What Kinds of Website and Mobile Phone–Delivered Physical Activity and Nutrition Interventions Do Middle-Aged Men Want?
Bibliographic record
Abstract
Within a health context, men in Western societies are a hard-to-reach population who experience higher rates of chronic disease compared with women. Innovative technology-based interventions that specifically target men are needed; however, little is known about how these should be developed for this group. This study aimed to examine opinions and perceptions regarding the use of Internet and mobile phones to improve physical activity and nutrition behaviors for middle-aged men. The authors conducted 6 focus groups (n = 30) in Queensland, Australia. Their analyses identified 6 themes: (a) Internet experience, (b) website characteristics, (c) Web 2.0 applications, (d) website features, (e) self-monitoring, and (f) mobile phones as delivery method. The outcomes indicate that men support the use of the Internet to improve and self-monitor physical activity and dietary behaviors on the condition that the website-delivered interventions are quick and easy to use, because commitment levels to engage in online tasks are low. Participants also indicated that they were reluctant to use normal mobile phones to change health behaviors, although smartphones were perceived to be more acceptable. This pilot study suggests that there are viable avenues to engage middle-aged men in Internet- or in mobile-delivered health interventions. This study also suggests that to be successful, these interventions need to be tailor-made especially for men, with an emphasis on usability and convenience. A wider quantitative study would bring further support to these findings.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".