Yan X, Newman MW, Park SY, Sander A, Choi SW, Miner J, Wu Z, Carlozzi N. Identifying Design Opportunities for Adaptive mHealth Interventions That Target General Well-Being: Interview Study With Informal Care Partners.
JMIR Form Res 2023;
7:e47813. [PMID:
37874621 PMCID:
PMC10630866 DOI:
10.2196/47813]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 08/25/2023] [Accepted: 09/08/2023] [Indexed: 10/25/2023] Open
Abstract
BACKGROUND
Mobile health (mHealth) interventions can deliver personalized behavioral support to users in daily contexts. These interventions have been increasingly adopted to support individuals who require low-cost and low-burden support. Prior research has demonstrated the feasibility and acceptability of an mHealth intervention app (CareQOL) designed for use with informal care partners. To further optimize the intervention delivery, we need to investigate how care partners, many of whom lack the time for self-care, react and act in response to different behavioral messages.
OBJECTIVE
The goal of this study was to understand the factors that impact care partners' decision-making and actions in response to different behavioral messages. Insights from this study will help optimize future tailored and personalized behavioral interventions.
METHODS
We conducted semistructured interviews with participants who had recently completed a 3-month randomized controlled feasibility trial of the CareQOL mHealth intervention app. Of the 36 participants from the treatment group of the randomized controlled trial, 23 (64%) participated in these interviews. To prepare for each interview, the team first selected representative behavioral messages (eg, targeting different health dimensions) and presented them to participants during the interview to probe their influence on participants' thoughts and actions. The time of delivery, self-reported perceptions of the day, and user ratings of a message were presented to the participants during the interviews to assist with recall.
RESULTS
The interview data showed that after receiving a message, participants took various actions in response to different messages. Participants performed suggested behaviors or adjusted them either immediately or in a delayed manner (eg, sometimes up to a month later). We identified 4 factors that shape the variations in user actions in response to different behavioral messages: uncertainties about the workload required to perform suggested behaviors, concerns about one's ability to routinize suggested behaviors, in-the-moment willingness and ability to plan for suggested behaviors, and overall capability to engage with the intervention.
CONCLUSIONS
Our study showed that care partners use mHealth behavioral messages differently regarding the immediacy of actions and the adaptation to suggested behaviors. Multiple factors influence people's perceptions and decisions regarding when and how to take actions. Future systems should consider these factors to tailor behavioral support for individuals and design system features to support the delay or adaptation of the suggested behaviors. The findings also suggest extending the assessment of user adherence by considering the variations in user actions on behavioral support (ie, performing suggested or adjusted behaviors immediately or in a delayed manner).
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)
RR2-10.2196/32842.
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