Bella Etingen

Chronic pain is the leading cause of disability worldwide and frequently co-occurs with mild traumatic brain injury among Veterans (mTBI + CP), creating complex treatment challenges and a need for novel, non-pharmacological interventions. This study evaluated a pilot intervention combining intermittent theta burst stimulation (iTBS), a neuromodulatory approach, with the evidence-based LoveYourBrain Yoga program to enhance rehabilitation outcomes. In a six-week open-label trial, ten Veterans with mTBI + CP received weekly iTBS followed by yoga sessions. Pilot quantitative outcomes included quality of life (Traumatic Brain Injury Quality of Life [TBI-QoL]) and functional ability (Mayo Portland Adaptability Inventory-4 [MPAI-4]), assessed pre- and post-intervention, alongside qualitative semi-structured interviews and interdisciplinary clinical notes. Significant improvements were observed in TBI-QoL Fatigue (p = 0.021) and MPAI-4 Grief and Loss (p = 0.016), with clinically meaningful but non-significant gains in Ability and Adjustment. Qualitative findings revealed improved pain management and enhanced self-management, with participants describing better emotional regulation, more effective coping strategies, and stronger social connections. Some benefits were more evident in qualitative data than in standardized measures. These pilot findings suggest that combining iTBS with mind–body therapy may provide complementary tools for pain management and functional recovery in Veterans with mTBI + CP, supporting further investigation of integrated neuromodulation and behavioral interventions.

Background: The integration of patient-generated health data (PGHD) into health care has the potential to significantly transform patient care and clinical practice. PGHD includes health-related data created by patients, enabling the collection of health data beyond traditional health care settings. The Veterans Health Administration (VA) has taken proactive steps to incorporate PGHD into health care through its Share My Health Data (SMHD) mobile app. Launched in 2023, the SMHD app allows veterans to securely share data from their personal digital health devices with the VA for clinical and research use. However, data characterizing patients who use such tools in real-world health care systems are lacking, creating an evidence gap for implementing PGHD-informed care equitably. Objective: This study aimed to identify the characteristics of patients using the VA SMHD mobile app, which allows veterans to share PGHD with the VA. Methods: We conducted a cross-sectional analysis of veterans who began using SMHD between October 2023 and September 2024 (n=3157, “SMHD users”). We collected demographic information, including age, sex, race/ethnicity, and rurality, and clinical information, including physiological and mental health conditions, from VA administrative data. We compared characteristics of SMHD users to a 10% random sample of veterans from the same underlying administrative data cohort that had never used the app (n=632,187, “nonusers”). Statistical analyses were performed using chi-square tests, independent t tests, and multivariable regression to assess the relationship between use and key characteristics. Results: Middle-aged veterans were more likely to be SMHD users (40-49 years: odds ratio [OR] 1.55, P<.001; 50-59 years: OR 1.37, P<.001), while veterans aged 60 years and over were less likely (60-69 years: OR 0.72, P<.001; ≥70 years: OR 0.24, P<.001). Female (OR 1.23, P<.001) and married (OR 1.31, P<.001) veterans were more likely to be SMHD users than male and unmarried veterans. In contrast, Black or African American (OR 0.62, P<.001) and rural (OR 0.82, P<.001) veterans were less likely to be SMHD users than White and urban veterans. Veterans in higher-income zip codes (OR 1.36, P<.001) were more likely to have used the app than those in lower-income zip codes. Clinically, SMHD users were more likely to have a service-connected disability (OR 1.81, P<.001), multiple physiological conditions (OR 1.86, P<.001), and multiple mental health diagnoses (OR 1.35, P<.001) versus none. Conclusions: Veterans who used the SMHD app differed significantly from nonusers across several demographic and clinical characteristics. These insights identify specific demographic and clinical subgroups with higher and lower app adoption, providing an evidence base to inform targeted implementation and outreach and support strategies to promote enhanced engagement in PGHD-informed care.

