Digital Phenotyping of Perceived Stress and Wearable Device Use Among Medical Professionals: A Multicentric Cross-Sectional Study

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Kosambiya, 2. Dr. Dharmik Sojitra, 3. Dr. Ramita Goel, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7771958/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: "Digital Vigilance Paradox" suggests that health-monitoring devices may increase stress for healthcare professionals instead of alleviating it, complicating their already demanding roles. Digital phenotyping offers a novel approach to objectively quantify behavioral and psychological health markers. Objectives: 1. To quantify the relationship between wearable device usages patterns and PSS-10 (Perceived Stress Scale) levels 2. To analyse gender-specific dimensions and latent psychological dimensions and create digital phenotypes for clinical assessment and timely intervention. Methods: Cross-sectional, multicentric study conducted to investigates the relationship between wearable device usage and perceived stress among 349 Indian medical professionals. Utilizing the modified PSS-10, Structured Questionnaire: Captured demographics, device usage patterns, and menstrual tracking behaviors. Inclusion criteria: age 18–65 years, active wearable use (>1 month) Results: Utilizing the PSS-10, identified a mean stress score of 15.38 (±6.41), with significant variations across cities (χ²=15.703, p=0.015), gender (χ²=8.437, p=0.015), and professional hierarchy (χ²=22.860, p=0.029). Females exhibited higher stress (65.8% moderate stress vs. 57.0% males), tracking menstrual health discrepancies (18.8% high stress, p=0.001). Cluster analysis revealed three psychological profiles: Low-Stress Casual Users (32.4%), Proactive Balanced Users (41.5%), and High-Stress Over-Monitors (26.1%), with the latter group demonstrating anxiety traits. Short-term users exhibited peak stress (PSS=16.97±5.804), suggesting an "adaptation phase" to device engagement. Conclusion : Digital Vigilance Paradox highlights wearables as both health tools and anxiety triggers for medical professionals, influenced by learned behaviors and gender, potentially increasing stress in vulnerable groups through hypervigilance. Digital Phenotyping validated three used profiles, enabling personalized care strategy, require urgent intervention like automated Tele-MANAS referrals. Psychiatry Wearable devices perceived stress digital phenotyping behaviorism cluster analysis Figures Figure 1 Figure 2 Figure 3 Introduction Smart wearable devices become globally ubiquitous, renowned for their ability to monitor health through integrated biosensors and wireless technology. These devices, worn close to the skin, continuously collect and transmit physiological data—heart rate variability, activity levels, and more—while providing instant biofeedback. Although touted for promoting physical health via features like goal setting and activity tracking, emerging research also highlights potential negative impacts on mental health. Kerner & Goodyear (2017) 1 suggest that smartwatches, while motivating for some, can reduce autonomy and increase stress or guilt when self-set goals are unmet, a phenomenon termed the “Digital Vigilance Paradox.” This paradox is especially relevant for healthcare professionals, whose daily routines already involve high stress, critical decision-making, and patient care responsibilities. For this group, the introduction of continuous self-monitoring may amplify psychological strain rather than relieve it, in part due to heightened sensitivity to physiological changes and potential over interpretation of normal data fluctuations. Despite extensive research on wearables’ health benefits and risks in the general population, there is a notable gap regarding their specific psychological effects on healthcare workers. Wearable devices now allow for real-time tracking of stress-related physiological metrics, offering opportunities for early detection of mental health risks through digital phenotyping—the objective, continuous measurement of behavioral and psychological states using passive and active data from devices. However, constant self-surveillance can also lead to over medicalization and increased anxiety, particularly when digital feedback conflicts with personal experience and undermines subjective autonomy. This study employs the PSS-10 to examine how ongoing biofeedback from wearables influences stress perceptions among medical professionals, aiming to disentangle technology-induced stressors and inform more user-sensitive device design and implementation in healthcare environments. By integrating the principles of the Perceived Stress Scale (Cohen et al., 1983) 2 , this study explores how continuous health parameter feedback from wearable devices affects stress perception among healthcare professionals in various medical settings. Aim: To assess the Digital Vigilance Paradox phenomenon, continuous health parameter monitoring to enhance healthcare well-being among medical professionals increases psychological stress among medical professionals, using digital phenotyping. Objectives To quantify the relationship between wearable device usage patterns (duration, frequency, and intensity) and perceived stress levels among medical professionals To analyze gender-specific dimensions (e.g., menstrual health tracking) and latent psychological dimensions (e.g., anxiety, self-efficacy). To create digital phenotypes for clinical assessment and timely intervention. Material and Methods Study Design A cross-sectional multicentric survey was conducted among medical professionals in Surat, Vadodara, Ahmedabad, and Ambala. Participants included consultants, general allopathic practitioners, postgraduate and undergraduate students. Investigators personally contacted participants, explaining the study and optioning consent. Students were surveyed in lecture halls under supervision, while consultants were approached individually. Data was collected via Google Forms under investigator supervision. Data collection utilized a self-administered structured questionnaire, including socio-demographic variables and the modified PSS-10 scale to assess stress levels related to health parameter monitoring by smart wearable devices. Information gathered included wearable device usage (brand, frequency, monitored metrics) and Perceived Stress levels using validated scales PSS-10. Data was extracted into Microsoft Excel, and analysis was performed using SPSS, version 26. Sample Size In this study, the sample size was determined using a conservative assumption of 50% prevalence, as no prior prevalence data was available. Consequently, a total of 385 participants were required to achieve a 95% confidence level with error of 5%. Due to sample loss and removal of incomplete data set, total of 349(91%) participant’s responses were analyzed. Participant recruitment site Medical Colleges, Government and Private Hospitals, and General Practitioner’s clinics of Surat, Vadodara, Ahmedabad and Ambala. Inclusion criteria Participants who gave consent to participate in the study, between the age group of 18 and 65 years, UG/PG students, Interns, Consultants and GPs who were using wearable smartwatches or devices. Sampling Technique Consecutive sampling Study tools A structured questionnaire, including core parameters of Perceived Stress Scale-10 (PSS) modified by investigators with 3 components of demography data like age, place, and designation. 1) Participants provided details on their smartwatch usage, including duration, purpose and patterns, along with general profile questions related to the device. 2) PSS-10; Cohen, Kamarch, & Mermelstein, 1983 2 is a widely used self-report tool for assessing psychological stress. The PSS-10 evaluates feelings of unpredictability, lack of control, and overload in daily life. Designed for community samples (Cohen & Janicki-Deverts, 2012) 3 with at least some high school education, the scale focuses on general stress rather than specific events or experiences. The scale consists of 10 items with scores ranging from 0 to 40; higher scores indicate greater stress. It includes two subscales: perceived helplessness and lack of self-efficacy. The PSS-10 scale was modified as per the need of current study by incorporating terms such as health goals, health parameters, heart rate, blood pressure, SpO₂, and sleep in the context of wearable device use. Two additional items were included: “I trust the health data on my smartwatch” and “I worry about my menstrual cycle data.” The modified scale was piloted with 25 participants, and reliability testing showed a Cronbach’s alpha of 0.88, indicating high internal consistency. Statistical Analysis The data was analyzed by descriptive statistics, chi-square (Χ 2 ) tests and Pearson’s correlation. Exploratory Factor Analysis (EFA), Cluster Analysis and Latent Class Analysis (LCA) were conducted to identify underlying factors related PSS. Ethical consideration and confidentiality : Approval was obtained from the Institutional Ethics Committee, No. SMIMER/IEC/Ref.No:118-01/02/2024/OUT/No.130, Dated 10/10/2024. All participants' autonomy and confidentiality were respected and maintained. Written informed consent was obtained from each participant after explaining the study's aim and objectives. Unique identity number was given to each participant and data was analyzed anonymously. Data files and information was kept under a password protected device. Results Participant Characteristics and Perceived Stress Levels A total of 349 medical professionals from four urban Indian cities (Surat, Ahmedabad, Vadodara, and Ambala) participated in the study. The mean age of participants was 23.6 years (SD 7.2, range 18–63), reflecting a predominantly young cohort. The sample was nearly balanced by gender (52.7% female) and spanned various professional designations, including a majority of UG medical students (60.2% UG) with smaller proportions of interns (4%), general practitioners (4.6%), postgraduate trainees (15.5%), specialists with MD/MS degrees (14.3%), and DM/MCh super-specialists (1.5%) (Table 1 ). Overall perceived stress levels were moderate: the mean PSS-10 score was 15.38 (SD 6.41), corresponding to moderate stress on average. In fact, most participants (61.6%) fell into the moderate stress category, with 36.1% reporting low stress and only 2.3% experiencing high stress (as shown in Table 1 ). This profile indicates that while severe stress was rare, a substantial majority of these medical professionals reported at least moderate perceived stress in their lives, underscoring the relevance of evaluating stress within this population. Table 1 Sociodemographic Characteristics and Perceived Stress Scores (PSS-10) of Medical Professionals (n = 349) Variables of interest Frequency (%) Sociodemographic Age (Years) Mean ± SD Range 23.61 ± 7.19 Years 18–63 Years Gender Male 165 (47.3%) Female 184 (52.7%) Designation UG 210 (60.2%) Intern 14 (4%) MBBS/GP 16 (4.6%) PG 54 (15.5%) MD/MS 50 (14.3%) DM/MCh 5 (1.5%) Scale used in study Perceived Stress Scale (PSS) Mean ± SD 15.38 ± 6.41 Low stress 126 (36.1) Moderate stress 215 (61.6) High stress 8 (2.3) Associations with Perceived Stress Scale (PSS) In line with our study objectives, we examined whether stress levels differed by key demographics and professional factors. Gender was significantly associated with perceived stress. Female participants had a higher mean PSS score (16.01 ± 6.37) than male participants (14.67 ± 6.40), a difference that reached statistical significance (p = 0.014; Table 2 ). In practical terms, a greater proportion of women were in the moderate-to-high stress range compared to men, suggesting that female medical professionals in this sample experienced higher perceived stress on average. By contrast, professional hierarchy (designation) was not significantly associated with stress levels (one-way ANOVA, p = 0.243). Although average PSS scores varied across roles – for example, the small subgroup of DM/MCh super-specialists reported the highest mean stress (23.1 ± 6.8) while general practitioners had the lowest (13.3 ± 6.3) – these differences did not achieve statistical significance (Table 2 ). Thus, stress did not consistently increase with higher professional