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Record W4285610179 · doi:10.2196/38370

An Observational Report of Screen Time Use Among Young Adults (Ages 18-28 Years) During the COVID-19 Pandemic and Correlations With Mental Health and Wellness: International, Online, Cross-sectional Study

2022· article· en· W4285610179 on OpenAlexvenueno aff
Michelle Teresa Wiciak, Omar Shazley, Daphne Santhosh

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicCross-sectional studyAnxietyMedicineMental healthObservational studyYoung adultScreen timeDepression (economics)Coronavirus disease 2019 (COVID-19)DemographyGerontologyPsychiatryPhysical therapyPhysical activityInternal medicineDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Screen time (ST) drastically increased during the COVID-19 pandemic, but there is little research on the specific type of ST use, degree of change from before COVID-19, and possible associations with other factors. Young adults are a particular interest since previous studies have shown the detriment ST has on a young person's health. With the combination of a life-changing pandemic, there are unreached depths regarding ST and young adults. This study aims to provide insight into these unknowns. OBJECTIVE: This study aims to assess ST in 3 domains (entertainment, social media [SM], and educational/professional) in young adults early in the COVID-19 pandemic; identify trends; and identify any correlations with demographics, mental health, substance abuse, and overall wellness. METHODS: An online, cross-sectional observational study was performed from September 2020 to January 2021 with 183 eligible respondents. Data were collected on ST, trauma from COVID-19, anxiety, depression, substance use, BMI, and sleep. RESULTS: The average total ST during COVID-19 was 23.26 hours/week, entertainment ST was 7.98 hours/week, SM ST was 6.79 hours/week, and ST for educational or professional purposes was 8.49 hours/week. For all categories, the average ST during COVID-19 was higher than before COVID-19 (P<.001). We found ST differences between genders, student status, and continent of location. Increased well-being scores during COVID-19 were correlated with greater change in total ST (P=.01). Poorer sleep quality (P=.01) and longer sleep duration (P=.03) were associated with a greater change in entertainment ST (P=.01). More severe depression and more severe anxiety was associated with the amount of entertainment ST (P=.047, P=.03, respectively) and greater percent change in SM (P=.007, P=.002, respectively). Greater stress from COVID-19 was associated with the amount of ST for educational/professional purposes (P=.05), change in total ST (P=.006), change in entertainment ST (P=.01), and change in ST for educational/professional purposes (P=.02). Higher Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) tobacco scores were associated with greater change in total ST (P=.004), and higher pack-years were associated with greater change in SM ST (P=.003). Higher alcohol scores (P=.004) and servings of alcohol per week (P=.003) were associated with greater change in entertainment ST. Quarantining did not negatively impact these variables. CONCLUSIONS: There is no doubt ST and worsening mental health increased during COVID-19 in young adults. However, these findings indicate there are many significant associations between ST use and mental health. These associations are more complex than originally thought, especially since we found quarantining is not associated with mental health. Although other factors need to be further investigated, this study emphasizes different types of ST and degree of change in ST affect various groups of people in discrete ways. Acknowledging these findings can help young adults optimize their mental health during pandemics.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.221
GPT teacher head0.529
Teacher spread0.308 · 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 teacher head, not a consensus.

Study designObservational
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

Citations18
Published2022
Admission routes1
Has abstractyes

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