Young People’s Attitudes and Motivations Toward Social Media and Mobile Apps for Weight Control: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Effective prevention at a young enough age is critical to halt the obesity epidemic. Mobile health (mHealth) apps would potentially reach large numbers at low-cost. While there is already a profusion of lifestyle apps, they are mostly non-evidence-based and evidently ineffective against rising obesity prevalence. OBJECTIVE: The aim of this study was to explore preferences and usage of lifestyle apps among young people in 6 countries. METHODS: A mixed methods study was conducted among young people aged 13 to 24 years residing in the United Kingdom, Belgium, Finland, Greece, Singapore, and New Zealand. Participants were recruited from Web advertisements on Facebook, asking for volunteers interested in mobile apps in general, not specific to lifestyle or health, to complete a short survey comprising 18 questions on demographics, weight gain, and mobile app preferences and then to join English-language online focus groups, which were held during 2017, in password-protected Web rooms, moderated by an experienced researcher. Descriptive statistics were carried out for the survey, and thematic analysis was applied to transcripts. RESULTS: A total of 2285 young people (610 adolescents aged 13-17 years and 1675 young adults aged 18-24 years) responded and completed the survey, with 72.0% (1645) reported being concerned about weight gain for themselves or friends. Later, 807 young people (376 adolescents and 431 young adults) were selected based on age and country to participate in 12 online focus groups, with 719 young people completing. Analysis revealed 4 main themes: (1) feelings toward personal weight; (2) perception of lifestyle apps and desired content for weight gain prevention; (3) social media apps, lifestyle apps, and motivation for downloading and retaining; and (4) data safety and data usage and confidentiality. Young people are interested in evidence-based advice in programs incorporating their preferences. CONCLUSIONS: Young people are commonly, and consistently across 6 countries, concerned about weight gain and obesity and would welcome evidence-based mHealth programs, provided the views of young people themselves are incorporated in the program content.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".