Journal of Aging and Physical Activity, 2016, 24, 32  -38 http://dx.doi.org/10.1123/japa.2014-0169 © 2016 Human Kinetics, Inc.

ORIGINAL RESEARCH

Association Between Body Mass Index, Physical Activity, and Health-Related Quality of Life in Canadian Adults Alina Cohen, Joseph Baker, and Chris I. Ardern Background: Obesity is associated with impairments in health-related quality of life (HRQL), whereas physical activity (PA) is a promoter of HRQL. Purpose: The aim of this study was to investigate the interaction between BMI and PA with HRQL in younger and older Canadian adults. Methods: Data from the 2012 annual component of the Canadian Community Health Survey (N = 48,041; ≥ 30 years) were used to capture self-reported body mass index (BMI- kg/m2), PA (kcal/kg/day, KKD), and HRQL. Interactions between PA and age on the BMI and HRQL relationship were assessed using general linear models and logistic regression. Results: Those younger (younger: µ = 0.79 ± 0.02; older: µ = 0.70 ± 0.02) and more active (active: µ = 0.82 ± 0.02; moderately active: µ = 0.77 ± 0.03; inactive: µ = 0.73 ± 0.01) reported higher HRQL. Older inactive underweight, normal weight, and overweight adults have lower odds of high HRQL. Conclusion: PA was associated with higher HRQL in younger adults. In older adults, BMI and PA influenced HRQL. Keywords: physical activity, obesity, BMI, aging, health-related quality of life

The living environment in Canada and other developed countries is characterized by a high energy intake and low daily energy expenditure due to the abundance of affordable processed foods as well as the high prevalence of sedentary behaviors (Brownson, Boehmer, & Luke, 2005; Juneau & Potvin, 2010; Mirowsky, 2011; Stamatakis, Ekelund, & Wareham, 2007). Taken together, these factors have contributed to an increase in body mass index (BMI) and decrease in total energy expenditure, which collectively increase the risk of chronic disease and impaired quality of life (Bize, Johnson, & Plotnikoff, 2007; Popkin, Adair, & Ng, 2012). Both physical inactivity and obesity have been linked to increased risk of cardiovascular disease, type 2 diabetes, some cancers, and allcause mortality (Han, Tajar, & Lean, 2011; Warburton, Nicol, & Bredin, 2006). Moreover, obesity is associated with significant impairments in health-related quality of life (HRQL), while regular physical activity (PA) is positively related to physical and mental HRQL (Bize et al., 2007; Imayama et al., 2011l; Kolotkin, Meter, & Williams, 2001). HRQL is a multidimensional concept that represents an individual’s perception of the impact of health problems on various spheres of life that includes physical, mental, and social aspects, as well as general well-being (Hickey, Barker, McGee, & O’Boyle, 2005). In a review by Bize et al. (2007), higher HRQL was consistently related to higher PA levels among healthy adults. PA also enhances well-being and increased physical functioning in people with poor health or advanced age (Dondzila et al., 2015; Rejeski & Mihalko, 2001). There is also an increasing recognition of the association between BMI and HRQL (Jensen, 2005; Katz, McHorney, & Atkinson, 2000; Korhonen, Seppala, Jarvenpaas, & Kautiainen, 2014). For example, in a cross-sectional study of 16–64-year-olds, being overweight was associated with poorer HRQL as well as inferior functional status, pain, anxiety, and restricted activity (Larsson, Karlsson, & Sullivan, 2002). Overweight and obesity are also associated with unhealthy aging, with longitudinal studies showing that weight gain is associated with a reduction in HRQL or with larger declines in HRQL Cohen, Baker, and Ardern are with the School of Kinesiology and Health Science, York University, Toronto, Ontario, Canada. Address author correspondence to Chris I. Ardern at [email protected] 32

