Abstract
Background
Increasing physical activity (PA) levels can decrease the burden of non-communicable diseases and improve functional ability in aging populations. Understanding current patterns in PA behaviours is essential for developing effective interventions. This study aimed to describe the usual PA by type of activity and amount in middle-aged and older adults.
Methods
A descriptive cross-sectional analysis of baseline data from the Physical Activity Scale for the Elderly in the Canadian Longitudinal Study on Aging was completed. Subgroup analyses were used to explore PA behaviour, by age and sex, socioeconomic variables, region and season. In addition, we estimated quintiles based on the amount of total PA completed in Canadians 45–85 years. Means and frequencies were reported using inflation weights.
Results
The 47,840 participants represented our target population of 12,365,513 Canadians 45–85 years old. The mean PASE score was 151 (SD 79.11) with 65% of persons 45–85 years completing at least 150-minutes of moderate-vigorous PA a week. Amount of PA and the proportion of individuals meeting the recommendation decreased for females, and with increasing age, lower income, and less education. Additionally, those with the lowest PA levels were more likely to report limitations in mobility and activities of daily living and had a higher prevalence of some chronic conditions (diabetes, musculoskeletal, and vision).
Conclusion
Physical activity behaviour among middle-aged and older adults varies based on several characteristics. Targeted interventions and promotion efforts are warranted, particularly for older females and those with lower income and education. Further investigation to determine directionality is needed.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-025-24032-0.
Keywords: CLSA, Descriptive analysis, Physical activity, Leisure activity, Social determinants
Introduction
Physical activity is an important modifiable behaviour for healthy aging [1]. Defined as any bodily movement produced by skeletal muscle that requires energy expenditure [2], physical activity (PA) levels vary across countries, age groups and sex, but it is estimated that over a quarter of adults worldwide are not achieving the recommended levels of PA for optimum health (150-minutes of moderate-to-vigorous PA) [3]. Increasing PA levels has the potential to decrease the economic burden on healthcare systems by over $500 million per year in Canada and $37 billion CAD worldwide [4, 5]. Understanding the patterns of PA behaviours and identifying individuals at greater risk for low levels of PA is essential for informing interventions and health promotion efforts.
Physical activity literature has begun to shift in focus from higher-intensity activities, referred to as moderate-vigorous (MVPA), to including the full spectrum of movement behaviours in daily life. The importance of lighter intensity activities (i.e., activities that do not elevate heart rate or cause perspiration) [6] is increasingly recognized in the discourse on PA and health and mortality, [7, 8] and national guidelines like the Canadian 24-hour movement guidelines [9]. Promoting a wider range of intensities as part of PA promotion is particularly relevant for populations who favour lighter intensities (e.g., older adults and women) [10], allowing for a more inclusive consideration of PA behaviour patterns that contribute to health. Therefore, it is extremely important to use measurement tools that capture a broad spectrum of physical activities.
Although low PA levels are considered a global health crisis, higher-income countries like Canada are said to experience a greater relative burden of inactivity [11]. Nonetheless, there is a dearth of studies that have comprehensively examined PA levels (i.e., include all intensities rather than only MVPA) among Canadian adults. This work describes the spectrum of usual PA (i.e., light to vigorous intensities) by type of activity and amount in middle-aged and older Canadians. Specific aims were to: (i) describe PA behaviour in Canadians aged 45–85 years, including by important subgroups related to age[12], sex, [12] socioeconomic status [13], and region and season [14]; and (ii) describe Canadians by the amount PA undertaken. Identifying subpopulations at greater risk of having lower PA, and patterns of PA behaviour, can inform targeted health promotion efforts for aging populations in Canada and similar high-income countries.
