Designing m-Health interventions for precision mental health support

Bidargaddi, N.; Schrader, G.; Klasnja, P.; Licinio, J.; Murphy, S.

doi:10.1038/s41398-020-00895-2

Cited by 92Digital Mental Health InterventionsMobile Health and mHealth ApplicationsMental Health Research Topics

Designing m-Health interventions for precision mental health support

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Bidargaddi, N., Schrader, G., Klasnja, P., Licinio, J., Murphy, S.·2020·Translational Psychiatry

DOI 10.1038/s41398-020-00895-2PMC PMC7341865

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Abstract

Mobile health (m-Health) resources are emerging as a significant tool to overcome mental health support access barriers due to their ability to rapidly reach and provide support to individuals in need of mental health support. m-Health provides an approach to adapt and initiate mental health support at precise moments, when they are most likely to be effective for the individual. However, poor adoption of mental health apps in the real world suggests that new approaches to optimising the quality of m-Health interventions are critically needed in order to realise the potential translational benefits for mental health support. The micro-randomised trial is an experimental approach for optimising and adapting m-Health resources. This trial design provides data to construct and optimise m-Health interventions. The data can be used to inform when and what type of m-Health interventions should be initiated, and thus serve to integrate interventions into daily routines with precision. Here, we illustrate this approach in a case study, review implementation issues that need to be considered while conducting an MRT, and provide a checklist for mental health m-Health intervention developers.

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A microrandomized trial can evaluate how different intervention components affect an individual in real time and assess how this effect varies over time.
“An MRT can evaluate how different intervention components affect an individual in real time (ie, directly after they have used it), and it assesses how this effect varies over time”
From Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial (2023) · cites this paper as [30]
The Micro-randomised trial Overview says MRTs compare intervention options using outcomes measured after each randomised decision, and the Case study says they can examine how those effects relate to time. But the Overview describes the primary estimate as an average effect, not an effect established for a particular individual. The paper also calls for outcomes to be measured within an appropriate near-term period, rather than necessarily directly after use. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Mobile technology enables observation of the immediate context through experience sampling and sensors and offers the possibility to recognize an individual's needs and provide relevant support by using recommendation systems.
“Mobile technology enables observation of the immediate context (eg, emotional state, location, and heart rate) through experience sampling and sensors and offers the possibility to recognize an individual’s needs and provide relevant support by using recommendation systems [30]”
From Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial (2023) · cites this paper as [30]

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[23]Bidargaddi, N. et al. JMU-to prompt or not to prompt? A microrandomized trial of time-varying push notifications to increase proximal engagement with a mobile health app. JMIR mHealth and uHealth. https://mhealth.jmir.org/2018/11/e10123/ (2018). PMC6293241 30497999

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Authors: Bidargaddi, N., Schrader, G., Klasnja, P., Licinio, J., & Murphy, S.
Affiliations: Flinders University (AU)
University of Michigan (US)
SUNY Upstate Medical University (US)
Harvard University (US)
Corresponding author: Bidargaddi, N.
ORCID: Bidargaddi, N.
Schrader, G.
Klasnja, P.
Licinio, J.
Murphy, S.
Venue: Translational Psychiatry
Publisher: Springer Science and Business Media LLC
Published: 2020-07-07
Volume: 10
Issue: 1
Pages: 222-222
ISSN: 2158-3188 (ISSN-L)
DOI: 10.1038/s41398-020-00895-2
PMC: PMC7341865
OpenAlex: W3038633450
Cited by: 92
References: 28
Citations by year: 2026: 11 · 2025: 16 · 2024: 21 · 2023: 13 · 2022: 18 · 2021: 11 · 2020: 2
FWCI: 1.73
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Translational Psychiatry (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Funders: National Institute on Alcohol Abuse and Alcoholism (R01AA023187)
National Institute on Drug Abuse (P50DA039838)
Topics: Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Keywords: mobile health, precision mental health, intervention optimization, implementation issues, translational research
MeSH terms: Humans, Mental Health, Telemedicine

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