seniority or training level, addressing our objective regarding hierarchical differences and indicating that even junior trainees experienced comparable stress to senior doctors. Table 2 Association Between Participant Characteristics, Wearable Device Usage Patterns, and Perceived Stress Levels (n = 349) PSS Variables of interest Low (0–13) Moderate (14–26) High (27–40) p value (Chi-square) Mean ± SD p value (t-test) Gender* Male (165) 70 94 1 8.310 14.67 ± 6.401 0.014 Female (184) 56 121 7 16.01 ± 6.369 Designation* UG (210) 78 128 4 11.730 14.98 ± 5.738 0.243 Intern (14) 3 11 0 18.14 ± 6.225 MBBS/GP (16) 6 10 0 13.31 ± 6.343 PG (54) 17 37 0 16.54 ± 6.927 MD/MS (50) 21 26 3 15.02 ± 7.948 DM/MCH (5) 1 3 1 23.14 ± 6.755 Duration of using smart wearable devices* few days (64) 28 34 2 14.64 12.89 ± 7.357 0.04 few weeks (16) 4 11 1 17.19 ± 6.348 few months (64) 16 48 0 16.97 ± 5.804 less than a year (62) 17 43 2 16.27 ± 6.093 more than a year (143) 61 79 3 15.18 ± 6.071 Goal setting No 44 74 0 4.19 0.128 Yes 82 141 8 No. of tracking of activity Step Count* No 58 103 2 1.559 0.461 Yes 68 112 6 Calorie Count No 77 150 4 3.619 0.165 Yes 49 65 4 Exercise* No 73 139 2 5.826 0.051 Yes 53 76 6 Sleep No 94 159 3 5.401 0.071 Yes 32 56 5 *Fisher’s exact test applied We also explored whether patterns of wearable device use related to perceived stress. The duration of smart wearable usage showed a notable association with stress (p = 0.040 by ANOVA; Table 2 ). Participants who had only recently begun using a wearable (for a “few days”) reported significantly lower stress (mean PSS ≈ 12.9) compared to those who had used devices for a “few weeks” (mean PSS ≈ 17.2). Those with longer-term use (several months up to > 1 year) had intermediate stress levels (mean PSS ~ 15–17) that did not differ markedly from the overall average. This pattern suggests that newcomers to wearable technology tended to be less stressed, whereas individuals with a brief period of use (weeks) reported the highest stress – a finding that may reflect stressed individuals adopting wearables, or early novelty effects, though longitudinal conclusions cannot be drawn from this cross-sectional data. In terms of specific wearable features usage, there were no significant differences in stress scores between users and non-users of most device features. Participants who regularly tracked steps, calories, sleep, or used goal-setting features did not have significantly different mean PSS scores than those who did not use these features (all p-values > 0.07). For instance, stress levels were similar whether or not individuals engaged with step counting (p = 0.46) or calorie tracking (p = 0.17). Notably, using the wearable for exercise tracking showed a trend toward lower stress levels (mean PSS ~ 14.5 with exercise tracking vs ~ 15.8 without), but this difference was only borderline significant (p = 0.051). Similarly, those using the device’s sleep monitoring tended to have slightly lower stress on average than non-users, but this did not reach conventional significance (p = 0.071). In summary, aside from the duration of use, simply using specific tracking functions was not strongly associated with reduced stress. This finding addresses our objective regarding usage patterns, indicating that how long someone had been using a wearable had a bigger impact on stress reports than which features they used, and that female gender (but not professional rank) was linked to higher stress. Exploratory Factor Analysis (EFA) To explore the latent dimensions of perceived stress in this cohort (another key objective of the study), we conducted an exploratory factor analysis (EFA) on the ten PSS items. Figure 2 illustrates the factor solution. A three-factor structure emerged, which together explained 60.2% of the total variance in responses. The factors can be interpreted as (1) Emotional Distress, (2) Self-Efficacy, and (3) Emotional Control, aligning with theoretical subcomponents of stress appraisal: Factor 1 – Emotional Distress This was the dominant factor, accounting for 34.8% of variance. It had high loadings from PSS items reflecting negative emotional experiences of stress. For instance, items about feeling upset by unexpected events, feeling nervous and “stressed”, feeling unable to control important things, and getting angry because of things outside of control all clustered on this factor. High scores on Factor 1 correspond to greater emotional turmoil and perceived helplessness in the face of stressors. In our sample, this suggests a core dimension of perceived stress centered on feelings of overwhelm, frustration, and lack of control over one’s circumstances – essentially an emotional distress component. Factor 2 – Self-Efficacy (Confidence) The second factor explained 15.9% of variance and captured the positive coping aspect of the PSS. Items with strong loadings here included those about feeling confident in handling personal problems and believing that difficulties can be mastered. This factor represents an individual’s sense of control and efficacy in dealing with stress – effectively the inverse of perceived stress. Participants with high scores on Factor 2 felt capable and in control, indicating better coping and lower subjective stress. The identification of this factor highlights that, within the overall stress measure, there is a distinct dimension related to one’s confidence and perceived ability to manage stressors. Factor 3 – Emotional Control The third factor accounted for 9.4% of variance and encompassed items related to maintaining emotional composure and achieving goals despite stress. Key contributions to this factor came from items such as being able to control irritations and feeling on top of things or that goals are achievable. We interpret Factor 3 as reflecting emotional regulation and goal-oriented control. A higher score on this factor denotes better control over one’s emotional reactions to stress and a sense that one can keep things in perspective. Although this factor explained a smaller share of variance, it is conceptually important, separating those who can remain calm and goal-directed under pressure from those who cannot. The three factors were only modestly correlated, reinforcing that they tap into distinct constructs of perceived stress. In aggregate, this factor structure of the PSS-10 suggests that the stress perceived by our participants has separable components: a predominant Emotional Distress element and two protective elements (Self-Efficacy and Emotional Control). This finding is consistent with the design of the PSS (which includes both negatively and positively phrased items) and meets our objective of uncovering latent psychological dimensions of stress. It implies that interventions could be tailored to address each component – for example, reducing emotional distress vs. bolstering coping confidence – depending on which factor an individual score high on. (Detailed factor loadings and variance explained are presented in Fig. 2 and Table 3 ). Table 3 EFA Factor Loadings Factor Item Description Factor Loadings Factor 1: Emotional Distress (34.83% variance) Feeling helpless and unable to control situations 0.869 Feeling angry due to lack of control 0.82 Feeling unable to manage important things 0.734 Feeling stressed and nervous 0.718 Feeling desires are unfulfilled 0.762 Feeling upset about health-related issues 0.643 Factor 2: Self-Efficacy (15.93% variance) Feeling confident about managing personal problems 0.876 Believing in one's ability to handle difficulties 0.81 Factor 3: Emotional Control (9.41% variance) Able to control irritations effectively 0.658 Feeling goals are achievable 0.656 Latent Class Analysis (LCA) To complement the cluster analysis and further investigate unobserved subgroups in our data, we conducted a latent class analysis (LCA) on participants’ responses to the stress and coping items (SQ1–SQ10, corresponding to the PSS items). Model fit indices, including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) (Fig. 3 ), indicated that a three-class solution provided the best fit, aligning with the three-factor structure of the PSS. These latent classes represent distinct psychological stress profiles among medical professionals and reflect the patterns identified through clustering, though they are derived solely from questionnaire responses. Class 1 – “Balanced” Profile This class, comprising approximately one-third of the sample, included individuals with moderate stress levels and a balanced emotional profile. Their PSS responses reflected a mix of both positive and negative experiences—for example, they occasionally felt nervous or upset but also expressed confidence in their coping abilities. Their emotional distress scores were neither extreme nor minimal, suggesting a capacity to manage day-to-day stress effectively. The gender distribution in this class was roughly equal (49% male, 51% female), indicating that both genders were equally represented in this intermediate stress category. Class 2 – “Resilient & Confident” Profile This class represented individuals with the lowest levels of perceived stress, minimal emotional distress, and high self-efficacy. Participants in this group rarely reported feeling overwhelmed and frequently expressed confidence in their ability to manage stressors. The gender distribution in this class showed a slight predominance of male participants (~ 52%), though both men and women were well-represented. This group aligns conceptually with the Proactive & Balanced cluster identified in the cluster analysis. Class 3 – “Stressed & Vulnerable” Profile This class comprised individuals who reported high stress levels and lower coping efficacy. Participants frequently endorsed feelings of helplessness and lack of control, scoring high on emotional distress and low on self-efficacy. This group was predominantly female (~ 62%), suggesting that women were overrepresented in the high-stress category. This gender disparity is consistent with the observed trend of higher mean stress scores among female participants. These individuals may be at greater risk for burnout or anxiety-related difficulties, highlighting the potential need for targeted stress management interventions. These findings underscore the role of gender as a meaningful factor in stress profile membership. Incorporating gender into the LCA model indicated that female participants had a significantly higher likelihood of being classified into the Stressed & Vulnerable group (odds ratio > 1 for Class 3 membership), whereas male participants had slightly higher odds of being classified into the Resilient & Confident group. The Balanced class served as the reference category with no strong gender bias. These insights suggest that interventions aimed at reducing stress among medical professionals may benefit from considering gender-related differences in stress perception and coping, with particular attention to groups at higher risk of psychological distress. Finally, to evaluate the ability of the latent class structure to be identified through questionnaire responses, we developed a predictive classification model using the 10 questionnaire items and assessed its performance in assigning individuals to their respective latent classes. The model achieved an overall classification accuracy of 85%. Performance metrics were strong across all classes: for the Balanced class, precision was 0.85 and recall 0.91 (F1-score 0.88); for the Resilient & Confident class, precision was 0.87 and recall 0.91 (F1-score 0.89). The Stressed & Vulnerable class, being the smallest and most heterogeneous group, had a lower recall of 0.70, suggesting some misclassification, but maintained a high precision of 0.86 (F1-score 0.78). These metrics (summarized in Table 4 ) indicate that the latent class structure is robust, allowing for reliable classification of individuals based on their psychological profiles even with cross-validation or new data. These findings suggest that questionnaire responses, reflecting underlying factors such as distress, efficacy, and control, provide a meaningful basis for identifying individuals at risk. This approach could support targeted interventions by facilitating the early identification of at-risk individuals (Class 3) based on their survey