Methods
Study population
The Canadian Longitudinal Study on Aging (CLSA) is a population-based study of 51,338 Canadians across all 10 provinces. Baseline data collection was completed in 2015; participants were 45–85 years old and free from any cognitive impairment at the time of recruitment [15, 16]. The CLSA excluded individuals living in long-term or institutional care settings at the time of recruitment, full-time members of the Canadian armed forces, persons living on federal First Nations reserves or provincial First Nations settlements, and residents of the Canadian territories and some remote regions. The CLSA’s sample is divided into two cohorts, the tracking cohort (21,241) which uses assessments completed by telephone interviews and the comprehensive cohort (30,097) which consists of in-person (in-home and at data collection site) and telephone assessments. Due to the nature of the comprehensive cohort assessments participants in this group were recruited from a 25–50 km range around one of the 11 data collection sites located in seven provinces. All sampling strategies of the CLSA were a single stage design where the individual participant is considered the primary sampling unit [15, 17].
Baseline data was selected for this analysis as the sample better represents the target population in comparison to the follow up data. As part of the baseline data all participants were contacted approximately 1.5 years after their initial assessments to complete a maintaining contact questionnaire (MCQ), to help improve participant retention and administer several additional modules including PA [16]. A total of 47,840 completed the MCQ; 3,498 participants did not complete this additional baseline assessment and were not included in the present analysis. As the purpose of this work was to describe and generalize to the target population no further exclusion criteria were applied.
Physical activity
The Physical Activity Scale for the Elderly (PASE) is a 7-day retrospective questionnaire [18]. The PASE was designed to capture a broader range of activities; meaning, unlike other questionnaires (e.g., International Physical Activity Questionnaire, Global Physical Activity Questionnaire), it captures light intensity PA as well as MVPA. Additionally, it includes a wide variety of types of PA, with participants reporting on walking, light sport and recreation, moderate sport and recreation, strenuous sport and recreation, exercise, light housework, (indoor and outdoor), heavy housework (indoor and outdoor), home repairs, caregiving roles, work or volunteer activities, and sedentary. Details on the activity types and questions can be found in the supplemental table A2. The PASE has shown fair to moderate convergent validity with direct measures of PA (e.g., steps a day r = 0.39–0.61, activity counts r = 0.43–0.64) and moderate correlations with other PA questionnaires (e.g., IPAQ r= 0.65 − 0.44) [19]. Additionally, the PASE has shown good test-retest reliability (ICC ≥0.90) in community-dwelling older adults [19]. The CLSA administered a slightly modified version of the PASE as part of the MCQ [20]. Question responses to the PASE were used individually and together to operationalize four PA outcomes.
PASE total score
was calculated for all participants with the required information available, rounded to a whole number as per the PASE instruction manual [21].
Proportion of total score
The average proportion of total score for each activity was reported. Values range from 0 (do not contribute to total PA score) to 1 (is the sole contributor to total PA score). To determine the proportion of total PA score represented by each activity, each activity score (calculated from weights and question responses) was divided by the total score for each person.
Prevalence of activity types
The percentage of participants who completed each activity in the previous 7-days was calculated.
150-minutes of MVPA guidelines
The total time spent in walking, moderate recreation, strenuous recreation, and exercise were calculated using the methods by Mayo et al. (2021), details available in supplemental appendix A [22]. Individuals with at least 150-minutes of MVPA in the last 7-days were said to have met the guideline.
Stratification
In total, six sets of subgroups based on correlates of PA were explored for aim 1: (i) age and sex (eight subgroups), (ii) household income (five subgroups), (iii) education (four subgroups), (iv) material deprivation (5 subgroups), (v) social deprivation domain (5 subgroups) and (vi) region and season (16 subgroups; four regions and four seasons). Subgroup details can be found in appendix B. For the second aim, participants were grouped into weighted quintiles based on total PASE score to describe amount of PA, where Q1 represents those with lowest 20% of PA levels and Q5 representing those with the highest 20% of PA levels.
