears strikingly similar across economic strata. This has profound implications for public health policy, which in many LMICs continues to implicitly prioritise affiuent or urban populations in NCD prevention strategies.

The Indian evidence is particularly valuable because of its longitudinal design, which overcomes a major limitation of much LMIC research that relies on cross-sectional data. By tracking changes in BMI over time, the study shows that women who remain chronically overweight have the highest risk of developing NCDs, while those who return to normal BMI experience a significantly lower risk. This dynamic perspective reinforces the importance of mid-life and reproductive-age interventions, a finding that aligns with cohort evidence from South Asia and Africa showing that weight gain during early adulthood strongly predicts later cardiometabolic disease.

The policy relevance of these results extends beyond India. Across LMICs, reproductive years represent a critical but underutilised window for obesity and NCD prevention among women. Pregnancy- related weight gain, declining physical activity, and changing diets contribute to sustained overweight, yet health systems often disengage once maternal and child health goals are met. Integrating weight management, nutrition counselling, and routine screening for hypertension and diabetes into maternal and primary care services could yield long-term benefits in India, Bangladesh, and sub-Saharan Africa alike.

At the same time, the study underscores persistent challenges in NCD surveillance. Reliance on self-reported diagnoses likely underestimates disease prevalence among poorer women with limited access to screening—a concern echoed in African and South Asian contexts. If underdiagnoses disproportionately affects disadvantaged groups, the observed convergence in NCD risk across wealth strata may in fact understate the true extent of inequality erosion driven by obesity.

In conclusion, longitudinal evidence from India adds to a growing body of global research demonstrating that obesity is transforming the social patterning of NCDs in LMICs. The convergence of disease risk across economic groups once overweight is established challenges outdated notions of NCDs as diseases of prosperity. Effective prevention will require population-wide strategies that prioritise healthy weight maintenance across the life course, rather than narrowly targeting the affiuent. As countries across South Asia and sub-Saharan Africa confront parallel transitions, addressing obesity among women must become central to equitable global NCD policy.

References

  1. Barik D (2025) Risk of Developing NCDs in Later Life among the Overweight and Obese Women in India: Insights from a Nationally Representative Longitudinal Margin: The Journal of Applied Economic Research 19(2): 1-18. https://doi. org/10.1177/00252921251394148
  2. Das S, Debnath M, Sarkar S, Rumana AS (2022) Association of overweight and obesity with hypertension, diabetes and comorbidity among adults in Bangladesh: evidence from nationwide Demographic and Health Survey 2017-2018 data. BMJ Open 12(7): e052822. [crossref]
  3. Yaya S, Ekholuenetale M, Bishwajit G (2018) Differentials in prevalence and correlates of metabolic risk factors of non-communicable diseases among women in sub-Saharan Africa: evidence from 33 BMC Public Health 18(1): 1168. [crossref]

Work Challenges and Adjustment among Novice Firefighters in Taiwan

DOI: 10.31038/PSYJ.2025764

Abstract

This study explored the work-related challenges and adjustment experiences of novice firefighters in Taiwan. Using a qualitative thematic analysis design, we conducted semi-structured, in-depth interviews with four novice firefighters (one woman and three men) aged 27–33 years (M = 29.75) who had 2–3.5 years of work experience. The findings revealed several salient challenge-related themes, including perceived unfairness and oppression in the workplace, ethical dilemmas in practice, organizational and interpersonal stressors, stress associated with insufficient work resources, and pressure arising from public expectations and commentary. Regarding adjustment and coping, participants expressed substantial concerns about using formal mental health resources and therefore relied primarily on personal resources to manage stress. Implications for practice and directions for future research are discussed.

Keywords

Novice firefighters, Work challenges, Work adjustment, Resilience

Work Challenges and Adjustment among Novice Firefighters in Taiwan

In recent years, global climate anomalies have contributed to increasingly complex and unpredictable disasters. In Taiwan, economic development and population concentration—together with larger, taller, and more diverse building structures—have expanded firefighters’ operational demands and increased exposure to hazardous labor conditions [1]. To address workforce shortages, the National Fire Agency launched the “Firefighting Workforce Enhancement Initiative” in 2018 and added 3,100 firefighting personnel [2]. Under these conditions, the training and development of novice firefighters becomes especially critical. Without resilience- building opportunities and a health-supportive work environment, novice personnel may experience excessive stress and maladaptation that ultimately contribute to early exit from the profession. Despite the practical significance of this issue, research on novice firefighters’ work challenges and psychological adjustment in Taiwan remains limited. Novice firefighters may face structural, systemic, and hierarchical constraints; high job demands may intensify perceived stress, while access to organizational resources may be restricted. Over time, an imbalance between excessive demands and insufficient resources may contribute to anxiety, depression, and stress-related symptoms, increasing the risk of physical and psychological burnout. Accordingly, this study aims to describe (a) the work-related challenges novice firefighters in Taiwan encounter and (b) their experiences of adjustment and coping.

Firefighters and Their Work

Firefighters’ work encompasses a broad range of duties. In Taiwan, novice firefighters commonly perform two major categories of frontline tasks: (1) disaster response and life-saving rescue, including fire suppression, rescue operations across disaster types, and the management and deployment of rescue resources; and (2) emergency medical services (EMS), including the planning, supervision, and delivery of prehospital emergency care, as well as communication and coordination with medical institutions. In this study, we use the term fire and emergency service work experience to denote frontline operational experience spanning disaster response/rescue, life-saving or technical rescue, and EMS. Firefighting is widely recognized as a high-risk occupation characterized by substantial physical demands, heavy psychological load, and high task complexity. As disaster responders, firefighters are required to enter and operate at hazardous scenes; even under life-threatening and terrifying conditions, professional responsibility typically precludes avoiding the scene or refusing assigned duties. Firefighting work is dangerous, unpredictable, and time-sensitive. Prolonged exposure to heavy workloads, shift-related strain, and traumatic events places firefighters at elevated risk for exhaustion and compromised physical and mental health [3]. Firefighters often must remain in a continuous “combat- ready” state, making firefighting a particularly high-stress occupation [4]. Long-term on-call shift systems can disrupt sleep and interfere with normal physical and psychological recovery processes [5]. Kuo [6] further noted that, driven by a sense of mission and responsibility, firefighters must meet public expectations; at disaster scenes, they may suppress physical discomfort and psychological distress in order to execute tasks calmly and courageously. When such reactions accumulate without adequate processing, firefighters may develop PTSD-related symptoms (e.g., hypervigilance, insomnia, exaggerated startle responses, irritability, and concentration difficulties). If psychological trauma is not addressed in a timely manner, symptoms may intensify over time.

Operational conditions also constrain emotional processing. During missions, firefighters often lack the time and space to process emotions and may temporarily set aside personal feelings until tasks are completed, at which point they begin to experience and interpret the impact of the event. Frequent exposure to disaster incidents may require firefighters to re-enter distressing rescue contexts before psychological equilibrium is restored, thereby accumulating negative emotions and traumatic experiences [7]. In addition, firefighters face public pressure through media coverage and public praise or criticism, which may further intensify occupational stress [8].

Novice Firefighters

Novice firefighters are commonly defined as those with 2–4 years of service [9]. They are expected to learn and perform core duties such as fire suppression and emergency medical response while adapting to the realities of frontline work. In Taiwan, however, empirical research on novice firefighters remains limited. Existing literature has primarily examined safety and training effectiveness, the influence of overall health on performance, the effects of stress, and the development of professional knowledge [10]. Because novice firefighters often serve as frontline personnel, they may experience substantial pressure associated with disaster response. Prior evidence suggests that shorter tenure is associated with stronger stress responses among firefighters [11,12]. Novice firefighters may also report higher stress than senior firefighters [13]. Moreover, they require an adjustment period to manage challenges such as the gap between training and real-world practice and the complexity of diverse duty types [14]. At the same time, they must learn to navigate bureaucratic features of the firefighting system and pressures related to obedience and hierarchical authority [15].

Job Demands and Resource Provision

Work environments shape employees’ health, well-being, and performance [16]. The Job Demands–Resources Model (JDRM) provides a useful framework for understanding how occupational conditions influence stress and adjustment [17]. Job demands refer to aspects of a job that require sustained physical and/or psychological effort and include negative factors at physical, psychological, social, or organizational levels; such demands entail specific physiological and/ or psychological costs. Examples include workload, time pressure, emotional load, and role conflict. Although moderate job demands may stimulate motivation and alertness, chronically excessive demands can initiate a health-impairment process that depletes resources and increases burnout risk, physical and mental health problems, and performance decrements.

In contrast, job resources refer to positive job characteristics at physical, psychological, social, or organizational levels. Job resources support goal attainment, reduce job demands and their associated costs, and promote personal growth, learning, and development. Resources may operate at the task level (e.g., autonomy), organizational level (e.g., career development opportunities), interpersonal level (e.g., supervisor and coworker support), and job design level (e.g., performance feedback). Adequate resources activate a motivational process, enhance engagement, and are associated with positive outcomes such as safety behaviors, job performance, and organizational commitment.

Safety-Oriented JDRM

Building on the JDRM, scholars have proposed a safety-oriented JDRM that foregrounds safety-relevant job demands and safety- relevant resources [18]. For firefighting, this model highlights four demand dimensions: (1) Workload, referring to the speed and volume of task completion under time constraints and pressure; high time pressure has been linked to increased firefighter fatality rates. (2) Physical demands, including high-intensity biomechanical activities such as running, carrying, and ladder climbing; excessive exertion contributes to fatigue that can undermine reaction time and adherence to safety procedures. (3) Emotional demands, referring to the psychological effort required to manage emotions and cope with affective reactions (e.g., fear, suppression); sustained emotional labor may deplete energy reserves and contribute to emotional exhaustion. (4) Complexity, involving cognitive demands associated with handling multiple difficult tasks, maintaining situational awareness, making rapid decisions, and conducting risk assessments—particularly in life- threatening contexts.

Structural Insufficiency of Resources

Fire service organizations may also face structural shortages of resources. Insufficient resources have been identified as a key factor undermining firefighters’ psychological health [19]. In addition, interpersonal conflict, discrimination, harassment, and negative perceptions of organizational justice can weaken resilience and are associated with anxiety, depression, and physical illness [20]. In particular, perceptions of organizational and systemic unfairness increase mental health risks and are closely tied to hierarchical stress. In such contexts, employees may engage in proactive behaviors that reshape their work conditions. These include job crafting (actively modifying one’s job or available resources) and self- undermining (stress-related mistakes that inadvertently increase one’s own job demands). Novice personnel may adjust their work behaviors in response to hierarchical pressure, or they may reduce engagement due to fear of making mistakes. When social support is limited, interpersonal resources become scarce and fragile, and this vulnerability is closely linked to trauma-related stress symptoms, depression, and emotional exhaustion [21]. Moreover, negative views of organizational systems (e.g., unfair policies or promotion practices) may further elevate mental health risks. Interpersonal conflict can also obstruct help-seeking and support access, producing a “double hit” of resource loss. Under conditions of high hindrance, high demands, and systemic resource deficiencies, job demands may substantially exceed resource provision, creating a structural imbalance that undermines job performance directly or indirectly through psychological distress.

The Buffering Effects of Resources

The JDRM also emphasizes personal resources, defined as individuals’ beliefs about their ability to control and influence their environment. Job resources can buffer strain associated with job demands and mitigate the negative effects of work stressors. This buffering function suggests that resources may be especially protective under high-demand conditions, supporting both well-being and work performance. Common personal resources include self-efficacy, optimism, and resilience. Such resources predict work engagement by strengthening confidence in one’s capacity to manage challenges and sustain involvement. Personal resources may also operate similarly to job resources by buffering the adverse effects of job demands on stress. In sum, when employees face high demands (e.g., heavy workload or emotional load), greater resources (e.g., autonomy, social support, performance feedback, and professional development opportunities) are generally associated with lower burnout and emotional exhaustion.

Organizational Culture

Organizational culture is another critical influence on firefighters’ well-being and help-seeking. A traditional culture emphasizing “toughness, silence, and self-sacrifice,” along with emotional taboo, may inhibit seeking psychological support [22]. In contrast, framing professional psychological help-seeking as a constructive form of self- care may facilitate a healthier organizational climate [23].

Resource adequacy in the fire service also matters. The quality, quantity, and suitability of personal protective equipment are central to firefighters’ sense of safety. Station facilities, vehicles, staffing, and occupational health and safety measures represent foundational resources that directly shape the safety and comfort of the work environment. Supervisor support is likewise influential: when perceived support from managers, family, or friends decreases, depressive symptoms tend to increase. Supervisors’ support, trust, and care may strengthen subordinates’ resilience. Leadership practices— such as reward and punishment systems, internal management, duty scheduling, performance evaluation, discipline, and performance demands—may function either as resources or as stressors, depending on how they are enacted and experienced.

Firefighters’ Adjustment and Coping

Carver et al. [24] developed a coping strategies inventory that categorizes coping behaviors into problem-focused coping, cognitive restructuring coping, emotional support coping, and avoidance coping. Chung and Chiou [25] suggested that firefighters who predominantly use problem-focused coping tend to recover gradually, whereas reliance on avoidance coping may increase risk for psychological disorders. Prior research indicates that firefighters often rely on individual coping strategies to manage work stress. Lee [26] and Chen [27] found that after exposure to death or major critical incidents, firefighters often cope privately; when adjustment is insufficient or support is unavailable, some may request transfer or resign. Some firefighters manage occupational stress through a passive stance (e.g., “you get used to it over time”) [28], whereas others use strategies such as active reflection for improvement or emotional detachment [29]. Firefighters may also cope by maintaining task focus and calm during operations [30] or by adopting passive coping patterns, allowing emotions to fade over time through emotional numbing, suppression, or self-isolation. Although these strategies may stabilize functioning in the short term, they may carry negative long-term consequences.

Resilience also shapes adjustment. Firefighters with higher resilience are more likely to appraise stress as a challenge rather than a threat [31]. Firefighters commonly report using conversation, exercise, leisure activities, and faith practices to reduce stress. Professional competence also increases with accumulated experience and knowledge, which can strengthen coping capacity. Individuals with stronger self-efficacy tend to believe they can control and change situations, thereby moderating the impact of perceived stress on burnout.

Peer support functions as an important buffer by enabling emotional exchange, experience sharing, mutual reminders, and opportunities for emotional ventilation. Colleagues’ competence, knowledge, and experience can enhance individual safety, and camaraderie is a strong predictor of firefighters’ mental health. Firefighters often reduce distress through peer dialogue—talking through tasks and coping approaches—and through experiential transfer from senior peers. Everyday conversations and informal gatherings may also help relieve emotional strain. In Taiwan, fire agencies have established counseling and guidance systems and offer courses and lectures. They may provide psychiatric services, psychological counseling, and consultation, sometimes combined with medication to improve sleep conditions [32]. Overall, building a comprehensive mental health support system—including accessible psychological services, effective communication channels, regular health check-ups, financial and emotional support, and health- and law-related information—may strengthen organizational support and enhance firefighters’ resilience.

Method

This study employed thematic analysis to examine the occupational experiences of novice firefighters in Taiwan. The analytic focus was directed toward describing both the substantive content and the processes embedded in their work, thereby highlighting the complexity of frontline practice and the trajectories through which participants adapted and coped.

Participants

A purposive sampling strategy was adopted. Four frontline novice firefighters were recruited through professional networks by the first author. The sample comprised one woman and three men, aged 27–33 years (M = 29.75), with work tenure ranging from 2 to 3.5 years— consistent with the career stage of novice firefighters (defined as within four years of service). Participants were drawn from brigades located in diverse service contexts: an eastern rural area, a central semi-urban area, a central rural area, and a northern metropolitan area. Two participants held certification as Emergency Medical Technicians–Paramedic (EMT-P), and one participant held Rescue Technician certification.

Data Collection

Data were generated through semi-structured, in-depth interviews. The first author conducted one individual interview with each participant, lasting approximately 60–90 minutes. Participants were invited to narrate their occupational challenges and their experiences of adaptation and coping. The interview guide encompassed questions on: motivations and expectations for entering the fire service; job tasks and sources of stress; interpersonal interactions; utilization of organizational resources; coping strategies; and perceived impacts of work on physical and mental health. Interviews were conducted in quiet, convenient locations selected by the first author. Prior to each interview, participants were informed of the study’s purpose and procedures, potential benefits and risks, and their rights. Ethical principles were observed throughout, including respect for autonomy and confidentiality. Written informed consent was obtained before interviews commenced. All interviews were audio-recorded and subsequently transcribed verbatim by the first author.

Data Analysis

Thematic analysis was conducted following Braun and Clarke’s [33] framework. The first and second authors conducted the analysis using Braun and Clarke’s thematic analysis. They engaged in repeated reading of transcripts, open coding, identification of cross-case patterns, and iterative refinement of thematic coherence. Themes were subsequently defined, named, and integrated into the analytic report. Throughout the process, the researchers maintained an open and reflexive stance, engaging in ongoing dialogue to ensure rigor. Particular attention was devoted to issues raised by participants concerning tenure, hierarchical power relations, the novice role, and work-related challenges, including organizational and structural barriers, resource utilization, and coping/adaptation processes.

Trustworthiness and Ethics

To enhance trustworthiness, multiple strategies were employed, including triangulation, reflexivity, thick description, and cross- checking. These measures ensured coherence between themes and textual evidence, strengthened descriptive appropriateness, and enhanced credibility. Ethical standards were rigorously observed: all participants signed informed consent forms and were informed of the study’s purpose, rights, and procedures for voluntary participation and withdrawal. Audio files and transcripts were de-identified and presented using pseudonyms to ensure anonymity. All study materials were securely stored by the first author, and data were used exclusively for academic purposes.

Results

Seven themes emerged from the thematic analysis. Themes are illustrated with representative participant narratives.

Unfairness and Oppression at Work

Novice firefighters characterized their work as highly complex, unpredictable, and psychologically demanding. One participant described each dispatch as opening a “mystery box,” because incidents could involve life-threatening conditions such as earthquakes, rockfalls, or fires. This uncertainty not only tested physical limits but also created substantial psychological burden.

Beyond the inherent danger of the job, participants emphasized that risk exposure was distributed unevenly across ranks. They reported that novices were frequently assigned the most hazardous roles during operations. For example, one participant noted that junior firefighters were expected to be the first to force entry into unknown environments. He also described death-related incidents in which novices could be tasked with handling particularly distressing duties (e.g., carrying severed body parts). He described this as an unspoken rule: “the most junior firefighter is expected to face unknown risks first or bear the greatest known risks.” Such expectations reflected a power imbalance between senior and novice firefighters and contributed to participants’ perceptions of unfairness and oppressive treatment.

Ethical Dilemmas in Practice

In addition to operational danger, participants reported repeated exposure to ethical and moral shocks, especially during emergency medical work. In out-of-hospital cardiac arrest (OHCA) cases, they often faced dilemmas about whether resuscitation was clinically meaningful—particularly when family members insisted on continuing resuscitation even when firefighters perceived it as futile. In these moments, participants described feeling ethically conflicted and powerless. One participant reflected: No intubation, no defibrillation… and then when we arrived at the hospital, the doctor said there was no chance—just stop resuscitation… I kept wondering… Was my CPR and oxygen meaningful?”Participants also described moral conflict from the opposite direction: even when resuscitation was successful, severe post-resuscitation outcomes could raise questions about long- term suffering and family burden. One participant stated: “Even though I brought him back—breathing, pulse—he would only survive with those machines. Sometimes I struggle inside… Should I save him or not?” Overall, participants emphasized that firefighters often confront ethically fraught situations without clear ethical guidance or standardized norms. For novices in particular, real-world emergency care required enduring ongoing tension among professional obligations, personal values, and ethical considerations.

Systemic Organizational and Interpersonal Pressures

Participants described hierarchical pressure and workplace bullying as major sources of stress. Some supervisors were portrayed as hostile toward newcomers (described as “newcomer killers”), frequently scolding, intimidating, or imposing punitive controls. One participant recalled that during the first six months he was reprimanded almost daily and received leave restrictions for minor issues: “For about the first six months, I was getting scolded almost all day… For small things—five minutes late, missing a signature—I was banned from taking leave for two months… he wrote it down and kept scolding me.” Another participant described feeling constantly monitored and scrutinized by supervisors and senior colleagues, which made his work experience “miserable.” He gave an example of being questioned for not attending activities that occurred on his scheduled days off: “There were two activities… both fell on my days off, and then I was called in and asked, ‘Why didn’t you participate?’”

Participants further reported that some senior firefighters undermined novices’ confidence through disparaging remarks. One participant described being mocked while studying for professional exams: “A senior saw me studying… and said, ‘You won’t pass,’… those sarcastic, belittling things.” They also noted that minor station-related issues (e.g., a gym not being tidied immediately or trash temporarily left) could trigger scolding, and supervisory pressure sometimes extended into rest time. Participants reported being unable to relax even during breaks; using a phone could invite suspicion, and taking leave could lead to criticism for “not helping the team” with dispatch duties. Accumulated pressure affected both physical and psychological functioning. One participant described missing a dispatch alarm due to a malfunctioning bell near his bed; fear of being reprimanded increased stress and contributed to sleep disturbance.

