Literature Collection

BACKGROUND: Posttraumatic stress disorder (PTSD) and substance use disorder (SUD) co-occur frequently and have deleterious impact. Seeking Safety (SS) - an evidence-based, present-focused, coping skills model - lends itself to mobile app delivery. OBJECTIVES: A novel SS mobile app is compared to a control app that lacks the interactivity, social engagement, and feature-richness of the SS app. We hypothesized that the SS app would outperform the control on primary outcome variables (substance use, trauma symptoms) and at least two secondary variables. METHODS: Outpatients with current PTSD and SUD (n = 116) were randomized to the apps; assessed were pre, post (12 weeks), and 3-month follow-up in this online study. RESULTS: The SS app outperformed the control on the primary outcomes, but not on secondary outcomes. Also both conditions evidenced significant change over time from pre to post, with gains sustained at follow-up. External medication and supports during the trial did not differ by condition. CONCLUSION: This first RCT on a SS mobile app had positive results for reduction in substance use and trauma symptoms compared to a control app. This is noteworthy as mental health mobile apps, in general, evidence few positive outcomes. Our substance use finding is also notable as psychosocial interventions in PTSD/SUD populations find it harder to achieve reduction in SUD than trauma symptoms. Our control app may have represented too strong a comparison and weakened our ability to find results on secondary outcomes by condition.

PURPOSE AND APPROACH: Women in recovery describe stigma, negative treatment, and limited support as barriers to achieving their health and parenting goals. Mobile health technologies carefully tailored to support the unique needs of recovery communities can provide less burdensome alternatives to in-person services for women transitioning out of substance use treatment. An iterative design process integrated women's interests into the structure, content, and interaction flow of a mobile health (mHealth) app. SETTING AND PARTICIPANTS: Participants included women in recovery from opioid, alcohol, and polysubstance use disorders in a comprehensive housing program in urban Arizona. METHODS: Five focus groups with 3-7 participants each (n = 27 total) informed creation of the mHealth app. Informed by theoretical models of usability and person-centered design, development involved an iterative series of focus groups in which we asked women to comment on interest in using each feature. This provided a qualitative priority framework for feature development. We then modified the app and repeated the process to gauge consensus and continually refine our prototype. RESULTS: Women were interested in access to resources, such as housing, counseling, and parenting advice in settings known to treat women in recovery with respect. They also asked for positive messages, chatting with peers, and access to expert answers. They were less interested in points-based learning modules and "scored" activities, leading us to develop a "daily challenges" concept that builds good habits, but does not feel like "classwork". Women's recommendations shaped an mHealth app tailored to maximize utility, access, and safety for this at-risk population. CONCLUSION: Integration of user-centered design with applied ethnographic techniques guided the development of a custom-tailored mHealth app responsive to lived experiences and needs of women in recovery. Future research should evaluate the potential for user-centered apps to increase self-efficacy, perceived social support, and to reduce risk of relapse.

BACKGROUND: Conversational agents (CAs) are increasingly used as a promising tool for scalable, accessible, and personalized self-management support of people with a chronic disease. Studies of CAs for self-management of chronic disease operate within a multidisciplinary domain: self-management originates from (behavioral) psychology and CAs stem from intervention technology, while diseases are typically studied within the biomedical context. To ensure their effectiveness, structured evaluations and descriptions of the interventions, integrating biomedical, behavioral, and technological perspectives, are essential. OBJECTIVE: We aimed to examine the design and evaluation of CAs for self-management support of chronic diseases, focusing on their characteristics, integration of behavioral change techniques, and evaluation methods. The findings will guide future research and inform intervention design. METHODS: We conducted a systematic search in the PubMed and Embase databases to identify studies that investigated CAs for chronic disease self-management, published from January 1, 2018, to April 15, 2024. Full-text journal articles, published in English, studying the efficacy or effectiveness of a CA in the context of self-management for chronic diseases in adults were included. Data extraction was guided by conceptual frameworks to ensure comprehensive reporting of intervention and methodologies: the behavioral intervention technology model and the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist. Risk of bias was assessed using the Risk of Bias 2 tool and the Risk of Bias in Non-randomized Studies-of Interventions (ROBINS-I) tool (version 2). RESULTS: In total, 25 studies were included, primarily focusing on text-based, rule-based CAs delivered via a mobile apps. The chronic diseases predominantly targeted were diabetes and cancer. Commonly identified clusters of behavior change techniques were "shaping knowledge," "feedback and monitoring," "natural consequences," and "associations." However, reporting of behavior change techniques and their delivery was lacking, and intervention descriptions were limited. Studies were mostly in the early phase, with a great variety in intervention descriptions, study methods, and outcome measures. CONCLUSIONS: Advancing the field of CA-based interventions requires transparent intervention descriptions, rigorous methodologies, consistent use of validated scales, standardized taxonomy, and reporting aligned with standardized frameworks. Enhanced integration of artificial intelligence-driven personalization and a focus on implementation in health care settings are critical for future research.

