Age and Ageing Advance Access published February 17, 2015 Age and Ageing 2015; 0: 1–7 doi: 10.1093/ageing/afv001
© The Author 2015. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For Permissions, please email: [email protected]
The role of perceived barriers and objectively measured physical activity in adults aged 65–100 PAUL GELLERT1, MILES D. WITHAM2, IAIN K. CROMBIE3, PETER T. DONNAN3, MARION E. T. MCMURDO4, FALKO F. SNIEHOTTA1 1
Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK Section of Ageing and Health, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK 3 Epidemiology and Public Health, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK 4 Department of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK 2
Address correspondence to: F. F. Sniehotta. Institute of Health and Society, Newcastle University, Faculty of Medical Sciences Newcastle University Baddiley-Clark Building Richardson Road NE2 4AX, UK. Tel: (+44) 191 208 3815; Fax: (+44) 191 208 6043. Email: [email protected]
Abstract Objective: to test the predictive utility of perceived barriers to objectively measured physical activity levels in a stratified sample of older adults when accounting for social-cognitive determinants proposed by the Theory of Planned Behaviour (TPB), and economic and demographic factors. Methods: data were analysed from the Physical Activity Cohort Scotland survey, a representative and stratified (65–80 and 80+ years; deprived and affluent) sample of 584 community-dwelling older people, resident in Tayside, Scotland. Physical activity was measured objectively by accelerometry. Results: perceived barriers clustered around the areas of poor health, lack of interest, lack of safety and lack of access. Perceived poor health and lack of interest, but not lack of access or concerns about personal safety, predicted physical activity after controlling for demographic, economic and TPB variables. Discussion: perceived person-related barriers ( poor health and lack of interest) seem to be more strongly associated with physical activity levels than perceived environmental barriers (safety and access) in a large sample of older adults. Perceived barriers are modifiable and may be a target for future interventions. Keywords: physical activity, older people, perceived barriers, theory of planned behaviour
Introduction Physical activity is an important determinant of well-being [1, 2], morbidity [3, 4] and mortality [5]. In the United Kingdom, national guidelines recommend 30 min or more of moderate to vigorous activity on at least 5 days per week [6]. While 16% of men’s and 12% of women’s self-reported levels of physical activity meet national recommendations in the population of over 65 year olds, only 5% of men and 0% of women over 65 years meet the recommended levels when using objective measures of physical activity [6]. Perceived barriers to being physically active explain some interindividual differences in activity levels [7].
59 perceived barriers to physical activity, which focussed predominantly on poor health and constraints related to the physical environment. In data from a large-scale survey of 8,881 community-dwelling older adults [8], health problems were the most frequently reported barriers to physical activity, but no independent associations were found for perception of neighbourhood safety. Supporting this finding, a study by Crombie et al. [9] found that the most powerful deterrent to being physically active was lack of interest. More recently, a cohort study of 1,937 older adults [10] found that the three most frequently cited perceived barriers were poor health, lack of company and lack of interest.
Perceived barriers to physical activity
Prediction of physical activity from perceived barriers and other psychological correlates
In a systematic review of 44 studies examining physical activity in adults aged 79 and older, Baert et al. [7] identified
A range of psychological variables, in particular social cognitions—an individual’s beliefs about being physically active
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P. Gellert et al. and its consequences—are reliably associated with activity levels. The Theory of Planned Behaviour (TPB) [11, 12] as an example of a social-cognitive theory is a parsimonious conceptual model making the assumption that other influences on behaviour, including all perceived or actual barriers to being physically active, would operate indirectly through behavioural intention and perceived behavioural control. This is termed the sufficiency hypothesis [12]. Perceived barriers are conceptualised as ‘control beliefs’ and are hypothesised to have an indirect influence on behaviour, mediated by perceived behavioural control. The sufficiency hypothesis has been a matter of recent debate, and aside from the question of how perceived barriers relate to physical activity, it is also important to understand whether perceived barriers predict physical activity over and above intentions and perceived behavioural control (For a critical debate, see Refs 13 and 14), and behavioural and normative beliefs. Also, to understand the potential role of perceived barriers, it is important to control for a range of known correlates of physical activity such as age, sex or deprivation status/educational attainment [7, 9–11].
Aims The aim of this paper is to investigate perceived barriers to being physically active in a population of older, communitydwelling adults by exploring the factorial structure of and testing the predictive capacity of barriers for physical activity behaviour. We hypothesised that perceived barriers would be predictive of objectively measured physical activity levels, in addition to demographics and social cognitions (Hypothesis 1). Furthermore, we were hypothesising that perceived barriers related to the individual (e.g. poor health and lack of interest) would be superior in predicting physical activity compared with constraints related to the physical environment such as neighbourhood safety (Hypothesis 2).
