PHYSICAL ACTIVITY, MUSCULOSKELETAL FITNESS, AND

PHYSICAL ACTIVITY, MUSCULOSKELETAL FITNESS, AND WElGHT GAIN IN THE CANADIAN POPULATION

Michelle D. Fortier

A thesis submitted to the Faculty of Graduate Studies in partial fulfillment of the requirements for the degree of Master of Science

Graduate Programme in Kinesiology and Health Science York University Toronto, Ontario September 2000

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Physical Activity, Musculoskeletal Fitness, and Weight Gain in the Canadian Population by

Michelle Diane Fortier a thesis submitted to the Faculty of Graduate Studies of York University in partial fulfilIment of the requirements for the degree of

Master of Science O 2000 Permission has been granted to the LIBRARY OF YORK UNIVERSITY to lend or seIl copies of this thesis, to the NATIONAL LIBRARY OF CANADA to microfilm this thesis and to lend or sel1 copies of the film. and to UNIVERSITY MICROFILMS to publish an abstract of this thesis. The author reserves other publication rights, and neither the thesis nor extensive extracts from it may be printed or otherwise reproduced without the author's written permission.

Abstract Background: Understanding population patterns for physical activity (PA) and musculoskeletal fitness (MSF) is important for public health interventions. PA requires further study as a potential predictor of changes in body mass, adiposity, and MSF. Objective: To examine the stability of PA and MSF over 7 y in the Canadian population, and to detennine if either baseline PA. follow-up PA, andlor changes in PA can predict 7-y changes in body mass, adiposity, or MSF.

Design: Data from the 1981 Canada Fitness Survey and its 7-y follow-up, the Campbell's Survey, were analyzed. A sample of 951 males and 958 females, aged 11-69, were divided into 2-y age groups in childhood (1 1-18y) and 10-y age groups in adulthood (19-69) for the stability analyses. For the prediction analyses, the sample consisted of 602 males and 644 females, aged 20-69.

Measurements: PA measures were activity energy expenditure (AEE), time on activity, and PA intensity. MSF indicators were sit-ups, push-ups, grip strength, and sit-and-reach flexibility. Anthropometric measures were comprised of body mass, the surn of five skinfolds (SF5), and waist circumference (WC). Results: 7-y interage stability coefficients ranged from -0.08 to 0.39 for AEE, -0.10 to 0.33

for time on activity. 0.42 to 0.80 for sit-ups, -0.07 to 0.73 for push-ups, 0.44 to 0.82 for grip strength, and 0.47 to 0.85 for sit-and-reach. Tracking was strongest in adulthood and MSF was generally more stable than PA at al1 ages. Tracking at the extremes of the distribution (quintiles) also was greater for MSF than for PA. Partial correlations, controlling for age and baseline adiposity, between PA

and 7-y adiposity changes were low and non-significant, with the exception of a low negative correlation between follow-up AEE and WC changes in females. WÏh few exceptions, partial correlations, controlling for age and baseline MSF,

between PA and 7-y MSF changes were low and non-significant. PA was not a significant predictor of ?-y changes in MSF, or body mass, SF5, and WC. Similarly, neither PA level, PA intensity, nor PA change categories significantly related to adiposity changes. Conclusions: MSF indicators exhibit moderate to high stability over 7 y, while PA is not a very stable characteristic in the Canadian population. PA is not predictive of 7-y changes in MSF,body mass or adiposity. KEYWORDS: TRACKING, PREVENTION, WEIGHT GAIN, PHYSICAL ACTIVITY, MUSCULOSKELETAL FITNESS

Acknowledaernents It is fitting that an acknowledgement section precedes a student's thesis

as many people deserve credit for the advice, support, and sympathetic ears graciously contributed throughout the thesis process. I have been most fortunate to have the backing of a loving family, wonderful friends, and a supportive graduate programme. These individuais have not only been instrumental in the completion of my thesis, but also in making the process an enjoyable one! Perhaps my show of gratitude should begin with the people who were there from the beginning - my family- My family has provided me with what I beIieve to be the most important attribute for success - belief in oneself. My parents instilled in me at an early age that hard work can take you anywhere you want to go. To my mom and dad - the unwavering support you have always given me has meant more than 1 a n adequately express and 1 will forever be grateful. To my brother Brian, you have been rny best friend through the many twists and turns of Iife - your encouragement and understanding have been invaluable. l feel blessed to have crossed paths with so many amazing people during my graduate school years at York. The friendships 1 have made will remain with

me throughout rny life. To "the Passy Girls" - Corien, Farheen, and Shaelyn -

you have given new meaning to 'living like a student' and your companionship has made me a better person. To MirÏsse, you are wonderful- never change. To Carrnen, you help put perspective on life's priorities. Finally, a special note of

gratitude to my boyhiend Mark - you encourage me to create the life I want thank you. Within the Kinesiology and Health Science Programme, several people have been instrumental to the fulfillment of my masters' degree. I would like to thank Peter Katzmarzyk, my thesis advisor, for his guidance; Barry Fowler, the programme director, for his support; Joanne Blake, the prograrn administrator, for her friendship and assistance; Nom Gledhill and Michael Riddell, for their help on my thesis cornmittee; and Merv Mosher, for ensuring rny TA expenence was a good one. Due in large part to al1 the aforementioned people, graduate studies at York University has been a very positive experience for me. However, as with many obstacles tackled, moments of difficulty have been present. In finishing, the following is a quote that has inspired me during such times:

"What Iies behind us and what lies before us are tiny matters cornpared to what Iies within us-"

- Ralph Waldo Emerson

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Table of Contents

Title Page

i

Copyright Page

ii

Certificate Page

iii

Abstract

iv

Acknowledgements

vi .--

Table of Contents

VIN

List of Tables

x

List of Figures

xiv

Introduction

1

Review of Literature

5

Stability of Physical Activity and Musculoskeletal Fitness -5 Prediction of Changes in Musculoskeletal Fitness from Physical Activity

9

Prediction of Changes in Body Mass and Adiposity from PhysicaI Activity

9

Confounding Variables

13

Hypotheses

15

References

16

Manuscript 1

27

Contributions of Authors Abstract

-28 29

.-.

Vlll

Introduction

30

Subjects and Methods

31

Results

34

Discussion

35

Acknowledgements

42

References

43

Manuscript 2

54

Contributions of Authors

55

Abst ract

56

Introduction

57

Subjects and Methodç

58

Results

62

Discussion

64

Acknowledgements

68

References

69

Discussion

80

Summary

86

References

y

89

List of Tables

Literature Review Table 1. Characteristics and results of the studies examining 24

the stability of physical activity Table 2. Characteristics and results of the studies examining

25

the stability of musculoskeletal fitness

Table 3. Characteristics and results of the studies evaluating physical activity as a predictor of changes in body mass and/or adiposity

2

6

Manuscript 1 Table 1. Sample sizes, means and standard deviations for 1981 and 1988 measures of physical activity in the

Canadian population

47

Table 2. Sample sizes, means and standard deviations for 1981 and 1988 measures of musculoskeletal fitness in the Canadian population

48

Table 3. Percentage of participants remaining in the lower or upper quintile (20%) of physical activity and musculoskeletal fitness over 7 y in the Canadian population-

4

9

Manuscript 2 Table 1. Setected descriptive statistics of study sample

72

Table 2. Pearson partial correlation coefficients (and sample sizes) between 7-y changes (A) in anthropometric measures and baseline measures of activity energy expenditure (AEE) and time on activity

73

Table 3. Regression coefficients (P) and standard errors (SE) for the prediction of changes in body mass, sum of five skinfolds, and waist circumference from baseline measures of activity energy expenditure (AEE), age, anthropometric measure, smoking status, alcohol consumption, and family incorne

74

Table 4. Regression coefficients (P) and standard errors (SE) for the prediction of changes in body mass, sum of five skinfolds, and waist circurnference frorn baseline rneasures of time on activity, age, anthropometric measure, smoking status, alcohol consumption, and farnily income

75

Table 5. ANCOVA results showing least squares means (M) and standard errors (SE) for 7-y body mass changes (kg) by physical activity (PA) change

76

Discussion Table 1-A. T-test results comparing the present sample (ages 11-69) to the entire Canada Fitness Survey (CFS) sample on select measures

91

Table 2-A. T-test results comparing the present sample (ages 20-69) to the entire Canada Fitness Survey (CFS) sarnple on select measures

92

Table 3-A. Pearson partial correlation coefficients (and sample sizes) between 7-y changes (A) in anthropometric measures and follow-up measures of activity energy expenditure (AEE) and tirne on activity

93

Table 4-A. Regression coefficients (p) and standard errors (SE) for the prediction of changes (A) in body mass, sum of five skinfolds, and waist circumference from follow-up measures of activity energy expeoditure (AEE), age, anthropometric measure, smoking status, alcohol consurnption, and farnily incorne

94

Table 5-A. Regression coefficients (p) and standard errors (SE) for the prediction of changes (A) in body mass, sum of five skinfofds, and waist circumference from follow-up rneasures of tirne on activity, age, anthropometric measure, smoking status, alcohol consumption, and family income

95

xii

Table 6-A. Pearson partial correlation coefficients (and sample sizes) between 7-y changes (A) in musculoskeletalfitness indicators and baseline, follow-up, and 7-change measures of activity energy expenditure and time on activity

96

Table 7-A. Regression coefficients (p), standard errors (SE), and sample sizes (n), for change (A) in push-ups, sit-ups, grip strength, and sit-and-reach flexibility, from baseline and follow-up measures of activity energy expenditure (AEE; kJakg -'day-') and tirne on activity (min-day-')

97

xiii

List of Fiaures Introduction Figure 1. Model illustrating the four principle issues addressed in thesis

4

Manuscript 1 Figure 1. Spearman 7-y interage correlations for a) activity energy expenditure (AEE) and b) time on activity in males (-a) and females (-0). Significant correlations (p 10.05) are indicated with

'+' for males and '*'for fernales

51

Figure 2. Spearman 7-y interage correlations for a) sit-ups and b) push-ups in males (-R)

and females (-0). Al1

correlations are significant (p 5 0.05), except for push-ups in 13-14-y-old males and 11-12, 13-14, 15-16, 17-18- and 50-59-y-old fernales

52

Figure 3. Spearman 7-y interage correlations for a) grip and ) strength and b) sit-and-reach flexibility in males (-i females (-*).

All correlations are significant (p 1 0.05)-

53

Manuscript 2 Figure 1. ANCOVA results for changes in body mass (BM), sum of five skinfolds (SF5), and waist circumference (WC)

xiv

by baseline physical activity (PA) levels ranging from a low of 1 to a high of 4 (based on kJ-kg -'-hr " of activity energy expenditure), for males (black bars) and females (white bars)-

78

Figure 2. ANCOVA results for changes in body mass (BM), sum of five skinfolds (SF5), and waist circumference (WC) by baseline physical activity (PA) intensities ranging from a low of 1 to a high of 4 (based on the frequency and MET values of activities performed), for males (black bars) and females (white bars)

79

Discussion Figure 1-A. ANCOVA results for changes in body rnass (BM). sum of five skinfolds (SF5). and waist circumference (WC) by baseline physical activity (PA) levels ranging from a low of 1 to a high of 4 (based on kJ-kg"-hr

-' of activity energy

expenditure), for males (black bars) and females (white bars)--

99

Figure 2-A. ANCOVA results for changes in body rnass (BM), sum of five skinfolds (SF5), and waist circumference (WC) by baseline physical activity (PA) intensities ranging from

a low of 1 to a high of 4 (based on the frequency and MET values of activities perfonned), for males (black bars) and females (white bars)

1O0

introduction

The prevalence of obesity is on the rise throughout industriatized and developing nations (1). Obesity reduces quality of life (2) and puts individuais at increased risk for the development of coronary heart disease (3, 4), non-insulin dependent diabetes mellitus (5),hypertension (6) and some cancers (7).In Canada, a recent estimate of the health care costs associated with obesity was $1-8 billion (1997),representing 2.4% of the annual health care expenditures (8). Knowledge of the determinants of obesity is essential to understand why the prevalence of this disease is increasing. Since this rise has occurred within a relatively short time, changes in gene frequencies at the population level have not likely played a large role (1). Obesity is intimately linked to energy balance. If a positive energy balance (energy intake exceeding energy expenditure) is maintained over tirne, weight gain will occur, and obesity (body mass index > 30 kgmm'2) rnay develop

