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Record W4378745908 · doi:10.2196/40650

The COVID-19 Pandemic and Daily Steps in the General Population: Meta-analysis of Observational Studies

2023· review· en· W4378745908 on OpenAlexvenueaboutno aff
Ziying Wu, Yilun Wang, Yuqing Zhang, Kim L. Bennell, Daniel K. White, Liusong Shen, Wei Ren, Jie Wei, Chao Zeng, Guanghua Lei

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typereview
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaXiangya Hospital, Central South UniversityMedical Research CouncilNational Health and Medical Research CouncilNational Natural Science Foundation of China
KeywordsObservational studyPandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Meta-analysisPopulationMedicineEnvironmental healthVirologyOutbreakDiseaseInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic has the potential to accelerate another pandemic: physical inactivity. Daily steps, a proxy of physical activity, are closely related to health. Recent studies indicate that over 7000 steps per day is the critical physical activity standard for minimizing the risk of all-cause mortality. Moreover, the risk of cardiovascular events has been found to increase by 8% for every 2000 steps per day decrement. OBJECTIVE: To quantify the impact of the COVID-19 pandemic on daily steps in the general adult population. METHODS: This study follows the guidelines of the MOOSE (Meta-analysis Of Observational Studies in Epidemiology) checklist. PubMed, EMBASE, and Web of Science were searched from inception to February 11, 2023. Eligible studies were observational studies reporting monitor-assessed daily steps before and during the confinement period of the COVID-19 pandemic in the general adult population. Two reviewers performed study selection and data extraction independently. The modified Newcastle-Ottawa Scale was used to assess the study quality. A random effects meta-analysis was conducted. The primary outcome of interest was the number of daily steps before (ie, January 2019 to February 2020) and during (ie, after January 2020) the confinement period of COVID-19. Publication bias was assessed with a funnel plot and further evaluated with the Egger test. Sensitivity analyses were performed by excluding studies with low methodological quality or small sample sizes to test the robustness of the findings. Other outcomes included subgroup analyses by geographic location and gender. RESULTS: A total of 20 studies (19,253 participants) were included. The proportion of studies with subjects with optimal daily steps (ie, ≥7000 steps/day) declined from 70% before the pandemic to 25% during the confinement period. The change in daily steps between the 2 periods ranged from -5771 to -683 across studies, and the pooled mean difference was -2012 (95% CI -2805 to -1218). The asymmetry in the funnel plot and Egger test results did not indicate any significant publication bias. Results remained stable in sensitivity analyses, suggesting that the observed differences were robust. Subgroup analyses revealed that the decline in daily steps clearly varied by region worldwide but that there was no apparent difference between men and women. CONCLUSIONS: Our findings indicate that daily steps declined substantially during the confinement period of the COVID-19 pandemic. The pandemic further exacerbated the ever-increasing prevalence of low levels of physical activity, emphasizing the necessity of adopting appropriate measures to reverse this trend. Further research is required to monitor the consequence of long-term physical inactivity. TRIAL REGISTRATION: PROSPERO CRD42021291684; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=291684.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.082
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.032
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.082
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0170.061
Bibliometrics0.0070.011
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.720
GPT teacher head0.549
Teacher spread0.171 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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

Citations9
Published2023
Admission routes2
Has abstractyes

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