Search

• Between 10,000 to 22,750 mental health apps exist, most without guidance for use. • Most providers have received no training in the use of apps in their practice. • Mobile health practice involves five key core competencies. • Technologies require thoughtful integration into clinical practice to be useful. Mobile technologies have become increasingly pervasive and integral in our daily lives. A growing number of mobile applications are focused on health applications with many specifically focused on helping the prevention, treatment, and management of mental health issues. These apps are slowly being introduced into clinical settings, either being brought in by clients who themselves are using these tools or by health systems or providers. Unfortunately, most practicing providers obtained their clinical training before the advent of mobile health tools and methods. Thus, a critical need exists to develop and disseminate resources to develop the essential skills needed to adopt mobile health techniques. We discuss the five core competencies in mobile health care: evidence, integration, security and privacy, ethics, and cultural considerations. Although this paper will not fully prepare a provider to use mobile apps in their practice, it provides an introduction that will helpfully guide a provider to additional resources and additional ways to develop these competencies. Finally, we discuss the future directions of the field, including a growing differentiation of the impact of mobile apps on clinical care and the need for training models and experiences to match this differentiation. [ABSTRACT FROM AUTHOR]

Mobile apps for mental health and wellness (MH apps) have the potential to support youth mental health, expanding access to the large proportion of youth with mental health concerns who do not access formal treatment. Survey data suggest that young people are highly interested in MH apps, with minoritized youth (eg, LGBTQ individuals) and youth with elevated depressive symptoms reporting especially high rates of downloading MH apps.1 In addition, systematic reviews and meta-analyses suggest that MH apps can be effective.2 Although many popular MH apps do not have direct empirical evidence supporting their efficacy,3 they often include elements of empirically supported treatments for children and adolescents.4 The MH app space moves at a fast pace, making it difficult to stay up-to-date. More than 10,000 MH apps exist, with hundreds released each year.5 Even interested clinicians may, understandably, not have time to sort through the large number of apps, scientific papers, and app directories. Furthermore, these resources are rarely designed to offer clear, actionable advice for clinicians. Clinicians could benefit from information and guidance to help patients safely navigate MH apps and to best use MH apps in treatment. Here, we provide 3 key points about youth MH apps ("takeaways"), each with an actionable implication for clinicians ("action items"). We also provide examples of specific questions for clinicians to support their use of MH apps with children and adolescents (Table 1). More information regarding where we derived these recommendations is available in Supplement 1, available online. [ABSTRACT FROM AUTHOR]

Abstract Mental health apps offer unique opportunities for self-management of mental health and well-being in mobile, cost-effective ways. There is an abundance of apps available to consumers, but selecting a useful one presents a challenge. Most available apps are not supported by empirical evidence and thus consumers have access to a range of untested apps, the benefits of which are not known or supported. While user ratings exist, and are likely to be considered by consumers when selecting an app, they do not actually yield information on app suitability. A possible alternative way for consumers to choose an app would be to use an app review platform. A number of attempts have been made to construct such a platform, and this paper introduces PsyberGuide, which offers a step towards providing objective and actionable information for publicly available mental health apps. Highlights • Apps are a useful tool in self-management of mental health and well-being • Most publicly available mental health apps have no direct scientific support • User ratings are an indication of app popularity but not clinical usefulness • Consumers and clinicians need additional ways to determine what "good" apps are • App rating platforms (for example, PsyberGuide) may be a way to address this [ABSTRACT FROM AUTHOR]

The Cognitive and Behavioral Response to Stress Scale (CB-RSS) is a self-report measure of the use and helpfulness of several cognitive and behavioral skills. Unlike other measures that focus on language specific to terms used in therapy, the CB-RSS was intended to tap the strategies in ways that might be understandable to those who had not undergone therapy. The measure was included in a clinical trial of cognitive-behavioral therapy for depression and completed by 325 participants at baseline and end of treatment (18 weeks). Psychometric properties of the scale were assessed through iterative exploratory and confirmatory factor analyses. These analyses identified two subscales, cognitive and behavioral skills, each with high reliability. Validity was addressed by investigating relationships with depression symptoms, positive affect, perceived stress, and coping self-efficacy. End of treatment scores predicted changes in all outcomes, with the largest relationships between baseline CB-RSS scales and coping self-efficacy. These findings suggest that the CB-RSS is a useful tool to measure cognitive and behavioral skills both at baseline (prior to treatment) as well as during the course of treatment. [ABSTRACT FROM AUTHOR]