responses. Table 4 Latent Class Analysis Predictive Accuracy Latent Psychological Class Precision Recall F1 Score Balanced Group 0.85 0.91 0.88 Resilient & Confident 0.87 0.91 0.89 Stressed & Vulnerable 0.86 0.7 0.78 Digital Phenotyping This study utilized cluster analysis, exploratory factor analysis and latent class analysis to derive three digital phenotypes. Validity of scale in the study Modified PSS scale used in the study reported Cronbach’s alpha which is 0.748, showing high reliability and internal consistency. Discussion The gender distribution was nearly balanced, with a slightly higher number of females (184, 52.7%) compared to males (165, 47.3%). Participants came from four cities, with the largest group from Surat (142, 40.7%), followed by Ambala (96, 27.5%), Ahmedabad (58, 16.6%), and Vadodara (53, 15.2%). This spread highlights the study's reach across different urban settings. In terms of academic and professional backgrounds, the majority of participants were undergraduate (UG) students (210, 60.2%), reflecting the young and aspiring nature of the group. Postgraduate (PG) students made up 15.5% (54 participants), while 50 (14.3%) had already achieved an MD/MS degree. Smaller numbers included MBBS/General Practitioners (GP) (16, 4.6%) and interns (14, 4.0%), with a few pursuing super-specializations—3 (0.9%) with a DM degree and 2 (0.6%) with an MCh degree. The study by Van Kraaij (2020) et al 4 reported the relationship Between Chronic Stress and Heart Rate Over Time Modulated by Gender in a Cohort of Office Workers: Cross-Sectional Study Using Wearable Technologies. reported 328 subjects, of which 142 were female and 186 were male participants, with a mean age of 38.9 (SD 10.2) years and a mean PSS score of 13.7 (SD 6.0). As main effects, gender (χ 2 1 = 24.02, P < .001) PSS observed among Male 13.0 (± 9.0) Female 15.0 (± 9.0) Setting Goal The findings suggest that a substantial portion of participants actively set goals, which correlates significantly (r = 0.18. p = .001) with their use of wearable devices. This suggests that individuals who actively set goals are more likely to utilize wearable devices effectively, which can enhance their self-improvement and productivity efforts. The positive impact of goal setting aligns with existing literature that emphasizes its role in motivating individuals to increase physical activity and achieve health-related objectives, as noted in previous studies on wearable technology interventions. 5 , 6 Meanwhile, age does not appear to significantly (r = .077,p = .149) influence perceived stress levels in this context which shows that age may not be a critical factor affecting how participants perceive stress when using wearable devices for goal-oriented activities. 7 Additionally, goal setting was found to be influential in increasing physical activity. 6 Educational hierarchy The findings highlight that interns face the highest levels of moderate stress, while postgraduate students also report notable stress levels. This aligns with previous research indicating that medical interns encounter unique challenges, including high workloads, academic pressures, and the transition into clinical responsibilities, which contribute to their elevated stress levels. 8 Conversely, MBBS/GPs experience the least stress, and This may be attributed to their more established roles within the healthcare system and potentially better coping mechanisms developed through experience. However, DM/MCh holders show significantly higher stress but with limited data reliability due to small sample sizes. This suggests that advanced training and specialization may introduce additional stressors that warrant further investigation. The observed differences in stress levels among these groups highlight the necessity for targeted support and interventions tailored to specific populations within the medical field. For instance, interns may benefit from structured mentorship programs and stress management training to help them navigate the demands of their roles effectively 9 Similarly, postgraduate students could be provided with resources to manage their workload and develop coping strategies for the unique pressures they face by post graduate students of a medical college in Thrissur, Kerala. 10 Duration of wearing wearable devices The findings suggest a non-linear relationship between device usage duration and stress, with stress peaking during early adoption (weeks to months) and declining slightly over time. While no statistically significant associations were observed, the trends highlight potential psychological adaptation or selection bias (e.g., stressed individuals may discontinue use over time). The lack of significance in usage patterns (full-day wear vs. intermittent) implies that stress may be influenced by factors beyond device engagement alone, such as workload or personal habits. The observed non-linear relationship between device usage duration and stress—characterized by an initial stress increase during early adoption (weeks to months) followed by a gradual decline—aligns with existing literature on psychological adaptation to technology. For instance, studies on wearable devices demonstrate that users often experience heightened stress during initial adoption phases, likely due to cognitive overload or behavioural adjustments, before adapting to the technology over time 11 , 12 This pattern mirrors findings in smartphone research, where early exposure to continuous connectivity can exacerbate stress, followed by habituation as users integrate devices into daily routines. 13,14 The lack of statistically significant associations in usage patterns (full-day vs. intermittent wear) suggests that stress may not be directly tied to device engagement alone but could instead reflect broader contextual factors, such as workload dynamics or personal coping strategies. 15 The decline in stress after the early-adoption phase may also indicate selection bias, where individuals with higher baseline stress levels discontinue device use, leaving a sample of more resilient or adaptive users 13 .This aligns with longitudinal studies showing that stressed individuals are more likely to abandon wearable technologies, potentially skewing results toward populations better equipped to manage stressors 17 Furthermore, the absence of significant differences between usage patterns implies that stress modulation is influenced by external variables, such as environmental demands (e.g., work pressure) or intrinsic habits (e.g., mindfulness practices) 18 Gender-specific dimensions of menstrual health tracking : Wearable devices are increasingly used to track menstrual health by monitoring physiological markers such as resting heart rate (RHR), heart rate variability (HRV), temperature, and respiratory rate, which fluctuate across menstrual phases. These metrics also correlate with stress responses, enabling dual insights into reproductive health and psychological well-being. Gender Influence (Covariate Effect) : The findings of this study indicate that gender plays a moderate role in class membership among individuals experiencing stress. Specifically, women are more likely to be categorized within the Stressed & Vulnerable group (Class 2), while men show a tendency to fall into the Resilient group (Class 1). This aligns with previous research that has consistently demonstrated that women often report higher levels of stress and are more susceptible to stress-related psychological sequelae compared to men 19 Given these differences, it is crucial for interventions and support programs aimed at enhancing emotional well-being to be tailored by gender. Such tailored approaches could provide more effective support for female respondents who are experiencing elevated stress levels 20 Furthermore, the analysis reveals the existence of three distinct psychological profiles among smart wearable users, indicating that gender has a meaningful, albeit not absolute, impact on emotional patterns. These insights suggest that health technology, workplace wellness initiatives, and educational programs can benefit from personalization based on gender-specific stress responses and coping mechanisms 18 Cluster Analysis: The analysis of user clusters highlights distinct psychological profiles among wearable users, offering valuable insights for targeted interventions. Cluster 1: The Low-Stress and Low-Engagement Group consists of younger, relaxed individuals who report low stress and anxiety but exhibit minimal engagement with health monitoring. Their casual use of wearables aligns with findings that many users prioritize basic tracking over active health management. 20 Encouraging this group to engage more meaningfully with wearable features could enhance their health outcomes. Cluster 2: The Proactive & Balanced Group represents the ideal wellness profile, marked by high confidence, emotional regulation, and active engagement with wearables for health goals. This group demonstrates lower stress levels and positive psychological states, making them prime candidates for advanced coaching or digital health platforms 21 Their proactive use of wearables underscores the potential of such technologies to empower users in achieving better health outcomes 22 Cluster 3: The High-Stress, High-Awareness Group experiences significant emotional distress but paradoxically shows high wearable usage, likely as a coping mechanism. While they demonstrate strong awareness of their health, they may feel overwhelmed by constant monitoring and alerts. This aligns with research indicating that excessive self-monitoring can exacerbate anxiety in vulnerable populations. Providing tailored support to help this group interpret wearable data without increasing distress could mitigate their psychological burden 23 In conclusion, Cluster 2 serves as a benchmark for positive engagement, while Clusters 1 and 3 require tailored strategies—motivation for meaningful usage in Cluster 1 and stress management interventions for Cluster 3—to maximize the benefits of wearable technologies. LCA’s strength lies in its ability to uncover hidden subgroups and integrate covariates like gender. The Stressed & Vulnerable class, predominantly female, highlights systemic inequities in stress management. Exploratory Factor Analysis factor analysis done for practical recommendations for individuals based on their scores in each factor. Factor analysis identifies underlying patterns in responses related to emotional well-being, self-confidence, and control over emotions. The analysis revealed three key factors, 60.2% of the total variance. Factor 1: Emotional Distress & Stress (34.83% Variance) : This factor reflects feelings of stress, helplessness, and frustration in response to health and life challenges. Individuals scoring high on this factor tend to: (1) Feel upset about their health, (2) Experience stress and nervousness, (3) Feel unable to handle situations, (4) Get angry when things are out of control. (5) Feel that their personal desires remain unfulfilled. This suggests that emotional distress and perceived helplessness play a significant role in shaping individuals' responses to health-related challenges. Factor 2: Self-Efficacy & Confidence (15.93% Variance) : This factor suggests sense of self-confidence and ability to handle situations effectively. Individuals scoring high on this factor (1) Feel confident in managing situations, (2) Believe they are capable of handling challenges, (3) Feel a sense of achievement when goals are met. This suggest A strong belief in one's abilities contributes to better coping mechanisms and a sense of control over personal health and well-being. Factor 3: Control Over Reactions & Goals (9.41% Variance) : This factor represents emotional regulation and goal orientation. High scorers in this factor tend (1) Control their emotions and reactions better, (2) Feel that their desired goals are within reach, (3) Ability to regulate emotions and work towards goals effectively contributes to better mental resilience and well-being. LCA to uncover latent psychological profiles based on the SQ items and examine how gender influences class membership as a covariate. Tele-MANAS Integration: Prioritize high-risk subgroups for immediate support. Algorithmic Transparency: Address distrust