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Record W4312105113 · doi:10.1093/geroni/igac059.271

TECHNOLOGY AND GROUP EXERCISE INTERVENTIONS FOR PEOPLE WITH DEMENTIA OR MILD COGNITIVE IMPAIRMENT: A SCOPING REVIEW

2022· review· en· W4312105113 on OpenAlexaff
Hannah Levine, Paavan Randhawa, Juyoung Park, Lillian Hung

Bibliographic record

VenueInnovation in Aging · 2022
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of British ColumbiaLangara College
Fundersnot available
KeywordsPsychosocialDementiaPsychological interventionCognitionPsychologyGerontologyCognitive impairmentPhysical therapyPhysical medicine and rehabilitationClinical psychologyMedicineApplied psychologyPsychiatryDisease

Abstract

fetched live from OpenAlex

Abstract We conducted a scoping review to analyze evidence about online group-based exercise programs. We searched six electronic databases and conducted team analysis with patient and family partners. Of 1,166 screened articles, the final review included 8 publications. Results identified three types of technology-based group exercise interventions for people with dementia or mild cognitive impairment (MCI): (a) exergames, (b) virtual cycling, and (c) video-conferencing platforms. These studies used psychosocial, physical function, biomarker, and cognitive outcome measures. The review identified three key impacts: (a) feasibility and accessibility; (b) physical, psychosocial, and cognitive benefits; and (c) adaptations necessary for persons with dementia or MCI. Over all, technology-based group exercise interventions were found to be accessible, feasible, and acceptable to persons with dementia or MCI. However, a “one-size-fits-all” game approach often did not work, suggesting that exercise interventions should be adaptable to meet the various needs of individual participants.

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)0.007
metaresearch head score (Gemma)0.027
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 score0.010
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.027
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0100.010
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.089
GPT teacher head0.433
Teacher spread0.344 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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