Exercise motivation: a cross-sectional analysis examining its relationships with frequency, intensity, and duration of exercise

Lindsay R Duncan
Craig R Hall
Philip M Wilson
O Jenny
Exercise motivation: a cross-sectional analysis examining its relationships with frequency, intensity, and duration of exercise
R E S E A R C H Open Access
Background: It is important to engage in regular physical activity in order to maintain a healthy lifestyle however a large portion of the population is insufficiently active. Understanding how different types of motivation contribute to exercise behavior is an important first step in identifying ways to increase exercise among individuals. The current study employs self-determination theory as a framework from which to examine how motivation contributes to various characteristics of exercise behavior. Methods: Regular exercisers (N = 1079; n = 468 males; n = 612 females) completed inventories which assessed the frequency, intensity, and duration with which they exercise, as well as the Behavioral Regulation in Exercise Questionnaire including four additional items assessing integrated regulation.Results: Bivariate correlations revealed that all three behavioral indices (frequency, intensity, and duration of exercise) were more highly correlated with more autonomous than controlling regulations. Regression analyses revealed that integrated and identified regulations predicted exercise frequency for males and females. Integrated regulation was found to be the only predictor of exercise duration across both genders. Finally, introjected regulation predicted exercise intensity for females only.Conclusions: These findings suggest that exercise regulations that vary in their degree of internalization can differentially predict characteristics of exercise behavior. Furthermore, in the motivational profile of a regular exerciser, integrated regulation appears to be an important determinant of exercise behavior. These results highlight the importance of assessing integrated regulation in exercise settings where the goal of understanding motivated behavior has important health implications.

Background

Engagement in regular physical activity is an important part of a healthy lifestyle. Research has shown that regular exercise is linked to the prevention of cardio-vascular disease, type 2 diabetes, cancer, hypertension, obesity, osteoporosis, and depression [1]. The physical activity guidelines set forth by the Canadian government recommend that adults engage in a cumulative total of sixty minutes of moderate intensity physical activity (occurring in bouts of at least 10 minutes) every day [2]. In addition to this, some research has shown that vigorous exercise can lead to health benefits above and beyond those offered by moderate intensity exercise. For example, the use of antidiabetic, antihypertenstion, and LDL-C-lowering drugs have been found to have an inverse relationship with vigorous physical activity [3]. Furthermore, in a study examining the relationships between physical activity and fatness in adolescents, it was found that lower percent body fat was related to vigorous physical activity but not to moderate intensity physical activity [4]. It appears that in order to achieve the health benefits associated with physical activity it is important to exercise regularly and at an appropriate intensity. Despite the vast amount of research that has demonstrated the link between physical activity and health, 63% of Canadians are not sufficiently active to obtain these health benefits [2]. One factor that is thought to contribute to an individual's physical activity levels is his or her motivation to exercise. In fact, various types of motivation have been found to influence effort expended during exercise sessions as well as intentions to continue exercising [5].

Self-Determination Theory (SDT) [6] has been proposed as one way of looking at motivation. SDT is a general theory which has frequently been applied in the exercise domain. The SDT framework posits that human motivation lies along a continuum which represents varying degrees of autonomy. Autonomy refers to behaviors being self-determined, or freely initiated by the individual [6]. The self-determination continuum is comprised of both intrinsic and extrinsic components. Intrinsic motivation occupies the most self-determined end of the continuum and involves motivation derived from the sheer pleasure and satisfaction of engaging in the behavior itself [6]. An exerciser who is intrinsically motivated might swim, for example, because they enjoy the feeling of their body moving through the water. Four distinct behavioral regulations comprise the extrinsic part of the motivational continuum. These four regulations successively decrease in their degree of selfdetermination from autonomous regulations to controlling regulations. Integrated and identified regulations represent the more autonomous forms of extrinsic motivation. Integrated regulation is represented by an individual's belief that a behavior is an important part of his or her identity and is consistent with his or her personal values [7]. An individual who demonstrates integration might go running because they believe they are 'a runner' and therefore running is consistent with their sense of identity. Identified regulation refers to being motivated to perform a behavior because it is personally significant and results in outcomes which are valued by the individual [6,7]. For example, individuals might engage in resistance training because they know that weight bearing activities are important for bone health. Controlling regulations (introjected and external) occupy the less self-determined end of the motivational continuum. Introjected regulation represents the desire to obtain intrapersonal rewards (e.g., pride) or to avoid self-inflicted punishments (e.g., guilt or shame) [7] while external regulation refers to the desire to obtain external rewards or avoid punishments [7]. An individual who exercises for external reasons might do so to appease their spouse or their physician. It is also possible that an individual will be amotivated. That is, they will engage in a behavior without feeling any motivation, or they will exhibit a complete lack of intention to perform a behavior. An individual's relative location along the self-determination continuum is determined by the degree to which he or she has achieved satisfaction of the basic psychological needs for competence, autonomy, and relatedness [8].

One important contention of SDT is that the external regulations and amotivation are less adaptive in nature while intrinsic motivation results in positive motivational consequences. Research has supported this contention with amotivation being linked to behavioral disengagement and negative psychological conditions [7]. Furthermore, intrinsic motivation is associated with persistence at a task as well as psychological health and well-being [6]. In an exercise context, research has examined individuals at various stages of exercise adoption and found that individuals with tendencies toward more regular exercise are more self-determined in their motivation [9].

In spite of these findings, it has been suggested that some people may persist at sport and exercise despite being extrinsically motivated [10]. This suggestion can be highlighted by research examining the relationships between obligatory exercise and motivation. In a study involving regular exercisers it was found that individuals who are preoccupied with exercise, or who exercise at greater frequency, tend to score higher on identified regulation [11]. Furthermore, individuals who experience negative emotional consequences (i.e., anger, depression) when they miss an exercise session tend to score highly on introjected regulation. In terms of exercise intensity, for individuals who show symptoms of exercise dependence, introjected regulation approached significance as a positive predictor of strenuous exercise behavior and identified regulation was found to be a positive predictor of strenuous exercise [12].

