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While many digital mental health interventions (DMHIs) have been found to be efficacious, patient engagement with DMHIs has increasingly emerged as a concern for implementation in real-world clinical settings. To address engagement, we must first understand what standard engagement levels are in the context of randomized controlled trials (RCTs) and how these compare with other treatments. This scoping review aims to examine the state of reporting on intervention engagement in RCTs of mobile app-based interventions intended to treat symptoms of depression. We sought to identify what engagement metrics are and are not routinely reported as well as what the metrics that are reported reflect about standard engagement levels. We conducted a systematic search of 7 databases to identify studies meeting our eligibility criteria, namely, RCTs that evaluated use of a mobile app-based intervention in adults, for which depressive symptoms were a primary outcome of interest. We then extracted 2 kinds of information from each article: intervention details and indices of DMHI engagement. A 5-element framework of minimum necessary DMHI engagement reporting was derived by our team and guided our data extraction. This framework included (1) recommended app use as communicated to participants at enrollment and, when reported, app adherence criteria; (2) rate of intervention uptake among those assigned to the intervention; (3) level of app use metrics reported, specifically number of uses and time spent using the app; (4) duration of app use metrics (ie, weekly use patterns); and (5) number of intervention completers. Database searching yielded 2083 unique records. Of these, 22 studies were eligible for inclusion. Only 64% (14/22) of studies included in this review specified rate of intervention uptake. Level of use metrics was only reported in 59% (13/22) of the studies reviewed. Approximately one-quarter of the studies (5/22, 23%) reported duration of use metrics. Only half (11/22, 50%) of the studies reported the number of participants who completed the app-based components of the intervention as intended or other metrics related to completion. Findings in those studies reporting metrics related to intervention completion indicated that between 14.4% and 93.0% of participants randomized to a DMHI condition completed the intervention as intended or according to a specified adherence criteria. Findings suggest that engagement was underreported and widely varied. It was not uncommon to see completion rates at or below 50% (11/22) of those participants randomized to a treatment condition or to simply see completion rates not reported at all. This variability in reporting suggests a failure to establish sufficient reporting standards and limits the conclusions that can be drawn about level of engagement with DMHIs. Based on these findings, the 5-element framework applied in this review may be useful as a minimum necessary standard for DMHI engagement reporting.

Patient-generated health data (PGHD) encompass health-related information created, recorded, and gathered by patients in their daily lives, and are distinct from data collected in clinical settings. PGHD can offer insight into patients’ everyday health behaviors and conditions, supporting health management and clinical decision-making. The Veterans Health Administration (VHA) has developed a robust infrastructure to collect PGHD, including automatically collected data from digital sensors and patient-entered data. This effort is guided by comprehensive policy and strategy documents to ensure the secure storage and effective use of PGHD. This paper describes the development and implementation of an infrastructure to support PGHD within the VHA and highlights envisioned clinical and research uses of PGHD to advance health care for US veterans. The PGHD database was built to Fast Healthcare Interoperability Resources standards, facilitating secure data storage and exchange of PGHD. Clinical tools, such as the provider-facing dashboards, make PGHD accessible from the electronic health records. Research and evaluation efforts focus on evaluating PGHD’s impact on patient engagement, clinical outcomes, and health care equity. The VHA’s comprehensive PGHD infrastructure represents a significant advancement in personalized health care and patient engagement. The integration of PGHD into clinical practice can enhance shared decision-making and self-management, while research and evaluation efforts can address how to maximize the benefits of PGHD for veterans. The VHA’s approach sets a benchmark for other US health care systems in leveraging PGHD to achieve the broad aims of enhancing stakeholder health care experiences, improving population health and health equity, and reducing costs.

BackgroundThe suicide rate in bipolar disorder (BD) is among the highest across all psychiatric disorders. Identifying modifiable variables that relate to suicidal thoughts and behaviors (STBs) in BD may inform prevention strategies. Social connectedness is a modifiable variable found to relate to STBs in the general population, but differences exist across subgroups of the general population and findings specifically in BD have been equivocal. We aimed to clarify how perceived social connectedness relates to STBs in BD.Method146 adults (86 BD, 60 healthy controls) completed clinical interviews (Hamilton Depression Rating Scale; Structured Clinical Interview for DSM-5) and self-report measures of loneliness (UCLA Loneliness Scale) and social support (Interpersonal Support Evaluation List). Analyses explored differences in indicators of social connectedness (loneliness and social support) between BD participants and healthy controls, and explored relationships between STBs (lifetime suicide attempts and current suicidal ideation) and indicators of social connectedness in BD participants.ResultsBD participants reported significantly higher loneliness and lower social support than healthy controls. In BD participants, perceived social support was significantly related to both ever having attempted suicide and number of lifetime attempts. Interestingly, perceived loneliness, but not social support, was significantly associated with current suicidal ideation.ConclusionsFindings expand the evidence base supporting a relationship between perceived social connectedness and STBs in BD. They suggest that this modifiable variable could be a fruitful treatment target for preventing STBs in BD.