than those who maintain a stable weight (Strandberg, Strandberg, Salomaa, Pitkala, & Miettinen, 2003; Williams, Young, & Brown, 2006). It should be noted that in older men and women, a BMI below 25 was associated with increased total mortality. In older age, the optimal weight with the lowest mortality is in the overweight category (BMI 25–29.9), whereas moderately obese individuals had only a modest increase in mortality risk (Orpana et al., 2010). It has also been observed that hospitalized older adults who were moderately overweight had a lower risk of mortality compared with those who are underweight (Bouillanne et al., 2009). These findings are important because almost half of the older adult population is overweight (BMI 25–29.9) and it is frequently assumed that these individuals have increased mortality (Adams et al., 2006; Oreopoulos, KalantarZadeh, Sharma, & Fonarow, 2009). As people age, BMI levels tend to increase and physical fitness levels tend to decrease (strength, endurance, agility, and flexibility). This results in difficulties in daily life activities and normal functioning (Kostic´, Pantelic´, Uzonovic´, & Djuraskovic, 2011; Riebe et al., 2009). Generally, performance level of daily activities of older adults decreases with aging, although it is well known that PA is important for independent living, prevention of chronic health problems, and quality of life (Brill, 2004). To date, little is known about the interrelationships between BMI, PA, and HRQL, and how their relationship might differ in younger and older adults. The aim of the current study was to explore the joint associations and interaction between BMI and PA in younger (30–59 years) and older (60–80+ years) Canadian adults. This age group (i.e., ≥ 30 years of age) is particularly important because by this point in the adult lifespan there is an accumulation of risk factors that may negatively impact HRQL. The prevalence of one or more risk factors as well as trajectories of overweight/ obesity and physical inactivity are also more pronounced beyond this point (Maldonado & Greenland, 2002; Powell & Blair, 1994).

Methods Sample This study used data from the 2012 annual component of the Canadian Community Health Survey (CCHS). Briefly, the CCHS is a

Association Between BMI and Activity Levels on Quality of Life   33

cross-sectional survey that collects information related to health status, health care utilization, and health determinants for the Canadian population. It surveys a large sample of respondents and is designed to provide reliable estimates at the health region level. Using computer-assisted interviews, the CCHS recruits persons aged 12 and over living in private dwellings in the 10 provinces and three territories, representing 98% of the Canadian population. Informed consent was obtained from each respondent by Statistics Canada, in accordance with Canadian federal legislative requirements. The present analysis was limited to respondents between the ages of 30–80+ years of age to examine these relationships within a sample with adequate chronic disease risk factors, obesity, and physical inactivity to relate to HRQL scores. Respondents with answers of “not stated” or “refused” for other study variables were excluded, leaving a sample of n = 48,041 (representing an estimated 21,523,898 Canadians) with complete data for all study variables.

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Measures Physical Activity.  PA was assessed using an adaptation of the

Minnesota Leisure Time Physical Activity Questionnaire (Taylor et al., 1978). Respondents were asked about participation in 21 specified activities, plus up to three additional participant-reported activities, indicating participation frequency within the past three months as well as average session duration. Average daily energy expended during leisure time PA was calculated, weighting activities by their MET values. Metabolic equivalent (MET) is a measure of energy expenditure. One MET is the rate of energy expenditure while sitting at rest, which, for most people, is an oxygen uptake of approximately 3.5 ml/(kg-min). The energy expenditure of other activities is expressed in multiples of METs. For example, standing requires approximately 2 METs (Ainsworth et al., 2000). Results were expressed in kilocalories per kilogram per day (KKD). A Physical Activity Index categorized participants as active (≥ 3.0 KKD), moderately active (1.5–2.9 KKD), or inactive (< 1.5 KKD), whereby 3.0 KKD reflects, on average, the equivalent of 60 min of moderate-intensity activity daily (Schmitz, Kruse, & Tress, 2000). Research suggests that engaging in 30 min of moderate-intensity PA five days/week or 20 min of vigorous-intensity PA on three days/ week is the recommended amount of activity to achieve substantial health benefits. These values are based on the MET range of 3–6 for moderate-intensity activity and 150 min/week (Haskell et al., 2007). The Physical Activity Index demonstrates very good reliability (r = .90), criterion validity (r = .36), and validity when compared with physical activity measured by alternative questionnaire-based methods (r = .77) (Craig, Russell, & Cameron, 2002).

Anthropometry.  Height and weight were self-reported and BMI was calculated as weight (kg) per height (m2). Participants were classified based on Canadian and World Health Organization guidelines as underweight (< 18.5 kg/m2), normal weight (18.5–24 kg/ m2), overweight (25–29.9 kg/m2), or obese (≥ 30 kg/m2) (Health Canada, 2003; World Health Organization, 2000). Age.  Participants were asked to select their age from one of 16 possible categories. For the purposes of this analysis, only categories 6 (30–34 years) through 16 (80+ years) were analyzed. Health-Related Quality of Life.  An estimate of HRQL was derived from the Health Utilities Index (HUI). Briefly, the HUI is the value assigned to duration of life as modified by the impairments, functional states, perceptions, and social opportunities that are influenced by disease, injury, treatment, or policy (Patrick & Erickson, 1993). It also provides a standardized measure of health status and HRQL to describe the experience of patients undergoing therapy;

