Journal of ObesityVolume 2011 (2011), Article ID 561432, 5 pagesdoi:10.1155/2011/561432

Research Article

Population-Based Estimates of Physical Activity for Adults with Type 2 Diabetes: A Cautionary Tale of Potential Confounding by Weight Status

Ronald C. Plotnikoff,1,2,3,4 Steven T. Johnson,⁵ Constantinos A. Loucaides,3,4,6 Adrian E. Bauman,⁷ Nandini D. Karunamuni,³ and Michael A. Pickering3,4

¹School of Education, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia²Centre for Health Promotion Studies, School of Public Health, University of Alberta, Edmonton, AB, Canada, T6G 2R3³Physical Activity and Population Health Research Laboratory, Faculty of Physical Education and Recreation, University of Alberta, Edmonton, AB, Canada, T6G 2R3⁴Alberta Centre for Active Living, Edmonton, AB, Canada, T5M 3K6⁵Centre for Nursing and Health Studies, Faculty of Health Disciplines, Athabasca University, Athabasca, AB, Canada, T95 3A3⁶The Open University of Cyprus, 1055 Nicosia, Cyprus⁷Centre for Physical Activity and Health, School of Public Health, University of Sydney, Sydney, NSW 2006, Australia

Received 26 May 2010; Revised 10 August 2010; Accepted 13 August 2010

Academic Editor: Robert J. Ross

Copyright © 2011 Ronald C. Plotnikoff et al. Ronald C. Plotnikoff et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

At a population level, the method used to determine those meeting physical activity guidelines has important implications, as estimating “sufficient” physical activity might be confounded by weight status. The objective of this study was to test the difference between three methods in estimating the prevalence of “sufficient activity” among Canadian adults with type 2 diabetes in a large population sample () while considering the role of weight status as a potential confounder. Our results revealed that estimates of physical activity levels vary by BMI categories, depending on the methods examined. Although physical activity levels were lower in the obese, their energy expenditure estimates were not different from those who were overweight or of a healthy weight. The implications of these findings are that biased estimates of physical activity at a population level may result in inappropriate classification of adults with type 2 diabetes as “sufficiently active” and that the inclusion of body weight in estimating physical activity prevalence should be approached with caution.

1. Introduction

At a population level, the method used to determine those meeting physical activity guidelines has important implications [1]. For example, estimating “sufficient” physical activity (PA) might be confounded by weight status. This is possible since an increase in mean weight status of a population may result in spurious estimates of increasing physical activity-related energy expenditure over time [2]. This hypothesis is particularly important for those who work with type 2 diabetes populations because weight loss is a salient clinical target and can be achieved through energy restriction alone or in combination with an increase in energy expenditure. Consequently, as argued above, valid changes (i.e., increases or decreases) in PA patterns might not be entirely and therefore correctly captured. If this were to occur, assessing population-based strategies geared towards increasing physical activity and reducing body weight among adults with type 2 diabetes might be limited by measurement error.

Low physical activity levels in adults with type 2 diabetes have been widely reported [3–9] yet there appear to be some irregularities among these data. For example, being older [3, 6] and being female [7, 9] were reported to be negatively associated with physical activity yet in other studies, age [7, 9] and sex [6] had no significant relationship with physical activity. We contend that this discordance reflects methodological differences in data synthesis and the thresholds used to quantify physical activity levels sufficient for health benefit, notably the use of weight dependent thresholds [9].

In response, we postulated that the methods for assessing self-reported physical activity might serve to explain some of the discordance. The objective of this study was to compare three different methods to classify individuals with type 2 diabetes as “sufficiently active” using a validated measure of physical activity instrument [10]. We hypothesized that the prevalence of those classified as “sufficiently” active (i.e., proportional estimates) would be different according to the method used to calculate weekly physical activity and that these differences would be confounded with the inclusion of body weight in indirectly estimating physical activity-related energy expenditure.