Background: The Veterans Health Administration is promoting patient-reported outcome measure (PROM) collection for measurement-based mental health care. Understanding veteran preferences about how and when to complete PROMs is critical to support their implementation. Objective: We examined veteran preferences for timing and use of different technology platforms to complete mental health–related PROMs. Methods: We invited a national sample of 1373 veterans to complete a survey; 858 (62.5%) responded. Surveys asked about veteran preferences for how and when to complete mental health–related PROMs. We characterized responses using descriptive statistics and estimated multiple logistic regression models to examine associations between veteran demographic and health characteristics and preferences for completing PROMs. Results: Most veterans preferred completing PROMs between appointments (607/801, 75.8%) using features of a patient portal (410/801, 51.2%), during appointments (589/801, 73.5%) verbally (413/801, 51.6%), and while at the medical center (480/801, 59.9%) on paper (189/801, 23.6%) or a tablet computer (180/801, 22.5%). Hispanic (vs non-Hispanic) veterans had 3.32 (95% CI 1.04-10.58) times higher odds of preferring to complete PROMs at the medical center, and veterans with lower (vs higher) socioeconomic status had lower odds (odds ratio 0.61, 95% CI 0.40-0.93) of preferring to complete PROMs in between appointments but 1.97 (95% CI 1.23-3.16) times higher odds of preferring to complete PROMs during appointments. Conclusions: As the Veterans Health Administration and other health care systems seek to expand the integration of PROM data into health care services, adaptive and flexible approaches to PROM administration that align with patient preferences, including those that leverage technology platforms in the remote collection of these data, may bolster implementation. Our results indicate that such implementation efforts should consider patient ethnicity and socioeconomic status. Our findings further suggest that these efforts could benefit from incorporating PROM administration into online patient portals, developing mobile health apps that support PROM completion through patients’ personal devices in between clinical encounters, and engaging care team members in PROM administration during appointments.

Trauma‐focused psychotherapy is the front‐line treatment option for posttraumatic stress disorder (PTSD); however, approximately two thirds of veterans who initiate such evidence‐based psychotherapy (EBP) for PTSD discontinue treatment before completing an adequate number of therapy sessions. We examined the association between whole health (WH) care, comprising WH services (e.g., health and wellness coaching, wellness groups) and complementary and integrative health (CIH) therapies, and EBP completion for PTSD. We completed a national retrospective database analysis of Veterans Health Administration administrative records for veterans with PTSD who initiated an EBP in Fiscal Years 2018–2022 and estimated multiple logistic regression models assessing the association between WH care use and EBP completion (i.e., completing eight EBP sessions within 14 weeks). We identified 100,177 veterans with PTSD who initiated EBP (34.8% completed EBP). Of these veterans, 9,824 (9.8%) had used WH services, and 3,396 (3.6%) had used CIH therapies. When controlling for demographic and health‐related variables, WH care use was associated with increased odds of EBP completion. The strongest associations were between the use of WH services both before EBP initiation and concurrent with EBP use, OR = 1.39, 95% CI [1.28, 1.51], and the use of CIH therapies concurrent with EBP use, OR = 1.38, 95% CI [1.20, 1.60], or both before EBP initiation and concurrent with EBP use, OR = 1.30, 95% CI [1.17, 1.45]. The results suggest that parallel use of WH care may bolster patient completion of EBP for PTSD, a critical component of the effectiveness of these psychotherapies.

Introduction: The Veterans Health Administration (VHA) prioritizes use of connected care technologies to enhance access and outcomes. The context in which connected care is implemented is crucial, yet difficult to measure, due to its subjective and complex nature. This evaluation examined alignment among stakeholder perceptions of context related to connected care implementation across VHA. Methods: A national, cross-sectional survey assessed perceptions of 11 contextual factors relevant to connected care implementation as identified in published reviews within the implementation science literature. Across 142 VHA facilities and 18 regions, surveys were sent to four stakeholder groups: clinical team members, connected care coordinators, facility leadership, and regional leadership. Mean scores for each factor were compared between stakeholder groups using Welch's ANOVA and Bonferroni-corrected post-hoc comparisons. Results: A total of 5541 respondents (36.1% response rate) participated. Organizational Culture and Climate was rated the most favorable contextual factor (mean = 3.9, SD = 0.7), while Financial Resources was perceived as least favorable (mean = 3.0, SD = 1.0). Significant differences emerged between the perceptions of frontline workers (clinical team members, connected care coordinators) and leadership (facility, regional). Clinical team members rated nearly all contextual factors less favorably than facility leadership. Coordinators similarly rated most factors less favorably than leadership. Discussion: Findings highlight a misalignment between leadership and frontline workers in their perceptions of organizational context for implementing connected care technologies. Leadership viewed key contextual factors (e.g. Organizational Readiness to Change, Leadership Support) more favorably than frontline workers. This misalignment may impact implementation success, suggesting a need for strategies to better align stakeholder perceptions.

