Using Online Computer Tailoring to Promote Physical Activity: A Randomized Trial of Text, Video, and Combined Intervention Delivery Modes
Bibliographic record
Abstract
Website-delivered interventions are increasingly used to deliver physical activity interventions, yet problems with engagement and retention result in reduced effectiveness. Hence, alternative modes of online intervention delivery need to be explored. Therefore, this study aimed to evaluate the acceptability and effectiveness of a computer-tailored physical activity intervention delivered on the Internet in 3 delivery modes: video, text, or both. Australian adults (N = 803), recruited through e-mail, were randomized into the three delivery modes and received personal physical activity advice. Intervention content was identical across groups. Repeated measures analyses of variance were used to compare the three groups regarding acceptability, website usability, and physical activity. Participants in the video group accepted the content of the physical activity advice significantly better (F = 5.59; p < .01), and spent significantly more time on the website (F = 21.19; p < .001) compared with the text and combination groups. Total physical activity improved significantly over time in all groups (F = 3.95; p < .01). Although the combination group increased physical activity the most, few significant differences between groups were observed. Providing video-tailored feedback has advantages over the conventional text-tailored interventions; however, this study revealed few behavioral differences. More studies, examining alternative delivery modes, that can overcome the limitations of the present study, are needed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".