the long-term outcomes associated with disease or therapy; the efficacy, effectiveness, and efficiency of health care interventions; and the health status of general populations. The HUI describes an individual’s functional health status using eight basic attributes: vision, hearing, speech, ambulation, dexterity, emotion, cognition, and pain. Each attribute has five or six levels, ranging from normal to severely limited (or the complete absence of) functioning. For example, levels on the ambulation attribute range from 1 (“able to get around the neighborhood without difficulty, and without walking equipment) to 6 (“cannot walk at all”). A higher score on the individual attributes indicates a poorer level of functioning (i.e., a score of 5 represents poorer functioning than a score of 1). A multiattribute scoring algorithm synthesizes the descriptive information into a single global utility score, which ranges from –0.36 (worst health state) through 0.00 (dead) to 1.00 (full health) (Feeny et al., 2002). A score of 1.00 indicates no disability or perfect health in which all attributes of health status are at their highest functional level; a score of 0.89–0.99 indicates mild disability in which at least one attribute is at a reduced level of function that can be readily corrected and/or does not prevent any activities; a score of 0.70–0.88 indicates moderate disability in which at least one attribute is at a reduced level of function that cannot be corrected and/or prevents some activities; a score less than 0.70 indicates severe disability in which at least one attribute is at a reduced level of function that cannot be corrected and prevents many activities. The HUI system has strong test–retest reliability, validity, and internal consistency (Cronbach’s α = .81), and it is a very good discriminator of diseaserelated quality of life at the population level (Furlong, Feeny, Torrance, & Barr, 2001; Horsman, Furlong, Feeny, & Torrance, 2003). Covariates.  Given their previously established relationships with

the primary variables examined in these analyses, sex, marital status (married/common-law, widowed/separated/divorced, single/ never married), level of education (less than secondary school grad, secondary school grad, some postsecondary school, postsecondary grad), smoking status (yes/no), high blood pressure (yes/no), diabetes (yes/no), and mood disorder (yes/no) were included as covariates in the analyses. Respondents with answers of ‘‘not stated’’ or ‘‘refused’’ for all study variables were excluded from the analyses.

Statistical Analysis Participants were categorized into young-to-middle aged (30–59 years) and older (60–80+ years) groups. Categorical variables are presented as frequencies and percentages, and continuous variables as means and standard deviations (SD). The original HUI score was negatively skewed (skewness = –2.33); therefore, it was transformed by squaring to ensure normality assumptions were met. General linear models were used to determine the associations between BMI, PA, and age after adjusting for sex, education, marital status, smoking status, high blood pressure, diabetes, and mood disorder with HRQL scores. Regression diagnostics were assessed and there were no influential outliers and residuals were normally distributed. All of these variables and their interactions were not significant and were dropped from the analyses, with the exception of BMI, PA, and age. Because the three-way interaction between these categories of PA, BMI, and age were significant (p ≤ .001), PA-BMI analyses were further stratified by age. To further explore the relationship between PA and BMI on clinically-relevant impairments in HRQL, HUI scores were dichotomized (high: ≥ 0.89 [high functional level] vs. low: < 0.89 [moderate or severe disability which cannot be corrected]) (Horsman et al., 2003) and compared using multiple logistic regression. Statistical analyses were performed using SAS v 9.3 (SAS Institute Inc., NC, USA) with statistical significance set at α = .05.

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34  Cohen, Baker, and Ardern

To ensure representativeness of the results to the Canadian population, the master weight in the SAS survey procedures were applied.

Results Demographic and health characteristics of the population are presented in Table 1. Participants were, on average, overweight (younger: µ = 26.5 ± 0.06; older: µ = 26.6 ± 0.06), with about 55% of Table 1  Characteristics of Participants in the Canadian Community Health Survey, 2012 30–59 Years

60–80+ Years

n = 25,175

n = 22,866

Male

11,325 (49.5)

9,672 (47.3)

Female

13,850 (50.5)

13,194 (52.8)

Married/common-law

16,422 (74.8)

12,667 (66.6)

Widowed/separated/divorced

3,637 (10.9)

8,553 (27.6)

Single/never married

5,035 (14.3)

1,586 (5.72)

Less than secondary school grad

2,553 (9.20)

6,490 (27.4)

Secondary school grad

4,298 (17.0)

3,737 (16.9)

Some postsecondary

965 (4.40)

7,66 (4.00)

Postsecondary grad

16,540 (69.4)

10,814 (51.7)

26.5 (0.06)

26.6 (0.06)

404 (1.69)

399 (2.11)

Normal weight

9,634 (42.9)

8,015 (38.1)

Overweight

8,304 (35.3)