BACKGROUND The Veterans Health Administration is promoting patient-reported outcome measure (PROM) collection for measurement-based mental health care. Understanding veteran preferences about how and when to complete PROMs is critical to support their implementation. OBJECTIVE We examined veteran preferences for timing and use of different technology platforms to complete mental health–related PROMs. METHODS We invited a national sample of 1373 veterans to complete a survey; 858 (62.5%) responded. Surveys asked about veteran preferences for how and when to complete mental health–related PROMs. We characterized responses using descriptive statistics and estimated multiple logistic regression models to examine associations between veteran demographic and health characteristics and preferences for completing PROMs. RESULTS Most veterans preferred completing PROMs between appointments (607/801, 75.8%) using features of a patient portal (410/801, 51.2%), during appointments (589/801, 73.5%) verbally (413/801, 51.6%), and while at the medical center (480/801, 59.9%) on paper (189/801, 23.6%) or a tablet computer (180/801, 22.5%). Hispanic (vs non-Hispanic) veterans had 3.32 (95% CI 1.04-10.58) times higher odds of preferring to complete PROMs at the medical center, and veterans with lower (vs higher) socioeconomic status had lower odds (odds ratio 0.61, 95% CI 0.40-0.93) of preferring to complete PROMs in between appointments but 1.97 (95% CI 1.23-3.16) times higher odds of preferring to complete PROMs during appointments. CONCLUSIONS As the Veterans Health Administration and other health care systems seek to expand the integration of PROM data into health care services, adaptive and flexible approaches to PROM administration that align with patient preferences, including those that leverage technology platforms in the remote collection of these data, may bolster implementation. Our results indicate that such implementation efforts should consider patient ethnicity and socioeconomic status. Our findings further suggest that these efforts could benefit from incorporating PROM administration into online patient portals, developing mobile health apps that support PROM completion through patients’ personal devices in between clinical encounters, and engaging care team members in PROM administration during appointments.

Background: Chronic pain is a debilitating condition that disproportionately impacts US veterans who manage numerous negative pain-related outcomes. There is an urgent need for accessible, engaging, and innovative treatments that can help veterans with chronic pain better self-manage their pain at home and improve their daily functioning. Technology-delivered acceptance- and mindfulness-based interventions for pain have shown strong efficacy, particularly when they are engaging and tailored to specific client needs. However, more research is needed to assess the impact of such interventions, particularly in terms of pain-related functioning and quality of life. Objective: The primary aim of this study is to test the efficacy of Veteran Acceptance and Commitment Therapy for Chronic Pain (VACT-CP), an online self-management pain program, compared to an active online control (Online Pain School) for improving pain-related functioning in a 3-site randomized controlled trial. The secondary aim is to explore psychological flexibility as a potential mediator between pain severity and pain-related functioning. Methods: This study will use a mixed methods approach to examine the efficacy of VACT-CP in a 2-arm, multisite, randomized controlled superiority trial including 200 participants with chronic musculoskeletal pain compared to Online Pain School. Participants will be assigned to 1 of these 2 online interventions. Both interventions will include 7 modules delivered over 7 weeks, with each module lasting approximately 15 minutes. Mixed effects models will be used to analyze the primary hypothesis that participants in the VACT-CP group will have greater improvement in pain-related functioning (Brief Pain Inventory–Interference subscale) than those in the active control group (Online Pain School). The main acceptance and commitment therapy process mediator (ie, Multidimensional Psychological Flexibility Inventory), pain-related functioning outcomes (Brief Pain Inventory–Interference subscale), and quality of life (Veterans RAND 36-Item Health Survey) will be measured at baseline, end of treatment, and 3 and 6 months after treatment. In addition, qualitative exit interviews will be conducted with a random set of 30 VACT-CP users (n=10, 33% per site) to obtain intervention usability, feasibility, and acceptability information. Results: The recruitment for this study began in January 2025. It is expected to continue through January 2027. Data collection is expected to be completed by June 2027, and primary data analyses are expected to be completed by early 2028. Conclusions: Online interventions such as VACT-CP and Online Pain School have the potential to expand access to behavioral interventions that improve quality of life and provide nonpharmacological pain treatment options for veterans experiencing chronic pain. However, research on their impact and underlying mechanisms of change is required to support this area of potential at-home programming. Trial Registration: ClinicalTrials.gov NCT06058624; https://clinicaltrials.gov/study/NCT06058624 International Registered Report Identifier (IRRID): PRR1-10.2196/70601

