JMIR Publications

ABSTRACT

Background:

Mobile health (mHealth) interventions can deliver personalized behavioral support to users in their daily context. These interventions have been increasingly adopted to support individuals who need low-cost and low-burden support. Prior research has demonstrated the feasibility and acceptability of a mHealth intervention app (CareQOL) designed for use in informal care partners. To further optimize the intervention delivery, we need to investigate how care partners, many who lack the time for self-care, react and act in response to different behavioral suggestions.

Objective:

The goal of this study is to understand factors that impact care partners’ decision-making and actions in response to different behavioral suggestions. Insights from this study will help optimize future, more tailored and personalized behavioral interventions.

Methods:

We conducted semi-structured interviews with participants that recently completed a three-month randomized controlled feasibility trial of the CareQOL mHealth intervention app. 23 out of 36 participants from the RCT treatment group took part in these interviews. To prepare for each interview, the team first selected representative behavioral suggestions (e.g., targeting different health aspects) and presented them to participants during the interview to probe their influences on participants’ thoughts and actions. The time of delivery, self-reported perceptions of the day, and user ratings of a suggestion were presented to participants during the interviews to assist recall.

Results:

Interview data showed that after receiving a suggestion, participants took various actions in response to different suggestions. Participants performed suggested behaviors or adjusted them either immediately or in a delayed manner (e.g., sometimes up to a month later). We identified four factors that shape the variations in user actions in response to different behavioral suggestions: (1) uncertainties about the workload required for performing suggested behaviors, (2) concerns about one’s ability to routinize suggested behaviors; (3) in-the-moment willingness and ability to plan for suggested behaviors; (4) overall capability for engaging with the intervention.

Conclusions:

Our study shows that care partners utilize mHealth behavioral suggestions differently regarding the immediacy of actions and the adaptation to suggested behaviors. Multiple factors influence people’s perceptions and decisions on when and how to take actions. Future systems should consider these factors to tailor behavioral support for individuals and design system features to support delay or adaptation of suggested behaviors. Findings also suggest extending the assessment of user adherence by considering the variations in user actions on behavioral support (i.e., performing suggested or adjusted behaviors immediately or in a delayed manner). Clinical Trial: ClinicalTrials.gov NCT04556591; https://clinicaltrials.gov/ct2/show/NCT04556591