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Record W4385974376 · doi:10.2196/45091

Short-Form Video Exposure and Its Two-Sided Effect on the Physical Activity of Older Community Women in China: Secondary Data Analysis

2023· article· en· W4385974376 on OpenAlexvenueno aff
Chen Wu, Si Chen, Shan Wang, Sijing Peng, Jiepin Cao

Bibliographic record

VenueJMIR mhealth and uhealth · 2023
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersUniversity of North Carolina at Chapel HillShandong University
KeywordsPhysical activityMedicineGerontologyCohortDemographyDuration (music)PsychologyPhysical therapy

Abstract

fetched live from OpenAlex

Background: There is a tendency for older adults to become more physically inactive, especially older women. Physical inactivity has been exacerbated since the COVID-19 pandemic. Lockdowns and information-based preventive measures for COVID-19 increased the number of short-form video app users and short-form video exposure, including content exposure and the duration of exposure, which has demonstrated important effects on youths' health and health-related behaviors. Despite more older adults viewing short-form videos, less is known about the status of their short-form video exposure or the impacts of the exposure on their physical activity. Objective: This study aims to describe physical activity-related content exposure among older adults and to quantify its impacts along with the duration of short-form video exposure on step counts, low-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Methods: We analyzed a subsample (N=476) of older women who used smartphones and installed short-form video apps, using the baseline data collected from an ongoing cohort study named the Physical Activity and Health in Older Women Study (PAHIOWS) launched from March to June 2021 in Yantai, Shandong Province, China. The information on short-form video exposure was collected by unstructured questions; physical activity-related content exposure was finalized by professionals using the Q-methodology, and the duration of exposure was transformed into hours per day. Step counts, LPA, and MVPA were assessed with ActiGraph wGT3X-BT accelerometers. Multiple subjective and objective covariates were assessed. Linear regression models were used to test the effects of short-form video exposure on step counts, LPA, and MVPA. MVPA was dichotomized into less than 150 minutes per week and 150 minutes or more per week, and the binary logistic regression model was run to test the effects of short-form video exposure on the achievement of spending 150 minutes or more on MVPA. Results: Of 476 older women (mean age 64.63, SD 2.90 years), 23.7% (113/476) were exposed to physical activity-related short-form videos, and their daily exposure to short-form videos was 1.5 hours. Physical activity-related content exposure increased the minutes spent on MVPA by older women (B=4.14, 95% CI 0.13-8.15); the longer duration of short-form video exposure was associated with a reduced step count (B=-322.58, 95% CI -500.24 to -144.92) and minutes engaged in LPA (B=-6.95, 95% CI -12.19 to -1.71) and MVPA (B=-1.56, 95% CI -2.82 to -0.29). Neither content exposure nor the duration of exposure significantly increased or decreased the odds of older women engaging in MVPA for 150 minutes or more per week. Conclusions: Short-form video exposure has both positive and negative impacts on the physical activity of older adults. Efforts are needed to develop strategies to leverage the benefits while avoiding the harms of short-form videos.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.083
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.092
GPT teacher head0.417
Teacher spread0.326 · 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 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

Citations8
Published2023
Admission routes1
Has abstractyes

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