Using SDT as a context for examining the motivation of 598 male and female university students engaged in a variety of exercise classes (e.g., weight training, aerobics, swimming), it was found that students who were classified as 'more frequent exercisers' showed higher levels of intrinsic motivation and the autonomous forms of extrinsic regulation compared to 'less frequent exercisers' [13]. A gender analysis revealed that females reported higher levels of intrinsic motivation and autonomous regulations while exercise behavior among males was more externally regulated and amotivated.

Further research examined the relationships between exercise regulations and various motivational consequences (i.e., behavioral intention, effort and importance associated with exercise participation, and current exercise behavior) among university students [5]. Results revealed that identified regulation was the strongest predictor of each of the three exercise behaviors in both males and females. Intrinsic regulation was also found to predict effort and importance for males and females, as well as behavioral intention for females only. These findings were consistent with previous research and with SDT. Interestingly, it was found that introjected regulation was a positive predictor of all three motivational consequences for females only. This finding suggested that females may experience a sense of pride associated with exercise or some degree of guilt or shame if they do not exercise.

Overall, it appears that exercise-related motivation varies according to the amount of exercise an individual undertakes. Previous research has considered exercise behavior in several different ways, from intention to exercise and self-reported exercise frequency [9] to a measure including exercise intensity [5] and indicates that different types of behavior (e.g., exercise frequency and intensity) may be differentially regulated. Since SDT suggests that the regulations along the continuum are distinct, it is possible that they can be individually manipulated. If key motivational forces can be identified in regular exercisers and specific motivational deficits can be identified in less frequent exercisers, perhaps it is possible to target the most relevant types of motivation in order to increase exercise behavior among those who are insufficiently active. If this is the case, understanding the unique role that each regulation plays in exercise behavior has important practical implications for exercise interventions.

One main limitation to research regarding motivation to exercise has been that the three basic measures of exercise behavior, frequency, intensity and duration have not been investigated within a single study. Another limitation is the lack of a measure of integrated regulation. Many of the studies examining exercise motivation have used the Behavioral Regulation in Exercise Questionnaire (BREQ) [14] which does not measure integration. More recently, a measure of integrated regulation which is complementary to the BREQ has been developed [15]. The inclusion of an integrated subscale allows for the full spectrum of motives to be measured which is important in order to gain a complete understanding of how individuals are motivated to engage in exercise. Therefore, the purpose of this study was to examine the relationships between three exercise behaviors (frequency, intensity, and duration) and the various behavioral regulations according to the SDT framework, including integrated regulation. Based on the contention of SDT that freechoice behaviors are most closely related to more selfdetermined motives, it was hypothesized that all three exercise behaviors would be most closely related to autonomous regulations and intrinsic motivation.

Methods

Participants

Participants (N = 1054) were male (n = 460) and female (n = 594) volunteer (M age = 24.15, SD = 9.61) regular exercisers. For the purposes of this study, 'regular exercise' was defined as consistently engaging in at least two exercise sessions (of any kind) each week for the past six months. The sample was largely composed of students with 75% of participants reporting 'student' as their primary occupation. Self-report data revealed the sample was quite active (M frequency = 4.07 sessions per week, SD = 1.77; M duration = 67.31 minutes per session, SD = 28.23; and M intensity = 69.71 weekly METS, SD = 39.65). Participants listed the exercise activities in which they typically participate. The most commonly cited exercise activities were running (62.6%), weight training (61.2%), playing sports (58.7%), walking (48.5%), and exercising on cardio equipment (e.g., treadmills, stationary bikes, elliptical trainers; 46.8%).

Measures

Exercise behavior was assessed using a self-report measure in which participants indicated the number of times they exercise in a typical week, the average duration of each session, and the type of exercises they engage in.

The Leisure Time Exercise Questionnaire (LTEQ) [16] was used to assess participants self-reported exercise intensity. Participants indicated the frequency of mild, moderate, and strenuous activity they engage in for at least 15 minutes during a typical week. A composite exercise behavior score was then calculated using the weighted sum of each exercise intensity according to the following formula: (mild × 3) + (moderate × 5) + (strenuous × 9). The result was a weekly MET (units of metabolic equivalence) value. The LTEQ has been found to be valid and reliable when compared to objective measures of physical activity [17].

The Behavioral Regulation in Exercise Questionnaire-version 2 (BREQ-2) [18] is a 19-item self-report measure which was adapted from the original BREQ [14] and assesses exercise regulations according to the SDT framework. The BREQ-2 includes 5 subscales assessing intrinsic (e.g., "I enjoy my exercise sessions;" n = 4), identified (e.g., "It's important to me to exercise regularly;" n = 4), introjected (e.g., "I feel guilty when I don't exercise;" n = 3), and external (e.g., "I feel under pressure from my family/friends to exercise;" n = 4) regulations as well as amotiviation (e.g., "I don't see why I should have to exercise;" n = 4). Each item is rated on a 5-point scale ranging from 0 = "not true for me" to 4 = "very true for me." Recently, the BREQ-2 has been extended to include four additional items assessing integrated regulation (e.g., "I exercise because it is consistent with my values;" n = 4) [15]. The integrated subscale was included in the current study. A reliability analysis revealed internal consistency values ranging from .76 to .90 for the various regulations for males and females, with the exception of amotivation for males being .54 (see Table 1 for specific values).

Procedures

All study procedures were approved by a university research ethics board. Participants were approached by the researcher prior to or following their workouts in their regular gym setting. Once informed consent was obtained, the participants completed the BREQ-2R, LTEQ, and demographic information.

Results

Preliminary Data Screening

The data was screened in order to detect missing values and outliers as well as to test for conformity with the assumptions of multiple regression (normality, linearity, and homoscedasticity). The analysis revealed no missing values and no cases of extreme responses (values > 4 standard deviation units from the mean on any measure). An examination of the distribution properties and histograms of each variable indicated that the amotivation variable deviated substantially from normality and warranted transformation. A logarithmic transformation with the addition of a constant to each value was conducted in order to normalize the amotivation variable [19]. All subsequent analyses were conducted using the transformed amotivation variable. No violations to the assumptions of linearity or homoscedasticity were observable through an examination of scatterplots of the residuals indicating that the data was suitable to undergo regression analyses.