in data among compulsive users. The study's findings have significant clinical implications for addressing stress among wearable device users. For high-stress over-monitors, immediate action involves real-time prompts to contact Tele-MANAS (14416) when stress biomarkers exceed thresholds, coupled with CBT integration. Proactive balanced users benefit from gamified challenges, while low-stress casual users receive preventive nudges. Systemic measures include Tele-MANAS API integration and contextualized clinician dashboards. Gender differences reveal higher stress prevalence in females, with menstrual tracking anxiety and workplace dynamics as root causes. Behaviorism insights highlight operant conditioning effects, extinction learning, and a goal-setting paradox. Clinical redesign strategies involve exposure therapy and positive reinforcement techniques to address these behavioral patterns effectively. Conclusion The findings of study suggests that perceived stress levels differ significantly according to demographic factors (city, gender, designation) and health perceptions (menstrual cycle worry, health data reliability concerns). Females, interns, and residents of certain cities (Ambala) reported higher stress levels. Additionally, frequent worry about menstrual cycles and doubts about health data reliability are significantly associated with higher perceived stress. These insights are valuable for targeted interventions aimed at stress reduction, particularly within medical communities relying on wearable technology for health monitoring. For individuals experiencing high emotional distress (Factor 1): Adopt stress management techniques (e.g., mindfulness, relaxation exercises). Use wearable health devices to monitor stress-related indicators (e.g., heart rate variability). For individuals with high self-efficacy (Factor 2): Utilize goal-setting features in smart devices to stay motivated. Track health progress to maintain consistency in fitness and wellness routines. For individuals with high emotional control (Factor 3): Implement behavioral strategies (e.g., cognitive-behavioral techniques) to reinforce positive habit. The development of these prediction models would enable continuous monitoring of long-term stress levels that could support to better understand patient progress and well-being. Limitations of the study include reliance on self-reported bias (PSS-10 score) may underrepresent physiological stress. Due to Demographic Skew: Overrepresentation of undergraduates (60.2%) limits generalizability to senior professionals. Non availability of Sensor Data leads to lack of HRV/sleep metrics restricts biomarker validation. Further longitudinal studies are needed to clarify causality and explore how wearable device usage interacts with stress dynamics over extended periods. Abbreviations EFA Exploratory Factor Analysis LCA Latent Class Analysis PSS 10 Perceived Stress Scale 10 Declarations Ethical approval and consent to participate The study was conducted in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments and was approved by the Institutional Review Board from the Institutional Ethics Committee, Surat Municipal Institute of Medical Education and Research, Surat, Gujarat, India, No. SMIMER/IEC/Ref.No:118-01/02/2024/OUT/No.130, Dated 10/10/2024. All participants' autonomy and confidentiality were respected and maintained. Written informed consent was obtained from each participant after explaining the study's aim and objectives. Unique identity number was given to each participant and data was analyzed anonymously. Data files and information was kept under a password protected device. Consent for publication Not applicable. Clinical trial number Not applicable. Competing interests The authors declare no competing interests Funding No funding was received for this study. Authors Contribution Study Conceptualized and Designed by RK. Material preparation and data collection were performed by every authors. Data analysis and interpretation were performed by RK and DS. The first draft of the manuscript was written by RK and supported by all. All authors contributed to and have approved the final manuscript. Acknowledgement The authors would like to thank IRB, the study participants and Dr G.K. Vankar, Professor Emeritus, Department of Psychiatry, PIMSR, Parul University, Vadodara, India. Data availability The data supporting the findings of this study are available upon request from the corresponding author. 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Sensors 52479. https://doi.org/10.3390/s23052479) O'Hara D (2019), June 6 Wearable technology for mental health. American Psychological Association. https://www.apa.org/members/content/wearable-technology .) Kang HS, Exworthy M (2022) Wearing the Future-Wearables to Empower Users to Take Greater Responsibility for Their Health and Care: Scoping Review. JMIR Mhealth Uhealth 10(7):e35684. 10.2196/35684 PMID: 35830222; PMCID: PMC9330198.) Hodes GE, Epperson CN (2019) Sex Differences in Vulnerability and Resilience to Stress Across the Life Span. Biol Psychiatry 86(6):421–432. 10.1016/j.biopsych.2019.04.028 Epub 2019 May 7. PMID: 31221426; PMCID: PMC8630768 Avishek Choudhury O, Asan Impact of using wearable devices on psychological Distress: Analysis of the health information national Trends survey. Int J Med Informatics, 156, 2021,104612, ISSN 1386–5056, https://doi.org/10.1016/j.ijmedinf.2021.104612 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":239206,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScree Plot Illustrating Factor Structure of Perceived Stress Scale (PSS-10)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7771958/v1/a2169eb96592df1b762b1647.png"},{"id":92855141,"identity":"575a46c4-b665-4a14-a5f2-087c6bb4002f","added_by":"auto","created_at":"2025-10-06 11:12:40","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84400,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScree Plot Illustrating Factor Structure of Perceived Stress Scale (PSS-10)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7771958/v1/77ad23dadfaab507dd1b9011.jpeg"},{"id":92856157,"identity":"00af11c9-06e9-430f-91e9-488318c2d538","added_by":"auto","created_at":"2025-10-06 11:28:40","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100603,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModel Fit Indices (AIC and BIC) for Latent Class Analysis Determining Optimal Number of Psychological Classes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7771958/v1/a92867c004ed3368a98522e7.jpeg"},{"id":92944739,"identity":"616b5118-4039-4545-94c3-cc925c6c5980","added_by":"auto","created_at":"2025-10-07 12:19:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1820850,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7771958/v1/1ce82cf1-e874-4491-9bc1-357a17bd28a8.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDigital Phenotyping of Perceived Stress and Wearable Device Use Among Medical Professionals: A Multicentric Cross-Sectional Study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSmart wearable devices become globally ubiquitous, renowned for their ability to monitor health through integrated biosensors and wireless technology. These devices, worn close to the skin, continuously collect and transmit physiological data\u0026mdash;heart rate variability, activity levels, and more\u0026mdash;while providing instant biofeedback. Although touted for promoting physical health via features like goal setting and activity tracking, emerging research also highlights potential negative impacts on mental health. Kerner \u0026amp; Goodyear (2017)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e suggest that smartwatches, while motivating for some, can reduce autonomy and increase stress or guilt when self-set goals are unmet, a phenomenon termed the \u0026ldquo;Digital Vigilance Paradox.\u0026rdquo; This paradox is especially relevant for healthcare professionals, whose daily routines already involve high stress, critical decision-making, and patient care responsibilities. For this group, the introduction of continuous self-monitoring may amplify psychological strain rather than relieve it, in part due to heightened sensitivity to physiological changes and potential over interpretation of normal data fluctuations.\u003c/p\u003e\u003cp\u003eDespite extensive research on wearables\u0026rsquo; health benefits and risks in the general population, there is a notable gap regarding their specific psychological effects on healthcare workers. Wearable devices now allow for real-time tracking of stress-related physiological metrics, offering opportunities for early detection of mental health risks through digital phenotyping\u0026mdash;the objective, continuous measurement of behavioral and psychological states using passive and active data from devices. However, constant self-surveillance can also lead to over medicalization and increased anxiety, particularly when digital feedback conflicts with personal experience and undermines subjective autonomy. This study employs the PSS-10 to examine how ongoing biofeedback from wearables influences stress perceptions among medical professionals, aiming to disentangle technology-induced stressors and inform more user-sensitive device design and implementation in healthcare environments.\u003c/p\u003e\u003cp\u003eBy integrating the principles of the Perceived Stress Scale (Cohen et al., 1983)\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, this study explores how continuous health parameter feedback from wearable devices affects stress perception among healthcare professionals in various medical settings.\u003c/p\u003e\u003cp\u003eAim: To assess the Digital Vigilance Paradox phenomenon, continuous health parameter monitoring to enhance healthcare well-being among medical professionals increases psychological stress among medical professionals, using digital phenotyping.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjectives\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo quantify the relationship between wearable device usage patterns (duration, frequency, and intensity) and perceived stress levels among medical professionals\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo analyze gender-specific dimensions (e.g., menstrual health tracking) and latent psychological dimensions (e.g., anxiety, self-efficacy).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo create digital phenotypes for clinical assessment and timely intervention.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003cp\u003eA cross-sectional multicentric survey was conducted among medical professionals in Surat, Vadodara, Ahmedabad, and Ambala. Participants included consultants, general allopathic practitioners, postgraduate and undergraduate students. Investigators personally contacted participants, explaining the study and optioning consent. Students were surveyed in lecture halls under supervision, while consultants were approached individually. Data was collected via Google Forms under investigator supervision. Data collection utilized a self-administered structured questionnaire, including socio-demographic variables and the modified PSS-10 scale to assess stress levels related to health parameter monitoring by smart wearable devices. Information gathered included wearable device usage (brand, frequency, monitored metrics) and Perceived Stress levels using validated scales PSS-10. Data was extracted into Microsoft Excel, and analysis was performed using SPSS, version 26.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSample Size\u003c/strong\u003e\u003cp\u003eIn this study, the sample size was determined using a conservative assumption of 50% prevalence, as no prior prevalence data was available. Consequently, a total of 385 participants were required to achieve a 95% confidence level with error of 5%. Due to sample loss and removal of incomplete data set, total of 349(91%) participant\u0026rsquo;s responses were analyzed.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eParticipant recruitment site\u003c/strong\u003e\u003cp\u003eMedical Colleges, Government and Private Hospitals, and General Practitioner\u0026rsquo;s clinics of Surat, Vadodara, Ahmedabad and Ambala.