Background Evidence-based digital health programs have shown efficacy in being primary tools to improve emotional and mental health, as well as offering supplementary support to individuals undergoing psychotherapy for anxiety, depression, and other mental health disorders. However, information is lacking about the dose response to digital mental health interventions. Objective The objective of the study was to examine the effect of time in program and program usage on symptom change among individuals enrolled in a real-world comprehensive digital mental health program (myStrength) who are experiencing severe anxiety or depression. Methods Eligible participants (N=18,626) were adults aged 18 years and older who were enrolled in myStrength for at least four weeks as part of their employee wellness benefit program, who completed baseline, the 2-week, 2-month, and 6-month surveys querying symptoms of anxiety (Generalized Anxiety Disorder–7 [GAD-7]) and depression (Patient Health Questionnaire–9 [PHQ-9]). Linear growth curve models were used to analyze the effect of average weekly program usage on subsequent GAD-7 and PHQ-9 scores for participants with scores indicating severe anxiety (GAD-7≥15) or depression (PHQ-9≥15). All models were adjusted for baseline score and demographics. Results Participants in the study (N=1519) were 77.4% female (1176/1519), had a mean age of 45 years (SD 14 years), and had an average enrollment time of 3 months. At baseline, participants reported an average of 9.39 (SD 6.04) on the GAD-7 and 11.0 (SD 6.6) on the PHQ-9. Those who reported 6-month results had an average of 8.18 (SD 6.15) on the GAD-7 and 9.18 (SD 6.79) on the PHQ-9. Participants with severe scores (n=506) experienced a significant improvement of 2.97 (SE 0.35) and 3.97 (SE 0.46) at each time point for anxiety and depression, respectively (t=–8.53 and t=–8.69, respectively; Ps<.001). Those with severe baseline scores also saw a reduction of 0.27 (SE 0.08) and 0.25 (SE 0.09) points in anxiety and depression, respectively, for each additional program activity per week (t=–3.47 and t=–2.66, respectively; Ps<.05). Conclusions For participants with severe baseline scores, the study found a clinically significant reduction of approximately 9 points for anxiety and 12 points for depression after 6 months of enrollment, suggesting that interventions targeting mental health must maintain active, ongoing engagement when symptoms are present and be available as a continuous resource to maximize clinical impact, specifically in those experiencing severe anxiety or depression. Moreover, a dosing effect was shown, indicating improvement in outcomes among participants who engaged with the program every other day for both anxiety and depression. This suggests that digital mental health programs that provide both interesting and evidence-based activities could be more successful in further improving mental health outcomes.

Abstract Background: Multiple health behavior change interventions (MHBCIs) are successful for changing maladaptive behaviors related to individual chronic conditions (CC), but less is known about MHBCIs in the context of managing multiple CC. This study examined effects of enrollment on clinical outcomes in multiple remote monitoring programs, including diabetes (DM), hypertension (HTN), and weight loss (WL). Methods: Participants were adults enrolled in at least one of three programs for DM, HTN, and WL with an optional mental health (MH) component; identified as “at-risk” at baseline ((A1c ≥7%, HTN ≥130/80, or BMI≥30 kg/m2); with ≥ 1 month of data available over a 12-month evaluation period. Outcomes consisted of mean blood glucose (BG), systolic blood pressure (SBP), and percent WL. Data were aggregated to the month level and mixed-effects models tested the effects of multiple program enrollment and the supplemental effect of MH enrollment on outcomes. All models controlled for demographics, time on program, baseline disease status, and engagement. 2,818 adults (55% female) were included, with mean age of 53 years (SD=10.0). Results: A significant interaction was shown between time on program and multiple program enrollment for DM (B=-0.48, SE=0.29) and HTN (B=-0.42, SE=0.12) (z=-3.57, z=-3.89; ps<0.001, respectively). Each program enrollment in addition to DM was associated with a 5.8 mg/dL reduction in BG, and each program enrollment in addition to HTN was associated with a 4.8 mmHg reduction in SBP. Significant interactions were found for time on program and MH enrollment for DM (B=-1.25, SE=0.29), HTN (B=-0.81, SE=0.19), and WL (B=-0.15, SE=0.03) (z=-4.36, z=-4.25, and z=-5.27; ps<0.001, respectively). Additional enrollment in MH was associated with a reduction of 15 mg/dL in blood glucose, of 9.6 mmHg in SBP, and 1.8% WL. Conclusions: Findings support the success of MHBCIs in management of CC and emphasize the supplemental effect a MH program has on improving outcomes.