Descriptive Statistics, T-tests, and Bivariate Correlations

Descriptive statistics for both males (n = 460) and females (n = 594) are presented in Table 1. The data confirmed that the males and females were all highly active, reporting mean exercise frequency scores of 4.20 (SD = 1.84) and 3.97 (SD = 1.70) workouts per week respectively. Furthermore, mean scores for the exercise intensity variable (weekly METS) were slightly higher than values reported in previous research [5,20].

Mean scores for the subscales of the BREQ revealed an expected pattern in which individuals reported participating in exercise for more autonomous reasons compared to more controlling reasons [5,21]. Specifically, for both genders, identified was the most strongly endorsed regulation followed by intrinsic, integrated, introjected, external, and amotivation respectively.

T-tests revealed that males and females differed significantly (p < .017 to control for type 1 error) in terms of the typical duration of exercise sessions (t(931.79) = 2.72, p = .007). In this case, males reported exercising for longer durations than females (Table 1). No significant difference was observed between males and females number of exercise sessions per week (frequency; t (1052) = 2.07, p = .039) or for exercise intensity (t(1052) = -.78, p = .436).

Correlations were conducted between each of the variables of the BREQ and the three exercise behaviors (frequency, intensity, and duration; Table 2 and Table 3). The analyses revealed a theoretically consistent pattern of relationships in which adjacent subscales from the BREQ were more strongly and positively correlated with subscales theorized to be more proximal along the motivation continuum. This finding is consistent with previous research [5] and supports the concept of a motivational continuum as proposed by SDT [6][7][8].

Strong correlations were found between the identified and integrated subscales for both males (r = .74, p = .0001) and females (r = .78, p = .0001). Some researchers have indicated that bivariate correlations >.70 between variables may suggest collinearity [19]. An examination of the collinearity diagnostics revealed that when the condition index was high (>10), no two variables had variance proportions exceeding the recommended threshold (.50) [22] and therefore it was determined that the subscales were not collinear.

In line with self-determination theory, all three exercise behaviors were more strongly correlated with intrinsic motivation and the more autonomous forms of extrinsic motivation for males and females. For males, exercise frequency and intensity were most strongly related to identified regulation, while duration of exercise was most strongly related to integrated regulation. Similar to males, for females, identified regulation had the strongest relationship with exercise intensity and integrated regulation was most strongly related to frequency. For females, however, integrated regulation was also most strongly related to duration of exercise.

Simultaneous Multiple Regression Analyses

Regression analyses were conducted to examine the relationships between exercise regulations and the three exercise behaviors. Based on the results of the t-tests revealing a significant difference between males and females for one of the dependent variables (i.e., duration of exercise), and previous research which has conducted regression analyses separately by gender [5], the current analyses involved separate regression analyses for males and females.

Results of the analyses revealed that integrated and identified regulations were significant predictors of exercise frequency for both males and females (Table 4). In terms of duration of exercise, integrated regulation was found to be a significant and positive predictor for males and females (Table 5). Finally, introjected regulation was found to be a positive predictor of exercise intensity for females only, while none of the behavioral regulations were a unique predictor of intensity among men (Table 6).

Discussion

The purpose of this study was to examine the relationships between three exercise behaviors (frequency, intensity, and duration) and various behavioral regulations. For males and females, both identified and integrated regulations were found to be positive predictors of exercise frequency. These findings are consistent with our original hypothesis and with the contention of SDT that free choice behaviors can be predicted by more autonomous motives. Previous research examining the relationship between behavioral regulations and various motivational consequences (i.e., behavioral intention, effort and importance, and exercise behavior) found that more autonomous regulations predicted behavioral intention however integrated regulation was not assessed [5]. These findings highlight the importance of including a measure of integrated regulation when assessing exercise motivation.

In the current investigation, integrated regulation was the strongest (and the only significant) predictor of exercise duration for males and females. This finding suggests that individuals are more likely to engage in longer bouts of physical activity if they feel that exercising is consistent with their identity. If free choice behaviors, such as exercise, are associated with autonomous motivation in general, why did integrated regulation emerge as the only significant predictor of exercise duration? Perhaps it is the nature of the exercise behavior under investigation. Without a doubt, individuals who are characterized as regular exercisers are aware of the many physiological and psychological benefits that are associated with routine (i.e., regular frequency) exercise. As a result, it is not surprising that regular exercisers have aligned their values and goals with routine  Table 3 Bivariate Correlations between BREQ, exercise frequency, duration, intensity for females exercise. It also seems logical that individuals who value the benefits associated with regular exercise have incorporated that behavior into their sense of identity. With exercise frequency, more is generally thought of as better, but with exercise duration, longer is not necessarily better. The duration of exercise depends on the goals of the exercise program and the intensity of the exercise and it can range from a few minutes to a few hours [23]. As a result, individuals may not build their exercise-related values and goals based on a 'longer is better' conception. It is possible however, that longer bouts of exercise play a stronger role in confirming ones exercise identity compared to shorter bouts of exercise, in part, as a function of the amount of time the individual invests each week in exercise participation.

The results of the current study revealed the importance of integrated regulation in the prediction of regular physical activity. Integration involves identifying that engaging in a behavior is an important part of one's identity, is proposed to be the most autonomous of the external regulations, and tends to be associated with behavioral persistence and more adaptive psychological outcomes [24]. The current analyses revealed that, in the motivational profile of a regular exerciser, the creation of an identity surrounding exercise, that is, believing that being 'an exerciser' is an important part of 'who I am' is  crucial. In light of this, exercise practitioners should work to develop programs which seek to integrate exercise into an individual's personal value system and help to influence people to include the word 'exerciser' as a selfdescriptor. For individuals who are moderately active, it may be possible to influence integrated regulation through a goal setting intervention in which goal setting could be used as a conduit for enhancing exercise-related identity. For example, the exerciser could be encouraged to establish exercise-related goals and use self-monitoring to track their progress toward these goals. In turn, this self-monitoring could be used as a means to substantiate the individual's exercise identity. Interestingly, introjected regulation was the only significant predictor of exercise intensity, and this was the case for females only. Exercise intensity was not predicted by the autonomous regulations or intrinsic motivation. This finding is consistent with previous findings that introjected regulation predicted exercise behavior and the effort and importance associated with exercise for females [5] and suggests that intense exercise is driven by a sense of obligation, rather than more adaptive and personally significant motives. Research examining the motivating forces behind exercise dependence has found a similar link. For example, it has been found that introjected regulation was the strongest predictor of exercise dependence [25]. In addition, researchers examined individuals who exhibited symptoms of exercise dependence and found that introjected regulation approached significance as a positive predictor of exercise intensity [12].