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003cp\u003eParticipants who gave consent to participate in the study, between the age group of 18 and 65 years, UG/PG students, Interns, Consultants and GPs who were using wearable smartwatches or devices.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSampling Technique\u003c/strong\u003e\u003cp\u003eConsecutive sampling\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStudy tools\u003c/strong\u003e\u003cp\u003eA structured questionnaire, including core parameters of Perceived Stress Scale-10 (PSS) modified by investigators with 3 components of demography data like age, place, and designation.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e1) Participants provided details on their smartwatch usage, including duration, purpose and patterns, along with general profile questions related to the device.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e2) PSS-10; Cohen, Kamarch, \u0026amp; Mermelstein, 1983\u003csup\u003e2\u003c/sup\u003e is a widely used self-report tool for assessing psychological stress. The PSS-10 evaluates feelings of unpredictability, lack of control, and overload in daily life. Designed for community samples (Cohen \u0026amp; Janicki-Deverts, 2012)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e with\u003c/sup\u003e at least some high school education, the scale focuses on general stress rather than specific events or experiences. The scale consists of 10 items with scores ranging from 0 to 40; higher scores indicate greater stress. It includes two subscales: perceived helplessness and lack of self-efficacy. The PSS-10 scale was modified as per the need of current study by incorporating terms such as health goals, health parameters, heart rate, blood pressure, SpO₂, and sleep in the context of wearable device use. Two additional items were included: \u0026ldquo;I trust the health data on my smartwatch\u0026rdquo; and \u0026ldquo;I worry about my menstrual cycle data.\u0026rdquo; The modified scale was piloted with 25 participants, and reliability testing showed a Cronbach\u0026rsquo;s alpha of 0.88, indicating high internal consistency.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003cp\u003eThe data was analyzed by descriptive statistics, chi-square (Χ\u003csup\u003e2\u003c/sup\u003e) tests and Pearson\u0026rsquo;s correlation. Exploratory Factor Analysis (EFA), Cluster Analysis and Latent Class Analysis (LCA) were conducted to identify underlying factors related PSS.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEthical consideration and confidentiality\u003c/b\u003e: Approval was obtained from the Institutional Ethics Committee, No. SMIMER/IEC/Ref.No:118-01/02/2024/OUT/No.130, Dated 10/10/2024. All participants' autonomy and confidentiality were respected and maintained. Written informed consent was obtained from each participant after explaining the study's aim and objectives. Unique identity number was given to each participant and data was analyzed anonymously. Data files and information was kept under a password protected device.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eParticipant Characteristics and Perceived Stress Levels\u003c/h2\u003e\u003cp\u003e A total of 349 medical professionals from four urban Indian cities (Surat, Ahmedabad, Vadodara, and Ambala) participated in the study. The mean age of participants was 23.6 years (SD 7.2, range 18\u0026ndash;63), reflecting a predominantly young cohort. The sample was nearly balanced by gender (52.7% female) and spanned various professional designations, including a majority of UG medical students (60.2% UG) with smaller proportions of interns (4%), general practitioners (4.6%), postgraduate trainees (15.5%), specialists with MD/MS degrees (14.3%), and DM/MCh super-specialists (1.5%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall perceived stress levels were moderate: the mean PSS-10 score was 15.38 (SD 6.41), corresponding to moderate stress on average. In fact, most participants (61.6%) fell into the moderate stress category, with 36.1% reporting low stress and only 2.3% experiencing high stress (as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This profile indicates that while severe stress was rare, a substantial majority of these medical professionals reported at least moderate perceived stress in their lives, underscoring the relevance of evaluating stress within this population.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemographic Characteristics and Perceived Stress Scores (PSS-10) of Medical Professionals (n\u0026thinsp;=\u0026thinsp;349)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eVariables of interest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFrequency (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003eSociodemographic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge (Years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;SD\u003c/p\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.61\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;7.19 Years\u003c/p\u003e\u003cp\u003e18\u0026ndash;63 Years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e165 (47.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e184 (52.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eDesignation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e210 (60.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIntern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMBBS/GP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (4.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54 (15.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMD/MS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (14.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDM/MCh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eScale used in study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003ePerceived Stress Scale (PSS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.38\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow stress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126 (36.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate stress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e215 (61.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh stress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (2.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssociations with Perceived Stress Scale (PSS)\u003c/h3\u003e\n\u003cp\u003eIn line with our study objectives, we examined whether stress levels differed by key demographics and professional factors. Gender was significantly associated with perceived stress. Female participants had a higher mean PSS score (16.01\u0026thinsp;\u0026plusmn;\u0026thinsp;6.37) than male participants (14.67\u0026thinsp;\u0026plusmn;\u0026thinsp;6.40), a difference that reached statistical significance (p\u0026thinsp;=\u0026thinsp;0.014; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In practical terms, a greater proportion of women were in the moderate-to-high stress range compared to men, suggesting that female medical professionals in this sample experienced higher perceived stress on average. By contrast, professional hierarchy (designation) was not significantly associated with stress levels (one-way ANOVA, p\u0026thinsp;=\u0026thinsp;0.243). Although average PSS scores varied across roles \u0026ndash; for example, the small subgroup of DM/MCh super-specialists reported the highest mean stress (23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8) while general practitioners had the lowest (13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3) \u0026ndash; these differences did not achieve statistical significance (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Thus, stress did not consistently increase with higher professional seniority or training level, addressing our objective regarding hierarchical differences and indicating that even junior trainees experienced comparable stress to senior doctors.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation Between Participant Characteristics, Wearable Device Usage Patterns, and Perceived Stress Levels (n\u0026thinsp;=\u0026thinsp;349)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003ePSS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariables of interest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003cp\u003e(0\u0026ndash;13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003cp\u003e(14\u0026ndash;26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003cp\u003e(27\u0026ndash;40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003cp\u003e(Chi-square)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003cp\u003e(t-test)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale (165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.67\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6.401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale (184)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.01\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eDesignation*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUG (210)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.98\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;5.738\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntern (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.14\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMBBS/GP (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13.31 \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u003c/p\u003e\u003cp\u003e6.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePG (54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.54\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6.927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMD/MS (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.02\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;7.948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDM/MCH (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.14 \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e 6.755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eDuration of using smart wearable devices*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efew days (64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.89\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;7.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efew weeks (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.19\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efew months (64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.97\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;5.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eless than a year (62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.27\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emore than a year (143)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.18\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGoal setting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNo. of tracking of activity\u003c/p\u003e\u003cp\u003eStep Count*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.461\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCalorie Count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExercise*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e5.