Methods Participants and procedure
Data from a sample of 584 community-dwelling older people aged 65 and over, resident in Tayside, Scotland, from the Physical Activity Cohort Scotland (PACS) were analysed. Participants were recruited from 17 primary care practices. Sampling was stratified according to age (65–80 and 80+ years) and deprivation status (Scottish Index of Multiple Deprivation score decile 1–4 versus 5–10; http://www.isdscotland.org). Potential participants were excluded from the cohort if they were a resident in institutional care, unwilling to participate, wheelchair or bedbound, had cognitive impairment sufficient to prevent written informed consent or were enrolled in another research study. Those using walking aids were not excluded from the study. Full details of the cohort recruitment process have been published previously [15]. Those responding positively to the request to take part in the study were screened for eligibility by telephone, and if
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eligible, an appointment was made for a home visit. Written informed consent was obtained from participants, and the study was approved by the Tayside Committee on Medical Research Ethics (09/S1401/57). Each participant received two home visits spaced 7 days apart to provide and collect accelerometers. All questionnaires and the accelerometers were provided at the first visit. Data were collated, anonymised and processed by the Health Informatics Centre (HIC, http://www.dundee.ac.uk/hic). Sample size calculations
A sample size of 600 (150 per stratum) permitted individual correlations as low as 0.11 to be detected with 80% power. For multiple regression analyses, this sample size would allow consideration of 30 variables with a multiple correlation coefficient of 0.2 or R 2 of 4%. Post hoc power analysis to estimate the sensitivity revealed an achieved power of 1 − β > 0.95 (N = 584; α = 0.95; R2 = 0.30; number of predictors = 11) indicating that there was sufficient power for the present analyses. Measures administered at home visits Barriers to physical activity
Participants were asked to what degree 16 potential barriers made it more difficult for them to engage in 30 min of moderate-intensity physical activity on 5 or more days a week. (Barrier items were derived from the Scottish Health Education Population Survey [16] and a previous qualitative study by the authors [9] and are displayed in Supplementary data, Table S1 available in Age and Ageing online). ‘TPB variables’ were measured using standard procedures [15]. For the present paper, measures of behavioural intention (e.g. I intend to do 30 min of moderate-intensity physical activity on 5 or more days in the forthcoming week?), perceived behavioural control (e.g. How confident are you that you will be able to do 30 min of moderate-intensity physical activity on 5 or more days in the forthcoming week?), normative (e.g. ‘My family things that I should do at least 30 min of moderate-intensity physical activity on 5 or more days a week’) and behavioural beliefs (e.g. For me, doing at least 30 min of moderate-intensity physical activity on 5 or more days would keep me fit’) were recorded on a 20-item Likert scale. ‘Deprivation status’ was introduced as a co-variate into the models and measured using the Scottish Index of Multiple Deprivation, which identifies small area concentrations of multiple deprivation—income, employment, health, education, housing, access and crime—across all of Scotland in a consistent way. The index deciles (http://www.isdscotland.org) ranging from most deprived (1) to least deprived (10) were used as the basis for structuring the sample for the present study. ‘Physical activity’ was objectively measured by the RT3 accelerometer (Stayhealthy inc., Monrovia, CA, USA), which is a triaxial accelerometer previously shown to discriminate
Perceived barriers to physical activity walking from sedentary activity in older people [17], and which is responsive to interventions designed to increase physical activity [18]. Activity counts per minute were recorded for each of the 7 days. Studies have shown selfreport measures of physical activity to systematically overestimate the amount of physical activity compared with more accurate accelerometry measures [6]. Data analysis
We used exploratory factor analysis (EFA; principal component analysis with varimax rotation) to assess the interrelationships among the barriers and to identify the number of underlying dimensions. The Kaiser criterion and a scree plot were used to justify the number of factors. To test the predictive ability of perceived barriers to being physically active, a series of hierarchical regression analyses were performed. The final model (Model 4) is testing the hypothesis that perceived barriers are predictive of behaviour when accounting for socio-demographic and social-cognitive determinants and Hypothesis 2 that person-related perceived barriers are superior predictors of physical activity compared with perceived environmental barriers. A first model (Model 1; baseline model) contained sociodemographic predictors of physical activity (age, gender, deprivation status), followed by Model 2 that added direct predictors outlined by the TPB (perceived behavioural control and behavioural intention) and thereby tested the sufficiency hypothesis [12]. Perceived behavioural control and behavioural intention were expected to account for independent variance in behaviour, whereas the socio-demographic factors were hypothesised to become insignificant, according to the sufficiency hypothesis. Model 3 introduced behavioural and normative beliefs to Model 2 and served as a baseline model for Model 4, which constitutes the main hypothesis test for Hypotheses 1 and 2. In Model 4, barriers to be physically active (as assessed by the questionnaires on barriers) were expected to explain additional independent variance in physical activity, when controlling for all other variables mentioned before (Hypothesis 2). All perceived barrier factors were entered simultaneously in Model 4. To account for missing data, multiple imputation generating five data sets was employed [19].
