A Mobile App Adopting an Identity Focus to Promote Physical Activity (MoveDaily): Iterative Design Study
Bibliographic record
Abstract
BACKGROUND: Web-based and mobile interventions to influence physical activity behavior have had limited effects on sustained behavior change. One reason may be that the interventions aim to change largely habitual behavior. Following an identity-oriented approach could be a successful strategy to behavior change because people are committed to behave in line with their self-perception of identity. OBJECTIVE: In this paper, we take a closer look at the role of motivation in long-term adherence to lifestyle interventions. The paper outlines a method for web-based or mobile intervention development that allows exploration of integrating behavior change theory into the design process. We will describe the development of a mobile app that allows people to be self-determined and to value and self-regulate physical activity by adopting an identity-oriented approach. METHODS: This paper describes a Research through Design (RtD) process in which design activities are carried out as part of the knowledge-generating process. Two RtD phases were completed, followed by a conceptual design phase. In the first RtD phase, 8 participants used diary cards to study initial attitudes toward starting with small changes in physical activity. In the second RtD phase, 26 participants used a web-based app to study changes in physical activity. We used an adapted version of the Self-Report Habit Index (SRHI) to evaluate individuals' perceptions of a particular behavior with respect to the three facets of a habit. The conceptual design phase consolidated the results from first two RtD phases into a design of a mobile app that combines an identity approach with gamification principles. The conceptual design was evaluated in a user-experience study with 4 participants. RESULTS: In the first RtD phase, we found that interacting daily with diary cards and reflecting on physical activity patterns is a promising strategy but works better through a digital medium. In the second RtD phase, SHRI ratings from all participants generally increased each week. In the conceptual design phase, we found that the concept of the mobile app was positively evaluated by participants. However, participants mentioned that terms such as "identity" do not resonate with them and that scenarios could be simpler. CONCLUSIONS: This paper provides deeper insights into designing for electronic health (eHealth) interventions and services and suggests a new way that motivation can be shaped by the design of an intervention and adherence to physical activity. To the best of our knowledge, this was the first iterative design study in which the effects of adopting an identity approach to both motivation and physical activity were included and observed. Initial promising results were found for using a web-based intervention where habits and identification with the personal importance of a behavior were repetitively triggered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".