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Record W1773649057 · doi:10.14740/jocmr2021w

Impact of Physical Activity on the Self-Perceived Quality of Life in Non-Frail Older Adults

2015· review· en· W1773649057 on OpenAlexvenueno aff
Ulla Svantesson, Janelle Jones, Kristin Wolbert, Marie Alricsson

Bibliographic record

VenueJournal of Clinical Medicine Research · 2015
Typereview
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
Fundersnot available
KeywordsGerontologyMedicineFear of fallingQuality of life (healthcare)CognitionPhysical activityHealthy agingPopulation ageingPerspective (graphical)Falling (accident)Cognitive declinePublic healthPopulationPhysical therapyInjury preventionDementiaPoison controlEnvironmental healthPsychiatryDisease

Abstract

fetched live from OpenAlex

As the population of older adults increases, healthy aging has become a global public health issue. Physical activity can help older adults reclaim or maintain a healthy aging process. The purpose of this paper is to investigate the relationship between physical activity, physical performance, quality of life and cognition in non-frail adults aged 65 and older. English articles in peer-reviewed journals about healthy, non-frail adults aged 65 and older were included in the present review. Additionally, articles were obtained from reviewing the reference lists of the aforementioned articles. Research proves an overwhelmingly positive correlation between physical activity and the reduction of preventable chronic illnesses, lower healthcare costs, improved cognition, improved muscle function, decreased fear of falling, and thereby, inevitably, an increased self-perceived quality of life. There is research evidence on healthy aging and the effect of physical activity, which could be of importance in a public health perspective.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.613
GPT teacher head0.682
Teacher spread0.069 · 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 designNot applicable
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

Citations54
Published2015
Admission routes1
Has abstractyes

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