In addition to interpersonal dynamics, participants described institutional unfairness that amplified helplessness. One participant stated that performance evaluations were opaque and highly subjective—dependent on “the supervisor’s mood”—yet directly influenced salary and promotion. When effort was not matched by recognition or reward, participants reported frustration and demoralization. Another participant described being reassigned to another brigade despite holding strong paramedic qualifications, which he experienced as unfair and identity-undermining.

Finally, participants described rigid systems and dysfunctional equipment as chronically depleting. They reported pressure to meet unreasonable key performance indicators and to complete large volumes of administrative tasks despite inadequate staffing and limited experience. Under directives from higher-level leadership, core emergency work was sometimes displaced by unrelated activities (e.g., sports events) and could even become entangled with election- related events—at the expense of essential rescue and EMS duties. Participants also described being constrained by outdated protocols: even after learning improved clinical approaches, they were expected to follow older procedures because regulations had not been updated and senior members insisted on “the old way.” Collectively, these systemic conditions were perceived as obstructing professional development, eroding motivation for learning and innovation, and fostering high pressure, helplessness, diminished self-efficacy, and weakened professional identification.

Scarcity of Job Resources

Participants consistently emphasized that job resources were insufficient relative to the demands they faced. At the basic operational level, one participant reported chronic shortages of essential supplies and incomplete distribution of equipment. For example, ambulance gauze ran out and was not replenished for an extended period; paramedic medication kits were not fully issued, forcing firefighters to purchase supplies themselves or seek support from physicians: “Resources are very limited—equipment, gear, consumables. The gauze on the ambulance ran out… and the whole station had none because it wasn’t issued… The medication kit wasn’t provided, so I bought it myself… This affects how well I can do the job, and it affects my quality of life.”Another participant highlighted chronic understaffing. On some days, only three personnel were on duty, increasing the likelihood of station closure or even single-person dispatch, which substantially elevated operational risk. Resource scarcity also extended to compensation. One participant stated that pay was disproportionate to high risk and long working hours, estimating an hourly wage lower than that of part-time student workers. He also described institutional limits on overtime pay, whereby excess overtime was converted to compensatory leave rather than paid overtime. Additionally, leave taken across months could reduce the salary of a given month, despite leave being largely nonvoluntary in practice. Participants experienced these arrangements as discouraging and demoralizing. Participants further described resource deficits in organizational infrastructure and administrative systems. One participant reported frequent crashes in electronic systems with little improvement. Shared computers lacked internet access and could not support routine administrative tasks; outdated equipment was neither functional nor replaced. These barriers disrupted both administrative operations and professional emergency duties, increasing stress, draining energy, and undermining morale.

Pressure From Public Expectations and Commentary

Participants described substantial pressure arising from public expectations, scrutiny, and online commentary. When rescue or response efforts were perceived as inadequate, participants experienced intense criticism and what they viewed as unreasonable accusations, which increased emotional disturbance and work stress. They noted that even cautious decision-making could be judged negatively, resulting in complaints or online attacks. One participant listed common disputes, such as being criticized for driving “too slowly,” questioned for running red lights during emergency transport, reported for siren noise at night, or asked by bystanders to move hoses during active fire response. He recalled: “Once, during a fire response, I was still dealing with it, and there was a hose on the ground. Someone said he wanted to drive home and asked me to move the hose… I said, ‘I’m still fighting a fire!’”Overall, participants expressed a sense that “whatever we do, the public will criticize.” They reported that minor mistakes could be amplified, leaving them uncertain about how to respond and pressured by constant evaluation. This perceived scrutiny affected their occupational identity and sense of meaning in the work.

Personal Ajustment and Coping

In response to the imbalance between high demands and limited resources, participants described diverse coping strategies that ranged from proactive competence-building to emotional self-protection and cognitive reframing. Proactive learning and competence-building. Some participants demonstrated strong agency. One participant repeatedly volunteered for training opportunities despite low seniority and initial supervisory rejection: “I’m pretty junior, so it wouldn’t be my turn… I recommended myself… he rejected me two or three times before finally letting me go.” Another participant described enrolling in an instructor-training program to rekindle motivation: “Last year… I changed my mindset… I decided to recover my passion… I applied for instructor training, and I got in.”

Participants also described practice-based improvement, especially in EMS. One participant reported reviewing the quality of CPR and airway management after OHCA cases, requesting simulation training, and striving to improve care quality. Another described systematically refining EMS procedures— practicing operational details, adjusting workflows, strengthening competence, and promoting team improvements. He explained how training translated to smoother field performance: “When I arrived on scene, I followed the training model… and the next time, I didn’t waste time… it went smoothly, and I completed the EMS work successfully.” He further described keeping organized records, publicly noting procedural gaps, advocating updated protocols, and optimizing team workflows. These efforts increased accomplishment and self-efficacy; when improved performance was recognized during later dispatches, confidence was reinforced in a positive feedback cycle.

Self-protective Emotional Strategies

In contrast, some coping strategies emphasized denial, minimization, or emotional detachment. One participant described himself as “digesting emotions quickly,” “letting it pass,” and being “forgetful,” noting that positive events could override work-related distress: “I digest (negative emotions) pretty fast… after sleeping a few times, I’m fine… If something happy happens, it covers up the unpleasantness.” Another participant rationalized supervisory harshness by framing militarized management as something men “should” endure, interpreting survival of strict training as evidence of becoming competent.

Compartmentalization and Technical Focus

One participant described being briefly frightened at the scene but switching immediately to professional judgment and action. After returning to the station, he reviewed dispatch footage to evaluate technical execution and team communication, using procedural focus to avoid being absorbed in distress: “Back at the station, I watch the footage again… if there are problems, we discuss and improve next time. That way, I don’t think too much… or get stuck on why it happened.”

Relational Coping and Future-oriented Strategies

Some participants described selectively collaborating with reliable senior firefighters to support smoother teamwork. Others attempted to transform unfair treatment into motivation for self-improvement— strengthening training, obtaining key EMS certifications, and preparing for potential future transfers to other units.

Cognitive Reframing

One participant reduced distress by reframing EMS work as “taxi” service: complete necessary tasks without judging whether patients were misusing EMS resources and without ruminating: “We arrive a hospital and see he (the patient) is actually okay—fine, take measurements, transport him, then happily go back to rest… I imagine my work is like a taxi driver.”

In summary, across accounts, the above strategies reflect novice firefighters’ attempts to preserve functioning, maintain competence, and sustain meaning under chronic stress in work.

Concerns About Using Counseling Resources

Although participants acknowledged that fire agencies provide mental health resources (including on-site counseling), they expressed substantial concerns about using them. One participant noted that counseling appointments required taking personal leave; after exhausting shifts, he preferred to use leave for sleep and rest. Another participant expressed fear of stigma and confidentiality breaches, reporting limited trust in counseling privacy and concern that supervisors might learn about counseling use: “Privacy… If I go talk to a counselor… will my supervisor know and ‘check on’ me?… Work stress is private… Counselors keep records—are they stored somewhere? Would people know?… Would I be labeled as having a mental problem?”

One participant questioned whether counseling could address structural sources of distress—such as authoritarian supervision, unfair treatment, or outdated equipment—given that these conditions would remain unchanged after counseling: “If I’m bullied long-term… would talking to a counselor make me better?… I still have to go back, and the supervisor is the same… So is counseling useful? I have a question mark.”

Overall, participants viewed counseling as potentially providing temporary emotional ventilation but offering limited practical benefit for stressors rooted in persistent organizational and structural conditions. Consequently, they tended to rely more on personal coping resources than on formal psychological services.

Discussion

The novice firefighters in this study characterized firefighting as an occupation involving high risk, heavy physical demands, substantial psychological load, and high complexity. They also emphasized that the work environment and organizational culture strongly shape mental health and necessitate coping strategies to manage occupational stress. These findings are consistent with Bakker and Demerouti’s argument that the work environment influences employees’ health, well-being, and performance. Taken together, the results suggest that novice firefighters’ early-career adjustment is not determined solely by the inherent dangers of frontline work, but also by organizational arrangements, leadership practices, resource provision, and public scrutiny—factors that jointly produce a structural pattern in which demands often exceed available resources. Participants described having to cope with diverse operational and administrative challenges, as well as bureaucratic features of the firefighting system and hierarchical pressures requiring obedience. Their narratives also highlighted pronounced hierarchical stress, including implicit workplace rules that positioned novices to assume high-risk and high-impact tasks, and supervisory practices experienced as punitive or oppressive. In addition, the findings echo prior evidence that firefighters are exposed to societal expectations and external evaluation—including media coverage and public praise or criticism—which can intensify occupational stress.

Smith and Dyal’s safety-oriented JD–R model is particularly useful for interpreting these results, as it emphasizes both safety- related job demands and safety-related resource provision. The current findings partially converge with this framework and with the four dimensions of firefighting job demands described by Smith and Dyal. Participants repeatedly reported working under stringent time constraints and pressure, which heightened stress. They also described emotional demands, especially in emergency medical and urgent rescue contexts, where fear, shock, and emotional suppression were common. Such emotional demands can deplete psychological energy and contribute to emotional exhaustion. In addition, participants emphasized cognitive complexity: managing multiple difficult tasks in life-threatening contexts required sustained situational awareness, rapid decision-making, risk assessment, and the execution of emergency interventions.

Notably, when these demands were experienced as persistent and cumulative, participants’ accounts suggested a health-impairment process consistent with the JD–R perspective. For example, some participants described wanting only to rest during time off and reported limited capacity for additional activities, including psychological counseling. This pattern underscores that excessive demands may constrain recovery opportunities and gradually erode psychological resources, thereby increasing risks for burnout and compromised functioning over time.

In the safety-oriented JD–R model, resource provision is not a peripheral issue but a central determinant of safety and well- being. Participants in this study described a broad range of resource constraints, including shortages of supplies, malfunctioning or obsolete systems, insufficient infrastructure (e.g., computers and internet access), and understaffing that increased operational risk. They also expressed dissatisfaction with compensation and overtime arrangements that were perceived as disproportionate to workload, risk, and time demands. These findings align with Payne and Kinman’s argument that insufficient resources represent a key factor undermining firefighters’ psychological health, and with research emphasizing that adequate equipment, facilities, staffing, and occupational health and safety measures influence the safety and comfort of firefighters’ work environments.

At the interpersonal and organizational levels, participants described authoritarian supervision, frequent reprimands for minor issues, disparaging remarks, and a sense of constant scrutiny. They also reported unfair or inconsistent evaluation and managerial practices, leaving them uncertain about behavioral standards and concerned that outcomes depended on supervisors’ subjective judgments. These experiences are consistent with prior literature identifying interpersonal conflict and low organizational justice as critical risk factors that erode resilience and increase vulnerability to anxiety, depression, and physical illness. They also reinforce the importance of supervisory support: deficiencies in managerial support have been linked to elevated depressive symptoms, whereas supervisors’ trust, care, and support may strengthen resilience. In the present study, perceived unfairness and hierarchical oppression appeared to operate as chronic “hindrance demands,” increasing stress while simultaneously restricting access to key resources such as psychological safety, guidance, and professional recognition.

A salient contribution of this study is the centrality of ethical dilemmas, particularly in OHCA cases. Participants described moral conflict when family requests for resuscitation diverged from firefighters’ assessment of futility, and they also described ambivalence when resuscitation succeeded but survival implied severe impairment and long-term dependence on life-sustaining equipment. These experiences suggest that novice firefighters may be exposed to moral distress—ethical discomfort arising when one’s professional judgment conflicts with external demands or constrained options—yet they may lack clear norms, ethical guidance, and structured opportunities to process these conflicts. Accordingly, ethical strain should be considered a meaningful component of job demands in EMS-related duties, not merely an emotional byproduct of trauma exposure.

Participants’ coping strategies reflected both active and avoidant patterns. Some described relatively active coping approaches, including cognitive reframing, transforming frustration into motivation for professional development, pursuing training and certifications, and strengthening technical competence. These strategies appeared to enhance self-efficacy, a key personal resource within the JD–R framework, and are consistent with evidence that believing one can control or change situations may mitigate the impact of stress on burnout. Participants also described drawing on interpersonal resources, such as selectively collaborating with supportive senior colleagues, which partially supports the safety-oriented JD–R proposition that social support buffers the psychological impact of high-demand work. At the same time, several participants reported passive or self-protective coping—emotional suppression, compartmentalization, denial, replacement, and self- isolation—used to maintain immediate functioning. These patterns align with prior research suggesting that some firefighters use passive stances or emotional detachment to cope, while others engage in active reflection and improvement. Although avoidant strategies may provide short-term stabilization and enable continued performance, they may carry longer-term risks. Lin similarly noted that passive coping can temporarily stabilize psychological states but may yield adverse long-term consequences for health and work functioning.

Participants also indicated that rescue and EMS work often involves immediate shock, but firefighters must rapidly regain composure to complete tasks according to standard procedures. This aligns with evidence that firefighters cope by focusing on the task and maintaining calm during operations. It also echoes Yeh’s observation that firefighters often lack time and space to process emotions during missions and must temporarily set aside feelings until tasks are completed. However, in the present study, novices did not clearly report structured post-mission emotional processing; instead, they tended to rely on compartmentalization, suppression, or redirection of attention. This suggests that novice firefighters may have limited awareness of cumulative trauma risks and may underestimate the potential harm of unprocessed emotional and psychological impact. If distress is repeatedly ignored, cumulative strain may contribute to psychological depletion, emotional exhaustion, intrusive re- experiencing, and triggered recall, with trauma-related symptoms becoming more pronounced over time.

Although participants were aware that fire agencies provide mental health services (including counseling), they tended to perceive such services as offering only temporary emotional relief and limited practical utility for problem solving. They also reported concerns about privacy and the confidentiality of counseling records, which reduced willingness to seek help. These findings imply that existing governmental and organizational mental health resources may not fully match novice firefighters’ needs. Importantly, participants’ skepticism was not solely attitudinal; it reflected a perception that counseling cannot resolve structural sources of distress (e.g., authoritarian supervision, institutional injustice, and equipment/resource deficits). Thus, improving service uptake likely requires both strengthened confidentiality safeguards and organizational reforms that address upstream stressors and demonstrate institutional accountability.

In summary, the findings indicate that novice firefighters’ job demands arise not only from the inherent danger of frontline tasks, but also from organizational oppression and institutional injustice— manifested as hierarchical pressure, workplace bullying, and opaque managerial practices—combined with structural resource deficits (e.g., shortages of supplies, malfunctioning systems, and insufficient interpersonal support). Under such conditions, personal coping and adaptation may help individuals maintain day-to-day functioning but remain insufficient to compensate for systemic problems. Novice firefighters in this study appeared to operate in a structural imbalance in which demands substantially exceeded available resources, highlighting the need for multi-level interventions that extend beyond individual resilience.

Implications

Fire service organizations should prioritize novice development by ensuring reasonable work allocation and adequate resources, supported by a constructive work environment, healthy organizational culture, and fair performance evaluation systems. Providing sufficient equipment and accessible psychological resources may foster a more supportive workplace and reduce the risk of work-related psychological harm. More specifically, leaders and supervisors should establish transparent and equitable assessment mechanisms, acknowledge and address resource shortages, and ensure stable provision of emergency medical supplies and other essential materials. Institutional protections for novice personnel should be strengthened to prevent unfair or oppressive treatment arising from hierarchical differences. Supervisors should proactively attend to novices’ needs and shift their role from “monitor” to a buffering resource—providing guidance on professional values, emotional support, and tangible resources, and helping novices navigate supports for processing distress and potential trauma-related reactions. Future research may examine the effectiveness of different coping strategies in response to specific challenges (e.g., hierarchical oppression and ethical dilemmas) and investigate how organizational resources and culture shape novice firefighters’ adjustment over time.

Conclusion

Within highly hierarchical fire service organizations, novice firefighters may experience a structural pattern of high demands and low resources, including hierarchical pressure, institutional injustice, resource scarcity, prolonged high-risk work, and compensation perceived as disproportionate to workload and risk. Despite these conditions, novice firefighters strive to adapt and often demonstrate resilience and commitment to professional practice. However, they primarily rely on individual coping strategies—such as emotional suppression, compartmentalization, and attentional redirection— to manage stress and emotional reactions, strategies that may carry hidden risks of long-term psychological depletion and escalation of trauma-related symptoms. Accordingly, supporting novice firefighters’ sustainable adaptation requires coordinated efforts that combine individual- and peer-level supports with organizational reforms in leadership practices, procedural justice, resource provision, and the design and credibility of mental health services.

References

  1. National Fire Agency (2024) Manpower in Fire Agency. Report from National Fire Agency, Ministry of the Interior, Taiwan.
  2. National Fire Agency (2018) Manpower supplementation in Fire Report from National Fire Agency, Ministry of the Interior, Taiwan.
  3. Chang CM (2009) A study on occupational safety and health management of firefighters-An example of Taipei National Chiao Tung University.
  4. Kuo NC (2014) The study of work stress turnover intension job satisfaction emotion regulation- for example of fire-fighter. I-Shou University.
  5. Yeh TC (2021) Research on mental health of firefighters and psychological impact after rescue disaster: A case study of Yilan County Fire Bureau. Southeast University of Science and Technology.
  6. Kuo CW (2015) A survey study of firefighters’ stress and PTSD. Report from National Fire Agency Ministry of the Interior Taiwan.
  7. Lin CH (2023) The study to explore the vicarious trauma experiences and mental adjustment process of polices and firefighters participated in the disaster incident. National Taipei University of Education.
  8. Chen, W. H (2001) Stress coping of firefighters. Extinguishing and Protection Monthly 4: 17-24.
  9. Lewis A, Hall TE, Black A (2011) Career stages in wildland firefighting: implications for voice in risky International Journal of Wildland Fire 20: 115-124.
  10. Choudhury NA, Saravanan P (2025) Mapping critical decisions and cues in firefighting: A structured analysis using the critical decision Journal of Cognitive Engineering and Decision Making.
  11. Goh KK, Jou S, Lu ML, Yeh LC, Kao et al (2021) Younger, more senior, and most vulnerable? Interaction effects of age and job seniority on psychological distress and quality of life among firefighters. Psychological Trauma: Theory Research Practice and Policy 13: 56-65. [crossref]
  12. Heydari A, Ostadtaghizadeh A, Ardalan A, Ebadi A, Mohammadfam I.(2022) Exploring the criteria and factors affecting firefighters’ resilience: A qualitative study. Chinese Journal of Traumatology 25: 107-114.
  13. Liao SC (2017) A comparison of perceived stress heart rate variability respiratory function and standing balance in firefighters with different seniority. University of Taipei.
  14. Hsiao TC (2020) Novice firefighters’ task features stressors and stress coping: A team in New Taipei Fo Guang University.
  15. Lin CK (2023) The impact of subordinate voice behavior on subordinate resilience under authoritarian leadership: A case study of firefighters. National Tainan University.
  16. Bakker AB, Demerouti E (2017) Job Demands-resources theory: Taking stock and looking Journal of Occupational Health Psychology 22: 273-285. [crossref]
  17. Demerouti E, Bakker AB, Nachreiner F, Schaufeli WB (2001) The job demands- resources model of Journal of Applied Psychology 86: 499-512. [crossref]
  18. Smith TD, Dyal M (2016) A conceptual safety-oriented job demands and resources model for the fire service. International Journal of Workplace Health Management 9: 443-460.
  19. Payne N, Kinman G (2019) Job demands, resources and work-related well-being in UK firefighters. Occupational Medicine 69: 604-609. [crossref]
  20. Igboanugo S, Bigelow PL, Mielke JG (2021) Health outcomes of psychosocial stress within firefighters: A systematic review of the research Journal of Occupational Health 63: e12219. [crossref]
  21. Wang L, Chen F, Zhang Y, Ye M (2023) Association between social support, and depressive symptoms among firefighters: The mediating role of negative Safety and Health at Work14: 431-437.
  22. Schuhmann B (2022) The assessment of burnout and resilience in firefighters. Nova Southeastern University.
  23. Eryılmaz İ, Dirik D, Öney T (2024) A phenomenological study on psychological resilience of aircraft rescue and fire fighting professionals. Current Psychology 43: 20286-20308.
  24. Carver CS, Scheier MF, Weintraub JK (1989) Assessing coping strategies: a theoretically based Journal of Personality and Social Psychology 56: 267-83. [crossref]
  25. Chung YY, Chiou JJ (2013) The relationships of post-traumatic stress disorder and coping behavior among first-line firefighters. Journal of Crisis Management 10: 69-78.
  26. Lee SF (2012) The study of the strikes and learning needs facing death events for firefighters in Chiayi County. National Chung Cheng University.
  27. Chen TY (2019) A narrative analysis about pressure adjustment of rescuers participating in major disasters: A case study of firefighters. National Taina University.
  28. Hsu CW (2022) A Study of work stress and the needs of psychological counseling system of firefighters-Taking the Fire Department of New Taipei City as an example. Ming Chuan University.
  29. Blaney L, Brunsden V (2015) Resilience and health promotion in high-risk professions: A pilot study of firefighters in Canada and the United International Journal of Interdisciplinary Organizational Studies 10: 23-32.
  30. Paterson C, Leduc C, Maxwell M, Aust B, Amann et al (2021) Evidence for implementation of interventions to promote mental health in the workplace: a systematic scoping review protocol. Systematic Reviews10: 41. [crossref]
  31. Ogińska-Bulik N, Kobylarczyk M (2016) Association between resiliency and posttraumatic growth in firefighters: The role of stress International Journal of Occupational Safety and Ergonomics 22: 40-48. [crossref]
  32. Gao PT (2025) A narrative study on the resilience of frontline firefighters: Experiences from disaster and emergency response. National Changhua University.