(i). The sedentary lifestyle comrnon to today's society, with 62% of Canadians

(9),may contnbute to a considered physically inactive ( 4 2 . 6 k~mkg-'=day-') positive energy balance and ultimately to the development of obesity. Physical inactivity poses a serious health risk in its own right, as the health implications of physical inactivity include increased risk of morbidity and mortality (10, I I ) . Further research is required on the relationship between physical activity (PA) and long-term weight change (12). Musculoskeletal fitness (MSF) is one of several components of healthrelated physical fitness (HRPF), which can be affected favorably by regular PA

and relates to health status (13). MSF is comprised of the strength and endurance aspects of muscular fitness, as well as the fiexibility elernent of morphoIogica1fitness (14). Recent evidence for a positive relationship between

MSF and health in a Canadian sample of male and female adults (15-69 y) (15), supports prior research findings that thiç component of HRPF is associated with functional ability and health (16), particularly in older adults (17, 18). While musculoskeletal fitness is an integral part of well-being, lirnited data exist on the stability of indicators for this cornponent of fitness across the lifespan. Further, knowledge is Iacking as to whether PA levels andfor changes in PA can affect changes in MSF over time. There are three aims of the present research (see Figure 1). First, the 7-y stability of PA and MSF patterns will be assessed for Canadians across the ages of 11-69 y. From a health promotion perspective, it is essential to know the course of change for a given trait over time in order to design interventions for an appropriate target group. At present, knowledge on the stability of indicators for

MSF is confined to childhood and adolescence, with the exception of one study, which has tracked indicators into middle-adulthood (19). A second aim of this study is to investigate whether measures of adulthood (20-69 y) PA can predict 7-y changes in indicators of MSF. If PA'is found to be stable within adulthood, then an active Iifestyle in young adulthood should carry on into older adulthood. Further, if adult PA is positively associated with changes in MSF, such that high PA predicts gains in MSF, then it is possible

to expect that the physically active young adult will becorne a physically active older adult - at decreased risk for functional limitations related to low MSF. Finally, this research will determine whether adult PA can predict ?-y changes in body mass and adiposity. If PA is found to be stable frorn childhood to adulthood, then an inactive child will tend to become an inactive adult. Further, if adult PA levels predict changes in adiposity, then it is possible to expect that the physically inactive child will become a physically inactive adult - at increased risk for gains in body mass and adiposity and their associated comorbidities. This study is timely because even though the relationship between PA and obesity has drawn considerable research interest of late (20-22), consistency in findings has yet to be established (23) and repeated studies are necessary to elucidate the relationship (24). In addition, though recent evidence suggests that MSF is an important factor in health status (15), gaps in the Iiterature exist concerning the stability of MSF levels across the lifespan. Further, the impact of PA on changes in MSF during adulthood has not been previously investigated.

The large sample size and longitudinal design of the present study will make a significant contribution to furthering the understanding on each of these issues.

r-5

MSF - 1981

A A A A

1981 - 1988 Push-ups Sit-ups Grip Strength Sit-and-reach Flexibility

r

PROSPECTIVE PREDlCTlONS

APA

RETROSPECTIVE PREDlCTlONS

1981 - f988 A Body Mass A Surn of Five SkinfoIds A Waist Circurnference

Figure 1. Model illustrating the four principle issues addressed in thesis: 1) the

tracking of physical activity (PA) and rnusculoskeletal fitness (MSF) frorn 1981 to 1988: 2) the 7-y prospective predictions and associations between baseline PA and changes (A) in MSF (push-ups, sit-ups, grip strength, and sit-and-reach flexibility) and anthropornetric measures (body rnass, sum of five skinfolds, and waist circurnference), 3) the 7-y retrospective predictions and associations behveen follow-up PA and changes in MSF and anthropometric measures, and 4) the relationship between changes in PA and changes in MSF and

anthropometric measures.

Review of Literature

Stability of Physical Activity and Musculoskeletiil Fitness Tracking, or stability, refers to the maintenance of relative rank or position within a group over time (25). The stability of PA and MSF has been estimated using inter-age correlations between repeated rneasures. Correlations e 0.30 are Iow, while those ranging from 0.30 to 0.60 are moderate, and those >0.60 are high (25). Factors such as the time interval between measurements, age at first observation, stage of biological maturation, and change in environment or testing methodology can affect these correlations (26). A recent overview by Malina (25) of the tracking of PA and physical fitness

across the lifespan assessed PA broken down by stage of development chiIdhood, adolescence and adulthood, whife physical fitness was broken down by components - strength, fiexibility, motor fitness, and aerobic power. The review found that for PA, tracking from childhood to adulthood is low to moderate, while for MSF (strength, endurance, and fiexibility), significant tracking is observed across childhood and adolescence with predominately moderate stability. Limited data exist on MSF Ievels that span adolescence into adulthood

(25)-

Physical A ctivity Table 1 presents a summary of studies in which the stability of PA was examined. Three studies focused on the tracking of PA from childhood or adolescence to adulthood. The Cardiovascular Risk in Young Finns Study used a questionnaire to examine the frequency, intensity, and duration of physical activities performed by adolescent males and fernales, aged 12 to 18 at baseline,

over 3- and 6-y follow-up intervals. The shorter, 3-y inter-age correlations (0.33 I

r < 0.54) were stronger than the longer, 6-y correlations (0.17 I r I 0.43) for both males and fernales. Tracking was also found to improve with increasing age at baseline (27). The Amsterdam Growth Study used a semi-structured interview to evaluate the duration and intensity of activities for male and female youths, age 13 at baseline, at six intervals over a 15-y time period. As expected, longer time intervals produced lower correlations, with tracking onfy significant at the shortest, 5-y tirne interval ( ~ 0 . 3 7 ,maIes; r=0.25, fernales). The 15-and 1O-y inter-age correlations were non-significant, illustrating an instability of PA in males and females (0.05 I r 5 0.09,mates; 0.1 7 2 r 5 0.16, females) (28). A third study analyzed the tracking of PA measured by questionnaire for 203 Danish males and females, aged 15-19 at baseline, over an 8-y follow-up period. Tracking was found to be rnoderate and significant for males (r=0.31, p~0.001), and Iow and non-significant for females ( ~ 0 . 2 0 (29). )

The stability of PA in adultkood has been less studied than that of childhood and adolescent PA (25). However, from the avaitable research, it appears that adult PA tracks slightly better, though still at a low to moderate level. The Seven-Year Longitudinal Follow-up in the Coronary Artery Risk Development in Young Adults Study looked at the stability of self-reported PA in black and white American males and fernales, aged 18-30 at baseline (30). For both blacks and whites, the results indicate moderate stability, with better tracking in males (r=0.42/0.49, blackfwhite) than females (r=0.34/0.41, blacWwhite). An al1 male sample with a mean baseline age of 43, was used in

the prospective study of 6,092 Harvard Colfege alumni (31). An index of estimated weekly energy expenditure was derived from questionnaire responses in 1962 or 1966, 1977 and 1988 on flights of stairs climbed, city blocks walked, and participation in sports or recreation. Rank correlation coefficients ranged from low (r=0.27, 196211966 to 1988) to moderate (r=0.38. 1977 to 1988), with minimal differences occurring when stratified by decile of age. An additional study using an all male sample involved 1400 participants with a rnean baseline

age of 47.5 y (32). An index of PA was computed from questionnaire results and was retested after a 4-y follow-up. The Spearman's correlation coefficient of 0.32 indicated rnoderate stability for the PA index score.

Musculoskeletal Fitness HRPF has been defined as those components of physical fitness that are affected favorably or unfavorably by habituai PA and relate to health status (13). HRPF is generally operationalized as having five cornponents: morphological fitness, muscular fitness, rnotor fitness, cardiorespiratory fitness, and metabolic fitness. The present research focuses on the strength and endurance aspects of muscular fitness, as well as on the trunk flexibility element of morphological fitness - together known as musculoskeletal fifness (14). Table 2 sumrnarizes studies in which the stability of MSF is examined. Lirnited longitudinal data exists for measures of MSF throughout the lifespanTwo studies have previously measured the stability of rnuscular endurance via bent-knee sit-ups. The first study tracked 414 American school children from age 9 to 12. Tracking correlations were similar for males (0.46)

and females (0.47) (33). Comparable results were obtained from the Saskatchewan Child Growth and Developrnent Study, which annually tested the physical performance of 1O 6 boys from age 10 through 16 y. The stability of individuaI differences in bent-knee sit-ups was found to be moderate ( ~ 0 . 4 0 ) (34). The stability of muscular endurance measured via push-ups has apparently not been previously exarnined. Muscular grip strength is commonly measured with an adjustable hand grip dynamometer. Grip strength has been shown to track at a moderate, to moderately high level during adolescence. The Medford Boy's Growth Study obtained tracking correlations of 0.40 through early adolescence (from age 7 to 12 y) and 0.34for late adolescence (12-17 y) (35). The Adolescent Growth Study measured the grip strength of 183 participants from age 11 through 17, with rnoderately high stability seen in both males (r=0.60) and females ( ~ 0 . 6 2 ) (36) The sit-and-reach test is commonly used for the evaluation of trunk flexibility (14). The stability of trunk flexibility in males was assessed in the Leuven Growth Study of Belgian Boys and its' continuation, the Leuven Longitudinal Study on Lifestyle, Fitness, and Health (19). lnterage correlation coefficients ranged from 0.68 to 0.82 from adolescence (ages 13, 15, and 18 at baseline) to adulthood (age 30), indicating high stability (37). Even greater stability ( ~ 0 . 9 0 )was found between 5-y interage correlations within middle adulthood (30-35 y and 35-40 y) (19). Significant tracking of trunk flexibility was also reported for a sample of Southern Californian boys and girls (9 y at

baseline). lnterperiod correlation coefficients of the sit-and-reach test were generally high, with coefficients greater than 0.70 over a 3 y period (33).

As this review has illustrated. correlations between baseline and follow-up measures of PA and MSF range from low to moderate to high, with varying gender differences compounding these inconsistencies. It is possible that the variation in findings may be due to the different rneasurement tools used. However, it is equally possible that the inconsistencies are the result of true instability in the traits. Further study is necessary to clarify this relationship. Focus on the stability of PA and MSF in adulthood is particularly required, as Iimited research exists at present. Prediction of Changes in Musculoskeletal Fitness from Physical Activity The prediction of changes in MSF from PA has apparently not been addressed in the literature. As previously noted, there is high stability within cornponents of MSF over time. Further, research has shown that a strong genetic basis exists for one's level of MSF (38). According[y,it is unlikely that changes in a biological trait, such as MSF, will be strongly related to participation in a behavior, such as PA. Prediction of Changes in Body Mass and Adiposity from Physical Activity The extent to which future changes in body mass and adiposity can be predicted has received considerable attention. Several factors examined for their potential to predict changes in body mass andfor adiposity include: respiratory exchange ratio - the relative contribution of carbohydrate and Iipid oxidation (3941), insulin resistance -the condition in which a normal concentration of insulin

produces a less than normal biologic effect (42). leptin - a hormone secreted by adipocytes in response to body fat stores (43, 44), spontaneous physical activity

- fidgeting (45, 46), dietary intake (12, 47), resting metabolic rate (RMR) (40, 41), therrnic effect of feeding (TEF) (41). and PA (12, 48). RMR, TEF, and PA are the major components of daily energy expenditure

(EE). RMR, the largest component, constitutes the minimum energy required to maintain physiologic functions at rest. TEF, a smaller component, refers to the energy expended during the digestion and processing of food. PA. the most variable component, has been defined as "any bodily movement produced by skeletal muscle that results in a substantial increase over the resting energy expenditure" (7).Leisure-time PA is further specified as activities undertaken during an individual's discretionary time. The focus of this review will be on studies examining leisure-time PA as a predictor of changes in body mass andlor adiposity. Table 3 presents a literature summary of the characteristics and results of six studies evaluating PA as a predictor variable. Similar to a 1996 review article (12) on this topic, the results frorn these studies are equivocal. Some findings indicate a significant negative relationship between baseline PA and subsequent changes in body mass (4749). while others suggest none (50, 51),and still others support a relationship

between follow-up PA and changes in body mass (51, 52). From the Health Professionals Follow-up Study (48), a negative association was found between PA and changes in body rnass in a large (n=l9,478), prospective sample of men. Age infiuenced this association, with PA

a significant predictor of body mass change in males 45-54 y (p<0.001) and 5564 y (p<0.001), but not for men aged 65 and older. Similarly, a smaller

longitudinal study (47) investigating the relationship between PA and body mass change. discovered leisure PA at baseline to be negatively associated with gains in body rnass. However. gender interacted with the association in this study. as significant relationships were found for females (n=152, p = 0.0003). but not males (n=142). An all-female (n=507) sample from the Healthy Women Study (49), gives further support to a negative relationship between PA and body mass change in women. Prospective analyses uncovered that females who reported greater levels of baseline PA have significantly (p=0.003) less gains in body mass over a 3-y follow-up interval. Investigators from the Finnish Twin Cohort Study (50) failed to find a relationship between baseline PA and body rnass change. Instead, sirnilarly low and non-significant correlations were observed between PA and body mass change for both males (F-0.03, p=0.49) and fernales (r=-0.03, p=0.08). Using both a cross-sectional and longitudinal design, the NHANES-I Epidemiologic Follow-up Study examined the relationships between PA, body mass, and body mass change among 3515 men and 5810 women (51). Results from the crosssectional component of this study showed recreational PA to be significantly (p=O.Ol) inversely related to body mass at both baseline and follow-up. However, longitudinal analyses revealed baseline PA as not predictive of subsequent body mass changes over a 10-y period.