2. Method

2.1. Subjects

The current study is a component of the Alberta Longitudinal Exercise and Diabetes Research Advancement (ALEXANDRA) Study, a prospective assessment of physical activity determinants [8]. The participants in this study were residents in the province of Alberta, Canada () with type 2 diabetes and were assessed at three time points: baseline, 6 months, and 18 months. Demographic characteristics, recruitment, and response rates have been previously described [8]. Briefly, participants were years of age, moderately overweight to obese ( kg/m²), and represented equally by sex (51.4% male), and 72.0% of the sample indicated were Canadian while 28.0% were either Arab, Asian, African, European, Aboriginal, or Latin/South American. The demographic characteristics of this study population generally reflect Canada’s and Alberta’s adult type 2 diabetes population in terms of age and sex distributions [8, 11]. Participants were recruited by (1) mailing questionnaires and consent forms to individuals from the Canadian Diabetes Association registry, requesting completion from those with diabetes or (2) through a random digit dialing method to recruit individuals living with diabetes in Alberta; households that were contacted could also nominate a family member or friend with diabetes. This study received ethical approval from the Health Research Ethics Board.

2.2. Physical Activity Assessment

Physical activity was assessed with the Godin Leisure-Time Exercise Questionnaire (GLTEQ) [10]. Participants were dichotomized as “sufficiently active” or “inactive” based on three different classifications as presented in Table 1: (1) the estimated kilocalories method (Kcal)(hours per week of moderately intense activities [4 METS] + hours per week of vigorously intense activities [7.5 METS] body weight (kg)); (2) the Met.mins method (minutes per week of moderately intense activities [4 METS] + minutes per week of vigorously intense activities [7.5 METS]); (3) the unweighted moderate and vigorous method (MVPA mins) (minutes per week of moderately intense activities + minutes per week of vigorously intense activities). Thresholds for categorization of “sufficiently active” for each method, respectively, were 800/week for the kilocalories which is based on previous population surveys [2] and reflects achieving 150 mins of moderate activity/week for an 80 kg person [2]; 600/week for Metmins (reflecting 150 mins of moderate activity/week); 150 minutes/week for MVPA mins. These thresholds were selected based on public health guidelines [12] and diabetes-specific [13, 14] guidelines for achieving moderate activity of at least 150 mins per week.

Table 1: Methods of assessment.

Method
Calculation
“Sufficiently active” thresholds

Kcal
Hours per week of moderate intense activities (4 METS) + hours of vigorous intense activities (7.5 METS) body weight (kg)
800/week

Metmins
Minutes per week of moderate intense activities (4 METS) + minutes of vigorous intense activities (7.5 METS)
600/week

MVPA mins
Minutes per week of moderate intense activity + minutes per week of vigorous intense activity (unweighted for intensity)
150

Related to the first two methods, in calculating the indirect estimate of weekly energy expenditure, the number of minutes was computed by multiplying the frequency and duration of (i) weekly minutes of moderate physical activity 4.0 METS and (ii) weekly minutes of vigorous physical activity 7.5 METS. The weekly minutes for moderate and vigorous were then summed for a total Met score. One minute of vigorous physical activity is equivalent to 1.875 minutes of moderate activity (7.5/4.0) based on the average Met levels for vigorous activity (Met level = 7.5) and moderate activity (Met level = 4.0) set by Brown and Bauman [2] and employed in the original paper of the ALEXANDRA study [8]. This weighting provides more credit for participating in vigorous activity. Individuals who accumulated 600 Met-minutes per week (Method Two) were classified as “adequately active for health benefit” while those who did not were classified as “inadequately active” [2]. This criterion reflects achieving 150 minutes of moderate activity [4.0 Mets] or 80 minutes of vigorous [7.5 Mets] activity per week, or any combination thereof [2].

2.3. Statistical Analysis

Using only baseline data, for each physical activity assessment method, one-way analysis of variance (ANOVA; with BMI as the independent variable) was used to examine whether physical activity (as a continuous variable) varied significantly across normal weight (), overweight (BMI 25.0–30.0), or obese () categories. Chi-square tests were conducted to examine differences in the proportion of participants classified as “sufficiently” active across body weight status categories. The Kappa statistic was employed to examine agreement between the three methods, and effect sizes using Cohen’s h [15] were reported to assess the magnitude of differences between the methods.