The Development of Oppositional Defiant Disorder(ODD) in Youth: A Review of Risk, Protective, and Ameliorating Factors

DOI: 10.31038/PSYJ.2025763

Abstract

Oppositional Defiant Disorder (ODD) is a disruptive behavioral condition characterized by persistent patterns of angry, irritable mood, argumentative behavior, and vindictiveness (APA, 2022). This comprehensive review explores the diagnostic criteria, prevalence, and developmental trajectory of ODD in youth, with a focus on the interplay of risk, protective, and ameliorating factors. The article examines the distinctions between medical and educational diagnoses, including the implications of DSM-5-TR and special education law in the United States (IDEIA). It highlights the disorder’s comorbidity with ADHD, mood disorders, and conduct disorder, and discusses how cultural, gender, and socioeconomic factors influence diagnosis and symptom presentation. The review also delves into dispositional, genetic, cognitive, neurobiological, and environmental risk factors, emphasizing the importance of early intervention and strong familial and peer relationships as protective mechanisms. Evidence-based treatments are presented as effective strategies for managing ODD symptoms. The article underscores the critical role of educators and psychologists in identifying, evaluating, and supporting students with ODD through informed assessment and intervention practices.

Oppositional Defiant Disorder

Oppositional Defiant Disorder (ODD) is a complex externalizing behavioral condition characterized by persistent patterns of angry or irritable mood, argumentative and defiant behavior, and vindictiveness in children and adolescents [1]. Understanding ODD requires a comprehensive, multidisciplinary approach that considers its diagnostic criteria, prevalence, and developmental trajectory, as well as the interplay of dispositional, genetic, cognitive, neurobiological, and environmental risk factors. In clinical settings, psychiatrists and licensed psychologists typically provide a medical diagnosis of ODD, while in educational contexts, school psychologists, particularly those trained in clinical assessment and diagnostic frameworks, are responsible for evaluating students for special education eligibility under the classification of emotional disability (ED) [2]. This review will provide a comprehensive overview of ODD and present the developmental trajectory of risk and protective factors for youth with ODD. The article will also explore the distinctions between medical and educational diagnoses, with particular attention to the implications of DSM-5-TR criteria and special education law in the United States (IDEIA). The article will examine how comorbid conditions such as ADHD, mood disorders, and conduct disorder complicate both identification and intervention. Additionally, it will highlight how cultural, gender, and socioeconomic factors influence symptom presentation and diagnostic outcomes, and provide a review of effective early intervention and evidence-based treatments, such as Parent-Child Interaction Therapy (PCIT) and Teacher-Child Interaction Training (TCIT), in equipping educators and psychologists to support youth with ODD effectively.

Overview of ODD

Diagnostic Features and Criteria

Oppositional Defiant Disorder (ODD) poses distinct challenges across both healthcare and educational systems. In clinical settings, professionals use standardized diagnostic tools to identify and manage the disorder. In contrast, educators must assess whether a student’s behavioral patterns meet the criteria for special education services. It’s important to distinguish between a clinical diagnosis and an educational classification: the DSM-5-TR provides the framework for diagnosing ODD in medical contexts, while eligibility for school- based support is determined by federal legislation such as the Youth with Disabilities Education Improvement Act (IDEIA) and state- specific guidelines like Indiana’s Article 7. Together, these systems help ensure that children with ODD receive comprehensive support that addresses both their behavioral health and academic needs.

DSM-5-TR

According to DSM-5-TR, ODD is characterized by a persistent pattern of angry/irritable mood, argumentative/defiant behavior, or vindictiveness lasting at least six months (APA, 2022). Diagnosis requires the presence of at least four symptoms from these categories, exhibited during interactions with at least one individual who is not a sibling). Regarding angry/irritable mood, an individual may experience symptoms where they often lose their temper, are touchy or easily annoyed, and/or are often angry and resentful. An individual may also experience symptoms of argumentative/defiant behavior where they often argue with authority figures, defy or refuse to comply with requests from authority figures or with rules, deliberately annoy others, and/or often blame others for their mistakes or misbehavior. The individual may have also been spiteful or vindictive at least twice within the past six months. The disturbance in behavior must be negatively impacting their social, educational, occupational, or other important areas of functioning, and the behaviors cannot be better explained by other mental health conditions (e.g., psychotic, substance use, depressive, or bipolar disorder). Finally, the DSM-5-TR includes three specifiers: mild (symptoms are confined to one setting); moderate (symptoms are present in at least two settings); and severe (symptoms are present in three or more settings).

IDEIA and State Laws

Youth with ODD, and disruptive behaviors, are often considered for special education services under the classification of Emotional Disorder (ED). IDEIA, the federal law that governs special education in the United States, broadly defines ED as a condition that exhibits at least one of the provided characteristics for a long period of time and to a marked degree that adversely affects a child’s educational performance [3]. Characteristics include: an inability to learn that cannot be explained by intellectual, sensory, or health factors; an inability to build or maintain satisfactory interpersonal relationships with peers and teachers; inappropriate types of behavior or feelings under normal circumstances; a general pervasive mood of unhappiness or depression; and/or a tendency to develop physical symptoms or fears associated with personal or school problems. While IDEIA provides a general overview of ED, it does not provide guidelines for how to evaluate the category.

Although IDEIA outlines the general criteria for ED, it does not provide detailed procedures for how to assess or determine eligibility. Each state in America is responsible for developing its own evaluation standards. For instance, in the state of Indiana, Article 7 serves as the state’s special education framework and builds upon the federal definition by adding further clarification. Indiana’s criteria include episodes of psychosis and emphasize the need for a comprehensive evaluation process. This process must include assessments of academic achievement and emotional functioning, a thorough developmental and social history, a functional behavior assessment, and relevant medical or psychological information. The goal is to ensure that the student’s learning challenges are not better explained by other factors such as cognitive or sensory impairments [4].

Prevalence

While there are limited population- or national-level data on the prevalence of Oppositional Defiant Disorder (ODD) in the United States, current estimates suggest a prevalence rate of approximately 3.3%, with most community samples ranging from 3% to 6% [5]. This variability may be influenced by differences in diagnostic practices, sample demographics, and access to mental health services across regions. Boys are slightly more likely than girls to develop and be diagnosed with ODD during childhood, possibly due to more overt behavioral symptoms that draw attention in educational and clinical settings. However, research indicates that gender differences in prevalence tend to diminish during adolescence and adulthood, with similar rates observed across sexes. These findings highlight the importance of early identification and culturally sensitive assessment practices to ensure accurate diagnosis and appropriate intervention.

Comorbidity

Youth with ODD are at risk of having internalizing and externalizing comorbidity. Youth with ODD have higher rates of comorbidity with attention deficit hyperactivity disorder (ADHD) and mood and anxiety disorders [6,7]. Youth who develop ODD during childhood and adolescence are at an increased risk for developing conduct disorder (CD) and/or antisocial personality disorder (APD). There is also evidence of comorbidity between posttraumatic stress disorder (PTSD) and ODD [8]. Youth with ODD are also more likely to use tobacco, alcohol, and drugs [9].

Factors Associated with ODD

Cultural Factors

Cultural differences in the diagnosis of Oppositional Defiant Disorder (ODD) may stem from misdiagnosis or overdiagnosis among youth from marginalized backgrounds. African American youth, for example, have been reported to exhibit higher rates of defiant behaviors and conduct problems in some research samples [10,11]. In special education settings, Black males are disproportionately identified as having Emotional Disturbance (ED), often due to elevated reports of behavioral concerns compared to their white peers—even when the actual intensity and frequency of behaviors are similar across racial groups. This overrepresentation may reflect systemic biases in behavioral interpretation and referral practices rather than true differences in symptom severity. As a result, culturally responsive assessment and intervention practices are essential to ensure accurate identification and equitable support for students with behavioral challenges.

Gender Differences

While the prevalence of ODD is similar among boys and girls during adolescence and adulthood, boys and girls may present differently in symptomology, severity, and associated comorbidity [12]. Boys may be found to experience symptoms that include annoying others, blaming others, being aggressive, and they may present with greater functional impairments at school and in the community than girls. Girls may demonstrate less observable oppositional characteristics, including more relational aggression (e.g., refusing to talk to someone, being malicious, avoiding blaming, spreading rumors, attempting to harm someone’s relationships with others, etc.). Additionally, boys may be more likely to have externalizing comorbidity (e.g., ADHD) while girls may be more likely to have internalizing comorbidity due to significantly diminished self-regulation skills compared to boys with ODD (e.g., anxiety, depression, and somatic complaints) [13].

Socioeconomic Differences

Children and adolescents from low-income households are more frequently identified as exhibiting symptoms associated with Oppositional Defiant Disorder (ODD) [14]. Environmental stressors common in economically disadvantaged neighborhoods, such as exposure to community violence, can contribute to the development or intensification of oppositional behaviors. Limited access to mental health resources and safe recreational spaces may further exacerbate these challenges. Additionally, lower levels of parental education are often linked to inconsistent or harsh disciplinary practices, which can influence the emergence of ODD symptoms. Factors such as food insecurity, chronic stress, and reduced access to supportive peer networks also play a role in shaping behavioral outcomes in these populations.

Developmental Pathway of ODD

Overview of Developmental Course

The first symptoms of ODD are typically present as early as preschool but rarely later than early adolescence. If left untreated, the severity of the symptoms could gradually escalate to the development of Conduct Disorder (CD) and even to the severity level of Anti-social Personality Disorder (APD) [15]. A typical developmental progression of disruptive behavior may begin with severe hyperactivity and impulsivity in early childhood, followed by the emergence of ODD symptoms during the preschool years. As the child enters elementary school, these behaviors may intensify and evolve into conduct disorder. During adolescence, the individual may begin to engage in substance- related issues, and if left untreated, these patterns can culminate in antisocial personality disorder in adulthood. Children and adolescents with ODD may experience a variety of problems in adulthood, including relational problems, lower educational attainment, and workplace stress. School psychologists and other providers should be familiar with the risk factors and the developmental trajectory of ODD when evaluating and designing interventions for students with ODD.

Dispositional Risk Factors

Emotional/Temperamental Factors

Numerous emotional risk factors have been associated with the development of ODD. Children who experience difficulties regulating their emotions are more likely to exhibit irritability and vindictiveness [16]. For example, children who have higher levels of emotional reactivity and/or have a low frustration tolerance are at risk for developing ODD. Children and adolescents who have low self-control due to ADHD may also possess issues with emotional regulation making them vulnerable to developing comorbid ODD. Callous-unemotional traits (CU), a cluster of traits of psychopathic youth that include a lack of empathy and indifference toward the feelings of others, have been seen in children and adolescents whose ODD symptoms begin to exacerbate to CD or APD. Youth who have CU traits likely have characteristics of neuroticism that cause them to demonstrate lower levels of fear and anxiety – also increasing the likelihood of their symptoms exacerbating.

Genetic Factors

The heritability estimate for ODD is around 50%. Significant levels of CU traits may be a result of genetic influences (e.g., excess methylation in the OXTR gene predisposes adolescents to CU) [17]. AVPR1A, a gene located on chromosome 2 that plays a significant role in social behavior and interaction, may be associated with aggression in early and middle childhood. Epigenetic research demonstrates that both environmental influences and additive genetic effects, where multiple genes contribute to a single trait, may be involved in the development of Oppositional Defiant Disorder (ODD). Through gene-environment interactions, environmental exposures can either enhance or suppress the expression of genetic predispositions, thereby influencing the likelihood that ODD-related traits will manifest.

Cognitive Vulnerabilities

Youth with ODD may show cognitive deficits in executive functioning (EF) and low verbal intelligence, especially if they possess CU traits. The classification of EF into ‘hot’ and ‘cool’ is a critical element of etiopathological research on externalizing disorders. “Hot” EF involves affective, motivational, and emotional aspects of cognition, whereas “cool” EF focuses on planning, cognitive flexibility, working memory, and inhibition. Children with ODD may have characteristics of greater reward-seeking behaviors and problems with emotional self-control [18]. Regarding “cool” EF, children with ODD have lower behavioral inhibition, which mixed with poor emotional control may exacerbate impulse aggression. School-referred children with disruptive behavior symptoms are associated with poor motivational and cognitive control (also referred to as executive control), and they may be incapable of cognitively processing the negative consequences of their victim’s distress.

Neurobiological Factors

Children with ODD may have neurobiological abnormalities in the amygdala and prefrontal cortex – areas responsible for reasoning, judgment, impulsecontrol, andemotionalprocessing. Morespecifically, reduction in the left amygdala, anterior insula, frontal gyrus, cingulate cortex, and/or medial prefrontal cortex might be associated with ODD. Brain regions such as the amygdala, anterior cingulate cortex, insula, and orbitofrontal cortex are primarily involved in emotional or “hot” executive functions, while the dorsolateral prefrontal cortex and cerebellum are more associated with logical, “cool” executive functions and areas, such as the precuneus, control both types of functioning. Additionally, heart rate, serotonin levels, and basal cortisol levels are often reduced in adolescents with aggressive behaviors.

Environmental Risk Factors

Familial Factors

A variety of family risk factors, including low socioeconomic status (SES), parental separation, and maternal depression, have been associated with the development of ODD. Aside from parental pathology, other family factors that could lead to symptoms of childhood/adolescent-onset of ODD include exposure to poor disciplinary practices (e.g., forms of hostility or aggression), maltreatment and neglect (e.g., sexual, physical, or psychological abuse), single parenthood, and family disharmony (e.g., argumentative parents). A combination of surrounding oneself with deviant peers and having poor parental supervision with low involvement are predictors for adolescents to engage in rebellious and/or defiant behaviors. Children who are exposed to high levels of dysfunctional parenting and maternal depression are also at a higher risk of developing symptoms of defiant and antisocial behaviors. Finally, children who are frequently emotionally dysregulated are at risk of experiencing higher child-parent conflict that could enhance ODD symptomology.

Interpersonal Vulnerabilities

Children and adolescents often face a range of interpersonal challenges, particularly when it comes to initiating and sustaining positive relationships with peers. Those who are consistently rejected by their peers—such as being disliked or excluded—and those who associate with deviant or antisocial peer groups are at increased risk for developing oppositional defiant disorder (ODD). In some cases, youth with ODD may engage in bullying behaviors themselves, contributing to a hostile social environment. However, these individuals may also be targets of bullying, which can intensify symptoms of vindictiveness, especially when they feel compelled to retaliate against those who have harmed them. This cycle of aggression and retaliation can further complicate their social interactions and emotional regulation, reinforcing the behavioral patterns associated with ODD.

Early Adverse Experience

There is a relationship between childhood externalizing problems, including ODD, and exposure to traumatic events. Interpersonal trauma, referring to harmful experiences directly inflicted by another person (e.g., physical abuse, emotional neglect, witnessing domestic violence, etc.), and non-interpersonal trauma, referring to events not involving direct human interaction (e.g., severe accident, natural disaster, loss of a loved one, etc.), are predictive of ODD symptomology in boys, while only interpersonal trauma is a predictor in girls. Children who experience interpersonal trauma are more likely to develop problems with anger, emotional regulation, and disruptive behavior. Children and adolescents who are exposed to community violence in impoverished neighborhoods have an increased risk of developing antisocial attitudes and behaviors.

Protective Factors

There are a variety of protective factors that reduce the likelihood of developing or worsening symptoms of Oppositional Defiant Disorder (ODD). Early intervention is especially important, as it can prevent ODD symptoms from escalating into more severe conditions such as Conduct Disorder (CD) or Antisocial Personality Disorder (APD). One key protective factor is the presence of high-quality relationships, both within the family and among peers. These relationships are often supported by living in safe neighborhoods, having strong family support systems, and being surrounded by prosocial peers who model positive behavior. Additionally, strong executive functioning and emotional regulation skills serve as internal protective mechanisms that help children manage impulses and navigate social challenges more effectively.

Treatment of ODD

Clinical Treatment

Because parental factors are highly associated with ODD, clinical treatments should consider strengthening the relationships between caregivers and their children. Parent-Child Interaction Therapy (PCIT) is an evidence-based therapeutic technique originally developed for children with disruptive behaviors that focuses on strengthening familial relationships by altering parent-child interactions [19]. PCIT involves having a parent and child together in a playroom while a therapist remains on the other side of a one-sided mirror where they coach the parent (by talking to them through a headset of some form) on how to positively interact and build a healthy rapport with their child. This verbal rapport is typically developed through praise, positive reinforcement, and overall parental involvement by the parent. If implemented with fidelity, PCIT is an effective treatment for decreasing ODD symptoms and preventing the development of CD by helping parents quit certain behaviors (e.g., unhealthy discipline responses) while starting other behaviors (e.g., more parental involvement) [20].

School Intervention

Similarly, school psychologists who possess the appropriate training should consider facilitating Teacher-Child Interaction Training (TCIT) with the teacher and student. In general, TCIT is a “classroom-based program designed to provide teachers with behavior management skills that foster positive teacher-student relations and to improve student behavior by creating a more constructive classroom environment” [21]. In general, there are teaching sessions that encompass learning about positive reinforcement through praise, modeling, and various classroom management strategies to decrease disruptive behaviors [22,23]. These sessions typically involve the clinician, teacher, and child, and it may also require the teacher to practice their skills in small and large group settings.

Conclusion

This review article explored Oppositional Defiant Disorder (ODD) in youth, emphasizing its diagnostic criteria, prevalence, developmental trajectory, and associated risk and protective factors. The article distinguishes between clinical and educational diagnoses in the United States, highlighting the roles of DSM-5- TR and IDEIA in shaping assessment and intervention practices. It identifies a range of dispositional, genetic, cognitive, neurobiological, and environmental risk factors that contribute to the onset and progression of ODD, while also underscoring the importance of early intervention and facilitating strong interpersonal relationships as protective mechanisms. Evidence-based treatments such as Parent- Child Interaction Therapy (PCIT) and Teacher-Child Interaction Training (TCIT) are presented as effective strategies for managing symptoms and preventing escalation into more severe disorders like Conduct Disorder (CD) or Antisocial Personality Disorder (APD). Psychologists, therapists, and educators should possess a deep understanding of the psychopathology underlying Oppositional Defiant Disorder (ODD) and be equipped with evidence-based strategies to support affected students. This includes conducting informed evaluations, implementing targeted interventions, and fostering collaborative efforts among school staff and families to address behavioral and emotional challenges effectively.