Though not finding a prospective association between PA and body mass change, the NHANES-I Epiderniologic Follow-up Study (51) did uncover a retrospective, negative relationship (p=0.05) between PA at follow-up and changes in body rnass. A second study ernploying a similar retrospective prediction, examined whether leisure PA at follow-up could explain body rnass changes occurring over the previous 4 to 7 y (52). The results frorn this large sample (n=12,669) of Finnish males and femaIes further supports a significant (p4.001), negative association between PA at follow-up and changes in body mass. Change in PA levels from baseline to follow-up haç also been assessed for its ability to predict changes in body mass. Three studies have found a significant relationship between PA change and body mass, while one study found none. In a sample of rniddle-aged women, increased PA over a 3-y interval was related to the smallest increases in body mass (p=O.Ol) (49). Investigators frorn the Health Professionals Follow-up Study reported that increased vigorous PA over 4 y was negatively related to body mass change for men in the 45-54-y and 55-64-y age groups, but not in the 265y age group (48). Finally, data from the NHANES-I indicated that IO-y PA change was strongly related to the odds of gaining weight of intemediate severity (8.0-13.0 kg) in men. However, for women, PA change increased the odds of gaining the highest level of weight (>13 kg), such that women reporting low activity at both baseline and follow-up had an odds ratio of 7.1 compared to those reporting high levels at both time points (51). Contrary to these significant findings, tegression analyses

on data from a small study of middle-class Caucasian Arnericans, found that 3-y change in leisure activity was not a significant predictor of weight change in men or women (47). Confounding Variables The variability in the magnitude of PA'S impact on body mass change has been attributed to several factors (53). These include differences in genetics, gender, regional fat distribution, intensity of physical activities performed. lifestyle factors. and socioeconomic status.

A classic experimental study examining the genetic component of changes in body mass and fat distribution suggests that genetic factors may be involved in the storage of energy as fat (54). Conducted on twelve pairs of monozygotic.twins, an 84 day period of overfeeding led to three times more variance between pairs than within, for changes in body mass, percent body fat, and fat mass. An observational study on naturally occurring body mass change was conducted within the Finnish Twin Cohort Study (50). Baseline PA was examined to determine whether PA modifies the action of genes responsible for changes in body mass. The associations between PA and 6-y body mass index (BMI) change for rnonozygotic twins were significantly stronger than for dizygotic twins, thus further supporting the proposa1that heredity can modify the effects of PA on body mass change. Gender differences may also influence the extent to which leisure-time PA is associated with body mass change. As indicated earlier, a m a l 1 longitudinal investigation (47) found baseline PA negatively associated with gains in body

rnass for fernales only. Similarly, a cross-sectional study, though not attempting to predict future changes, found gender differences in the extent to which

prevailing levels of habitua1 PA were associated with some dimensions of body composition (55). In males, moderate intensity PA was significantly associated with four indices of body composition - BMI, waist-to-hip ratio (WHR), waist-toheight ratio, and waist circumference (WC), while vigorous PA was only associated with WHR. Females showed a substantiaily different pattern, having no association between moderate PA and body composition, and significant associations between vigorous PA and WHR, waist-to-height ratio, and WC (but not BMI). The impact of activity intensity on regional fat distribution was considered in a cross-sectional study of the Canada Fitness Survey (56). VVithin four PA intensity groups (METs ranging from 4 to >9), no differences were found for

BMI, while significant (p<0.05) differences were found for WHR. Males and females having high intensity PA levels had lower WHR, which the authors attributed to the lower mean WC found in the high METs group (83.9I8.0 cm) compared to the lowest METs group (85.819.0

cm).

Socioeconornic status and lifestyle factors have been associated with body mass and adiposity in both males and females. For example, low family incorne is related to ovenveight and obesity in both males (57, 58) and females (59, 60), as is high levels of alcohol consumption (61). On the contrary, smoking

appears to be negatively associated with body mass and adiposity (62, 63).

From the review of literature, the following hypotheses were forrnulated:

1 - i. The stability of physical activity and musculoskeletal fitness over 7 y in the

Canadian population will be weak to rnoderate, dernonstrating greater stability during adulthood than childhood. ii. Indicators of musculoskeletal fitness will track better than measures of

physical activity.

2- i. Physical activity will not be associated with, nor predictive of, 7-y changes in musculoskeletal fitness.

ii. Changes in physical activity from baseline to follow-up will not be associated with 7-y changes in rnusculoskeletal fitness.

iii. The retrospective associations (using 1988 PA measures) will be stronger than the prospective associations (using 1981 PA measures).

3- i. Physical activity will be associated with and will predict 7-y changes in body mass and adiposity.

ii. Changes in physical activity from baseline to follow-up will predict 7-y changes in body mass and adiposity. iii. The retrospective prediction (using 1988 PA measures) wilI be

stronger than the prospective prediction (using 1981 PA measures).

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Heitmann BL, Kaprio J, Hams JR, Rissanen A, Korkeila Ml Koskenvuo M. Are genetic determinants of weight gain modified by leisure-time physical activity? A prospective study of Finnish twins. Am J Clin Nutr 1997;66:6728. Williamson DF, Madans J, Anda RF, Kleinman JC, Kahn HS, Byers T. Recreational physical activity and ten-year weight change in a US national cohort. Int J Obes Relat Metab Disord 1993;17:279-286. Rissanen AM, Heliovaara M, Knekt P, Reunanen A, Aromaa ADeterminants of weight gain and overweight in adult Finns. Eur J Clin Nutr 1991;45:419-430. Stich V, Hainer V, Barbes P, De Glisezinksy 1, Parizkova J, Crampes F. Physical activity in the prevention and management of obesity. World Rev Nutr Diet 1997;82:219-28. Bouchard C, Tremblay A, Després J-P, et al. The response to long-term overfeeding in identical twins. N Engl J Med 1990;322: 1477-1482. Fentem PH, Mockett SJ. Physical activity and body composition: What do the national surveys reveal? Int J Obes Relat Metab Disord 1998;22, SuppI 2:S8-S14. Tremblay A, Despres J-P, Leblanc Clet al. Effect of intensity of physical activity on body fatness and fat distribution. Am J Clin Nutr 1990;51:153-7. Jeffery RW, Forster JL, Folsom AR, Luepker RV, Jacobs DRJ, Blackburn H. The relationship between social status and body mass index in the

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12.

Table 1: Characteristics and results of the studies exarnining the stability of physical activity. Time lnterval & Age at Testing

Sample

Results -

Reference

-

-

Activity child to adult 961 males & females

6 years ages 12,15,& 18 at basellne

male/female: age 12+18, r=O.18 10,17 malelfemale: age 15-121, r=0.27 / 0,27 malelfemale: age l8+24, r=0,43 10,37

27

84 males 98 females

15 years (6 intervals) age 13 at baseline

malelfemale: age 13 +21, ~ 0 . 2 100.18 malelfemale: age 16 +21, ~ 0 . 3 170.25 10.17 rnalelfemale: age 13 +27, ~0.05

28

88 males

8 years ages 15-19 at baseline

malelfemale ~ 0 . 3 10.20 1

2,328 males 2,787 females

7 years ages 18-30 at baseline

black rnalelfemale: r=0.42 1 0.34 white malelfemale: r=0.49 / 0,41

6,092 males

26 years mean age=43 at star1 of follow-up

1962/66+1988: r=0,27

115 females

-

Activity adult

1962166-11977: ~ 0 . 3 9 l977+1988: r=0.38

1400 males

4 years mean age=47.5 at baseline

r=0.32

Cc) Cc)

02C - O U N

Co O v

Table 3: Characteristics and results of studies evaluating physical activity as a predictor of changes in body mass andlor adiposity. Sample 19,478 males

Measure of Activity

Outcorne Variable

vigorous PA hourshveek

weight change

Length of Follow-up 4 years

Results

- significant, negative association for men age 45-64 years (pc0,OOI) - change in PA , significant negative

48

associations in 45-54 y and 55-64 y age groups males and females tertiles of MET values 1571 MZ same-sex classified PA lnto twin pairs low, medium, high 3029 DZ same-sex twin pairs

3515 males 5810 females

142 males 152 females

507 females

12,669 males & females

weight change change in BMI

6 years

absoiute weight change weight gain categories

10 years

Baecke Questionnaire PA score (leisure, sport, & work)

weight change

2 years

kcal/wk leisure PA

weight change

leisure PA 3 categories

weight change

self-report PA level (low, medium, high)

- non-slgnificant, negative associatlons

50

between PA and weight change in males (r=-0.028, p=0,19) and in females (r=-0.034, p=0.08) - Min pair associations on change ln BMI were strongest at the highest PA levels (p for trend = 0,002)

- no association with baseline PA

- PA at foltow-up, strong negative relationship between PA level and weight gain (pc0.05) change in PA, significant negatlve relationship (p<0,05)

-

- females: baseline leisure activity, significant negative relationship (p= 0.0003) males: nls 3-y change in PA: nls

3 years

baseline kcal, significant negative relationship (p=0,003) change ln PA, significant negative relationship (p=0.01)

4-7 years

-

significant, negative associatlon between follow-up PA & weight gain (pc0,OOI)

52

Seven-year stability of physical activity and rnusculoskeletal fitness in the Canadian population

Michelle D. ~ortier' Peter T. ~atzmarzyk' Robert M. ~ a l i n a ~ Claude ~ o u c h a r d ~

'~epartrnentof Kinesiology and Health Science, York University North York, Ontario 'lnstitute for the Study of Youth Sports Michigan State University East Lansing, Michigan 3~ennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana

KEYWORDS: TRACKING, CANADA FITNESS SURVEY, PREVENTION, PHYSICAL FITNESS, AGE Running Head: Stability of Activity and Fitness Address for Correspondence: Peter T. Katzrnarzyk Department of Kinesiology and Health Science York University 4700 Keele Street North York, Ontario M3J 1P3 Phone: (416) 736-2100 ext. 30308 Fax: (416)736-5774 E-mail: katzmarz(ir2vorku.ca

Contributions of Authors

Michelle D. Fortier: conception, data analysis, interpretation and writing.

Peter T. Katzmarzyk: aided in conception, data analysis, interpretation and writing.

Robert M. Malina: aided in interpretation of results and writing.

Claude Bouchard: aided in study design, interpretation of results, and writing.

Abstract

Background: Knowledge of population patterns of physical activity (PA) levels and musculoskeletal fitness are important in the selection of appropriate target groups for public health interventions- Objective: To examine the stability of PA levels and rnusculoskeletal fitness in the Canadian population. Design: The sample included 951 males and 958 fernales, aged 11-69 y, for whom measures of body mass were available in the 1981 Canada Fitness Survey and its 7-y follow-up, the Campbell's Survey. Participants were divided into 2-y age groups in childhood (11-18 y) and 10-y age groups in adulthood (19-69 y). Measures of PA levels were estimated activity energy expenditure (AEE) and time spent on activity, while indicators of musculoskeletal fitness consisted of sit-ups, push-ups, grip strength, and sit-and-reach (trunk flexibility). Results: 7-y interage correlations ranged from -0.08 to 0.39 for AEE, -0.1 O to 0.33 for time on activity,

0.42 to 0.80 for sit-ups, -0.07 to 0.73 for push-ups, 0.44 to 0.82 for grip strength and 0-47 to 0.85 for sit-and-reach. In general, significant tracking correlations for

PA levels were Iimited to adulthood, whereas significant tracking of muscufoskeletal fitness was observed at al1 ages for al1 indicators except pushups. Males exhibited greater stability in push-ups than fernales. The average

percentage of participants remaining in the lower and upper quintiles of the distribution over 7 y was greater for musculoskeletal fitness (36%-68%) than for

PA level (30%-34%). Conclusion: PA level is not a very stable characteristic in the Canadian population; however, indicators of musculoskeletal fitness are moderately stable over 7 y.