Considering that the mean age of the study sample was 63 years, a sensitivity analysis was also conducted with Metmins set at the lower cutoff (i.e., weekly minutes of moderate physical activity 3.0 METS and weekly minutes of vigorous physical activity 6.0 METS to calculate total Metmins). ANOVAS and chi-square analyses described above were repeated using this new Metmin variable.

3. Results

When examining physical activity levels for each method, ANOVA analyses revealed significant () differences for Metmins and MVPA mins but total physical activity did not vary across BMI categories using the estimated kilocalories method (see Table 2). Chi-square analyses also revealed significant () differences in the proportion of those classified as sufficiently active for Metmins and MVPA mins methods but not for the kilocalories method (see Table 3). Further, it is noteworthy that the obese group generally had similar proportions that were sufficiently active, regardless of the physical activity measure (see Table 3).

Table 2: Means and one-way ANOVA results for each of the three methods of assessment.

BMI

Kcal
Metmins
MVPA mins

Mean
SD
()
Mean
SD
()
Mean
SD
()

25
341
746.5
2051.4
(99)
853.6
1516.5
(135)
172.0
243.9
(115)

25–30
570
798.2
1675.6
(170)
767.7
1221.4
(208)
158.3
231.7
(181)

30
704
650.4
1450.1
(175)
557.4
928.1
(182)
112.7
165.7
(156)

;
;
;

Table 3: Proportion of adults classified as “sufficiently” active based on the three methods stratified by BMI.

BMI

Sufficiently active based on Kcal (%)
Sufficiently active based on Metmins (%)
Sufficiently active based on MVPA mins (%)

25
341
99 (29.0)
135 (39.6)
115 (33.7)

25–30
570
170 (29.8)
208 (36.5)
181 (31.8)

30
704
175 (24.9)
182 (25.9)
156 (22.2)

Significantly different ()

Estimates for those meeting “sufficient activity” guidelines for each method across BMI categories are shown in Tables 4(a), 4(b), and 4(c). In terms of relative comparisons, the Metmins method was consistently higher for all weight categories. The largest proportion was found for the normal weight category in the Metmins methods (39.6%) and the smallest for the obese category in the MVPA mins method (22.2%). The magnitudes of the differences were the greatest when comparing the Metmins and MVPA mins methods, with effect sizes ranging from  .03 to  .22. The discordance between the Kcal and the MVPA mins methods was moderately lower than that between the Metmins and Kcal methods (effects sizes from  .09 to  .12) and the lowest for the Kcal and the MVPA mins methods (.05 to  .10).

Table 4

Metmins

Body mass index
25
25–30
30

Inactive (%)
Active (%)
Inactive (%)
Active (%)
Inactive (%)
Active (%)

Kcal

    Inactive
206 (60.4)
36 (10.6)
361 (63.3)
39 (6.8)
506 (71.9)
23 (3.3)

    Active
0 (0)
99 (29.0)
1 (0.1)
169 (29.7)
16 (2.2)
159 (22.6)

Kappa = 0.77 ()
Kappa = 0.84 ()
Kappa = 0.85 ()

MVPA mins

Body mass index
25
25–30
30

Inactive (%)
Active (%)
Inactive (%)
Active (%)
Inactive (%)
Active (%)

Kcal

    Inactive
220 (64.5)
22(6.5)
385 (67.5)
15 (2.6)
521(74.0)
8 (1.1)

    Active
6 (1.8)
93 (27.3)
4 (0.7)
166 (29.1)
27 (3.8)
148 (21.0)

Kappa = 0.81 ()
Kappa = 0.92 ()
Kappa = 0.86 ()

Metmins

Body mass index
25
25–30
30

Inactive (%)
Active (%)
Inactive (%)
Active (%)
Inactive (%)
Active (%)

MVPA mins

    Inactive
206 (60.4)
20 (5.9)
362 (63.5)
27 (4.7)
522 (74.1)
26 (3.7)