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. 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. 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. 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. 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 Participant recruitment is an ongoing challenge in health research. Recruitment may be especially difficult for studies of access to health care because, even among those who are in care, people using services least often also may be hardest to contact and recruit. Opt-out recruitment methods (in which potential participants are given the opportunity to decline further contact about the study (opt out) following an initial mailing, and are then contacted directly if they have not opted out within a specified period) can be used for such studies. However, there is a dearth of literature on the effort needed for effective opt-out recruitment. Methods In this paper we describe opt-out recruitment procedures for two studies on access to health care within the U.S. Department of Veterans Affairs. We report resource requirements for recruitment efforts (number of opt-out packets mailed and number of phone calls made). We also compare the characteristics of study participants to potential participants via t-tests, Fisher’s exact tests, and chi-squared tests. Results Recruitment rates for our two studies were 12 and 21%, respectively. Across multiple study sites, we had to send between 4.3 and 9.2 opt-out packets to recruit one participant. The number of phone calls required to arrive at a final status for each potentially eligible Veteran (i.e. study participation or the termination of recruitment efforts) were 2.9 and 6.1 in the two studies, respectively. Study participants differed as expected from the population of potentially eligible Veterans based on planned oversampling of certain subpopulations. The final samples of participants did not differ statistically from those who were mailed opt-out packets, with one exception: in one of our two studies, participants had higher rates of mental health service use in the past year than did those mailed opt-out packets (64 vs. 47%). Conclusions Our results emphasize the practicality of using opt-out methods for studies of access to health care. Despite the benefits of these methods, opt-out alone may be insufficient to eliminate non-response bias on key variables. Researchers will need to balance considerations of sample representativeness and feasibility when designing studies investigating access to care.

Background: Despite their prevalence and reported patient interest in their use, uptake of health-related apps is limited. The Veterans Health Administration (VHA) has developed a variety of apps to support veterans; however, uptake remains low nationally. Objective: We examined the prevalence of VHA health-related app use and how veterans learned about these apps in order to identify factors associated with their use. Methods: As part of a VHA quality improvement initiative, we recruited a national cohort of veterans to obtain feedback on their use of technology for health and collected data from them via a cross-sectional survey. The survey data were supplemented with VHA administrative data. We used descriptive statistics to examine demographic and health characteristics, health-related technology use, and how veterans learned about apps. We assessed factors associated with app use using bivariate analyses and multiple logistic regression models. Results: We had complete data on 1259 veterans. A majority of the sample was male (1069/1259, 84.9%), aged older than 65 years (740/1259, 58.8%), White (1086/1259, 86.3%), and non-Hispanic (1218/1259, 96.7%). Most respondents (1125/1259, 89.4%) reported being very comfortable and confident using computers, over half (675/1259, 53.6%) reported being an early adopter of technology, and almost half (595/1259, 47.3%) reported having used a VHA health-related app. Just over one-third (435/1259, 34.6%) reported that their VHA care team members encouraged them to use health-related apps. Respondents reported learning about available VHA health-related apps by reading about them on the VHA’s patient portal (468/1259, 37.2%), being told about them by their VHA health care team (316/1259, 25.1%), and reading about them on the VHA’s website (139/1259, 11%). Veterans who self-reported having used VHA health-related apps were more likely to receive care at the VHA (OR [odds ratio] 1.3, 95% CI 1.0-1.7), be in worse health (as assessed by Hierarchical Condition Community score; OR 1.1, 95% CI 1.0-1.2), report owning a desktop or laptop computer (OR 1.8, 95% CI 1.1-3.1), have posttraumatic stress disorder (OR 1.4, 95% CI 1.1-1.9), and report having VHA health care team members encourage them to use the apps (OR 2.7, 95% CI 2.1-3.4). Conclusions: We found strong associations between self-reported use by veterans of VHA health-related apps and multiple variables in our survey. The strongest association was observed between a veteran self-reporting app use and having received encouragement from their VHA health care team to use the apps. Veterans who reported receiving encouragement from their VHA care team members had nearly 3 times higher odds of using VHA apps than veterans who did not report receiving such encouragement. Our results add to growing evidence suggesting that endorsement of apps by a health care system or health care team can positively impact patient uptake and use.