Background: There is an increasingly diverse range of mobile apps and digital health devices available to help patients manage their health. Despite evidence for the effectiveness of such technologies, their potential has not been fully realized because adoption remains low. Such limited uptake can have direct implications for the intended benefits of these technologies. Objective: This study aimed to understand what matters most to US military veterans when deciding whether to use digital health technologies (DHTs) such as mobile health apps or devices to manage their health and compare these factors between veterans with and without prevalent chronic physical and mental health conditions. Methods: We conducted a cross-sectional analysis of survey data collected from a national sample of veterans who receive care from the Veterans Health Administration (VHA), which was predominantly gathered as part of the last wave of a larger longitudinal data collection effort. Results: Among respondents (n=857), 86.7% (736/849) reported currently using or having previously used ≥1 devices to manage their health, and 78.4% (639/815) also reported using either VHA or non-VHA health apps. Considerations most frequently endorsed as “very important” by veterans when deciding whether to use DHTs included receiving secure messages from their health care team about DHTs, knowing data from DHTs would be used to inform their care, and receiving recommendations from providers to use DHTs. Conversely, considerations most frequently endorsed as “not at all important” included seeing information about DHTs on social media, having community support to use DHTs, and receiving encouragement from peers to use DHTs. Considerations did not significantly differ between veterans with or without prevalent chronic health conditions; however, a greater proportion of veterans with prevalent mental health conditions reported the following considerations to be “very important:” seeing information about DHTs on social media, having community support to use DHTs, having other veterans encourage DHT use, and having help from family, friends, or other important people to use DHTs. Conclusions: Understanding what matters most to patients when they are deciding to adopt a technology for their health can, and should, inform implementation strategies and other approaches to enhance health-related technology use. Our results suggest that, for veterans, recommendations from health care team members and knowing that the data from DHTs will be used in clinical care are more important than information from social media, community sources, or peers when deciding to use DHTs, although perceptions of importance regarding the latter may differ among patients with different conditions. Our findings suggest that communication from health care team members to patients, perhaps either in-person or electronically, could help encourage DHT adoption and use.

BACKGROUND The integration of patient-generated health data (PGHD) into health care has the potential to significantly transform patient care and clinical practice. PGHD includes health-related data created by patients, enabling the collection of health data beyond traditional health care settings. The Veterans Health Administration (VA) has taken proactive steps to incorporate PGHD into health care through its Share My Health Data (SMHD) mobile app. Launched in 2023, the SMHD app allows veterans to securely share data from their personal digital health devices with the VA for clinical and research use. However, data characterizing patients who use such tools in real-world health care systems are lacking, creating an evidence gap for implementing PGHD-informed care equitably. OBJECTIVE This study aimed to identify the characteristics of patients using the VA SMHD mobile app, which allows veterans to share PGHD with the VA. METHODS We conducted a cross-sectional analysis of veterans who began using SMHD between October 2023 and September 2024 (n=3157, “SMHD users”). We collected demographic information, including age, sex, race/ethnicity, and rurality, and clinical information, including physiological and mental health conditions, from VA administrative data. We compared characteristics of SMHD users to a 10% random sample of veterans from the same underlying administrative data cohort that had never used the app (n=632,187, “nonusers”). Statistical analyses were performed using chi-square tests, independent t tests, and multivariable regression to assess the relationship between use and key characteristics. RESULTS Middle-aged veterans were more likely to be SMHD users (40-49 years: odds ratio [OR] 1.55, P<.001; 50-59 years: OR 1.37, P<.001), while veterans aged 60 years and over were less likely (60-69 years: OR 0.72, P<.001; ≥70 years: OR 0.24, P<.001). Female (OR 1.23, P<.001) and married (OR 1.31, P<.001) veterans were more likely to be SMHD users than male and unmarried veterans. In contrast, Black or African American (OR 0.62, P<.001) and rural (OR 0.82, P<.001) veterans were less likely to be SMHD users than White and urban veterans. Veterans in higher-income zip codes (OR 1.36, P<.001) were more likely to have used the app than those in lower-income zip codes. Clinically, SMHD users were more likely to have a service-connected disability (OR 1.81, P<.001), multiple physiological conditions (OR 1.86, P<.001), and multiple mental health diagnoses (OR 1.35, P<.001) versus none. CONCLUSIONS Veterans who used the SMHD app differed significantly from nonusers across several demographic and clinical characteristics. These insights identify specific demographic and clinical subgroups with higher and lower app adoption, providing an evidence base to inform targeted implementation and outreach and support strategies to promote enhanced engagement in PGHD-informed care.

