User Interest in Digital Health Technologies to Encourage Physical Activity: Results of a Survey in Students and Staff of a German University
Bibliographic record
Abstract
BACKGROUND: Although the benefits for health of physical activity (PA) are well documented, the majority of the population is unable to implement present recommendations into daily routine. Mobile health (mHealth) apps could help increase the level of PA. However, this is contingent on the interest of potential users. OBJECTIVE: The aim of this study was the explorative, nuanced determination of the interest in mHealth apps with respect to PA among students and staff of a university. METHODS: We conducted a Web-based survey from June to July 2015 in which students and employees from the University of Potsdam were asked about their activity level, interest in mHealth fitness apps, chronic diseases, and sociodemographic parameters. RESULTS: A total of 1217 students (67.30%, 819/1217; female; 26.0 years [SD 4.9]) and 485 employees (67.5%, 327/485; female; 42.7 years [SD 11.7]) participated in the survey. The recommendation for PA (3 times per week) was not met by 70.1% (340/485) of employees and 52.67% (641/1217) of students. Within these groups, 53.2% (341/641 students) and 44.2% (150/340 employees)-independent of age, sex, body mass index (BMI), and level of education or professional qualification-indicated an interest in mHealth fitness apps. CONCLUSIONS: Even in a younger, highly educated population, the majority of respondents reported an insufficient level of PA. About half of them indicated their interest in training support. This suggests that the use of personalized mobile fitness apps may become increasingly significant for a positive change of lifestyle.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".