Trends in Persuasive Technologies for Physical Activity and Sedentary Behavior: A Systematic Review
Bibliographic record
Abstract
Persuasive technology (PT) is increasingly being used in the health and wellness domain to motivate and assist users with different lifestyles and behavioral health issues to change their attitudes and/or behaviors. There is growing evidence that PT can be effective at promoting behaviors in many health and wellness domains, including promoting physical activity (PA), healthy eating, and reducing sedentary behavior (SB). SB has been shown to pose a risk to overall health. Thus, reducing SB and increasing PA have been the focus of much PT work. This paper aims to provide a systematic review of PTs for promoting PA and reducing SB. Specifically, we answer some fundamental questions regarding its design and effectiveness based on an empirical review of the literature on PTs for promoting PA and discouraging SB, from 2003 to 2019 (170 papers). There are three main objectives: (1) to evaluate the effectiveness of PT in promoting PA and reducing SB; (2) to summarize and highlight trends in the outcomes such as system design, research methods, persuasive strategies employed and their implementaions, behavioral theories, and employed technological platforms; (3) to reveal the pitfalls and gaps in the present literature that can be leveraged and used to inform future research on designing PT for PA and SB.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".