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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".