The collaborative care model is an evidence-based practice for treatment of depression in which designated care managers provide clinical services, often by telephone. However, the collaborative care model is infrequently adopted in the Department of Veterans Affairs (VA). Almost all VA medical centers have adopted a co-located or embedded approach to integrating mental health care for primary care patients. Some VA medical centers have also adopted a telephone-based collaborative care model where depression care managers support patient education, patient activation, and monitoring of adherence and progress over time. This study evaluated two research questions: (1) What does a dedicated care manager offer in addition to an embedded-only model? (2) What are the barriers to implementing a dedicated depression care manager? This study involved 15 qualitative, multi-disciplinary, key informant interviews at two VA medical centers where reimbursement options were the same- both with embedded mental health staff, but one with a depression care manager. Participant interviews were recorded and transcribed. Thematic analysis was used to identify descriptive and analytical themes. Findings suggested that some of the core functions of depression care management are provided as part of embedded-only mental health care. However, formal structural attention to care management may improve the reliability of care management functions, in particular monitoring of progress over time. Barriers to optimal implementation were identified at both sites. Themes from the care management site included finding assertive care managers to hire, cross-discipline integration and collaboration, and primary care provider burden. Themes from interviews at the embedded site included difficulty getting care management on leaders' agendas amidst competing priorities and logistics (staffing and space). Providers and administrators see depression care management as a valuable healthcare service that improves patient care. Barriers to implementation may be addressed by team-building interventions to improve cross-discipline integration and communication. Findings from this study are limited in scope to the VA healthcare system. Future investigation of whether alternative barriers exist in implementation of depression care management programs in non-VA hospital systems, where reimbursement rates may be a more prominent concern, would be valuable.

The Veterans Health Administration (VHA) is deploying an automated texting system (aTS) to support patient self-management. We conducted a qualitative evaluation to examine factors influencing national rollout of the aTS, guided by the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, which is intended to support the evaluation of novel technologies. Semistructured interviews were conducted with 33 staff and 38 patients who were early adopters of the aTS. Data were analyzed following deductive and inductive approaches using a priori codes and emergent coding based on the NASSS. We identified themes across NASSS domains: (1) Condition: The aTS was considered relevant for a range of patient needs; however, perceptions of patient suitability were guided by texting experience and clinical complexity rather than potential benefits. (2) Technology: Onboarding of the aTS presented difficulty and the staff had different opinions on incorporating patient-generated data into care planning. (3) Value: Supply-side value relied on the flexibility of the aTS and its impact on staff workload whereas demand-side value was driven by patient perceptions of the psychological and behavioral impacts of the aTS. (4) Adopters: Limited clarity on staff roles and responsibilities presented challenges in incorporating the aTS into clinical processes. (5) Organization: Staff were willing to try the aTS; however, perceptions of leadership support and clinic readiness hindered usage. (6) Wider system: Staff focused on enhancing aTS interoperability with the electronic medical record. (7) Embedding and adaptation over time: The interplay of aTS versatility, patient and staff demands, and broader societal changes in preferences for communicating health information facilitated aTS implementation. VHA's new aTS has the potential to further engage patients and expand the reach of VHA care; however, patients and staff require additional support to adopt, implement, and sustain the aTS. The NASSS highlighted how the aTS can be better embedded into current practices, which patients might benefit most from its functionality, and which aspects of aTS messages are most relevant to self-management. ClinicalTrials.gov NCT03898349; https://clinicaltrials.gov/ct2/show/NCT03898349.

The research marketplace has seen a flood of open-source or commercial mobile health (mHealth) platforms that can collect and use user data in real time. However, there is a lack of practical literature on how these platforms are developed, integrated into study designs, and adopted, including important information around cost and effort considerations. We intend to build critical literacy in the clinician-researcher readership into the cost, effort, and processes involved in developing and operationalizing an mHealth platform, focusing on Intui, an mHealth platform that we developed. We describe the development of the Intui mHealth platform and general principles of its operationalization across sites. We provide a worked example in the form of a case study. Intui was operationalized in the design of a behavioral activation intervention in collaboration with a mental health service provider. We describe the design specifications of the study site, the developed software, and the cost and effort required to build the final product. Study designs, researcher needs, and technical considerations can impact effort and costs associated with the use of mHealth platforms. Greater transparency from platform developers about the impact of these factors on practical considerations relevant to end users such as clinician-researchers is crucial to increasing critical literacy around mHealth, thereby aiding in the widespread use of these potentially beneficial technologies and building clinician confidence in these tools.

      Purpose of the review Digital mental health interventions (DMHIs) are an effective and accessible means of addressing the unprecedented levels of mental illness worldwide. Currently, however, patient engagement with DMHIs in real-world settings is often insufficient to see a clinical benefit. In order to realize the potential of DMHIs, there is a need to better understand what drives patient engagement. Recent findings We discuss takeaways from the existing literature related to patient engagement with DMHIs and highlight gaps to be addressed through further research. Findings suggest that engagement is influenced by patient-, intervention-, and system-level factors. At the patient level, variables such as sex, education, personality traits, race, ethnicity, age, and symptom severity appear to be associated with engagement. At the intervention level, integrating human support, gamification, financial incentives, and persuasive technology features may improve engagement. Finally, although system-level factors have not been widely explored, the existing evidence suggests that achieving engagement will require addressing organizational and social barriers and drawing on the field of implementation science. Summary Future research clarifying the patient-, intervention-, and system-level factors that drive engagement will be essential. Additionally, to facilitate an improved understanding of DMHI engagement, we propose the following: (a) widespread adoption of a minimum necessary 5-element engagement reporting framework, (b) broader application of alternative clinical trial designs, and (c) directed efforts to build upon an initial parsimonious conceptual model of DMHI engagement.