References

  1. American Psychiatric Association [APA]. (2022) Disruptive, impulse-control, and conduct disorders.
  2. Tobin RM,House (2016) DSM-5 diagnosis in the schools. The Guilford Press
  3. S. Department of Education. (2004) Youth with Disabilities Education Act.
  4. Indiana State Board of (2024) Special Education Rules. Article 7.
  5. Aggarwal A,Marwaha (2024) Oppositional defiant disorder. StatPearls – NCBI Bookshelf.
  6. Boat TF, Wu J T, Disorders T. E. T. S. S. I. D. P. F. C. W. M., Families, B. O. C. Y. A., &Education, D. O. B. a. S. S. A. (2015) Prevalence of oppositional defiant disorder and conduct disorder. Mental Disorders and Disabilities Among Low-Income Children – NCBI
  7. Halldorsdottir T, Fraire MG, Drabick, D AG, Ollendick TH. (2023) Co-occurring conduct problems and anxiety: Implications for the functioning and treatment of youth with oppositional defiant disorder. International Journal of Environmental Research and Public Health 20: 3405
  8. Mikolajewski AJ,Scheeringa MS. (2022) Links between Oppositional Defiant Disorder Dimensions, Psychophysiology, and Interpersonal versus Non-interpersonal Trauma. Journal of psychopathology and behavioral assessment 44: 261-275.
  9. Leadbeater BJ, Merrin GJ, Contreras A,Ames (2023) Trajectories of oppositional defiant disorder severity from adolescence to young adulthood and substance use, mental health, and behavioral problems. Journal of the Canadian Academy of Child and Adolescent Psychiatry 32: 224-235
  10. Mash EJ,Barkley (Eds.) (2014) Child psychopathology (3rd ed.) New York: Guilford Press. (ISBN: 978-1-4625-1668-1)
  11. Lambert MC, Katsiyannis A, Epstein MH,Cullinan D. (2022) An Initial Study of the Emotional and Behavioral Characteristics of Black Students School Identified as Emotionally Disturbed. Behavioral Disorders 47: 108 117.
  12. Trepat E, Ezpeleta (2011) Sex differences in oppositional defiant disorder. Psicothema 23: 666–671.
  13. Zhang W, Qiao L, Wang M, Liu Z, Chi P.et al.(2025) Bidirectional relation of self- regulation with oppositional defiant disorder symptom networks and moderating role of gender. Development and Psychopathology 37: 1616–1627.
  14. Hawes DJ, Gardner F, Dadds MR, Frick PJ, Kimonis ER, et (2023) Oppositional defiant disorder. Nature Reviews Disease Primers 9: 31.
  15. Steinberg (2022) Psychosocial problems in adolescence. In Adolescence . New York, NY: McGraw-Hill
  16. Nobakht HN, Steinsbekk S,Wichstrøm (2024), Development of symptoms of oppositional defiant disorder from preschool to adolescence: the role of bullying victimization and emotion regulation. Journal of Child Psychiatry 65: 343-353
  17. Ghosh A, Ray A, Basu (2017) Oppositional defiant disorder: current insight. Psychol Res Behav Manag,10: 353-367
  18. Anning KL, Langley K, Hobson C, et (2024) Cool and hot executive function problems in young children: linking self-regulation processes to emerging clinical symptoms. European Child and Adolescent Psychiatry 33: 2705-2718.
  19. Bjørseth Å,Wichstrøm (2016) Effectiveness of Parent-Child Interaction Therapy (PCIT) in the Treatment of Young Children’s Behavior Problems. A Randomized Controlled Study. PloS one 11: e0159845.
  20. Fooladvand M, Nadi MA, Abedi A,Sajjadian (2021) Parenting styles for children with oppositional defiant disorder: Scope review. Journal of education and health promotion 10: 21
  21. Fernandez MA, Adelstein JS, Miller SP, Areizaga MJ, Gold DC, et (2015) Teacher- Child Interaction Training: A Pilot Study with Random Assignment. Behavior Therapy 46: 463-477.
  22. Stankus, Jaclynn S, Jancart Karl L,McGoey, Kara (2022) “The Effect of Teacher- Child Interaction Training (TCIT) on Children who are Exhibiting Disruptive Behaviors within the Classroom Setting,” Perspectives on Early Childhood Psychology and Education.6: 2.
  23. Barker C, de Lugt (2022) A review of evidence-based practices to support students with oppositional defiant disorder in classroom settings. International Journal of Special Education 37: 85-98.

Talking to Patients: Part 2 – Mind Genomics Cartography of Reactions to Messages Pertaining to Patient Experience with COVID-19

DOI: 10.31038/MGSPE.2025523

Abstract

In a Mind Genomics experiment, 101 respondents each evaluated unique sets of 24 vignettes pertaining to interactions with a medical professional concerning the personal experience with COVID-19. The focus was to determine which messages made the respondents feel “comfortable” communicating. The results suggest two clearly different mind-sets, those responsive and comfortable with communication about the “facts” of COVID-19, versus those comfortable with communication about emotional support of the patient. These two mind-sets transcend gender, age, and previous experience with COVID-19. A simulation of the same topic by AI revealed that AI picked up these two mind-sets but suggested even deeper subgroups within each mind-set. The paper shows the value of incorporating Mind Genomics and AI into the education of medical professionals to provide deeper knowledge of how to communicate with patients.

Keywords

Artificial Intelligence, COVID-19, Empathy in healthcare, Medical education, Mind Genomics, Patient communication

Introduction

Healthcare professionals can improve their communication skills with patients by actively listening, demonstrating empathy, using clear language, and adopting a patient-centered approach. By involving patients in decision-making processes, providing comprehensive information, and collaborating on treatment decisions, healthcare providers can build trust and rapport. Measurement of communication strategies can be done through patient surveys, feedback forms, focus groups, and patient outcomes data. Common barriers to effective communication include time constraints, language barriers, cultural differences, health literacy limitations, and emotional or psychological challenges. Addressing these barriers can lead to more effective patient-provider interactions and improved health outcomes.

Mind Genomics is an emerging science that studies how people behave and respond to different messages on important topics, such as COVID. This method helps experts understand what people like or dislike about certain messages and how they react in various situations. By using AI, healthcare workers can create personalized texts about COVID in the Mind Genomics app to see how comfortable patients are with them. This approach is based on facts and aims to help healthcare workers establish deeper connections and have more effective conversations with their patients.

Mind Genomics differentiates itself from standard consumer research techniques by taking a unique approach to gathering insights on consumer preferences. Instead of asking respondents to directly rate the importance of various aspects, Mind Genomics presents them with combinations of messages and analyzes their responses to determine the impact of each message. This method avoids forcing people to intellectualize and allows for a more natural, intuitive understanding of consumer preferences. Using statistical techniques such as regression modeling, Mind Genomics identifies which messages most strongly communicate the desired messages. The pattern of strong performing messages provides insights on consumer behavior and preferences, doing so in a fashion which is robust, rapid, and designed for exploration in areas that may be as yet “terra incognita,” viz., unknown lands. This approach not only improves patient satisfaction but also makes training medical professionals more effective by giving them real-world feedback on their messaging strategies.

Mind Genomics has been used in topic areas ranging from medicine (focus of the current paper), to food, law, social issues, and matters of the everyday). The methods have been explicated in a variety of papers [1-6]. This paper adds to the corpus of knowledge created by Mind Genomics, doing so with the focus on COVID, and specifically on the communication between patient and medical professional, e.g., doctor or nurse practitioner.

Step 1: Create the Raw Materials

The Mind Genomics technique asks the user to provide four questions, each with four answers. The four questions and four answers to each question were developed using AI, as discussed in the accompanying paper. Table 1 shows the four questions and four answers to each question (elements), generated by AI, but edited by the researchers to make the answers succinct and easy to understand. For each question and each answer, the AI was instructed to make the text 15 words or less, to write in the way people talk, and to make the text understandable to a 12-year-old.

Table 1: The four questions and the four answers to each question.

Step 2: Create the Test Stimuli

The test stimuli consist of message combinations from an experimental design. The experimental design specifies the combinations as follows:

  1. Each element appears five times in 24 vignettes and is absent from 19 vignettes.
  2. A vignette must contain at most one answer to a question.
  3. A vignette must have a minimum of two elements and a maximum of four This requirement means that in some cases a vignette does not have an element (answer) from one of the four questions, and in some other cases a vignette does not have an element from two or the four questions. This approach ensures that vignettes do not contain contradictory information from the same question.
  4. The 16 elements appear independently, enabling OLS regression to assess the strength of each element in driving the response.
  5. Each respondent assesses a distinct set of 24 A permutation scheme ensures uniqueness while preserving the mathematical properties of the experimental design, altering only the combinations. This leads to greater coverage of potential combinations. The permutation scheme allows research to explore various ideas and combinations, rather than relying on prior knowledge of what will work best. This shift from experimental confirmation to exploration is central to the Mind Genomics perspective [7].

Step 3: Execute the Study

Mind Genomics studies are conducted online through the BimiLeap.com platform. Luc.id, now Cint, supplies respondents based on user specifications. Participants were adults aged 25-54 living in the United States. Email invitations were sent to the respondents. Participants were directed to an orientation page about the study. The study was not labeled that way. The respondents were informed they would read a set of phrases and rate them as a single idea.

The orientation began with a self-profiling questionnaire that collected the respondent’s age and gender, followed by their answers to seven profile questions in Table 2. BimiLeap.com uses this information to form subgroups of respondents based on their self-identification.

Table 2: Self-profiling questions for classification questionnaire (top) and rating scale for the 24 vignettes (bottom).

The respondents did not receive extensive orientation. The goal was to show them the vignettes and capture their immediate reactions, without creating any expectations. This brief introduction is typical for most Mind Genomics studies, except those related to the law, where case background is pertinent. A brief introduction will suffice for most issues.

Step 4: Create the Database and Estimate the Regression Equation for the Total Panel

Each respondent assessed a unique set of 24 vignettes in random order. The respondent initially assessed vignette #1 as a training vignette. The rating for the first vignette was discarded. The respondent assessed all 24 vignettes. The 24th vignette was a repeat of the training vignette.

The respondent scored the vignette on a 5-point scale. The analysis focused on ratings 4 and 5, which indicate “patient feels comfortable with what the patient has heard.” They were transformed into a new binary variable, R54x. When the respondent rates a vignette a 4 or 5, R54x become 100. When the respondent rates a vignette 3, 2, or 1, R54x become 0. As a prophylactic measure to ensure some variation in the binary variable R54x (necessary for regression analysis) a tiny random number (<10-5) was added to the new binary variable.

The Mind Genomics platform recorded both the rating that the respondent assigned, then the transformed value (R54x), as well as the response time. The response time was the number of seconds (to the nearest 100th second) elapsing between the time the vignette appeared on the screen to the time that the respondent assigned a rating. Response times of 8 seconds or longer were automatically transformed to 8 seconds under the assumption that the respondent was multi-tasking.

The platform’s database included 2424 records, one for each vignette per respondent among 101 participants. Each record included a respondent ID, self-profiling data, vignette order in the 24-set, 16 columns for coding element absence, and the rating, response time, and transformed binary rating R54x.

For the OLS regression, the 16 columns for the elements were coded “1” if present in the vignette and “0” if absent. This is known as dummy coding. The coding indicates the predictor’s state: absent (0) or present (1).

The analysis occurs twice: first for groups, then for individuals. Groups are defined by the self-profiling questionnaire. All 24 vignettes from each respondent were compiled for analysis. The analysis involved the OLS regression without an additive constant, represented as: R54x = k1A1 + k2A2 … k16D4

The regression equation shows the impact of each factor. Parallel analyses showed at statistic of about 2.0 corresponding to a coefficient close to 11 in the regression model with an additive constant. The coefficient of 11 in a model or equation is equivalent to a coefficient of about 20 for a model without an additive constant, estimated on the same data. Given the foregoing argument, it appears that one could make an argument for coefficients of 21 or above as show strong performance when the model or equation is estimated without an additive constant

Table 3 presents the model parameters estimated for 101 respondents. Five elements are statistically significant (coefficient > 20), indicating that AI effectively generated strong, inspiring elements. These elements use informal language.

Table 3: Coefficients for the total panel for the equation relating R54x (comfortable) to the elements.

Step 5: Estimate Regression Equations for the Self-defined Subgroups

Recall that at the start of the Mind Genomics session the respondent completed a self-defining questionnaire, shown in Table 2.  The regression analysis for each group comprising 10 or more respondents generates a great deal of data. In order to make the analysis easier, Table 4 (age, gender) and Table 5 (self-defined attitudes and behavior) show only those coefficients of 25 or higher.

Table 4: Coefficients for gender and for age for the equations relating R54x (comfortable) to the elements.

Table 4 shows only four strong performing elements, suggesting that if there are group differences, the groups are probably not defined by gender nor by age. Table 5 shows that the elements “resonate” for the 11 respondents who have defined themselves as having had Long COVID, but otherwise the pattern is once again elusive

Table 5: Coefficients for the four subsets of respondents based on COVID experience for the equation relating R54x (comfortable) to the elements.

Step 6: Create Mind-Sets by K-means Clustering

Variability among individuals derives from the “human condition,” which is the inescapable reality that people differ from each other on issues, even on the same issues of the world of everyday. Perhaps this variation is an intractable inconvenience? That would be acceptable, of course, and dealt with by oversampling people until the real average emerges out of the intractable variation. But what if this variation represents various ways of thinking about things, rather than random differences? What if there are basic distinctions in cognitive patterns that are not always related to a person’s identity or previous experiences?

A recurrent theme in Mind Genomics is that individuals vary in their daily lives but that this variation at the level of the everyday experience can be traced to mind-sets, patterns of thinking. The mind-sets emerge from the world of the granular and are descriptive rather than normative. The mind-sets “make sense” of the variation by showing that the variation can be generated by parsimonious set of groups. Furthermore, these groups are discoverable by simple studies such as the study presented here. Furthermore, one cannot always anticipate how a person would think based on their demographics, or even their actions. As of this writing (Winter 2024-2025), the mind- sets must be retrieved via an examination of reaction patterns to the vignettes. The method is simple: use the individual coefficients from a research, such as ours, to determine what individuals react to in terms of inspiration.

Reducing this tumultuous inter-person variety to well-behaved, explainable, parsimonious number of mind-sets is one way of using clustering—a well-accepted statistical procedure. Clustering reduces a seeming random cloud of different objects into a few interpretable groups, clusters, or mind-sets in the language of Mind Genomics. The processes are strictly mathematical, Mind Genomics uses k-means clustering [8]. People in a cluster think and respond similarly to the elements (viz., feel comfortable with the message as conveyed by the medical professional).

The particular strategy used by k-means clustering follows these simple steps:

  1. Using the data from the 24 vignettes evaluated by one respondent, compute the 16 coefficients which emerge from relating the binary dependent variable, inspire (R54x) to the 16 The equation is the same as that above, viz., R54x = k1A1 + k2A2… k16D4
  2. Although each respondent evaluated a different set of 24 vignettes, the original set-up ensured that each of the 101 respondents would evaluate a proper set of vignettes, permitting regression modeling at the level of the individual respondent.
  3. The result of the analysis is a matrix of 101 rows, one row per respondent, and 16 columns one column for each of the 16 The number in the cell is the coefficient for that respondent for the specific element.
  4. The k-means process computes the “distance,” D, between every pair of respondents, by the expression (1-R). The “R” is the Pearson linear correlation between two sets of When R is 1, the two sets of numbers are perfectly related to each other. In our case, this means that the two respondents react identically to the elements. The distance is 1-1 or 0. In contrast, when the two respondents are opposites, R = -1. The distance is (1- -1) or 2.
  5. The k-means algorithm puts the 101 respondents first into two groups, so that the distances of people in each group are small, but the distances of the two group centroids are large. Then the k-means algorithm does the same thing for three groups, and so forth.
  6. The process is entirely objective.
  7. Once the k means algorithm finishes, we end up with two and then with three We can create the equations for the two groups and then create the equations for the three groups. In each case, we look at the strong performing elements.
  8. The remaining effort moves from objective mathematics to subjective We want to make sure that we have easy- to-interpret clusters (interpretability) and as few clusters as possibility (parsimony)
  9. For this study, two clusters ended up providing the better Three clusters ended up having many of the same elements in common.

Table 6 compares the two mind-sets emerging from the k-means clustering. To make the patterns easier to distinguish, the tables show the very strong performing coefficients (25 and higher) in shade. The choice of a cut-off of 25 was made subjectively, to provide a way to distinguish between these two mind-sets. When we use this cut-off, we end up with Mind-Set 1 feeling comfortable by “information-rich messages,” and Mind-Set 2 feeling comfortable with “emotion-rich messages.”

Table 6: Coefficients of two mind-sets (MS1 of 2, MS 2 of 2) emerging from k-means clustering.

It is important to keep in mind that it would be impossible for the respondents to “game” the system. Each respondent saw 24 vignettes in rapid order, and essentially ended up judging each vignette intuitively. Yet, it is striking how clear the mind-sets are. The results of this study support the “insight productivity” emerging from the seemingly “impossible” Mind Genomics task of judging so many vignettes so rapidly.

Step 7: Estimate the Regression Models Using Response Time as the Dependent Variable

Table 7 shows the estimated number of seconds in the response time attributed to each element. The table shows three columns, one column for Total Panel, and then the two remaining columns for the two mind- sets. Long response times are operationally defined as 1.3 seconds or longer. Short response times are operationally defined as 0.3 seconds or shorter. The data suggests no response times meeting the criteria for “long response times.” The absence of long or short response times suggests a lack of deep interest in the topic of COVID-19 [9,10].

Table 7: Response times attributable to individual elements for the Total Panel and for the two mind-sets.

How AI Summarizes the Two Mind-Sets

After the analysis is completed by the Mind Genomics platform, BimiLeap.com, the program is instructed to review the coefficients for each subgroup and answer a variety of prompts. Those prompts are based on the elements 21 or higher for the subgroup. Table 8 shows how the AI “summarizes” these subgroups.

Table 8: AI summarization of the two mind-sets.

What Would AI Have Uncovered Had It Been Prompted to Look for Mind-Sets?

Our final analysis returns to AI, to determine whether or not AI would have uncovered these mind-sets [11-13]. Table 9 (top) shows the instructions given to the AI. Table 9 (bottom) shows the five different mind-sets emerging from the AI, AI-generated Mind-Sets A and B are similar to the empirical Mind-Set 1, AI-generated Mind-Sets D and E are similar to the empirical Mind-Set 2, and AI-generated Mind-Set C is similar to both empirical mind-sets.

Table 9: Using AI to simulate possible mind-sets in the populations regarding communicating with the patient about COVID-19.

Discussion and Conclusions

Mind Genomics experiments improve medical communication by understanding patient mind-sets and preferences. By analyzing patterns in patient responses to different types of communication, healthcare professionals can tailor their approach to better meet individual needs. This personalized approach can lead to improved patient satisfaction, adherence to treatment plans, and overall health outcomes.

AI can further enhance the understanding of patient communication preferences by analyzing large amounts of data and identifying patterns that may not be immediately apparent to human researchers. By developing a corpus of knowledge based on Mind Genomics experiments, medical students and nurse practitioners can learn about the diverse mind-sets of patients and how to effectively communicate with them. This knowledge can help healthcare professionals provide more personalized care and support ongoing professional development.

The value of Mind Genomics experiments goes beyond improving patient satisfaction; it can also lead to better health outcomes. When patients feel heard, understood, and cared for, they are more likely to follow treatment plans and adhere to medical advice. By analyzing patterns in patient responses to different types of communication, healthcare providers can tailor their communication style to better connect with and engage their patients.

Incorporating the findings of Mind Genomics experiments into training can help young medical professionals develop the communication skills needed to excel in their clinical practice and provide more personalized care to their patients. Empathy plays a crucial role in effective communication between doctors and patients, as it allows them to understand and connect with their emotions, concerns, and perspectives.

Mind Genomics experiments can be used to enhance the communication skills and patient-centered care of organizations. By incorporating these findings into training and practice, healthcare providers can better understand patient communication preferences and tailor their communication strategies to meet their unique needs. This can lead to better patient understanding, adherence to treatment plans, and overall satisfaction with care.

Acknowledgment

The authors gratefully acknowledge the ongoing support and encouragement of Dr. Rizwan Hamid of the Global Healthcare Management Forum in Brooklyn. Dr. Hamid is a continuing source of encouragement for young medical professionals to create a more patient-focused, knowledge-driven healthcare system. The authors are grateful to Vanessa A. and Angela A. for their ongoing help in preparing these and other manuscripts for publication.

References

  1. Moskowitz HR, Porretta S, Silcher M (2008) Concept research in food product design and John Wiley & Sons. [crossref]
  2. Moskowitz HR, Reisner M, Lawlor JB, Deliza R (2009) Packaging research in food product design and development. John Wiley & Sons. [crossref]
  3. Moskowitz HR, Gofman A, Beckley J, Ashman H (2012) Mind Genomics®: A Systematic Consumer Research. InRule Developing Experimentation: A Systematic Approach to Understand & Engineer the Consumer Mind. Bentham Science Publishers. [crossref]
  4. Moskowitz HR, Wren J, Papajorgji P (2020) Mind genomics and the Mauritius: LAP Lambert Academic Publishing. [crossref]
  5. Kornstein B, Rappaport S, Moskowitz H (2023) Communication styles regarding child obesity: investigation of a health and communication issue by a high school student researcher, using mind genomics and artificial intelligence. Mind Genom Stud Psychol Exp. [crossref]
  6. Mendoza CL, Mendoza CI, Braun M, Deitel Y, Rappaport S, et (2023) Empowering Young Researchers: Searching for What to Say to Young People to Avoid Becoming Obese. Endocrinol Diabetes Metab J. [crossref]
  7. Gofman A, Moskowitz H (2010) Isomorphic permuted experimental designs and their application in conjoint Journal of sensory studies. [crossref]
  8. Dubey A, Choubey AP (2017) A systematic review on k-means clustering Int J Sci Res Eng Technol (IJSRET, ISSN 2278–0882). [crossref]
  9. Azuma T, Van Orden GC (1997) Why SAFE is better than FAST: The relatedness of a word’s meanings affects lexical decision Journal of memory and language. [crossref]
  10. Chumbley JI, Balota DA (1984) A word’s meaning affects the decision in lexical Memory & Cognition. [crossref]
  11. Moskowitz HR, Rappaport SD, Saharan S, Wingert S, Anderson T, Mulvey T, Mulvey M (2024) Mind-Sets for Prescription Weight Loss Products That Are Advertised Directly to Consumers: Using Mind Genomics Thinking with AI for Synthesis and Acta Scientific Pharmaceutical Sciences (ISSN: 2581-5423). [crossref]
  12. Viswanathan S, Omidvar-Tehrani B, Renders JM (2022) What is Your Current Mindset?: Categories for a satisficing exploration of mobile point-of-interest InProceedings of the 2022 CHI Conference on Human Factors in Computing Systems. [crossref]
  13. Moskowitz HR, Rappaport SD, Saharan S, Mulvey T. Envisioning the World STEM Teaching Organisation: Combining AI with Mind Genomics to Map a Sustainable InNon-Profit Organisations, Volume III: Society, Sustainability and Accountability 2024 Aug 13 (pp. 151-175) Cham: Springer Nature Switzerland. [crossref]

Evaluating Functional Recovery in Stroke Patients: Neurostimulation-Assisted Walking Device in Stroke Patients with Drop Foot

DOI: 10.31038/JCRM.2025834

Abstract

Background: Walking recovery is a key concern for post-stroke patients, with up to 46% of first-ever stroke survivors initially unable to walk and 40% of all post-stroke patients requiring rehabilitation. Regaining a more physiological walking pattern can lead to improved gait performance and, consequently, greater independence in activities of daily living.