It is not surprising that a sense of obligation drives exercise intensity for women. This may even be the case for women who are not exercise dependent. One study found that college-aged women believe that intense physical activity expends more energy than longer sessions of lower intensity physical activity (such as household chores) although this is not necessarily the case [26]. This study also found that college-aged women focused on the rate of caloric expenditure as an indication of an effective activity. Further research found that throughout an acute exercise session, women experienced a significant decrease in positive affect between the first minute of exercise and the minute before they reached their ventilatory threshold [27]. Together, these findings demonstrate that while women feel the need to exercise intensely, they do not tend to enjoy this type of activity. The problem that arises with this is that people who experience more controlling types of behavioral regulation tend not to persist at an activity for extended periods of time [28,29]. While women recognize the importance of more intense exercise, they are not exhibiting motivation that will lead them to persist at higher intensity exercise. Exercise programs and interventions that are autonomy enhancing may prove to be effective in increasing the frequency with which women engage in intense exercise.

While this study did provide some insight into the link between motivation and exercise behavior, the results must be interpreted with some degree of caution as all of the exercise behavior measures were taken by self-report. Specifically, it is difficult to accurately assess exercise intensity using self-report measures [30]. Future research would do well to examine the link between motivation and objectively measured exercise behavior. Further, the current sample was composed primarily of undergraduate students. While this sample did allow for the depiction of the motivational profile of a regular exerciser, it may be specific to the sample under investigation. For example, the motivational drive and exercise-related identity of a middle-aged male is likely to be strikingly different from that of a university-aged female. Over the lifespan, the role that exercise plays in an individual's life is likely to change. In the current investigation it was noted that males and females differed in terms of the role that introjected regulation played in motivating regular exercise behavior. Perhaps this difference would be attenuated (or enhanced) for males and females in different age groups. While general motivational patterns are likely to remain constant (e.g., exercisers experiencing primarily by autonomous motives), it may be more realistic to examine motivational profiles that are specific to different demographic groups. There are two important strengths associated with this study. First, we were able to measure the entire spectrum of motives proposed by SDT [6]. This allowed us to identify the most important motivational forces behind various characteristics of exercise behavior. Second, we examined the motivation behind three different characteristics of exercise in the same study. This allowed us to gain some understanding about how motivation affects decisions to engage in exercise of varying frequency, intensity, and duration. It may be possible to influence persistence among exercise initiates using interventions that target their motivational profile and influence their development toward a profile more similar to that of a regular exerciser. Overall, the current investigation provides an important first step in determining the motivational profile of a regular exerciser which will provide a necessary point of reference with which to develop such interventions.

Conclusions

The results of the present study revealed that various characteristics of exercise are differentially regulated. Furthermore, in the motivational profile of a regular exerciser, identified and integrated regulations are important contributors to exercise frequency while integrated regulation plays an important role in determining exercise duration. The influence that integration has on exercise behavior indicates that an individual's exerciserelated identity can be influential in determining their exercise behavior. This finding points to the importance of measuring integrated regulation in an exercise context and the need for practitioners to develop programs that aim to enhance exercise-related identity in order to increase exercise participation among individuals.

-.14** -.12** -.16** -.28** -.35*** -.44*** -.10** .25** -**p < .01. *** p < .001.

Table 1
1Descriptive statistics for age, frequency, duration, intensity and BREQ subscalesVariables
Females (n = 594)
Males (n = 460)
M
S D
a
M
S D

Age
24.15
9.81
24.15
9.35

Frequency (times/week)
3.97
1.70
4.20
1.84

Duration (mins)
65.21 26.80
70.02 29.78

Intensity (LTEQ-METS)
70.55 38.42
68.63 41.19

Intrinsic motivation
3.06
0.75
.87
3.07
0.82
.88

Integrated regulation
2.70
1.02
.90
2.76
1.00
.89
Identified regulation
3.22
0.68
.76
3.16
0.74
.79

Introjected regulation
1.97
1.08
.80
1.72
1.15
.82

External regulation
0.82
0.84
.84
0.84
0.87
.85

Amotivation
0.13
0.31
.54
0.20
0.47
.79

Note. M = Mean; SD = standard deviation; BREQ = Behavioral Regulation in
Exercise Questionnaire; LTEQ = Leisure Time Exercise Questionnaire; BREQ
scale, 0 = "not true for me" to 4 = "very true for me.

Duncan et al. International Journal of Behavioral Nutrition and Physical Activity 2010, 7:7
http://www.ijbnpa.org/content/7/1/7

Table 2
2Bivariate Correlations between BREQ, exercise frequency, duration, intensity for malesVariable
1
2
3
4
5
6
7
8
9

1. Frequency
-

2. Duration
.26**
-

3. Intensity
.45**
.19**
-

4. Intrinsic motivation
.30**
.24**
.20**
-

5. Integrated regulation
.41***
.30***
.21***
.65***
-

6. Identified regulation
.42***
.29***
.22***
.65***
.78***
-

7. Introjected regulation
.25**
.23**
.17**
.32**
.50***
.53***
-

8. External regulation
.03
.03
.07
-.08
.09
.05
.35**
-

9. Amotivation
-.20**
-.06
-.14
-.33**
-.35***
-.44***
-.13**
.25**
-

**p < .01.
*** p < .001.

Table 4
4Multiple regression analysis predicting exercise frequency from exercise regulationsVariable
F
d f
R 2adj
B
S EB
b
t

Males
18.25***
6, 453
.18

Intrinsic motivation
-.02
.13
-.01
-.16
Integrated regulation
.38
.13
.20
2.82**

Identified regulation
.62
.19
.25
3.23**

Introjected regulation
.04
.09
.02
.42

External regulation
-.01
.10
-.01
-.13

Amotivation
-.22
.75
-.02
-.30

Females
38.50***
6, 587
.28

Intrinsic motivation
-.03
.11
-.02
-.30

Integrated regulation
.65
.09
.39
7.20***
Identified regulation
.38
.15
.15
2.51**

Introjected regulation
.09
.07
.06
1.35

External regulation
-.15
.08
-.07
-1.92

Amotivation
.17
.71
.01
.23

*p < .05.
** p < .01.
*** p < .001.

