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Record W2770387110 · doi:10.1177/0733464817743332

The Effectiveness of Combining Visual Art Activities and Physical Exercise for Older Adults on Well-Being or Quality of Life and Mood: A Scoping Review

2017· review· en· W2770387110 on OpenAlexaff
Roswiyani Roswiyani, Linda Kwakkenbos, Jan Spijker, Cilia Witteman

Bibliographic record

VenueJournal of Applied Gerontology · 2017
Typereview
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsMcGill UniversityJewish General Hospital
FundersRadboud Universiteit
KeywordsCINAHLPsycINFOMoodPsychological interventionQuality of life (healthcare)MedicineMEDLINEGerontologyPopulationClinical psychologyPhysical therapyPsychologyPsychiatryNursing

Abstract

fetched live from OpenAlex

Visual art activities and physical exercise are both low-intensity and low-cost interventions. The present study aims to comprehensively describe published literature on the effectiveness of a combination of these interventions on well-being or quality of life (QoL) and mood of older adults. Embase, CINAHL, Ovid Medline (R), PsycINFO, and Web of Science databases were searched for studies published between 1990 and 2015 that evaluated interventions combining visual art therapy and exercise for people aged 50 years or older with at least one resultant well-being or QoL or mood outcome. We found 10 studies utilizing different combination programs and outcome measures, and most had small sample sizes. Seventy percent of the studies reported that combining both interventions was effective in improving well-being or QoL and mood in older adults. Future studies are, however, requisite to investigate whether in the respective population such a combination is more effective than either of the interventions alone.

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 score0.675
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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 head0.110
GPT teacher head0.432
Teacher spread0.323 · 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

Citations29
Published2017
Admission routes1
Has abstractyes

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