Materials and methods: 18 post-stroke patients with drop foot completed a treatment program comprising a neurostimulation-assisted walking device (BTL WALK). Functional gait measurements, including the 10-Meter Walk Test (10MWT), 6-Minute Walk Test (6MWT), Stair Climb Test (SCT), and Manual Muscle Grading scale (MMT) were assessed both before and after the program. Additionally, the Barthel Index was used to evaluate the impact of the treatment on independence in daily activities.

Results: Statistically significant improvements were observed in all monitored parameters, with changes of -17.7% in the 10-Meter Walk Test (10MWT), 14.6% in 6-Minute Walk Test (6MWT), -37.0% in Stair Climb Test (SCT), 8.86% in Barthel Index, and 27.8% in Manual Muscle testing scale (MMT). No significant relationship was found between any parameter or its change and the time since stroke onset.

Conclusions: This study highlights the potential of BTL WALK training in mitigating drop foot in post-stroke patients. Improvements in walking speed, distance, and stair-climbing enhanced independence in daily activities and increased muscle strength, serving as a key motivational factor for continued rehabilitation, especially for patients several years post-stroke.

Keywords

Stroke, Drop foot, Neurostimulation-assisted walking, Gait training, Independence in daily activities

Introduction

Stroke remains a leading cause of mortality and long-term disability, with up to 40% of survivors requiring active rehabilitation [1]. Lower extremity motor impairment is present in 44.1% of individuals experiencing a first-ever stroke, and 46.0% are initially unable to walk [2]. Walking recovery is often one of the first concerns raised by post-stroke patients when consulting medical staff, highlighting its critical role in rehabilitation [3]. Among those who regained some walking ability within 3 to 5 days post-stroke, 76.4% were discharged to home care, compared to only 21% of non-ambulatory patients [1]. Walking is not only a key factor in determining discharge to home care but also a fundamental component in restoring independence in daily life [3].

Up to 20% of stroke survivors experience drop foot, a condition characterized by an impaired ability to dorsiflex the foot during the swing phase of gait. This persistent distal weakness in the hemiparetic leg may be associated with weakness of the anterior muscles, spasticity of the posterior leg muscles, or both. Additionally, the condition can be further complicated by the development of plantar-flexion contracture at the ankle [4]. Drop foot leads to disability by promoting undesirable compensatory movement patterns, restricting mobility, and increasing the risk of falls [4,5]. One potential treatment for drop foot in individuals with hemiparesis is Electrical Stimulation (ES) applied to the peroneal nerve and the tibialis anterior muscle [5].

The most common therapeutic strategies for drop foot are ankle-foot orthosis (AFO) and electrical stimulation [6]. Ankle-foot orthoses (AFOs) are frequently prescribed for individuals with drop foot to maintain ankle neutrality and enhance limb clearance during the swing phase [4,7]. However, this intervention presents several limitations. Restricted ankle mobility associated with AFO use may contribute to contracture development. Furthermore, AFOs do not actively promote dynamic function or facilitate motor recovery [4,6]. Challenges in sit-to-stand transitions and patient discomfort are also commonly reported [4]. Electrical stimulation presents an alternative therapeutic modality for drop foot, targeting branches of the common peroneal nerve distal to the knee. In contrast to AFOs, ES permits physiological ankle range of motion and facilitates active dorsiflexion and eversion without mechanical constraint [4,6]. Clinical evidence indicates that ES, by unrestricted ankle motion, may offer superior functional outcomes compared to AFOs, particularly in complex environments characterized by inclines, uneven terrain, and pedestrian traffic [6,7].

Studies evaluating the immediate effects of ES during therapy have reported positive impacts on walking quality. However, research investigating its long-term effectiveness has yielded divergent results [8,9]. Guzel et al. observed statistically significant improvements in gait quality following a combined conventional physiotherapy, ES, and balance-weighting rehabilitation program in post-stroke patients. Similarly, Street et al. reported positive outcomes in individuals with multiple sclerosis [10,11]. In both patient populations, significant improvements were also observed following ES therapy when compared to the use of AFO [12,13].

This study aims to assess the impact of BTL WALK therapy on post-stroke patients. The research will evaluate the effects on gait parameters, lower limb disability, and the influence of time since stroke on individual outcomes [14,15].

Materials and Methods

The study was conducted at the Hamzova Léčebna Luže-Košumberk rehabilitation center between April and December 2025. Its design adhered to the ethical principles of the 1975 Declaration of Helsinki, as adopted by the Convention on Human Rights and Biomedicine of the Council of Europe and endorsed by the General Assembly of the World Medical Association [17].

Inclusion criteria required participants to be first-time stroke survivors, medically stable, with no cognitive impairment, and presenting with lower extremity involvement in the form of drop foot. Exclusion criteria included febrile conditions, cachexia of any etiology, tuberculosis or other bacterial infections, suspected malignancy, bleeding disorders, menses, the presence of electronic or metal implants in the treatment area, skin inflammation, trophic skin changes, irritated or damaged skin, cardiovascular diseases, sensation disorders, lower limb fractures, dislocations, or conditions adversely affected by motion, electroanalgesia without a confirmed pain etiology, psychopathological syndromes, and inflammatory conditions in the treatment area.

Participants underwent a 4-week treatment program consisting of neurostimulation-assisted walking (BTL WALK) therapy, administered five times per week. Functional assessments were performed before and after treatment, including the 10-Meter Walk Test (10MWT), the 6-Minute Walk Test (6MWT), and the Stair Climb Test (SCT) to evaluate mobility. Muscle strength was assessed by the Manual Muscle Test (MMT). Additionally, participants completed the Barthel Index questionnaire, which assessed their subjective level of disability related to gait impairment.

Before therapy, the patient was seated with the hip, knee, and ankle positioned at a 90° angle. The BTL WALK Pro device (BTL Industries, Ltd.) was placed on the inner calf, with electrode positioning adjusted to stimulate the common peroneal nerve that innervates the peroneal muscles and the tibialis anterior muscle (Figure 1). Following the initiation of therapy, stimulation intensity was gradually increased until ankle dorsiflexion was achieved. The electrode was then further adjusted to maintain the foot in a neutral position, preventing excessive eversion or inversion. The patient was encouraged to walk, with continuous adjustments made to electrode placement and stimulation intensity based on their response. Once optimized, a walking session was conducted, during which drop foot pathology was mitigated through muscle stimulation.

Figure 1: Image depicting the placement of BTL WALK on the inner calf to treat drop foot. Permission was granted by BTL Industries, Ltd.

To assess objective gait improvements, several functional tests were performed. The 10MWT evaluated gait speed, where the patient was instructed to walk at a comfortable pace over a 10-meter distance. The total time taken to complete the walk was recorded in seconds. No physical assistance was provided unless required for safety reasons. Timing began when the first foot crossed the start line and stopped when the first foot crossed the end line at 10 meters [18].

The 6MWT assessed functional exercise capacity by measuring the maximum distance the patient could walk in six minutes. The test was conducted in a long, straight hallway with cones marking each end of the walking course. The patient was instructed to wear comfortable clothing and non-slip footwear and walk at their own pace. They were allowed to rest if needed, but the timer continued running, and the total distance walked in meters was recorded [19].

Lower limb strength and balance were assessed using the SCT, which measured the time taken to ascend a standard flight of 12–14 steps, each 17–19 cm high. The patient was instructed to climb the stairs at their normal pace, using handrails only if necessary for balance. Timing began at the first step and stopped upon reaching the top, with results recorded in seconds [20].

Strength of the tibialis anterior, peronei, soleus, and gastrocnemius muscles was measured using the 0, meaning no contraction, to 5, meaning normal strength against full resistance, scale of the MMT. Muscle tests were performed by trained examiners following standardized limb positions and stabilization to minimize substitution and optimize repeatability.

In addition to these functional tests, the Barthel Index was used to evaluate the patient’s level of functional independence in performing ten essential daily activities. The assessment was conducted through direct observation and patient interviews, with activities scored based on the level of independence—categorized as independent, partially independent, or dependent. The total score ranged from 0 to 100, with higher values indicating greater independence [21].

The sample size was determined based on the capacity of the rehabilitation center and the minimum required sample size calculation. This calculation was conducted using 6MWT data from a previous study on post-stroke patients, assuming a study power of 80% and an estimated effect size of 15 meters [22,23]. The minimum required sample size of nine patients was increased to 20 to account for facility capacity and potential dropout risk.

For statistical processing and data evaluation, a customized script was developed in the Matlab environment. The data were initially analyzed using the Shapiro-Wilk test to assess normality. Except for the 10-Meter Walk Test (10MWT), the assumption of normal distribution was rejected for all parameters. Consequently, these data were analyzed using the Wilcoxon Signed-Rank test and presented as median values with interquartile range (IQR). In contrast, the 10MWT data, which met the criteria for normality, were compared using a T-test and reported as mean values with Standard Deviation (SD). The Pearson correlation coefficient was used to determine if there was a correlation between the time since stroke onset and the therapy outcome. P-values less than 0.05 were considered indicative of statistical significance.

Results

A total of 18 post-stroke patients, 16 men and 2 women, with a mean age of 70.22 ± 12.70 years and a mean time of symptom duration of 8.7 years. No adverse events occurred during the program, and none of the patients experienced any discomfort that would have hindered their participation. The therapy was generally well tolerated and resulted in improved walking function in the majority of patients.

Monitored parameters showed statistically significant improvements, mean score change, and mean within-patient percentage change in Table 1. After the treatments, 83.33% of patients improved in 10 MWT, 88.89% of patients improved in 6MinWT, 83.33% of patients improved in SCT, and 50% of patients improved in BI. These patients showed mean improvements in the aforementioned parameters of -18.41%, +23.06%, -28.46%, and 21.61%. The changes in MMT measurements showed the number of patients that improved after the therapies was 22.22% in the tibialis anterior, 33.33% in the peronei muscle, 27.78% in the soleus muscle, and 27.78% in the gastrocnemius muscle. The average change for the MMT score was 27.78%.

Table 1: Results of functional tests and functional independence questionnaire obtained before and after the treatment program. P values lower than 0.05 were considered statistically significant.

 

Before

After Diff %Diff %Mean within-patient percentage change P (0.05)
Mean (SD) Mean (SD) Mean score change (SD)

T-test

Other: Wilcoxon Signed Rank test

10MWT (seconds)

24.86 (13.50)

20.44 (10.43) -4.43 (5.41) -17.77% -14.20% 0.006

6MWT (meters)

166.17 (75.37) 190.50 (80.67) 24.33 (39.10) 14.64% 19.53%

0.002

SCT (seconds)

68.00 (63.54)

42.83 (20.55) -25.17 (51.53) -37.01% -22.69% 0.006

Barthel Index

75.28 (11.13) 81.94 (5.18) 6.67 (8.91) 8.86% 10.80%

0.035

IQR: Interquartile range, Diff: difference, 10MWT: 10-Meter Walk Test, 6MWT: 6-Minute Walk Test, SCT: Stair Climb Test.

The bar graph in Figure 2 visually represents the improvements achieved. The 6MWT and Barthel Index showed increases in the distance covered and the degree of independence in activities of daily living, respectively. Conversely, both the 10MWT and SCT showed decreases in the time needed to walk a set distance or climb a flight of stairs. These results all suggest an overall improvement in lower limb function over the course of the BTL WALK treatment program.

Figure 2: The baseline and post-treatment values of the 10-Meter Walk Test (10MWT), the 6-Minute Walk Test (6MWT), and the Stair Climb Test (SCT) to evaluate mobility.

Table 2 displays the Pearson correlation coefficients, which show the relationship between the time since stroke onset and individual outcome measures. The results indicate that time since stroke onset did not have a statistically significant effect on any of the indicators.

Table 2: P-value and Pearson correlation coefficient between time since stroke onset and individual outcome measures obtained before starting and after completing the treatment program, and the difference between them.

 

Time vs 10MWT

Time vs 6MWT Time vs SCT Time vs Barthel Index
P (0.05) r P (0.05) r P (0.05) r P (0.05)

r

Before

0.928

0.027 0.662 -0.128 0.355 -0.268 0.578 0.163

After

0.873 0.047 0.57 -0.166 0.591 -0.157 0.757

-0.091

Diff

0.934

0.024 0.711 -0.109 0.358 0.266 0.336

-0.278

Time: Time since stroke onset, r: Pearson correlation coefficient, Diff: difference, 10MWT: 10-Meter Walk Test, 6MWT: 6-Minute Walk Test, SCT: Stair Climb Test.

Discussion

The present study documents statistically significant changes in post-stroke patients’ gait as a result of a rehabilitation program based on BTL WALK gait training. These conclusions are supported by existing evidence, both in terms of statistical significance and the extent of the changes achieved [16,22,24]. Comparing the recalculated 10MWT values for speed (before: 0.51 m/s, after: 0.59 m/s) with similar studies, it is evident that the absolute values of gait speed differ significantly across publications. This may be due to variations in measurement methodology (patient instruction, use of aids, treadmill, etc.) and the different composition of patients in terms of indication, age, time from onset of stroke, severity, or brain damage, etc. Patients in the acute and subacute post-stroke phase tend to be more limited in movement and their gait speed reaches an average of around 0.25 m/s [25]. The current research includes patients who are a few weeks post-stroke, but also those who have been suffering from the consequences of stroke for many years. Correlation analysis did not reveal a statistically significant relationship between time since stroke and any outcome measure or its change. The main influence on walking limitation is likely the extent of brain damage [26].

The observed improvements in walking speed, functional exercise capacity, balance, and lower limb strength, as demonstrated through various functional assessments, can be further explained by the underlying mechanism of BTL WALK in post-stroke drop foot rehabilitation. BTL WALK facilitates neuromuscular activation by delivering electrical impulses to the dorsiflexor muscles, inducing muscle contraction, and enabling ankle dorsiflexion during the swing phase of gait. In post-stroke patients experiencing drop foot or impaired foot clearance, this timely and coordinated muscle activation enhances gait performance, contributing to a more physiological walking pattern [27]. Moreover, intensive and prolonged gait training incorporating ES promotes motor relearning, which, over time, can lead to sustained improvements in gait mechanics, potentially reducing the need for assistive devices [28]. This device can significantly affect the quality of life of patients in everyday life if they use the device as a compensatory aid.

Improvements observed in functional gait assessments were also positively reflected in subjective evaluations of activities of daily living. Although these changes were less pronounced compared to speed and distance-based assessments, they nonetheless reached statistical significance. Future research should consider utilizing more sensitive assessment tools capable of detecting the impact of even minor gait pattern deviations on an individual’s independence in daily activities.

The conclusions of this study must be interpreted in consideration of its limitations. The primary limitation is the absence of a control group, which would have enabled a direct comparison with patients receiving conventional therapy or no treatment, thereby accounting for the potential effects of spontaneous motor recovery. Additionally, the small sample size may have influenced the findings, particularly in the analysis of the impact of time since stroke onset. Notably, patient inclusion was not stratified based on time post-stroke, which adds a distinctive aspect to the study but also presents a limitation. Future research should aim to investigate specific subgroups of stroke survivors, particularly those several years post-stroke, as clinical evidence in this population remains limited.

Despite its limitations, this study provides valuable insights, particularly by demonstrating the positive effects of BTL WALK therapy on gait in patients undergoing rehabilitation not only in the early post-stroke phase but also several years after stroke onset. The finding that mobility and independence in daily activities can still be improved in chronic stroke survivors may serve as an important motivational factor for continued rehabilitation efforts.

Conclusions

The present study highlights the potential of BTL WALK training in mitigating drop foot in post-stroke patients. The observed improvements in walking speed, distance covered, and stair-climbing ability contributed to enhanced independence in daily activities, which may serve as a crucial motivational factor for continued rehabilitation, particularly for individuals undergoing therapy several years post-stroke. The therapy is an effective intervention for rehabilitating patients with limited mobility and gait impairments resulting from brain damage, promoting functional recovery and improved movement patterns.

References

  1. van de Port IG, Kwakkel G, Schepers VP, Lindeman E (2006) Predicting mobility outcome one year after stroke: a prospective cohort study. Journal of Rehabilitation Medicine 38: 218-223. [crossref]
  2. Louie DR, Simpson LA, Mortenson WB, Field TS, Yao J, et al. (2022) Prevalence of Walking Limitation After Acute Stroke and Its Impact on Discharge to Home. Physical Therapy 102. [crossref]
  3. Preston E, Ada L, Dean CM, Stanton R, Waddington G (2011) What is the probability of patients who are nonambulatory after stroke regaining independent walking? A systematic review. International Journal of Stroke: Official Journal of the International Stroke Society 6: 531-540. [crossref]
  4. O’Dell MW, Dunning K, Kluding P, Wu SS, Feld J, et al. (2014) Response and prediction of improvement in gait speed from functional electrical stimulation in persons with poststroke drop foot. PM & R: The Journal of Injury, Function, and Rehabilitation 6: 587-601. [crossref]
  5. Lairamore CI, Garrison MK, Bourgeon L, Mennemeier M (2014) Effects of functional electrical stimulation on gait recovery post-neurological injury during inpatient rehabilitation. Perceptual and Motor Skills 119: 591-608. [crossref]
  6. Li X, Li H, Liu Y, Liang W, Zhang L, et al. (2024) The effect of electromyographic feedback functional electrical stimulation on the plantar pressure in stroke patients with foot drop. Frontiers in Neuroscience 18: 1377702. [crossref]
  7. Nevisipour M, Honeycutt CF (2020) The impact of ankle-foot-orthosis (AFO) use on the compensatory stepping response required to avoid a fall during trip-like perturbations in young adults: Implications for AFO prescription and design. Journal of Biomechanics 103: 109703. [crossref]
  8. Böhm H, Döderlein L, Dussa U, Ch (2020) Functional electrical stimulation for foot drop in the upper motor neuron syndrome: does it affect 3D foot kinematics during the stance phase of walking? Fuß & Sprunggelenk 18: 115-124.
  9. Robison J, Gibbons R, Achelis D, Bent B, Wajda D, et al. (2022) Augmenting gait in a population exhibiting foot drop with adaptive functional electrical stimulation. medRxiv27.22273623.
  10. Güzel Şükran, Umay Ebru, Ozturk Erhan, ÇAKCI Aytül (2022) The Efficiency of Functional Electrical Stimulation and Balance-Weighted Rehabilitation Therapy in Stroke Patients with Foot-Drop: A Pilot Study. Fiziksel Tıp ve Rehabilitasyon Bilimleri Dergisi 25: 1-10.
  11. Street T, Taylor P, Swain I (2015) Effectiveness of functional electrical stimulation on walking speed, functional walking category, and clinically meaningful changes for people with multiple sclerosis. Archives of Physical Medicine and Rehabilitation 96: 667-672. [crossref]
  12. Renfrew LM, Paul L, McFadyen A, Rafferty D, Moseley O, et al. (2019) The clinical- and cost-effectiveness of functional electrical stimulation and ankle-foot orthoses for foot drop in Multiple Sclerosis: a multicentre randomized trial. Clinical Rehabilitation 33: 1150-1162. [crossref]
  13. Wang He, Li Yaning, Yu Shaohong (2025) Effect of electrical stimulation in the treatment on patients with foot drop after stroke: a systematic review and network meta-analysis. Journal of Stroke and Cerebrovascular Diseases 34: 108279. [crossref]
  14. Friðriksdóttir R (2020) Functional Electrical Stimulation as a treatment option for foot drop, with a focus on gait velocity: A retrospective quality improvement study.
  15. Bethoux F, Rogers HL, Nolan KJ, Abrams GM, Annaswamy TM, et al. (2014) The effects of peroneal nerve functional electrical stimulation versus ankle-foot orthosis in patients with chronic stroke: a randomized controlled trial. Neurorehabilitation and Neural Repair 28: 688-697. [crossref]
  16. Lairamore CI, Garrison MK, Bourgeon L, Mennemeier M (2014) Effects of functional electrical stimulation on gait recovery post-neurological injury during inpatient rehabilitation. Perceptual and Motor Skills 119: 591-608. [crossref]
  17. Hendriks A (1997) Council of Europe-Convention for Protection of Human Rights and Dignity of the Human Being with Regard to the Application of Biology and Biomedicine: Convention on Human Rights and Biomedicine. Kennedy Institute of Ethics Journal 7: 277-290.
  18. Peters DM, Fritz SL, Krotish DE (2013) Assessing the reliability and validity of a shorter walk test compared with the 10-Meter Walk Test for measurements of gait speed in healthy, older adults. Journal of Geriatric Physical Therapy 36: 24-30. [crossref]
  19. Agarwala P, Salzman SH (2020) Six-Minute Walk Test: Clinical Role, Technique, Coding, and Reimbursement. Chest 157: 603-611. [crossref]
  20. Gagliano-Jucá T, Li Z, Pencina KM, Traustadóttir T, Travison TG, et al. (2020) The Stair Climb Power Test as an Efficacy Outcome in Randomized Trials of Function Promoting Therapies in Older Men. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences 75: 1167-1175. [crossref]
  21. Collin C, Wade DT, Davies S, Horne V (1988) The Barthel ADL Index: a reliability study. International Disability Studies 10: 61-63. [crossref]
  22. Jongbae Ch, Sungryoung M, Jongeun Y (2019) Effect of Electric Stimulation Training on Walking Ability of Patients with Foot Drop after Stroke. J Int Acad Phys Ther Res, Journal of International Academy of Physical Threrapy Research 10: 1903-1906.
  23. Charan J, Biswas T (2013) How to calculate sample size for different study designs in medical research?. Indian Journal of Psychological Medicine 35: 121-126. [crossref]
  24. Wilkinson Hart IA (2016) Team flow: The missing piece in performance [Thesis for the degree of Doctor of Philosophy, University of Southhampton]. Southhampton University Research Repository.
  25. Cheng DK, Dagenais M, Alsbury-Nealy K, Legasto JM, Scodras S, et al. (2021) Distance-limited walk tests post-stroke: A systematic review of measurement properties. NeuroRehabilitation 48: 413-439. [crossref]
  26. Scott JN, Buchan AM, Sevick RJ (1999) Correlation of neurologic dysfunction with CT findings in early acute stroke. The Canadian journal of neurological sciences. Le Journal Canadien des Sciences Neurologiques 26: 182-189. [crossref]
  27. Shin HE, Kim M, Lee D, Jang JY, Soh Y, et al. (2022) Therapeutic Effects of Functional Electrical Stimulation on Physical Performance and Muscle Strength in Post-stroke Older Adults: A Review. Annals of Geriatric Medicine and Research 26: 16-24. [crossref]
  28. Dantas MTAP, Fernani DCGL, Silva TDD, Assis ISA, Carvalho AC, et al. (2023) Gait Training with Functional Electrical Stimulation Improves Mobility in People Post-Stroke. International Journal of Environmental Research and Public Health 20: 5728. [crossref]

