Internet Use and Effects on Mental Well-being During the Lockdown Phase of the COVID-19 Pandemic in Younger Versus Older Adults: Observational Cross-Sectional Study
Notice bibliographique
Résumé
BACKGROUND: Majority of individuals, including both younger and older adults, had to adapt to digital means to cope with lockdown measures and pandemic-induced lifestyle changes during the COVID-19 pandemic. While internet accessibility was beneficial during the pandemic, existing literature suggests that excessive use could lead to the rise of problematic internet use in adolescents and younger adults. However, the effects on older adults remain unclear. OBJECTIVE: This study aimed to examine differences in internet use during the lockdown phase of the COVID-19 pandemic and explore how age differences in mental health could be explained by time spent on the internet. METHODS: A door-to-door survey of a nationally representative sample of 602 adults in Singapore was carried out using computer-assisted personal interviewing during the early phase of the COVID-19 pandemic (October to November 2020). Participants were categorized into younger (21-59 years old) and older (60 years or above) age groups. We assessed self-reported measures of depression, anxiety, and stress; psychosocial adaptability; ability to perform essential activities; social support; health status; digital media use patterns, and time spent on the internet. Procedures complied with existing safe distancing measures. RESULTS: Older adults reported being less able to use digital platforms to meet needs and acquire information updates compared with younger adults during the lockdown period of the pandemic. Older adults spent significantly less time on the internet for both work and personal uses per day (mean 146.00 min, SD 9.18 min) compared with younger adults (mean 433.27 min, SD 14.32 min). Significant age differences in depression, anxiety, and stress were found, with younger adults showing poorer mental health. Mediation analysis showed that age differences in depression, anxiety, and stress were partially explained by time spent on the internet. These variables together explained 43%, 40%, and 40% of the variances in depression, anxiety, and stress scores, respectively. CONCLUSIONS: The findings showed that younger adults spent significantly more time on the internet compared with older adults during the lockdown phase of the pandemic. They were also ahead in their ability to use digital resources to meet needs and engage socially compared with older adults. Despite this, the mental health of younger adults was poor, and this was partially accounted for by the amount of time spent on the internet. Since past research suggests that excessive time spent on the internet could lead to disordered use, the benefits brought by digital technologies could have been attenuated during the lockdown phase of the pandemic. Considering this potential negative effect, it is imperative to educate both young and old adults in the appropriate use of information and communication technology.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».