Introduction A recent consensus statement on physical activity (PA) in the prevention and treatment of obesity and its comorbidities has called for continued monitoring of PA patterns at a population level (1). This reflects the growing body of evidence supporting the role of physical inactivity in the etiology of obesity (2) and as a predictor of morbidity and mortality from cardiovascular diseases, type II diabetes mellitus, and other chronic illnesses (3). Patterns of health-related physical fitness are also worthy of further, population-based study, as fitness indicators are also important predictors of morbidity and rnortality (4). Musculoskeletal fitness, for example, was associated with an index of general health status in a sample of Canadians (5). Further, poor muscular fitness was associated with an increased risk of mortality among Japanese men (6). Tracking, or stability, refers to the maintenance of relative rank or position over time. Knowledge of an indicator's stability allows one to predict future levels of the indicator and to potentially identify individuals at risk early enough to confer benefits from preventive rneasures (7). lnterage correlations between repeated measures of the trait are generally used to estimate stability. Correlations ~ 0 . 3 0 are considered to be indicative of low stability, while those ranging from 0.30 to 0.60 are moderate, and those 20.60 are high (8). Previous research examining the stability of PA level has found low to moderate correlations, with greater stability reported for shorter time intervals between measurements and older ages at first obsewation (9,10). Musculoskeletal fitness appears to track better than PA (1l ) , as the components

of muscular strength (12, 13) and endurance (14, 15) are moderately stable, and trunk fiexibility generally exhibits high stability (11, 16). The purpose of this study was to examine the stability of PA levels and components of musculoskeletal fitness in the Canadian population over 7 y

(1981-1988). While previous tracking studies have been conducted on PA levels and physical fitness, the large population-based sample and wide age range (1 169 y) of the present study will provide a valuable addition to the literature.

Subiects and Methods Sample

The 1981 Canada Fitness Survey (CFS) was based on a representative sample of the Canadian population (17), and it included information on 23,400 individuals frorn urban and rural areas of each province. The 1988 Campbell's Survey on WelLBeing in Canada was a folIow-up of the CFS and consists of 4,345 participants who took part in the original survey (18). Participants in these studies were given an explanation of the testing protocol prior to examination and provided informed consent indicating their awareness of the survey requirements (19). Health Screening was conducted using a 7-item Physical Activity

Readiness Questionnaire (20),resting heart rate and blood pressure rneasurements, and direct observation. The present analysis uses a subsample of 951 males and 958 females aged 11-69 y for whom body rnass rneasures were available in both 1981 and 1988. To establish representativeness between the present sample to the CFS, the variables used in this study (age, PA measures, and rnusculoskeletalfitness indicators) were converted to 2-scores,

by age groups, using the entire CFS sarnple as the reference sarnple. The variables examined in this study differed by a maximum of 0.15 standard deviation units. Thus, the present sample can be considered as acceptably representative of the larger CFS sample at baseline. Measures

Physical A ctivity Various rneasures of PA were obtained from 1981 and 1988 responses to an 11-page questionnaire based on the Minnesota Leisure Tirne Activity Questionnaire (21). The CFS questionnaire is moderately reliable for most rneasures of PA (22). The present analysis includes two indicators of PA level. A measure of daily time on activity (min-day-') was deterrnined from the total time spent per year on al1 physical activities. Further, a measure of activity energy expenditure (AEE) associated with daily leisure-time physical activities was calculated as follows:

AEE k~-kg-'-da~-' = Z (Ni x Di x METsi 1 365) where N is the nurnber of occasions of activity (0 in the past 12 months, D is the average duration in hours of that activity, and METs is the estimated energy cost of the activity expressed as kilojoules expended per kilogram of body weight per hour of activity (kJ-kg"-hr "). The METs value for activities at various intensity levels has been previously established by a panel of exercise physiology experts

(23)

Musculoskeletal Fifness Muscular strength and endurance were measured using the standardized procedures of the CFS (24). Grip strength was measured with a Stoelting adjustable dynamometer. Participants held the dynamometer in line with the forearm at thigh level, and were instructed to squeeze vigorously so as to exert maximal force. The maximum strength of both hands was surnmed to provide a single index of strength (kg). Muscular endurance was measured as the maximum number of sit-ups perfonned in 60 s (nmmin-') with participants in a supine position with legs fiexed 90" at the knees, and from the greatest number of push-ups produced without time limit. Male and fernale protocols differed for push-ups. Males lifted the body while balancing from the toes whereas females lifted while baiancing frorn the knees. Trunk flexibility was assessed using a sitand-reach test. The test measured how far a participant could reach towards the toes, with the knees flat on the floor. The test was repeated twice and the maximum value was recorded to the nearest 0.5 cm. A trunk flexibility score of 25 cm is equivalent to touching the floor. Statistical Analyses To investigate the stability of PA level and musculoskeIetal fitness across the Iifespan, the sample was divided into 2-y age groups over youth (ages 11-18 y) and 10-y age groups over adulthood (ages 19-69 y). Spearrnan rank-order

interage correlations between 1981 and 1988 measures of PA levels and musculoskeIetal fitness variables were computed. Tracking at the high and low ends of the distribution was determined by calculating the percentage of participants remaining in the upper and lower quintiles (20%) over the 7-y period.

Results Tables 1 and 2 show the sample sizes, rneans and standard deviations for measures of PA and musculoskeletal fitness in 1981 and 1988, by age category, -') from 1981 to 1988 in adults for males and fernales. AEE ( k ~ = d a ~increased aged 30 y and older at baseline, while the 7-y changes in youth AEE were more variable. Expressing AEE per kg of body weight resulted in a different pattern of change, such that AEE (k~mk~-'-day-') tended to decrease over the 7-y period. All components of musculoskeletal fitness decreased over the 7 y in the adult age groups, whereas changes in musculoskeletal fitness during youth vari& between components. (Insert Table 1 here) {Insert Table 2 here) Spearman 7-y interage correlations for measures of PA level are presented in Figure 1. For both AEE and time on activity, correlations from the youth age groups were low and non-significant, with the exception of tirne on activity in males i5-16 y of age (r=0.33, p=0.04). AEE correlations during adulthood showed significant, low-to-moderate stability in al1 age groups for males, but only in the 19-20 and 30-39-y age groups for females. The opposite gender pattern ernerged for adult time on activity correlations, with significant low-to-moderate stability found in al1 but the 60-69-y age group for females, whereas significant stability in males was only seen in the 19-20 and 30-39-y age groups. {Insert Figure 1 here)

The 7-y interage correlations for cornponents of musculoskeletal fitness are presented in Figures 2 and 3. Small sarnple sizes for measures in the 60-69y age group prevented analysis beyond the 50-59-y age group. The stability of

sit-ups, grip strength, and sit-and-reach was significant, with moderate-to-high tracking in al1 age groups, and generally greater stability in adulthood than childhood. Correlations for push-ups were generally higher for males than females. While al1 but the 13-14-y age group showed significant tracking for push-ups in males, only the 19-29, 30-39, and 40-49-y female age groups displayed significant tracking {Insert Figure 2 here} The percentages of participants remaining in the lower or upper quintiles (20%) of the distribution over 7 y are presented in Table 3. On average, the

percentages of individuals remaining in the lower or upper quintiles of PA level ranged from 31% to 34% for mafes and frorn 30% to 34% for fernales. The percentage of participants remaining in the outer quintiles of musculoskeletal fitness ranged from 49% to 68% for males and 36% to 66% for females. {Insert Table 3 here)

Discussion In general, physical activity levels tend to decline throughout adolescence and adulthood (18). In the present study, estimated AEE (k~.day-') increases over 7 y for youth aged 11-16 y at baseline. However, when AEE is adjusted for body mass (k~gkg-'.day-'),the results show a general decline in AEE for this age

range. Thus, the increases in body mass experienced in the transition from adolescence to adulthood are Iikely driving the increase in the unadjusted AEE. The total time on activity indicates 7-y increases over the 11-16-y age range. In combination, these PA measures irnply that the increased tirne spent on PA must be involving low intensity activities and that, in general, activity levels exhibit 7-y decreases as one progresses through childhood into adulthood. In Iine with previous research (8), results from the present study indicate that stability of PA level is greater in adulthood than youth (Figure 1). lnterage correlations for youth are generally low, ranging from 0.03 to 0.33, which is consistent with studies of similar follow-up length, having correlations in the range of 0.17 to 0.43 (9, 10, 25). Comparable to the CardiovascuIar Risk in Young Finns Study (Q),tracking correlations in the present sample generally increase in older age groups during youth, with the notable exception of the 1718-y age group which rnay reflect the transition frorn school to the workforce.

Due to the fixed 7-y follow-up design of this study, evaluation of the tracking of PA level under various tirne intervals was not possible. Previous research has shown that shorter follow-up lengths generate higher interage correlations (9, 10,

26). PA interage correlations during adulthood are low to moderate, ranging from 0.04 to 0.39. Gender differences are apparent in the adulthood 40-49, 5059, and 60-69-y age groups. Males tend to track better on AEE, while fernales are more stable than males on time on activity. The tracking of PA Ievel in adulthood has been less studied than during childhood and adolescence (8).

However, The Leuven Longitudinal Study on Lifestyle, Fitness, and Health examined the 5-y stability of PA among adult males and found Iow tracking coefficients for time active during leisure time (h/wk; r=0.20, aged 30-35 y; r=029, aged 35-40 y) and moderate coefficients for an active leisure time index , 30-35 y; r=0.30, aged 35-40 y) (1 1). The gender effect (METS; ~ 0 . 3 4aged

evident in the present results cannot be compared exactly to existing Iiterature, as this is the first study, to Our knowledge, to compare the tracking of PA levels between males and females during middle adulthood. However, a similar gender difference has been previously found in early adulthood, with males showing greater stability in PA level than females (27). The 7-y changes in indicators of musculoskeIetal fitness during chitdhood

(1i-19 y) show substantial variation between components (Table 2). For grip strength, both males and females generally show 7-y increases. The opposite is true for sit-ups, in which males and females generally display 7-y decreases. A gender difference is apparent for push-ups, in which males increase at each age, while fernales show 7-y decreases at each age. Trunk flexibility does not undergo substantial7-y changes during childhood, and females generally obtain higher sit-and-reach scores than males. Throughout adulthood (20-69 y) there are consistent decreases from 1981 to 1988 for al1 musculoskeletal fitness indicators. Results of the present study suggest that the stability of musculoskeletal fitness is greater than for PA level. Few longitudinal data exist for measures of

~usculoskeletal ftness throughout the lifespan (8). thus drawing comparisons to past studies is Iimited beyond childhood and early adulthood. The stability of sit-ups in the present study ranges from moderate to high, with tracking correlations between 0.44 and 0.80 for al1 ages. Previously, the stability of sit-ups has been assessed over a 3-y pefiod in American school children, 9 y at baseline, in which tracking correlations were moderate, with no gender diwerence (mate, r=0.46; female, r=0,47) (15). In the Saskatchewan ChiId Growth and Developrnent Study, lower stability over a longer tirne interval (6-y), with moderate tracking (r=0.40) was observed in males 10 y at baseline (14). High 5-y sit-up stability (r=0.69) was reported for a sample of 130 adult males, aged 35 at baseline, in the Leuven Longitudinal Study (11). The stability of push-ups has apparentiy not been examined previously. In the present study, a gender difference is evident with males having greater stability than females in al1 but one age group (13-14-y). The range of interage correlations across the lifespan is also greater for males (-0.07 to 0.73) than for females (0.14 to 0.39). lnterage correlations for grip strength exhibit a pattern sirnilar to that of situps, indicating moderate to high stability throughout the lifespan. These results are consistent with previous studies. Moderate tracking correlations of 0.40 through early adolescence (7-12 y) and 0.34 for late adolescence (12-17 y) were found in the Medford Boy's Growth Study (12). The Adolescent Growth Study rneasured 6-y tracking (age 11 at baseline) and found high stability (males, ~ 0 . 6 0 females, ; r=0.62) (13). As with the stability of sit-ups, only one previous