Results In total, 3,343 letters of invitation were sent to potential participants. Overall, 63% replied to their invitation (either positively or negatively) and 17% (584/3,343) responded positively and indicated a willingness to participate. This sample comprised 45.7% men with a mean age of 78.5 (SD = 7.7; ranging from 65 to 105 years) years. 48.7% of the participants were married, 39.0% were widowed and 12.3 were divorced. Regarding the household income, 43.0% had an income below £10,000, 32.7% had an income between £10,000 and £20,000, and 24.3% earned above £20,000 per year. A total of 36.6% of the sample finished primary/ middle school only, 40.8% had a secondary or vocational
degree and 22.6% had a college or university degree. 73.3% of the sample were receiving treatment of at least one chronic disease when the study was conducted. Accelerometry data were available for 94% (547/584) of participants. To permit hypothesis testing and to accommodate missing data, the 37 missing accelerometer data points were imputed using multiple imputation technique. The mean accelerometry counts of activity per day recorded by the accelerometer device for the sample of 547 participants were 146,119 (SD = 79,707) counts per day. The physical activity counts based on the pooled mean value estimation over the five imputed data sets (N = 584) were calculated as M = 146,166, corresponding to an estimated mean of 177 (SD = 89) min per day. Table 1 shows means (M), standard deviations (SD) and intercorrelations for the psychological predictors of behaviour, physical activity, age, gender and multiple deprivation index. The correlation matrix provides preliminary evidence that the associations among the study variables are in the expected direction. The strongest correlations are among the TPB constructs (r = 0.51–0.86), whereas the perceived barriers factors show only weak associations (r = 0.15–0.36) as they were varimax rotated. Means of TPB variables were slightly above/below the theoretical scale mean indicating that participants had in general socialcognitive resources related to physical activity. Factor structure of the perceived barriers
The principal component analyses (PCA) extracted four components with an Eigenvalue over 1. After varimax rotation (Supplementary data, Table S1 available in Age and Ageing online), the PCA solution accounted for 52% (sums of squared loadings) of the overall variance. This analysis suggests four domains of perceived barriers; those related to health and age (M = 2.51; SD = 1.33), to access (M = 1.35; SD = 0.78), general interest in physical activity (M = 1.57; SD = 0.89) and safety (M = 1.45; SD = 0.83). Predictive role of perceived barriers
Table 2 shows the results from hierarchical linear regression analyses testing key assumptions of the TPB. Model 1 tested the utility of age, gender and deprivation index as a predictor of physical activity as a baseline model. All three predictive variables were significant, with age being the strongest predictor and the model accounting for 17– 21% (varying over the five imputed datasets) of the variance in physical activity measures. Model 2 added behavioural intention and perceived behavioural control to age, gender and deprivation index. This addition improved the prediction significantly by accounting for 7–8% additional variance over and above Model 1. All variables except for behavioural intention significantly contributed to the equation. In Model 3, behavioural and normative beliefs were added to the regression equation, serving as baseline model for the hypothesised Model 4 in the next step, where barriers to be physically active were included. As hypothesised,
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P. Gellert et al. Table 1. Descriptive statistics for perceived barriers, TPB variables, physical activity and demographic variables 1
2
3
4
5
6
7
8
9a
9b
9c
9d
−0.40**
−0.12** 0.01
0.12** 0.03 −0.02
0.37** −0.18** −0.12** 0.09*
0.35** −0.18** −0.12* 0.08* 0.86**
0.33** −0.25** −0.15** 0.09** 0.51** 0.49**
0.36** −0.24** −0.12 0.12** 0.71** 0.67** 0.65**
−0.39** 0.22** 0.18** −0.15** −0.65** −0.56** −0.44** −0.57**
−0.10* 0.02 0.12** −0.09* −0.09* −0.08 −0.13** −0.12**
−0.17** −0.10* 0.07 0.02 −0.28** −0.26** −0.16** −0.15**
−0.23** 0.20** 0.09* −0.10* −0.19** −0.17** −0.40** −0.23**
0.34**
0.34** 0.30**
0.36** 0.32** 0.15**
1.35 0.78 0.63
1.57 0.89 0.60
1.45 0.83 0.40
.................................................................................... 1. Physical activity 2. Age 3. Gendera 4. Deprivation index 5. PBC 6. Behavioural intention 7. Behavioural beliefs 8. Normative beliefs 9. Perceived barriers 9a. Health/age 9b. Access 9c. Interest 9d. Safety Mean SD Cronbach’s α b
146,119 79,707 –
78.50 7.66 –
5.20 2.73 –
–
4.10 2.15 0.98
4.12 2.17 0.90
4.87 1.03 0.82
4.79 1.63 0.95
2.51 1.33 0.76
PBC, perceived behavioural control. a 0, men; 1, women. b Cronbach’s α was used as a measure of reliability; perceived barriers, which were loading on one factor, were considered to be a unidimensional scale. *P < 0.05. **P < 0.01.