Objective Monitoring depressive symptoms during treatment can guide clinical decision making and improve outcomes. The aim of this study was to explore values on the Patient Health Questionnaire (PHQ-9) that could predict response to treatment. Method Data came from two independent trials, including three treatment modalities of cognitive–behavioral treatment for depression. Four hundred eighty-seven participants who either met the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition criteria for major depressive disorder or had PHQ-9 scores consistent with a diagnosis of depression were included in our analyses. Participants either received 18 weeks of telephone or face-to-face ( n = 279) or 8 weeks of Web-delivered ( n = 208) cognitive–behavioral therapy. Depressive symptoms, evaluated using the PHQ-9, were reported every 4 weeks in the telephone and face-to-face trial and weekly in the Web-delivered intervention trial. Results Optimal cut points for predicting end-of-treatment response were consistent in both trials. Our results suggested using cut points of a PHQ-9 ≥ 17 at Week 4, and PHQ-9 ≥ 13 at Week 9 and PHQ-9 ≥ 9 at Week 14. Conclusions Consistent cut points were found within the included trials. These cut points may be valuable for algorithms to support clinical decision making. [ABSTRACT FROM AUTHOR]

Abstract: To address health problems that have a major impact on global health requires research designs that go beyond randomized controlled trials. One such design, the participant preference trial, provides additional information in an ecologically valid manner, once intervention efficacy has been demonstrated. The current study presents illustrative data from a participant preference trial of an internet-based smoking cessation intervention. Participants (N=7763) from 124 countries accessed the intervention and were allowed to choose from nine different site components to aid their quit attempt. Of consenting participants, 36.7% completed at least one follow-up assessment. Individuals with depression were more likely to choose a mood management module and participants who smoked a higher number of cigarettes were more likely to choose a cigarette counter and a nicotine replacement therapy guide. Furthermore, depressed participants selecting the mood management component were more likely to report at least one successful 7 day quit (37.2% vs. 22.2%) in the 12 months following the intervention. Thus, participants with depressive symptoms appear to make choices on the basis of their needs and to benefit from these decisions. This suggests that providing the ability to customize previously validated resources may be a successful way to widely disseminate interventions. [Copyright &y& Elsevier]

• DMHIs can expand the scale, efficiency, and effectiveness of psychological services. • Issues of reach, adoption, implementation, and maintenance limit DMHI impact. • Gaps in DMHI research and practice are examined and potential solutions presented. • It is discussed how new technologies and advances in the profession will shape DMHIs' future. • Suggestions to advance DMHI equity in research and practice are provided. In recent years, digital mental health interventions (DMHIs) have emerged as a paradigm shift in care delivery that could expand the scale, efficiency, and effectiveness of psychological services. However, DMHI impact is constrained by issues related to limited reach, poor adoption, implementation barriers, and insufficient long-term maintenance. Organized by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, this paper surveys the current state of DMHIs, highlighting research and practice gaps as well as potential strategies to move the field forward. Similarly, we discuss the role that emerging technologies and changes in the profession will play in shaping DMHIs in years to come. Finally, concrete and actionable steps to advance equity in the DMHI field are provided, with an emphasis on strategies to increase the representativeness of marginalized populations in DMHI research, the inclusion of these groups in the design and testing of DMHIs, and how to improve the contextual and cultural fit of DMHIs. [ABSTRACT FROM AUTHOR]

Psychotherapy nonattendance is a costly and pervasive problem. While prior research has identified stable patient-level predictors of attendance, far less is known about dynamic (i.e., time-varying) factors. Identifying dynamic predictors can clarify how clinical states relate to psychotherapy attendance and inform effective "just-in-time" interventions to promote attendance. The present study examines whether daily mood, as measured by responses to automated mobile phone-based text messages, prospectively predicts attendance in group cognitive-behavioral therapy (CBT) for depression. Fifty-six Spanish-speaking Latino patients with elevated depressive symptoms (46 women, mean age=50.92years, SD=10.90years), enrolled in a manualized program of group CBT, received daily automated mood-monitoring text messages. Patients' daily mood ratings, message response rate, and delay in responding were recorded. Patients' self-reported mood the day prior to a scheduled psychotherapy session significantly predicted attendance, even after controlling for patients' prior attendance history and age (OR=1.33, 95% CI [1.04, 1.70], p=.02). Positive mood corresponded to a greater likelihood of attendance. Our results demonstrate the clinical utility of automated mood-monitoring text messages in predicting attendance. These results underscore the value of text messaging, and other mobile technologies, as adjuncts to psychotherapy. Future work should explore the use of such monitoring to guide interventions to increase attendance, and ultimately the efficacy of psychotherapy. [ABSTRACT FROM AUTHOR]