Table 5
5Multiple regression analysis predicting exercise duration from exercise regulationsVariable
F
d f
R 2adj
B
S E B
b
t

Males
9.48***
6, 453
.10

Intrinsic motivation
2.11
2.26
.06
.93

Integrated regulation
4.80
2.26
.16
2.13*

Identified regulation
4.69
3.26
.12
1.44

Introjected regulation
2.44
1.49
.09
1.64

External regulation
-1.30
1.73
-.04
-.75

Amotivation
22.24
12.75
.09
1.74
Females
6.61***
6, 587
.05

Intrinsic motivation
2.80
2.01
.08
1.39

Integrated regulation
4.98
1.61
.19
3.09**

Identified regulation
-2.84
2.74
-.07
-1.04

Introjected regulation
1.77
1.18
.07
1.50

External regulation
-1.58
1.40
-.05
-1.13

Amotivation
-18.29
12.68
-.06
-1.44

*p < .05.
** p < .01.
*** p < .001

Table 6
6Multiple regression analysis predicting exercise intensity from exercise regulationsVariable
F
d f
R 2 adj
B
S E B
b
t

Males
5.28***
6, 453
.05

Intrinsic motivation
4.54
3.12
.09
1.42
Integrated regulation
1.51
3.20
.04
.47

Identified regulation
3.68
4.63
.07
.79

Introjected regulation
1.85
2.11
.05
.88

External regulation
3.33
2.45
.07
1.36

Amotivation
-28.20
18.09
-.08
-1.56

Females
11.22***
6, 587
.09

Intrinsic motivation
5.47
2.82
.11
1.94

Integrated regulation
3.02
2.26
.08
1.34
Identified regulation
4.42
3.84
.08
1.15

Introjected regulation
3.85
1.66
.11
2.32*

External regulation
.40
1.97
.01
.20

Amotivation
-32.11
17.78
-.08
-1.81

*p < .05.
** p < .01.
*** p < .001

Duncan et al. International Journal of Behavioral Nutrition and Physical Activity 2010, 7:7 http://www.ijbnpa.org/content/7/1/7
Authors' contributions LRD has contributed to the collection, analysis, and interpretation of data as well as drafting the manuscript. CRH has contributed to the interpretation of the data and revisions of the manuscript. PMW has contributed to drafting the background information, analyzing the data and critically revising the manuscript. JO has contributed to the data collection as well as drafting and revising the manuscript. All authors have given final approval of the version to be published.Competing interestsThe authors declare that they have no competing interests.
. Lrd -Ma Authors' Information, Phd Candidate, United StatesSchool of Kinesiology, The University of Western Ontario, Canada. CRH-PhD, Professor, School of Kinesiology, The University of Western Ontario, Canada. PMW-PhD, Associate Professor, Faculty of Applied Health Science, Department of Physical Education and Kinesiology, Brock University, Canada. JO-PhD, Lecturer, Department of Kinesiology, California State University, East BayAuthors' information LRD -MA, PhD Candidate, School of Kinesiology, The University of Western Ontario, Canada. CRH-PhD, Professor, School of Kinesiology, The University of Western Ontario, Canada. PMW-PhD, Associate Professor, Faculty of Applied Health Science, Department of Physical Education and Kinesiology, Brock University, Canada. JO-PhD, Lecturer, Department of Kinesiology, California State University, East Bay, United States.

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Health-related quality of life and strain in caregivers of Australians with Parkinson's disease: An observational study

David H Kelly
Jennifer L Mcginley
Frances E Huxham
Hylton B Menz
Jennifer J Watts
Robert Iansek
Anna T Murphy
Mary Danoudis
Brooke Adair
Meg E Morris
Health-related quality of life and strain in caregivers of Australians with Parkinson's disease: An observational study
R E S E A R C H A R T I C L E Open Access
Background:The relationship between health-related quality of life (HRQoL) in people with Parkinson's disease and their caregivers is little understood and any effects on caregiver strain remain unclear. This paper examines these relationships in an Australian sample. Methods: Using the generic EuroQol (EQ-5D) and disease-specific Parkinson's Disease Questionnaire-39 Item (PDQ-39), HRQoL was evaluated in a sample of 97 people with PD and their caregivers. Caregiver strain was assessed using the Modified Caregiver Strain Index. Associations were evaluated between: (i) caregiver and care-recipient HRQoL; (ii) caregiver HRQoL and caregiver strain, and; (iii) between caregiver strain and care-recipient HRQoL. Results: No statistically significant relationships were found between caregiver and care-recipient HRQoL, or between caregiver HRQoL and caregiver strain. Although this Australian sample of caregivers experienced relatively good HRQoL and moderately low strain, a significant correlation was found between HRQoL of people with PD and caregiver strain (rho 0.43, p < .001). Conclusion: Poor HRQoL in people with PD is associated with higher strain in caregivers. Therapy interventions may target problems reported as most troublesome by people with PD, with potential to reduce strain on the caregiver.

Background

Little is known about health-related quality of life (HRQoL) in people with Parkinson's disease (PD) and their caregivers in Australia, or of the level of strain experienced by family caregivers of people with PD. This paper investigates those issues and explores their relationships.