Table 2. Summary of hierarchical multiple regression analysis predicting physical activity Model 1
Model 2
Model 3
Model 4
B
SE
B
SE
B
SE
B
SE
−4,271*** −18,721** 3,884***
411 6,421 1,110
−3,721*** −13,689* 3,158**
400 6,239 1,061
−3,530*** −12,252* 2,978**
405 6,236 1,055
−3,581*** −10,322 2,891**
410 6,202 1,046
7,816** 3,055
2,701 2,663
6,125* 2,220
2,812 2,677
2,875 1,839
2,965 2,684
6,993 1,627
4,099 2,954
4,482 1,794
4,289 2,972
−7,051* 4,181 −10,263** −3,908 0.30–0.31 0.02–0.03
3,219 4,187 3,588 4,028
.................................................................................... Co-variates Age per year Gendera Deprivation per decile TPB PBC Behavioural intention Beliefs Behavioural Normative Perceived barriers Health/age Access Interest Safety Adjusted R 2 ΔR 2
0.17–0.21
0.27–0.28 0.07–0.08
0.28–0.29 0.01
Significant values in bold. N = 583 based on five imputed data sets; higher barrier values refer to higher levels of perceived barriers. PBC, perceived behavioural control. a 0, men; 1, women. *P < 0.05. **P < 0.01. ***P < 0.001.
Model 3 does not explain significant additional variance compared with Model 2. The inclusion of the four perceived barriers to being physically active in Model 4—the main hypothesised model— improved the prediction, explaining an additional 2–3% of variance in physical activity over and above the first two blocks of variables. Barriers regarding poor health/age as
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well as lack of interest are significant in the final model, and the effect of gender became non-significant.
Discussion This study examined the factorial structure of barriers to being physically active in a large sample of older adults
Perceived barriers to physical activity stratified by age and deprivation status. It was hypothesised that perceived barriers to physical activity would be predictive of objectively measured physical activity levels, in addition to demographics and social cognitions (Hypothesis 1). Further, perceived barriers related to the individual (e.g. poor health and lack of interest) were hypothesised to be superior in predicting physical activity compared with constraints related to the physical environment (Hypothesis 2). Factorial structure
The factor analyses suggested four separate groups of barriers to physical activity, namely poor health/age and general lack of interest in physical activity, as well as lack of access and concerns about safety. The first two factors might reflect person-related factors ( poor health and lack of interest), whereas the later ones (concerns about safety and lack of access) might represent interpersonal or environment-related factors. This detected factorial structure is in line with findings of previous research such as a meta-analysis of Baert et al. [7] who identified perceived barriers to physical activity in old age and grouped them into (intra)personal perceived barriers (56% of all barriers such as poor health or lack of interest) and constraints related to the physical environment and barriers on a interpersonal or community level (44%). Testing the sufficiency hypothesis
Findings from the current study dispute the sufficiency hypothesis of the TPB (Model 2) which proposes that variables external to the model (i.e. the co-variates of age, gender and deprivation) influence behaviour only indirectly through the model constructs. Looking into the results of Model 4, the sufficiency hypothesis is challenged by the introduction of the four perceived barrier factors as these perceived barriers have an additional effect on subsequent physical activity and bypass the TPB constructs. Perceived person-related barriers: poor health and lack of interest are predictive in addition to TPB variables
Regressing physical activity onto the four factors of perceived barriers simultaneously, results have shown poor health/age and lack of interest to be associated with physical activity levels (confirming Hypothesis 2), which is not in accordance with the sufficiency hypothesis of the TPB (confirming Hypothesis 1). Ayotte et al. [20] outlined an indirect effect of perceived barriers on physical activity via more proximal social-cognitive factors, such as goals and self-regulation, confirming the sufficiency hypothesis. In contrast, the current study has shown a strong direct effect of two of the four barrier factors in addition to social cognitive factors which doubts the sufficiency hypothesis. Ayotte et al. [20] used a unidimensional scale that incorporates personal and environmental perceived barriers, whereas in the present study environmental but not personal perceived barriers were in line with sufficiency hypothesis. As our study has
