Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics

Kamath, Jayesh; Bi, Jinbo; Russell, Alexander; Wang, Bin

doi:10.20900/jpbs.20200010

Cited by 3 publications

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Characteristics of Mobile Health Platforms for Depression and Anxiety: Content Analysis Through a Systematic Review of the Literature and Systematic Search of Two App Stores

Leong¹,

Sridhar²,

Blasiak³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

Background Mobile health (mHealth) platforms show promise in the management of mental health conditions such as anxiety and depression. This has resulted in an abundance of mHealth platforms available for research or commercial use. Objective The objective of this review is to characterize the current state of mHealth platforms designed for anxiety or depression that are available for research, commercial use, or both. Methods A systematic review was conducted using a two-pronged approach: searching relevant literature with prespecified search terms to identify platforms in published research and simultaneously searching 2 major app stores—Google Play Store and Apple App Store—to identify commercially available platforms. Key characteristics of the mHealth platforms were synthesized, such as platform name, targeted condition, targeted group, purpose, technology type, intervention type, commercial availability, and regulatory information. Results The literature and app store searches yielded 169 and 179 mHealth platforms, respectively. Most platforms developed for research purposes were designed for depression (116/169, 68.6%), whereas the app store search reported a higher number of platforms developed for anxiety (Android: 58/179, 32.4%; iOS: 27/179, 15.1%). The most common purpose of platforms in both searches was treatment (literature search: 122/169, 72.2%; app store search: 129/179, 72.1%). With regard to the types of intervention, cognitive behavioral therapy and referral to care or counseling emerged as the most popular options offered by the platforms identified in the literature and app store searches, respectively. Most platforms from both searches did not have a specific target age group. In addition, most platforms found in app stores lacked clinical and real-world evidence, and a small number of platforms found in the published research were available commercially. Conclusions A considerable number of mHealth platforms designed for anxiety or depression are available for research, commercial use, or both. The characteristics of these mHealth platforms greatly vary. Future efforts should focus on assessing the quality—utility, safety, and effectiveness—of the existing platforms and providing developers, from both commercial and research sectors, a reporting guideline for their platform description and a regulatory framework to facilitate the development, validation, and deployment of effective mHealth platforms.

show abstract

Characteristics of Mobile Health Platforms for Depression and Anxiety: Content Analysis Through a Systematic Review of the Literature and Systematic Search of Two App Stores

Leong¹,

Sridhar²,

Blasiak³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

Characteristics of Mobile Health Platforms for Depression and Anxiety: Content Analysis Through a Systematic Review of the Literature and Systematic Search of Two App Stores (Preprint)

Leong¹,

Sridhar²,

Blasiak³

et al. 2021

Preprint

View full text Add to dashboard Cite

BACKGROUND Mobile health (mHealth) platforms show promise in the management of mental health conditions such as anxiety and depression. This has resulted in an abundance of mHealth platforms available for research or commercial use. OBJECTIVE This study aimed to characterize the current state of mHealth platforms designed for anxiety and/or depression that are available for research, commercial use or both. METHODS A systematic review was conducted using a two-pronged approach. (i) A systematic literature search of PubMed, EMBASE, CINAHL and PsycINFO was conducted to identify platforms available for research purposes. (ii) A simultaneous search of two major mobile app stores – Apple App Store and Google Play Store – to identify commercially available platforms. Key characteristics of the mHealth platforms such as platform name, targeted condition, targeted group, purpose, technology type, intervention type, commercial availability, regulatory information were synthesized. RESULTS The literature and app stores searches yielded 169 and 179 mHealth platforms respectively. Most platforms developed for research purposes were designed for depression (n=113) whereas the app stores search reported a higher number of platforms were developed for anxiety (n=58 and n=27 for Android and iOS operating systems, respectively). The most common purpose of platforms in both searches was treatment (n=122 and n=129 for the literature and app stores searches, respectively). In regard to the types of intervention, cognitive behavioral therapy and referral to care/counselling emerged as the most popular options offered by the platforms identified in the literature and app stores searches, respectively. Most platforms from both searches did not have a specific target age group. Additionally, most platforms found in the app stores lacked clinical and real-world evidence, while only small number of platforms found in published research were available commercially. CONCLUSIONS A considerable number of mHealth platforms designed for anxiety and/or depression are available for research, commercial use or both. Characteristics of these mHealth platforms vary greatly. Future effort should focus on accessing the quality – utility, safety and effectiveness – of the existing platforms and providing developers, from both commercial and research sectors alike, a reporting guideline for their platform description as well as a regulatory framework to facilitate the development, validation and deployment of effective mHealth platforms. CLINICALTRIAL CRD42020193956

show abstract

Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives

Kamath¹,

León-Barriera²,

Jain³

et al. 2022

WJP

View full text Add to dashboard Cite

Depression is a serious medical condition and is a leading cause of disability worldwide. Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations, lack of objective assessments, and assessments that rely on patients' perceptions, memory, and recall. Digital phenotyping (DP), especially assessments conducted using mobile health technologies, has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes. DP includes two primary sources of digital data generated using ecological momentary assessments (EMA), assessments conducted in real-time, in subjects' natural environment. This includes active EMA, data that require active input by the subject, and passive EMA or passive sensing, data passively and automatically collected from subjects' personal digital devices. The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients' clinical status. Preliminary investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression status. These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients. Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines. The current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations. A clinically-relevant model for incorporating DP in clinical setting is presented. This model, based on investigations conducted by our group, delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making process. Benefits, challenges, and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.

show abstract

Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics

Cited by 3 publications

References 43 publications

Characteristics of Mobile Health Platforms for Depression and Anxiety: Content Analysis Through a Systematic Review of the Literature and Systematic Search of Two App Stores

Characteristics of Mobile Health Platforms for Depression and Anxiety: Content Analysis Through a Systematic Review of the Literature and Systematic Search of Two App Stores

Characteristics of Mobile Health Platforms for Depression and Anxiety: Content Analysis Through a Systematic Review of the Literature and Systematic Search of Two App Stores (Preprint)

Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives

Contact Info

Product

Resources

About