Articlegold2022

Clinical Targets and Attitudes Toward Implementing Digital Health Tools for Remote Measurement in Treatment for Depression: Focus Groups With Patients and Clinicians

Valeria de Angel · Serena Lewis · Katie M White · Faith Matcham · Matthew H. Hotopf

15 citations0/0 files storedJMIR Mental Health

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Digital Mental Health Interventions Mobile Health and mHealth Applications Mental Health Research Topics

Abstract

BACKGROUND: Remote measurement technologies, such as smartphones and wearable devices, can improve treatment outcomes for depression through enhanced illness characterization and monitoring. However, little is known about digital outcomes that are clinically meaningful to patients and clinicians. Moreover, if these technologies are to be successfully implemented within treatment, stakeholders' views on the barriers to and facilitators of their implementation in treatment must be considered. OBJECTIVE: This study aims to identify clinically meaningful targets for digital health research in depression and explore attitudes toward their implementation in psychological services. METHODS: A grounded theory approach was used on qualitative data from 3 focus groups of patients with a current diagnosis of depression and clinicians with >6 months of experience with delivering psychotherapy (N=22). RESULTS: Emerging themes on clinical targets fell into the following two main categories: promoters and markers of change. The former are behaviors that participants engage in to promote mental health, and the latter signal a change in mood. These themes were further subdivided into external changes (changes in behavior) or internal changes (changes in thoughts or feelings) and mapped with potential digital sensors. The following six implementation acceptability themes emerged: technology-related factors, information and data management, emotional support, cognitive support, increased self-awareness, and clinical utility. CONCLUSIONS: The promoters versus markers of change differentiation have implications for a causal model of digital phenotyping in depression, which this paper presents. Internal versus external subdivisions are helpful in determining which factors are more susceptible to being measured by using active versus passive methods. The implications for implementation within psychotherapy are discussed with regard to treatment effectiveness, service provision, and patient and clinician experience.

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References

2006Using thematic analysis in psychology 1989Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology 2011Therapeutic Alliance and Outcome of Psychotherapy: Historical Excursus, Measurements, and Prospects for Research 2018Realizing the Mass Public Benefit of Evidence-Based Psychological Therapies: The IAPT Program 2015Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health.2016Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild 2022Digital health tools for the passive monitoring of depression: a systematic review of methods 2019Creating a Digital Health Smartphone App and Digital Phenotyping Platform for Mental Health and Diverse Healthcare Needs: an Interdisciplinary and Collaborative Approach

Cited by

2024Healthcare Professionals' Views on the Use of Passive Sensing and Machine Learning Approaches in Secondary Mental Healthcare: A Qualitative Study 2024Perception of Apps for Mental Health Assessment With Recommendations for Future Design: United Kingdom Semistructured Interview Study 2025Application of a Sociotechnical Framework to Uncover Factors That Influence Effective User Engagement With Digital Mental Health Tools in Clinical Care Contexts: Scoping Review 2025

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