shown that the differential operationalization of personal and environmental perceived barriers might give further insights about which type of barriers confirm the sufficiency hypothesis, yet further research is needed. In line with previous studies [8, 10, 21], poor health/age had the highest mean value out of all four perceived barrier factors in the present study. Physical activity interventions should target these perceived health and age barriers by including coping planning how to overcome these perceived health barriers [22, 23]. Since lack of interest in physical activity was a major factor, interventions should target increasing motivation but also adapt intervention strategies to potential motivational changes in old age. For instance, some research has shown that older adults prefer the process of being active itself rather than the outcomes of physical activity [24]. Furthermore, older adults’ motivation tends to be driven more by affective rather than cognitive beliefs about consequences of physical activity [25]. Perceived environmental barriers: concerns about safety and lack of access are not independent predictors of physical activity once PBC and behavioural intention have been taken into account
In line with other research [8, 26], perceived environmental barriers (concerns about safety and perceived lack access) did not have any direct effect on physical activity levels, compared with more person-related barriers, such as lack of interest and poor health, which is in line with the sufficiency hypothesis (as there is no direct effect of these barriers on physical activity when TPB predictors are in the model). Salmon et al. [26] also found that personal barriers, such as lack of time and other priorities, were predictive, whereas environmental barriers were not strongly related to participation in physical activity. Also Giles-Corti and Donovan [27] found the physical environment to be secondary to individual and social environmental barriers. A supportive physical environment is necessary, but it may be insufficient to increase recommended levels of physical activity. Complementary strategies are required that aim to influence individual and social environmental factors. Limitations
Despite objective measures, the relationships under study were tested cross-sectionally and need replication in longitudinal and experimental designs. Also the four empirically detected barriers need cross-validation by means of confirmatory factor analysis in a follow-up study and in other samples. To reduce potential selection bias due to age or deprivation status, the present study sample was stratified by these variables. Additionally, the present results were based on missing value imputation to account for selective study missingness, yet bias cannot be ruled out completely. The use of accelerometry overcomes various sources of bias, for example bias due to problems with recall. The PACS cohort used one of the few accelerometers specifically validated for older adults, but bias due to wear time or deviation from the protocol for wearing the devices is possible. The TPB
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P. Gellert et al. measures in the PACS cohort were following standard procedures for assessment [15], but the research justifying these measures has predominantly been based on younger adults.
Supplementary data Supplementary data mentioned in the text are available to subscribers in Age and Ageing online.
Implications for future studies
Future studies might assess whether the perceived barriers vary with increasing age. They should also focus on further disentangling actual barriers and perceived ones, as well as increasing insight into the causal impact of perceived barriers by means of ecological experimental designs. An aim of future research might be to inform intervention development in changing perceived and actual barriers as an additional factor in intervention designs and to adopt interventions specific to the barriers and needs of older adults [23, 25].
Conclusion In conclusion, four types of perceived barriers emerged: poor health, lack of interest, lack of access and lack of safety. Person-related barriers (poor health and lack of interest) had a direct effect on physical activity, even after accounting for demographic variables and social-cognitive factors proposed by TPB disputing the theory’s sufficiency hypothesis. Conversely, perceived environmental barriers (lack of safety and lack of access) did not have any direct effect on physical activity alongside TPB.
Key points • Perceived barriers to physical activity cluster around the themes of perceived health, interest, access and safety. • Perceived poor health and lack of interest are associated with objective physical activity. These effects are not accounted for by the Theory of Planned Behaviour.
Conflicts of interest None declared.
Funding This study was funded by Scottish Executive grant CZH/4/ 518. The sponsor was the University of Dundee. Neither funder nor sponsor had a role in the design, conduct or interpretation of the study. F.F.S. is funded by Fuse, the Centre for Translational Research in Public Health, a UKCRC Public Health Research Centre of Excellence. Funding for Fuse from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council and the National Institute for Health Research, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged.
