Implementing a Sedentary Behavior Change Smartphone App in Cardiac Rehabilitation
Bibliographic record
Abstract
BACKGROUND: Smartphone apps used in research offer a variety of capabilities to track and influence behavior; however, they often do not translate well into real-world use. Implementation strategies for using apps to reduce sedentary behavior in cardiac rehabilitation are currently unknown. OBJECTIVE: The aim of this study was to explore (1) barriers and enablers for use of a behavioral smartphone app (Vire and ToDo-CR program) for reducing sedentary behavior in cardiac rehabilitation participants and (2) implementation strategies for future smartphone apps aimed at reducing sedentary behavior in this population. METHODS: In-depth semistructured interviews were conducted with cardiac rehabilitation participants in the ToDo-CR randomized controlled trial. Participants had used the Vire app and a wearable activity tracker for 6 months. Interviews were audio recorded and transcribed. The researchers used thematic analysis and deductive mapping of themes to the Theoretical Domains Framework and the Capability, Opportunity, and Motivation-Behavior model. Sociodemographic and clinical variables were recorded. RESULTS: Fifteen participants aged 59 ± 14 years were interviewed. Most were male, tertiary educated, and employed, and had varying experiences with smartphone apps and wearable activity trackers. Five core themes explaining the user experiences of cardiac rehabilitation participants with the Vire app were identified: (1) being tech savvy can be both an enabler and a barrier, (2) app messaging needs to be clear-set expectations from the beginning, (3) get to know me-personalization is important, (4) curious to know more instant feedback, and (5) first impression is key. The themes and subthemes mapped to 12 of the 14 Theoretical Domains Framework domains. Improving engagement and implementation of future smartphone apps for sedentary behavior may be aided by building psychological capability, physical opportunity, and reflective motivation. CONCLUSIONS: Shifting to in-the-moment behavioral nudges, setting clear expectations, assisting participants to monitor their sitting time, increasing the frequency of tailoring, and understanding more about the participant as well as their experiences and needs for reducing sedentary behavior in cardiac rehabilitation are important future directions.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".