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e5.401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Fisher\u0026rsquo;s exact test applied\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe also explored whether patterns of wearable device use related to perceived stress. The duration of smart wearable usage showed a notable association with stress (p\u0026thinsp;=\u0026thinsp;0.040 by ANOVA; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Participants who had only recently begun using a wearable (for a \u0026ldquo;few days\u0026rdquo;) reported significantly lower stress (mean PSS\u0026thinsp;\u0026asymp;\u0026thinsp;12.9) compared to those who had used devices for a \u0026ldquo;few weeks\u0026rdquo; (mean PSS\u0026thinsp;\u0026asymp;\u0026thinsp;17.2). Those with longer-term use (several months up to \u0026gt;\u0026thinsp;1 year) had intermediate stress levels (mean PSS\u0026thinsp;~\u0026thinsp;15\u0026ndash;17) that did not differ markedly from the overall average. This pattern suggests that newcomers to wearable technology tended to be less stressed, whereas individuals with a brief period of use (weeks) reported the highest stress \u0026ndash; a finding that may reflect stressed individuals adopting wearables, or early novelty effects, though longitudinal conclusions cannot be drawn from this cross-sectional data. In terms of specific wearable features usage, there were no significant differences in stress scores between users and non-users of most device features. Participants who regularly tracked steps, calories, sleep, or used goal-setting features did not have significantly different mean PSS scores than those who did not use these features (all p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.07). For instance, stress levels were similar whether or not individuals engaged with step counting (p\u0026thinsp;=\u0026thinsp;0.46) or calorie tracking (p\u0026thinsp;=\u0026thinsp;0.17). Notably, using the wearable for exercise tracking showed a trend toward lower stress levels (mean PSS\u0026thinsp;~\u0026thinsp;14.5 with exercise tracking vs\u0026thinsp;~\u0026thinsp;15.8 without), but this difference was only borderline significant (p\u0026thinsp;=\u0026thinsp;0.051). Similarly, those using the device\u0026rsquo;s sleep monitoring tended to have slightly lower stress on average than non-users, but this did not reach conventional significance (p\u0026thinsp;=\u0026thinsp;0.071). In summary, aside from the duration of use, simply using specific tracking functions was not strongly associated with reduced stress. This finding addresses our objective regarding usage patterns, indicating that how long someone had been using a wearable had a bigger impact on stress reports than which features they used, and that female gender (but not professional rank) was linked to higher stress.\u003c/p\u003e\n\u003ch3\u003eExploratory Factor Analysis (EFA)\u003c/h3\u003e\n\u003cp\u003eTo explore the latent dimensions of perceived stress in this cohort (another key objective of the study), we conducted an exploratory factor analysis (EFA) on the ten PSS items.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the factor solution. A three-factor structure emerged, which together explained 60.2% of the total variance in responses. The factors can be interpreted as (1) Emotional Distress, (2) Self-Efficacy, and (3) Emotional Control, aligning with theoretical subcomponents of stress appraisal:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFactor 1 \u0026ndash; Emotional Distress\u003c/strong\u003e\u003cp\u003eThis was the dominant factor, accounting for 34.8% of variance. It had high loadings from PSS items reflecting negative emotional experiences of stress. For instance, items about feeling upset by unexpected events, feeling nervous and \u0026ldquo;stressed\u0026rdquo;, feeling unable to control important things, and getting angry because of things outside of control all clustered on this factor. High scores on Factor 1 correspond to greater emotional turmoil and perceived helplessness in the face of stressors. In our sample, this suggests a core dimension of perceived stress centered on feelings of overwhelm, frustration, and lack of control over one\u0026rsquo;s circumstances \u0026ndash; essentially an emotional distress component.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFactor 2 \u0026ndash; Self-Efficacy (Confidence)\u003c/strong\u003e\u003cp\u003eThe second factor explained 15.9% of variance and captured the positive coping aspect of the PSS. Items with strong loadings here included those about feeling confident in handling personal problems and believing that difficulties can be mastered. This factor represents an individual\u0026rsquo;s sense of control and efficacy in dealing with stress \u0026ndash; effectively the inverse of perceived stress. Participants with high scores on Factor 2 felt capable and in control, indicating better coping and lower subjective stress. The identification of this factor highlights that, within the overall stress measure, there is a distinct dimension related to one\u0026rsquo;s confidence and perceived ability to manage stressors.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFactor 3 \u0026ndash; Emotional Control\u003c/strong\u003e\u003cp\u003eThe third factor accounted for 9.4% of variance and encompassed items related to maintaining emotional composure and achieving goals despite stress. Key contributions to this factor came from items such as being able to control irritations and feeling on top of things or that goals are achievable. We interpret Factor 3 as reflecting emotional regulation and goal-oriented control. A higher score on this factor denotes better control over one\u0026rsquo;s emotional reactions to stress and a sense that one can keep things in perspective. Although this factor explained a smaller share of variance, it is conceptually important, separating those who can remain calm and goal-directed under pressure from those who cannot.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe three factors were only modestly correlated, reinforcing that they tap into distinct constructs of perceived stress. In aggregate, this factor structure of the PSS-10 suggests that the stress perceived by our participants has separable components: a predominant Emotional Distress element and two protective elements (Self-Efficacy and Emotional Control). This finding is consistent with the design of the PSS (which includes both negatively and positively phrased items) and meets our objective of uncovering latent psychological dimensions of stress. It implies that interventions could be tailored to address each component \u0026ndash; for example, reducing emotional distress vs. bolstering coping confidence \u0026ndash; depending on which factor an individual score high on. (Detailed factor loadings and variance explained are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEFA Factor Loadings\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItem Description\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFactor Loadings\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eFactor 1: Emotional Distress (34.83% variance)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeeling helpless and unable to control situations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.869\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeeling angry due to lack of control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeeling unable to manage important things\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.734\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeeling stressed and nervous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeeling desires are unfulfilled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeeling upset about health-related issues\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.643\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eFactor 2: Self-Efficacy (15.93% variance)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeeling confident about managing personal problems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.876\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelieving in one's ability to handle difficulties\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eFactor 3: Emotional Control (9.41% variance)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAble to control irritations effectively\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.658\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeeling goals are achievable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eLatent Class Analysis (LCA)\u003c/h2\u003e\u003cp\u003eTo complement the cluster analysis and further investigate unobserved subgroups in our data, we conducted a latent class analysis (LCA) on participants\u0026rsquo; responses to the stress and coping items (SQ1\u0026ndash;SQ10, corresponding to the PSS items). Model fit indices, including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), indicated that a three-class solution provided the best fit, aligning with the three-factor structure of the PSS. These latent classes represent distinct psychological stress profiles among medical professionals and reflect the patterns identified through clustering, though they are derived solely from questionnaire responses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClass 1 \u0026ndash; \u0026ldquo;Balanced\u0026rdquo; Profile\u003c/strong\u003e\u003cp\u003eThis class, comprising approximately one-third of the sample, included individuals with moderate stress levels and a balanced emotional profile. Their PSS responses reflected a mix of both positive and negative experiences\u0026mdash;for example, they occasionally felt nervous or upset but also expressed confidence in their coping abilities. Their emotional distress scores were neither extreme nor minimal, suggesting a capacity to manage day-to-day stress effectively. The gender distribution in this class was roughly equal (49% male, 51% female), indicating that both genders were equally represented in this intermediate stress category.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClass 2 \u0026ndash; \u0026ldquo;Resilient \u0026amp; Confident\u0026rdquo; Profile\u003c/strong\u003e\u003cp\u003eThis class represented individuals with the lowest levels of perceived stress, minimal emotional distress, and high self-efficacy. Participants in this group rarely reported feeling overwhelmed and frequently expressed confidence in their ability to manage stressors. The gender distribution in this class showed a slight predominance of male participants (~\u0026thinsp;52%), though both men and women were well-represented. This group aligns conceptually with the Proactive \u0026amp; Balanced cluster identified in the cluster analysis.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClass 3 \u0026ndash; \u0026ldquo;Stressed \u0026amp; Vulnerable\u0026rdquo; Profile\u003c/strong\u003e\u003cp\u003eThis class comprised individuals who reported high stress levels and lower coping efficacy. Participants frequently endorsed feelings of helplessness and lack of control, scoring high on emotional distress and low on self-efficacy. This group was predominantly female (~\u0026thinsp;62%), suggesting that women were overrepresented in the high-stress category. This gender disparity is consistent with the observed trend of higher mean stress scores among female participants. These individuals may be at greater risk for burnout or anxiety-related difficulties, highlighting the potential need for targeted stress management interventions.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThese findings underscore the role of gender as a meaningful factor in stress profile membership. Incorporating gender into the LCA model indicated that female participants had a significantly higher likelihood of being classified into the Stressed \u0026amp; Vulnerable group (odds ratio\u0026thinsp;\u0026gt;\u0026thinsp;1 for Class 3 membership), whereas male participants had slightly higher odds of being classified into the Resilient \u0026amp; Confident group. The Balanced class served as the reference category with no strong gender bias. These insights suggest that interventions aimed at reducing stress among medical professionals may benefit from considering gender-related differences in stress perception and coping, with particular attention to groups at higher risk of psychological distress.\u003c/p\u003e\u003cp\u003eFinally, to evaluate the ability of the latent class structure to be identified through questionnaire responses, we developed a predictive classification model using the 10 questionnaire items and assessed its performance in assigning individuals to their respective latent classes. The model achieved an overall classification accuracy of 85%. Performance metrics were strong across all classes: for the Balanced class, precision was 0.85 and recall 0.91 (F1-score 0.88); for the Resilient \u0026amp; Confident class, precision was 0.87 and recall 0.91 (F1-score 0.89). The Stressed \u0026amp; Vulnerable class, being the smallest and most heterogeneous group, had a lower recall of 0.70, suggesting some misclassification, but maintained a high precision of 0.86 (F1-score 0.78). These metrics (summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) indicate that the latent class structure is robust, allowing for reliable classification of individuals based on their psychological profiles even with cross-validation or new data. These findings suggest that questionnaire responses, reflecting underlying factors such as distress, efficacy, and control, provide a meaningful basis for identifying individuals at risk. This approach could support targeted interventions by facilitating the early identification of at-risk individuals (Class 3) based on their survey responses.