Obesity and physical inactivity are major modifiable risk factors for numerous chronic diseases and conditions in adulthood, including premature mortality. Physical activity (PA) is also positively related to physical and mental health-related quality of life (HRQL) (6), and obesity is associated with significant impairments in HRQL (24). However, new evidence suggests that 85% of adult Canadians do not meet current PA recommendations (9), whereas 61% are overweight or obese (34).
HRQL is defined as “an individual’s or group’s perceived physical and mental health over time” (8). The US Centers for Disease Control and Prevention has identified 4 valid and reliable population surveillance measures: Self-Rated Health (SRH), and numbers of recent Physically Unhealthy Days, Mentally Unhealthy Days, and Activity Limitation Days (8).
Although not eliminating the risk associated with excess body weight/abdominal fat, several studies have shown that PA/fitness is beneficial at all body mass index (BMI) or waist circumference levels: mortality and cardiovascular health risk in lean but inactive/unfit persons often approaches or sometimes eclipses that of obese but active/fit persons (11,17,26,27,35,37). Few studies have examined whether a similar pattern occurs for HRQL.
In large US adult population studies, both underweight and increasing levels of overweight/obesity are negatively associated with SRH (12,21,30), Unhealthy Days, and Activity Limitation Days (12,14,30). Associations of overweight and moderate obesity varied by sex and age, presenting less risk for men, especially ≥ 45 yr. Although the health consequences of underweight may increase with age, those associated with obesity decline (12,21). For PA, significantly lower proportions of US adults meeting PA recommendations reported fair/poor SRH, or ≥14 Unhealthy Days in the past month, compared with inactive adults, at all ages (7). Elsewhere, the likelihood of poor SRH decreased with increasing PA, for all age/sex groups (13), and active adults were more likely to report good SRH (1). In recent US studies, overweight/obese adults of all ages meeting recommended PA levels had lower odds ratio (OR) of fair/poor SRH and ≥14 Unhealthy Days than their inactive counterparts (16), and regular PA was associated with better HRQL regardless of BMI: similar to biomedical outcomes, the prevalence of fair/poor SRH, ≥14 Unhealthy Days, or ≥14 Activity Limitation Days was comparable in obese–active and healthy weight–inactive persons (25).
Notably, overweight and moderate obesity may be of less significance for morbidity/mortality in older adults (2,23), PA and HRQL indicators may have different meanings depending on age (33), increasing age may affect HRQL both positively and negatively in overweight/obese persons (39), and older respondents may give disproportionately positive health assessments (18). Women may suffer a greater burden of disease attributable to overweight/obesity than men, largely due to lower HRQL (29), and women’s self-assessments of health are based on a broader range of health and non-health factors (5).
Although physical inactivity and obesity are separately associated with adverse HRQL, and recent US research suggests PA is the more important correlate, these relationships need to be better understood across various populations and subpopulations. The objectives of this study were to 1) explore the combined association of PA and BMI to HRQL in Canadian adults and older adults and 2) assess age/sex–based differences in the associations of BMI/PA to HRQL.
This study used data from the Canadian Community Health Survey (CCHS) Cycle 3.1 (January to December 2005). In-depth methodology has been published (4). Briefly, the CCHS is a cross-sectional survey conducted by Statistics Canada to collect information on health determinants, health status and health system utilization for the Canadian population. Using computer-assisted interviews, the CCHS targets persons ≥ 12 yr living in private dwellings in the 10 provinces and three territories, covering 98% of the population. Informed consent was obtained from each respondent by Statistics Canada, in accordance with Canadian federal legislative requirements. The present analysis included all respondents ≥ 18 yr, excluding pregnant women, with complete data for all study variables (n = 110,986), representing an estimated 22,563,527 Canadians. Data from a subsample with objectively measured height and weight in addition to self-reported measures were used to validate results (n = 3974). Ethics approval was not required for the current project as it involved secondary analysis of publicly available data.