Statistical analysis
Analyses were completed using R studio (Rversion 4.1.2). Inflation sampling weights provided by the CLSA were used to estimate descriptors for the target population (R package ‘Svy’). The following was defined in the ‘svydesign’ function: sampling weights, the primary sampling unit (participant id’s), and the strata (geographical strata) [17, 23]. Sample and population descriptors were calculated using mean (standard deviation (SD)) and frequency (percent). Weighted and unweighted estimates of demographic, health, clinical, social, and environmental characteristics were calculated for the analytic sample. Physical activity behaviour was described using the four PA outcomes for each of the designated subgroups in aim 1. For aim 2, proportions and means for each PA quintile were estimated for correlates (e.g., age, sex, chronic conditions, environment). Missing data and patterns of missingness were explored (R package ‘mice’ using md.pattern function). Pairwise deletion was used, meaning the sample size varied between analyses based on which variables were included. Research ethics approval for this secondary analysis was obtained from the Hamilton Integrated Research Ethics Board (application #7342).
Results
Sample and target population
The 47,840 participants represented a population of 12,365,513 Canadians 45–85 years old. Six-hundred and thirty-three participants (1.3%) were missing information required to calculate the total PASE score. Our estimated population was 51.8% female, and 95.0% reported being of European descent. Over 75.0% of the target population was taking at least one prescription medication, more than 85.0% reported good or better health, over half received a degree or diploma, and 75.2% were in a relationship. The average PASE score was 151 (SD79.11) with 64.8% of Canadians 45–85 years meeting the 150-minutes of MVPA. Most participants (~ 70%) reported that the PA captured over the previous 7-days on the PASE was representative of their PA over the last 12 months. Full details on the sample, target population, and missing data are available in the appendix C.
General patterns in physical activity behaviour
Age and sex
Total PASE scores decreased with increasing age and were consistently lower in females (approximately 20-points lower in each age group). Figure 1 presents the proportion of total PASE score from each activity; for example, work made up a greater proportion of overall PASE scores for younger ages. The proportion of the score made up by light indoor housework increased with age, and heavy outdoor activities appeared to account for a larger proportion of scores in males than females. Exploring the percent of people who participated in each activity revealed similar results; home repairs and heavy outdoor activities were more common for males compared to caregiving which appeared to be more common for females. The youngest age group had the highest percent of people participating in strenuous recreation activities. Walking and light indoor housework were the most common activities across age groups and sexes. Additionally, the percent of people reaching 150-minutes of MVPA decreased in both males and females with age (details available in appendix D).
Fig. 1.
Proportion of PASE Scores for Males and Females. Each square represents the average proportion of the total PASE contributed by that activity for the subgroup. Where darker squares represent a greater proportion of the total score for that activity
Household income
Total PASE score increased with household income; however, the increase was most prominent between the two lowest income groups (28-point difference). When accounting for age, the increases in PASE score between income groups was smaller in higher age groups. For example, the 45–54 year old age group had a 44-point difference between the <$20,000 and the $20,000-$50,000 groups compared to the 17-point difference for the 75 + age group between the same income categories (Fig. 2).
Fig. 2.
Mean PASE Scores Across Age Groups for Each Household Income Level
Work appeared to make up a larger proportion of PASE scores for those with higher incomes; whereas, the proportion of total score from indoor housework decreased with higher income. However, after accounting for age, these patterns were less prominent across income groups and more pronounced when looking at age groups (Fig. 3). There was only one noticeable difference for the prevalence of each activity, a higher percentage of individuals in the ≥$100,000 income group participated in strenuous recreation activities. An increase in the percentage of Canadians reaching 150-minutes of MVPA with increasing income level was consistent even after accounting for age. More details available in appendix D.
Fig. 3.
Proportion of PASE Score by Age Groups Across Household Income. Each square represents the average proportion of the total PASE contributed by that activity for a subgroup. Darker squares represent a greater proportion of the total score for that activity
Education
Total PASE scores increased with education level. The largest increase was seen between those who have graduated and have not graduated from secondary school (17-points). Only minor differences were seen for proportion of score and percent of people completing each activity, with no clear overarching patterns (appendix F). As with total score, the percent of Canadians who achieved recommended levels of MVPA increased with education level; the largest jump was between the two lowest education groups (9.0%).