BACKGROUND Proactive detection of mental health (MH) needs among people with diabetes mellitus (DM) could facilitate early intervention, improve overall health and quality of life, and reduce individual and societal health and economic burdens. Passive sensing and ecological momentary assessment are newer methods that may be leveraged for proactive detection. OBJECTIVE The primary aim of this study was to conceptualize, develop, and evaluate a novel machine learning approach for predicting MH risk in people with DM. METHODS A retrospective study was designed to develop and evaluate a machine learning model, utilizing data collected on 142,432 individuals with DM enrolled in the Livongo for Diabetes program. First, participants’ MH statuses were verified using prescription and medical and pharmacy claims data. Next, passive sensing signals were extracted from participant behavior in the program. Data sets were then assembled to create participant-period instances and descriptive analyses were conducted to understand the correlation between MH status and passive sensing signals. Passive sensing signals were then entered into the model to train and test its performance. The model was evaluated on seven measures: sensitivity, specificity, precision, area under the curve, F1 score, accuracy, and confusion matrix.Results: In training and 3 subsequent test sets, the model achieved a confidence score of greater than 0.5 in all but three measures. RESULTS In training and 3 subsequent test sets, the model achieved a confidence score of greater than 0.5 in all but three measures. CONCLUSIONS Results demonstrate the utility of a passively informed MH risk algorithm and invite further exploration.

Background Proactive detection of mental health needs among people with diabetes mellitus could facilitate early intervention, improve overall health and quality of life, and reduce individual and societal health and economic burdens. Passive sensing and ecological momentary assessment are relatively newer methods that may be leveraged for such proactive detection. Objective The primary aim of this study was to conceptualize, develop, and evaluate a novel machine learning approach for predicting mental health risk in people with diabetes mellitus. Methods A retrospective study was designed to develop and evaluate a machine learning model, utilizing data collected from 142,432 individuals with diabetes enrolled in the Livongo for Diabetes program. First, participants’ mental health statuses were verified using prescription and medical and pharmacy claims data. Next, four categories of passive sensing signals were extracted from the participants’ behavior in the program, including demographics and glucometer, coaching, and event data. Data sets were then assembled to create participant-period instances, and descriptive analyses were conducted to understand the correlation between mental health status and passive sensing signals. Passive sensing signals were then entered into the model to train and test its performance. The model was evaluated based on seven measures: sensitivity, specificity, precision, area under the curve, F1 score, accuracy, and confusion matrix. SHapley Additive exPlanations (SHAP) values were computed to determine the importance of individual signals. Results In the training (and validation) and three subsequent test sets, the model achieved a confidence score greater than 0.5 for sensitivity, specificity, area under the curve, and accuracy. Signals identified as important by SHAP values included demographics such as race and gender, participant’s emotional state during blood glucose checks, time of day of blood glucose checks, blood glucose values, and interaction with the Livongo mobile app and web platform. Conclusions Results of this study demonstrate the utility of a passively informed mental health risk algorithm and invite further exploration to identify additional signals and determine when and where such algorithms should be deployed.

Abstract Older adults are faced with an increased risk of comorbid chronic disease such as diabetes. While multiple health behavior change interventions (MHCIs) are known to improve clinical outcomes more than targeted interventions, less is known whether such effects persist in older populations. The objective of the study was to examine the effects of multiple chronic condition (CC) remote monitoring program enrollment and mental health program enrollment on glucose and blood pressure reduction, adjusting for self-monitoring behaviors. In a sample of 594 older adults (age 55+, 14% 65+ years, 46.8% female) evaluated over a 12-month period, statistical models showed that older adults with uncontrolled diabetes (A1c >= 7.0%) had a 7.9 pt. reduction in blood glucose for each additional program enrolled and a 22.7 pt. reduction in blood glucose when enrolled in mental health compared to those not enrolled. Similarly, older adults with uncontrolled hypertension (BP >= 130/80) had a 4.8 pt. reduction in systolic blood pressure for each additional program enrolled and a 7.2 pt. reduction in systolic blood pressure when enrolled in mental health compared to those not enrolled. The findings indicate the potential for multiprogram digital health interventions that incorporate mental health to further improve clinical outcomes in older adults suffering from multiple chronic diseases, namely diabetes and hypertension.