Abstract: Behavioral intervention technologies (BITs) are web-based and mobile interventions intended to support patients and consumers in changing behaviors related to health, mental health, and well-being. BITs are provided to patients and consumers in clinical care settings and commercial marketplaces, frequently with little or no evaluation. Current evaluation methods, including RCTs and implementation studies, can require years to validate an intervention. This timeline is fundamentally incompatible with the BIT environment, where technology advancement and changes in consumer expectations occur quickly, necessitating rapidly evolving interventions. However, BITs can routinely and iteratively collect data in a planned and strategic manner and generate evidence through systematic prospective analyses, thereby creating a system that can “learn.” A methodologic framework, Continuous Evaluation of Evolving Behavioral Intervention Technologies (CEEBIT), is proposed that can support the evaluation of multiple BITs or evolving versions, eliminating those that demonstrate poorer outcomes, while allowing new BITs to be entered at any time. CEEBIT could be used to ensure the effectiveness of BITs provided through deployment platforms in clinical care organizations or BIT marketplaces. The features of CEEBIT are described, including criteria for the determination of inferiority, determination of BIT inclusion, methods of assigning consumers to BITs, definition of outcomes, and evaluation of the usefulness of the system. CEEBIT offers the potential to collapse initial evaluation and postmarketing surveillance, providing ongoing assurance of safety and efficacy to patients and consumers, payers, and policymakers. [Copyright &y& Elsevier]

Objective: A technical expert panel convened by the Agency for Healthcare Research and Quality and the National Institute of Mental Health was charged with reviewing the state of research on behavioral intervention technologies (BITs) in mental health and identifying the top research priorities. BITs refers to behavioral and psychological interventions that use information and communication technology features to address behavioral and mental health outcomes. Method: This study on the findings of the technical expert panel. Results: Videoconferencing and standard telephone technologies to deliver psychotherapy have been well validated. Web-based interventions have shown efficacy across a broad range of mental health outcomes. Social media such as online support groups have produced disappointing outcomes when used alone. Mobile technologies have received limited attention for mental health outcomes. Virtual reality has shown good efficacy for anxiety and pediatric disorders. Serious gaming has received little work in mental health. Conclusion: Research focused on understanding reach, adherence, barriers and cost is recommended. Improvements in the collection, storage, analysis and visualization of big data will be required. New theoretical models and evaluation strategies will be required. Finally, for BITs to have a public health impact, research on implementation and application to prevention is required [ABSTRACT FROM AUTHOR]

Limited English Proficiency (LEP) Latinxs experience a longer duration of untreated depression and anxiety. LEP Latinxs have difficulty accessing mental healthcare due to insufficient Spanish-speaking behavioral/mental health clinicians to meet demand. These under-resourced healthcare systems are less likely to be the site for the implementation of innovations. Digital interventions can provide an effective option for overcoming these barriers; yet, when digital evidence-based treatments are available, uptake and engagement is often low. This manuscript presents the protocol for the SUPERA (SUpport from PEeRs to expand Access) study which will evaluate the implementation of an evidence-based, Spanish language, digital cognitive-behavioral therapy (dCBT) intervention (i.e., SilverCloud) in safety-net primary care clinics for LEP Latinx patients with depression or anxiety. We will conduct an effectiveness-implementation hybrid trial (Type 2) design comparing engagement and clinical outcomes in two modalities of dCBT delivery (peer-supported vs. unsupported). We will also compare provider-level outreach (using a clinic patient registry) versus inreach (traditional provider referral) to compare rates of initiation, completion, and cost. Participants will be 426 LEP Latinx adults ≥18 years of age, PHQ-9 ≥ 10 or GAD-7 ≥ 8, with access to the internet via smartphone, and not currently receiving individual psychotherapy. We will collect baseline, post-intervention (8 weeks), and follow up (3 months) data. The long-term goal of this research is to aid in the implementation of digital mental health interventions that can be sustainably implemented in low-resourced settings, while reducing the reliance on professionals, overcoming workforce deficits, and increasing relevance for diverse populations. [ABSTRACT FROM AUTHOR]

