IntelliCare: An Eclectic, Skills-Based App Suite for the Treatment of Depression and Anxiety

The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of randomized controlled trials

The rapid advances and adoption of smartphone technology presents a novel opportunity for delivering mental health interventions on a population scale. Despite multi-sector investment along with wide-scale advertising and availability to the general population, the evidence supporting the use of smartphone apps in the treatment of depression has not been empirically evaluated. Thus, we conducted the first meta-analysis of smartphone apps for depressive symptoms. An electronic database search in May 2017 identified 18 eligible randomized controlled trials of 22 smartphone apps, with outcome data from 3,414 participants. Depressive symptoms were reduced significantly more from smartphone apps than control conditions (g=0.38, 95% CI: 0.24-0.52, p<0.001), with no evidence of publication bias. Smartphone interventions had a moderate positive effect in comparison to inactive controls (g=0.56, 95% CI: 0.38-0.74), but only a small effect in comparison to active control conditions (g=0.22, 95% CI: 0.10-0.33). Effects from smartphone-only interventions were greater than from interventions which incorporated other human/computerized aspects along the smartphone component, although the difference was not statistically significant. The studies of cognitive training apps had a significantly smaller effect size on depression outcomes (p=0.004) than those of apps focusing on mental health. The use of mood monitoring softwares, or interventions based on cognitive behavioral therapy, or apps incorporating aspects of mindfulness training, did not affect significantly study effect sizes. Overall, these results indicate that smartphone devices are a promising self-management tool for depression. Future research should aim to distil which aspects of these technologies produce beneficial effects, and for which populations.

Exploring User Learnability and Learning Performance in an App for Depression: Usability Study

BACKGROUND

Mental health apps tend to be narrow in their functioning, with their focus mostly being on tracking, management, or psychoeducation. It is unclear what capability such apps have to facilitate a change in users, particularly in terms of learning key constructs relating to behavioral interventions. Thought Challenger (CBITs, Chicago) is a skill-building app that engages users in cognitive restructuring, a core component of cognitive therapy (CT) for depression.

OBJECTIVE

The purpose of this study was to evaluate the learnability and learning performance of users following initial use of Thought Challenger.

METHODS

Twenty adults completed in-lab usability testing of Thought Challenger, which comprised two interactions with the app. Learnability was measured via completion times, error rates, and psychologist ratings of user entries in the app; learning performance was measured via a test of CT knowledge and skills. Nonparametric tests were conducted to evaluate the difference between individuals with no or mild depression to those with moderate to severe depression, as well as differences in completion times and pre- and posttests.

RESULTS

Across the two interactions, the majority of completion times were found to be acceptable (5 min or less), with minimal errors (1.2%, 10/840) and successful completion of CT thought records. Furthermore, CT knowledge and skills significantly improved after the initial use of Thought Challenger (P=.009).

CONCLUSIONS

The learning objectives for Thought Challenger during initial uses were successfully met in an evaluation with likely end users. The findings therefore suggest that apps are capable of providing users with opportunities for learning of intervention skills.

Evidence-Based Psychotherapy in Primary Care

The functional and financial effects of untreated psychiatric disorders within primary care have led to the development of novel service delivery models to improve access to high-quality, evidence-based mental health treatments. Cognitive-behavioral therapy (CBT) is an efficacious and effective psychotherapeutic approach for treating a broad range of mental health conditions. CBT is a practical, skill-building approach that emphasizes self-efficacy and self-management of symptoms while working toward defined and measurable treatment goals. Although significant barriers to the full dissemination of CBT remain, collaborative care and integrated behavioral health programs embedded within primary care clinics can enhance treatment outcomes by using CBT. Identifying core CBT principles used in the treatment of anxiety (e.g., exposure), depression (e.g., behavioral activation), and insomnia (e.g., stimulus control) is an important step toward improving the quality of care for these conditions. High-impact, low-intensity CBT programs hold promise in improving access to this evidence-based treatment across a broader population.

Patient-Driven Innovation for Mobile Mental Health Technology: Case Report of Symptom Tracking in Schizophrenia

This patient perspective piece presents an important case at the intersection of mobile health technology, mental health, and innovation. The potential of digital technologies to advance mental health is well known, although the challenges are being increasingly recognized. Making mobile health work for mental health will require broad collaborations. We already know that those who experience mental illness are excited by the potential technology, with many actively engaged in research, fundraising, advocacy, and entrepreneurial ventures. But we don't always hear their voice as often as others. There is a clear advantage for their voice to be heard: so we can all learn from their experiences at the direct intersection of mental health and technology innovation. The case is cowritten with an individual with schizophrenia, who openly shares his name and personal experience with mental health technology in order to educate and inspire others. This paper is the first in JMIR Mental Health's patient perspective series, and we welcome future contributions from those with lived experience.