BackgroundTechnology is rapidly advancing our understanding of how people with diabetes mellitus experience stress. ObjectiveThe aim of this study was to explore the relationship between stress and sequelae of diabetes mellitus within a unique data set composed of adults enrolled in a digital diabetes management program, Livongo, in order to inform intervention and product development. MethodsParticipants included 3263 adults under age 65 who were diagnosed with diabetes mellitus and had access to Livongo through their employer between June 2015 and August 2018. Data were collected at time of enrollment and 12 months thereafter, which included demographic information, glycemic control, presence of stress, diabetes distress, diabetes empowerment, behavioral health diagnosis, and utilization of behavioral health-related medication and services. Analysis of variance and chi-square tests compared variables across groups that were based on presence of stress and behavioral health diagnosis or utilization. ResultsFifty-five percent of participants (1808/3263) reported stress at the time of at least 1 blood glucose reading. Fifty-two percent of participants (940/1808) also received at least 1 behavioral health diagnosis or intervention. Compared to their peers, participants with stress reported greater diabetes distress, lower diabetes empowerment, greater insulin use, and poorer glycemic control. Participants with stress and a behavioral health diagnosis/utilization additionally had higher body mass index and duration of illness. ConclusionsStress among people with diabetes mellitus is associated with reduced emotional and physical health. Digital products that focus on the whole person by offering both diabetes mellitus self-management tools and behavioral health skills and support can help improve disease-specific and psychosocial outcomes.

Family members of Veterans with posttraumatic stress disorder (PTSD) face high levels of burden that are poorly addressed by existing mental health services. Widely distributed mobile interventions could play a role in addressing these unmet needs. The purpose of this study was to characterize caregiver burden in those seeking a mobile app for self-management of stress symptoms and to develop a model to guide mobile interventions for family members. Those living with a Veteran with PTSD (n = 212) and interested in using a mobile intervention agreed to participate. The majority reported moderate-to-severe levels of depression (60%) and/or caregiver burden (59%). Relationship quality, communication, and self-efficacy for caregiving were the strongest predictors of negative outcomes (p's < .001), and qualitative results identified several additional unmet needs (e.g. relationship concerns, safety concerns). This study identifies potential mechanisms by which a mobile app could improve family functioning in the context of PTSD.

Effective treatments for posttraumatic stress disorder (PTSD) remain underutilized and individuals with PTSD often have difficulty accessing care. Telehealth, particularly clinical videoconferencing (CVT), can overcome barriers to treatment and increase access to care for individuals with PTSD. The purpose of this review is to summarize the literature on the delivery of PTSD treatments through office-based and home-based videoconferencing, and outline areas for future research. Evidence-based PTSD treatments delivered through office-based and home-based CVT have been studied in pilot studies, non-randomized trials, and randomized clinical trials. The studies have consistently demonstrated feasibility and acceptability of these modalities as well as significant reduction in PTSD symptoms, non-inferior outcomes, and comparable dropout rates when compared with traditional face-to-face office-based care. Finally, it has been shown that using CVT does not compromise the therapeutic process. Office-based and home-based CVT can be used to deliver PTSD treatments while retaining efficacy and therapeutic process. The use of these modalities can increase the number of individuals that can access efficacious PTSD care.

Psycho-OncologyVolume 27, Issue 1 p. 350-353 CLINICAL CORRESPONDENCE Cancer distress coach: Pilot study of a mobile app for managing posttraumatic stress Sophia K. Smith, Corresponding Author Sophia K. Smith sophia.smith@duke.edu orcid.org/0000-0002-7809-0339 School of Nursing, Duke University, Durham, NC, USA Duke Cancer Institute, Durham, NC, USA Correspondence Dr Sophia K. Smith, Box 3322, Durham, NC 27710, USA. Email: sophia.smith@duke.eduSearch for more papers by this authorEric Kuhn, Eric Kuhn VA National Center for PTSD, Palo Alto, CA, USA School of Medicine, Stanford University, Stanford, CA, USASearch for more papers by this authorJonathan O'Donnell, Jonathan O'Donnell School of Medicine, Duke University, Durham, NC, USASearch for more papers by this authorBridget F. Koontz, Bridget F. Koontz Duke Cancer Institute, Durham, NC, USASearch for more papers by this authorNicole Nelson, Nicole Nelson Duke University, Durham, NC, USASearch for more papers by this authorKiera Molloy, Kiera Molloy Duke University, Durham, NC, USASearch for more papers by this authorJianhong Chang, Jianhong Chang School of Nursing, Duke University, Durham, NC, USASearch for more papers by this authorJulia Hoffman, Julia Hoffman US Department of Veteran Affairs, Palo Alto, CA, USASearch for more papers by this author Sophia K. Smith, Corresponding Author Sophia K. Smith sophia.smith@duke.edu orcid.org/0000-0002-7809-0339 School of Nursing, Duke University, Durham, NC, USA Duke Cancer Institute, Durham, NC, USA Correspondence Dr Sophia K. Smith, Box 3322, Durham, NC 27710, USA. Email: sophia.smith@duke.eduSearch for more papers by this authorEric Kuhn, Eric Kuhn VA National Center for PTSD, Palo Alto, CA, USA School of Medicine, Stanford University, Stanford, CA, USASearch for more papers by this authorJonathan O'Donnell, Jonathan O'Donnell School of Medicine, Duke University, Durham, NC, USASearch for more papers by this authorBridget F. Koontz, Bridget F. Koontz Duke Cancer Institute, Durham, NC, USASearch for more papers by this authorNicole Nelson, Nicole Nelson Duke University, Durham, NC, USASearch for more papers by this authorKiera Molloy, Kiera Molloy Duke University, Durham, NC, USASearch for more papers by this authorJianhong Chang, Jianhong Chang School of Nursing, Duke University, Durham, NC, USASearch for more papers by this authorJulia Hoffman, Julia Hoffman US Department of Veteran Affairs, Palo Alto, CA, USASearch for more papers by this author First published: 29 December 2016 https://doi.org/10.1002/pon.4363Citations: 13Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume27, Issue1January 2018Pages 350-353 RelatedInformation

