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Record W4388201514 · doi:10.3390/ejihpe13110165

Enhancing Physical and Psychosocial Health of Older Adults in Saudi Arabia through Walking: Comparison between Supervised Group-Based and Non-Supervised Individual-Based Walking

2023· article· en· W4388201514 on OpenAlexaff
Palash Karmakar, Ming Yu Claudia Wong, Mezna A. AlMarzooqi, Nouf Alghamdi, Kai-ling Ou, Yanping Duan, Ryan E. Rhodes, Chun‐Qing Zhang

Bibliographic record

VenueEuropean Journal of Investigation in Health Psychology and Education · 2023
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPsychosocialPhysical healthGerontologyPhysical activityPsychologyPhysical medicine and rehabilitationPhysical therapyMedicineMental healthPsychiatry

Abstract

fetched live from OpenAlex

Walking is widely recognized as one of the most common and effective forms of physical activity, particularly for older adults. This study examined the comparative effects of two types of walking interventions, including supervised group-based intervention (SGBI) and non-supervised individual-based intervention (NSIBI), on frailty syndrome, cognitive functions or skills, and health-related quality of life among sedentary older Saudi individuals. A 15-week double-blinded, randomized controlled trial (RCT) including three groups (two were intervention groups while the other was the control group) was conducted among older adults who were inactive to examine the effect of different forms of walking interventions on frailty syndrome, cognitive functions, and health-related quality of life. A total of 107 participants, including 65 males and 42 females, were divided into three groups, which include SGBI, NSIBI, and the control group. Frailty syndrome was measured using the physical performance test (PPT), while cognitive function and health-related quality of life were assessed using the Mini-mental State Examination (MMSE) and the Short Form 36 (SF 36) health survey questionnaire. One-way repeated measures analysis of variance (ANOVA) and one-way analysis of covariance (ANCOVA) with the pre- and post-tests were performed for within- and between-group differences. while post-test data for the control group participants were absent due to the change in elderly center regulation, and they were excluded from the analysis. Hence, the comparison was stated only between the intervention groups. Both of the intervention groups (SGBI and NSIBI) showed significant within-subject differences in the Physical Function subscale of the health-related quality of life scale only, with F(1,20) = 23.03, p < 0.001, and F(1,18) = 27.22, p < 0.001, respectively. On the other hand, the Physical Performance Test revealed significant [F(2,51) = 9.21, p < 0.001] between-group differences in the post-test based on the baseline values. In addition, the average step count of older adults was increased from 4000 steps per session to around 7000 steps per session in the intervention group. The average heart rate of the NSIBI group did not show a visible change, and the resting heart rate of both groups showed a slightly declining trend throughout the intervention period. The walking intervention significantly increased participants’ physical function, which is a component of health-related quality of life and physical performance (frailty level), along with average daily step counts for older adults in Saudi Arabia. Regular engagement in the recommended level of walking is strongly advisable for Saudi Arabian older adults to maintain their overall quality of life at this stage.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.053
GPT teacher head0.374
Teacher spread0.321 · 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
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

Citations7
Published2023
Admission routes1
Has abstractyes

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