Background: Tele-oncology addresses geographic barriers to cancer care, but implementation challenges persist in rural settings. AI-enhanced predictive analytics offer opportunities for optimizing deployment through personalized, data-driven strategies; however, evidence in rural tele-oncology contexts remains limited, and critical equity considerations remain underexamined. Objective: This scoping review aimed to map evidence on AI-enhanced predictive analytics in tele-oncology implementation, with particular attention to rural and underserved populations, to identify research gaps and inform implementation science priorities. Methods: We searched 5 databases (PubMed, Embase, CINAHL, Web of Science, and IEEE Xplore) using 4 concept domains (tele-oncology, rural implementation barriers, AI or predictive analytics, implementation science) from January 2015 through November 2025. Two independent reviewers screened 330 unique records (title or abstract; Cohen κ=0.78), with the principal investigator resolving conflicts. Of 138 full-text reviews (κ=0.82), 4 studies met inclusion criteria. Data extraction captured study characteristics, AI applications, implementation factors, and outcomes. We used narrative thematic analysis to map findings into three themes: (1) the current tele-oncology implementation landscape in rural and underserved settings, (2) potential AI applications addressing implementation challenges, and (3) implementation considerations for AI systems themselves. Results: Four included studies (1 pilot feasibility study, 1 proof-of-concept validation study, 1 cross-sectional predictive study, and 1 platform development study; published 2019‐2025) demonstrated limited evidence at the intersection of AI, tele-oncology, and rural health equity. Patient characteristics predicted telehealth modality preferences with 86.2% accuracy, revealing that male patients exhibited 66% increased odds of video selection versus female patients ( P =.004), and urban residents showed 101% increased odds compared to rural counterparts ( P <.001). Liu et al demonstrated that disadvantaged populations engaged with AI-generated health literacy content 2.52-fold more frequently than nondisadvantaged counterparts. However, all 4 studies documented substantial implementation barriers (patient, provider, organizational, and system levels) persisting despite technological sophistication. Organizational threshold effects, where remote monitoring interventions succeeded with adequate provider capacity but failed under resource constraints—suggest that algorithmic innovations cannot overcome structural limitations in rural facilities. No studies explicitly examined algorithmic bias, cross-population validation, or potential harms in rural contexts. Geographic concentration in high-resource countries (United States n=2, Greece n=1, and Singapore n=1) and limited oncology-specific focus underscore structural gaps in knowledge generation for underserved populations. Conclusions: Current evidence remains insufficient to support definitive practice recommendations. The observed evidence gap may reflect broader structural inequities in knowledge generation: populations with the greatest implementation challenges appear to remain substantially underrepresented in AI and digital health literature. Future research should prioritize comparative effectiveness studies in authentic rural contexts with implementation science outcomes, equity-centered cross-population validation, specification of translation mechanisms linking AI predictions to implementation strategies, health economic analyses, and mechanistic research on sociotechnical integration factors, ensuring technological innovation reduces rather than perpetuates disparities in cancer care.