Background Digital technology is increasingly being used to enhance health care in various areas of medicine. In the area of serious mental illness, it is important to understand the special characteristics of target users that may influence motivation and competence to use digital health tools, as well as the resources and training necessary for these patients to facilitate the use of this technology. Objective The aim of this study was to conduct a quantitative expert consensus survey to identify key characteristics of target users (patients and health care professionals), barriers and facilitators for appropriate use, and resources needed to optimize the use of digital health tools in patients with serious mental illness. Methods A panel of 40 experts in digital behavioral health who met the participation criteria completed a 19-question survey, rating predefined responses on a 9-point Likert scale. Consensus was determined using a chi-square test of score distributions across three ranges (1-3, 4-6, 7-9). Categorical ratings of first, second, or third line were designated based on the lowest category into which the CI of the mean ratings fell, with a boundary >6.5 for first line. Here, we report experts’ responses to nine questions (265 options) that focused on (1) user characteristics that would promote or hinder the use of digital health tools, (2) potential benefits or motivators and barriers or unintended consequences of digital health tool use, and (3) support and training for patients and health care professionals. Results Among patient characteristics most likely to promote use of digital health tools, experts endorsed interest in using state-of-the-art technology, availability of necessary resources, good occupational functioning, and perception of the tool as beneficial. Certain disease-associated signs and symptoms (eg, more severe symptoms, substance abuse problems, and a chaotic living situation) were considered likely to make it difficult for patients to use digital health tools. Enthusiasm among health care professionals for digital health tools and availability of staff and equipment to support their use were identified as variables to enable health care professionals to successfully incorporate digital health tools into their practices. The experts identified a number of potential benefits of and barriers to use of digital health tools by patients and health care professionals. Experts agreed that both health care professionals and patients would need to be trained in the use of these new technologies. Conclusions These results provide guidance to the mental health field on how to optimize the development and deployment of digital health tools for patients with serious mental illness.

Many public health agencies, including the U.S. Department of Veterans Affairs (VA), have identified the use of mobile technologies as an essential part of a larger strategy to address major public health challenges. The VA's National Center for PTSD (NCPTSD), in collaboration with VA's Office of Mental Health and Suicide Prevention and the Defense Health Agency inside the U.S. Department of Defense (DoD), has been involved in the development, evaluation, and testing of 15 mobile apps designed specifically to address the needs and concerns of veterans and others experiencing symptoms of posttraumatic stress disorder (PTSD). These applications include seven treatment-companion apps (designed to be used with a provider, in conjunction with an evidence-based therapy) and eight self-management apps (designed to be used independently or as an adjunct or extender of traditional care). There is growing evidence for the efficacy of several of these apps for reducing PTSD and other symptoms, and studies of providers demonstrate that the apps are engaging, easy-to-use, and provide a relative advantage to traditional care without apps. While publicly available apps do not collect or share personal data, VA has created research-enabled versions of many of its mental health apps to enable ongoing product enhancement and continuous measurement of the value of these tools to veterans and frontline providers. VA and DoD are also collaborating on provider-based implementation networks to enable clinicians to optimize implementation of mobile technologies in care. Although there are many challenges to developing and integrating mHealth into care, including cost, privacy, and the need for additional research, mobile mental health technologies are likely here to stay and have the potential to reach large numbers of those with unmet mental health needs, including PTSD-related concerns.