Physical activity (PA) was assessed using an adaptation of the Minnesota Leisure Time Physical Activity Questionnaire (36). Respondents were asked about participation in 21 specified activities, plus up to three additional volunteered activities, indicating participation frequency (past 3 months) and average session duration. Average daily energy expended during leisure time PA was calculated, weighting activities by their MET values; results were expressed in kilocalories per kilogram per day (KKD). A physical activity index categorized participants as active (A; ≥3.0 KKD), moderately active (MA; 1.5–2.9 KKD), or inactive (IA; <1.5 KKD), whereby 3.0 KKD, reflects, on average, the equivalent of 60 min of moderate-intensity activity daily (22).
BMI was calculated as weight (kg) per height (m2). Height and weight were self-reported and also directly measured in a subsample of respondents. Participants were classified based on Canadian (15) and World Health Organization guidelines (38) as underweight (UW; <18.5 kg·m−2), healthy weight (HW; 18.5–24.9 kg·m−2), overweight (OW; 25–29.9 kg·m−2), or obese (OB; ≥30 kg·m−2).
Participants were asked, “Would you say your health in general is excellent, very good, good, fair, or poor?” SRH was dichotomized to estimate the probability of rating one’s health as “fair” or “poor,” versus “excellent,” “very good,” or “good.” This widely used measure is a significant independent predictor of morbidity, mortality, and health care utilization (19).
Participation and Activity Limitation
Participants were asked five questions as follows: “Do you have any difficulty hearing, seeing, communicating, walking, climbing stairs, bending, learning, or doing any similar activities?”; “Does a long-term physical condition or mental condition or health problem, reduce the amount or the kind of activity you can do at home?”; “…at school?;” “…at work?”; and “…in other activities, for example, transportation or leisure?” Response choices were “never,” “sometimes,” or “often.” A composite variable Participation and Activity Limitation (PAL) was created from the five questions, and dichotomized to estimate the probability of experiencing any of the above limitations “sometimes” or “often,” versus “never.”
Total Disability Days in the Past 14 d
Participants were asked, “During that period [past 14 d], did you stay in bed at all because of illness or injury, including any nights spent as a patient in a hospital?” and then “How many days did you stay in bed for all or most of the day?” They were then asked, “Not counting days spent in bed during those 14 d, were there any days that you cut down on things you normally do because of illness or injury?” and then “How many days did you cut down on things for all or most of the day?” Although the questions were asked regarding both physical and mental disability days, the composite Total Disability Days in the past 14 d (TDD) variable was used for this study because physical ill health accounted for 99% of disability days reported. TDD was dichotomized to estimate the probability of experiencing ≥1 disability day (≥1 TDD) versus 0 disability days.
Race/ethnicity (white/nonwhite), marital status (married-common law/single-divorced-widowed), smoking status (current/nonsmoker), education (<postsecondary graduate/postsecondary graduate), household income (<$30,000/$30,000–$59,999/≥$60,000/“missing”), number of comorbidities (from 25 conditions potentially affecting BMI/PA and HRQL: continuous 0–8+), and residence (urban/rural) were included as covariates in multivariate analysis. For income, high nonresponse necessitated retaining a “missing” category. Respondents with answers of “not stated” or “refused” for other study variables were excluded.
Participants were categorized based on combined BMI–PA levels, as follows:
Prevalences were calculated by BMI, PA, and BMI–PA, estimating the number of Canadians reporting fair/poor SRH, sometimes/often PAL, or ≥1 TDD. Multivariate logistic regression was used to predict the odds of these three outcomes, based on BMI, PA, and BMI–PA, controlling for covariates. Healthy weight and active were the referent groups. Statistically significant differences from the referent categories and between categories were identified by 95% confidence intervals (CI) not including 1 and non-overlapping 95% CI, respectively. Analyses were stratified by sex and age (18–44, 45–64, ≥65 yr).
Appropriate weights (provided) were applied to calculate summary statistics for the Canadian population; bootstrapping techniques were used to calculate 95% CI, using bootstrap weights provided by Statistics Canada.