study has been perfomed, to our knowledge, which addresses the stability of grip strength in adults. The Leuven Longitudinal Study found a high correlation between baseline grip strength and its 5-y follow-up in adult males (11). The stability of trunk flexibility in the present study is high for males, across the lifespan, and fluctuates between rnoderate and high stability for females during childhood then levels off into high stability in adulthood. The stability of trunk flexibility in males was assessed in the Leuven Growth Study of Belgian Boys and its' continuation, the Leuven Longitudinal Study on Lifestyle, Fitness, and Health (1 1). Results from these studies indicate high tracking for the sit-and-reach test. Correlation coefficients ranged frorn 0.68 to 0.82 from adolescence (ages 13, 15, and 18 at baseline) to adulthood (age 30) (16),and even higher stability (r=0.90)between 5-y interage correlations within middle adulthood (30-35 y and 35-40 y) (11). High stability levels for trunk flexibility were also reported for a sarnple of Southern Californian boys and girls (9 y ai baseline). lnterperiod correlation coefficients of the sit-and-reach test with baseline rneasures were generally above 0.70 over a 3 y period (Z 5 ) . Analysis of the percentage of participants remaining in the lower or upper quintiles indicates that rnovement away from these quintiles over 7 y is greater for PA level than for rnusculoskeletal fitness (Table 3). The average percentage o f participants remaining in the outer quintiles range from 30%-34% for PA level and 36%-68% for rnusculoskeletal fitness. These results support the correlation data for PA levels and musculoskeletal fitness, which suggest that PA level has greater plasticity than musculoskeIetal fitness and is thus subject to more

change. The differences in percentages of participants remaining in the upper and lower quintiles of PA level are negligible for both males and females. Past studies have reported rnoderate differences, with the percentage remaining sedentary larger than the percentage remaining active (9, 10). The range of ages exarnined in the present sample is a rnarked strength of this study. As a society with an aging population base, it is essential to gain further understanding of health patterns during middle adulthood, a period for which little research on patterns of PA level and musculoskeletal fitness exist. Further, the inclusion of the PA measure - time on activity, adds greater scope to the present understanding of patterns of PA in the Canadian population. The stability of time on activity has seldorn been addressed (11, 25) and, to Our knowledge, never across the Iifespan. This PA measure requires greater examination because health promotional tools, such as Canada's Physical Activity Guide for Healthy Active Living (28), often relate recommendations in terms of the time on activity required for health benefits. Knowledge of the decline in time on activity occurring in adult males, as suggested in this study, provides information that could help establish appropriate target groups when designing health interventions. A final strength of this study is the examination of musculoskeletal fitness within adulthood. Since studies have shown that musculoskeletal fitness, particularly muscular strength, is related to health (5), independence (29),and functional performance in older adults (30, 31), the stability of this fitness cornponent, across adulthood, is an important consideration.

The finding that musculoskeletal fitness tracks better than PA is consistent with previous studies (8). There are two potential explanations for this. PA is a behavior, while musculoskeletalfitness components are traits or characteristics of a person. Thus, it is reasonable to assume that PA would demonstrate greater plasticity over time. A second consideration is the reliability with which PA can be measured relative to musculoskeletal fitness. Given that PA is a behavior, the measurernent error for any estimate is undoubtedly greater than for measurement error associated with flexibility or muscular strength and endurance, which are biological attributes. Based on the present findings, interventions aimed at increasing levels of PA should be targeted at al1 age groups. The low level of tracking observed for

PA at al1 ages suggests that directed efforts at increasing PA levels could be productive at any age. While tracking is greater in adulthood than during childhood for musculoskeletalfitness, there was significant stability at al1 ages. Hence, no specific cut-off age is apparent for which interventions to improve rnusculoskeletal fitness would be most successful. In conclusion, the stability of muscuIoskeletalfitness generally exceeds that of PA level at a11 ages, in both males and fernales in the Canadian population. Public health initiatives to increase the activity and fitness of Canadians, should focus on augmenting existing values and on providing techniques for maintaining PA and fitness once optimal levels are reached.

This research was supported by The Polar Research Grant on Controlled Heart Rate Zone Exercise from the American College of Sports Medicine Foundation (P.T.K.).

Thank you to Cora Craig and her colleagues at the

Canadian Fiiness and Lifestyle Research lnstitute for supplying the Canada Fitness Survey and Campbell's Survey data. C. Bouchard is partially supported by the George A. Bray Chair in Nutrition.

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24. Fitness Canada. Standardized Test of Fitness: Operations Manual. 2nd ed. Ottawa, ON: Ministry of Fitness and Amateur Sport, 1981. 25. Andersen LB, Haratdsdottir J. Tracking of cardiovascular disease risk factors including maximal oxygen uptake and physical activity from late teenage to adulthood- An 8-year follow-up study.

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Table 1. Sample sizes, means and standard deviations for 1981 and 1988 measures of physical activity in the Canadian population. Activity Energy Expenditure

Age (Y)

(k~ d a y ' )

( k J kg-'*da$') 1981

1988

Males 11-12 13-14 15-16 17-18 19-29 30-39 40-49 50-59 60-69

15112 2 0 k 16 19k23 19k24 12 î 14 8k11 8 & 12 I O * 12 I O * 11

21 k 24 16k12 15k14 l 4 I 18 I O * 10 11 1 1 0 917 12* II 14 A 12

Females 71-12 13-14 15-16 17-18 19-29 30-39 40-49 50-59 60-69

13 118 14 k 17 14 116 12 2 18 8110 6 I8 7î10 6 17 718

11 I l 0 14 21 13 k 16 9IIO 8 I8 8I7 loi10

9I9 918'

Time on Activity (rninday-')

Table 2. Sample sizes, means and standard deviations for 1981 and 1988 measures of musculoskeletal fitness in the Canadian population. Age (Y)

Males 11-12 13-14 15-16 17-18 19-29 30-39 40-49 50-59 60-69

Females 11-12 13-14 15-16 17-18 19-29 30-39 40-49 50-59 60-69

Sit-ups (nnmirfl)

Push-ups (n)

Grip Strength (kg)

Sit-and-reach (cm)

Table 3. Percentage of participants remaining in the lower or upper quintile (20%) of physical activity and musculoskeletal fitness over 7 years in the Canadian population. Activity Energ! Expenditure Age (y)

Lower

Males

11-12 13-14 15-16 17-18 19-29 30-39 40-49 50-59 60-69 Average Females 11-12 13-14 15-16 17-18 19-29 30-39 40-49 50-59 60-69 Average

20.0 28,6 50.0 33,3 37.8 35.6 40.9 20.0 16,7 34.1 0.0 12.5 42.9 14.3 34.8

38.3 24.0 35.3 11.1 29.9

Upper

Time on Activity Lower

Upper

Sit-ups Lower

Upper

Push-ups Lower

Upper

Grlp Strength Lower

Upper

Sit-and-Reach tower

Upper

Figure Legends

Figure 1. Spearman 7-y interage correlations for a) activity energy expenditure (AEE) and b) time on activity in males (-i and )females (-e). Significant correlations (p 5 0.05)are indicated with '0' for males and '*'for fernalesFigure 2. Spearman 7-y interage correlations for a) sit-ups and b) push-ups in males (-4)and females (-*). AI1 correlations are significant (p <0.05),except for push-ups in 13-14-y-old males and 11-12, 13-14, 15-16,17-18- and 50-59-yold females. Figure 3. Spearman 7-y interage correlations for a) grip strength and b) sit-andand ) females (-e). AI1 correlations are significant reach flexibility in males (-i (p 1 0.05).

Physical activity and seven-year changes in body mass and adiposity in the Canadian population

Michelle D. ~ortier' Peter T. ~atzmarzyk'

Claude I3ouchard2

'Departrnent of Kinesiology and Health Science, York University North York, Ontario '~enningtonBiomedical Research Center, Louisiana State University, Baton Rouge, Louisiana

Running Head: Physical activity and weig ht change Address for Correspondence: Peter T. Katzmarzyk Department of Kinesiology and Health Science York University 4700 Keele Street North York, Ontario M3J 1P3 Phone: (416) 736-2100 ext. 30308 Fax: (416) 736-5774 E-mail: [email protected]

Contributions of Authors

Michelle D. Fortier: conception, data analysis, interpretation and writing.

Peter T. Katzmarzyk: aided in conception, data analysis, interpretation and writing .

Claude Bouchard: aided in study design, interpretation of results, and writing.

Abstract Background: Physical activity is potentially a strong predictor of changes in

body mass and adiposity. Objective: To determine the associations among baseline levels of physica! activity (PA), changes in PA, and changes in body

mass and adiposity in the Canadian population. Design: The sample consists of 602 males and 644 fernales, aged 20-69, from the 1981 Canada Fitness Survey and its 7-y follow-up, the Campbell's Survey. Measures of PA, derived from questionnaire, consisted of activity eneergy expenditure (AEE) and time on activity. Participants were grouped into categories of PA by AEE and PA intensity (based on MET values of activities). PA change was determined from movement between tertiles of AEE from baseline to follow-up. Indicators of body size and adiposity were body mass, the sum of five skinfolds (SF5). and waist circumference (WC). Results: Partial correlations between baseline levels of PA and ?-y changes in body mass, SF5, or WC, controlling for age and baseline level of adiposity, were low and non-significant. Based on results from multiple regression analyses, PA was not a significant predictor of 7-y changes in body mass or adiposity. Neither PA level, PA intensity, nor PA change categories were significantly related to changes in body mass, SF5, or WC. Conclusions: Levels of PA are not predictive of 7-y changes in body rnass or adiposity in the Canadian population.

KEYWORDS: WEIGHT GAIN, LONGITUDINAL STUDY, OBESIW, LlFESTYLE

Introduction Knowledge of the deteminants of obesity is essential to understanding the genesis of the current obesity epidernic. Obesity results from a chronic energy imbalance, whereby energy intake exceeds energy expenditure. As of yet, there is no consensus as to whether the increasing prevalence of obesity is primarily the result of decreased energy expenditure or increased energy intake. Given that long-tenn weight loss is rarely achieved once an obese state is reached (A),

the ability to predict obesity prior to its development is critical to countering the obesity epidemic. Of the rnany potential contributors to body rnass and adiposity change, leisure-time physical activity has drawn considerable interest (2, 3). A large proportion of Canadians lead a sedentary lifestyle. Recent estirnates are that

62% of canadians are physically inactive (~12.6k~-kg"-day-')(4), and this rnay contribute to a positive energy balance and ultimately to the developrnent of obesity. Since physical activity (PA) is the most variable component of daily energy expenditure, it is a candidate to predict why some individuals gain weight over time while others do not. However, results are equivocal. For example, PA was related to future weight gain in two studies (5, 6),while two others failed to find a relationship (7, 8). PA levels have low to moderate stability over time (9),

and as a result, the effect of changing PA Ievels on changes in body mass and adiposity warrants further examination (5, 8).

The purpose of this study is to examine the reiationship between PA and 7-y changes in body mass and adiposity in the Canadian population. The

changes in adiposity between the 1981 Canada Fitness Survey and 1988 Campbell's Follow-up Sunley were exarnined in relation to baseline observations of PA and 7-y changes in PA levels.

Subiects and Methods Sample The 1981 Canada Fitness Survey (CFS) was based on a representative sample of the Canadian population (IO),containing information on 23,400 individuals from urban and rural areas of each province. The 1988 Campbell's Survey on Well-Being in Canadians was a follow-up of the CFS and consists of

4,345 participants who took part in the original survey (11). Participants were given an explanation of the testing protocol and informed consent was obtained

(12). The sample used in the present study includes al1 males (n=602) and fernales (n=644) aged 20-69y for whom information was available for baseline

PA and 1981 and 1988 body mass. To establish the representativeness between the present sample to the CFS,measures for the variables used in this study were converted to Z-scores, controlling for age, using the entire CFS sample as the reference sarnple. The variables examined in this study differed by a maximum of 0.22 standard deviation units. Thus, the present sample can

be considered as acceptably representative of the larger CFS sample at baseline. Measures Anthropometry Anthropometric dimensions were taken following the procedures of the Canada Fitness Survey (13). Body mass was measured to the nearest 0.1 kg using a standing beam balance scale (Seca), and waist circumference ('WC) was measured to the nearest mm using an anthropometric tape. Five skinfolds, taken at the subscapular, suprailiac, triceps, biceps, and medial caff sites, were measured on the right side of the body with a Harpenden caliper to the nearest

0.2 mm. The sum of five skinfolds (SF5) was calculated to provide a single index of subcutaneous adiposity.

Physical Activity Several measures of PA were obtained from 1981 and 1988 responses to an 11-page questionnaire based on the Minnesota Leisure Time Activity Questionnaire (14). The CFS questionnaire has been found to be moderately reliabie for most measures of PA (15). A continuous rneasure of daily tirne on

activity (min~day-l)was determined from the total tirne spent per year on all physical activities. Activity energy expenditure (AEE) was calculated as follows:

AEE k~.k~''.day-'= E (Ni x Dix METsi 1 365) where N is the number of occasions of activity (1) in the past 12 months, D is the average duration in hours of that activity, and METSis the energy cost of the

activity expressed as kilojoules expended per kifogram of body weight per hour of activity (kJ-kg -'-hr -'). The METSvalue for activities of various intensity levels has been previously established by a panel of exercise physiology experts (16). AEE was used to group subjects into four categories of PA levels.

