Although impressive progress has been made toward developing empirically-supported psychological treatments, the reality remains that a significant proportion of people with mental health problems do not receive these treatments. Finding ways to reduce this treatment gap is crucial. Since app-supported smartphone interventions are touted as a possible solution, access to up-to-date guidance around the evidence base and clinical utility of these interventions is needed. We conducted a meta-analysis of 66 randomized controlled trials of app-supported smartphone interventions for mental health problems. Smartphone interventions significantly outperformed control conditions in improving depressive (g=0.28, n=54) and generalized anxiety (g=0.30, n=39) symptoms, stress levels (g=0.35, n=27), quality of life (g=0.35, n=43), general psychiatric distress (g=0.40, n=12), social anxiety symptoms (g=0.58, n=6), and positive affect (g=0.44, n=6), with most effects being robust even after adjusting for various possible biasing factors (type of control condition, risk of bias rating). Smartphone interventions conferred no significant benefit over control conditions on panic symptoms (g=-0.05, n=3), post-traumatic stress symptoms (g=0.18, n=4), and negative affect (g=-0.08, n=5). Studies that delivered a cognitive behavior therapy (CBT)-based app and offered professional guidance and reminders to engage produced larger effects on multiple outcomes. Smartphone interventions did not differ significantly from active interventions (face-to-face, computerized treatment), although the number of studies was low (n≤13). The efficacy of app-supported smartphone interventions for common mental health problems was thus confirmed. Although mental health apps are not intended to replace professional clinical services, the present findings highlight the potential of apps to serve as a cost-effective, easily accessible, and low intensity intervention for those who cannot receive standard psychological treatment.

The current COVID-19 pandemic has created a global context likely to increase eating disorder (ED) risk and symptoms, decrease factors that protect against EDs, and exacerbate barriers to care. Three pathways exist by which this pandemic may exacerbate ED risk. One, the disruptions to daily routines and constraints to outdoor activities may increase weight and shape concerns, and negatively impact eating, exercise, and sleeping patterns, which may in turn increase ED risk and symptoms.Relatedly, the pandemic and accompanying social restrictions may deprive individuals of social support and adaptive coping strategies, thereby potentially elevating ED risk and symptoms by removing protective factors. Two, increased exposure to ED-specific or anxiety-provoking media, as well as increased reliance on video conferencing, may increase ED risk and symptoms. Three, fears of contagion may increase ED symptoms specifically related to health concerns, or by the pursuit of restrictive diets focused on increasing immunity. In addition, elevated rates of stress and negative affect due to the pandemic and social isolation may also contribute to increasing risk. Evaluating and assessing these factors are key to better understanding the impact of the pandemic on ED risk and recovery and to inform resource dissemination and targets.

As the COVID-19 pandemic has largely increased the utilization of telehealth, mobile mental health technologies -such as smartphone apps, virtual reality, chatbots, and social media -have also gained attention. These digital health technologies offer the potential of accessible and scalable interventions that can augment traditional care. In this paper, we provide a comprehensive update on the overall field of digital psychiatry, covering three areas. First, we outline the relevance of recent technological advances to mental health research and care, by detailing how smartphones, social media, artificial intelligence and virtual reality present new opportunities for "digital phenotyping" and remote intervention. Second, we review the current evidence for the use of these new technological approaches across different mental health contexts, covering their emerging efficacy in self-management of psychological well-being and early intervention, along with more nascent research supporting their use in clinical management of long-term psychiatric conditions -including major depression; anxiety, bipolar and psychotic disorders; and eating and substance use disorders -as well as in child and adolescent mental health care. Third, we discuss the most pressing challenges and opportunities towards real-world implementation, using the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to explain how the innovations themselves, the recipients of these innovations, and the context surrounding innovations all must be considered to facilitate their adoption and use in mental health care systems. We conclude that the new technological capabilities of smartphones, artificial intelligence, social media and virtual reality are already changing mental health care in unforeseen and exciting ways, each accompanied by an early but promising evidence base. We point out that further efforts towards strengthening implementation are needed, and detail the key issues at the patient, provider and policy levels which must now be addressed for digital health technologies to truly improve mental health research and treatment in the future.

Objectives: Although the efficacy of smartphone-delivered interventions for mental health problems is emerging, randomized controlled trials (RCTs) of smartphone interventions are characterized by high rates of attrition and low adherence. High attrition and low adherence may threaten the validity of RCT findings, so a better understanding of these phenomena is needed. We examined attrition and adherence in 70 RCTs of smartphone interventions. Method: Four online databases were searched for RCTs of mental health interventions delivered via smartphones. Results: The mean meta-analytic study attrition rate was 24.1% (95% CI [19.3, 29.6]) at short-term follow up and 35.5% (95% CI [26.7, 45.3]) at longer-term follow up. These rates varied according to target mental health condition. Attrition rates were significantly lower in trials that delivered an acceptance-based intervention, offered participants monetary compensation, and reminded participants to engage in the intervention, and were significantly higher in trials that used an online enrollment method (relative to telephone or in-person enrollment). No participant-level baseline characteristic reliably predicted attrition. Evidence of attrition bias came from many RCTs not conducting intention-to-treat analyses. However, the mean difference in the between-groups effect size on primary outcomes in trials that reported both per protocol an intention-to-treat analyses was only ⌬d ϭ 0.18. Adherence rates were also suboptimal based on our qualitative synthesis; several participants failed to download the intervention, and intervention usage consistently declined over the course of the trial. Conclusion: Study attrition and low adherence are common, problematic, and may undermine the validity of findings in RCTs of smartphonedelivered interventions for mental health problems. What is the public health significance of this article?This study suggests that a significant proportion of people enrolled in randomized trials of mental health smartphone apps drop out from the study and fail to use the intervention as intended. Developing retention strategies may be important for improving mental health problems.

CBT is efficacious for eating disorders. Although CBT was equally efficacious to certain psychological treatments, the fact that CBT outperformed all active psychological comparisons and interpersonal psychotherapy specifically, offers some support for the specificity of psychological treatments for eating disorders. Conclusions from this study are hampered by the fact that many trials were of poor quality. Higher quality RCTs are essential. (PsycINFO Database Record