Validity and characteristics of patient-evaluated adherence to medication via smartphones in patients with bipolar disorder: exploratory reanalyses on pooled data from the MONARCA I and II trials

Updated strategies for the management of poor medication adherence in patients with bipolar disorder

INTRODUCTION

Suboptimal adherence is a well-established, pervasive problem in individuals with bipolar disorder (BD) causing disability, suffering, and cost.

AREAS COVERED

This review covers new research since January 2016 regarding internal (patient-centered) and external (system level) barriers and facilitators to adherence. Measures of adherence, the efficacy of psychosocial adherence enhancement interventions in individuals with BD, and, finally, novel delivery systems for BD medication are also covered. Measures of adherence continue to fall broadly into objective measures (i.e. drug levels) and more subjective, self-report measures and a combination of these likely provides the most comprehensive picture. Efficacious components of psychosocial adherence enhancement interventions include psychoeducation, motivational interviewing, and cognitive behavioral strategies, yet methods for delivery vary. Long-acting injectable (LAI) medications for BD are the drug delivery system with the most promise for BD. Combining psychosocial components with novel drug delivery systems has the potential for establishing and maintaining medication adherence.

EXPERT OPINION

Psychosocial interventions improve adherence in individuals with BD. Psychoeducation is a necessary but not sufficient component in psychosocial interventions. LAIs should be considered earlier for adherence improvement than many treatment guidelines currently suggest. Comparative studies are lacking as is research into novel systems of medication delivery.

Effectiveness of mobile applications for patients with severe mental illness: A meta-analysis of randomized controlled trials

BACKGROUND

A systematic review and meta-analysis was conducted to evaluate the effectiveness of mobile applications used by patients diagnosed with mental disorders.

METHODS

An electronic literature search in five databases including PubMed, Embase, the Cochrane Library, CINAHL, and PsychInfo was conducted. The keywords used were "mental disorder," "mental illness," "mobile phone," "smartphone," "mHealth," "application," and "app". The search was restricted to randomized controlled trials (RCTs) written in English and Korean.

RESULTS

Fourteen RCTs, involving 1307 patients diagnosed with depression, schizophrenia, and bipolar disorder were included in the analysis. The included studies were published between 2012 and 2020 and used mobile applications. The risk of bias tool was used to assess methodological quality and the overall risk of bias of the included studies was moderate. The pooled data favored mobile application interventions in reducing the disease-related symptoms of depression (standardized mean difference [SMD] = -0.255, 95% CI: -0.370 to -0.141), mania symptoms (SMD = -0.279, 95% CI: -0.456 to -0.102), and positive (SMD = -0.205, 95% CI: -0.388 to -0.022) and negative psychotic symptoms (SMD = -0.406, 95% CI: -0.791 to -0.020). In subgroup analysis, the incorporation of feedback, notification, and data tracking features in the mobile application intervention produced better outcomes.

CONCLUSION

This review provided evidence that mobile applications could well-assist patients diagnosed with mental disorders. Greater benefits could be achieved by well-designed interventions incorporating strategies with thoughtful consideration of the disease characteristics. Mobile applications present the potential to be effective supplements to clinical treatment.

Digital Health Around Clinical High Risk and First-Episode Psychosis

PURPOSE OF REVIEW

This review aims to examine relapse definitions and risk factors in psychosis as well as the role of technology in relapse predictions and risk modeling.

RECENT FINDINGS

There is currently no standard definition for relapse. Therefore, there is a need for data models that can account for the variety of factors involved in defining relapse. Smartphones have the ability to capture real-time, moment-to-moment assessment symptomology and behaviors via their variety of sensors and have high potential to be used to create prediction and risk modeling. While there is still a need for further research on how technology can predict and model relapse, there are simple ways to begin incorporating technology for relapse prediction in clinical care.

Longitudinal trends in the quality, effectiveness and attributes of highly rated smartphone health apps

Background

While there are numerous mental health apps on the market today, less is known about their safety and quality. This study aims to offer a longitudinal perspective on the nature of high visibility apps for common mental health and physical health conditions.

Methods

In July 2019, we selected the 10 top search-returned apps in the Apple App Store and Android Google Play Store using six keyword terms: depression, anxiety, schizophrenia, addiction, high blood pressure and diabetes. Each app was downloaded by two authors and reviewed by a clinician, and the app was coded for features, functionality, claims, app store properties, and other properties.

Results

Compared with 1 year prior, there were few statistically significant changes in app privacy policies, evidence and features. However, there was a high rate of turnover with only 34 (57%) of the apps from the Apple’s App Store and 28 (47%) from the Google Play Store remaining in the 2019 top 10 search compared with the 2018 search.

Discussion

Although there was a high turnover of top search-returned apps between 2018 and 2019, we found that there were few significant changes in features, privacy, medical claims and other properties. This suggests that, although the highly visible and available apps are changing, there were no significant improvements in app quality or safety.