Prevalence of SRH, by sex/age, was calculated for the participant subsample with both measured and self-reported height and weight. Sample size limitations precluded the inclusion of the underweight group, and multivariate analysis was not done because inferences from CCHS subsamples may not be generalizable to the population. The general bias resulting from the use of self-reported versus directly measured BMI in the CCHS 3.1 has previously been quantified (31).
Demographic and health characteristics of the population are presented in Table 1. Average BMI was just into the overweight range, with 50% of the population overweight or obese (58% of males and 42% of females). About 50% were inactive, whereas only 25% were active. Overall, 11% reported fair/poor SRH, 30% reported sometimes/often PAL, and 17% (females > males) reported ≥1 TDD in the past 14 d—representing more than 2.5, 6.7, and 3.8 million Canadians, respectively.
Because of small numbers of underweight participants per age/sex groups when further subdivided into PA categories, results with wide 95% CI should be interpreted with caution. However, the fact that these results are often quite different from the healthy weight sample supports their inclusion as a separate group.
The prevalence of fair/poor SRH increased with age within all BMI, PA, and BMI–PA categories for both sexes (Table 2). Overweight males did not have a greater prevalence of fair/poor SRH compared with healthy weight males. Underweight and obese males 18–64 yr had similar (higher) prevalence of fair/poor SRH; however, underweight was associated with a higher prevalence in males ≥65 yr. Underweight and obese females had progressively higher prevalence of fair/poor SRH at all ages compared with healthy weight females. Underweight was associated with a higher prevalence in older females, with the prevalence of fair/poor SRH similar for underweight and obese at ≥65 yr. The prevalence of fair/poor SRH also increased with decreasing PA level across all age–sex groups. Inactivity became increasingly associated with a higher prevalence of fair/poor SRH with increasing age: one-third of inactive older adults reported fair/poor SRH.
Figure 1A illustrates the combined effects of BMI and PA on the prevalence of fair/poor SRH. Across all age–sex groups, healthy weight–active persons had the lowest prevalence of fair/poor SRH and obese–active persons had similar or lower prevalence of fair/poor SRH compared with healthy weight–inactive persons. Within each weight category, the prevalence of fair/poor SRH was significantly greater among inactive compared with active persons. Within each PA category, the prevalence of fair/poor SRH was greater among obese compared with healthy weight persons, with no increase for overweight persons. The highest prevalence of fair/poor SRH occurred in underweight–inactive (older males especially) and obese–inactive persons.
The odds of fair/poor SRH did not increase significantly with age in either sex (Table 3). Obese males had increased odds of fair/poor SRH compared with healthy weight, but this association was significantly weaker in older (≥65 yr) compared with younger (18–44 yr) males; overweight males did not show increased odds of fair/poor SRH. Obese females had increased odds of fair/poor SRH at all ages, whereas overweight was associated with increased odds in females 18–44 yr only. Underweight males and females also had increased odds of fair/poor SRH. Both inactive males and females had significantly greater odds of fair/poor SRH compared with their active counterparts; moderately active males 18–44 yr and females 18–44 yr and 45–64 yr also showed increased odds. Odds ratios (ORs) were only mildly attenuated by including both BMI and PA in the model (models (b) in Table 3) versus each in their own model (models (a) in Table 3), demonstrating the independent effects of each.
For the combined BMI–PA categories, a pattern similar to the prevalence estimates emerged: obese–active individuals had, on average, similar or lower odds of fair/poor SRH compared with healthy weight–inactive individuals. Obese males ≥65 yr and females 45–64 yr and ≥65 yr did not have greater odds of fair/poor SRH if active (younger obese persons had increased odds of fair/poor SRH across all PA levels). Overweight males did not have increased odds of fair/poor SRH, unless inactive; overweight females had increased odds of fair/poor SRH if inactive or only moderately active, but not if active. Underweight–inactive males and females had greater odds of fair/poor SRH; underweight females ≥65 yr had greater odds of fair/poor SRH regardless of PA level. Inactivity was associated with increased odds of fair/poor SRH, across all age–sex groups, regardless of weight status.
