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Record W4386050168 · doi:10.2196/46350

The Effect of Social Networks on Active Living in Adolescents: Qualitative Focus Group Study

2023· article· en· W4386050168 on OpenAlexvenueno aff
Sander Hermsen, Femke van Abswoude, Bert Steenbergen

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupThematic analysisActive ageingQualitative researchActive livingPsychological interventionPsychologyInformation and Communications TechnologySocial network (sociolinguistics)Physical activityGerontologyDevelopmental psychologyMedicineSocial mediaSociologyPolitical scienceOlder peoplePhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Participation in organized sports and other forms of active living have important health benefits in adolescence and adulthood. Unfortunately, the transition to secondary school has been shown to be a barrier to participation. Social networks can play important roles in activating adolescents, and information and communication technology (ICT) interventions can augment this role. To date, there are few insights into what adolescents themselves think and feel about barriers to and motivators for active living, the role of their social networks in active living, and the potential of ICT for physical activity (PA). OBJECTIVE: This study aimed to gather insights into the perspectives of adolescents aged 12 to 14 years on active living and sports participation, motivators and demotivators for active living, and the potential roles of their social network and of ICT. METHODS: A total of 26 adolescents aged 12 to 14 years from different levels of Dutch secondary schools participated in 1 of 5 semistructured focus group interviews, in which they talked about sports and PA, their social networks, their ICT use, and the role of social networks and ICT in PA. All interviews were transcribed and analyzed using a thematic qualitative approach. RESULTS: The study showed that all participants were physically active, although the transition to secondary school made this difficult, mostly because of time constraints. Participants saw positive physical and mental health effects as important benefits of active living. They regarded social benefits as strong motivators for active living: being together, making friends, and having fun together. However, the social network could also demotivate through negative peer judgment and negative feedback. Participants were willing to share their own positive experiences and hear about those from close peers and friends but would not share their own (and were not interested in others') negative experiences or personal information. Participants were mainly interested in descriptive norms set by others and obtained inspiration from others for PA. With respect to using ICT for active living, participants stated a preference for social challenges among friends, personalized feedback, goals, activities, and rewards. Competition was seen as less important or even unattractive. If mentioned, participants felt that this should be with friends, or peers of a similar level, with fun being more important than the competition itself. CONCLUSIONS: This study shows that adolescents feel that their social network is and can be a strong driver of active living. They are willing to use ICT-based solutions that make use of social networks for PA as long as these solutions involve their current (close) network and use an approach based on being together and having fun together.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0060.005
Scholarly communication0.0020.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.097
GPT teacher head0.518
Teacher spread0.421 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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