Insomnia and depression frequently co-occur. Significant barriers preclude a majority of patients from receiving first line treatments for both disorders in a sequential treatment episode. Although digital versions of cognitive behavioral therapy for insomnia (CBT I) and for depression (CBT D) hold some promise to meet demand, especially when paired with human support, it is unknown whether heterogeneity of treatment effects exist, such that some patients would be optimally treated with single or sequential interventions. Describe the protocol for a two-phase, prescriptive comparative effectiveness study to develop and evaluate an individualized intervention rule (IIR) for prescribing the optimal digital treament of co-occurring insomnia and depression. The proposed sample size is 2300 U.S. military veterans with insomnia and depression recruited nationally (Phase 1 = 1500; Phase 2 = 800). In each phase, the primary endpoint will be remission of both depression and insomnia 3 months following a 12-week intervention period. Phase 1 is a 5-arm randomized trial: two single digital interventions (CBT-I or CBT-D); two sequenced interventions (CBT-I + D or CBT-D + I); and a mood monitoring control condition. A cutting-edge ensemble machine learning method will be used to develop the IIR. Phase 2 will evaluate the IIR by randomizing participants with equal allocation to either the IIR predicted optimal intervention for that individual or by randomization to one the four CBT interventions. Study procedures are ongoing. Results will be reported in future manuscripts. The study will generate evidence on the optimal scalable approach to treat co-occurring insomnia and depression. [ABSTRACT FROM AUTHOR]

Rates of clinically elevated depressive symptoms among ambulatory oncology patients are higher than in the general population and are associated with poorer health-related quality of life. Furthermore, a reduction in depressive symptoms may be associated with improved cancer survival. Several interventions have demonstrated efficacy in reducing oncologic depressive symptoms, including cognitive-behavioral stress management (CBSM). However, more work is needed to understand how to best implement CBSM into practice, such as through stepped-care approaches and digital health interventions linked to electronic health records (EHR). This manuscript presents the protocol of the My Well-Being Guide study, a pragmatic type 1 effectiveness-implementation hybrid study. This trial will test the effectiveness of My Well-Being Guide , a seven-week structured, CBSM-based digital health intervention designed to reduce depressive symptoms. This trial will also evaluate My Well-Being Guide's implementation across two health systems. The final sample (N = 4561) will be oncology patients at Northwestern Medicine or University of Miami Health System who are ≥18 years of age; have a cancer diagnosis; elevated depressive symptoms on the Patient-Reported Outcomes Measurement Information System Depression; and primary language is English or Spanish. Data collection will occur at baseline, and 2-, 6-, and 12-months post baseline. Outcome domains include depressive symptoms and implementation evaluation. This study may provide valuable data on the effectiveness of our depressive symptom management digital health intervention linked to the EHR and the scalability of digital health interventions in general. [ABSTRACT FROM AUTHOR]

This trial examined whether a stepped care program for depression, which initiated treatment with internet cognitive behavioral therapy, including telephone and messaging support, and stepped up non-responders to telephone-administered cognitive behavioral therapy (tCBT), was noninferior, less costly to deliver, and as acceptable to patients compared to tCBT alone. Adults with a diagnosis of major depressive episode (MDE) were randomized to receive up to 20 weeks of stepped care or tCBT. Stepped care (n = 134) was noninferior to tCBT (n = 136) with an end-of-treatment effect size of d = 0.03 and a 6-month post-treatment effect size of d = −0.07 [90% CI 0.29 to 0.14]. Therapist time in stepped care was 5.26 (SD = 3.08) hours versus 10.16 (SD 4.01) for tCBT (p < 0.0001), with a delivery cost difference of $-364.32 [95% CI $-423.68 to $-304.96]. There was no significant difference in pre-treatment preferences (p = 0.10) or treatment dropout (39 in stepped care; 27 in tCBT; p = 0.14). tCBT patients were significantly more satisfied than stepped care patients with the treatment they received (p < 0.0001). These findings indicate that stepped care was less costly to deliver, but no less effective than tCBT. There was no significant difference in treatment preference or completion, however satisfaction with treatment was higher in tCBT than stepped care. clinicaltrials.gov Identifier: NCT01906476. • Stepped care used internet cognitive behavioral therapy (iCBT) + telephone CBT [tCBT]). • Stepped care was noninferior to tCBT for depression at both post-treatment and 6-month follow-up. • Stepped care cost approximately half to administer, compared to tCBT. • Treatment dropout for stepped care was not significantly different from tCBT. [ABSTRACT FROM AUTHOR]