Material and social deprivation index
Material deprivation scores had less of a pattern in total PASE score with the highest mean score in Q2 (157 SD81.37) and the lowest score in the most deprived group (Q5; 146 SD79.39). A clearer pattern was seen for social deprivation score where PA levels decreased with increasing deprivation. No strong patterns were seen for PASE score proportions or types of activities completed for either deprivation scale. The percent of people reaching 150-minutes of MVPA was similar across quintiles for both domains (difference between Q1 and Q5 material 7%, social 2%, appendix H).
Region and season
Mean total PASE score varied between regions with residents in the Prairies (156 SD81.69) having the highest followed by British Columbia (152 SD78.82), Central (150 SD78.45) and Atlantic Canada (147 SD79.19) having the lowest. However, when season was considered, the Prairies only retained the highest PA amount for Jan-Mar, with Central Canada having the highest from Jul-Sep, and British Columbia having slightly higher PASE scores for the remaining two seasons (Fig. 4). Atlantic Canada remained the lowest PA levels three of four seasons with the most pronounced differences in the colder months.
Fig. 4.
Variations in mean PASE scores between regions and seasons in Canada
The Prairies had a higher proportion of their PASE total score coming from work and British Colombia had a smaller proportion of total score from heavy outdoor housework. Regions were very similar in the percentage of people completing each type of activity. British Columbia has a slightly higher percentage of people completing 150-minutes of MVPA a week (71%) compared to the other three regions (61–64%). Visual depictions are available in appendix H.
Physical activity behaviour
Physical activity level quintiles
Physical activity quintiles were: Q1 ≤85, Q2 86–121, Q3 122–158, Q4 159–217, and Q5 ≥218 (Table 1). The percent of females gradually decreased with increasing PA quintile. There was a 10% difference in the percent of people making over $150,000 per household between the lowest and the highest quintile. There was a 15.0% shift from those with no secondary school graduation to those with a degree or diploma when going from Q1 to Q5. The prevalence of diabetes, musculoskeletal, neurological and vision conditions was at least 10% higher in the least active compared to the most active. Almost a quarter (22.7%) of individuals in Q1 reported fair or poor general health, which was reduced to 12.9% in Q2 and only 7.5% in the most active group. There was over a 15.0% decrease in the presence of an ADL/IADL limitation going from Q1 to Q5. Disparities of PA across household income, education, self-reported health, and sex can be seen in Fig. 5.
Table 1.
Weighted sample description by total PASE score quintiles
| 1 st Quintile 20th Percentile and under (≤85) | 2nd Quintile 20th−40th Percentiles (86–121) | 3rd Quintile 40th-60th Percentiles (122–158) | 4th Quintile 60th −80th Percentiles (159–217) | 5th Quintile 80th Percentile and higher(≥218) | |
|---|---|---|---|---|---|
| PASE ScoreMean (SD) | 55.42 (20.88) | 104 0.76 (10.65) | 139.92 (10.56) | 184.21 (16.81) | 274.46 (49.40) |
| AgeMean (SD) | 64.50 (11.14) | 62.03 (10.22) | 60.65 (9.65) | 57.84 (8.90) | 53.75 (6.90) |
| Sex (% Female) | 1,593,498 (63.41%) | 1,405,949 (57.90%) | 1,242,232 (52.04%) | 1,184,589 (48.53%) | 894,034 (36.91%) |
| Household Income | |||||
| <$20,000 | 300,676 (12.97%) | 167,649 (7.36%) | 109,404 (4.82%) | 74,606 (3.21%) | 42,569 (1.81%) |
