Psychometric Properties of the Weight Loss Readiness Test in Active Duty Military Personnel Enrolled in a Weight Management Trial.

INTRODUCTION: The Weight Loss Readiness Test (WLRT) was developed to encourage consideration of factors influencing readiness to engage in weight loss. The WLRT is used clinically, most notably to assess motivation before initiating Navy weight management programs, yet little is known about its psychometric properties. MATERIALS AND METHODS: This study examined the reliability, convergent and predictive validity, and factor structure of the WLRT in a sample of active duty service members enrolling in a Navy-based weight management program (N = 178, identified as female = 61%, mean age = 29.7 years, mean baseline body mass index = 33.1 kg/m2). All procedures were approved by the respective Institutional Review Boards and research committees. RESULTS: Exploratory factor analysis revealed a 5-factor structure explaining 52% of the variance that best fit the data with low to moderate correlations between factors: (1) Motivation, (2) Exercise-Related Confidence, (3) Non-Exercise Confidence, (4) Cues, and (5) Anticipated Satisfaction. Internal reliability of subscales was acceptable to good (α = 0.755-0.903). Generally, convergent validity was found between the identified subscales and other measures of motivation, confidence, and disinhibited eating in expected directions. No relationships were found between the subscales and predictive validity outcomes (weight change, program attendance). CONCLUSIONS: Results indicate adequate structural and convergent validity in the WLRT, but that weight loss readiness, as measured by the WLRT, does not provide predictive validity regarding weight loss or attendance outcomes in this sample. Nonetheless, this measure offers clinical utility in fostering thoughtful conversations about weight loss. The WLRT uniquely focuses on long-term maintenance of behavior change and differentiates between exercise-related and non-exercise confidence. Future studies should further probe the utility of this measure in other populations and the contexts in which it is being used.

Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals.

Healthcare professionals are known to suffer from workplace stress and burnout, which can negatively affect their empathy for patients and quality of care. While existing research has identified factors associated with wellbeing and empathy in healthcare professionals, these efforts are typically focused on the group level, ignoring potentially important individual differences and implications for individualized intervention approaches. In the current study, we implemented N-of-1 personalized machine learning (PML) to predict wellbeing and empathy in healthcare professionals at the individual level, leveraging ecological momentary assessments (EMAs) and smartwatch wearable data. A total of 47 mood and lifestyle feature variables (relating to sleep, diet, exercise, and social connections) were collected daily for up to three months followed by applying eight supervised machine learning (ML) models in a PML pipeline to predict wellbeing and empathy separately. Predictive insight into the model architecture was obtained using Shapley statistics for each of the best-fit personalized models, ranking the importance of each feature for each participant. The best-fit model and top features varied across participants, with anxious mood (13/19) and depressed mood (10/19) being the top predictors in most models. Social connection was a top predictor for wellbeing in 9/12 participants but not for empathy models (1/7). Additionally, empathy and wellbeing were the top predictors of each other in 64% of cases. These findings highlight shared and individual features of wellbeing and empathy in healthcare professionals and suggest that a one-size-fits-all approach to addressing modifiable factors to improve wellbeing and empathy will likely be suboptimal. In the future, such personalized models may serve as actionable insights for healthcare professionals that lead to increased wellness and quality of patient care.

Effects of Chronic Pain Diagnoses on the Antidepressant Efficacy of Transcranial Magnetic Stimulation.

OBJECTIVE: Major depressive disorder (MDD) and chronic pain are highly comorbid and bidirectionally related. Repetitive transcranial magnetic stimulation (rTMS) over the dorsolateral prefrontal cortex is effective in treating MDD, but additional research is needed to determine if chronic pain interferes with rTMS for MDD. METHODS: Participants were 124 veterans ( Mage = 49.14, SD = 13.83) scheduled for 30 sessions of rTMS across 6 weeks. Depression severity was monitored weekly using the Patient Health Questionnaire-9 (PHQ-9). Having any pain diagnosis, low back pain, or headache/migraine were assessed by chart review. We fit latent basis models to estimate total change by pain diagnosis in depression scores and quadratic latent growth models to examine differences in growth rates. Then, we computed χ2 tests of group differences in response (PHQ-9 reduction ≥50%) and remission rates (final PHQ-9 < 5). RESULTS: A total of 92 participants (74%) had a documented pain diagnosis, 58 (47%) had low back pain, and 32 (26%) had headache/migraine. In growth models, depression scores initially decreased (linear slope estimate = -2.04, SE = 0.26, p < .0001), but the rate of decrease slowed over time (quadratic slope estimate = 0.18, SE = 0.04, p < .001). Overall change was not different as a function of any pain diagnosis ( p = .42), low back pain (p = .11 ), or headache/migraine ( p = .28). However, we found that low back pain was a negative predictor of response ( p = .032). CONCLUSIONS: These data support rTMS as a viable treatment option for comorbid populations. Although patients with comorbid chronic pain conditions are likely to receive benefit from rTMS for depression, adjunctive pain treatment may be indicated.

