JMIR Publications

ABSTRACT

Background:

Mobile health (mHealth) applications hold great promise as vehicles for delivering high-reach, scalable health behavior change interventions, given the ubiquity of smartphones. However, to improve uptake and sustain consumer engagement, mHealth interventions need to be responsive to individuals’ needs and preferences, which may change over time. Currently, user centered approaches to mHealth treatment Currently, user centered approaches to mHealth treatment development rely on preferences data collected at the end of the intervention and outside the mobile context via individual surveys or interviews, focusing almost exclusively on in-person and group-based methods, leaving untapped potential to assess users’ dynamic needs, preferences, and goals via mHealth tools.

Objective:

We created the Ecological Daily Needs Assessment (EDNA), a mobile needs assessment tool employed during an mHealth intervention to determine individualized, contextually relevant user needs and preferences. We provide a use example of EDNA in the development of an app-based healthy lifestyle intervention for young adults with psychiatric conditions – a population with significant barriers to treatment engagement.

Methods:

We created a fully remote lifestyle application called HealthyBodies HealthyMinds, which included an automated needs assessment employing ecological momentary assessment (EMA) to study microprocesses influencing needs and preferences for treatment development purposes. Participants could download the app via the study website or from links on social media, and could consent to study participation through their device. Individual user needs were collected daily during participation in a basic behavioral weight loss framework that included daily goal setting and self-monitoring. Participants were prompted up to 6 times daily – when they were most likely to eat or exercise – to determine in-the-moment needs and preferences for app-assisted health behavior change.

Results:

Twenty-four participants engaged in the health-intention setting prompts (22 female; 2 male). Twenty-three participants responded to at least one needs assessment prompt. The mean length of participation in the study was 5.6 days (SD 4.7), with mean of 2.8 (1.1) responses per day. The earliest time to termination was 1 day; 2 individuals completed the entire 2 weeks. Specific feedback included preferences for automating self-monitoring, a known predictor of success in weight loss treatment. Users also expressed desire to personalize message content and automate messaging timing based on use patterns. Moreover, there was evidence for individually dynamic needs and preferences, as participants not only reported different needs for help with weight loss but also each participant reported different needs over time, with no two users having the same trajectory of needs.

Conclusions:

The present study demonstrates the feasibility of collecting ecological, frequently-sampled patient feedback via smartphones in the course of mHealth treatment development and early testing. The EDNA technique provides a in idiographic, individually dynamic and contextually relevant alternative and complement to the traditional needs assessment format for assessing individually dynamic user needs and preferences during treatment development or adaptation.