8,222 (39.5)

Obese

5,694 (20.2)

4,653 (20.3)

Yes

13,654 (49.9)

12,934 (55.4)

No

11,456 (50.1)

9,849 (44.6)

Variable

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Sex, frequency (%)

Marital status, frequency (%)

Education, frequency (%)

Body mass index, mean (SD) Body mass index, frequency (%) Underweight

Smoking, frequency (%)

High blood pressure, frequency (%) Yes

3,832 (13.3)

10,351 (43.7)

No

21,262 (86.7)

12,421 (56.3)

Yes

1,298 (4.67)

3,699 (16.8)

No

23,840 (95.3)

19,136 (83.2)

Yes

2,415 (8.37)

1,602 (6.43)

No

22,709 (91.6)

21,201 (93.6)

Diabetes, frequency (%)

Mood disorder, frequency (%)

Physical activity, frequency (%) Active

6,638 (24.9)

4,966 (22.9)

Moderately active

6,558 (26.6)

5,294 (24.1)

Inactive

11,749 (48.6)

11,685 (53.1)

0.79 (4.81)

0.70 (4.03)

Health Utilities Index, mean (SD)

Note. Values are means and standard deviations (SD) or frequencies and weighted percentages. All n’s and frequencies are unweighted (all percentages are weighted).

the younger adult population and 60% of the older adult population overweight or obese. Nearly 49% of the younger adults and 53% of the older adults were inactive, whereas only 25% of the younger adults and 23% of the older adults were active. As presented in Table 2, individuals who were more active reported higher HRQL scores compared with those who were moderately active or inactive (active: µ = 0.82 ± 0.02; moderately active: µ = 0.77 ± 0.03; inactive: µ = 0.73 ± 0.01; p < .001). As well, younger adults reported higher HRQL scores compared with older adults (younger: µ = 0.79 ± 0.02; older: µ = 0.70 ± 0.02; p < .001). In Table 2  Mean Health-Related Quality of Life Scores Stratified by Sociodemographics and Health History Variable

Mean (± SD)

F-value

P-value

Young (30–59 years)

0.79 (0.015)

18.92

< .001

Older (60–80+ years)

0.70 (0.015) 1.41

.236

6.07

.002

6.08

< .001

1.97

.117

7.02

.008

17.20

< .001

6.15

.013

11.32

< .001

7.73

< .001

Age

Sex Male

0.77 (0.014)

Female

0.75 (0.017)

Marital status Married/common-law

0.79 (0.011)

Widowed/separated/divorced

0.66 (0.038)

Single/never married

0.73 (0.029)

Education Less than secondary school grad

0.67 (0.022)

Secondary school grad

0.78 (0.019)

Some postsecondary

0.77 (0.043)

Postsecondary grad

0.78 (0.016)

Body mass index Underweight

0.50 (0.181)

Normal weight

0.78 (0.018)

Overweight

0.78 (0.015)

Obese

0.74 (0.018)

Smoking Yes

0.73 (0.017)

No

0.80 (0.014)

High blood pressure Yes

0.69 (0.019)

No

0.78 (0.013)

Diabetes Yes

0.66 (0.045)

No

0.77 (0.011)

Mood disorder Yes

0.60 (0.050)

No

0.78 (0.012)

Physical activity Active

0.82 (0.017)

Moderately active

0.77 (0.030)

Inactive

0.73 (0.014)

Note. All values are weighted.

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Association Between BMI and Activity Levels on Quality of Life   35

regard to BMI, underweight individuals reported the lowest HRQL scores (µ = 0.50 ± 0.18), but this observation was not significant at the bivariate level (p = .12). In addition, individuals who were married/common-law, postsecondary graduates, nonsmokers, and were otherwise healthy (i.e., nondiabetic, normotensive, and no reported mood disorder) had higher HRQL scores (all p < .05). No significant differences were observed on the basis of sex (p = .24). In younger adults, PA was more strongly related to higher HRQL scores (Table 3). This was evident by higher HRQL scores at all levels of PA, regardless of BMI category. In general, active and normal weight older adults reported the highest HRQL scores. Although older adults who were underweight reported the lowest HRQL scores at every physical activity level (active: 0.135; moderately active: 0.673; inactive: 0.537), it should be noted that BMI and PA were related to HRQL even if the underweight group was excluded from the analysis. Within younger adults, all but the active/underweight and moderately active/underweight groups had lower odds of high HRQL (≥ 0.89 HUI score) compared with active/normal weight (OR = 1.00, referent) individuals (Figure 1a). Compared with active/ normal weight older adults, odds of high HRQL were 63–86% lower across all other activity-by-BMI groups (inactive/underweight [0.14, 0.03–0.66]; inactive/normal weight [0.37, 0.16–0.87]; inactive/ overweight [0.34, 0.15–0.79]) (Figure 1b).