Commentary: Rethinking Digital Dating Abuse Through Gendered Perceptions and Lived Experience of Young Adults

DOI: 10.31038/PSYJ.2025762

 

As digital communication becomes ever more embedded in the daily lives of young adults, the dynamics of intimate relationships are increasingly shaped—and strained—by technology. While digital platforms offer constant connection, they also create new contexts in which control, surveillance, coercion, and aggression can emerge [1]. The publication “College Students Perceptions of Digital Dating Abuse: Insights From Gender Schema Theory” advances the field by examining how college students judge the abusiveness of digital dating abuse (DDA) behaviors, how these judgments differ based on gender, and how personal histories of victimization and perpetration influence these views [2]. Although researchers have clearly defined several digital behaviors as abusive, through the increasing norms of online communication and engagement, it is unclear how college students view these behaviors. Situated within the framework of gender schema theory, this publication provides vital insight into how young adults make sense of digital dating behaviors in their relational lives, and why some forms of DDA remain under-recognized despite their documented harm.

Gender as a Lens for Interpreting DDA

A central contribution of the publication is its clear demonstration that gender strongly shapes how college students interpret DDA. Across all behaviors, male-to-female DDA was viewed as more abusive than female-to-male DDA, reflecting longstanding evidence that violence by men against women is perceived as more harmful and threatening [3,4]. Even in digital contexts without physical contact, cultural scripts linking men with physical power and women with vulnerability shape how students assess potential harm and escalation.Women also consistently rated digital sexual coercion and digital monitoring/control as more abusive than men, aligning with research showing that men are generally more tolerant of aggression and less likely to label behaviors as abusive [5,6]. Gender schema theory [7] offers a useful explanation: cultural expectations that associate masculinity with dominance and femininity with nurturance guide how individuals recognize and evaluate relational harm. Women’s heightened perceptions may also stem from their disproportionate exposure to the emotional, psychological, and sexual consequences of DDA [8-10], including fear of escalation, reputational harm, and coercive control [11,12]. Men, by contrast, may normalize persistent messaging or monitoring due to socialization that minimizes relational intrusion and discourages acknowledging vulnerability.A particularly noteworthy nuance is that women also viewed female-to-male digital monitoring/control as more abusive than men did. Female-perpetrated aggression violates cultural stereotypes positioning women as passive or emotionally compliant [13], making such behavior appear more deviant—and therefore more abusive—to female respondents. Men’s lower ratings in these scenarios may reflect norms that downplay male victimization, reinforce emotional invulnerability, and obscure harm when the perpetrator does not match stereotypical images of an aggressor [14]. As a result, male victims may be less visible to peers, less likely to receive support, and less likely to identify their experiences as abuse [15].

Hierarchies of Harm: Why Monitoring Is Under-Recognized

One of the publication’s strongest contributions is its systematic comparison of how different types of DDA are ranked in severity. Students consistently viewed digital direct aggression as most abusive, followed by digital sexual coercion, and finally by digital monitoring/control. This hierarchy parallels findings from broader intimate partner violence research, where overt threats or sexual aggression are more easily recognized as abusive than psychological or controlling behaviors [16,17]. But digital monitoring/control might present unique risks precisely because it is so easily normalized among young adults.

Research shows that a majority of college students engage in at least one digital monitoring/control behavior—such as checking a partner’s social media or sending excessive messages—without labeling these actions as abusive [18]. Adolescents and young adults often view digital access, shared passwords, or location tracking as signs of trust, intimacy, or commitment [19]. In this context, the publication’s results reflect a broader cultural shift in which persistent digital connection is expected and surveillance becomes routine.

Yet digital monitoring/control is not benign. Studies demonstrate strong associations between monitoring behaviors and poor mental health outcomes, attachment anxiety, and eventual escalation to offline aggression [20,21]. The publication’s findings that students consistently under-recognize this category of harm reinforce the need for educational programs that clearly differentiate between healthy connectedness and intrusive control.

Experience Shapes Perception: Minimization Among Victims and Perpetrators

A significant theoretical insight from the publication is the identification of what might be termed a desensitization effect: individuals with prior experiences of DDA—either as victims or perpetrators—rate abusive behaviors as less harmful than those without such histories. This aligns with research on cognitive dissonance, self-justification, and normalization processes in intimate partner violence.

Victims who remain in or return to unhealthy relationships may reinterpret or downplay DDA behaviors to maintain relational coherence. Perpetrators, meanwhile, may minimize the negativity of their actions, especially when DDA behaviors feel mundane or common among their peers. However, the asymmetry noted by the authors, in which perpetrators minimized digital monitoring/control but not necessarily digital direct aggression or digital sexual coercion, might signal a deeper structural issue: digital monitoring/control is so culturally embedded that even those who commit it may not recognize its harm potential.

This has profound implications for prevention programming. Traditional interventions often assume that individuals can identify unhealthy behaviors but struggle with behavioral change. However, if young adults do not interpret their own actions—or their partner’s actions—as abusive, generative dialogue about harm and relational boundaries becomes more challenging. Prevention efforts must therefore address both recognition and reinterpretation: helping students recalibrate their internal thresholds for what constitutes harmful digital conduct.

Conclusion

The publication “College Students Perceptions of Digital Dating Abuse: Insights From Gender Schema Theory” provides an important and much-needed contribution to the evolving discourse on DDA. By grounding its investigation in gender schema theory and foregrounding the diverse and often contradictory ways college students interpret digital behaviors, it illuminates the perceptual landscape that shapes young adults’ responses to DDA. The findings reveal that DDA is not simply a technological phenomenon but a deeply social one—embedded in gendered expectations, cultural narratives, and personal histories. As institutions, educators, and scholars seek to reduce DDA and foster healthier digital relationships, this study offers a foundation for designing prevention efforts that are evidence-based, culturally attuned, and responsive to the realities of young adults. Its insights serve not only as an academic contribution but as a call for more intentional, nuanced, and inclusive strategies to confront the normalization of digital harm in intimate relationships.

References

  1. Estévez A, Urbiola I, Iruarrizaga I, Onaindia J, Jauregui P (2017) Emotional dependency in dating relationships and psychological consequences of internet and mobile abuse. Anales de psicología 33: 260.
  2. Weathers MR, Gangel MJ (2025) College students’ perceptions of digital dating abuse: Insights from gender schema theory. Journal of Interpersonal Violence. [crossref]
  3. Allen E, Bradley MS (2017) Perceptions of harm, criminality, and law enforcement response: Comparing violence by men against women and violence by women against men. Victims & Offenders 13: 373-389.
  4. Webster K, Diemer K, Honey N, Mannix S, Mickle J, et al. (2018) Australians’ attitudes to violence against women and gender equality. Findings from the 2017 National Community Attitudes towards Violence against Women Survey.
  5. Bryant SA, Spencer GA (2003) University students’ attitudes about attributing blame in domestic violence. Journal of Family Violence 18: 369-376.
  6. Dardis CM, Edwards KM, Kelley EL, Gidycz CA (2017) Perceptions of dating violence and associated correlates: A study of college young adults. Journal of Interpersonal Violence 32: 3245-3271. [crossref]
  7. Bem SL (1981) Gender schema theory: A cognitive account of sex typing. Psychological Review 88: 354-364.
  8. Dick RN, McCauley HL, Jones KA, Tancredi DJ, Goldstein S, et al. (2014) Cyber dating abuse among teens using school-based health centers. Pediatrics 134: e1560-1567. [crossref]
  9. Reed LA, Tolman RM, Ward LM (2017) Gender matters: Experiences and consequences of digital dating abuse victimization in adolescent dating relationships. Journal of Adolescence 59: 79-89. [crossref]
  10. Reed LA, Conn K, Wachter K (2020) Name-calling, jealousy, and break-ups: Teen girls’ and boys’ worst experiences of digital dating. Children and Youth Services Review 108:104607.
  11. Gracia-Leiva M, Puente-Martínez A, Ubillos-Landa S, González-Castro JL, Páez-Rovira D (2020) Off- and online heterosexual dating violence, perceived attachment to parents and peers and suicide risk in young women. International Journal of Environmental Research and Public Health 17: 3174. [crossref]
  12. Marganski A, Melander L (2018) Intimate partner violence victimization in the cyber and real world: Examining the extent of cyber aggression experiences and its association with in-person dating violence. Journal of Interpersonal Violence 33: 1071-1095. [crossref]
  13. West C, Zimmerman HD (1987) Doing gender. Gender and Society 1(2): 125-151.
  14. Turchik JA., Edwards KM (2012) Myths about male rape: A literature review. Psychology of Men & Masculinity 13: 211.
  15. Felson RB, Paré PP (2005) The reporting of domestic violence and sexual assault by nonstrangers to the police. Journal of Marriage and Family 67: 597-610.
  16. Capezza NM, D’Intino LA, Flynn MA, Arriaga XB (2017) Perceptions of psychological abuse: The role of perpetrator gender, victim’s response, and sexism. Journal of Interpersonal Violence 36: 1414-1436. [crossref]
  17. Masci BSF, Sanderson S (2017) Perceptions of psychological abuse versus physical abuse and their relationship with mental health outcomes. Violence and victims 32: 362-376. [crossref]
  18. Reed LA, Tolman RM, Ward LM (2016) Snooping and sexting: Digital media as a context for dating aggression and abuse among college students. Violence Against Women 22: 1556-1576.
  19. Redondo I, Ozamiz-Etxebarria N, Jaureguizar J, Dosil-Santamaria M (2024) Cyber dating violence: How is it perceived in early adolescence? Behavioral Sciences 14: 1074.
  20. Borrajo E, Gámez-Guadix M, Calvete E (2015) Cyber dating abuse: Prevalence, context, and relationship with offline dating aggression. Psychological Reports 116: 565-585. [crossref]
  21. Gámez-Guadix M, Borrajo E, Calvete E (2018) Abuso, control y violencia en la pareja a través de internet y los smartphones: Características, evaluación y prevención. Papeles Psicólogo 39: 218-227.

Talking to Patients: Part 1 – Using AI to Suggest How to Talk with a Patient Regarding COVID-19

DOI: 10.31038/MGSPE.2025522

Abstract

Using AI (ChatGPT 3.5) and Mind Genomics thinking, the paper shows how the medical professional can learn how to understand and converse with patients on topics such as COVID-19. The approach comprises an initial query to AI about the topic, using the Idea Coach feature on BimiLeap.com. The query can be modified and resubmitted, providing the medical professional with a real-time learning tool based on AI. Once the queries and iterations are completed and the program goes offline, the information generated by AI is subject to additional critical thinking by AI. The output comprises key themes, perspectives, analyses of responses of audiences (positive, negative, alternative viewpoints), as well as suggested innovation. The paper proposes the approach as a just-in-time teaching system for the medical professional who needs an understanding of how patients may think about a condition, and how one might communicate with the patient.

Keywords

Artificial Intelligence; medical communication; Mind Genomics; patient interaction

Introduction

Many medical professionals struggle with the job of communicating with patients. This shortcoming in effective interactions with patients can lead to misunderstandings, misinformation, frustration, and ultimately, the loss of trust in the medical professional. Furthermore, poor communication skills can hinder the diagnostic process, as patients may not feel comfortable sharing important information about their health. As a result, it is crucial for medical professionals, especially students, but also new doctors and nurse practitioners to learn how to talk to patients in a clear, empathic, respectful and ultimately productive manner.

One solution to this problem is the integration of artificial intelligence (AI) technology as a colleague and tutor for medical professionals. AI can provide simulated patient interactions for students to practice and improve their communication skills in a safe environment. AI can also provide real-time feedback and suggestions on how to improve communication with patients, helping the medical professional become more confident and effective in their interactions. In addition, AI can serve as a resource for medical professionals to access information on different communication strategies, cultural nuances, and techniques for building rapport with patients. AI can provide personalized guidance based on the individual needs and preferences of each healthcare provider, helping them to tailor their communication style to better meet the needs of their patients. This personalized approach can lead to more positive patient experiences and ultimately, better health outcomes. Furthermore, AI can assist medical professionals in gathering important information about their patients, such as their medical history, treatment preferences, and communication preferences. This information can help medical professionals build stronger relationships with their patients, as they can better understand and address their individual needs and concerns. By using AI technology, medical professionals can enhance their ability to provide patient-centered care and improve overall patient satisfaction.

Using the Mind Genomics Platform as Access Point to AI

The Mind Genomics platform, with its access to ChatGPT 3.5, is designed to help the medical professional communicate effectively with patients, as will be shown in this paper and the companion paper. With the Idea Coach feature of the Mind Genomics platform, BimiLeap. com, the user can request AI to provide different ways to ask patients about how they are feeling, what concerns them, and so forth. The AI returns with language designed to elicit the necessary information from the patient. The exercise helps the medical professional practice their communication skills in a safe and controlled environment. The happy outcome is that the medical professional ends up learning the nature of insightful and empathic questions, the language which shows compassion, and builds rapport with patients.

The topic of this paper is the interaction with the patient regarding COVID-19 (henceforth abbreviated as COVID). The Idea Coach feature provides a variety of alternative questions, explaining the subtleties of the question where relevant. With the Idea Coach feature, the medial professional can input the specific questions they need help with, and the Idea Coach can provide feedback on the wording, tone, and overall effectiveness of their questions. This personalized coaching can help to refine the medical professional’s communication style and help them learn how to ask relevant and sensitive questions about a complex topic like COVID. For example, the Idea Coach could suggest ways to frame questions about COVID symptoms, exposure history, and vaccination status in a clear and non-judgmental manner. It could also provide guidance on how to address patient concerns and convey important information about the virus and preventive measures. Through repeated practice and feedback from AI. With direct, easy to understand feedback, the medical professional would soon become more confident and skilled in communicating effectively with patients about COVID and other health-related topics.

A Worked Example: How AI can Show Ways for the Doctor to Talk to the Patient.

Table 1 presents the instructions to AI (Idea Coach in BimiLeap. com). The instructions or prompts are written in simple English (the program can work in other languages as well). The instructions are straightforward, simple and direct. At the same time, they convey very little information. All that is known is the topic (COVID), the identity of the respondent (woman, age 25-35), and some simple requests about format (questions 15 words or fewer, simple language). The simplicity of writing the prompt to the AI removes the factor of expertise as a necessity. Anyone can write these simple instructions.

Table 1 first shows the “key idea” and then the question corresponding to that key idea. The question itself is shown in italics. The question itself (in italics) was generated immediately upon request in a so-called iteration. The user typed in the request at the top of Table 1, submitted the requests by selecting the proper “box” on the screen, and the 14 questions emerged immediately. This iteration could be repeated if desired. After the iterations were completed, and the BimiLeap.com program shut down by the user, the underlying program reviewed the results of each iteration through “critical thinking”.The first part of the critical thinking was to state the “key idea” of each question. That “key idea” is presented BEFORE the actual question.

Table 1: Instructions to the AI requesting how the doctor should talk to the patient (top), and the critical thinking and related specific questions emerging from AI (bottom).

During the “critical thinking” period, where AI analyzes its own work “off-line,” AI often comes up with additional questions that were not presented to the user at the time the iteration was occurring, viz., when the user was interacting with BimiLeap. Table 2 shows 12 additional questions suggested by AI that became available when the results of AI’s off-line analyses were completed. The user receives this additional information in the form of an Excel workbook, called the Idea Book.

Table 2: Twelve additional, relevant questions to ask the patient. These questions were generated as part of the AI’s critical thinking analysis of its own work.

Critical Thinking by AI Regarding the 15 Questions

Critical thinking by the Idea Coach feature in BimiLeap is designed to provide deeper insights of a practical nature for the user, insights which teach. Critical thinking begins with the identification of themes and perspectives within the set of questions that the AI had generated. The critical thinking is “built-in.” That is, for every iteration, the critical thinking questions and analyses are done automatically. Thus, for this study, the user actually did eight iterations. We are looking at one iteration. The critical thinking analysis done off- line was done separately for each of the eight iterations, providing a useful compendium of material from which to understand the nature of questions that one could ask. The objective of repeating the critical thinking for each iteration was to create a resource for the medical professional. Table 3 shows the 12 themes and perspectives emerging from the questions. Table 3 goes a bit deeper into the theme, considering different questions that one might ask.

Table 3: Twelve themes and perspectives identified by AI.

AI Suggests Three Audiences: Those Who Accept, Those Who Oppose, and Those Who Think Differently

By hearing different points of view on COVID, medical professionals can learn how each patient reacts and adjust their communication methods accordingly. This helps them handle tough talks and give each patient individualized care. By staying informed about evolving beliefs and attitudes, healthcare providers can identify trends, misconceptions, and misinformation, build trust with patients, and ensure accurate information sharing. Also, seeing things from different points of view prepares the medical professional for possible relationship problems, like language or cultural hurdles. By consistently engaging with diverse viewpoints, medical professionals enhance their ability to dispel myths, address misconceptions, and provide accurate information which resonates with diverse patient experiences.

Table 4 presents possible responses by three populations to the issues surrounding COVID. These populations are those who are interested, those who oppose, and those who “think differently,” because the “facts” that they believe to be true are not true according to orthodox medical belief.

Table 4: Responses of three likely audiences; Interested, Opposing, and Alternative Viewpoints.

Innovations

The feature of critical thinking generated by AI in the Mind Genomics platform are suggested innovations. The AI can only go so far, and the innovations may already be in place. Yet, simply having AI suggest these ideas, and then using AI to expand the interest creates a framework where AI becomes a true collaborator. In this spirit, Table 5 presents 12 innovations as AI conceptualizes them at the early stages [1-15].