| $20,000-$50,000 | 760,862 (32.83%) | 663,931 (29.16%) | 662,613 (29.17%) | 526,079 (22.65%) | 352,617 (15.0%) |
| $50,000-$100,000 | 730,957 (31.54%) | 815,114 (35.80%) | 870,089 (38.31%) | 866,227 (37.30%) | 893,321 (38.01%) |
| $100,000-$150,000 | 312,812 (13.50%) | 361,940 (15.90%) | 337,404 (14.86%) | 477,539 (20.56%) | 606,031 (25.79%) |
| >$150,000 | 212,228 (9.16%) | 267,962 (11.77%) | 291,710 (12.84%) | 377,923 (16.27%) | 455,780 (19.39%) |
| Education | |||||
| < Secondary School graduation | 708,806 (28.33%) | 518,235 (21.38%) | 448,187 (18.86%) | 418,294 (17.20%) | 294,125 (12.17%) |
| No Post-Secondary | 334,207 (13.36%) | 371,478 (15.33%) | 340,948 (14.35%) | 338,868 (13.93%) | 310,614 (12.85%) |
| Some Post-Secondary | 195,859 (7.93%) | 213,665 (8.82%) | 215,611 (9.07%) | 231,096 (9.50%) | 223,671 (9.25%) |
| Degree or Diploma | 1,262,905 (50.48%) | 1,320,117 (54.47%) | 1,371,884 (57.72%) | 1,444,050 (59.37%) | 1,588,459 (65.72%) |
| Self-reported General Health | |||||
| Good or Better | 1,942,178 (77.34%) | 2,111,379 (87.08%) | 2,154,483 (90.34%) | 2,230,005 (91.39%) | 2,240,721 (92.52%) |
| Fair/Poor | 568,903 (22.66%) | 313,177 (12.92%) | 230,315 (8.61%) | 210,039 (8.61%) | |
| Diagnosed with Medical Condition (% yes) | |||||
| Cancer | 434,035 (16.18%) | 331,712 (13.67%) | 322,903 (13.54%) | 255,873 (10.49%) | 206,253 (8.53%) |
| Cardiovascular | 1,464,996 (16.18%) | 1120,800 (13.67%) | 978,621 (13.54%) | 908,399 (10.49%) | 697,012 (8.53%) |
| Diabetes | 649,577 (24.22%) | 401,582 (16.56%) | 340,773 (14.31%) | 351,987 (14.43%) | 287,110 (11.88%) |
| Musculoskeletal | 1,641,195 (61.57%) | 1,335,815 (55.52%) | 1,208,304 (51.03%) | 1,180,390 (48.64%) | 1,028,833 (42.79%) |
| Neurological | 351,716 (13.21%) | 194,291 (8.05%) | 153,742 (6.47%) | 117,121 (4.81%) | 73,046 (3.02%) |
| Respiratory | 559,938 (20.90%) | 442,760 (18.30%) | 353,508 (14.87%) | 309,688 (12.76%) | 315,664 (13.06%) |
| Vision | 1,023,655 (38.47%) | 659,128 (27.37%) | 544,648 (22.98%) | 432,324 (17.90%) | 217,394 (9.04%) |
| Limitation in one or more ADL/IADL | |||||
| Yes | 483,496 (19.31%) | 160,050 (6.59%) | 90,444 (3.79%) | 74,213 (3.04%) | 71,472 (2.95%) |
| No | 2,020,916 (80.69%) | 2,267,312 (93.41%) | 2,294,273 (96.21%) | 2,365,897 (96.96%) | 2,348,593 (97.05%) |
| Mobility limitation (uses a gait aid) | |||||
| Yes | 560,878 (22.32%) | 259,010 (10.67%) | 195,080 (8.17%) | 148,558 (6.09%) | 111,805 (4.62%) |
| No | 1,952,266 (77.68%) | 2,169,259 (89.33%) | 2,192,079 (91.83%) | 2,292,572 (93.91%) | 2,310,333 (95.38%) |
| Social SupporMean (SD) (0-100) | 79.42 (19.98) | 82.34 (17.43) | 83.58 (17.01) | 84.87 (15.93) | 84.75 (15.84) |
| Social participation – not counting PA | |||||
| Yearly | 152,251 (5.68%) | 105,160 (4.34%) | 78,529 (3.30%) | 76,207 (3.13%) | 79,516 (3.28%) |
| Monthly | 650,314 (24.25%) | 604,861 (24.98%) | 557,435 (23.39%) | 519,818 (21.33%) | 612,896 (25.31%) |
| Weekly | 1,627,427 (60.67%) | 1,500,372 (61.97%) | 1,500,824 (62.98%) | 1,583,489 (64.98%) | 1,467,522 (60.60%) |
| Daily | 211,669 (7.89%) | 197,278 (8.15%) | 242,879 (10.19%) | 252,416 10.36(%) | 261,194 (10.79%) |
| Self-perceived Social Status | |||||
| 1–2 (low) | 370,695 (14.77%) | 220,142 (9.52%) | 203,946 (8.81%) | 171,915 (7.25%) | 140,907 (5.97%) |
| 3–4 | 360,737 (14.38%) | 305,961 (13.24%) | 274,750 (11.87%) | 262,971 (11.09%) | 256,707 (10.88%) |
| 5–6 | 931,678 (37.13%) | 913,536 (39.52%) | 906,922 (39.18%) | 940,728 (39.68%) | 844,197 (35.77%) |
| 7–8 | 709,631 (28.28%) | 763,930 (33.05%) | 811,798 (35.07%) | 863,360 (36.42%) | 980,970 (41.57%) |
