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Record W3137601440 · doi:10.2196/26293

Changes in Workers’ Sedentary and Physical Activity Behaviors in Response to the COVID-19 Pandemic and Their Relationships With Fatigue: Longitudinal Online Study

2021· article· en· W3137601440 on OpenAlexafffundvenue
Mohammad Javad Koohsari, Tomoki Nakaya, Gavin R. McCormack, Ai Shibata, Kaori Ishii, Koichiro Oka

Bibliographic record

VenueJMIR Public Health and Surveillance · 2021
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Calgary
FundersJapan Society for the Promotion of ScienceCanadian Institutes of Health Research
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSedentary behaviorSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Physical activitySedentary lifestyleMedicinePsychologyEnvironmental healthVirologyPhysical therapyOutbreakDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: Sedentary behaviors and physical activity are likely to be affected by the COVID-19 outbreak, and sedentary lifestyles can increase subjective fatigue. The nonpharmaceutical policies imposed as a result of the COVID-19 pandemic may also have adverse effects on fatigue. OBJECTIVE: This study has two aims: to examine the changes in sedentary behaviors and physical activity of company workers in response to the COVID-19 pandemic in Japan and to examine relationships between changes in these sedentary behaviors and physical activity and changes in fatigue. METHODS: Data from a nationwide prospective online survey conducted in 2019 and 2020 were used. On February 22, 2019, an email with a link to participate in the study was sent to 45,659 workers, aged 20 to 59 years, who were randomly selected from a database of approximately 1 million individuals. A total of 2466 and 1318 participants, who self-reported their occupation as company workers, answered the baseline and follow-up surveys, respectively. Surveys captured fatigue, workday and daily domain-specific sedentary behaviors and physical activity, and total sedentary behaviors and physical activity. We used multivariable linear regression models to estimate associations of changes in sedentary behaviors and physical activity with changes in fatigue. RESULTS: Increases in public transportation sitting during workdays, other leisure sitting time during workdays, and other leisure sitting time were associated with an increase in the motivation aspect of fatigue (b=0.29, 95% CI 0-0.57, P=.048; b=0.40, 95% CI 0.18-0.62, P<.001; and b=0.26, 95% CI 0.07-0.45, P=.007, respectively). Increases in work-related sitting time during workdays, total sitting time during workdays, and total work-related sitting time were significantly associated with an increase in the physical activity aspect of fatigue (b=0.06, 95% CI 0-0.12, P=.03; b=0.05, 95% CI 0.01-0.09, P=.02; and b=0.07, 95% CI 0-0.14, P=.04, respectively). The motivation and physical activity aspects of fatigue increased by 0.06 for each 1-hour increase in total sitting time between baseline and follow-up (b=0.06, 95% CI 0-0.11, P=.045; and b=0.06, 95% CI 0.01-0.10, P=.009, respectively). CONCLUSIONS: Our findings demonstrated that sedentary and active behaviors among company workers in Japan were negatively affected during the COVID-19 outbreak. Increases in several domain-specific sedentary behaviors also contributed to unfavorable changes in workers' fatigue. Social distancing and teleworking amid a pandemic may contribute to the sedentary lifestyle of company workers. Public health interventions are needed to mitigate the negative effects of the COVID-19 pandemic or future pandemics on sedentary and physical activity behaviors and fatigue among company workers.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.202
GPT teacher head0.415
Teacher spread0.213 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations65
Published2021
Admission routes3
Has abstractyes

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