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© The Author 2015. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For Permissions, please email: [email protected]
The role of perceived barriers and objectively measured physical activity in adults aged 65–100 PAUL GELLERT1, MILES D. WITHAM2, IAIN K. CROMBIE3, PETER T. DONNAN3, MARION E. T. MCMURDO4, FALKO F. SNIEHOTTA1 1
Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK Section of Ageing and Health, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK 3 Epidemiology and Public Health, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK 4 Department of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK 2
Address correspondence to: F. F. Sniehotta. Institute of Health and Society, Newcastle University, Faculty of Medical Sciences Newcastle University Baddiley-Clark Building Richardson Road NE2 4AX, UK. Tel: (+44) 191 208 3815; Fax: (+44) 191 208 6043. Email: [email protected]
Abstract Objective: to test the predictive utility of perceived barriers to objectively measured physical activity levels in a stratified sample of older adults when accounting for social-cognitive determinants proposed by the Theory of Planned Behaviour (TPB), and economic and demographic factors. Methods: data were analysed from the Physical Activity Cohort Scotland survey, a representative and stratified (65–80 and 80+ years; deprived and affluent) sample of 584 community-dwelling older people, resident in Tayside, Scotland. Physical activity was measured objectively by accelerometry. Results: perceived barriers clustered around the areas of poor health, lack of interest, lack of safety and lack of access. Perceived poor health and lack of interest, but not lack of access or concerns about personal safety, predicted physical activity after controlling for demographic, economic and TPB variables. Discussion: perceived person-related barriers ( poor health and lack of interest) seem to be more strongly associated with physical activity levels than perceived environmental barriers (safety and access) in a large sample of older adults. Perceived barriers are modifiable and may be a target for future interventions. Keywords: physical activity, older people, perceived barriers, theory of planned behaviour
Introduction Physical activity is an important determinant of well-being [1, 2], morbidity [3, 4] and mortality [5]. In the United Kingdom, national guidelines recommend 30 min or more of moderate to vigorous activity on at least 5 days per week [6]. While 16% of men’s and 12% of women’s self-reported levels of physical activity meet national recommendations in the population of over 65 year olds, only 5% of men and 0% of women over 65 years meet the recommended levels when using objective measures of physical activity [6]. Perceived barriers to being physically active explain some interindividual differences in activity levels [7].
59 perceived barriers to physical activity, which focussed predominantly on poor health and constraints related to the physical environment. In data from a large-scale survey of 8,881 community-dwelling older adults [8], health problems were the most frequently reported barriers to physical activity, but no independent associations were found for perception of neighbourhood safety. Supporting this finding, a study by Crombie et al. [9] found that the most powerful deterrent to being physically active was lack of interest. More recently, a cohort study of 1,937 older adults [10] found that the three most frequently cited perceived barriers were poor health, lack of company and lack of interest.
Perceived barriers to physical activity
Prediction of physical activity from perceived barriers and other psychological correlates
In a systematic review of 44 studies examining physical activity in adults aged 79 and older, Baert et al. [7] identified
A range of psychological variables, in particular social cognitions—an individual’s beliefs about being physically active
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P. Gellert et al. and its consequences—are reliably associated with activity levels. The Theory of Planned Behaviour (TPB) [11, 12] as an example of a social-cognitive theory is a parsimonious conceptual model making the assumption that other influences on behaviour, including all perceived or actual barriers to being physically active, would operate indirectly through behavioural intention and perceived behavioural control. This is termed the sufficiency hypothesis [12]. Perceived barriers are conceptualised as ‘control beliefs’ and are hypothesised to have an indirect influence on behaviour, mediated by perceived behavioural control. The sufficiency hypothesis has been a matter of recent debate, and aside from the question of how perceived barriers relate to physical activity, it is also important to understand whether perceived barriers predict physical activity over and above intentions and perceived behavioural control (For a critical debate, see Refs 13 and 14), and behavioural and normative beliefs. Also, to understand the potential role of perceived barriers, it is important to control for a range of known correlates of physical activity such as age, sex or deprivation status/educational attainment [7, 9–11].
Aims The aim of this paper is to investigate perceived barriers to being physically active in a population of older, communitydwelling adults by exploring the factorial structure of and testing the predictive capacity of barriers for physical activity behaviour. We hypothesised that perceived barriers would be predictive of objectively measured physical activity levels, in addition to demographics and social cognitions (Hypothesis 1). Furthermore, we were hypothesising that perceived barriers related to the individual (e.g. poor health and lack of interest) would be superior in predicting physical activity compared with constraints related to the physical environment such as neighbourhood safety (Hypothesis 2).
