Technology Facilitates Physical Activity Through Gamification: A Thematic Analysis of an 8-Week Study
Bibliographic record
Abstract
Gamification technology has served as behaviour change mechanism for increasing the engagement and motivation of consumers in many areas including health and wellness domains. While research on physical activity (PA) and motivation to participate in PA in the context of older adults exist, there are fewer studies on the usage of gamified technology by older adults over longer periods of time. We conducted a mixed-method, eight-week, synchronous, three-condition experimental study with older adults in the 50+ age group. Participants were randomized into Group 1 (gamified), Group 2 (non-gamified) and a control group. The weekly semi-structured interview questions focused on their motivation for PA, setting up goals, accomplishments, fears or barriers, rewards and tracking in PA. Thematic analysis of the interview data showed distinct variations in emergent themes for the three groups over an eight-week period. This further indicated that gamification elements can be customized to participants for older adults and tailored to suit their current health conditions and prevalent barriers to participate in PA.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".