Posttraumatic stress disorder (PTSD) is a global public health problem. Unfortunately, many individuals with PTSD do not receive professional care due to a lack of available providers, stigma about mental illness, and other concerns. Technology-based interventions, including mobile phone applications (apps) may be a viable means of surmounting such barriers and reaching and helping those in need. Given this potential, in 2011 the U.S Veterans Affairs National Center for PTSD released PTSD Coach, a mobile app intended to provide psycho-education and self-management tools for trauma survivors with PTSD symptoms. Emerging research on PTSD Coach demonstrates high user satisfaction, feasibility, and improvement in PTSD symptoms and other psychosocial outcomes. A model of openly sharing the app's source code and content has resulted in versions being created by individuals in six other countries: Australia, Canada, The Netherlands, Germany, Sweden, and Denmark. These versions are described, highlighting their significant adaptations, enhancements, and expansions to the original PTSD Coach app as well as emerging research on them. It is clear that the sharing of app source code and content has benefited this emerging PTSD Coach community, as well as the populations they are targeting. Despite this success, challenges remain especially reaching trauma survivors in areas where few or no other mental health resources exist.

Objective: Posttraumatic stress disorder (PTSD) is highly prevalent in the population, but relatively few affected individuals receive treatment for it. Smartphone applications (apps) could help address this unmet need by offering sound psychoeducational information and evidence-based cognitive behavioral coping tools. We conducted a randomized controlled trial to assess the efficacy of a free, publicly available smartphone app (PTSD Coach) for self-management of PTSD symptoms. Method: One hundred 20 participants who were an average of 39 years old, mostly women (69.2%) and White (66.7%), recruited primarily through online advertisements, were randomized to either a PTSD Coach (n = 62) or a waitlist condition (n = 58) for 3 months. Web-administered self-report measures of PTSD, PTSD symptom coping self-efficacy, depression, and psychosocial functioning were conducted at baseline, posttreatment, and 3 months following treatment. Results: Following the intent-to-treat principle, repeated-measures analyses of variance (ANOVAs) revealed that at posttreatment, PTSD Coach participants had significantly greater improvements in PTSD symptoms (p = .035), depression symptoms (p = .005), and psychosocial functioning (p = .007) than did waitlist participants; however, at posttreatment, there were no significant mean differences in outcomes between conditions. A greater proportion of PTSD Coach participants achieved clinically significant PTSD symptom improvement (p = .018) than waitlist participants. Conclusion: PTSD Coach use resulted in significantly greater improvements in PTSD symptoms and other outcomes relative to a waitlist condition. Given the ubiquity of smartphones, PTSD Coach may provide a wide-reaching, convenient public health intervention for individuals with PTSD symptoms who are not receiving care.

This paper presents existing research describing how telehealth and eHealth technologies can be used to improve mental health services for trauma survivors, either by enhancing existing treatment approaches or as a stand-alone means of delivering trauma-relevant information and interventions. The potential ways in which telemedicine technologies aide in overcoming barriers to care is first addressed in terms of providing mental health treatment. We then outline how different telehealth and eHealth tools can be used for key therapeutic tasks, including the provision of self-guided interventions, remote delivery of psychotherapy, and augmentation of psychological treatments. We conclude by discussing key emergent issues that are shaping current and future use of telemedicine technologies as part of the continuum of care for trauma survivors.

Posttraumatic stress disorder (PTSD) is common and undertreated among Veterans Affairs (VA) primary care patients. A brief primary care intervention combining clinician support with a self-management mobile app (Clinician-Supported PTSD Coach, CSPTSD Coach) may improve patient outcomes. This study developed and refined an intervention to provide clinician support to facilitate use of the PTSD Coach app and gathered VA provider and patient qualitative and quantitative feedback on CS-PTSD Coach to investigate preliminary acceptability and implementation barriers/ facilitators. VA primary care providers and mental health leadership (N = 9) completed a survey and interview regarding implementation barriers and facilitators structured according to the Consolidated Framework for Implementation Research (CFIR). Clinicians who delivered CS-PTSD Coach (N = 3) and patients (N = 9) who received it provided feedback on the intervention and implementation process. CS-PTSD Coach has high provider and patient acceptability. Important implementation factors included that CS-PTSD Coach be compatible with the clinics' current practices, have low complexity to implement, be perceived to address patient needs, and have strong support from leadership. Diverse factors related to CS-PTSD Coach delivery facilitate implementation, provide an opportunity to problem-solve barriers, and improve integration of the intervention into primary care.

