Mobile Apps for Mental Health

With the widespread use of smartphones, there is an opportunity for clinicians and researchers to incorporate this technology into mental health treatment. Numerous mental health applications are available for download, but there is a limited research base to support most apps. Also, many apps are self-report and are not focused on providing and/or facilitating treatment.

Here we review 2 apps currently in development. Both use innovative technologies embedded within smartphones to capture objective data on patients to provide timely treatment and relapse prevention.

Mobilyze

Mobilyze, an app developed by researchers at Northwestern University, is designed as a multimodal treatment for depression. It uses built-in sensors, such as GPS, ambient light, and recently made calls, in an attempt to predict the patient’s moods, emotions, and activities. It has been subject to an initial 8-person pilot study: results indicated that participants were satisfied with the app and both depressive and anxiety symptoms improved.¹

A machine learning approach is used: users are prompted via messages to describe their locations, activity, social context, and emotional state. The app learns to associate real-world data with those obtained via the sensors. Supplemental messages on the phone, a Web-based behavioral activation intervention, and telephone coaching were also provided as part of the initial study and contributed to the overall symptom reduction.¹ Larger-scale research is being conducted at Northwestern University.

The design of the app shows promise in using passive data collection to treat a potentially debilitating psychiatric condition. Persons with depression often evidence changes in behavior, including sleeping more (or less), moving slowly, interacting less with the world, and losing interest in activities they usually enjoy. A smartphone app is well-positioned to monitor changes in behavior and can provide real-world feedback to the individual.²

CrossCheck

CrossCheck is another app currently in an NIMH-funded clinical trial that uses a range of features similar to those of Mobilyze. Unlike Mobilyze, which focuses on depression, CrossCheck’s emphasis is on re-lapse prevention in patients with schizophrenia. CrossCheck uses a combination of observational and self-report data to determine the participant’s “relapse signature.” Individualization of a relapse signature is particularly important in schizophrenia, given the heterogeneity of presentations involving positive, negative, or disorganized symptoms.

Observational data collected from sensors can model sleep patterns (eg, using light sensors, screen lock time), frequency and duration of conversations (using the microphone), traveling range (using GPS data), and movement or sedentary behaviors (from accelerometer/screen orientation data). Self-report data are collected 1 to 3 times weekly to assess symptoms, quality of life, and functioning.

Self-report responses serve as another data point in predicting a relapse signature. Marked, perhaps simultaneous, changes in the objective and subjective variables may occur before and during a relapse. Thus, active measurement of these variables can catch a relapse early. Once relapse warnings have been triggered, contact is made with both the patient and the treatment team.

There are several concerns related to the real-life practicality of the CrossCheck app. First, in a study of a self-management app for symptoms of schizophrenia also created by the developer of CrossCheck, expert mental health practitioners expressed concern that individuals with schizophrenia may sell a smartphone provided to them for treatment purposes.³ However, this was not found to be a concern of other researchers.⁴ Second, issues related to privacy and confidentiality need to be thoughtfully addressed in the context of HIPAA. Third, many individuals with schizophrenia have paranoia, and one can imagine that being constantly and remotely monitored may negatively affect some patients. Initial investigations by the developers found no relationship between baseline paranoia and willingness to use a smartphone for a behavioral intervention.⁴ Fourth, one compelling potential clinical application for this app would be for inclusion as part of a community treatment team for individuals with severe mental illness; however, there could very likely be civil rights concerns related to constant or even mandated remote monitoring of patients. Some of these issues will undoubtedly be addressed in feasibility studies and field trials.

The future of mobile health technology

In general, mobile apps have distinct limitations. In an e-mail correspondence, Northwestern University Professor (and director of the Center for Behavior Intervention Technology) David Mohr, PhD, wrote:

Regarding limitations, the biggest is that the vast majority of health apps that are downloaded are never used, and those that are used, are usually only used once or twice. For the few people who do use them, they are typically already engaged in the behavior. For example, someone who runs may download an app that tracks running. But that person would probably not stop running if he or she did not have the app. The people who need to start exercising aren’t usually the ones using the apps.

One [strategy to improve usability] is to embed these apps in care systems. Placing some human presence in the system can greatly increase people’s adherence. It might be data going back to a physician or coach, or learning how to optimize data flow and communications within a peer network. The other is to improve the usability-one idea being the use of sensor data.

Although Mobilyze and CrossCheck are still in the early stages of development, they are representative of the future integration of mobile health technologies in monitoring, treatment, and relapse prevention. There may be considerable generalizability beyond depression and schizophrenia-to similarly complex and often treatment-resistant disorders, such as addiction and acquired brain injury, or to chronic health conditions with episodic declines in function, such as diabetes mellitus. There is a limited research base at present, but there is significant potential for mobile technologies to serve as an adjunct to routine clinical practice for complex and treatment-resistant disorders.

Disclosures:

Dr Brooks is a Voluntary Assistant Professor of Research in the department of physical medicine and rehabilitation at the Miller School of Medicine at the University of Miami; and he is a clinical nueropsychologist in private practice in Miami and Hollywood, Fla. He specializes in the assessment and cognitive rehabilitation of individuals with neurological disorders. Dr Brooks’s Web site is larrybrookssphd.com. Dr Schirmer is a clinical and forensic psychologist at Napa State Hospital in Napa, Calif; he is Adjunct Professor at Saybrook University in Oakland, Calif. The authors report no conflicts of interest concerning the subject matter of this article.

References:

1. Burns MN, Begale M, Duffecy J, et al. Harnessing context sensing to develop a mobile intervention for depression. J Med Internet Res. 2011;13:e55.

2. Mohr DC, Burns MN, Schueller SM, et al. Behavioral intervention technologies: evidence review and recommendations for future research in mental health. Gen Hosp Psychiatry. 2013;35:332-338.

3. Ben-Zeev D, Kaiser SM, Brenner CJ, et al. Development and usability testing of FOCUS: a smartphone system for self-management of schizophrenia. Psychiatr Rehabil J. 2013;36:289-296.

4. Ben-Zeev D, Brenner CJ, Begale M, et al. Feasibility, acceptability, and preliminary efficacy of a smartphone intervention for schizophrenia. Schizophr Bull. 2014;40:1244-1253.