    Active
0 (0)
115 (33.7)
0 (0)
181 (31.8)
0 (0)
156 (22.2)

Kappa = 0.87 ()
Kappa = 0.90 ()
Kappa = 0.90 ()

The sensitivity tests (ANOVA’s and chi-square analyses) revealed significant differences for Metmins (; ; ) but not for estimated Kcals (; ; ; ). Kappa values across the three respective BMI categories (25, 25–30, and 30) for the three methods were  .90,  .92, and  .76 for the Kcal and Metmins methods; 0.90, 0.89, and 0.85 for the MVPA mins and Metmins methods; 0.81, 0.92, and 0.86 for the MVPA mins and Kcal methods.

4. Discussion

The objective of this study was to test the difference between three methods in estimating the prevalence of “sufficient activity” among Canadian adults with type 2 diabetes in a large sample, while considering the role of weight status as a potential confounder. In addition to the unique population, our study expands on the Brown and Bauman [2] methodology by including a third measure of physical activity (MVPA mins) in addition to kilocalories and Metminutes scores in comparing physical activity prevalence.

Overall, our results for all methods are generally in agreement with previous prevalence estimates of physical activity which show that approximately 60–70% of adults with type 2 diabetes are not “sufficiently active” [3–7, 9]. However, our results suggest that estimates of physical activity levels vary by BMI categories, depending on the methods examined. Upon further comparison of methods considered appropriate for estimating physical activity, we suggest that the Metmins method may be more appropriate even though it may categorize a larger proportion as sufficiently active relative to the other methods when stratifying by weight status.

When comparing the difference between the methods, we found the magnitude to be small (i.e.,  .03 to  .22) according to Cohen’s definition [15]: there were nonetheless differences worthy of comment. Most notably, the largest effect sizes were found between the MVPA mins and the Metmins methods suggesting higher discordance between these methods. Overall, when classifying individuals as “sufficiently active” the magnitude of the differences between the methods appears to be consistent regardless of the stratification by weight status but the magnitude of difference appears to be lower for the Metmins method. Although this difference may seem statistically trivial, the impact of such differences at a population level may be profound.

It is noteworthy (see Table 3) that although physical activity levels were lower in the obese, their energy expenditure estimates were similar with those who were overweight or of a healthy weight. This finding supports existing evidence indicating that the energy cost of physical activity is greater in the obese [16]. Further, it is acknowledged that physical activity energy expenditure is important for weight maintenance and that weight maintenance is problematic because physical activity levels are low among this population. Consequently, because energy expenditure estimates at a population level are not always considered with physical activity surveillance, taking into account physical activity energy expenditure may be important in the obese population.

This study is not without caveats. First, our data were based on self-reported physical activity and may reflect a population who are more highly active and with lower body weights status. Second, the somewhat broad use of metabolic equivalents (MET) for estimating energy expenditure is imprecise as it should incorporate resting metabolic rate to more accurately gauge energy expenditure on an individual level. Third, since there is no established “gold standard” for self-reported physical activity, the addition of an objective measure (i.e., accelerometer) would have provided stronger support for our argument for the use of the Metmins method when assessing physical activity in this population. Finally, future studies on this topic should examine physical activity change scores in longitudinal designs.

5. Conclusions

The implications of our study are that biased estimates of physical activity at a population level may result in inappropriate classification of adults with type 2 diabetes as “sufficiently active” and that the inclusion of a weight-dependent estimate (i.e., Kcal method) of physical activity prevalence should be approached with caution as 80% of individuals with type 2 diabetes are overweight or obese [17]. If a weight-dependent estimate of physical activity is used however, an estimate of weight stability should be included within the temporal reference with which physical activity behaviors are collected.

Acknowledgments

This study was funded by the Alberta Heritage Foundation for Medical Research and the Canadian Diabetes Association. Dr. R. C. Plotnikoff holds a salary award from the Canadian Institutes of Health Research (Chair in Applied Public Health).

