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Record W4281256881 · doi:10.2196/36234

The Effects of Overweight and Obesity on Obstacle Crossing During Walking: Protocol for a Systematic Review

2022· review· en· W4281256881 on OpenAlexvenueno aff
Matthias Chardon, Fábio Augusto Barbieri, Tiago Penedo, Paulo Cezar Rocha dos Santos, Nicolas Vuillerme

Bibliographic record

VenueJMIR Research Protocols · 2022
Typereview
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorAgence Nationale de la RechercheUniversidade Estadual PaulistaUniversité Grenoble Alpes
KeywordsOverweightProtocol (science)ObstaclePhysical medicine and rehabilitationMedicineObesityPsychologyComputer sciencePhysical therapyAlternative medicinePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Overweight and obesity are significant global health concerns that involve deficits in gait and balance that affect daily activities. Although much is reported about the effect of overweight and obesity on gait during unobstructed walking, not much is known about how overweight and obesity could impact gait under more challenging conditions, such as environments with obstacles. OBJECTIVE: The aim of this study is to systematically review and synthesize the available data regarding the effects of overweight and obesity on obstacle crossing during walking. METHODS: This review will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. PubMed, Web of Science, Scopus, and SPORTDiscus will be systematically searched with no limitations on publication date. Only full-text English-language articles published in a peer-reviewed journal will be included. Included articles must have compared obstacle crossing during walking in individuals with overweight or obesity to individuals of normal body weight. A total of 2 independent reviewers will select the articles and extract the following 4 sets of data: (1) study characteristics, (2) sample description, (3) obstacle crossing task protocol, and (4) main results obtained. If a considerable number of homogeneous papers are included, a meta-analysis will be conducted. A preliminary search was conducted in November 2021. RESULTS: The results will include the article selection flowchart as well as tables and figures synthesizing the extracted data on the effects of overweight and obesity on obstacle crossing during walking. The preliminary search identified 73 original records, of which 5 articles met the inclusion criteria. CONCLUSIONS: This review will present researchers and clinicians with an overview of published studies that have compared the performance of obstacle crossing for individuals with overweight and obesity to those of normal body weight. Gaining insight into the control strategies adopted by individuals with overweight and obesity is critical for safe and successful obstacle crossing in this population. We therefore believe that our findings could be useful for identifying people at risk of falls and developing and implementing fall prevention programs for individuals with overweight and obesity. TRIAL REGISTRATION: PROSPERO CRD42021269949; https://tinyurl.com/3yrwccu4. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36234.

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)0.083
metaresearch head score (Gemma)0.102
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.083
Threshold uncertainty score0.437

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.102
Meta-epidemiology (narrow)0.0070.006
Meta-epidemiology (broad)0.0170.020
Bibliometrics0.0110.010
Science and technology studies0.0050.005
Scholarly communication0.0070.008
Open science0.0050.005
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0770.012

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 head0.274
GPT teacher head0.620
Teacher spread0.346 · 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
GenreProtocol

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

Citations1
Published2022
Admission routes1
Has abstractyes

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