Sensing behavioral symptoms of mental health and delivering personalized interventions using mobile technologies

Unlike most other health conditions, the treatment of mental illness relies on subjective measurement. In addition, the criteria for diagnosing mental illnesses are based on broad categories of symptoms that do not account for individual deviations from these criteria. The increasing availability of personal digital devices, such as smartphones that are equipped with sensors, offers a new opportunity to continuously and passively measure human behavior in situ. This promises to lead to more precise assessment of human behavior and ultimately individual mental health. More refined modeling of individual mental health and a consideration of individual context, assessed through continuous monitoring, opens the way for more precise and personalized digital interventions that may help increase the number of positive clinical outcomes in mental healthcare. In this paper, we provide a conceptual review of such techniques for measuring, modeling, and treating mental illness and maintaining mental health.

Health App Use Among Individuals With Symptoms of Depression and Anxiety: A Survey Study With Thematic Coding

BACKGROUND

Researchers have largely turned to commercial app stores, randomized trials, and systematic reviews to make sense of the mHealth landscape. Few studies have approached understanding by collecting information from target end users. The end user perspective is critical as end user interest in and use of mHealth technologies will ultimately drive the efficacy of these tools.

OBJECTIVE

The purpose of this study was to obtain information from end users of mHealth technologies to better understand the physical and mental health apps people use and for what purposes.

METHODS

People with depressive or anxious symptoms (N=176) seeking entry into a trial of mental health and well-being apps for Android devices completed online questionnaires assessing depression and anxiety (Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7), past and current mental health treatment-seeking behavior, overall mobile device use, and use of mobile health apps. Participants reported the physical health and mental health apps on their devices and their reasons for using them. Data were extracted from the participant self-reports and apps and app purposes were coded in order to categorize them.

RESULTS

Participants were largely white, middle-aged females from the Midwest region of the United States recruited via a health care organization and Web-based advertising (135 female, 41 male, mean age 38.64 years, age range 19-75 years.) Over three-quarters (137/176, 77.8%) of participants indicated having a health app on their device. The top 3 kinds of apps were exercise, fitness, and pedometers or heart rate monitoring apps (93/176, 52.8%); diet, food, or calorie counting apps (65/177, 36.9%); and mental health/wellness apps (46/177, 26.1%). The mean number of mobile physical and mental health apps on a participant's phone was 2.15 (SD 3.195). Of 176 participants, 107 (60.8%) specifically reported the top 5 health apps that they used and their purposes. Across the 107 participants, a total of 285 apps were reported, with 139 being unique apps. The majority of these apps were free (129/139, 92.8%). Almost two-thirds of participants (67/107, 62.6%) reported using health apps at least on a daily basis.

CONCLUSIONS

Among those seeking support for their well-being via physical and mental health apps, people are using a variety of health apps. These people use health apps on a daily basis, especially free apps. The most common reason for using a health app is to track some health-related data; for mental health apps specifically, training or habit building was the most popular reason. Understanding the end user perspective is important because it allows us to build on the foundation of previously established mHealth research and may help guide future work in mHealth.

TRIAL REGISTRATION

Clinicaltrials.gov NCT02176226; https://clinicaltrials.gov/ct2/show/NCT02176226 (Archived by WebCite at http://www.webcitation.org/6rGc1MGyM).

The New Digital Divide For Digital BioMarkers

As smartphone and sensors continue to become more ubiquitous across the world, digital biomarkers have emerged as a scalable and practical tool to explore disease states and advance health. But as the digital divide of access and ownership begins to fade, a new digital divide is emerging. Who are the types of people that own smartphones or smart watches, who are the types of people that download health apps or partake in digital biomarker studies, and who are the types of people that are actually active with digital biomarkers apps and sensors - the people providing the high quality and longitudinal data that this field is being founded upon? Understanding the people behind digital biomarkers, the very people this emerging field aims to help, may actually be the real challenge as well as opportunity for digital biomarkers.

What might get in the way: Barriers to the use of apps for depression

Objective

Smartphones are being used with increasing frequency to deliver behavioral interventions for depression via apps. However, barriers specific to using an app for depression are poorly defined. The purpose of the current study is to identify barriers to the use of a mobile app to deliver treatment for depression. Secondarily, design implications will be provided based upon identified barriers.

Method

A card sorting task that ranked and grouped barriers to the use of apps for depression was completed. Participants first completed a card sorting task identifying barriers to face-to-face treatment, as a primer to identification of treatment barriers. The sample consisted of those above (n = 9) and below (n = 11) the threshold for a referral to psychotherapy, to capture anticipated barriers for likely end users. Cluster analyses were conducted to analyze the card sorting data. Multiple analyses were conducted to identify: 1) the most important barriers, and 2) how consistently barriers were ranked as important.

Result

The card sorting task identified a number of primary barriers to the use of apps for depression treatment, including concerns over intervention efficacy, app functioning, privacy, cost, and lack of guidance and tailored feedback. The top face-to-face treatment barrier was cost, overlapping with mobile barriers.