Methods Participants and procedure
Data from a sample of 584 community-dwelling older people aged 65 and over, resident in Tayside, Scotland, from the Physical Activity Cohort Scotland (PACS) were analysed. Participants were recruited from 17 primary care practices. Sampling was stratified according to age (65–80 and 80+ years) and deprivation status (Scottish Index of Multiple Deprivation score decile 1–4 versus 5–10; http://www.isdscotland.org). Potential participants were excluded from the cohort if they were a resident in institutional care, unwilling to participate, wheelchair or bedbound, had cognitive impairment sufficient to prevent written informed consent or were enrolled in another research study. Those using walking aids were not excluded from the study. Full details of the cohort recruitment process have been published previously [15]. Those responding positively to the request to take part in the study were screened for eligibility by telephone, and if
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eligible, an appointment was made for a home visit. Written informed consent was obtained from participants, and the study was approved by the Tayside Committee on Medical Research Ethics (09/S1401/57). Each participant received two home visits spaced 7 days apart to provide and collect accelerometers. All questionnaires and the accelerometers were provided at the first visit. Data were collated, anonymised and processed by the Health Informatics Centre (HIC, http://www.dundee.ac.uk/hic). Sample size calculations
A sample size of 600 (150 per stratum) permitted individual correlations as low as 0.11 to be detected with 80% power. For multiple regression analyses, this sample size would allow consideration of 30 variables with a multiple correlation coefficient of 0.2 or R 2 of 4%. Post hoc power analysis to estimate the sensitivity revealed an achieved power of 1 − β > 0.95 (N = 584; α = 0.95; R2 = 0.30; number of predictors = 11) indicating that there was sufficient power for the present analyses. Measures administered at home visits Barriers to physical activity
Participants were asked to what degree 16 potential barriers made it more difficult for them to engage in 30 min of moderate-intensity physical activity on 5 or more days a week. (Barrier items were derived from the Scottish Health Education Population Survey [16] and a previous qualitative study by the authors [9] and are displayed in Supplementary data, Table S1 available in Age and Ageing online). ‘TPB variables’ were measured using standard procedures [15]. For the present paper, measures of behavioural intention (e.g. I intend to do 30 min of moderate-intensity physical activity on 5 or more days in the forthcoming week?), perceived behavioural control (e.g. How confident are you that you will be able to do 30 min of moderate-intensity physical activity on 5 or more days in the forthcoming week?), normative (e.g. ‘My family things that I should do at least 30 min of moderate-intensity physical activity on 5 or more days a week’) and behavioural beliefs (e.g. For me, doing at least 30 min of moderate-intensity physical activity on 5 or more days would keep me fit’) were recorded on a 20-item Likert scale. ‘Deprivation status’ was introduced as a co-variate into the models and measured using the Scottish Index of Multiple Deprivation, which identifies small area concentrations of multiple deprivation—income, employment, health, education, housing, access and crime—across all of Scotland in a consistent way. The index deciles (http://www.isdscotland.org) ranging from most deprived (1) to least deprived (10) were used as the basis for structuring the sample for the present study. ‘Physical activity’ was objectively measured by the RT3 accelerometer (Stayhealthy inc., Monrovia, CA, USA), which is a triaxial accelerometer previously shown to discriminate
Perceived barriers to physical activity walking from sedentary activity in older people [17], and which is responsive to interventions designed to increase physical activity [18]. Activity counts per minute were recorded for each of the 7 days. Studies have shown selfreport measures of physical activity to systematically overestimate the amount of physical activity compared with more accurate accelerometry measures [6]. Data analysis
We used exploratory factor analysis (EFA; principal component analysis with varimax rotation) to assess the interrelationships among the barriers and to identify the number of underlying dimensions. The Kaiser criterion and a scree plot were used to justify the number of factors. To test the predictive ability of perceived barriers to being physically active, a series of hierarchical regression analyses were performed. The final model (Model 4) is testing the hypothesis that perceived barriers are predictive of behaviour when accounting for socio-demographic and social-cognitive determinants and Hypothesis 2 that person-related perceived barriers are superior predictors of physical activity compared with perceived environmental barriers. A first model (Model 1; baseline model) contained sociodemographic predictors of physical activity (age, gender, deprivation status), followed by Model 2 that added direct predictors outlined by the TPB (perceived behavioural control and behavioural intention) and thereby tested the sufficiency hypothesis [12]. Perceived behavioural control and behavioural intention were expected to account for independent variance in behaviour, whereas the socio-demographic factors were hypothesised to become insignificant, according to the sufficiency hypothesis. Model 3 introduced behavioural and normative beliefs to Model 2 and served as a baseline model for Model 4, which constitutes the main hypothesis test for Hypotheses 1 and 2. In Model 4, barriers to be physically active (as assessed by the questionnaires on barriers) were expected to explain additional independent variance in physical activity, when controlling for all other variables mentioned before (Hypothesis 2). All perceived barrier factors were entered simultaneously in Model 4. To account for missing data, multiple imputation generating five data sets was employed [19].