Informal caregivers are estimated to provide some $AUD30 billion support annually to people with disabilities in Australia alone [1]. In the USA, this figure was estimated to be as high as $US375 billion in 2007 [2] and currently £119 billion in the UK [3]. In the case of PD, family members and particularly spouses are usually the primary caregivers [4,5]. Their support often enables people with PD to remain at home despite increasing disability, significantly reducing the costs associated with a move to residential care [5,6]. Together with physical assistance, family members can provide emotional and practical support that helps maintain better quality of life in the person with PD and may reduce morbidity and mortality [7].

The vital support provided by informal caregivers is not always without cost to the carers. There is a growing body of literature detailing some of the negative sequelae of PD on some caregivers' lives. People providing care for others with advanced PD have demonstrated an increased incidence of depression [8,9], diminished quality of life [10], reduced economic circumstances [11,12] and reduced physical [5] and mental health [5,10,13]. These and other factors brought about by stress and burden can combine to cause increased levels of caregiver strain, which has been defined as "an enduring change . . . in a caregiver's fabric of well-being" [14].

With the importance of the care-giving role now recognised, research attention is turning to the interplay between caregiver and care-recipient. Understanding which aspects of the care-recipient's condition are most likely to negatively affect caregiver wellbeing may enable therapy interventions to be targeted to assist both patient and caregiver [15], such as addressing factors contributing to decreased HRQoL.

Previous research has shown that quality of life in people with PD and their caregivers is linked [5].

Health-related quality of life refers to facets of an individual's self-perceived well-being that are affected by disease or treatment [11], suggesting that it may respond to appropriate therapy interventions. Parkinson's disease is known to reduce HRQoL in both people with PD [11] and caregivers in general [7], yet much less is known about the relationship between HRQoL in care-recipient with PD and caregiver, or between caregiver HRQoL and caregiver strain in those who care for individuals with PD.

In this study, we report the HRQoL of an Australian sample of people with PD and their caregivers, and examine the level of strain in caregivers. A key aim was to consider the relationship between HRQoL in people with PD and their caregivers. Further aims were to determine the associations between caregiver HRQoL and caregiver strain; and HRQoL in PD and caregiver strain.

Methods

Study design

The study formed part of a larger clinical randomized controlled trial that investigated the efficacy of two physiotherapy programs relative to a control intervention to reduce falls and improve mobility in people with PD (see [16] for full details). Participants were drawn from general medical practitioners, neurologists, Parkinson's Victoria network and advertisements in local papers. As part of their baseline assessments, participants with PD were asked if they had a significant other person in their life whom they could designate as a caregiver. The current study thus utilized a cross-sectional design. The study design and content were approved by the Human Research Ethics Committee of The University of Melbourne (HREC Number 0829579.2).

Participants

Participants with PD had to meet the criteria for the primary investigation which were: a diagnosis of idiopathic PD; Hoehn and Yahr score less than V, equating to the ability to walk independently (with or without an assistive device); Mini Mental State Examination score [17] of 24 or greater; ability to participate safely in an exercise program and to provide informed consent. Caregivers were included in the current study if they were nominated as the main caregiver by the person with PD, were willing to provide informed consent, and were able to read and complete the necessary questionnaires.

Procedure

Information from care-recipients was collected as part of the baseline assessment protocol of the larger study [16], performed by trained physical therapists in an outpatient setting. Caregiver assessments were performed at the same time if they had accompanied their carerecipient, or by postal completion of questionnaires.

Outcome measures Health related quality of life

The EuroQoL (EQ-5D) was chosen as the common HRQoL tool for both people with PD and their caregivers [18]. It utilises five questions covering mobility, personal care, usual activities, pain / discomfort and anxiety / depression, each with three options and summing to a single score. The overall health state is subsequently defined by a five-digit number. Comparisons with healthy older people and other patient groups are available [18]. A Visual Analogue Scale (VAS) is included, depicting the subject's perceived health on a scale of 0 (death) -100 (perfect health) on that day. The EQ-5D is sensitive to the clinical features of the Parkinson's Disease Questionnaire-39 (PDQ-39) [19] and correlates strongly with both PDQ-39 and Hoehn and Yahr scores [20]. Country specific weightings have been developed for the EQ-5D which reflect the respective population data of the country from which they have been derived. As no Australian weights (Wt) are available, the VAS and time trade off (TTO) weighted scores were calculated using the United Kingdom weights and formulae as described by Szende et al. [21].

The disease-specific PDQ-39 [22] was also used to evaluate HRQoL in the participants with PD. The PDQ-39 uses 39 questions with five levels of choice and covers eight domains, including mobility, activities of daily living and emotional well-being. These are weighted to provide a single score, with higher scores denoting lower HRQoL. The PDQ-39's strong measurement qualities [11] make it the most widely used disease-specific tool in Parkinson's disease.

Caregiver Strain

Caregiver strain or enduring change in wellbeing was evaluated using the Modified Caregiver Strain Index (MCSI) [23,24]. This instrument uses 13 questions scored at three levels to assess the impact of caring on the provider's health, finances, social interactions, time demands and employment. Scores from 0 to 2 for each question lead to a maximum of 26, denoting maximal strain.

Other measures

Motor performance in participants with PD was assessed by the timed 6-metre walk test (6MWT), which has demonstrated reliability in people with PD [25]. The modified Hoehn and Yahr scale [26] was used to provide an overall view of the level of PD severity, where I denotes mild unilateral symptoms progressing to an inability to walk at level V.

Statistical analysis

Descriptive data were summed and tabulated. As scoring on the EQ-5D, PDQ-39 and MCSI is ordinal, the nonparametric Spearman rank correlations coefficient (r s ) was used to examine associations between measures. All analyses were performed using PASW Statistics 18.0 (SPSS) and IBM SPSS Statistics 19.0.

Results

The primary RCT included 210 people with PD, with data for the current study available from 97 caregiverrecipient dyads. Seven missing data points in PDQ-39 (0.15% of data) were filled using the expectation minimization algorithm [27].

