Mapping Physical Activity Participation Barriers and Facilitators in Older East Asian Canadian Immigrants: A Scoping Review Protocol
Bibliographic record
Abstract
Exercise and physical activity has long been considered within the scientific community as a holistic remedy to upkeep health and wellbeing, with its multifaceted benefits thoroughly documented within the literature. It comes as no shock that within the aging population, there is a significant association between the health status of an individual, and the amount of physical activity they perform and integrate into their lives (Merom et al., 2012). However, despite these findings, within marginalized aged populations, more specifically, East Asian older adult immigrants in Canada, it is reported that a substantial portion of their time is spent devoted to sedentary activities post immigration (Tong, 2019). Understandably, this can be attributed to a multiplex of factors contributing to their lack of participation and sedentary behaviour, including various SES factors, societal barriers, lack of knowledge or education, low intrinsic motivation, current health status, stigma or personally held biases and beliefs, or just overall reduced vitality and diminished initiative, to name a few. This places an avoidable and unjustified negative impact on their healthspan, which could be readily mitigated and effectively addressed if their participation within physical activity could somehow be enhanced. Gaps in the literature currently include what factors inhibit this population from higher participation rates towards existing physical activity programs in Canada, and how to reduce this gap to promote equitable access to the health benefits of physical activity observed among non-marginalized older adults. (Bryan & Walsh, 2012). The question (and objective of scoping literature review) is then posed: What barriers do East Asian older adult immigrants face towards physical activity programming participation in Canada? In addition, what are some facilitating factors that, if amplified, would possibly promote higher engagement and adhesion towards physical activity for this population? For the methodological framework of the scoping review, I will be following Arksey and O’Malley’s proposed framework. The following databases have been selected for preliminary search, Medline OVID, CINAHL, Scopus, Web of Science (WoS), and SPORTDiscus (via EBSCOhost). Inclusion criteria will include sources published in English, sources published between 1990-2025, as the 90s marked a notable increase in East Asian immigrants among new arrivals, and research pertaining to exercise participation. Data will be extracted and thematically analyzed through the generation of a data chart via Excel, and thematic analysis will take place through iterative coding via NVivo. Results will be shared primarily via academic publications, community outreach channels, and will be included in my final thesis project. It is anticipated that the map will be used to inform the target population, exercise program directors, and wider municipal or national policy makers of the current constraints faced by older East Asian immigrants towards participation within existing physical activity programming, and potential future directions to explore to encourage elevated participation rates. Ultimately, the goal is to optimize the health benefits that physical activity can provide to support aging in place for this population. Ethics approval at the current time is inessential as the current scoping review protocol does not call for live subjects.
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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 | 0.045 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.059 | 0.007 |
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".