Conclusion

This study identified perceived barriers to the use of mobile treatment apps. Identification of barriers implicates design recommendations for apps for depression.

There is an app for that! The current state of mobile applications (apps) for DSM-5 obsessive-compulsive disorder, posttraumatic stress disorder, anxiety and mood disorders

Mental health apps are viewed as a promising modality to extend the reach of mental health care beyond the clinic. They do so by providing a means of assessment, tracking, and treatment through a smartphone. Given that nearly 2/3 of the American population owns a smartphone, mental health apps offer the possibility of overcoming treatment barriers such as geographic location or financial barriers. Unfortunately, the excitement surrounding mental health apps may be premature as the current supporting literature regarding their efficacy is limited. The app marketplace is littered with apps claiming to treat or assess symptoms, but even those created by reputable organizations or those incorporating components of evidence-based treatments have not yet been validated in terms of their efficacy. This review aims to provide a comprehensive review of the current state of the mental health app literature by examining published reports of apps designed for DSM-5 anxiety and mood disorders, OCD, and PTSD. The breadth of apps reviewed includes those oriented around assessment, symptom tracking, and treatment as well as "multipurpose" apps, which incorporate several of these components. This review will also present some of the most popular mental health apps which may have clinical utility and could be prescribed to clients. While we discuss many potential benefits of mental health apps, we focus on a number of issues that the current state of the app literature presents. Overall there is a significant disconnect between app developers, the scientific community and health care, leaving the utility of existing apps questionable.

Anxiety: There is an app for that. A systematic review of anxiety apps

BACKGROUND

Smartphones and mobile devices have become ubiquitous, and with the rapid advance of technology, the number of health applications (apps) that are available for consumers on these devices is constantly growing. In particular, there has been a recent proliferation of anxiety apps. However, there has been no review of the quality or content of these anxiety apps and little is known about their purpose, the features they contain, and their empirical support. The goal of this systematic review was to assess the commercially available anxiety apps.

METHODS

A list of anxiety apps was collected in January 2017, using the Power Search function of iTunes and Google Play. Of 5,078 identified apps, 52 met our inclusion criteria (i.e., being defined as an anxiety/worry relief app, and offering psychological techniques aimed primarily at reducing anxiety) and were further reviewed.

RESULTS

The majority (67.3%) of the currently available anxiety apps were found to lack the involvement of health care professionals in their development, and very few (3.8%) of them have been rigorously tested.

CONCLUSIONS

At the moment, although anxiety apps have the potential to enhance access to mental health care, there is a marked discrepancy between the wealth of commercially available apps, and the paucity of data regarding their efficacy and effectiveness. Although the great promise of apps is their ability to increasing access to evidence-based mental health, the field is not quite there yet and the full potential of apps for treating anxiety has yet to be exploited.

Ecological momentary interventions for depression and anxiety

Ecological momentary interventions (EMIs) are becoming more popular and more powerful resources for the treatment and prevention of depression and anxiety due to advances in technological capacity and analytic sophistication. Previous work has demonstrated that EMIs can be effective at reducing symptoms of depression and anxiety as well as related outcomes of stress and at increasing positive psychological functioning. In this review, we highlight the differences between EMIs and other forms of treatment due to the nature of EMIs to be deeply integrated into the fabric of people's day-to-day lives. EMIs require unique considerations in their design, deployment, and evaluation. Furthermore, given that EMIs have been advanced by changes in technologies and that the use of behavioral intervention technologies for mental health has been increasing, we discuss how technologies and analytics might usher in a new era of EMIs. Future EMIs might reduce user burden and increase intervention personalization and sophistication by leveraging digital sensors and advances in natural language processing and machine learning. Thus, although current EMIs are effective, the EMIs of the future might be more engaging, responsive, and adaptable to different people and different contexts.

Moodivate: A self-help behavioral activation mobile app for utilization in primary care-Development and clinical considerations

Depressive symptoms are highly prevalent and are associated with considerable functional impairment, significant public health costs, and heightened mortality risk. Individuals experiencing impairment due to depressive symptomatology are most likely to report their symptoms to a primary care provider. As such, national guidelines highlight the need to assess and effectively treat depression via primary care. Despite these guidelines, the dissemination of evidence-based psychotherapy via primary care is limited, likely due to both provider- and patient-level treatment barriers. Mobile health (mHealth) technologies are promising for addressing these barriers and for promoting uptake of evidence-based depression treatment. Among evidence-based psychotherapies for depression, brief Behavioral Activation Treatment for Depression (BATD) has shown great promise and is particularly amenable to mHealth delivery. Herein, we discuss the development of a BATD mobile application, Moodivate, that was developed in order to disseminate BATD via primary care. This paper focuses on description of (1) rationale for Moodivate treatment development, (2) Moodivate treatment components, (3) ongoing clinical trial evaluation of Moodivate, and (4) clinical considerations for incorporating Moodivate into clinical practice.