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Mobile and connected devices like smartphones and wearable sensors can facilitate the collection of novel naturalistic and longitudinal data relevant to psychiatry at both the personal and population level. The National Institute of Mental Health’s Research Domain Criteria framework offers a useful roadmap to organize, guide and lead new digital phenotyping data towards research discoveries and clinical advances.

Clinical and research interest in the potential of mobile health apps for the management of mental health conditions has recently been given added impetus by growing evidence of consumer adoption. In parallel, there is now a developing evidence base that includes meta-analyses demonstrating reductions in symptoms of depression and anxiety, and reduction in suicidal ideation. While these findings are encouraging, recent research continues to identify a number of potential barriers to the widespread adoption of mental health apps. These challenges include poor data governance and data sharing practices; questions of clinical safety relating to the management of adverse events and potentially harmful content; low levels of user engagement and the possibility of 'digital placebo' effects; and workforce barriers to integration with clinical practice. Current efforts to address these include the development of new models of care, such as 'digital clinics' that integrate health apps. Other contemporary innovations in the field such as digital sensing and just-in-time adaptive interventions are showing early promise for providing accessible and personalised care.

Hundreds of randomized controlled trials (RCTs) have tested the efficacy of mobile health (mHealth) tools for a wide range of mental and behavioral health outcomes. These RCTs have used a variety of control condition types which dramatically influence the scientific inferences that can be drawn from a given study. Unfortunately, nomenclature across mHealth RCTs is inconsistent and meta-analyses commonly combine control conditions that differ in potentially important ways. We propose a typology of control condition types in mHealth RCTs. We define 11 control condition types, discuss key dimensions on which they differ, provide a decision tree for selecting and identifying types, and describe the scientific inferences each comparison allows. We propose a five-tier comparison strength gradation along with four simplified categorization schemes. Lastly, we discuss unresolved definitional, ethical, and meta-analytic issues related to the categorization of control conditions in mHealth RCTs.

The unmet need for mental health care continues to rise across the world. This article synthesizes the evidence supporting the components of a hypothetical model of integrated digital mental health care to meet population-wide mental health needs. This proposed model integrates two approaches to broadening timely access to effective care: integrated, primary care-based mental health services and digital mental health tools. The model solves for several of the key challenges historically faced by digital health, through promoting digital literacy and access, the curation of evidence-based digital tools, integration into clinical practice, and electronic medical record integration. This model builds upon momentum toward the integration of mental health services within primary care and aligns with the principles of the Collaborative Care Model. Finally, the authors present the major next steps toward implementation of integrated digital mental health care at scale.

Importance To solicit the view of an international panel of experts on the effects on mental health patients, including possible benefits and harms, of accessing their clinical notes. An online 3-round Delphi poll. Online. International experts identified as clinicians, chief medical information officers, patient advocates, and informaticians with extensive experience and/or research knowledge about patient access to mental health notes. An expert-generated consensus on the benefits and risks of sharing mental health notes with patients. A total of 70 of 92 (76%) experts from 6 countries responded to Round 1. A qualitative review of responses yielded 88 distinct items: 42 potential benefits, and 48 potential harms. A total of 56 of 70 (80%) experts responded to Round 2, and 52 of 56 (93%) responded to Round 3. Consensus was reached on 65 of 88 (74%) of survey items. There was consensus that offering online access to mental health notes could enhance patients' understanding about their diagnosis, care plan, and rationale for treatments, and that access could enhance patient recall and sense of empowerment. Experts also agreed that blocking mental health notes could lead to greater harms including increased feelings of stigmatization. However, panelists predicted there could be an increase in patients demanding changes to their clinical notes, and that mental health clinicians would be less detailed/accurate in documentation. This iterative process of survey responses and ratings yielded consensus that there would be multiple benefits and few harms to patients from accessing their mental health notes. Questions remain about the impact of open notes on professional autonomy, and further empirical work into this practice innovation is warranted.

As mental healthcare expands to smartphone apps and other technologies that may offer therapeutic interventions without a therapist involved, it is important to assess the impact of non-traditional therapeutic relationships.AimsTo determine if there were any meaningful data regarding the digital therapeutic alliance in smartphone interventions for serious mental illnesses. A literature search was conducted in four databases (PubMed, PsycINFO, Embase and Web of Science). There were five studies that discuss the therapeutic alliance when a mobile application intervention is involved in therapy. However, in none of the studies was the digital therapeutic alliance the primary outcome. The studies looked at different mental health conditions, had different duration of technology use and used different methods for assessing the therapeutic alliance. Assessing and optimising the digital therapeutic alliance holds the potential to make tools such as smartphone apps more effective and improve adherence to their use. However, the heterogeneous nature of the five studies we identified make it challenging to draw conclusions at this time. A measure is required to evaluate the digital therapeutic alliance.

