Supporting Community Members with Low Socioeconomic Status Engage in Physical Activity during the COVID-19 Pandemic
Bibliographic record
Abstract
Dear Editor-in-Chief: The pandemic has shown an overall reduction in people’s engagement with physical activity (PA) (1). Those with low income had the greatest reduction in PA, whereas those with high income experienced an increase in PA (1). This showcases COVID-19’s inequitable effect on people with low socioeconomic status (SES). Fearnbach et al. (1) highlight several protective factors that may reduce the PA decline, including PA self-monitoring, purchasing home-based equipment, and virtual fitness. We believe it is necessary to further explore the availability of these protective factors for community members with low SES because they may not have the time, literacy, or financial resources required for PA participation. We are not questioning the internal validity of the study of Fearnbach et al. (1), instead we would like to expand on their findings for low SES community members. Thus, the implementation of evidence-based strategies is important to support the engagement of low SES community members in PA. Although PA self-monitoring affects adherence to PA goals and levels, as discussed by Conroy et al. (2), low SES community members may lack access to the information or ability to document PA. Pampel et al. (3) emphasize the inverse relationship between SES and unhealthy behaviors, referring to the standing that the stratification system, typically measured by occupation, employment, income, wealth, deprivation, inequality, and stress, affects a low SES person’s capacity to cope. Given these circumstances, low SES community members may prefer not to engage in PA. There has been a surge in fitness equipment demand, which has raised equipment costs and resulted in supply shortages (4). In Canada, Buckner (4) found that StairMaster machines and treadmills have experienced a 200%+ surge in price since 2019. A basic training system without cardio equipment could cost over $200, whereas a single cardio machine may cost $1000 (4). Exercise bikes may cost as much as $3000 (4). These factors could pose challenges for low SES community members trying to access equipment for PA during the pandemic. Although virtual PA served as a protective factor against PA decline, virtual PA activity among low SES individuals may not be a viable protective factor because of the lack of accessibility to the Internet or devices needed to access virtual PA (5). This highlights how low SES community members may be unable to access virtual PA. It is important to support the engagement of low SES community members in PA. Jenum et al. (6) highlight how inexpensive, highly informative intervention programs were beneficial in increasing PA adherence among low SES individuals. Mediation programs that were the most advantageous to low SES individuals targeted their recognition of control, self-efficacy, and support from family and friends when confronted with PA adversity (6). In addition, alternatives during the pandemic include exercising outdoors and engaging in creative activities at home such as climbing stairs, hopping, and skipping during the pandemic (7). Furthermore, equipment can be improvised using materials such as ropes, brooms, and bottles (7). For individuals with technological access, they can follow online exercise classes (7). For those without access, telephone volunteer services could be used to support their engagement in PA (8). By implementing these strategies, it is possible to lessen the inequitable effect of COVID-19 on the PA levels of low SES community members. Nilanga Aki Bandara Balpreet Sasan Nathan Li Nicholas Feng Xuan Randy Zhou The University of British Columbia School of Kinesiology Vancouver, BC, CANADA
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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 | 0.004 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".