Previous research by Stephens et al. (17) used a similar rnethod of dividing subjects by energy expenditure into activity designations based on the exercise dosage required for cardiovascular health benefits. Participants were separated into three groups: sedentary - having an average EE under 6.3 k~=kg-'=dayk' (1-5 kcal-kg-l-day-'); rninirnally active - averaging between 6.3-12.6 k ~ - k ~ - ' = d a (1~-5-' 2.9 kcal.kg"=day-'1; and. adequately active - average EE greater than 12.6 kJ=kg-'=day-' (3.0 kcal=kg"-day"). For the purpose of the present analysis, the high-end group was further divided into adequately active - average EE between

12.6-20.9kJ-kg-'=day-' (3.0-4.9 kcal-kg-'day-'); and highly active - average EE greater than 20.9 kJ-kg-'=day-'(5.0 kcal-kg-'oday-'). It is hoped that this expanded PA level grouping will prevent "ceiling" effects and allow for the observation of whether high levels of PA affect body mass and adiposity changes differently than adequate Ievels. To examine the effect of changing PA level on changes in anthropometric rneasures, 1981 and 1988 AEE were divided into tertiles of low, moderate, and high PA. Five activity change groups were created based on rnovement between the tertiles from 1981 to 1988, as follows: group 1 - low AEE, stable from 1981 to 1988; group 2 - increased AEE from 1981 to 1988; group 3 - moderate AEE,

stable from 1981 to 1988; group 4 - decreased AEE from 1981 to 1988; and group 5 - high AEE, stable from 1981 to 1988. In addition to the AEE categories described above, the sample was separated into PA intensity groups following the procedures of Tremblay et al. (18 ) . Participants were divided into four groups according to the following criteria: group 1, subjects reporîing activities of <5 METs for 5 6 months in the past year; group 2, subjects reporting activities of 15 METs but <7 METs for 16 months in the past year; group 3, subjects reporting activities of 27 METs but <9 METs for 16 months in the past year; and group 3, subjects reporting

> 9 METs

for 26 months in the past year. Covariates

Family incorne was obtained from questionnaire data and subject responses were allotted into one of three groups: group 1, those with 1981 family incornes less than $14,999; group 2, $15,000 to 29,999; and group 3, $30,000 and over. Smoking status was designated as: group 1, current smoker; group 2, former smoker; and group 3, never smoked. A measure of the frequency of alcohol use was constructed by grouping responses according to: group 1

-

frequent use, for subjects who reported "1+ times a day", "4-7 times/weekn, or "13 times/weekW;group 2 - infrequent use, for subjects who reported "1-3

timeslmonth" or "< once a month"; and group 3 - non-drinker, for subjects who reported "do not drink alcohol".

Stafistical Analyses AI1 vanables were examined for normality and transformations were peiforrned on skewed variables. Partial correlations, corrected for age and baseline anthropometric measures, were used to investigate the relationship between 1981 AEE and time on activity and changes in body mass, SF5, and WC from 1981 to 1988. Multiple regression was used to predict changes in body m a s , SF5, and WC, from 1981 measures of AEE, tirne on activity, age, family

incorne, smoking, alcohol use, and baseline levels of the anthropometric indicators.

ANCOVA was used to test for differences in changes of body mass or adiposity within the 1981 categorical variables of PA IeveI and PA intensity. Further, ANCOVA was used to examine changes in body mass or adiposity in relation to categories of PA change. Covariates included in the models were age, family income, smoking status, alcohol use, and baseline level of the anthropometric indicators.

Table 1 provides descriptive statistics for baseline measures of age, body mass, SF5, WC, family income, smoking status, alcohol use, PA, and the five categories of PA change. As well, the 7-y changes in the anthropornetric measures are shown, with positive mean changes observed for males and fernales.

Pearson partial correlation coefficients between baseline PA and 7-y changes in body rnass, SF5, and WC are presented in Table 2. There are no significant relationships between either baseline AEE or time on activity and changes in body mass, SF5, or WC. Results of the multiple regression analyses are presented in Tables 3 and 4. Neither AEE nor time on activity are significant predictors of 7-y changes in

anthropometric rneasures. Baseline rneasures of body mass, SF5, and WC are the best predictors of their respective 7-y changes. For both sexes, the proportion of the variance attributable to the independent variables is quite low, ranging from 2% to 6% (Tables 3-4). ANCOVA results for PA level and PA intensity are illustrated in Figures 1

and 2, respectively. PA levels at baseline (Figure 1) show no significant patterns relating to 7-y changes in body rnass, SF5, or WC. PA intensity at baseline (Figure 2) is not related to anthropometric changes except for body mass change in females (F=2.70, p=0.04), where the results are statistically significant but not in the expected direction (Le. second intensity group has least weight gain). Results frorn the ANCOVA for PA change on 7-y changes in body mass are presented in Table 5. With respect to body mass change, there are no differences between groups in which PA levels increased, decreased, or maintained a low, moderate, or high level, relative to others in the sample. Similar results were obtained when changes in PA were related to changes in

SF5 and WC (results not shown).

Discussion

The present study used a representative sample of the Canadian population to investigate whether PA levels could predict changes in body mass or adiposity during adulthood. The results showed 7-y gains in anthropometric measures frorn baseline to follow-up, however, the amount of change incurred did not relate to PA status. The results suggest that PA levels do not have an appreciable effect on ?-y changes in body mass, SF5, or WCSeveral factors rnay have attenuated the expected relationship between PA Ievels and 7-y changes in body rnass and adiposity. First, without knowledge

of energy intake, an important aspect of the energy balance equation is missing and this potentially confounding variable could not be statistically controlled for. Thus, differences between AEE groups may have been tempered by variations in energy intake. Second, the use of a questionnaire to quantify PA, while common in epidemiological studies, is subject to inaccuracies frorn participant's subjective

recall of past PA performed and from questionnaire interpretation problems. Unfortunately, more accurate means of measuring PA, such as doubly labeled water or heart rate monitoring, are inconvenient and too expensive for large, population-based research (19). Whiie limitations may be present when perforrning longitudinal research involving rneasures of PA, some studies have found a significant negative relationship between baseline PA levels and subsequent changes in body mass (5, 6, 20). A possible reason for these significant findings is the use of relatively

short foiiow-up intervals ranging from 2 to 4 y. Studies finding no relationship between PA and anthropometric change, as in the present research, have typically used longer follow-up intervals. For example, the Finnish Twin Cohort Study (7) revealed low and non-significant associations between baseline PA level and 6-y weight gain and the NHANES-I Study found recreational PA Ievels reporkd at baseline had little relationship to weight gained over a IO-y follow-up period (8). It has been shown that PA exhibits greater stability over shorter tirne intervals (9). Hence, predictions of future body mass and adiposity changes from PA levels should be most accurate with short time intervals, since PA levefs at baseline and follow-up will be more similar. In the present study, PA measurements were taken 7 y apart with no inter-period measures, thus it is unknown at what point(s) PA levels rnay have changed during the study interval, nor for how long the changes may have been maintained. This factor may also have reduced the effect of PA level on body mass and adiposity change. The relationship of changes in PA over time has previously been significantly related to changes in weight (5, 6, 8). Results from the present study however, suggest that changing PA Ievels are not associated with 7-y body

mass and adiposity changes. The way in which PA is operationally defined presents a possible explanation for the contrary results found in the present analysis. The past studies have considered PA in terms of self-reported recreational PA Ievel (low, medium, and high) (8), hours per week of vigorous activity (5),and weekly kilocalo~esexpended (6). The present analysis

considers PA change in terms of rnovement, relative to the sample, between tertiles of AEE. Perhaps this particular rneasure of activity taps into a different aspect of the association between PA and body mass change. Some gender differences exist in the AEE and tirne on activity regression models. In females, a significant positive relationship exists between age and changes in SF5 and WC, but not in body rnass. This finding is supported by the literature as adiposity generally increases with age (21). Conversely, in rnales, age is related only to body mass and it shows a negative relationship. Interestingly, significant negative relationships are found between ail baseline anthropometric measures and their respective anthropometric changes in females, while for males, the same is true except between baseline body mass

and ifs 7-y change. Thus, in accordance with the literature (21), it appears that gender may interact in the differential manner in which age and baseline anthropornetry affect body mass and adiposity changes. PA is the rnost variable component of daily energy expenditure and would therefore seem to be a good candidate to explain differences in weight gain among individuals. On the other hand, PA is a behavior, and a cornponent of one's lifestyle. Thus it is difficult to rneasure accurately. Perhaps individual differences in PA levels that are too small to be detected with a questionnaire could be important in explaining long-tem weight gain. Alternately, the interactions arnong PA and other metabolic predictors of weight changes, such as resting rnetabolic rate or respiratory quotient, must also be considered to

enhance the prediction of changes in adiposity from components of daily energy expenditure. In summary, neither baseline PA levels, nor changes in PA frorn baseline to follow-up, were predictive of 7-y changes in body mass, SF5, or WC. Plainly, the development of obesity is cornplex and not IikeIy due to any single factor. The industrialized, computerized environment fosters conditions for low PA and high energy intake, which ultimately promotes a positive energy balance. Further multivariate longitudinal investigations are necessary to understand why some individuals gain weig ht over time white others do not under these same environmental conditions. Future studies should encompass repeated measurements of PA and lifestyle behaviors throughout the study interval so that changes can be assessed with shorter follow-up periods. The importance of understanding the determinants of body rnass and adiposity change is clear, the increasing prevalence of obesity in Our society represents a significant public health threat.

Acknowledgements This research was supported by The Polar Research Grant on Controlled Heart Rate Zone Exercise frorn the American College of Sports Medicine

Foundation (P.T.K.). Thank you to Cora Craig and her colleagues at the Canadian Fitness and Lifestyfe Research lnstitute for supplying the Canada

Fitness Survey and Campbell's Survey data. C. Bouchard is partially supported by the George A. Bray Chair in Nutrition.

References Leibel RL, Rosenbaurn M, Hirsch J. Changes in energy expenditure resulting frorn altered body weight. N Engl J Med 1995;332:621-8. Saris WHM. Physical inactivity and metabolic factors as predictors of weight gain. Nutr Rev 1996;54:Sll O-SI 15. Williamson DF. Dietary intake and physical activity as "predictors" of weight gain in observational, prospective studies of adults. Nutr Rev 1996;54:SlOlS109. Craig CL, Russell SJ, Cameron C, Beaulieu A. Foundation for Joint Action: Reducing Physical Inactivity. Ottawa, ON: Canadian Fitness and Lifestyle Research Institute, 1999. Coakley EH, Rimm EB, Colditz G, Kawachi 1, Willett W. Predictors of weight change in men: Results from The Health Professionals Follow-Up Study. Int

J Obes Relat Metab Disord 1998;22:89-96. Owens JF, Matthews KA, Wing RR, Kuller LH. Can physical activity mitigate the effects of aging in middle-aged women? Circulation l992;85:I265-l270. Heitmann BL, Kaprio J, Harris JR, Rissanen A, Korkeila M, Koskenvuo M.

Are genetic determinants of weight gain modified by leisure-time physical activity? A prospective study of Finnish twins. Am J Clin Nutr 1997;66:672-8. Williamson DF, Madans J, Anda RF, Kleinman JC, Kahn HS, Byers T. Recreational physical activity and ten-year weight change in a US national cohort. Int 3 Obes Relat Metab Disord 1993;Iï:Z79-286.

9.

Malina RM. Tracking of physical activity and physical fitness across the Iifespan. Res Q Exerc Sport 1996;67(Suppl3):48-57.

10. Fitness Canada. A Users Guide to CFS Findings: A Technical Reference Work Describing the CFS Sample, Data Items, and Forms of Data Access. Ottawa, ON: Ministry of Fitness and Amateur Sport, 1983. 11. Stephens T, Craig CL. The Well-Being of Canadians: Highlights of the 1988 Campbell's Survey. Ottawa, ON: Canadian Fitness and Lifestyle Research Institute, 1990. 12. Shephard RJ. Fitness of a Nation. Lessons from the Canada Fitness Survey. New York: Karger, 1986. 13. Fitness Canada. Standardized Test of Fitness: Operations Manual. 2nd ed. Ottawa, ON: Ministry of Fitness and Amateur Sport, 1981. 14. Taylor HL, Jacobs DR, Schucker B, Knudsen J, Leon AS, Debacker G. A questionnaire for the assessrnent of leisure time physical activities. J Chron Dis 7 978;W:741-55, 15. Weller IMR, Corey PN. A study of the reliability of the Canada Fitness Survey questionnaire. Med Sci Sports Exerc 1998;3O: 1530-6. 16. Bouchard C, Landry FI Shephard RJ, Skinner J, Godin G. Report of the work group on constructing a physical activity index. In: Stephens T, Craig CL, eds. Proceedings of the Workshop on Assessing Physical Fitness and

Activity Patterns in General Populations Surveys. Hyattsville, MD: National Center for Health Statistics, 1985:55-62.

