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Record W3207309483 · doi:10.1007/s12603-021-1688-1

Feasibility and Acceptability of Remote Physical Exercise Programs to Prevent Mobility Loss in Pre-Disabled Older Adults during Isolation Periods Such as the COVID-19 Pandemic

2021· article· en· W3207309483 on OpenAlexafffund
Fanny Buckinx, Mylène Aubertin‐Leheudre, Raoul Daoust, S. Hegg, Dominic Martel, M. Martel-Thibault, Marie‐Josée Sirois

Bibliographic record

VenueThe journal of nutrition health & aging · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsCentre hospitalier de l'Université LavalUniversité LavalUniversité de MontréalHôpital du Sacré-Cœur de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité du Québec à MontréalInstitut Universitaire de Gériatrie de Montréal
FundersFonds de Recherche du Québec - SantéRéseau québécois de recherche sur le vieillissement
KeywordsPhoneCoronavirus disease 2019 (COVID-19)Web applicationMedicineIsolation (microbiology)Physical therapyGerontologyMultimediaWorld Wide WebComputer scienceDisease

Abstract

fetched live from OpenAlex

This study aimed to assess the feasibility and acceptability of remote physical exercise (PE) to prevent mobility loss among pre-disabled older adults during the COVID-19 lockdowns. Participants followed a 12-week PE remote program in Zoom© supervised groups (Web-Ex group, n=11) or phone-supervised individual booklet-based home-program (Booklet group, n=33). The total rate of adherence was 82.5% in the Web-Ex group and 85.8% in the Booklet group. The level of satisfaction was « a lot » for 60% of the participants in the Web-ex group and for 37.9% of those included in the Booklet group. Respectively 10% and 31% of the participants rated the difficulty as « low » in the web-ex and Booklet groups. Remote physical exercise using a web technology or booklets at home with regular and personalized follow-up during the lockdown was feasible and acceptable among pre-disabled seniors.

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)0.008
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 score0.242
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.059
GPT teacher head0.450
Teacher spread0.391 · 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

Citations51
Published2021
Admission routes2
Has abstractyes

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