For both sexes, prevalence of sometimes/often PAL increased with age within all BMI and PA groups (Table 2). In males, obesity was associated with increased prevalence at all ages, but overweight only at ≥65 yr. In females, both overweight and obesity were associated with increased prevalence at all ages. Inactivity was associated with higher prevalence of sometimes/often PAL for all groups, but most dramatically in older adults.
Obese–active males had similar prevalence of sometimes/often PAL as healthy weight–inactive; obese–active females had higher prevalence of sometimes/often PAL in those <65 yr, but similar prevalence at older ages (Fig. 1B). Overweight–active was not associated with increased prevalence of sometimes/often PAL in either sex. Inactive persons had a significantly higher prevalence of sometimes/often PAL compared with their active counterparts across most age/weight categories.
The odds of sometimes/often PAL were not affected by weight status in younger males but increased slightly with obesity at ≥45 yr (see Table, Supplemental Digital Content 1, http://links.lww.com/MSS/A129, for logistic regression results). Overweight and obesity were associated with greater odds of sometimes/often PAL in females <65 yr, whereas underweight and obesity increased the odds in older females. Odds of sometimes/often PAL were greater in inactive compared with active persons, especially in older adulthood. OR were not greatly attenuated by including both BMI and PA in the model, illustrating the independent effects of each. For the combined BMI–PA groups, being overweight and obese did not increase the odds of fair/poor SRH for active persons (increased odds of sometimes/often PAL for obese adults 45–64 yr only). Inactivity was associated with increased odds of sometimes/often PAL in healthy weight individuals, especially at ≥65 yr, and underweight increased the odds for inactive adults ≥65 yr.
Prevalence of ≥1 TDD seemed to decrease with age in both men and women (Table 2). In males, prevalence of ≥1 TDD was not greatly affected by BMI, although underweight is a potential issue especially in older males. Obese females had increased prevalence of ≥1 TDD at all ages. Inactivity was associated with higher prevalence of ≥1 TDD in adults ≥65 yr. Females generally showed a higher prevalence of ≥1 TDD than their same-age male counterparts within most BMI and PA categories.
For males 18–44 and 45–64 yr, overlapping 95% CI indicated no differences in prevalence of ≥1 TDD for any BMI–PA group compared with healthy weight–active; inactivity was associated with a higher prevalence across all weight categories in older males (Fig. 1C). Obese females 18–44 and 45–64 yr had a higher prevalence of ≥1 TDD compared with healthy weight, regardless of PA level, whereas obese women ≥65 yr had a higher prevalence only if inactive (as did overweight–inactive + underweight–inactive women ≥65 yr).
Weight status did not affect the odds of ≥1 TDD in males 18–44 yr or in females of any age; underweight was associated with increased odds for males ≥45 yr (see Table, Supplemental Digital Content 1, http://links.lww.com/MSS/A129, for logistic regression results). PA did not affect the odds of ≥1 TDD in males 18–44 yr or 45–64 yr or in females 18–44 yr; inactivity was associated with increased odds in older males and females. Including both BMI and PA in the models did not attenuate ORs. Similarly, combined BMI–PA category had little effect at 18–44 and 45–64 yr, but inactivity was associated with increased odds of ≥1 TDD across all weight categories at ≥65 yr, especially if underweight.
Subsample: measured versus self-reported BMI.
Subsample participants were similar to the larger sample on BMI, prevalence of fair/poor SRH, and most demographic characteristics. Using measured BMI, 39% of participants were healthy weight, 35% were overweight, and 24% were obese compared with 48%, 34%, and 16%, respectively, using self-reported BMI. Across all age/sex groups, overlapping 95% CI indicated no statistically significant differences in the prevalence of fair/poor SRH when participants were categorized by measured versus self-reported BMI (data not shown).
