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Record W4317829806 · doi:10.1177/23337214231151357

Fear of Falls Following an Online Exercise Program for Aging Adults

2023· article· en· W4317829806 on OpenAlexafffund
Monica MacDonald, Martin Sénéchal, Brianna Leadbetter, Danielle R. B̀ouchard

Bibliographic record

VenueGerontology and Geriatric Medicine · 2023
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of New Brunswick
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsFalls in older adultsGerontologyPhysical medicine and rehabilitationFear of fallingFall preventionPsychologyMedicinePhysical therapyInjury preventionPoison controlMedical emergency

Abstract

fetched live from OpenAlex

Individuals who participate in regular exercise tend to report a lower fear of falling; however, it is unknown if this fear can be reduced following an online fall prevention exercise program. The main purpose of this study was to test if offering a peer-led fall prevention exercise program online reduced the fear of falling and if this potential improvement was greater than when the program was offered in person. The secondary objectives were to describe participants’ characteristics when participating online, the rate of falls and the context in which falls occur. A total of 85 adults aged 69.0 ± 7.8 years participated in the program offered online ( n = 44) and in-person ( n = 41). No significant differences in fear of falling before and after participation in the program were reported for either group: online (20.7 ± 5.1–21.8 ± 5.5) and in-person (20.6 ± 5.1–21.2 ± 5.3). Online participants reported a greater proportion of falls ( n = 9; 20.5% vs. n = 4; 9.8%; p = .14), mostly occurring outdoors ( n = 7) (77.8). A properly designed study is needed to test if the rate of falls is greater when an exercise program is offered online.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.060
GPT teacher head0.418
Teacher spread0.359 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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