References

P. T. Katzmarzyk and M. S. Tremblay, “Limitations of Canada's physical activity data: implications for monitoring trends,” Canadian Journal of Public Health, vol. 98, supplement 2, pp. S185–194, 2007.

W. J. Brown and A. E. Bauman, “Comparison of estimates of population levels of physical activity using two measures,” Australian and New Zealand Journal of Public Health, vol. 24, no. 5, pp. 520–525, 2000.

L. M. Hays and D. O. Clark, “Correlates of physical activity in a sample of older adults with type 2 diabetes,” Diabetes Care, vol. 22, no. 5, pp. 706–712, 1999.

E. H. B. Lin, W. Katon, M. Von Korff, C. Rutter, G. E. Simon, M. Oliver, P. Ciechanowski, E. J. Ludman, T. Bush, and B. Young, “Relationship of depression and diabetes self-care, medication adherence, and preventive care,” Diabetes Care, vol. 27, no. 9, pp. 2154–2160, 2004.

E. H. Morrato, J. O. Hill, H. R. Wyatt, V. Ghushchyan, and P. W. Sullivan, “Physical activity in U.S. adults with diabetes and at risk for developing diabetes, 2003,” Diabetes Care, vol. 30, no. 2, pp. 203–209, 2007.

K. M. Nelson, G. Reiber, and E. J. Boyko, “Diet and exercise among adults with type 2 diabetes: findings from the third national health and nutrition examination survey (NHANES III),” Diabetes Care, vol. 25, no. 10, pp. 1722–1728, 2002.

F. Nothwehr and T. Stump, “Health-promoting behaviors among adults with type 2 diabetes: findings from the health and retirement study,” Preventive Medicine, vol. 30, no. 5, pp. 407–414, 2000.

R. C. Plotnikoff, L. M. Taylor, P. M. Wilson, K. S. Courneya, R. J. Sigal, N. Birkett, K. Raine, and L. W. Svenson, “Factors associated with physical activity in Canadian adults with diabetes,” Medicine and Science in Sports and Exercise, vol. 38, no. 8, pp. 1–9, 2006.

J. G. Spangler and J. C. Konen, “Predicting exercise and smoking behaviors in diabetic and hypertensive patients. Age, race, sex, and psychological factors,” Archives of Family Medicine, vol. 2, no. 2, pp. 149–155, 1993.

G. Godin and R. J. Shephard, “A simple method to assess exercise behavior in the community,” Canadian Journal of Applied Sport Sciences, vol. 10, no. 3, pp. 141–146, 1985.

Health Canada, “Responding to the challenge of diabetes in Canada,” First Report of the National Diabetes Surveillance System (NDSS), Health Canada, Ottawa, Canada, 2003.

R. R. Pate, M. Pratt, S. N. Blair, et al., “Physical activity and public health. A recommendation from the Centres for Disease Control and Prevention and the American College of Sports Medicine,” Journal of American Medical Association, vol. 273, no. 5, pp. 402–407, 1995.

Canadian Diabetes Association, “Canadian Diabetes Association 2003 clinical practice guidelines for the prevention and management of diabetes in Canada,” Canadian Journal of Diabetes, vol. 27, supplement 2, pp. S1–S140, 2003.

R. J. Sigal, G. P. Kenny, D. H. Wasserman, C. Castaneda-Sceppa, and R. D. White, “Physical activity/exercise and type 2 diabetes: a consensus statement from the American Diabetes Association,” Diabetes Care, vol. 29, no. 6, pp. 1433–1438, 2006.

J. Cohen, Statistical Power Analysis for the Behavioral Sciences, Erlbaum, Hillsdale, NJ, USA, 1988.

R. C. Browning and R. Kram, “Energetic cost and preferred speed of walking in obese vs. normal weight women,” Obesity Research, vol. 13, no. 5, pp. 891–899, 2005.

National Institutes of Health, “U.S. Department of Health & Human Services. (n.d.). What are the health risks of overweight and obesity?” September 2008, http://www.nhlbi.nih.gov/health/dci/Diseases/obe/obe_risks.html.