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Record W3209165524

A retrospective review of older adults who attended a medical fitness facility in Winnipeg, MB and the impact of the medical fitness model on long term health outcomes

2020· review· en· W3209165524 on OpenAlexaboutno aff
Ranveer Brar

Bibliographic record

VenueMspace (University of Manitoba) · 2020
Typereview
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsnot available
Fundersnot available
KeywordsGerontologyTerm (time)Medicine
DOInot available

Abstract

fetched live from OpenAlex

Background: It is well established that being active helps prevent chronic diseases and reduce the risk of premature death. However, physical inactivity is common among older adults which may be associated with poor health outcomes. Furthermore, it is not clearly understood whether physical inactivity is related to increased health care utilization in older adults. As the proportion of older adults increases, it is important for this population to stay active and practice positive health behaviours to maintain functional independence, live longer and improve quality of life. The aim of this study was to determine the relationship between the medical fitness model (membership and attendance) and all-cause mortality, health care utilization and a major adverse cardiac event in older adults. Methods: We conducted a retrospective cohort study linking data from two medical fitness facilities in Winnipeg, MB to provincial health administrative databases between January 1st 2005 and December 31st, 2015. Our propensity model included age, sex, socioeconomic status, comorbidities and year of index date. We then applied stabilized inverse probability treatment weights to examine the association of all-cause mortality and a major adverse cardiac event in older adult members using time-dependent Cox regression models. Health care utilization (hospitalizations, outpatient primary care visits, emergency department visits) was assessed using negative binomial regression models. Results: We included 3,108 older adult members and 92,045 controls in our study. We had 1,765 members attend <1 weekly and 1,343 attend >1 weekly at the medical fitness facility. Compared to controls, older adult members had a 62% lower risk of all-cause mortality during the first 536 days (HR, 0.38 [95% CI, 0.29 – 0.48]) and 37% after 536 days (HR, 0.63 [95% CI, 0.56 – 0.70]). Older adult members were associated with a lower risk of hospitalizations (OR, 0.74 [95% CI, 0.70 – 0.80]), which strengthened the more frequently older adults attended (<1 weekly; OR, 0.80 [95% CI, 0.73 – 0.88], (>1 weekly; OR, 0.65 [95% CI, 0.59 – 0.73]). Less frequent users were more likely to visit a general practitioner (OR, 1.07 [95% CI, 1.03 – 1.11]). More frequent users were more likely to visit a general practitioner (OR, 1.06 [95% CI, 1.01 – 1.10]), less likely to visit the emergency department (OR, 0.79 [95% CI, 0.72 – 0.86]) and had a lower risk of a major adverse cardiac event after sustained exposure to the medical fitness facility (HR, 0.71 [95% CI, 0.57 – 0.89]). Conclusions: Our findings suggest that attending a medical fitness facility may be associated with survival benefits, as well as, decreased risk of hospitalizations, emergency department visits and cardiovascular events, for older adult members. The medical fitness model may be an alternative approach for public health efforts aimed at reducing physical inactivity and promoting positive health behaviours in older adult populations.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.519
Threshold uncertainty score0.969

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.405
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 designObservational
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
Published2020
Admission routes1
Has abstractyes

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