Mental health concerns have become increasingly prevalent; however, care remains inaccessible to many. While digital mental health interventions offer a promising solution, self-help and even coached apps have not fully addressed the challenge. There is now a growing interest in hybrid, or blended, care approaches that use apps as tools to augment, rather than to entirely guide, care. The Digital Clinic is one such model, designed to increase access to high-quality mental health services. To assess the feasibility, acceptability, and potential efficacy of the Digital Clinic model, this study aims to conduct a nonrandomized open trial with participants experiencing depression, anxiety, or both, at various levels of clinical severity. Clinicians were trained in conducting brief transdiagnostic evidence-based treatment augmented by a mental health app (mindLAMP); digital navigators were trained in supporting participants' app engagement and digital literacy while also sharing app data with both patients and clinicians. Feasibility and acceptability of this 8-week program were assessed against a range of benchmarks. Potential efficacy was assessed by calculating pre-post change in symptoms of depression (Patient Health Questionnaire-9; PHQ-9), anxiety (7-item Generalized Anxiety Disorder; GAD-7), and comorbid depression and anxiety (Patient Health Questionnaire Anxiety and Depression Scale; PHQ-ADS), as well as rates of clinically meaningful improvement and remission. Secondary outcomes included change in functional impairment, self-efficacy in managing emotions, and flourishing. Of the 258 enrolled participants, 215 (83.3%) completed the 8-week program. Most were White (n=151, 70.2%) and identified as cisgender women (n=136, 63.3%), with a mean age of 41 (SD 14) years. Feasibility and acceptability were good to excellent across a range of domains. The program demonstrated potential efficacy: the average PHQ-9 score was moderate to moderately severe at baseline (mean 13.39, SD 4.53) and decreased to subclinical (mean 7.79, SD 4.61) by the end of the intervention (t =12.50, P<.001, Cohen d=1.11). Similarly, the average GAD-7 score decreased from moderate at baseline (mean 12.93, SD 3.67) to subclinical (mean 7.35, SD 4.19) by the end of the intervention (t =13, P<.001, Cohen d=1.22). Participation in the program was also associated with high rates of clinically significant improvement and remission. Results suggest that the Digital Clinic model is feasible, acceptable, and potentially efficacious, warranting a future randomized controlled trial to establish the efficacy of this innovative model of care.

Low engagement with mental health apps continues to limit their impact. New approaches to help match patients to the right app may increase engagement by ensuring the app they are using is best suited to their mental health needs. This study aims to pilot how digital phenotyping, using data from smartphone sensors to infer symptom, behavioral, and functional outcomes, could be used to match people to mental health apps and potentially increase engagement. After 1 week of collecting digital phenotyping data with the mindLAMP app (Beth Israel Deaconess Medical Center), participants were randomly assigned to the digital phenotyping arm, receiving feedback and recommendations based on those data to select 1 of 4 predetermined mental health apps (related to mood, anxiety, sleep, and fitness), or the control arm, selecting the same apps but without any feedback or recommendations. All participants used their selected app for 4 weeks with numerous metrics of engagement recorded, including objective screentime measures, self-reported engagement measures, and Digital Working Alliance Inventory scores. A total of 82 participants enrolled in the study; 17 (21%) dropped out of the digital phenotyping arm and 18 (22%) dropped out from the control arm. Across both groups, few participants chose or were recommended the insomnia or fitness app. The majority (39/47, 83%) used a depression or anxiety app. Engagement as measured by objective screen time and Digital Working Alliance Inventory scores were higher in the digital phenotyping arm. There was no correlation between self-reported and objective metrics of app use. Qualitative results highlighted the importance of habit formation in sustained app use. The results suggest that digital phenotyping app recommendation is feasible and may increase engagement. This approach is generalizable to other apps beyond the 4 apps selected for use in this pilot, and practical for real-world use given that the study was conducted without any compensation or external incentives that may have biased results. Advances in digital phenotyping will likely make this method of app recommendation more personalized and thus of even greater interest.

This survey study examines how patients with a mental illness diagnosis who read at least 1 clinical note in the last year perceived its association with their medication adherence.

We appreciate Jacobson and colleagues’ thoughtful commentary on our meta-review of mobile phone-based interventions for mental health. In this response, we address 2 issues raised: requiring low to moderate heterogeneity (I2 < 50%) and requiring no evidence of publication bias for evidence to be classified as “convincing.” While we agree these represent a high bar, we disagree that these requirements are destined to fail. Other effect sizes reported in the literature, including effect sizes related to mental health interventions and effect sizes related to mobile health (mHealth) interventions (although not their combination) have met requirements for convincing evidence. Jacobson and colleagues argue that features of the mHealth interventions may produce heterogeneity when meta-analyses combine across intervention types. However, several of the effect sizes we reviewed were based on relatively homogeneous portions of the literature and many of the effect sizes we reviewed showed low to moderate heterogeneity. Ideally, future meta-analyses will examine intervention features as moderators of treatment effects. While an absence of publication bias may be a stringent criterion, all but 2 of the 34 effect sizes we reviewed did not report formal tests of publication bias. Clearly, there is a need to reach consensus on how the strength of evidence for mHealth interventions can be evaluated. From our perspective, convincing evidence will ultimately come from large-scale randomized controlled trials employing rigorous comparison conditions along with meta-analyses that do not combine across control condition types, that examine theoretically important moderators, and report formal tests of publication bias. It is this kind of evidence that the public, the clinicians, and the scientific community may need to encourage adoption of mHealth interventions for mental health treatment and prevention.