Discussion This study examined the interactions among BMI and PA with HRQL in younger and older Canadian adults. Overall, findings indicated that both younger and older study participants were generally overweight and inactive. Results also showed that those who were more physically active reported higher HRQL scores and younger adults reported higher HRQL scores compared with older adults. In younger adults, results suggested that PA is an independent predictor of HRQL, but in older adults, both BMI and level of PA were associated with HRQL. However, obesity was associated with Table 3  Mean Health-Related Quality of Life (HRQL) Scores for Younger and Older Canadian Adults by Physical Activity Level and Body Mass Index Category Mean HRQL Score by Activity Level Body Mass Index Category

Active

Moderately Active

Inactive

Younger adults Underweight

0.895

0.661

0.766

Normal weight

0.843

0.857

0.747

Overweight

0.871

0.832

0.784

Obese

0.780

0.761

0.758



0.673

0.537

Older adults Underweight Normal weight

0.816

0.695

0.660

Overweight

0.687

0.781

0.692

Obese

0.766

0.787

0.643

Note. Main effects for younger adults: BMI: F(3,11) = 1.95; P = .12; PAI (physical activity index): F(2,11) = 3.42, P = .03; BMI*PAI: F(6,11) = 0.47, P = .83. Main effects for older adults: BMI: F(3,11) = 4.10, P < .01; PAI: F(2,11) = 2.53, P = .08; BMI*PAI: F(6,11) = 5.78, P < .001. † Estimate suppressed because the coefficient of variation is > 33.3%.

lower HRQL in both younger and older adults as well as at all levels of PA. The lower HRQL of individuals with obesity compared with normal weight may partly be explained by their increased disease risk, but other factors, such as negative perceptions of body weight, mild physical impairments, and stigmatization, may also play a role (Kolotkin et al., 2001; Wilson, Latner, & Hayashi, 2013). As well, people are less active today than they were decades ago (Church et al., 2011). While studies find that sports and leisure activity levels have remained relatively stable, leisure activities represent only a small part of daily PA (Brownson et al., 2005; Juneau & Potvin, 2010; Stamatakis et al., 2007). PA associated with work, home, and transportation has declined due to economic growth, technological advancements, and social changes. Along with this decrease in PA there has been an increase in sedentary activities (i.e., watching television and using the computer), which together may have contributed to an increase in the prevalence of obesity (Brownson et al., 2005; Juneau & Potvin, 2010; Stamatakis et al., 2007). On the other hand, underweight older adults reported the lowest HRQL scores at every PA level compared with all other groups, which suggests that being underweight may be especially serious at older age. However, because the underweight/active older adult group contained a limited number of observations, this estimate was suppressed. Among the many criticisms of BMI, its use in older adults may present particular challenges in light of the decrease in stature, accumulation of excess body fat, decrease in lean body tissue, and decrease in the amount of body fluids generally associated with advancing age (Bedogni et al., 2001; Gallagher et al., 1996). In fact, it should be noted that in the older adult population (≥ 65 years), a higher BMI may provide protection against nutritional and energy deficiencies, metabolic stresses, the development of wasting and frailty, and the loss of muscle and bone density caused by chronic diseases (Kvamme at el., 2012). As a consequence, health risk profiles are more closely aligned with abdominal visceral fat than BMI at older ages (Bedogni et al., 2001; Bhurosy & Jeewon, 2013; Gallagher et al., 1996; Stewart, 2006). Indeed, the relationship between BMI and morbidity and mortality tends to vary with age, and is further complicated by the presence of diseases and lack of age-specific anthropometric cut-points (Bedogni et al., 2001; Bhurosy & Jeewon, 2013; Gallagher et al., 1996; Stewart, 2006). All of the above could contribute to a lower HRQL score in older adults. As well, findings indicate that older adults who are underweight/inactive, normal weight/inactive, and overweight/inactive are significantly less likely to report high HRQL than their more physically active peers. Although BMI was not significant at the bivariate level (p = .12), when included in the model with PA (for both younger and older adults) it became statistically significant (p ≤ .001). This finding suggests that BMI may moderate the relationship between PA, age, and HRQL, meaning that the relationship between PA and HRQL depends on the BMI category. For example, there is a negative relationship between PA and HRQL in older/underweight adults; whereas for normal weight/older adults, the opposite is true. As with most research there are strengths and limitations to our investigation. The strengths of our study include (i) the use of a large nationally-representative Canadian population dataset; (ii) the measurement of HRQL with the HUI, a questionnaire with good test–retest reliability and validity; and (iii) adjustment for known confounding variables (Costet et al., 1998; Wang & Chen, 1999). In addition, secondary analysis of population data often involves limitations that warrant mention. First, because the CCHS is a cross-sectional study, the data do not allow us to determine causality in the PA, BMI, and HRQL relationship. Although PA is able to improve HRQL, individuals with impaired HRQL may be less able to participate in PA, such that higher HRQL may be the