This study examined the associations of leisure time PA and BMI with HRQL in Canadian adults by sex and age. Results show that BMI and PA are each strong independent predictors of adverse HRQL; however, when taken in combination, PA emerges as the more important factor. An active individual’s weight status seems to have little association with SRH and PAL. Inactive persons, however, have a greater likelihood of fair/poor SRH, and sometimes/often PAL, regardless of their weight status. Results and patterns were especially striking and consistent for SRH, an indicator associated with changes in functional ability (20) and a consistent and powerful independent predictor of morbidity and mortality (19). Associations were weaker for TDD, with neither PA nor BMI showing much influence in persons <65 yr, and the most striking associations involving inactivity in persons ≥65 yr, especially if underweight. Although the prevalence of ≥1 TDD was lower in persons ≥ 65 yr (12%/15% M/F) versus < 65 yr (15%/21% M/F), which may reflect a retired versus employed outside the home difference, it is possible that causes of disability days in younger persons are of a more acute nature not directly influenced by BMI or PA (or even caused by higher PA) versus more chronic issues experienced by older adults more reflective of overall health and therefore more influenced by BMI and PA.
In general, this study found less association of overweight with HRQL in males, and in older adults, and greater association of underweight with HRQL in males, especially those ≥65 yr. No age/sex differences were apparent in the association of PA to HRQL. Our findings provide further evidence that patterns similar to those found for biomedical outcomes—including longitudinal studies showing PA/fitness to be beneficial across all BMI groups in terms of CVD and all-cause mortality, the health benefits of “leanness” largely limited to those who were active/fit (11,17,26,35,37)—may also apply to HRQL, such that obese–active individuals experience similar or better HRQL than healthy weight–inactive individuals. Our results showing less influence of overweight on HRQL in adults ≥65 yr match patterns reported in the morbidity/mortality literature in older adults as well (2,23).
Few studies have examined the combined association of BMI and PA on HRQL. In a recent US study, participation in regular PA was associated with better HRQL regardless of weight status, and the prevalence of fair/poor SRH, ≥14 Unhealthy Days, or ≥14 Activity Limitation Days was similar in obese–active persons and healthy weight–inactive persons (25). Similarly, overweight and obese adults meeting recommended PA levels had better SRH and lower OR of ≥14 Unhealthy Days than inactive adults; in fact, obese–active individuals had better SRH and Unhealthy Days versus healthy weight–inactive, highlighting the importance of regular PA on HRQL in overweight/obese persons despite their excess weight (16). Supporting these findings, our Canadian results also suggest that PA supersedes BMI in determining HRQL in adults and older adults; however, these associations vary with sex and age, particularly with respect to the overweight and underweight groups.
Previous studies have examined the association of either PA or BMI with HRQL. The proportions of US adults reporting ≥14 Unhealthy Days and fair/poor SRH were significantly lower among those attaining recommended PA levels versus insufficiently active or inactive adults for all age/sex groups (7). Korean adults had lower prevalence/odds of poor SRH with increasing PA level across all age/sex groups, in both healthy and physically impaired or chronically ill individuals (13). For older adults, a systematic review concluded that physical function and well-being were related to higher levels of PA (33). Several US studies have explored the BMI–HRQL association: adults who were underweight, overweight, or obese had increased odds of fair/poor SRH, whereas underweight and obese adults fared worse in Unhealthy Days and Activity Limitation Days, with a greater influence of underweight and little effect of overweight in males (12); the proportion of adults reporting fair/poor SRH increased with increasing BMI for overweight/obese, whereas obese and severely obese individuals had greater odds of >14 Unhealthy Days or >14 Activity Limitation Days (14); the proportion of adults free of chronic conditions reporting excellent health decreased with increasing BMI, but odds of reduced SRH were not increased for overweight persons (30); and underweight and severe obesity were consistently associated with increased disability and poorer health status, whereas associations for overweight and moderate obesity varied by age and sex, suggesting that a single “ideal body weight category” may not apply to all persons or health outcomes (21). Our findings lend further support to the benefits of PA to HRQL for men and women of all ages and further highlight potential age/sex–based differences in the BMI–HRQL relationship, especially for overweight and underweight.
