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Record W4412466071 · doi:10.1186/s12889-025-23613-3

Facilitators and barriers of reducing sedentary behavior in sedentary and non-sedentary older adults: a descriptive qualitative study based on the COM-B model and TDF

2025· article· en· W4412466071 on OpenAlexaboutno aff
Siqing Chen, Kaijie Yang, Albert Ko, Edward L. Giovannucci, Matthew Stults‐Kolehmainen, Lili Yang

Bibliographic record

VenueBMC Public Health · 2025
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersJinhua Science and Technology BureauZhejiang University
KeywordsMedicinePsychological interventionBiostatisticsQualitative researchSittingGerontologyPopulationSedentary lifestyleChecklistSedentary behaviorPublic healthPhysical activityPhysical therapyPsychologyNursingEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Prolonged sedentary behavior is a critical health risk for older adults. However, little is known about the distinct barriers and facilitators experienced by sedentary and non-sedentary older adults. Understanding these factors is essential for designing effective behavior change interventions. PURPOSE: The study aims to identify and categorize the barriers and facilitators to reducing sedentary behavior among sedentary and non-sedentary older adults using the Capability, Opportunity, Motivation-Behavior (COM-B) model and Theoretical Domains Framework (TDF), thereby informing future mobile health (mHealth) interventions designed to reduce sedentary time in this population. METHODS: Data were collected through semi-structured interviews with older adults, conducted at two community hospitals in China between July 2024 and September 2024. The interviews focused on older adults' psychological and physical capabilities, social and physical opportunities, and reflective and autonomous motivations related to sedentary behavior. According to the Canadian 24-Hour Movement Guidelines, participants were classified as sedentary (> 8 h/day sitting time) or non-sedentary (≤ 8 h/day) based on a participant characteristics questionnaire with verbal confirmation during the interview. The data were analyzed thematically, and the identified themes were mapped onto the COM-B model and TDF. Study procedures followed the COREQ checklist for qualitative research reporting. RESULTS: The study included 29 older adults, comprising 19 sedentary (65.5%) and 10 non-sedentary (34.5%). The following ten higher-order themes were identified: Lack of Knowledge (and Limited Knowledge); Lack of Methods (and Available Methods); Sedentary Triggers (and Interruptions); Lack of Management (and Self-management); Lack of Social Support (and Available Social Support); Lack of Environmental Support (and Available Environment Support); Perceptions and Conflicts (and Importance and Effort); Lack of Confidence (and Confidence); Limited Belief (and Understanding Health Benefits); and Limited Motivation (and Sufficient Motivation). CONCLUSION: Sedentary older adults face barriers such as low awareness of health risks, lack of regulation strategies, and insufficient social support, while non-sedentary older adults demonstrate higher confidence, better self-regulation, and engage in structured activities supported by cues such as mobile health reminders.

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)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0010.002
Open science0.0010.002
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 head0.062
GPT teacher head0.372
Teacher spread0.310 · 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 designQualitative
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

Citations4
Published2025
Admission routes1
Has abstractyes

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