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(a)

(b) Figure 1 — (a) Odds of high health-related quality of life (HRQL) score according to body mass index (BMI) and physical activity (PA) category in younger adults. (b) Odds of high HRQL score according to BMI and PA category in older adults. † Estimate suppressed because the coefficient of variation is > 33.3%. CI = confidence interval. 36

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Association Between BMI and Activity Levels on Quality of Life   37

cause rather than the consequence of higher PA. Healthier individuals may also be more likely to be active, as factors leading to poor health or activity limitations may preclude PA participation. Whereas obesity may lead to impaired HRQL, factors leading to weight gain may contribute to impaired HRQL. Being underweight may also arise from underlying illness, especially in those ≥ 60 years of age, which could both impair HRQL and inhibit PA participation (Bhurosy & Jeewon, 2013). Moreover, our reliance on self-reported BMI and PA might have resulted in underestimations of BMI and overestimations of PA.

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Conclusion In this study, participation in regular PA was associated with greater quality of life regardless of BMI in the younger adult population. In the older population, however, both BMI and level of PA were related to HRQL scores. In this population, being underweight at any PA level resulted in reduced HRQL scores. Given the high prevalence of inactivity and obesity in North America and beyond, promotion of PA at all ages is likely to exert a net positive influence on HRQL scores that may contribute to the attainment of improved BMIs, and should be a focus of continuing populationhealth promotion.

References Adams, K.F., Schatzkin, A., Harris, T.B., Kipnis, V., Mouw, T., BallardBarbash, R., Hollenbeck, A., Leitzmann, M.F. (2006). Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old. The New England Journal of Medicine, 355, 763–778. PubMed Ainsworth, B.E., Haskell, W.L., Whitt, M.C., Irwin, M.L., Swartz, A.M., Strath, S.J. . . .Leon, A.S. (2000). Compendium of physical activities: an update of activity codes and MET intensities. Medicine and Science in Sports and Exercise, 32(9 Suppl.), S498–S504. PubMed Bedogni, G., Pietrobelli, A., Heymsfield, S.B., Borghi, A., Manzieri, A.M., Morini, P. . . . Salvioli, G. (2001). Is body mass index a measure of adiposity in elderly women? Obesity Research, 9(1), 17–20. PubMed doi:10.1038/oby.2001.3 Bhurosy, T., & Jeewon, R. (2013). Pitfalls of using body mass index (BMI) in assessment of obesity risk. Current Research in Nutrition and Food Science, 1(1), 71–76. doi: 10.12944/CRNFSJ.1.1.07 Bize, R., Johnson, J.A., Plotnikoff, R.C. (2007). Physical activity level and health-related quality of life in the general adult population: a systematic review. Preventive Medicine, 45(6), 401–415. PubMed doi:10.1016/j.ypmed.2007.07.017 Bouillanne, O., Dupont-Belmont, C., Hay, P., Hamon-Vilcot, B., Cynober, L., & Aussel, C. (2009). Fat mass protects hospitalized elderly persons against morbidity and mortality. American Journal of Clinical Nutrition (Burbank, Los Angeles County, Calif.), 90, 505–510. PubMed Brill, P.A. (2004). Functional Fitness in Older Adults. Champaign, IL: Human Kinetics. Brownson, R.C., Boehmer, T.K., & Luke, D.A. (2005). Declining rates of physical activity in the United States: what are the contributors? Annual Review of Public Health, 26, 421–443. PubMed doi:10.1146/ annurev.publhealth.26.021304.144437 Church, T.S., Thomas, D.M., Tudor-Locke, C., Katzmarzyk, P.T., Earnest, C.P., Rodarte, R.Q., Martin, C.K., Blair, S.N., Bouchard, C. (2011) Trends over 5 decades in U.S. occupation-related physical activity and their associations with obesity. PLoS One, 6(5), e19657. doi:10.1371/ journal.pone.0019657 Costet, N., Le Galès, C., Buron, C., Kinkor, F., Mesbah, M., Chwalow, J., Slama, G. (1998). French cross-cultural adaptation of the Health Utilities Index Mark 2 (HUI2) and 3 (HUI3) classification systems. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 7(3), 245–256. PubMed