| 9–10 (high) | 136,319 (5.43%) | 108,174 (4.68%) | 117,156 (5.06%) | 131,887 (5.56%) | 136,985 (5.81%) |
| Rural | 420,900 (16.75%) | 549,574 (22.63%) | 538,361 (22.55%) | 523,659 (21.45%) | 570,950 (23.57%) |
| Urban | 2,092,244 (83.25%) | 1,878,696 (77.37%) | 1,848,798 (77.45%) | 1,917,470 (78.55%) | 1,851,189 (76.43%) |
Fig. 5.
Population Patterns Across PASE Total Score Quintiles. Bar sections represent the percent of people from each outcome present in each PA quintile
Discussion
This is the first paper to provide a detailed description of usual PA, exploring both the types of activities and amount of light to vigorous PA in Canadians 45–85 years old across all 10 provinces. This descriptive analysis demonstrated the relationships between several social determinants of health and PA behaviours. Notably, these relationships were most pronounced in those from the lowest socioeconomic groups. Targeted PA promotion efforts appear warranted based on the heterogeneity of PA behaviours by age, income level, and region in middle-aged and older Canadians. While several factors appeared to have salient relationships with PA levels, future research is needed to determine the directionality of these relationships.
Describing Canadians by their total PA levels demonstrated a clear gap in several social determinants of health between the most active (top 20% of PASE scores Q5) and the least active (bottom 20% of PASE scores Q1). Individuals in the bottom 20% of PA were more likely to be female, have lower household income and education levels, and report worse health. A similar intersectionality was seen in two population-based analyses of adults (18 + years) meeting recommended levels of MVPA in Brazil (n = 58,429) [24] and Canada (n= 15,510) [13]. Mielke et al. (2022) found that 48.0% of white males in Brazil from the highest income quartile with a university degree, compared to only 9.0% of non-white females with low education and income met recommended MVPA levels [24]. Colley et al. (2023), showed similar patterns using latent class analysis of the Canadian Health Measures Survey, where individuals least likely to meet guidelines were more likely to be female, older age, and have lower income and education [13]. While previous research has suggested that only examining MVPA may result in underrepresentation of PA in females and older adults [10], the present work builds on the above findings by demonstrating that even when accounting for lighter intensity activities these patterns persist. These disparities in PA participation are concerning and suggest further investigation into the determinants of PA behaviour in these individuals is needed, and may improve interventions for these populations.
The exploration of socioeconomic factors showed a noteworthy distinction for the lowest levels. For both education and household income, the lowest categories demonstrated the most considerable differences in mean PASE scores compared to other categories. This could indicate the presence of a threshold for these two factors where a greater negative effect on PA is seen up to a certain point (i.e., secondary school graduation and $20,000+). The relationship with household income appeared to be attenuated by age but did not account for the overall trend of increasing PA with household income. Interventions and policies targeting PA promotion among individuals from lower income and education groups is of particular importance for improving population health in Canada.