BACKGROUND: Although mobile health apps integrated with Internet of Things-enabled devices are increasingly used to satisfy the growing needs for home-based older adult care resulting from rapid population aging, their effectiveness is constrained by 3 key challenges: a focus on specific functions rather than on holistic and integrated support, absence of a solid theoretical framework for development, and a lack of personalized, real-time feedback to address diverse care needs. To overcome these limitations, we developed a knowledge-based clinical decision support system using mobile health technology-an intelligent and integrated older adults care model (SMART system). OBJECTIVE: This study aims to systematically outline the development process and outcomes of a knowledge base and trigger rules for the SMART system. METHODS: Our study adopted a user-centered approach guided by the nursing process and intervention mapping (IM) framework. We first identified older adult care needs through semistructured, in-depth interviews. Guided by the nursing process and informed by guidance from the World Health Organization's Integrated Care for Older People and World Health Organization International Classification of Functioning, Disability, and Health, along with the North American Nursing Diagnosis Association-I nursing diagnosis, we then determined care problems along with their underlying causes and risk factors and diagnostic criteria. Building on these findings, we applied the first 3 steps of the intervention mapping framework to formulate corresponding long-term and short-term care objectives, select appropriate evidence-based interventions, and match practical implementation approaches, which were grounded in rigorous evidence derived from systematic literature reviews, clinical guidelines, and expert insights. We also developed a set of trigger rules to link abnormalities in older adults with corresponding care problems and interventions in the SMART knowledge base. RESULTS: The semistructured in-depth interviews identified 5 types of care needs-daily life care, health care, external support, social participation, and self-development-which formed the foundation of the SMART knowledge base. Based on this, we identified 138 care problems, each with associated causes and risk factors and diagnostic criteria. The objective matrix comprised 138 long-term and 195 short-term care objectives. Guided by 15 expert-defined selection criteria, we then selected 450 evidence-based interventions, each paired with at least 1 feasible and practical implementation approach. Additionally, we developed diagnostic rules to match the assessment data with relevant care problems and their causes and risk factors and intervention trigger rules to formulate personalized interventions based on individual characteristics, ensuring tailored care aligned with specific care objectives. CONCLUSIONS: This study outlines the development process and outcomes of the SMART knowledge base and trigger rules. The study methodology offers theoretical support for developing knowledge bases and trigger rules of similar clinical decision support systems for home-based older adult care.

Providing human support for users of behavioral health technology can help facilitate the necessary engagement and clinical integration of digital tools in mental health care. A team conducted digital navigator training that taught participants how to promote patrons' digital literacy, evaluate and recommend health apps, and interpret smartphone data. The authors trained 80 participants from 21 organizations, demonstrating this training's feasibility, acceptability, and need. Case studies explore the implementation of this training curriculum. As technology's potential in mental health care expands, training can empower digital navigators to ensure that the use of digital tools is informed, equitable, and clinically relevant.

BACKGROUND: Type 1 diabetes (T1D) management requires following a complex and constant regimen relying on child or caregiver behaviors, skills, and knowledge. Psychological factors such as diabetes distress (DD), depression, and burnout are pertinent considerations in the treatment of pediatric T1D. Approximately 40% of youth and 61% of caregivers experience DD. Implementation of DD screening as part of clinical best practice is recommended and may facilitate treatment referral, perhaps leading to improved health or well-being for youth with T1D and their caregivers. By building on existing institutional infrastructure when available, screening via digital health platforms (applications, or "apps") may allow for timely screening of, and response to, DD. OBJECTIVE: This work details the creation, implementation, and refinement of a process to screen for DD in youth and their caregivers in the context of routine T1D care using a digital health platform. METHODS: DD screening was implemented in an outpatient endocrinology clinic over 1 year as part of a larger screen-to-treat trial for children aged 8-12.99 years and their caregivers. Validated measures were sent via digital health platform to be completed prior to the clinic visit. Results were initially reviewed manually, but a digital best practice alert (BPA) was later built to notify staff of elevated scores. Families experiencing DD received resources sent via the digital health platform. For this secondary analysis, child demographics and glycated hemoglobin A1c (HbA1c) were collected. RESULTS: During the screening period, absolute completion rates were 36.78% and 38.83%, with adjusted screening rates at 52.02% and 54.48%, for children and caregivers, respectively. A total of 21 children (mean HbA1c 8.04%, SD 1.39%) and 26 caregivers (child mean HbA1c 8.04%, SD 1.72%) reported elevated DD. Prior to BPA development, resources were sent to all but 1 family. After BPA implementation, all families were sent resources. CONCLUSIONS: Early findings indicate that DD education, screening, and response can be integrated via digital platforms in a freestanding outpatient endocrinology clinic, thereby facilitating timely treatment referral and provision of resources for those identified with distress. Notably, in the observed 1-year screening period, screening rates were low, and barriers to implementation were identified. While some implementation challenges were iteratively addressed, there is a need for future quality improvement initiatives to improve screening rates and the identification of, or response to, DD in our pediatric patients and their families.