Strengths and limitations
This study is the first to use nationally representative Canadian population data to examine the relationship of BMI and PA to HRQL, adding to recently published US reports. Also, the large sample allowed age/sex–based analysis and inclusion of the underweight group previously seldom studied. A basic analysis on a subsample of participants with measured BMI data, showing similar patterns, validates results obtained with self-reported BMI.
The cross-sectional data limit the inference of causal relationships and reverse causation may exist. Although PA can improve HRQL, individuals with impaired HRQL may be less likely to participate in PA and improved HRQL may be the cause of higher PA rather than the consequence. Healthier individuals may be more likely to be active; conversely, factors leading to poor health or activity limitations may also preclude PA participation. Likewise, whereas obesity may lead to impaired HRQL, factors leading to weight gain may also contribute to impaired HRQL. Underweight may also arise from underlying illness, especially in individuals ≥65 yr: illness could both impair HRQL and inhibit PA participation, resulting in the underestimation of the effects of obesity (28). Our analysis controlled for comorbidities to reduce this bias; however, severity of comorbidities could vary widely between conditions and between individuals, and this information was not available. Longitudinal studies are needed for additional insight.
A second limitation is the reliance on self-reported BMI and PA, risking underestimation of BMI and overestimation of PA. Underreporting of weight increases with age and increasing weight, women underreporting more than men, and overreporting of height increases with age and becomes substantial in adults ≥65 yr (10,31). Thus, underestimation of BMI, especially in older adults, might potentially explain some of our results. If obese individuals were misclassified as merely overweight, the obese category thus comprising a falsely elevated proportion of extremely obese individuals, the negative association of overweight and obesity to HRQL may be overestimated (32). Some respondents may also misreport their PA to reflect the socially desirable nature of PA participation (3), leading to overestimation of PA. However, because only leisure time PA was measured, conservative PA estimates may result if other daily activity is missed.
A third limitation is that the underweight group in our analysis suffered, in some instances, from small cell sizes, once subdivided by sex, age, and PA category. However, the consistent nature and direction of results for this group are evidence of its importance and the inappropriateness of collapsing it into the healthy weight category as is often done. Future research should strive for larger sample sizes in this group for comparison with other weight status groups, perhaps through targeted sampling methods, especially in the older adult population.
Finally, BMI does not necessarily reflect body composition in terms of fat versus fat-free mass or fat distribution. Particularly for younger males, a BMI in the overweight range may reflect increased muscle rather than fat mass, perhaps partly explaining the lack of effect of overweight on HRQL in males.
Our results further support the promotion of PA for reasons beyond merely the goal of weight loss or maintenance. Although both BMI and PA are strong independent predictors of adverse HRQL, PA emerges as the more important factor when taken in combination. This reinforces the importance of maintaining an active lifestyle for prevention and treatment of obesity-related outcomes, independent of weight loss. Future research should include objectively measured BMI, body composition/fat distribution, PA and fitness, and a larger sample of underweight individuals across age groups. Further, although known biological mechanisms exist to explain the health benefits of PA at all BMI levels with respect to biomedical outcomes (11), several of which may also play an indirect role in HRQL outcomes, the specific mechanisms involved remain to be explored. Longitudinal studies in particular would provide additional insight into the roles played by weight status and PA in the maintenance of good physical health and HRQL.
K. M. Herman was supported by a Doctoral Research Award from the Canadian Institutes of Health Research (Institute of Population and Public Health – Public Health Agency of Canada).
The authors would like to thank Dr. Gilles Paradis for his critical review of the final draft and revision of this article.
The authors declare no conflict of interest.
The results of this study do not constitute endorsement by the American College of Sports Medicine.
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