An updated synthesis of environmental factors influencing mobility and social participation in older adults: results from a scoping review
Bibliographic record
Abstract
BACKGROUND: As population ages globally, understanding how to better support mobility and social participation is increasingly important. Although several studies have explored the associations between the neighborhood environment, mobility and social participation, no integrated synthesis of current evidence is available yet. This study aimed to understand how the neighborhood environment currently affects mobility and social participation in older adults. METHOD: A scoping review followed a rigorous methodology based on the PRISMA guidelines. Seven databases were searched using a predefined set of keywords to retrieve empirical studies published between October 2013 and February 2025. Relevant articles were retained based on clear inclusion (inform about how the neighborhood environment influences mobility and social participation of individual aged 60 and older in high income country) and exclusion criteria (too narrow or specific, not from empirical study). Data was extracted, categorized and synthesized in Tables 2 and 3. RESULTS: Of the 38 selected articles, the majority report results of qualitative studies (16/38; 42.1%), quantitative (12; 31.6%) and others were mixed (10; 26.3%). Mainly conducted in Canada (7; 18.4%), most of these studies examined how the neighborhood environment is associated with both mobility and social participation (49; 63.63%). Among the 77 neighborhood attributes considered, they mainly focused on 'Products and technology' (32/77; 41.6%), 'Services, systems and policies' (17; 22.1%) and 'Natural and human-made changes' (17; 22.1%). These attributes mainly concerned 'Access to amenities and services', 'Adequate public transportation', 'Nature and green space', 'Social cohesion', 'Gathering places', 'Aesthetics', 'Social support' and 'Neighborhood security'. CONCLUSIONS: Mobility and social participation of older adults are highly related and often influenced by the same attributes. Results suggest potential interventions, such as creating supportive environments that enable people to continue doing what matters to them. Despite a rigorous process, relevant studies might have been overlooked, and time constraint limited both the scope of this study and inclusion of knowledge-users. Further research should consider a wider range of settings and focus on underexplored attributes to better promote mobility and social participation among older adults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.059 | 0.051 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".