Table 5: Twelve innovations suggested by AI for the patients and medical professionals dealing with COVID.

Discussion and Conclusions

Using AI as a coworker and teacher for medical workers is important for improving soft skills like knowing how people with COVID think. AI can model conversations with patients to help workers learn how to understand and talk to patients in tough situations. One of the best things about using AI in medical education is that it can figure out the different ways that people may talk to medical professionals when they need help. AI can help the professional understand and adapt to the different needs and communication styles of patients by giving them specific advice and feedback in real time.

Medical workers can learn how to have tough talks and get along with patients by simulating real-life contacts with patients using AI technology. This can help build trust and make patients happier, which can lead to better results in the long run. Moreover, AI can give doctors information about how patients behave and what they like, which lets them adjust how they talk to each patient to best meet their needs. This personalized method can help people talk to each other better and help patients do better. Finally, AI can help doctors learnmore about the mental and emotional parts of patient care, in addition to making it easier for them to talk to each other.

As a whole, incorporating AI into medical education gives students a one-of-a-kind chance to improve their soft skills and talk to patients more clearly. At the same time, with AI technology, medical workers of all types can learn to understand, connect with, and give patient-centered care which meets each patient’s unique needs.

Acknowledgment

The authors gratefully acknowledge the ongoing support and encouragement of Dr. Rizwan Hamid of the Global Healthcare Management Forum in Brooklyn. Dr. Hamid is a continuing source of encouragement for young medical professionals to create a more patient-focused, knowledge-driven healthcare system.

The authors are grateful to Vanessa A. and Angela A. for their ongoing help in preparing these and other manuscripts for publication.

References

  1. Allen MR, Webb S, Mandvi A, Frieden M, Tai-Seale M, Kallenberg G (2024) Navigating the doctor-patient-AI relationship-a mixed-methods study of physician attitudes toward artificial intelligence in primary care. BMC Primary Care 25(1): 42. [crossref]
  2. Al-Zyoud W, Oweis T, Al-Thawabih H, Al-Saqqar F, Al-Kazwini A, et al. (2021) The psychological effects of physicians’ communication skills on covid-19 patients. Patient Preference and Adherence: 677-90. [crossref]
  3. Casini L, Delnevo G, Mirri S, Monti L, Prandi C, Roccetti M, et (2019) What Do Patients Tell Doctors on the Internet? Ask AI How to Valorize Online Medical Conversations. In2019 28th International Conference on Computer Communication and Networks (ICCCN) IEEE.
  4. Debad SJ, Metcalfe J (2024) DOCTORS AND ARTIFICIAL INTELLIGENCE: WORKING TOGETHER FOR A HEALTHIER FUTURE. SPARK-ing Big Questions: What is the Future of Health Technology? 31: 70.
  5. Delnevo G, Roccetti M, Mirri S (2018) Modeling patients’ online medical conversations: a granger causality approach. InProceedings of the 2018 IEEE/ACM international conference on connected health: applications, systems and engineering technologies.
  6. Gopichandran V, Sakthivel K (2021) Doctor-patient communication and trust in doctors during COVID 19 times—A cross sectional study in Chennai, India. Plos One. 16(6): e0253497. [crossref]
  7. Mehta-Shah, N., S. Mehta, and R. Zemel, in Consumer-based New Product Development for the Food Industry, ed. S. Porretta, H. Moskowitz, and A. Gere,(2021) The Royal Society of Chemistry, 8, pp. 119-131.
  8. Mendel T, Nov O, Wiesenfeld B (2024) Advice from a doctor or AI? Understanding willingness to disclose information through remote patient monitoring to receive health advice. Proceedings of the ACM on Human-Computer Interaction 8(CSCW2): 1-34.
  9. Mheidly N, Fares J (2020) Leveraging media and health communication strategies to overcome the COVID-19 infodemic. Journal of Public Health Policy 41(4): 410-20. [crossref]
  10. Patra M, Hamiduzzaman M, McLaren H, Siddiquee NA (2024) A scoping review of changes to patient-doctor communication during COVID-19. Health Communication. 39(1): 25-48. [crossref]
  11. Reddy BV, Gupta A (2020) Importance of effective communication during COVID-19 Journal of Family Medicine and Primary Care 9(8): 3793-6. [crossref]
  12. Theriot A, Urrutia-Alvarez N, McKinley EM (2021) An analysis of pandemic panic buying motivators among undergraduate college students using mind genomics cognitive Psychology 12(9): 1457-71.
  13. Tripathy AK, Carvalho R, Puthenputhussery A, Chhabhaiya N, Anthony B (2015) MediAssistEdge—Simplifying diagnosis procedure & improving patient doctor In2015 International Conference on Technologies for Sustainable Development (ICTSD) IEEE.
  14. Wittenberg E, Goldsmith JV, Chen C, Prince-Paul M, Johnson RR (2021) Opportunities to improve COVID-19 provider communication resources: A systematic Patient Education and Counseling 104(3): 438-51. [crossref]
  15. Yashar-Gershman SG, Rosenberg AT, Sawhney M, Machicao MF, Moskowitz HR, Bernstein HH (2024) Developing a novel screening tool to address pediatric COVID-19 vaccine hesitancy at point of care. Vaccine 42(9): 2260-70. [crossref]

Chinese T2DM Patients’ Psychological Insulin Resistance: A Comprehensive Structural Equation Model Analysis

DOI: 10.31038/EDMJ.20261014

Abstract

Psychological insulin resistance (PIR) constitutes a significant barrier to achieving timely insulin initiation among adults living with type 2 diabetes mellitus (T2DM). This study utilizes structural equation modeling (SEM) to explore the influence of self-efficacy, social support, diabetes distress, and diabetes stigma on PIR. A cross-sectional survey of 289 T2DM patients demonstrated that PIR is shaped by complex emotional and sociocultural pathways. High self-efficacy and strong social support reduce insulin hesitancy, whereas elevated distress and stigma significantly intensify reluctance toward insulin therapy. These findings provide important guidance for developing psychosocial interventions aimed at improving insulin uptake and long-term glycemic outcomes.

Keywords

Insulin reluctance, Self-efficacy, Diabetes distress, T2DM, China, SEM

Introduction

Type 2 diabetes mellitus (T2DM) is a rapidly escalating public health challenge in China, with the country representing the largest diabetic population globally. Despite therapeutic advancements, maintaining glycemic control remains difficult for many individuals due to progressive β-cell dysfunction. Insulin therapy is often required as the disease advances; however, psychological insulin resistance (PIR) frequently delays the transition to insulin. PIR refers to a complex spectrum of emotional fears, misconceptions, perceived burdens, stigma, and cultural beliefs surrounding insulin therapy.

In China, sociocultural dynamics play a profound role in shaping patient perceptions of insulin therapy. Many individuals view insulin as an indicator of treatment failure or severe disease progression. Fear of injections, hypoglycemia, lifestyle disruption, dependency, and judgment from others further heighten psychological resistance. The collectivist nature of Chinese society places significant influence on family beliefs, which can either support or hinder insulin acceptance. Additionally, limited diabetes education and inadequate emotional counseling exacerbate misunderstandings about insulin, reinforcing PIR [1].

Existing research highlights key psychosocial determinants of PIR, including self-efficacy, social support, diabetes distress, and diabetes stigma. Individuals with high self-efficacy exhibit stronger confidence in diabetes self-management and are more open to initiating insulin [2]. Social support provides emotional and practical assistance, buffering anxiety associated with insulin therapy. In contrast, elevated diabetes distress increases emotional burden and avoidance behaviors, while stigma contributes to internalized shame and negative self-perception. Few studies, however, have examined these factors within a unified analytical framework in Chinese populations.

Structural equation modeling (SEM) offers a comprehensive statistical method for examining direct and indirect effects across multiple interrelated psychosocial constructs. This study aims to employ SEM to explore how self-efficacy, social support, distress, and stigma interact to shape PIR among Chinese adults with T2DM [3]. Understanding these pathways is crucial for designing culturally sensitive interventions to reduce PIR and facilitate timely insulin initiation.

Methods

Study Design and Setting

This cross-sectional study was carried out between March and September 2023 in the Endocrinology Department of a tertiary teaching hospital in Anhui Province, China. The hospital serves as a major referral center for diabetes management, providing access to a large and diverse patient population. Ethical approval was obtained from the institutional review board prior to study commencement.

Sampling and Recruitment

A convenience sampling method was used to recruit adults attending outpatient diabetes clinics. Inclusion criteria were: age ≥18 years, confirmed diagnosis of T2DM for at least one year, and ability to independently complete questionnaires. Patients with severe psychiatric illness, cognitive impairment, terminal illness, or short-term insulin use for acute conditions were excluded. A total of 289 participants met eligibility criteria and completed the study.

Data Collection Procedures

Written informed consent was obtained from all participants. Trained research assistants supervised questionnaire completion, ensuring accuracy and minimizing missing data. Clinical information including HbA1c levels, treatment regimens, and diabetes duration was extracted from electronic medical records [4].

Measures

Participants completed validated Chinese versions of the following instruments: the Psychological Insulin Resistance Scale (PIR), the Diabetes Management Self-Efficacy Scale, the Multidimensional Scale of Perceived Social Support (MSPSS), the Diabetes Distress Scale (DDS-17), and the Type 2 Diabetes Stigma Scale. All instruments demonstrated high internal consistency with Cronbach’s α > 0.70.

Statistical Analysis

Data analysis was performed using SPSS 26.0 and AMOS 26.0. Descriptive statistics summarized demographic and clinical characteristics. Pearson correlations were computed to examine associations between variables [5]. Structural equation modeling (SEM) was used to evaluate hypothesized relationships, with model fit assessed through χ²/df, RMSEA, GFI, AGFI, CFI, TLI, and IFI indices. Bootstrapping with 5000 iterations tested indirect effects and mediation pathways.

Results

Participant Characteristics

A total of 289 adults with T2DM participated in the study. The mean age was 56.8 ± 10.3 years, with 52% being male. The average duration of diabetes was 8.4 ± 4.2 years. HbA1c levels averaged 8.1%, indicating poor glycemic control across the sample. Approximately 38% of patients reported that their physician had advised insulin initiation, yet they remained reluctant due to psychological barriers [6] (Figure 1).

Figure 1: Conceptual Structural Equation Model.
This figure visually represents the expected directional influences of key psychosocial variables on Psychological Insulin Resistance (PIR).

Psychosocial Profiles and PIR Levels

The mean PIR score was 56.7 ± 11.4, indicating moderate psychological resistance toward insulin therapy. Self-efficacy scores reflected moderate overall confidence in diabetes self-management. Social support was perceived as relatively strong, particularly from family members 7-10]. Diabetes distress scores were elevated, with emotional burden and regimen-related frustration being the most prominent factors. Moderate levels of diabetes stigma were also reported (Table 1).

Table 1: Participant Demographics

Variable

Mean/n SD/%

Interpretation

Age (years)

56.8

10.3

Middle-aged population with chronic disease burden
Gender (Male)

151

52%

Slightly higher male representation
Diabetes Duration (years)

8.4

4.2

Indicates long-term disease progression
HbA1c (%)

8.1

Poor glycemic control across sample

Correlation Analysis

Pearson correlation analysis demonstrated significant associations between PIR and key psychosocial variables. PIR was strongly negatively correlated with self-efficacy (r = -0.55) and social support (r = -0.41), indicating that patients with greater confidence and stronger support systems exhibited lower insulin hesitancy. PIR was positively correlated with diabetes distress (r = 0.58) and diabetes stigma (r = 0.32), confirming that emotional burden and stigmatized beliefs contribute substantially to resistance (Figure 2).

Figure 2: Distribution of PIR Scores.
Simulated distribution illustrating typical clustering of PIR scores among T2DM patients.

Structural Equation Modeling (SEM)

The structural model demonstrated excellent goodness of fit, with χ²/df = 1.56, RMSEA = 0.044, GFI = 0.960, AGFI = 0.933, CFI = 0.980, and TLI = 0.972. Standardized path coefficients indicated that self-efficacy (β = -0.61, P < 0.001) and social support (β = -0.39, P = 0.001) significantly reduced PIR. Conversely, diabetes distress (β = 0.61, P < 0.01) and diabetes stigma (β = 0.28, P = 0.001) significantly increased PIR. Bootstrapping confirmed that distress partially mediated the relationship between social support, stigma, and PIR (Table 2).

Table 2: Structural Equation Model Path Coefficients.

Pathway

β Coefficient p-value

Interpretation

Self-efficacy → PIR

-0.61

<0.001

Higher self-efficacy reduces PIR
Social support → PIR

-0.39

0.001

Support systems lower resistance
Diabetes distress → PIR

+0.61

<0.01

Higher distress increases PIR
Diabetes stigma → PIR

+0.28

0.001

Stigma elevates PIR scores

Discussion

This expanded analysis demonstrates the powerful interplay of psychological and sociocultural variables influencing PIR in Chinese T2DM populations. Self-efficacy emerged as the strongest protective factor, emphasizing the necessity of individualized diabetes education and confidence-building strategies. Patients with higher self-efficacy perceive insulin not as a sign of failure but as a therapeutic tool supporting long-term health outcomes. Clinical teams must therefore prioritize training that enhances problem-solving skills, self-management behaviors, and understanding of insulin mechanisms.

Diabetes distress was revealed as a substantial positive predictor of PIR. Emotional burden—including frustration, burnout, and anxiety—magnifies fear of hypoglycemia, perceived complexity of insulin routines, and concerns about lifelong dependency. Interventions such as targeted counseling, diabetes burnout management programs, stress-reduction techniques, and frequent follow-up can meaningfully mitigate distress levels.

Social support demonstrated a significant buffering effect, reducing PIR through emotional, informational, and practical reinforcement. Chinese cultural contexts place high value on family involvement; thus, educating families on insulin benefits and dispelling myths can improve acceptance. Conversely, diabetes stigma was associated with increased PIR. Stigmatized individuals may perceive insulin as an indicator of personal failure or social inadequacy. Addressing stigma through public health communication, peer-support groups, and clinician–patient trust-building is essential.

Conclusion

PIR among Chinese adults with T2DM is shaped by interconnected psychosocial mechanisms. Strengthening self-efficacy, enhancing social support, reducing distress, and addressing stigma collectively improve insulin acceptance. These findings emphasize the need for comprehensive, culturally sensitive interventions integrated into diabetes care pathways. Diabetes distress emerged as one of the strongest predictors of PIR, indicating that emotional exhaustion, frustration with disease management, and fear of future complications significantly undermine patients’ readiness to adopt insulin. Interventions must therefore extend beyond traditional education to include psychological counseling, distress screening, cognitive-behavioral support, and stress-reduction strategies. Addressing distress proactively may not only reduce PIR but also improve glycemic control, treatment adherence, and overall quality of life.

References

  1. Duan J, Li H, Zhang Y, et al. (2025) Type 2 diabetes prediction model in China: a five-year risk assessment study. Chin J Endocrinol Metab 42(2): 112-119. [crossref]
  2. Luo X, Zhang S, Wang Y, et al. (2025) Health management of type 2 diabetes mellitus and its complications in Chinese community settings: a cohort analysis. J Diabetes Res 2025: 998742. [crossref]
  3. Wang R, Li Q, Chen Z, et al. (2025) Disease burden of type 2 diabetes among young adults in China: trends and forecast (1990-2030) Int J Diabetes Endocrinol 12(1): 27-36.
  4. Zeng Z, Wu C, Huang Y, et al. (2023) Health-related quality of life in Chinese individuals with type 2 diabetes: impact of fear of hypoglycaemia and treatment burden. Qual Life Res 32(9): 2451-2462. [crossref]
  5. Deng W, Li M, Zhou Y, et al. (2024) National burden and risk factors of diabetes mellitus in China (1990-2021): analysis of mortality and DALYs. Public Health Rep 139(4): 315-327. [crossref]
  6. Lu X, Zhang D, Wang B, et al. (2024) Type 2 diabetes mellitus in adults: pathogenesis, metabolic dysfunction syndrome and personalized management—A Chinese perspective. Metab Syndr Relat Disord 22(5): 201-214. [crossref]
  7. Zhao X, Li Y, Chen H, et al. (2022) Treatment of type 2 diabetes mellitus using traditional Chinese medicine: efficacy of Jinlida granules in Chinese patients. Chin J Integr Med 28(8): 584-591. [crossref]
  8. Zhang P, Liu Y, Chen D, et al. (2024) Effectiveness of a digital-health intervention for improving T2DM control in rural China: a cluster randomized trial. Chin Prim Health Care 10(3): 145-154.
  9. He Q, Wang J, Li Z, et al. (2023) Diabetes self-management and its correlates among Chinese adults with type 2 diabetes: a cross-sectional study. BMC Endocr Disord 23(1): 107. [crossref]
  10. Wang J, Liu H, Huang Z, et al. (2023) Trends in the burden of type 2 diabetes mellitus attributable to high body mass index in China (1990-2019): a joinpoint regression and age-period-cohort analysis. Front Endocrinol 14: 119388. [crossref]

Behavior of Deuterium on Boric Acid: Raman Spectrometric Measurements: Preliminary Results

DOI: 10.31038/GEMS.2025774

Abstract

We show, in short, using Raman spectroscopy, that upon dissolving boric acid [H3BO3] in heavy water [D2O], all stoichiometric phases form solid crystalline phases and can be detected. The pure D3BO3phase is very rare, but it can be proved. Intermedia forms were not found. Deuterium in higher concentrations in nature is, however, possible in connection with supercritical fluids coming from the mantle region.

Keywords

Raman spectroscopy, heavy water [D2O], solid phases: H3BO3, H2DBO3, HD2BO3, and D3BO3, pegmatites

Introduction

Boron, the fifth element of the periodic table, is relatively rare on Earth, with a concentration of 2.5 ppm in acid rocks. In granite rocks, the average concentration is 15 ppm [1]. Generally, boron is present in the Earth’s crust in its compounds, as tourmaline and other complex minerals. The author has also recently found boron as a mineral in the upper crust [2]. In mineral-forming minerals, especially in pegmatites, boron appears as boric acid [H3BO3], often as a daughter mineral in fluid and melt inclusions in different minerals (quartz, tourmaline, hambergite, beryl, topaz). Raman data of boric acid are difficult for the general public to access. Most publications on this topic are not open access. Therefore, we provide a brief overview of the Raman spectra, including those of deuterium-bearing phases. Because Raman spectroscopy is a relatively straightforward and accurate method for determining the Raman shift by the exchange of hydrogen of boric acid [H3BO3] by deuterium [D], we will show, using the leading Raman bands, the stoichiometric exchange of hydrogen by deuterium.

Methods and Samples

Microscopy, Raman Spectroscopy

Besides a polarization microscope for transmission (JenaLab Pol), we performed all microscopic and Raman spectroscopic studies with a petrographic polarization microscope (BX 43) equipped with an XY- or rotating stage, coupled to the EnSpectr Raman spectrometer R532 (Enhanced Spectrometry, Inc., Mountain View, CA, USA) in transmission. The Raman spectra were recorded in the spectral range of 0–4000 cm−1 using the 30 mW of a single-mode 532 nm laser, an entrance aperture of 20 µm, a holographic grating of 1800 g/mm, and a spectral resolution of 4 cm−1. Solid boric acid (sassolite) crystals are easy to identify under crossed Nicols under the transmission light by their peculiar interference color.

Samples

Deuterium-rich boric acid phases are prepared by dissolving commercial nB-boric acid in pure D2O (heavy water (99.9%) from the PElementeSamples, Belchertown, MA) in surplus. nB refers to boron with natural abundance of the boron isotopes: 80.22% 11B and 19.78% 10B. Droplets of such solutions are placed on the microscope slice, with a deepening in the middle, and the solid boric acid phases slowly crystallize as the heavy water vaporizes at room temperature. Through this slow process, a fractionation of the solid phases occurs according to the schema:

2H3BO3 + 3D2O → 2D3BO3 + 3H2O                             (1)

H3BO3 + D2O → HD2BO3 + H2O                                   (2)

2H3BO3 + D2O → 2H2DBO3 + H2O                               (3)

Besides stoichiometric phases, subordinate phases are theoretically possible as well. However, as we will see clearly, the stoichiometric phases are dominant. The relatively large standard deviations of the D-influenced Raman lines allow them to be incorporated as traces. That means that at low deuterium concentration, we mainly observe it inserting into the Raman main line at 880 cm-1, which results in a slight shift to lower values. In our experiments to produce D-bearing boric acid phases, the formation of small amounts of water [H2O], shown in equations (1) to (3), is detectable [3,4]. In our case, 8% (g/g) water was estimated by Raman.

Results

Gmelin’s handbook (1954) [5] gives only 7 Raman lines for boric acid: 503 (4), 880 (6), 1065 (0), 1155 (0), 3180 (3), 3256 (1), and 3574 (3). The numbers in brackets are relative intensities. Boric acid shows, according to Krishnan (1963) [6], 11 Raman lines (4Ag + 2E1g + 5E2g), including three lines from the lattice spectrum. During our study, we have found all 11 Raman lines/bands (Table 1 and Figure 1).