Craig, C.L., Russell, S.J., Cameron, C. (2002). Reliability and validity of Canada’s Physical Activity Monitor for assessing trends. Medicine and Science in Sports and Exercise, 34, 1462–1467. PubMed doi:10.1097/00005768-200209000-00010 Dondzila, C., Gennuso, K.P., Swartz, A.M., Tarima, S., Lenz, E.K., Stein, S.S. . . .Strath, S.J. (2015). Dose-response walking activity and physical function in older adults. Journal of Aging and Physical Activity, 23(2), 194–199. doi:10.1123/japa.2013-0083. Feeny, D., Furlong, W., Torrance, G.W., Goldsmith, C.H., Zhu, Z., DePauw, S. . . Boyle, M. (2002). Multi-attribute and single-attribute utility functions for the health utilities index mark 3 system. Medical Care, 40(2), 113–128. PubMed doi:10.1097/00005650-20020200000006 Furlong, W., Feeny, D., Torrance, G.W., & Barr, R.D. (2001). The Health Utilities Index (HUI) system for assessing health-related quality of life in clinical studies. Annals of Medicine, 33(5): 375–384. PubMed Gallagher, D., Visser, M., Sepúlveda, D., Pierson, R.N., Harris, T., Heymsfield, S.B. (1996). How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? American Journal of Epidemiology, 146(3), 228–239. PubMed Han, T.S., Tajar, A., Lean, M.E. (2011). Obesity and weight management in the elderly. British Medical Bulletin, 97, 169–196. doi:10.1093/ bmb/ldr002 Haskell, W.L., Lee, I.M., Pate, R.R., Powell, K.E., Blair, S.N., Franklin, B.A. . . .Bauman, A. (2007). Physical activity and public health— updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation, 116(9), 1081–1093. doi:10.1161/CIRCULATIONAHA.107.185649 Health Canada. (2003). Canadian guidelines for body weight classification in adults. Minister of Public Works and Government Services Canada. Ottawa, Canada. Hickey, A., Barker, M., McGee, H., O’Boyle, C. (2005). Measuring healthrelated quality of life in older patient populations: A review of current approaches. PharmacoEconomics, 23(10), 971–993. PubMed Horsman, J., Furlong, W., Feeny, D., & Torrance, G.W. (2003). The Health Utilities Index (HUI®): Concepts, measurement properties and applications. Health and Quality of Life Outcomes, 16(1), 54. PubMed Imayama, I., Alfano, C.M., Cadmus Bertram, L.A., Wang, C., Xiao, L., Duggan, C. . . . McTiernan, A. (2011). Effects of 12-month exercise on health-related quality of life: A randomized controlled trial. Preventive Medicine, 52(5), 344–351. PubMed Jensen, G.L. (2005). Obesity and functional decline: epidemiology and geriatric consequences. Clinics in Geriatric Medicine, 21, 677–687. doi:10.1016/j.cger.2005.06.007 Juneau, C.E., Potvin, L. (2010). Trends in leisure-, transport-, and workrelated physical activity in Canada 1994-2005. Preventative Medicine, 51, 384–386. PubMed Katz, D.A., McHorney, C.A., Atkinson, R.L. (2000). Impact of obesity on health-related quality of life in patients with chronic illness. Journal of General Internal Medicine, 15, 789–796. PubMed doi:10.1046/j.15251497.2000.90906.x Kolotkin, R.L., Meter, K., & Williams, G.R. (2001). Quality of life and obesity. Obesity Reviews, 2(4), 219–229. PubMed doi:10.1046/j.1467789X.2001.00040.x. Korhonen, P.E., Seppala, T., Jarvenpaas, S., Kautiainen, H. (2014). Body mass index and health-related quality of life in apparently healthy individuals. Quality of Life Research, 23, 67–74. PubMed Kostic´, R., Pantelic´, S., Uzunovic´, S., Djuraskovic, R. (2011). A comparative analysis of the indicators of the functional fitness of the elderly. Facta Universitatis Series Physical Education and Sport, 9(2), 161–171. Kvamme, J.M., Holmen, J., Wilsgaard, T., Florholmen, J., Midthjell, K., Jacobsen, B.K. (2012). Body mass index and mortality in elderly men and women: the Tromsø and HUNT studies. Journal of Epidemiology and Community Health, 66, 611–617. doi:10.1136/jech.2010.123232 Larsson, U., Karlsson, J., Sullivan, M. (2002). Impact of overweight and obesity on health-related quality of life—a Swedish population study. International Journal of Obesity and Related Metabolic Disorders, 26, 417–424. PubMed