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLatent Class Analysis Predictive Accuracy\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLatent Psychological Class\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrecision\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRecall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF1 Score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBalanced Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResilient \u0026amp; Confident\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStressed \u0026amp; Vulnerable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDigital Phenotyping\u003c/strong\u003e\u003cp\u003eThis study utilized cluster analysis, exploratory factor analysis and latent class analysis to derive three digital phenotypes.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eValidity of scale in the study\u003c/strong\u003e\u003cp\u003eModified PSS scale used in the study reported Cronbach\u0026rsquo;s alpha which is 0.748, showing high reliability and internal consistency.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe gender distribution was nearly balanced, with a slightly higher number of females (184, 52.7%) compared to males (165, 47.3%). Participants came from four cities, with the largest group from Surat (142, 40.7%), followed by Ambala (96, 27.5%), Ahmedabad (58, 16.6%), and Vadodara (53, 15.2%). This spread highlights the study's reach across different urban settings. In terms of academic and professional backgrounds, the majority of participants were undergraduate (UG) students (210, 60.2%), reflecting the young and aspiring nature of the group. Postgraduate (PG) students made up 15.5% (54 participants), while 50 (14.3%) had already achieved an MD/MS degree. Smaller numbers included MBBS/General Practitioners (GP) (16, 4.6%) and interns (14, 4.0%), with a few pursuing super-specializations\u0026mdash;3 (0.9%) with a DM degree and 2 (0.6%) with an MCh degree.\u003c/p\u003e\u003cp\u003eThe study by Van Kraaij (2020) et al \u003csup\u003e4\u003c/sup\u003e reported the relationship Between Chronic Stress and Heart Rate Over Time Modulated by Gender in a Cohort of Office Workers: Cross-Sectional Study Using Wearable Technologies. reported 328 subjects, of which 142 were female and 186 were male participants, with a mean age of 38.9 (SD 10.2) years and a mean PSS score of 13.7 (SD 6.0). As main effects, gender (χ 2 1\u0026thinsp;=\u0026thinsp;24.02, P\u0026thinsp;\u0026lt;\u0026thinsp;.001) PSS observed among Male 13.0 (\u0026plusmn;\u0026thinsp;9.0) Female 15.0 (\u0026plusmn;\u0026thinsp;9.0)\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSetting Goal\u003c/strong\u003e\u003cp\u003eThe findings suggest that a substantial portion of participants actively set goals, which correlates significantly (r\u0026thinsp;=\u0026thinsp;0.18. p\u0026thinsp;=\u0026thinsp;.001) with their use of wearable devices. This suggests that individuals who actively set goals are more likely to utilize wearable devices effectively, which can enhance their self-improvement and productivity efforts. The positive impact of goal setting aligns with existing literature that emphasizes its role in motivating individuals to increase physical activity and achieve health-related objectives, as noted in previous studies on wearable technology interventions.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Meanwhile, age does not appear to significantly (r\u0026thinsp;=\u0026thinsp;.077,p\u0026thinsp;=\u0026thinsp;.149) influence perceived stress levels in this context which shows that age may not be a critical factor affecting how participants perceive stress when using wearable devices for goal-oriented activities.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Additionally, goal setting was found to be influential in increasing physical activity. \u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEducational hierarchy\u003c/strong\u003e\u003cp\u003eThe findings highlight that interns face the highest levels of moderate stress, while postgraduate students also report notable stress levels. This aligns with previous research indicating that medical interns encounter unique challenges, including high workloads, academic pressures, and the transition into clinical responsibilities, which contribute to their elevated stress levels. \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Conversely, MBBS/GPs experience the least stress, and This may be attributed to their more established roles within the healthcare system and potentially better coping mechanisms developed through experience. However, DM/MCh holders show significantly higher stress but with limited data reliability due to small sample sizes. This suggests that advanced training and specialization may introduce additional stressors that warrant further investigation.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe observed differences in stress levels among these groups highlight the necessity for targeted support and interventions tailored to specific populations within the medical field. For instance, interns may benefit from structured mentorship programs and stress management training to help them navigate the demands of their roles effectively \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Similarly, postgraduate students could be provided with resources to manage their workload and develop coping strategies for the unique pressures they face by post graduate students of a medical college in Thrissur, Kerala.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDuration of wearing wearable devices\u003c/strong\u003e\u003cp\u003eThe findings suggest a \u003cb\u003enon-linear relationship\u003c/b\u003e between device usage duration and stress, with stress peaking during early adoption (weeks to months) and declining slightly over time. While \u003cb\u003eno statistically significant associations\u003c/b\u003e were observed, the trends highlight potential psychological adaptation or selection bias (e.g., stressed individuals may discontinue use over time). The lack of significance in usage patterns (full-day wear vs. intermittent) implies that stress may be influenced by factors beyond device engagement alone, such as workload or personal habits.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe observed non-linear relationship between device usage duration and stress\u0026mdash;characterized by an initial stress increase during early adoption (weeks to months) followed by a gradual decline\u0026mdash;aligns with existing literature on psychological adaptation to technology. For instance, studies on wearable devices demonstrate that users often experience heightened stress during initial adoption phases, likely due to cognitive overload or behavioural adjustments, before adapting to the technology over time \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e This pattern mirrors findings in smartphone research, where early exposure to continuous connectivity can exacerbate stress, followed by habituation as users integrate devices into daily routines. \u003csup\u003e13,14\u003c/sup\u003e The lack of statistically significant associations in usage patterns (full-day vs. intermittent wear) suggests that stress may not be directly tied to device engagement alone but could instead reflect broader contextual factors, such as workload dynamics or personal coping strategies.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe decline in stress after the early-adoption phase may also indicate selection bias, where individuals with higher baseline stress levels discontinue device use, leaving a sample of more resilient or adaptive users \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.This aligns with longitudinal studies showing that stressed individuals are more likely to abandon wearable technologies, potentially skewing results toward populations better equipped to manage stressors \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Furthermore, the absence of significant differences between usage patterns implies that stress modulation is influenced by external variables, such as environmental demands (e.g., work pressure) or intrinsic habits (e.g., mindfulness practices) \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGender-specific dimensions of menstrual health tracking\u003c/b\u003e: Wearable devices are increasingly used to track menstrual health by monitoring physiological markers such as resting heart rate (RHR), heart rate variability (HRV), temperature, and respiratory rate, which fluctuate across menstrual phases. These metrics also correlate with stress responses, enabling dual insights into reproductive health and psychological well-being. \u003cb\u003eGender Influence (Covariate Effect)\u003c/b\u003e: The findings of this study indicate that gender plays a moderate role in class membership among individuals experiencing stress. Specifically, women are more likely to be categorized within the Stressed \u0026amp; Vulnerable group (Class 2), while men show a tendency to fall into the Resilient group (Class 1). This aligns with previous research that has consistently demonstrated that women often report higher levels of stress and are more susceptible to stress-related psychological sequelae compared to men \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eGiven these differences, it is crucial for interventions and support programs aimed at enhancing emotional well-being to be tailored by gender. Such tailored approaches could provide more effective support for female respondents who are experiencing elevated stress levels\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Furthermore, the analysis reveals the existence of three distinct psychological profiles among smart wearable users, indicating that gender has a meaningful, albeit not absolute, impact on emotional patterns. These insights suggest that health technology, workplace wellness initiatives, and educational programs can benefit from personalization based on gender-specific stress responses and coping mechanisms \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eCluster Analysis:\u003c/h3\u003e\n\u003cp\u003eThe analysis of user clusters highlights distinct psychological profiles among wearable users, offering valuable insights for targeted interventions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 1: The Low-Stress and Low-Engagement Group\u003c/b\u003e consists of younger, relaxed individuals who report low stress and anxiety but exhibit minimal engagement with health monitoring. Their casual use of wearables aligns with findings that many users prioritize basic tracking over active health management. \u003csup\u003e20\u003c/sup\u003e Encouraging this group to engage more meaningfully with wearable features could enhance their health outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 2: The Proactive \u0026amp; Balanced Group\u003c/b\u003e represents the ideal wellness profile, marked by high confidence, emotional regulation, and active engagement with wearables for health goals. This group demonstrates lower stress levels and positive psychological states, making them prime candidates for advanced coaching or digital health platforms\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Their proactive use of wearables underscores the potential of such technologies to empower users in achieving better health outcomes \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 3: The High-Stress, High-Awareness Group\u003c/b\u003e experiences significant emotional distress but paradoxically shows high wearable usage, likely as a coping mechanism. While they demonstrate strong awareness of their health, they may feel overwhelmed by constant monitoring and alerts. This aligns with research indicating that excessive self-monitoring can exacerbate anxiety in vulnerable populations. Providing tailored support to help this group interpret wearable data without increasing distress could mitigate their psychological burden \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn conclusion, Cluster 2 serves as a benchmark for positive engagement, while Clusters 1 and 3 require tailored strategies\u0026mdash;motivation for meaningful usage in Cluster 1 and stress management interventions for Cluster 3\u0026mdash;to maximize the benefits of wearable technologies.