OBJECTIVE Posttraumatic stress disorder (PTSD) is a major public health concern. Although effective treatments exist, affected individuals face many barriers to receiving traditional care. Smartphones are carried by nearly 2 thirds of the U.S. population, offering a promising new option to overcome many of these barriers by delivering self-help interventions through applications (apps). As there is limited research on apps for trauma survivors with PTSD symptoms, we conducted a pilot feasibility, acceptability, and potential efficacy trial of PTSD Coach, a self-management smartphone app for PTSD. METHOD A community sample of trauma survivors with PTSD symptoms (N = 49) were randomized to 1 month using PTSD Coach or a waitlist condition. Self-report assessments were completed at baseline, postcondition, and 1-month follow-up. Following the postcondition assessment, waitlist participants were crossed-over to receive PTSD Coach. RESULTS Participants reported using the app several times per week, throughout the day across multiple contexts, and endorsed few barriers to use. Participants also reported that PTSD Coach components were moderately helpful and that they had learned tools and skills from the app to manage their symptoms. Between conditions effect size estimates were modest (d = -0.25 to -0.33) for PTSD symptom improvement, but not statistically significant. CONCLUSIONS Findings suggest that PTSD Coach is a feasible and acceptable intervention. Findings regarding efficacy are less clear as the study suffered from low statistical power; however, effect size estimates, patterns of within group findings, and secondary analyses suggest that further development and research on PTSD Coach is warranted. (PsycINFO Database Record

STUDY OBJECTIVES This paper describes CBT-I Coach, a patient-facing smartphone app designed to enhance cognitive behavioral therapy for insomnia (CBT-I). It presents findings of two surveys of U.S. Department of Veterans Affairs (VA) CBT-I trained clinicians regarding their perceptions of CBT-I Coach before it was released (n = 138) and use of it two years after it was released (n = 176). METHODS VA-trained CBT-I clinicians completed web-based surveys before and two years after CBT-I Coach was publicly released. RESULTS Prior to CBT-I Coach release, clinicians reported that it was moderately to very likely that the app could improve care and a majority (87.0%) intended to use it if it were available. Intention to use the app was predicted by smartphone ownership (β = 0.116, p < 0.05) and perceptions of relative advantage to existing CBT-I practices (β = 0.286, p < 0.01), compatibility with their own needs and values (β = 0.307, p < 0.01), and expectations about the complexity of the app (β = 0.245, p < 0.05). Two years after CBT-I Coach became available, 59.9% of participants reported using it with patients and had favorable impressions of its impact on homework adherence and outcomes. CONCLUSIONS Findings suggest that before release, CBT-I Coach was perceived to have potential to enhance CBT-I and address common adherence issues and clinicians would use it. These results are reinforced by findings two years after it was released suggesting robust uptake and favorable perceptions of its value.

Objective: This study aims to evaluate the feasibility and potential effectiveness of two approaches to using the PTSD Coach mobile application in primary care: Self-Managed PTSD Coach and Clinician-Supported PTSD Coach. This study also aims to gather preliminary data to investigate if clinician support improves the benefits of using PTSD Coach on posttraumatic stress disorder (PTSD) severity and specialty mental healthcare utilization.Method: Twenty primary care veterans with PTSD symptoms were randomized to either Self-Managed PTSD Coach consisting of one 10-min session providing instructions for application use or Clinician-Supported PTSD Coach consisting of four 20-min sessions focused on setting symptom reduction goals and helping veterans fully engage with application content.Results: Research procedures and intervention conditions appear feasible as indicated by high rates of assessment and intervention retention and high clinician fidelity and satisfaction. Both treatments resulted in reductions in PTSD symptoms, with 7 Clinician-Supported PTSD Coach and 3 Self-Managed PTSD Coach participants reporting clinically significant improvements. Clinician-Supported PTSD Coach resulted in more specialty PTSD care use postintervention and possibly greater reductions in PTSD symptoms.Conclusions: Both PTSD Coach interventions are feasible and potentially helpful. The addition of clinician support appears to increase the effectiveness of self-management alone. A larger-scale randomized controlled trial is warranted to confirm these encouraging preliminary findings. Published by Elsevier Inc.