17. Stephens T, Craig CL, Ferris BF. Adult physical activity in Canada: Findings from the Canada Fitness Survey 1. Can J Public Health 1986;77:285-90.

18. Tremblay A, Despres J-P, Leblanc Clet al. Effect of intensity of physical activity on body fatness and fat distribution. Am J Clin Nutr l99O;5l :153-7.

19. Ainsworth BE, Montoye HJ, Leon AS. Methods of assessing physical activity during leisure and work. In: Bouchard C, Shephard RJ, Stephens T, eds. Physical Activity, Fitness and Health. Champaign, IL: Human Kinetics,

I994:l46-159. 20. Klesges RC, Klesges LM, Haddock CK, Eck LH. A longitudinal analysis of the

impact of dietary intake and physical activity on weight change in adults. Am J Clin Nutr l992;55:8 18-22.

21. Flegal KM. Trends in body weight and overweight in the U.S. population. Nutr Rev 1996;54:S97-SI 00.

Table 1. Selected descriptive statistics of study sample.

n

Characteristic Age at baseline (mean

* s.d.)

602

Anfhropometric rneasures at baseline (mean k sd.) Body mass (kg) 602 SF5 (mm) 593 WC (cm) 601 Anfhropometric 7-y change (mean Is-d.) Body mass (kg) SF5 (mm) WC (cm)

602 509 523

Physical Acfivify af baseline (mean Is.d.) AEE (kJ-kg -'-day -') Time on Activity (min-day-')

602 602

Physical Activity change (%) Low, Low Moderate, Moderate High, High lncreased Decreased Family lncome at baseline (%) ~ $ 1 999 4 $15000-29999 ~ $ 3 O00 0 Smoking Sfatus at baseline (%)

Never Former Current Alcohol Use at baseline (%) Non-drinker lnfrequent Frequent

Males

n

FemaIes

Table 2. Pearson partial correlation coefficients* (and sample sizes) between 7-y changes (A) in anthropometric measures and baseline measures of activity energy expenditure (AEE) and time on activity.

AEE a Males ABody mass

-0.01 (n=602)

ASum of skinfolds

-0.02 (n=509)

Awaist circurnference

-0.04 (n=523)

Females ABody mass

-0.05 (n=644)

ASum of skinfolds

-0.02 (n=527)

AWaist circurnference

-0.04 (n=560)

-0.00 (n=560)

* covariates include age and baseline anthropornetric measure a

kJ.kg -'.day min-day"

-'

lnsert Table 3 here (Reg Coefficients for baseline AEE)

Insert Table 4 here (reg coefficients for baseiine time on activity)

Table 5. ANCOVA results showing least squares rneans* (M) and standard errors (SE) for 7-y body mass changes (kg) by physical activity (PA) change. Males PA Chancre

M

SE

Low, Low Moderate, Moderate High, High lncreased Decreased

p-value

"Means are adjusted for the effects of baseline body mass, age, smoking status, alcohol consurnption, and family income.

Figure Legends

Figure 1. ANCOVA results for changes in body mass (BM), sum of five skinfolds (SF5), and waist circumference (WC), by physical activity (PA) levels ranging from a low of 1 to a high of 4 (based on kJ-kg -'-hr of activity energy expenditure), for males (black bars) and females (white bars).

-'

Figure 2. ANCOVA results for changes in body mass (BM), sum of five skinfolds (SF5), and waist circumference (WC), by physical activity (PA) intensities ranging from a low of 1 to a high of 4 (based on the frequency and MET values of activities performed), for males (black bars) and femaIes (white bars).

Males: F=0.58, p=0.6; Fernales :F=0.70, p=0.5

7

PA Level

-E al

30-01 25.0

-1

20.0

i

1

1

m

r 15.0 i (P r u 10.0 1 VI LL,

u,

Males: F=1.50, p=0.2; Fernales: F=1.34, p=0.3

5.0 o.o

f i n T* ' 3

-

1

2

6.0

; I

PA Level

3

Males: F=0.48, p=0.7; Fernales: F=0.92, p=0.4

PA Level

-

4

6.0 ,

Males: F= 0.58, p=0-6; Females: F=2,70, p=0.04

1

30.0 ,

Males: F=0.34, p=0.8; Females: F=0.82, p=0.5

1

6.0 , I

PA intensity

3

Males: F=0.47, p=0.7; Females: F=0.93, p=0.4

4

Discussion The results of this study can be generalized to the Canadian population as the analyses were performed on a sample derived frorn the Canada Fitness Survey - a nationally representative survey. This tenet is based on the belief that the present subsample is composed of similar demographics to that of the larger CFS. In order to substantiate this clairn, the representativeness of the present sample to the CFS was investigated. The gender distribution for the present sample of adults (20-69 y) is 48% males and 52% females, while among adults from the CFS,there were 49% males and 51% females. T-tests were perforrned to examine the nutl hypothesis that no differences are existent between selected variables of the present sample and those of the CFS. The two samples were compared within age categories by grouping subjects according to the methods used in the first manuscript (i.e. 2-y age groups in chiidhood and deciles in adulthood). Z-scores for the present sample were then calculated for selected measures by subtracting the mean score on a measure (from the CFS) from a subject's score, then dividing by the standard deviation (from the CFS) for the measure. Table 1-A presents the t-test results relating to the sample used in the first manuscript (ages 11-69) and Table 2-A presents the t-test results relating to the sample used in the second manuscript (ages 20-69). The p-values presented reflect the probability that the nul1 hypothesis should be rejected based on a mean 2-score differing significantly frorn zero. Significant pvalues are present for al1 measures except age, grip strength, and flexibility.

However, upon inspection of the Z-scores (in standard deviation units), it is apparent that no variable deviated more than 0.22 standard deviations (see Table 2-A. sit-ups Z-score) from the entire CFS sample. Clearly, the large sample size used in the present study is responsible for the small sample differences to assume statistical significance. Thus, it is within reason to allow findings from the present research to be generalized to the Canadian population from which the CFS was drawn. The first manuscript presented in this thesis, Seven-year stability o f

physical activity and musculoskeletal fitness in the Canadian population, focused on the tracking of indicaton for PA and MSF across the Iifespan. Essentially it was deterrnined that within the Canadian population, PA does not track very well

and MSF does. A previous study on the sarne sample examined the 7-y stability of indicators of obesity and adipose tissue distribution (1). The researchers found that both obesity and adipose tissue distribution tracked significantly over 7-y, with BMI rernaining relatively constant across the age range in males and females, while SF5 and WC exhibited greater stability in adulthood. Gender differences were evident for the childhood age groups, with females generally having more stability than males. An earlier study that investigated the stability of body mass and triceps skinfold thickness (TSF) from childhood to adulthood used a sample of approximately 2500 young adults who were followed from childhood as part of the Muscatine Coronary Risk Factor Project (2). Tracking correlations for weight ranged from 0.51 to 0.78 in females and 0.57 to 0.88 in

males, while TSF tracking correlations ranged from 0.41 to 0.56 in females and from 0.26 to 0.72 for males. Results from the Muscatine study provide further evidence that body mass and adiposity track from childhood into the young adult years. The rationale for examining the stabilivj of body mass and adiposity centers on prediction and prevention. If the development of obesity can be predicted from present levels of body mass (Le. high stability), then interventions

to prevent weight gain should ideally be targeted at an early stage, prior to the onset of obesity. With a similar explanation, the purpose of examining the stabiiity of PA and MSF across the lifespan has at its foundation the aspiration to learn at what point(s) PA and MSF patterns become established so that health promoters rnay know when interventions to increase levels would be most suitable. An objective of the present research was to examine PA and MSF, from childhood to late adulthood, to investigate whether particular age groups stand out as potential points of instability. Since ample evidence exists supporting the contention that physical inactivity is a risk factor for the development of obesity and it's various cornorbidities (3-6), knowledge of the stability of this trait is akin to knowledge of the stability of body rnass. For both factors, early interventions rnay curtail the development into an obese state. In the second manuscript presented in this thesis, Physical activity and seven-year changes in body mass and adiposity in the Canadian population,

prospective associations between PA and changes in body mass, SF5 and WC

were examined and it was conciuded that baseline PA is not predictive of 7-y anthropornetric changes. As suggested in the manuscript, a factor that may partially explain the observed Iack of relationships is the absence of knowledge on the time point(s) in which PA IeveIs rnay have changed during the study interval. If shifts in PA levels are parallel with changes in body rnass, then the observed body mass changes should be closely related to the most ternporally proximal PA level recorded (7). Furthermore, as determined in the first manuscript, PA levels have only low to moderate stability in this sarnple, hence, baseline PA levels are not expected to be the sarne as follow-up levels. Together this suggests that follow-up PA levels. being more proximal to the final reported measures of body mass and adiposity, should predict changes better than baseline PA levels. Previous studies considering this issue conducted retrospective analyses relating follow-up PA levels to body mass changes. Results were successful in predicting body rnass changes in a IO-y longitudinal study of a US national cohort (7) and in a large sarnple of adult Finns with a 5.7 y median study interval time (8). To examine this possibility in the present sample, further analyses were performed (see Tables 3-Ato 5-A, Figures 1-A and 2-A). Similar to the prospective analyses (see Manuscript 2), the retrospective analyses revealed insignificant associations between AEE, time on activity and changes in body mass, SF5, and WC. One exception is seen for the partial correlation analysis in

which follow-up AEE has a low negative relationship with changes in WC in females. While neither baseline nor follow-up PA levels were generally predictive of anthropometric change in the present study, there was one important distinction behveen the two. The range in variance accounted for by the retrospective regression modeis ( R ~ 0.17 : - 0.28)was substantially larger than that observed in the prospective rnodels ( R ~ 0.02 :

- 0.06).

This finding, however, is not due to

differences in the beta weights of baseline and follow-up PA levels; rather it is attributable to the large, significant beta weights for follow-up anthropometric measure. Baseline and follow-up measures of smoking status, alcohol use and famiIy income were not significant predictors of body mass and adiposity change.

The prediction of MSF change from PA levels in adulthood, though not addressed in either of the manuscripts, is worthy of further study as the ability to predict future levels of MSF may prevent premature loss of functional ability and independence. Furthemore, the associations between changes in PA and changes in MSF has apparentiy not yet been addressed and should provide valuable information on the basis of MSF change. Pearson partial correlation analyses, corrected for age and baseline MSF indicators, were performed to examine the relationships between 7-y changes in push-ups, sit-ups, grip strength, and sit-and-reach flexibility and PA at baseline, follow-up, and its 7-y change. Table 6-A presents the correlation coefficients and sarnple sizes for each of the associations. In general, PA does not relate to

changes in MSF over 7 y. However, particularly in females, PA does show some significant relationships. Both baseline and follow-up AEE are positively related to changes in push-ups, as was baseline time on activity. As well, follow-up AEE is positively associated with 7-y flexibility changes. In males, the only significant finding was a negative relationship between baseline AEE and grip strength change. Multiple regression analyses, with covariates consisting of age, MSF rneasure, smoking, alcohol, and family income, were perforrned to examine whether PA at baseline or follow-up could predict 7-y changes in push-ups, situps, grip strength, andfor sit-and-reach flexibility. Table 7-A presents the beta

weights (for AEE and time on activity), standard errors, and sample sizes for each analysis. Only twice was PA significantly related with MSF change. With follow-up time on aciivity in males, a significant but very low beta weight is present for changes in push-ups; for females, baseline AEE is significantly predictive of 7-y changes in push-ups. As the results indicate, PA appears not to be a good predictor of MSF changes. Rather, as shown in Manuscript 1, indicators of MSF track at moderate to high levels, thus baseline measures themselves rnay be effective predictors of future levels. The results from Manuscript 1, along with the present correlation analyses, also support the contention that MSF has a substantial genetic basis that rnay interact with the potential impact of PA eliciting a change in MSF Ievels. Indeed, previous research has suggested that various individual characteristics,

such as age, sex, cuvent phenotype level and molecular markers may affect one's sensitivity to training (9).