Optimizing the overall health of individuals prior to pregnancy can improve both pregnancy and lifelong health outcomes. Despite extremely high financial expenditure on birth in the United States, maternal and infant mortality rates continue to rise. Moreover, significant racial and ethnic disparities persist in perinatal health outcomes. Preconception care, or health care provided before or between pregnancies, has the capacity to meet these challenges. Preconception care can be integrated into every health care visit, including visits with primary care, reproductive health, and mental health care clinicians. Increasing awareness among clinicians of the benefits of preconception care and equipping them with innovative strategies to implement this care into practice, the number of people receiving optimal care could be increased. Recent innovations in preconception care include group care, health applications (apps), reminders in electronic health records, social marketing campaigns, social media movements, community-based partnerships, health care policy and access, and improving public and clinician education on preconception health topics. Ultimately, improving preconception health is best done in partnership between the consumer and clinician. Midwives, whose care is person-centered and partnership-focused, are well positioned to champion the innovation and implementation of equitable preconception care. The purpose of this State of the Science review, therefore, is to synthesize the literature on recent evidence-based innovations that may be used to improve preconception health and counseling.

BACKGROUND: Digital health interventions are emerging as an approach to support obesity management through self-management and remote care. However, utilization, impact, and practicality remain unclear. This study aims to map research on digital health interventions for obesity management among adults, describing their characteristics, uses, and outcomes, and identifying gaps. METHODS: A comprehensive scoping review following Arksey and O'Malley's methodological framework, in accordance with the Joanna Briggs Institute's guidelines and reported in line with the PRISMA-ScR guidelines, examined studies published between 2015 and 2025 across four databases: PubMed, Google Scholar, Scopus, and APA PsycNet. RESULTS: A total of 43 studies met the eligibility criteria. Digital health interventions for obesity encompassed consultations and education on healthy lifestyle, behavioral change strategies, physical activity, dietary management, weight goal setting, intermittent fasting, gamification, and psychological support. Digital health interventions were delivered through telehealth, mobile apps, web-based programs, multicomponent digital approaches integrating several digital tools, and hybrid models combining digital delivery with face-to-face communication. These interventions often supported by devices such as digital scales, wearable trackers, and telemonitoring units to enhance self-monitoring, adherence, and engagement. The interventions were implemented across clinical, workplace, and community settings, including adaptations developed during the COVID-19 pandemic. The included studies showed varying and inconsistently reported outcomes, with some showing significant weight loss, improvements in metabolic markers and behavioral outcomes, including dietary adherence, physical activity, and self-monitoring, as well as favorable feasibility outcomes and the potential to maintain continuity of service delivery during the COVID-19 outbreak. CONCLUSIONS: Digital health interventions used telehealth, mobile applications, web-based, multicomponent, and hybrid models across different settings, including healthcare settings (e.g., clinical and primary care) and community settings. Digital devices, such as scales, wearables, and activity trackers, were often incorporated to support self-tracking, adherence, and engagement. Reported outcomes included weight loss, improved self-monitoring, and behavioral changes such as enhanced dietary adherence and increased physical activity, and a favorable feasibility outcome; however, these outcomes were not consistently reported across all studies. Key gaps included short follow-up periods and limited evidence from LMICs. Future research should prioritize sustainable, equitable, scalable, culturally adapted, and cost-effective digital interventions.