The presented analyses provided several unique insights into PA behaviour among middle-aged and older Canadians. Our findings highlight several patterns related to the types of activities favoured that could be targeted as part of PA promotion efforts. For example, the lower participation in heavier housework reported among females, and lower household activity in younger age groups. Interestingly, there was not salient patterns across either the education or income (when accounting for age), suggesting that while the amount of PA differs across income and education levels, the activities that make up PA behaviour are similar. Another noteworthy trend that may be unique to Canada and countries with similar climates was the presence of high PA throughout the colder months in certain regions of Canada. The PA patterns in British Columbia and the Prairies contrast those reported in previous literature and from Central and Atlantic Canada [14]. Regardless of the season, Atlantic Canada appears less active overall and may benefit from specific PA promotion efforts in addition to national messaging.
Finally, while several variables exhibited relationships with PA behaviours, including the presence of mobility and ADL limitations and certain chronic conditions, this analysis was cross-sectional. Therefore, we cannot infer the directionality of these relationships and future work exploring longitudinal associations of these variables is warranted. There are several other limitations to consider when interpreting the data from this study. Firstly, the PASE was designed for older adults (65 + years) and has not been validated among middle-aged adults. However, the questionnaire was designed to be a comprehensive measure of PA in later life as it captures both light-intensity PA and work-related activities. The inclusion of light PA allows us to provide a more complete description of PA and to explore whether previously noted relationships with MVPA extended to total PA. Finally, the CLSA sample only represents community-dwelling Canadians from the 10 provinces (i.e., not the territories) and excludes persons living in Federal and other provincial First Nations settlements and full-time Canadian armed forces. Additionally, the exclusion of persons with cognitive impairment at the time of CLSA recruitment may have artificially inflated PA levels in the older age groups of our sample, as people with cognitive impairment are often less active [25, 26]. The exclusion criteria in combination with the detailed description of population characteristics should be considered before generalizing these results.
Conclusion
Middle-aged and older Canadians’ PA behaviour is heterogeneous in terms of both the amount and types of PA completed. Social determinants of health clearly influence PA levels in Canada, and targeted interventions and promotion efforts are warranted. Noticeable relationships between PA behaviours and health outcomes, including limitations in mobility and ADLs and chronic conditions, warrant further investigation via longitudinal analyses to determine directionality.
Supplementary Information
Acknowledgements
This research was made possible using the data/biospecimens collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation, as well as the following provinces, Newfoundland, Nova Scotia, Quebec, Ontario, Manitoba, Alberta, and British Columbia. This research has been conducted using the CLSA dataset Baseline Tracking Dataset Version 4.0, Baseline Comprehensive Dataset Version 7.0, under Application Number 190233. The CLSA is led by Drs. Parminder Raina, Christina Wolfson and Susan Kirkland. NDVI metrics, indexed to DMTI Spatial Inc. postal codes, were provided by CANUE (Canadian Urban Environmental Health Research Consortium). The opinions expressed in this manuscript are the author’s own and do not reflect the views of the Canadian Longitudinal Study on Aging.
Authors’ contributions
CD and MKB developed the research question and initial methods. CD conducted analysis with feedback and support from MKB, LEG, and JR. All authors contributed to the interpretation of results and manuscript revisions.
Funding
Access to CLSA data was supported by funding from the Canadian Institutes for Health Research through a CLSA catalyst grant (funding reference # 187253). MKB is supported by a Tier 2 Canada Research Chair in Mobility, Aging and Chronic Disease; LEG is supported by the McLaughlin Foundation Professorship in Population and Public Health.
Data availability
Data are available from the Canadian Longitudinal Study on Aging (www.clsa-elcv.ca) for researchers who meet the criteria for access to de-identified CLSA data.
Declarations
Ethics approval and consent to participate
Research ethics approval for this secondary analysis was obtained from the Hamilton Integrated Research Ethics Board (application #7342).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data are available from the Canadian Longitudinal Study on Aging (www.clsa-elcv.ca) for researchers who meet the criteria for access to de-identified CLSA data.