Participant Characteristics

Participants with PD were predominantly men (64%) in their late 60s (Table 1). There was a spread of disease severity as measured by the modified Hoehn and Yahr status [26] with the largest number (54%) moderately disabled at level 3 and above. A further 32% were classified as level 2 or 2.5. Although only two percent required supported accommodation, the higher number with moderate to severe disease suggests considerable demands on some caregivers, as carer strain is known to increase with Hoehn and Yahr level [28].

Most caregivers (84%) were spouses/partners, with 13% being their children. Sixty-six percent of caregivers were over 60 years old, seven percent were over 80 and six percent were less than 40 years old.

HRQoL in PwPD and their caregivers and caregiver strain

HRQoL scores in both people with PD and their caregivers using the EQ-5D VAS were negatively skewed, suggesting that this Australian sample had relatively good HRQoL. The mean EQ-5D VAS score for the participants with PD was 72 (SD = 16) ranging from a low of 15 to the maximum score of 100, with a median of 73 and interquartile range of 25 (60-85). This score was, as expected, lower than the caregivers' mean of 83 (SD = 13), ranging from 50 to 100, with a median of 85 and interquartile range 16 (77-93); Mann Whitney U: p < 0.001. The results of TTO and VAS Wt components of the EQ-5D essentially reflected the VAS scores. The level of caregiver strain reflected by MCSI scores was similarly somewhat low, with most caregivers scoring less than 16 out of a maximum 26 (median = 5, inter-quartile range = 8, range 0-25). Despite the low median, some caregivers were at near-maximal strain.

Association between HRQoL in caregiver and carerecipient

As expected, HRQoL scores using the EQ-5D VAS were lower in participants with PD (mean = 72.1, SD = 16.4; range 15-100). Similar distributions were shown using the EQ-5D TTO and VAS Wt methods. No significant correlations were found between HRQoL in caregiver and care-recipient, whether using the generic EQ-5D for PD, or the disease-specific PDQ-39 (See Figure 1). HRQoL in this sample of caregivers and care-recipients was not related.

Association between caregiver HRQoL and caregiver strain

No significant correlation was found between caregiver strain and caregiver HRQoL irrespective of the measure used (EQ-5D VAS: r s = −0.13, p = 0.11; TTO: r s = −0.08, p = 0.21; VAS Wt: r s = −0.09, p = 0.19). Most caregivers reported relatively low levels of strain (13 or less), coupled with relatively high HRQoL of 75 or more. Of interest, there was no significant correlation between caregiver strain and caregiver age (r s = −0.15, p = 0.079).

Association between HRQoL in PwPD and caregiver strain

Both the EQ-5D and PDQ-39 were used to evaluate the association between HRQoL in the participants with PD and the level of strain experienced by their caregivers (as indicated by MCSI scores). A small but significant negative relationship was found using the EQ-5D TTO and VAS Wt, but not the unweighted VAS (see Table 2), suggesting that poorer HRQoL in people with PD is associated with higher caregiver strain experienced by their caregivers. This became more obvious when the disease-specific PDQ-39 scores were used. The correlation of PDQ-39 with MCSI scores (r s = 0.3 at p < .001) confirmed that poorer self-rated health in people with PD was associated with increased strain in their caregivers.

Discussion

This study is one of the first on HRQoL and caregiver strain in an Australian sample of people caring for family members with PD. By comparison with the literature, both the caregivers and the care-recipients in our sample demonstrated relatively high levels of HRQoL, although that of the people with PD was, as expected, somewhat lower than their caregivers. Previous studies have reported reduced HRQoL in groups of people with PD of similar age and disease duration [29,30] when using the EQ-5D VAS. Others have noted greater emotional distress [31] and increased rates of depression in caregivers of people with PD [5,32], findings that suggest caregiver HRQoL may also be reduced. In the current study, however, the mean EQ-5D VAS scores in our cohort of caregivers were similar to non-caregiving Australians of comparable age [33]. Further, it appeared that caregivers in our sample had higher scores for HRQoL than cohorts in the Netherlands [15,34], Spain [10,35] and the UK [36,37]. However, as the Australian value was within one standard deviation of other results, this difference should be interpreted with caution. Unlike previous studies [4,38], we failed to find an association between HRQoL in the caregiver and carerecipient. Although Martinez-Martin et al.'s sample was similar to ours in age and mean Hoehn and Yahr status, they used different assessment tools for caregiver HRQoL (the caregiver-specific SQLC: Scale of Quality of Life of Caregivers [39]) and care-recipient HRQoL (EQ-5D [18] and PDQ-8 [40]). The slightly different domains covered potentially impact on attempted comparisons across groups. Differences in culture or facilities between Spain and Australia may also affect the HRQoL of caregiver or of the people with PD. Peters et al. [38] found a moderate correlation in HRQoL between the mental component score of this measure in people with PD and their caregivers in their UK sample of > 700 dyads using the SF12 [41]. Despite using the same instrument for both groups, differences between their cohort and ours may explain why they found a relationship between the two. The Peters et al. cohort was similar in age but may have had more long-standing disease; 35% of patients had been diagnosed for five to ten years and 32% for longer than 10 years, whereas the mean disease duration for our participants was 7.5 years. Another potential difference lies in the fact that the EQ-5D captures information about how people perceive their HRQoL at that time, whereas the SF-12 and PDQ-39 ask about perceptions over the past four weeks. This may lead to underreporting from the EQ-5D.  Contrary to expectations, no relationship was found between caregiver HRQoL and caregiver strain, perhaps related to the relatively high HRQoL and low levels of strain reported by our cohort. As more than half of our caregivers reported optimal health, the impact of a ceiling effect to the EQ-5D cannot be discounted. An Australian study found a strong tendency of the EQ-5D to allocate an excess of responses a 1.00 value and therefore reduce the capacity of the instrument to discriminate at the upper end of the range [33]. Although MCSI scores were more evenly distributed, some 80% of caregivers reported scores in the lower half of the scale, meaning relatively low levels of strain. At the individual level however, some caregivers reported near-maximal strain, reinforcing the potential of extreme strain in family caregivers.