OBJECTIVE: This systematic review and meta-analysis evaluated the effectiveness of mobile app-based psychological interventions in reducing distress (depressive and anxiety symptoms) and improving quality of life among adults in primary care (PC). METHODS: We included randomized controlled trials (RCTs) involving adults with depression and/or anxiety identified through searches of PsycINFO, PubMed, Embase, and the Cochrane Library from database inception to April 11, 2025. Standardized mean differences (SMDs) and relative risks (RRs) were pooled using fixed- and random-effects models. Study quality was assessed using the Cochrane Risk-of-Bias 2 tool. RESULTS: Eleven RCTs (N = 2915) met inclusion criteria, contributing 15 comparisons to the meta-analysis (N = 2588; intervention vs. treatment as usual). Pooled analyses showed a significant reduction in overall distress (SMD = -0.44; 95% CI -0.68 to -0.21, p < 0.001) and a moderate decrease in depressive symptoms (SMD = -0.53, 95% CI -0.81 to -0.24, p < 0.001). Limited evidence from four comparisons showed no significant effect on anxiety (SMD = -0.25, 95% CI -0.68 to 0.18, p = 0.26). A small but significant improvement in quality of life was found (SMD = 0.16, 95% CI 0.06 to 0.25, p = 0.001). Participants using apps were more likely to drop out at post-treatment than controls (RR = 1.45, 95% CI 1.18 to 1.79, p < 0.001). CONCLUSIONS: Mobile app-based interventions appear to reduce distress and depressive symptoms and modestly improve quality of life in PC, although no significant effects were observed for anxiety. These findings support the potential role of app-based interventions as accessible tools within primary care. However, the limited and heterogeneous reporting of follow-up assessments restricts conclusions about the durability of these effects over time. Higher dropout rates also highlight the need to improve adherence strategies.

Obesity management in primary care should begin with lifestyle interventions for patients ready to engage in behavior change. Using the 5As counseling model (assess, advise, agree, assist, arrange) in a patient-centered, nonjudgmental manner enhances motivation and adherence. Comprehensive lifestyle interventions (combining reduced caloric intake, increased physical activity, and behavioral changes) are most effective, typically achieving 5% to 10% weight loss over 6 months. Physicians should refer patients to multidisciplinary programs when possible or recommend digital or commercial alternatives with personalized feedback. Key components include setting specific, measurable, achievable, relevant, and time-based (SMART) goals; calculating caloric deficits; and promoting self-monitoring. Various dietary approaches can be effective and should be tailored to patient preferences. Physical activity lasting at least 150 minutes per week at moderate intensity or at least 75 minutes per week of vigorous activity is recommended. Digital tools (eg, apps, wearable trackers) can enhance engagement and outcomes. Physicians should also address barriers such as food insecurity, time constraints, and lack of support. Although adjunctive strategies such as sleep optimization and workplace modifications may be beneficial, they should not replace core lifestyle changes.

BACKGROUND: Depression is pervasive, and rates are rising in the United States. Most people with depression receive care from primary care clinicians, but gaps in the quality of care exist. Team-based approaches to depression care have been shown to aid in treatment and management; yet, challenges exist in implementation. Digital health apps have been shown to be effective in improving depression symptoms and enhancing patient engagement in some populations. Many, however, do not share data with clinical care teams. OBJECTIVE: This study aimed to understand the barriers to and facilitators for implementation of a digital health program that supports coordinated use by clinical care teams and patients, via a mobile app and care team-facing web interface, for depression in primary care. METHODS: This study was part of a larger intervention study that included 4 primary care practices: 2 intervention and 2 control sites. The intervention sites used a patient-facing mobile app and a care team-facing web interface, and the control sites continued usual care. The study team conducted interviews from May to October 2021. Patient and care team participants were recruited toward the end of their study involvement. Separate semistructured interview guides were developed for patient and care team participants. Interviews were recorded and transcribed. Data were coded using Atlas.ti.9, and data analysis was completed using a grounded theory approach. RESULTS: Interviews with patient (n=8) and care team (n=8) participants revealed 3 main topics for program implementation: app/interface usability, tracking, and program recommendations. For app/interface usability, overall, navigation for both patient and care team participants was simple and straightforward. Although app content was relevant, patient participants desired additional educational resources and information to aid in their depression treatment and management. In terms of tracking, care team participants indicated that data obtained via the interface enabled them to monitor patients in between visits; and in some circumstances, these data facilitated conversations with patients about treatment plans. Tracking medication adherence differed among patient participants due to established routines of taking medications consistently, lack of motivation to track, or lack of interest in tracking. Care team participants reported the ability to respond more quickly to side effects. Patients commented on tracking difficulties: confusing response options, insufficient goal attainment response options, not being able to provide details or write notes, and no ability to change goals. Some patient and care team participants perceived that tracking encouraged communication with one another; others perceived tracking as having no impact on shared decision-making. CONCLUSIONS: Results suggest implementation of a digital health program for depression treatment and management in primary care practices could impact patient medication adherence, produce faster turnaround time for medication optimization, encourage goal setting, and foster communication between patients and care team members. Program enhancements could optimize patient and care team member engagement.