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Maldonado, G., & Greenland, S. (2002). Estimating causal effects. International Journal (Toronto, Ont.). Epidemiology (Cambridge, Mass.), 31, 422–429. Mirowsky, J. (2011). Cognitive decline and the default American lifestyle. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 66(Suppl. 1), i50–i58. PubMed Oreopoulos, A., Kalantar-Zadeh, K., Sharma, A.M., Fonarow, G.C. (2009). The obesity paradox in the elderly: potential mechanisms and clinical implications. Clinics in Geriatric Medicine, 25, 643–659. PubMed Orpana, H.M., Berthelot, J.M., Kaplan, M.S., Feeny, D.H., McFarland, B., Ross, N.A. (2010). BMI and Mortality: results from a national longitudinal study of Canadian adults. Obesity (Silver Spring, Md.), 18, 214–218. PubMed doi:10.1038/oby.2009.191 Patrick, D.L., Erickson, P. (1993). Health status and health policy: Quality of life in health care evaluation and resource allocation. New York: Oxford University Press. Popkin, B.M., Adair, L.S., Ng, S.W. (2012). Global nutrition transition and the pandemic of obesity in developing countries. Nutrition Reviews, 70, 3–21. PubMed doi:10.1111/j.1753-4887.2011.00456.x Powell, K., Blair, S. (1994). The public health burdens of sedentary living habits: theoretical but realistic estimates. Medicine and Science in Sports and Exercise, 26, 851–856. PubMed doi:10.1249/00005768-199407000-00007 Rejeski, W.J., Mihalko, S.L. (2001). Physical activity and quality of life in older adults. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 56(2), 23–35. PubMed Riebe, D., Blissmer, B.J., Greaney, M.L., Garber, C.E., Lees, F.D., Clark, P.G. (2009). The relationship between obesity, physical activity, and physical function in older adults. Journal of Aging and Health, 21(8), 1159–1178. PubMed Schmitz, N., Kruse, J., Tress, W. (2000). Application of stratum-specific likelihood ratios in mental health screening. Social Psychiatry and Psychiatric Epidemiology, 35, 375–379. PubMed

Stamatakis, E., Ekelund, U., Wareham, N.J. (2007). Temporal trends in physical activity in England: the Health Survey for England 1991 to 2004. Preventative Medicine, 45, 416-423. PubMed Stewart, K.J. (2006). Physical activity and aging. Annals of the New York Academy of the Sciences, 1055(1), 193–206. Strandberg, T.E., Strandberg, A., Salomaa, V.V., Pitkala, K., Miettinen, T.A. (2003). Impact of midlife weight change on mortality and quality of life in old age. Prospective cohort study. International Journal of Obesity and Related Metabolic Disorders, 27(8), 950–954. PubMed Taylor, H., Jacobs, D.R., Schucker, B., Knudsen, J., Leon, A.S., DeBacker, G. (1978). A questionnaire for the assessment of leisure time physical activities. Journal of Chronic Diseases (Basel, Switzerland), 31, 741–755. PubMed Warburton, D.E.R., Nicol, C.W., Bredin, S.S.D. (2006). Health benefits of physical activity: the evidence. Canadian Medical Association Journal, 174(6), 801–809. doi:10.1503/cmaj.051351 Wang, Q., Chen, G. (1999). The health status of the Singaporean population as measured by a multi-attribute health status system. Singapore Medical Journal, 40(6), 389–396. PubMed Williams, L.T., Young, A.F., Brown, W.J. (2006). Weight gained in two years by a population of mid-aged women: how much is too much? International Journal of Obesity, 30(8), 1229–1233. PubMed doi:10.1038/ sj.ijo.0803262 Wilson, R.E., Latner, J.D., Hayashi, K. (2013). More than just body weight: the role of body image in psychological and physical functioning. Body Image, 10, 644–647. PubMed doi:10.1016/j. bodyim.2013.04.007 World Health Organization. (2000). Obesity: Preventing and managing the global epidemic report of a WHO consultation on obesity. Geneva (Switzerland): World Health Organization, 5–15.

JAPA Vol. 24, No. 1, 2016

Association Between Body Mass Index, Physical Activity, and Health-Related Quality of Life in Canadian Adults.

Obesity is associated with impairments in health-related quality of life (HRQL), whereas physical activity (PA) is a promoter of HRQL...
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