Table 1: Raman lines of boric acid [H3nBO3 ] according to Krishnan (1963) and own measurements using the 532 nm laser.

H3BO3

Intensity (relative)

Assignment

Krishnan, 1963

This work

Raman band (cm-1)

Species

Raman band (cm-1)

   

60

Ag 76.0 ± 1.6

w

Lattice- oscillation

128

E1g 119.1 ± 3.8

w

Lattice- oscillation

210

E2g 207.8 ± 1.6

m

OH-O bonds

499

E2g 497.2 ± 3.8

m

O-B-O bending

884

Ag 879.8 ± 0.3

vs

B-O stretching

1085

Ag 1092.0 ± 7.2

w

B-O-H bending

1172

E2g 1166.6 ± 2.7

w

O-H stretching

1384

E2g 1383.5 ± 6.3

w

BOH bending

  2294.1 ± 2.7

vw

BOH bending

3165

Ag 3173.6 ± 4.2

w

O-H stretching

3251

Ag 3246.4 ± 4.1

w

O-H stretching

Irel – relative intensity: vs – very strong, s – strong, m – medium, w – weak, vw -very weak.

Figure 1: Raman spectrum of boric acid [H3BO3] using nB and the 532 nm Raman excitation in the Raman range from 50 to 4000 cm-1.

The differences in the Raman shift result from the use H3nBO3 and the fractionated crystallization in a droplet of distilled water on the microscope slide. The data in Table 1 are from Krishnan (1963) – [6] and own measurements (mean of 20 measurements per Raman line).

Krishnan (1963) [6] takes the Raman spectrum of crystalline boric acid using the mercury 253.7 nm line.

In a further step, we determined the Raman shift by substituting hydrogen with deuterium. Conspicuously act deuterium on the 880 cm-1 and the 129 cm-1 lines. Figure 2 shows the change in dependence of the substitution degree. Because the 880 cm-1 line (the main line of boric acid) shifts firmly with the substitution of hydrogen by deuterium, the assignment can not be correct – it should be exchanged by B-O-H stretching. As we see from Figure 2, the deuterium substitution influences the lattice oscillation powerfully.

Figure 2: Raman spectrum of boric acid [H3BO3] using nB and the 532 nm Raman excitation in the Raman range from 50 to 4000 cm-1 with the exchange of hydrogen by deuterium.

Figure 2 shows the Raman spectrum of boric acid, where the hydrogen (H) atom is stepwise replaced by deuterium (D), and the extracted results are summarized in Table 2 and Figure 3. In contrast to the Raman spectrum of pure H3BO3 (Figure 1), the spectrum of the deuterium-bearing boric acid is more complex, showing the presence of different D-bearing species (see Table 2 too), especially in the range between 812 – 880 cm-1, and in the range between 75 – 129 cm-1. Conspicuous for the D-bearing boric acid is the Raman band at 2415 cm-1. Pure H3BO3 shows only a very weak doublet in this region at 2235 and 2294 cm-1.

The data in Table 2 are plotted in Figure 3. The correlation coefficient R2 = 0.99914.

Table 2: Position of the main Raman lines at about 880 cm-1 and the Raman shift by including deuterium into the formula (mean of 20 measurements each).

Formula

Raman position Standard deviation

Irel

H3BO3

879.56 0.41 s
H2DBO3 854.43

0.38

s

HD2BO3

834.33 0.80 m
D3BO3 812.78 1.46

vw

Irel – relative intensity: vs – very strong, s – strong, m – medium, w – weak, vw -very weak.

Figure 3: Raman shift of the H3BO3 main line at 880 cm-1 in dependence on the number of deuterium (D) in the formula (subscripted numbers). Raman shift RS =879.236 – 24.729 D + 0.895 D2; SD = 0.9926 (RS – Raman shift, SD – standard deviation).

Table 3 summarizes the data for the low-frequency range, and Figure 4 shows the results plotted.

Table 3: Position of the main Raman lines 129 cm-1 and the Raman shift by including deuterium into the formula (mean of 20 measurements each).

Formula

Raman position Standard deviation Irel
H3BO3 129.0 2.3

vs

H2DBO3

116.8 0.6 vs
HD2BO3 103.9 1.4

m

D3BO3

75.3 0.9

m

Irel – relative intensity: vs – very strong, s – strong, m – medium, w – weak.

Figure 4: Raman shift of the H3BO3 main line at 129 cm-1 in dependence on the number of deuterium (D) in the formula (subscripted numbers). Raman shift RS = 128.25 – 5.1 D – 4.1 D2; SD = 0.99293 (RS – Raman shift, SD – standard deviation).

The Raman lines for all deuterium-bearing species in the lattice oscillation range are all relatively intense. By contrast, the intensity of the Raman bands starting at 880 cm-1 declines very rapidly toward lower frequencies. The band for D3BO3 is, in comparison to H3BO3, very weak (see Figure 5). Sometimes the Raman band of H2DBO3 is significantly intense than the 880 and 129 cm-1 bands of pure H3BO3. This behavior results from the species formed by the exchange reaction of boric acid with D2O.

Figure 5: The four boric acid species (H3BO3 , H2DBO3 , HD2BO3 , and D3BO3 ) around 840 cm-1 . The different intensities result from the concentrations of the species in the measured crystal.

Figure 6 shows a Raman spectrum of nearly pure HD2BO3. The Raman band of H BO is completely missing. Table 4 gives the Raman data.

Figure 6: Raman spectrum of nearby pure HD2BO3. The intensity of the 2402 cm-1 band is strongly dependent on orientation.

Table 4: Raman bands of nearly pure HD2BO3 obtained from fractionated crystallization. Measurements using the 532 nm laser and 30 mW on the sample.

Raman band of HD2BO3

Raman band (cm-1) Species Relative intensity

Nature of vibration

73.5

Ag

m

Lattice-oscillation

116.4

E1g

s

Lattice-oscillation

206.0

E2g

s

OH-O bonds

466.5

E2g

m

O-B-O bending

836.5

Ag

vs

B-O stretching

995.6

 

w

B-O-H bending

1375.8

E2g

w

O-H stretching

2377.2

E2g

m

BOH bending

2402.3

E2g

m

BOH bending

3244.1

Ag

vw

O-H stretching

3251.0

Ag

vw

O-H stretching

Irel – relative intensity: vs – very strong, s – strong, m – medium, w – weak, vw -very weak.

Discussion

In this short contribution, we demonstrate with Raman spectroscopy that by dissolving boric acid [H3BO3] into heavy water [D2O], stoichiometric phases like the equations (1) to (3) forms. The formation of pure D3BO3 after equation (1) is reduced. The formation of the non-stoichiometric mixtures could not be proved. The concentration of deuterium in nature is low; therefore, the different D-bearing phases are only very rare. Higher deuterium concentrations are expected in supercritical fluids that form pegmatites [7]. To obtain Raman spectra of the single D-bearing phases, an expensive separation, for example by fractionated crystallisation, is required.

References

  1. Rösler HJ, Lange H (1975) Geochemische Leipzig. Pg: 700.
  2. Thomas R (2025) Boron in some Variscan deposits in the German Geol Earth Mar Sci 7: 1-5.
  3. Thomas R (2000) Determination of water contents of granitic melt inclusions by confocal laser Raman microprobe spectroscopy. American Mineralogist 85: 868-872.
  4. Thomas R, Kamenetsky VS, Davidson P (2006) Laser Raman spectroscopic measurements of water in unexposed glass inclusions. American Mineralogist 91: 467-470.
  5. Gmelin L (1954) Gmelins Handbuch der anorganischen Chemie: Bor (Ergänzungsband). Verlag Chemie, Weinheim. Pg: 253.
  6. Krishnan K (1963) The Raman spectrum of boric acid. Proceedings of the Indian Academy of Sciences – Sec A. 57: 103-108.
  7. Thomas R (2024) NaHCO3-NaDCO3 and 13CO2-rich fluid inclusion in pegmatite quartz from Bornholm Island/Denmark. Geol Earth Mar Sci 6: 1-5.

The Complex Pathophysiology of Metabolic Dysfunction-Associated SteatoHepatitis (MASH) may Complicate FDA Accelerated Approval Regulatory Paradigm

DOI: 10.31038/EDMJ.20261012

 
 

Metabolic dysfunction-Associated SteatoHepatitis (MASH), previously known as NonAlcoholic SteatoHepatitis (NASH), continues to be the most common cause of chronic liver disease in the industrialized world [1] and can progress to liver fibrosis, as well as end-stage cirrhosis leading to death [2]. MASH is also associated with an increased risk of cardiovascular (CV) morbidity and mortality, as well as obesity and type 2 diabetes mellitus (T2DM) [3]. Cirrhosis associated with MASH increases the risk of hepatocellular carcinoma [4]. Most MASH clinical trials use liver biopsy [5] as the primary endpoint to support regulatory submissions to FDA, based upon the original NIH published guidelines for NASH Clinical Trial Design [6] which influenced the recommendations in the December 2018 Draft NASH FDA Guidance [7]. However, a growing number of trials include imaging and blood based biomarkers as secondary and/or exploratory endpoints, allowing for comparison with liver biopsy for calculations of their sensitivity, specificity and predictive values [8] to help support the move away from liver biopsy to these surrogate assessments for both initial MASH diagnosis and as clinical trial endpoints to support FDA approval [9].

Recent FDA approvals of drugs to treat patients with MASH have been applauded by patient advocacy groups [10] and supported by clinicians [11]. However, lost in the media headlines is the fact that these regulatory actions were “accelerated approvals” by FDA which then must be followed by successful Phase 4 confirmatory clinical outcomes trials in order for these drugs to stay on the U.S. market [12]. One can consider accelerated approvals as a contract between the pharmaceutical company and FDA. Under this initial approval, the agency allows the drug to be sold earlier, allowing broader patient access based upon surrogate or intermediate clinical endpoints (“biomarker”) that is deemed to be “reasonably likely to predict clinical benefit” [12] in confirmatory clinical outcome studies to be conducted post-accelerated approval. FDA expectations are that the pharmaceutical company starts the outcomes trial before accelerated approval is granted, it then is fully enrolled, completed and submits the data to FDA and the submitted clinical data is viewed by the agency as “substantial evidence” [13] adequate to support conversion of the accelerated approval to a traditional (full) approval. Previously the agency had limited authority to withdraw accelerated approvals when confirmatory outcomes studies failed, but in 2022, Congress granted FDA additional authority to rapidly withdraw accelerated approval drugs from the U.S. market, as part of the Food and Drug Omnibus Report Act of 2022 (FDORA) [14]. However, prior to the withdrawal of any accelerated approval, FDA has to provide the pharmaceutical company with due notice and an explanation of the proposed withdrawal. This is followed by an opportunity for the company to have a meeting with the Commissioner or the Commissioner’s designee and an opportunity for written appeal, including an advisory committee meeting on the proposed withdrawal [14]. It was soon after the granting of this new authority that FDA withdrew the accelerated approval of Pepaxto (melphalan flufenamide) [15]. The FDA decision memo stated that “the grounds for withdrawing approval have been met because: (1) the confirmatory study conducted as a condition of accelerated approval did not confirm Pepaxto’s clinical benefit and (2) the available evidence demonstrates that Pepaxto is not shown to be safe or effective under its conditions of use” [16].

FDA Guidance “Noncirrhotic Nonalcoholic Steatohepatitis With Liver Fibrosis: Developing Drugs for Treatment” (December 2018) lists the various endpoints to be utilized to verify clinical benefit after demonstrating positive impact on liver history and receiving accelerated approval [7]. Each element of clinical benefit listed deserves individual analysis. The “Progression to cirrhosis on histopathology” [7] sounds promising on its face. A clinical trial design where the Phase 3 patients which supported accelerated approval are “rolled over” into the Phase 4 confirmatory outcomes trial with the primary endpoint measure by liver biopsy histology. However, a significant question is how long these patients should be treated and should the placebo group be followed for the same interval? The published literature describes various lengths of time for progression of fibrosis from early stages to later stages, with all in years. An article authored by Singh S et al. (2015) described “In this systematic review and meta-analysis of paired liver biopsy studies in patients with NAFLD, contrary to conventional paradigm, we found that both patients with NAFL and NASH develop progressive hepatic fibrosis, progressing by 1 fibrosis stage (from baseline stage 0 fibrosis) over 14.3 and 7.1 years, respectively. A small subset of these patients may develop rapidly progressive hepatic fibrosis” [17]. Will patients want to be part of a placebo group for at best 7 years? Since some companies pushed back on the FDA desire for 18 month endpoints in Phase 3, the willingness to fund a confirmatory study that runs significantly longer than their Hatch-Waxman Exclusivity is less likely. Regardless, this design would require a third liver biopsy to be successful, which many patients do not want to undergo. ”Reduction in hepatic decompensation events” [7] is an appealing endpoint since these are the types of clinical events (e.g., variceal bleeding, portal hypertension, hepatic encephalopathy, ascites, spontaneous bacterial peritonitis (SBP), hepatorenal syndrome, hospitalizations, liver related deaths) that clinicians and patients want to avoid. However, many of these events can be impacted by the amount and quality of care these patients receive. If a patient is in a rural area, the hope is that they receive appropriate supportive care. In contrast, in some tertiary/academic centers, patients could have better outcomes by receiving state-of-the-art procedures and medications that minimize these adverse events [18].

There is hope that the MELD score (Model for End Stage Liver Disease) [7] will be an easy calculation using laboratory data and other patient information to derive a number that has demonstrated correlation with clinical status over time. The original publication by Wiesner R et al. (2003) was intimidating to some clinicians “The MELD equation used to calculate the severity score was as follows: MELD score = [9.57 × loge creatinine mg/dL + 3.78 × loge bilirubin mg/dL + 11.20 × loge INR + 6.43 (constant for liver disease etiology)]” [19]. Currently, this function has been automated and is available for anyone with internet access and the patient’s laboratory values (e.g., bilirubin, serum sodium, INR, serum creatine, albumin0, along with their medical history [20]. In contrast, “Liver transplant” [7] has been a problematic endpoint in the past for FDA to interpret in the context of treatments of other liver diseases given the variability on timing, as well as heterogeneity of the transplant criteria and implementation within the U.S. and around the world. Despite changes to the organ allocation system over the years, continued variability in this transplant process makes it difficult to differentiate an improvement due to drug treatment versus seasonal or other variabilities within transplant centers or heterogeneity across centers [21]. It is possible for a drug to have a positive impact on either NASH Activity Score (NAS) and/ or NASH Clinical Research Network (CRN) fibrosis score [7] but that efficacy signal is lost in the noise of the liver transplant system.

Finally, “All-cause mortality” [7] prized as the most desirable endpoint for support of clinical efficacy in confirmatory drug trials, is the most difficult to obtain. In the case of MASH, the underlying factors that contribute to fatty liver with resulting hepatic inflammation and fibrosis, are also risk factors for hyperlipidemia [22] obesity and T2DM, all of which contribute to cardiovascular (CV) risk, including myocardial infarction (MI) and stroke (CVA) [23]. The specific mechanism of action for any MASH drug may provide either a selective advantage or disadvantage in confirmatory clinical outcome trials depending upon that drugs impact on a patient’s weight, LDL cholesterol, fasting blood glucose, blood pressure and emerging factors related to inflammation and thus the degree to which they are able to “reasonably likely to predict clinical benefit” [12] in confirmatory trials. From a practical perspective, how can such an accurate prediction be made if the drug is effective in a significant percentage over placebo, but under 50% of patients treated? Even if the drug produces complete resolution of NASH and reduction of hepatic fibrosis, what if the many years of untreated risks have produced coronary artery disease (CAD) which remains unaffected by even the most potent MASH drug, resulting in a fatal CV event during the confirmatory clinical outcome study. With the occurrence of a number of such CV events, the confirmatory trial could be labeled a “failure”. This simplistic perspective would do a disservice to MASH patients, since given the complex nature of this disease it is unlikely that any one drug would significantly treat all of its aspects. It is reasonable to assume that combinations of drugs with different mechanisms of action will be necessary to completely treat MASH, just as combinations of various drugs are routinely used for the effective treatment of T2DM [24]. FDA has approved MASH treatments based upon the accelerated approval pathway which has been in effect since the early 1990’s. Given that FDA law undergoes Congressional revision every five years with Prescription Drug User Fee Act (PDUFA) renewals [25] there have been multiple opportunities for Capitol Hill to modify use of the accelerated approval provisions. FDA has used the regulatory tools it has been provided and the results are MASH drug approvals. Although these initial drugs have been described as “modest” and “incremental advances”, their availability on the U.S. market has raised both patient and practitioner awareness, increasing screening programs and thus MASH diagnosis of patients who are then able to enroll in clinical trials to study new therapeutic agents. I hope that these benefits to underserved patients with MASH will not be forgotten during a future “drug failure” news cycle, questioning FDA and their use of the accelerated approval pathway.

The author declares that he has no competing interests.

Funding Information: No Funding Sources

Keywords

U.S. Food & Drug Administration; FDA; Accelerated approval, Confirmatory trials, Clinical outcome study, MASH, NASH, Biomarkers

References

  1. Diehl AM, Day C (2017) Cause, pathogenesis, and treatment of nonalcoholic New England Journal of Medicine. [crossref]
  2. Ekstedt M, Franzén LE, Mathiesen UL (2006) Long-term follow-up of patients with NAFLD and elevated liver enzymes. Hepatology. [crossref]
  3. Targher G, Day CP, Bonora E (2010) Risk of cardiovascular disease in patients with nonalcoholic fatty liver New England Journal of Medicine. [crossref]
  4. Ratziu V, Bellentani S, Cortez-Pinto H (2010) A position statement on NAFLD/ NASH based on the EASL 2009 special Journal of Hepatology. [crossref]
  5. Anania FA, Dimick-Santos L, Mehta R (2021) Nonalcoholic steatohepatitis: current thinking from the FDA Division of Hepatology and Nutrition. Hepatology. [crossref]
  6. Sanyal AJ, Brunt EM, Kleiner DE (2011) Endpoints and clinical trial design for nonalcoholic Hepatology. [crossref]
  7. S. FDA (2018) Noncirrhotic nonalcoholic steatohepatitis with liver fibrosis: developing drugs for treatment. FDA Guidance. [crossref]
  8. Di Mauro S, Scamporrino A, Filippello A (2021) Clinical and molecular biomarkers for diagnosis and staging of NAFLD. International Journal of Molecular Sciences. [crossref]
  9. Harvey BE (2022) NASH: regulatory considerations for clinical drug development and FDA approval, Acta Pharmacologica Sinica. [crossref]
  10. Global Liver Institute (2024) Annual GLI. [crossref]
  11. Harvey BE (2025) Improvements in FDA regulatory process leading to first drug approval for common liver Acta Pharmacologica Sinica. [crossref]
  12. S. FDA (2024) Expedited Programs for Serious Conditions — Accelerated Approval of Drugs and Biologics. FDA Guidance. [crossref]
  13. S. FDA (2023) Demonstrating substantial evidence of effectiveness with one adequate and well-controlled clinical investigation. FDA Guidance. [crossref]
  14. S. Congress (2022) Accelerated Approval Update under FDORA. Federal Report. [crossref]
  15. S. FDA (2024) Final decision to withdraw approval of Pepaxto. FDA Announcement. [crossref]
  16. S. FDA (2024) Final decision on proposal to withdraw approval of Pepaxto. FDA Documentation. [crossref]
  17. Singh S, Allen AM, Wang Z (2015) Fibrosis progression in NAFLD vs NASH: systematic review and meta-analysis. Clinical Gastroenterology and Hepatology. [crossref]
  18. Liu YB, Chen MK (2022) Epidemiology of liver cirrhosis and associated World Journal of Gastroenterology. [crossref]
  19. Wiesner R, Edwards E, Freeman R (2003) MELD and allocation of donor Gastroenterology. [crossref]
  20. OPTN (2025) MELD Calculator. Organ Procurement and Transplantation Network. [crossref]
  21. Goldberg DA, Gilroy R, Charlton M (2016) New organ allocation policy in U.S. liver Clinical Liver Disease (Hoboken). [crossref]
  22. Loomba R, Friedman SL, Shulman GI (2021) Mechanisms and disease consequences of NAFLD. Cell. [crossref]
  23. Khoshbaten M, Maleki SH, Hadad S (2023) NAFLD and carotid intima-media thickness: systematic review and meta-analysis. Health Science Reports. [crossref]
  24. Fleming GA, Harvey BE (2019) Regulatory considerations for early clinical development of drugs for diabetes, obesity, NASH and cardiometabolic disorders. Springer Nature. [crossref]
  25. S. FDA (2025) Prescription Drug User Fee Amendments. FDA Guidance. [crossref]