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Record W4400774187 · doi:10.1177/07334648241257799

Types of Home and Community-Based Physical Activity and Their Effects on the Older Adults’ Quality of Life: A Systematic Review and Meta-Analysis

2024· review· en· W4400774187 on OpenAlexaff
Zishuo Huang, Tingke Xu, Yunyun Huang, Qianru Zhao, Weizhen Dong, Jixiang Xu, Xinyu Liu, Yating Fu, Ying Wang, Chun Chen

Bibliographic record

VenueJournal of Applied Gerontology · 2024
Typereview
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Waterloo
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsMeta-analysisPsychological interventionGerontologyQuality of life (healthcare)Cochrane LibraryInclusion (mineral)MedicineDuration (music)Systematic reviewIntervention (counseling)MEDLINEPsychologyNursing

Abstract

fetched live from OpenAlex

Home and community-based physical activity (HCBPA) has been extensively utilized among older adults. Nevertheless, the varying types of HCBPA, including different duration, intensity, and frequency, have sparked controversy regarding their impact on the quality of life in older adults. This study aims to explore the effects of HCBPA on QoL in older adults. We conducted a systematic review and retrieved studies published from January 2000 to April 2023 from multiple databases (PubMed, Embase, Cochrane, and the Web of Science Library). Seventeen articles met the inclusion criteria for this study. Long-term HCBPA interventions may have a more pronounced positive impact on older adults' quality of life than short-term ones, with the intervention's intensity and frequency playing a key role in its effectiveness. The results of the meta-analyses showed significant differences in PCS but not in MCS, both with low certainty of evidence. Policymakers should prioritize the importance of promoting HCBPA interventions with appropriate duration, intensity, and frequency to create a more age-inclusive society.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.884
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0130.001
Bibliometrics0.0000.000
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 head0.180
GPT teacher head0.423
Teacher spread0.244 · 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 designSystematic review
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

Citations3
Published2024
Admission routes1
Has abstractyes

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