How can digital citizen science approaches improve ethical smartphone use surveillance among youth: traditional surveys versus ecological momentary assessments
Notice bibliographique
Résumé
Abstract Background Ubiquitous use of smartphones among youth poses significant challenges related to non-communicable diseases, including poor mental health. Although traditional survey measures can be used to assess smartphone use among youth, they are subject to recall bias. This study aims to compare self-reported smartphone use via retrospective modified traditional recall survey and prospective Ecological Momentary Assessments (EMAs) among youth. Methods This study uses data from the Smart Platform, which engages with youth as citizen scientists. Youth (N=436) aged 13-21 years in two urban jurisdictions in Canada (Regina and Saskatoon) engaged with our research team using a custom-built application via their own smartphones to report on a range of behaviours and outcomes on eight consecutive days. Youth reported smartphone use utilizing a traditional validated measure, which was modified to capture retrospective smartphone use on both weekdays and weekend days. In addition, daily EMAs were also time-triggered over a period of eight days to capture prospective smartphone use. Demographic, behavioural, and contextual factors were also collected. Data analyses included t-test and linear regression using SPSS statistical software. Results There was a significant difference between weekdays, weekends and overall smartphone use reported retrospectively and prospectively (p-value= <0.001), with youth reporting less smartphone use via EMAs. Overall retrospective smartphone use was significantly associated with not having a part-time job (β=0.342, 95%[CI]=0.146-1.038, p-value =0.010) and participating in a school sports team (β=0.269, 95%[CI]= 0.075-0.814, p-value=0.019). However, prospective smartphone use reported via EMAs was not associated with any behavioural and contextual factors. Conclusion The findings of this study have implications for appropriately understanding and monitoring smartphone use in the digital age among youth. EMAs can potentially minimize recall bias of smartphone use among youth, and other behaviours. More importantly, digital citizen science approaches that engage large populations of youth using their own smartphones can transform how we ethically monitor and mitigate the impact of excessive smartphone use. Author Summary Use of ubiquitous digital devices, particularly smartphones, has experienced an exponential increase among youth, a phenomenon that continues to influence youth health. Although retrospective measures have been used to understand smartphone use among youth, they are prone to measurement and compliance biases. There has been a growing interest in using ecological momentary assessments (EMAs) to assess smartphone to minimize biases associated with retrospective measures. This study uses the smart framework, which integrates citizen science, community based participatory research and systems science to ethically engage with youth citizen scientists using their own smartphones to understand smartphone use behaviours – reported by the same cohort of youth using both retrospective and prospective measures. The findings show a significant difference between smartphone use reported through retrospective and prospective EMAs, with youth reporting more smartphone use via retrospective measures. Furthermore, there were differences in contextual and behavioural factors that were associated with smartphone use reported via retrospective and prospective measures. The findings have implications for appropriately understanding and monitoring smartphone use in the digital age among youth. More importantly, digital citizen science approaches that engage large populations of youth using their own smartphones can transform how we ethically monitor and mitigate the impact of excessive smartphone use.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,043 | 0,109 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,007 | 0,006 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».