\u003c/p\u003e\u003cp\u003eLCA\u0026rsquo;s strength lies in its ability to uncover hidden subgroups and integrate covariates like gender. The Stressed \u0026amp; Vulnerable class, predominantly female, highlights systemic inequities in stress management.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eExploratory Factor Analysis\u003c/strong\u003e\u003cp\u003efactor analysis done for practical recommendations for individuals based on their scores in each factor. Factor analysis identifies underlying patterns in responses related to emotional well-being, self-confidence, and control over emotions. The analysis revealed three key factors, 60.2% of the total variance.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFactor 1: Emotional Distress \u0026amp; Stress (34.83% Variance)\u003c/b\u003e: This factor reflects feelings of stress, helplessness, and frustration in response to health and life challenges. Individuals scoring high on this factor tend to: (1) Feel upset about their health, (2) Experience stress and nervousness, (3) Feel unable to handle situations, (4) Get angry when things are out of control. (5) Feel that their personal desires remain unfulfilled. This suggests that emotional distress and perceived helplessness play a significant role in shaping individuals' responses to health-related challenges.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFactor 2: Self-Efficacy \u0026amp; Confidence (15.93% Variance)\u003c/b\u003e: This factor suggests sense of self-confidence and ability to handle situations effectively. Individuals scoring high on this factor \u003cb\u003e(1)\u003c/b\u003e Feel confident in managing situations, (2) Believe they are capable of handling challenges, (3) Feel a sense of achievement when goals are met. This suggest A strong belief in one's abilities contributes to better coping mechanisms and a sense of control over personal health and well-being.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFactor 3: Control Over Reactions \u0026amp; Goals (9.41% Variance)\u003c/b\u003e: This factor represents emotional regulation and goal orientation. High scorers in this factor tend \u003cb\u003e(1)\u003c/b\u003e Control their emotions and reactions better, (2) Feel that their desired goals are within reach, (3) Ability to regulate emotions and work towards goals effectively contributes to better mental resilience and well-being.\u003c/p\u003e\u003cp\u003eLCA to uncover latent psychological profiles based on the SQ items and examine how gender influences class membership as a covariate. Tele-MANAS Integration: Prioritize high-risk subgroups for immediate support. Algorithmic Transparency: Address distrust in data among compulsive users.\u003c/p\u003e\u003cp\u003eThe study's findings have significant clinical implications for addressing stress among wearable device users. For high-stress over-monitors, immediate action involves real-time prompts to contact Tele-MANAS (14416) when stress biomarkers exceed thresholds, coupled with CBT integration. Proactive balanced users benefit from gamified challenges, while low-stress casual users receive preventive nudges. Systemic measures include Tele-MANAS API integration and contextualized clinician dashboards. Gender differences reveal higher stress prevalence in females, with menstrual tracking anxiety and workplace dynamics as root causes. Behaviorism insights highlight operant conditioning effects, extinction learning, and a goal-setting paradox. Clinical redesign strategies involve exposure therapy and positive reinforcement techniques to address these behavioral patterns effectively.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003cp\u003eThe findings of study suggests that perceived stress levels differ significantly according to demographic factors (city, gender, designation) and health perceptions (menstrual cycle worry, health data reliability concerns). Females, interns, and residents of certain cities (Ambala) reported higher stress levels. Additionally, frequent worry about menstrual cycles and doubts about health data reliability are significantly associated with higher perceived stress. These insights are valuable for targeted interventions aimed at stress reduction, particularly within medical communities relying on wearable technology for health monitoring.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eFor individuals experiencing high emotional distress (Factor 1): Adopt stress management techniques (e.g., mindfulness, relaxation exercises). Use wearable health devices to monitor stress-related indicators (e.g., heart rate variability). For individuals with high self-efficacy (Factor 2):\u003c/p\u003e\u003cp\u003eUtilize goal-setting features in smart devices to stay motivated. Track health progress to maintain consistency in fitness and wellness routines. For individuals with high emotional control (Factor 3): Implement behavioral strategies (e.g., cognitive-behavioral techniques) to reinforce positive habit.\u003c/p\u003e\u003cp\u003eThe development of these prediction models would enable continuous monitoring of long-term stress levels that could support to better understand patient progress and well-being.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations of the study\u003c/b\u003e include reliance on self-reported bias (PSS-10 score) may underrepresent physiological stress. Due to Demographic Skew: Overrepresentation of undergraduates (60.2%) limits generalizability to senior professionals. Non availability of Sensor Data leads to lack of HRV/sleep metrics restricts biomarker validation. Further longitudinal studies are needed to clarify causality and explore how wearable device usage interacts with stress dynamics over extended periods.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEFA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eExploratory Factor Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLatent Class Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePSS 10\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePerceived Stress Scale 10\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003cp\u003e The study was conducted in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments and was approved by the Institutional Review Board from the Institutional Ethics Committee, Surat Municipal Institute of Medical Education and Research, Surat, Gujarat, India, No. SMIMER/IEC/Ref.No:118-01/02/2024/OUT/No.130, Dated 10/10/2024. All participants' autonomy and confidentiality were respected and maintained. Written informed consent was obtained from each participant after explaining the study's aim and objectives. Unique identity number was given to each participant and data was analyzed anonymously. Data files and information was kept under a password protected device.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e\u003cp\u003e Not applicable.\u003c/p\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthors Contribution\u003c/h2\u003e\u003cp\u003eStudy Conceptualized and Designed by RK. Material preparation and data collection were performed by every authors. Data analysis and interpretation were performed by RK and DS. The first draft of the manuscript was written by RK and supported by all. All authors contributed to and have approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e The authors would like to thank IRB, the study participants and Dr G.K. Vankar, Professor Emeritus, Department of Psychiatry, PIMSR, Parul University, Vadodara, India.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available upon request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKerner C, Goodyear V (2017) The motivational impact of wearable healthy lifestyle technologies: a self determination perspective on FitBits with adolescents. 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PMID: 31221426; PMCID: PMC8630768\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAvishek Choudhury O, Asan Impact of using wearable devices on psychological Distress: Analysis of the health information national Trends survey. Int J Med Informatics, 156, 2021,104612, ISSN 1386\u0026ndash;5056, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijmedinf.2021.104612\u003c/span\u003e\u003cspan address=\"10.1016/j.ijmedinf.2021.104612\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Department of Psychiatry, NAMO Medical Education \u0026 Research Institute, Silvassa, Dadra and Nagar Haveli, India.","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wearable devices, perceived stress, digital phenotyping, behaviorism, cluster analysis","lastPublishedDoi":"10.21203/rs.3.rs-7771958/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7771958/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e \u0026nbsp;\"Digital Vigilance Paradox\" suggests that health-monitoring devices may increase stress for healthcare professionals instead of alleviating it, complicating their already demanding roles. Digital phenotyping offers a novel approach to objectively quantify behavioral and psychological health markers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e 1. To quantify the relationship between wearable device usages patterns and PSS-10 (Perceived Stress Scale) levels 2. To analyse gender-specific dimensions and latent psychological dimensions and create digital phenotypes for clinical assessment and timely intervention.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Cross-sectional, multicentric study conducted to investigates the relationship between wearable device usage and perceived stress among 349 Indian medical professionals. Utilizing the modified PSS-10, Structured Questionnaire: Captured demographics, device usage patterns, and menstrual tracking behaviors. Inclusion criteria: age 18–65 years, active wearable use (\u0026gt;1 month)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eUtilizing the PSS-10, identified a mean stress score of 15.38 (±6.41), with significant variations across cities (χ²=15.703, p=0.015), gender (χ²=8.437, p=0.015), and professional hierarchy (χ²=22.860, p=0.029). Females exhibited higher stress (65.8% moderate stress vs. 57.0% males), tracking menstrual health discrepancies (18.8% high stress, p=0.001). Cluster analysis revealed three psychological profiles: Low-Stress Casual Users (32.4%), Proactive Balanced Users (41.5%), and High-Stress Over-Monitors (26.1%), with the latter group demonstrating anxiety traits. Short-term users exhibited peak stress (PSS=16.97±5.804), suggesting an \"adaptation phase\" to device engagement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Digital Vigilance Paradox highlights wearables as both health tools and anxiety triggers for medical professionals, influenced by learned behaviors and gender, potentially increasing stress in vulnerable groups through hypervigilance. Digital Phenotyping validated three used profiles, enabling personalized care strategy, require urgent intervention like automated Tele-MANAS referrals.\u003c/p\u003e","manuscriptTitle":"Digital Phenotyping of Perceived Stress and Wearable Device Use Among Medical Professionals: A Multicentric Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 11:12:35","doi":"10.21203/rs.3.rs-7771958/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c7901158-d81b-487e-ad38-139c2c8f6a25","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55715139,"name":"Psychiatry"}],"tags":[],"updatedAt":"2025-11-20T20:53:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 11:12:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7771958","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7771958","identity":"rs-7771958","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}