Summary

The first aim of this research was to assess the stability of PA and MSF across the Iifespan in the Canadian population. As hypothesized, PA dernonstrated weak to moderate stability, with interage correlation coefficients generally increasing from childhood to adulthood. The 7-y interage correlation coefficients obtained for indicators of MSF were generally higher than those hypothesized, with correlations ranging from moderate to high, with the exception of push-ups, which tracked at low levels in females during childhood. Similar to the stability of PA, correlation coefficients generally became stronger with increasing age. As hypothesized, indicators of MSF exhibited greatet stability than indicators of PA in the Canadian population. The second purpose of the present research was to assess the association between PA and 7-y changes in MSF. As hypothesized, PA was generally not related to, nor predictive of, MSF changes. Further, change in PA levels from baseline to follow-up did not relate to changes occurring in the MSF indicators of push-ups, sit-ups, grip strength, and sit-and-reach flexibility. The third purpose of this investigation was to determine whether PA could predict 7-y changes in body mass and adiposity. It was hypothesized that PA would indeed be predictive of such changes. On the contrary, neither baseline

levels, follow-up levels, nor changes in PA levels from baseline to follow-up, were predictive of anthropometric change. However, minimal support was obtained for the hypothesis that retrospective predictions are stronger than prospective predictions; the variance accounted for in the regression models using follow-up measures were substantially greater than was evident when baseline measures were usedThe findings from this research suggest that PA is not a useful measure for predicting future changes in MSF, body mass, or adiposity. Possibly this is due to the difficulty in accurately measuring PA within a large sample or perhaps

it is due to the transient nature of this variable as witnessed by its low 7-y stability. The design of future prospective studies may be well advised to incorporate more frequent repeated measures of PA and lifestyle variables, as controlling for fluctuating levels may be necessary before such behavioral variables can adequately predict biological changes. Further. as MSF has shown to have moderate to high 7-y stability, perhaps other biological indicators of

HRPF, such as cardiorespiratory fitness, may be more suited to the task of predicting future changes in MSF, body mass and adiposity. There are several reasons why the present research rneaningfully contributes to the existing fitness epiderniology literature. First, the prospective longitudinal design of the CFS has afforded the possibility to go beyond simple associations and consider cause and effect relationships; accordingly, it has shown that PA is not related to 7-y MSF, body mass, and adiposity changes.

Further, the longitudinal design - required for tracking analyses - was of a population-based nature, thus the findings can be generalized to the greater Canadian population. Next, the age groups measured for the stability of PA and MSF ranged from 11 to 69. Extremely limited data existed before now on the

tracking of these measures within mid-to-late adulthood, particularly in MSF. The results of this work lend support to the multifaceted nature of weight gain. While high PA intensity levels have been cross-sectionally related to lower levels of body fatness in this population (IO), it is clear that the developrnent of obesity over time is not simply due to a lack of PA. Rather, the interactions between such diverse factors as age, sex, macronutrient composition of energy intake, genetics, social economic status, and PA are more likely responsible for weight gained (11). Separating out the individual effects of these variables will likely continue to challenge researchers who seek to understand the nature of the obesity epidemic currently underway.

References

Katzmarzyk PT, Perusse L, Malina RM, Bouchard C. Seven-year stability of indicators of obesity and adipose tissue distribution in the Canadian population. Am J Clin Nutr I999;69:Il23-Q. Clarke WR, Lauer RM. Does childhood obesity track into adulthood? Crit Rev Food Sci Nutr 1993;33:423-430. Blair SN, Brodney S. Effects of physical inactivity and obesity on morbidity and rnortality: Current evidence and research issues. Med Sci Sports Exerc 1999;31:S646-S662. Hill JO, Melanson EL. Overview of the determinants of oveweight and obesity: current evidence and research issues. Med Sci Sports Exerc l999;3l :S515-S521.

Jebb SA, Moore MS. Contribution of a sedentary Iifestyle and inactivity to the etiology of ovennreight and obesity: Current evidence and research issues. Med Sci Sports Exerc l999;3l :S534-S541. Kelley DE, Goodpaster BH. Effects of physical activity on insulin action and glucose tolerance in obesity. Med Sci Sports Exerc l999;31:S6l9S623.

Williamson DF, Madans J, Anda RF, Kleinman JC, Kahn HS, Byers T. Recreational physical activity and ten-year weight change in a US national cohort. Int J Obes Relat Metab Disord 1993;17:279-286.

8.

Rissanen AM, Heliovaara M, KneM P, Reunanen A, Arornaa A. Detenninants of weight gain - and overweight in adult Finns. Eur J Clin Nutr

-

1991;45:419-430.

9.

Bouchard ClMalina RM, Perusse L. Genetics of Fitness and Physical Performance. Champaign, IL: Human Kinetics, 1997.

10.

Tremblay A, Despres J-P, Leblanc Cl et al. Effect of intensity of physical activity on body fatness and fat distribution. Am J Clin Nutr 1990;51:153-7.

11.

World Health Organization. Obesity. Preventing and Managing the Global Epidemic. Geneva: World Health Organization, 1998.

Table I.A. T-test results comparing the present sample (ages 11-69)to the entire Canada Fitness Survey (CFS) sample on select measures.

Variable

Musculoskeletal Fitness Push-ups 1548 Sit-ups 1534 Grip Strength 1617 Flexibility 1586 Physical A ctivity AEE

1770

Time on Activity

1771

0.08

0.02

3.17

0.002"

0.15

0.03

5.79

-=0.0001*

-0.04

0.03

-1.56

0.00

0-02

0.09

O. 1 0.9

O.06 0.04

0.02

2.93

0.003"

0.02

1.92

0.05"

SE, Standard Error; AEE, activity energy expenditure * p<0.05 for significance

Table 2-A. T-test results comparing the present sarnple (ages 20-69) to the entire Canada Fitness Survey (CFS) sample on select measures.

Variable Age

1246

0.01

0.03

0-46

0.6

Anthropometric Measures Body mass

1246

-0.07

0.02

-2.81

0.005"

SF5

1216

-0.08

0.03

-3.04

0.002"

WC

1244

-0.14

0.02

-5.46


Musculoskeletal Fitness Push-ups

975

0.08

0.03

2.37

0.02"

Sit-ups

964

0.22

0.03

7.01

Grip Strength

1030

-0.02

0.03

-0.65

0.5

Fiexibility

1004

-0.00

0.03

-0.00

1.O

AEE

1246

0.15

0.03

5.51


Time on Activity

1246

0.1 3

0.03

4.75

<0.0001*


Physical A ctivity

SE, Standard Error; SF5, sum of five skinfolds; WC, waist circumference; AEE, activity energy expenditure; * pe0.05 for significance

Table 3-A. Pearson partial correlation coefficients* (and sample sizes) between

7-y changes (A) in anthropometric rneasures and follow-up measures of activity energy expenditure (AEE) and time on activity. AEE a

Time on Activitv b

ABody mass

0.02 (n=554)

0.01 (n=561)

ASum of skinfolds

0.01 (n=486)

-0.06 (n=49 1)

-0.04 (n=500)

-0.06 (n=505)

ABody mass

-0.05 (n=603)

-0.01 (n=616)

ASum of skinfolds

-0.04 (n=505)

-0.01 (n=518)

AWaist circumference

-0.09 (n=538)

-0.05 (n=551)

Males

Awaist circumference Females

" covariates are age and baseline anthropometric measure a

kJ-kg -'.day min-day-' p < 0.05

"

Table 4-A. Regression coefficients (P) and standard errors (SE) of change (A) in body mass, sum of five skinfolds (SF5), and waist circumference (WC) from follow-up measures of activity energy expenditure (AEE), age, anthropometric measure, smoking, alcohol, and income, of the multiple regression analysis, for males and females separately.

a

Males AEE Age Anthrolo~~ow-up Smoking Alcohol lncorne Females AEE Age Anthrol,~~ow.u~ Smoking Alcohol lncome

SE

SE

BC

SE

-0,85 -19.47d 360.40~ 2,91 0.80 -0.19

2,02 3,42 27.82

-1,48 -8.21

1.80 2,50 19.45 2.75 0.67

0.01 -1 ,3gd 3.18d -0.03 0,05 -0.53

0.17 0.25 036 0.29 0,04 0,38

0,74 -2.46d 23,~4~ 0.38 O, 14 -0,86

O, 52 0.83 2.06

-0.31 -1.osd 15.6gd 0.58 0.04

O. 19 0.24 1.47 0.29 0.07

-0.01

O.33

-0.14 -2.24d 37 ,57d 0,28 0.26 -0.13

0.69 0.94 256 1,O6 0.25 1.18

Males (n.506): R' = 0.17; Females (n=539): R~ = 0.19 Males (n.451): R2 = 0.23; Fernales (n.456): R2 = 0.26 Males (n=462): R2 0.27; Females (n=485): R~ = 0.23 d p < 0,05

a

pb

0.90 0,11 1.21

225,54d 2,75 0.37 0.18

352 0.44 4.73

3.06

Table 5-A. Regression coefficients (P) and standard errors (SE) of change (A) in body mass, sum of five skinfolds (SF5), and waist circumference (WC) from follow-up measures of time spent on activity, age, anthropometric measure, smoking, alcohol, and incorne, of the multiple regression analysis, for males and fernales separately. ABody Mass

Males Time on Activity Age Anfhr~follow-up Smoking Alcohol lncome Females Time on Activity Age Ant~~~l,llowu, Smoking Alcohol lncome

" Males (n-510):

0"

ASF5 SE

R~= 0.17; Femaleç (n=550): R' = 0.19 ales (n=455): R* = 0.23; Females (n.467): R~ = 0.25 %lales (n-467): R* = 0.28; Fernales (n=496): R~ = 0.22 d p < 0.05

b

AWC SE

C

SE

Table 6-A. Pearson partial correlation coefficients* (and sarnple sizes) between 7-y changes (A) in rnusculoskeletal fitness indicators and baseline, follow-up, and 7-change measures of activity energy expenditure and tirne on activity in adults. ATOA

Males APUS~-UPS

-0.00(338) 0.09 (325) 0.10 (325) 0.01 (338) 0.08 (329) 0.06 (329)

Asit-UPS

-0.05 (339) 0.02 (326) 0.04 (326) -0.04(339) -0.09(330) -0.04(330)

Agrip strength

-0.11 (354) -0.01 (340) 0.02 (340) -0.13(354) 0.02 (344) 0.08 (344)

Aflexibility

-0.01 (342) 0-05(328) 0.06 (328) 0.03 (342) 0.03 (332) 0.00 (332)

Apush-ups

0.19 (329) 0.14 (319) 0.10 (319) 0.12(329) 0.06 (327) 0.01 (327)

Asit-UPS

0.10 (322) 0.10 (310) 0.08 (310) 0.07 (322) 0.01 (319) -0.01 (319)

Agrip strength

0.06 (349) 0.05 (339) 0.04 (339) 0.06 (349) 0.04 (348) 0.01 (348)

Aflexibility

0.08 (345) 0.12 (335) 0.10 (335) 0.06 (345) 0.08 (344) 0.05 (344)

'

* covariates consist of age and baseline rnusculoskeletal fitness component. AEE - activity energy expenditure (kJ-kg -'-day ")

TOA - time on activity (min-day-')

' baseline data

follow-up data

II

C

-

ii

C

O

- 0 - 0

Figure Legends

Figure 1-A. ANCOVA results for changes in body mass (BM), sum of five skinfolds (SF5), and waist circumference (WC), by follow-up physical activify (PA) levels ranging from a low of 1 to a high of 4 (based on kJkg -'-hr of activity energy expenditure), for males (black bars) and females (white bars).

-'

Figure 2-A. ANCOVA results for changes in body mass (BM), surn of five skinfolds (SFS), and waist circumference (WC), by follow-up physical activity (PA) intensities ranging from a low of 1 to a high of 4 (based on the frequency and MET values of activities performed), for males (black bars) and females (white bars).

=1

Males: F=0.22, p=0.9; Females: F=0.53, p=0.6

30.0

-E A

25.0 4

a 20.0

m

Males: F=0.96, p=0.4; Females: F=0.36, p=0.8

.. I 1

C V)

55.

7

0.0

6.0 . I

5

5.0

:

Males: F=0.45, p=0.7; Fernales: F=0.37, p=0.8

Males: F=0.30, p=0.7; Females: F=2.15, p=Q-1

5.0

PA Intensity

30.0

-O -

20.0

rn u,

5.0

0

25.0

1

!

Males: F=2.01, p=O.l ; Fernales: F=0.54, p=0.6

m

0.0 PA lntensity

6.0 , 5.0 1

Males: F=0.24, p=0.8; Fernales: F=2.23, p=0.1

2

PA Intensity