Exploring Users’ Experiences of the Uptake and Adoption of Physical Activity Apps: Longitudinal Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Although smartphone apps might support physical activity (PA), engagement with them tends to be low. OBJECTIVE: This study aimed to examine potential users' needs and preferences regarding their engagement with PA apps during a first exposure to a never-used PA app and after 2 weeks' usage. METHODS: A longitudinal, one-arm qualitative study was conducted with potential PA app users. At baseline, participants (N=20) were asked to explore 1 of 3 randomly allocated PA apps while thinking aloud. Semistructured interview techniques allowed participants to elaborate on their statements. After 2 weeks, follow-up interviews explored participants' (n=17) lived experiences of real-world app use. Verbal reports from both time points were analyzed using inductive thematic analysis. RESULTS: Features that promote a fair and simple user experience, support users' self-regulation skills, and address users' exercise motives were considered important for engagement both during a first exposure and after a 2-week use of PA apps. Features that support users' need for relatedness as well as those that facilitate users to implement their intentions were expected to be important for engagement mainly during a first exposure to PA apps. Proactive and tailored features that integrate behavioral, psychological, and contextual information to provide adaptive exercise plans and just-in-time support were considered relevant to sustain engagement over time. CONCLUSIONS: App features that address users' exercise motives, promote self-regulation, and fulfill users' need for relatedness might promote engagement with PA apps. Tailored and proactive features were expected to promote sustained engagement.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".