In comparison with the literature there were no studies found examining the relationship between caregiver strain using the MCSI with caregiver HRQOL. While there have been several which have examined its precursor, the Caregiver Strain Index (CSI), correlations were not reported [37,41]. However several studies have found a statistically significant negative correlation between the Caregiver Burden Index (CBI) and caregiver HRQOL (SQLC) [5,42], whilst another has found a significant negative association with the Zarit Caregiver Burden Inventory (ZCBI) [32].

Our findings of reasonable HRQoL and fairly low caregiver strain may relate to the nature of our sample. Depression is the most commonly cited predictor of reduced HRQoL in people with PD [43] and known to have a negative impact on caregiver HRQoL [44] and stress, strain or burden [31,44]. People with depression, whether patient or caregiver, may be less willing to participate in a research project, especially when the major study required 14 months commitment from participants. Further, dementia was an exclusion criterion (MMSE score of less than 24), potentially contributing to selection bias because cognitive issues in the carerecipient are known to increase caregiver burden [5] and distress [31].

Our group of caregivers was under less strain than has been reported in other papers, perhaps because of relatively higher HRQoL. For example, Peters et al. [38] reported a mean of 11.89 (SD 6.39) (maximum possible for maximal strain is 26 points) on the MCSI, whereas the median for our caregivers was only 5. This may reflect a longer disease duration and considerably poorer HRQoL of their Parkinson's group (mean PDQ-39 index score of 41 (SD 18), compared to 23 (SD 12) for the participants with PD in our study).

In support of the idea that better HRQoL in carerecipients reduces strain on the caregiver, a significant correlation existed between the HRQoL in the individuals with PD and the level of strain experienced by their caregivers. Greater correlations were found using the disease-specific PDQ-39 than the generic EQ-5D measure. The added strength of the PDQ-39 correlation is not surprising, given that it is disease-specific, comprises 39 detailed items, and evaluates HRQoL over the past four weeks, rather than a single day as in the EQ-5D. Peters et al [38] similarly reported higher correlations between the PDQ-39 and MCSI (r s = 0.56, p < .001) than the generic SF-12 in care-recipients and the MCSI (physical component: r s = −0.33; p < .001; mental component: r s = −0.41, p < .001 [note that SF-12 and MCSI are scored in different directions, leading to negative correlations]).

Understanding the direct impact of patient HRQoL on the levels of caregiver strain is an important result. It has been suggested that families are "the most valuable and also the most vulnerable resource" in the treatment of PD [28]. Recognition of this has led to the development of educational and support programs for family caregivers of people with PD [15,34]. It also suggests that HRQoL should be a routine part of the assessment of patients with PD by health professionals, in conjunction with caregiver strain. Results from the PDQ-39 can inform the treating staff about how important various problems are to the individual, enabling management to be directed towards what is important to the patient, rather than most obvious to the therapist. PDQ-39 items such as difficulties dressing, carrying shopping or getting around the home, can be improved with targeted intervention by physical therapists, occupational therapists or nurses. Prioritising treatment by patient perceptions of difficulty provides emotional as well as physical benefits as the people with PD feels "listened to" and their opinion valued. Improved HRQoL of the care recipients may then flow on to help minimise caregiver strain.

Limitations

The cross sectional design, appropriate for the aims of the study, meant that issues of causality between variables could not be investigated. A further limitation of this study is that depression was not measured. Depression is a notable factor that contributes to the HRQOL and caregiver strain [9,28,31,32,45] although it was beyond the scope of this study to include measures of depression. Similarly, other pertinent caregiver personal factors that may influence strain and HRQOL such as sense of coherence, coping styles and resilience were not examined. The above factors could be explored in future research on caregiver quality of life and caregiver strain.

This sample cannot be taken to be completely representative of all caregivers. The sample was one of convenience with ambulant clients who had volunteered. It is possible that volunteers with PD and their caregivers who were not overly burdened by their condition could have been more likely to volunteer, or the responses of caregivers may have been in a more positive direction than for the broader community of caregivers of people with PD. Nevertheless, the sampling methods were similar to most other studies which examined the HRQOL of caregivers throughout the world.

Conclusions

This sample of Australians with PD and their caregivers experienced relatively good HRQoL and there was a generally low level of caregiver strain. However, poorer HRQoL in people with PD was significantly associated with increased strain in caregivers. Assessment of HRQoL may enable targeted interventions that may indirectly reduce caregiver strain.

Competing interests

The authors declare that they have no competing interest.

Figure 1
1Correlations between HRQoL in care-givers and care-recipients with PD. CG = caregiver, PD = care-recipient with Parkinson's disease.

Abbreviations PD: Parkinson's disease; PwPD: people (or person) with PD; HRQoL: healthrelated quality of life; MCSI: modified Caregiver Strain Index; PDQ-39: Parkinson's Disease Questionnaire −39 items; EQ-5D: Euroquol 5D; 6MWT: timed 6 metre Walk Test; RCT: randomized controlled trial; VAS: Visual Analogue Scale; VAS Wt: weighted Visual Analogue Scale; TTO: Time trade-off method; SQLC: Scale of Quality of Life of Caregivers; SF12: Short Form 12.

Table 1
1Characteristics of participants with PDParticipant characteristics
PwPD (n = 97)

Age (years); mean (SD)
68.5 (9.5)

Gender, n females (%)
35 (36)

Disease duration (years); mean (SD)
7.5 (5.8)

Modified Hoehn and Yahr stage; n (%)

< 1.5
8 (8)

1.5
5 (5)

2.0
19 (20)

2.5
12 (12)

3
40 (41)

4
13 (13)

Timed 6 Metre Walk Test a (seconds); mean (SD)
5.8 (2.1)

a Two participants with missing data.

Table 2
2Relationship between measures of HRQoL in people with PD and care-giver strain (MCSI)Correlated variables
Spearman rho
p value

PD EQ-5D VAS
−0.16
0.061

PD EQ-5D VAS Wt
−0.20
0.025

PD EQ-5D TTO
−0.18
0.042

PD PDQ-39
0.43
0.001

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