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Record W4296915138 · doi:10.1123/japa.2021-0417

Online Exercise Programming Among Older Adults: A Scoping Review

2022· review· en· W4296915138 on OpenAlexaff
Matthieu Dagenais, Olivia Parker, Sarah Galway, Kimberley L. Gammage

Bibliographic record

VenueJournal of Aging and Physical Activity · 2022
Typereview
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsBrock University
Fundersnot available
KeywordsGerontologyInclusion (mineral)PsychologyPhysical activityPerceptionMedicinePhysical therapySocial psychology

Abstract

fetched live from OpenAlex

Online exercise programming may promote physical activity while at home, but little is known about its use among older adults. Using the Arksey and O'Malley framework, we describe the nature and extent of the research pertaining to the use of online exercise programming among adults 65 years of age and older. We ran two separate searches (January 2005-September 2020 and October 2020-October 2021), yielding 17 articles that met our inclusion criteria. A total of 1,767 participants (69% female) ranging from 65 to 94 years of age were included. Most studies delivered the online programs asynchronously. The majority of studies assessed the feasibility of online programs, with 14 studies investigating health-related outcomes such as physical, psychological, and social health. Future research should explore perceptions and experiences of online exercise programming among older adults and the mechanisms by which it impacts physical, psychological, social, and behavioral outcomes.

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)0.006
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0120.012
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.077
GPT teacher head0.415
Teacher spread0.337 · 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 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

Citations18
Published2022
Admission routes1
Has abstractyes

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