The Impact of COVID-19 on Health Behavior Engagement and Psychological and Physical Health Among Active Duty Military Enrolled in a Weight Management Intervention: An Exploratory Study.

INTRODUCTION: The COVID-19 pandemic profoundly affected the health and well-being of people globally. Some studies suggest individuals with overweight or obesity may have been more adversely impacted by pandemic restrictions. Additionally, military personnel may have been more vulnerable to stress during the pandemic because of job demands (e.g., work in close quarters). Our research group was conducting a randomized clinical trial of a weight management intervention in active duty military personnel with overweight or obesity when the COVID-19 pandemic struck. Thus, we collected additional pandemic-related data from participants enrolled during the COVID-19 pandemic and conducted exploratory analyses to understand how the pandemic affected their stress levels and perceived abilities to engage in health behaviors like exercise and healthy eating. The aims of this exploratory study were to: (1) assess associations between pandemic-related stress and health behaviors with body fat percentage (BF%) and psychological and physical health, and (2) explore how pandemic-related stress and health behaviors affected BF% during and after the intervention. MATERIALS AND METHODS: A total of 29 active duty Navy personnel (55% female, 69% White) were enrolled after the onset of pandemic restrictions and completed measures assessing pandemic-related stress and health behaviors as well as measures of general psychological and physical health before the intervention. BF% was collected at 5 timepoints, including baseline, during, and following the intervention. Bivariate correlations assessed associations at baseline. Linearmixed-effects longitudinal models explored how pandemic-related stress and health behaviors affected BF%. Post-hoc analyses evaluated the effects of pandemic factors on BF% at each timepoint. RESULTS: Most participants perceived of the pandemic as increasing stress and detracting from their abilities to engage in health behaviors (i.e., exercise and healthy eating). Higher pandemic stress was significantly associated with reporting exercise and healthy eating as more difficult, worse overall health, and more anxiety and general stress. Reporting exercise as harder during the pandemic also was associated with more psychological distress at baseline. A linearmixed- effects model controlling for age, sex, and number of intervention sessions attended revealed endorsing "exercise as harder" was associated with higher BF%. Post-hoc regression analyses revealed rating "exercise as harder" significantly predicted higher BF% 3 months post-intervention (B = 0.65, P = .01), whereas pandemic stress was not significantly associated with higher BF% at 3 month (B =0.14, P = .08) or 6 month (B = 0.21, P = .09) follow-up. CONCLUSIONS: As expected, most participants perceived of the pandemic as heightening stress and interfering with engagement in health behaviors. It is possible that pandemic-related stress may have exacerbated weight gain-promoting behaviors and/or interfered with achievement of desired weight management outcomes. Outside the context of the pandemic, it may be beneficial for healthcare providers to screen individuals for stress and perceptions of ease of engagement in health behaviors before enrollment in a weight management intervention. Further, tailoring interventions to mitigate stress and promote perceptions of ease in engaging in health behaviors may promote better weight management outcomes.

Compassion Dynamics in Medical Students: An Ecological Momentary Assessment Study.

Effective interventions to support compassionate patient- and self-care requires an understanding of how to best assess compassion. Micro-ecological momentary assessment (micro-EMA), a method in which participants provide brief responses in real-time within their own environments, can capture changes in compassion across time and contexts. This study examined a micro-EMA approach for measuring the temporal dynamics of compassion in medical students during the COVID-19 pandemic. Medical students (N = 47) completed demographic information and self-report questionnaires assessing empathy and compassion for self and others. Participants then completed six bursts of micro-EMA smartphone-delivered surveys. Each burst was 14 days, with 28 days between bursts. During each burst, participants received four daily micro-EMA surveys assessing compassion, stress, positive affect, and negative affect. Dynamic structural equation modeling was used to examine micro-EMA responses. The overall micro-EMA response rate was 83.75%. On average, daily compassion did not significantly change across the academic year. However, there was significant within-person variability in medical students' compassion trajectories over the training year (b = 0.027, p < .01). At concurrent timepoints, micro-EMA assessed compassion was associated with greater happiness (b = 0.142, p < .001) and lower stress (b = -0.052, p < .05) but was not associated with sadness. In lagged analyses, higher micro-EMA assessed compassion predicted higher next day happiness (b = 0.116, p < .01) and vice versa (b = 0.185, p < .01). Results suggest it is feasible to use micro-EMA to assess daily levels of compassion among medical students. Additionally, there is wide variability in day-to-day fluctuations in compassion levels among medical students, with some students showing substantial increases in daily compassion across the training year and others showing decreases. Positive affect as opposed to negative affect may have particularly strong associations with compassion. Further examination of antecedents and consequences of fluctuations in daily compassion could inform potent intervention targets.