Navigating the Online World of Lifestyle Health Information: Qualitative Study With Adolescents
Bibliographic record
Abstract
BACKGROUND: Adolescence is a critical life stage characterized by an interplay of biological, social, and environmental factors. Such factors influence lifestyle health-related trajectories, including dietary behaviors, physical activity levels, body weight, and sleep. Generation Z (born 1995-2015) is the most internet-dependent and technologically savvy generation in history with increasing rates of smartphone ownership across high- and low-income countries. Gaps exist in understanding what online platforms adolescents are using and barriers and facilitators of these platforms to seek lifestyle health information. OBJECTIVE: We evaluated adolescents' perceptions on the use of contemporary digital platforms (websites, social media platforms, smartphone apps) to seek lifestyle heath information or advice. METHODS: Virtual focus groups were held via Zoom teleconference between July 2021 and August 2021. Eligible participants were 13 years to 18 years old, were living in Australia, and had searched for online lifestyle health information in the previous 3 months. For this study, lifestyle health information referred to key behaviors and risk factors for chronic disease, namely, diet, physical activity, weight management, and sleep. Participants were recruited through an existing database of research participants and networks of the research team. Focus groups were analyzed using the framework approach, in which data are systematically searched to recognize patterns in the data and manage, analyze, and identify themes. Focus group audio files were transcribed verbatim and independently coded by 2 researchers (RR, SSJ). Through an iterative, reflexive process, a final coding matrix was agreed on by all researchers and used to thematically analyze the data. RESULTS: We held 5 focus groups (n=32; mean age: 16.3 [SD 1.4] years; 18/32, 56% female; 13/32, 41% spoke language other than English at home). Thematic analysis revealed participants searched for information both actively (eg, on Google or YouTube) and passively (eg, scrolling social media and using existing apps preloaded to their smartphone such as Apple Health, Samsung Health, or Google Fit apps). Participants identified that the most helpful information was well-presented in terms of aesthetic appeal and layout and came from a credible and reliable source (eg, any sponsorships disclosed), and they expressed the need for the information to be relatable. Mixed views were reported for the application of lifestyle health information found online. Some participants reported behavior change, while others noted that certain advice was hard to maintain and incorporate into their lifestyle. CONCLUSIONS: This study highlights the abundance and complexity of lifestyle health information online for adolescents. Adolescents in the digital age seek access to information that is appealing, credible, relevant, and actionable for lifestyle health behaviors. To appeal to needs of adolescents, future interventions for adolescents relating to lifestyle health must consider co-design methodological approaches. Furthermore, the regulation of lifestyle health information available online warrants further investigation.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".