Results In total, 3,343 letters of invitation were sent to potential participants. Overall, 63% replied to their invitation (either positively or negatively) and 17% (584/3,343) responded positively and indicated a willingness to participate. This sample comprised 45.7% men with a mean age of 78.5 (SD = 7.7; ranging from 65 to 105 years) years. 48.7% of the participants were married, 39.0% were widowed and 12.3 were divorced. Regarding the household income, 43.0% had an income below £10,000, 32.7% had an income between £10,000 and £20,000, and 24.3% earned above £20,000 per year. A total of 36.6% of the sample finished primary/ middle school only, 40.8% had a secondary or vocational
degree and 22.6% had a college or university degree. 73.3% of the sample were receiving treatment of at least one chronic disease when the study was conducted. Accelerometry data were available for 94% (547/584) of participants. To permit hypothesis testing and to accommodate missing data, the 37 missing accelerometer data points were imputed using multiple imputation technique. The mean accelerometry counts of activity per day recorded by the accelerometer device for the sample of 547 participants were 146,119 (SD = 79,707) counts per day. The physical activity counts based on the pooled mean value estimation over the five imputed data sets (N = 584) were calculated as M = 146,166, corresponding to an estimated mean of 177 (SD = 89) min per day. Table 1 shows means (M), standard deviations (SD) and intercorrelations for the psychological predictors of behaviour, physical activity, age, gender and multiple deprivation index. The correlation matrix provides preliminary evidence that the associations among the study variables are in the expected direction. The strongest correlations are among the TPB constructs (r = 0.51–0.86), whereas the perceived barriers factors show only weak associations (r = 0.15–0.36) as they were varimax rotated. Means of TPB variables were slightly above/below the theoretical scale mean indicating that participants had in general socialcognitive resources related to physical activity. Factor structure of the perceived barriers
The principal component analyses (PCA) extracted four components with an Eigenvalue over 1. After varimax rotation (Supplementary data, Table S1 available in Age and Ageing online), the PCA solution accounted for 52% (sums of squared loadings) of the overall variance. This analysis suggests four domains of perceived barriers; those related to health and age (M = 2.51; SD = 1.33), to access (M = 1.35; SD = 0.78), general interest in physical activity (M = 1.57; SD = 0.89) and safety (M = 1.45; SD = 0.83). Predictive role of perceived barriers
Table 2 shows the results from hierarchical linear regression analyses testing key assumptions of the TPB. Model 1 tested the utility of age, gender and deprivation index as a predictor of physical activity as a baseline model. All three predictive variables were significant, with age being the strongest predictor and the model accounting for 17– 21% (varying over the five imputed datasets) of the variance in physical activity measures. Model 2 added behavioural intention and perceived behavioural control to age, gender and deprivation index. This addition improved the prediction significantly by accounting for 7–8% additional variance over and above Model 1. All variables except for behavioural intention significantly contributed to the equation. In Model 3, behavioural and normative beliefs were added to the regression equation, serving as baseline model for the hypothesised Model 4 in the next step, where barriers to be physically active were included. As hypothesised,
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P. Gellert et al. Table 1. Descriptive statistics for perceived barriers, TPB variables, physical activity and demographic variables 1
2
3
4
5
6
7
8
9a
9b
9c
9d
−0.40**
−0.12** 0.01
0.12** 0.03 −0.02
0.37** −0.18** −0.12** 0.09*
0.35** −0.18** −0.12* 0.08* 0.86**
0.33** −0.25** −0.15** 0.09** 0.51** 0.49**
0.36** −0.24** −0.12 0.12** 0.71** 0.67** 0.65**
−0.39** 0.22** 0.18** −0.15** −0.65** −0.56** −0.44** −0.57**
−0.10* 0.02 0.12** −0.09* −0.09* −0.08 −0.13** −0.12**
−0.17** −0.10* 0.07 0.02 −0.28** −0.26** −0.16** −0.15**
−0.23** 0.20** 0.09* −0.10* −0.19** −0.17** −0.40** −0.23**
0.34**
0.34** 0.30**
0.36** 0.32** 0.15**
1.35 0.78 0.63
1.57 0.89 0.60
1.45 0.83 0.40
.................................................................................... 1. Physical activity 2. Age 3. Gendera 4. Deprivation index 5. PBC 6. Behavioural intention 7. Behavioural beliefs 8. Normative beliefs 9. Perceived barriers 9a. Health/age 9b. Access 9c. Interest 9d. Safety Mean SD Cronbach’s α b
146,119 79,707 –
78.50 7.66 –
5.20 2.73 –
–
4.10 2.15 0.98
4.12 2.17 0.90
4.87 1.03 0.82
4.79 1.63 0.95
2.51 1.33 0.76
PBC, perceived behavioural control. a 0, men; 1, women. b Cronbach’s α was used as a measure of reliability; perceived barriers, which were loading on one factor, were considered to be a unidimensional scale. *P < 0.05. **P < 0.01.
Table 2. Summary of hierarchical multiple regression analysis predicting physical activity Model 1
Model 2
Model 3
Model 4
B
SE
B
SE
B
SE
B
SE
−4,271*** −18,721** 3,884***
411 6,421 1,110
−3,721*** −13,689* 3,158**
400 6,239 1,061
−3,530*** −12,252* 2,978**
405 6,236 1,055
−3,581*** −10,322 2,891**
410 6,202 1,046
7,816** 3,055
2,701 2,663
6,125* 2,220
2,812 2,677
2,875 1,839
2,965 2,684
6,993 1,627
4,099 2,954
4,482 1,794
4,289 2,972
−7,051* 4,181 −10,263** −3,908 0.30–0.31 0.02–0.03
3,219 4,187 3,588 4,028
.................................................................................... Co-variates Age per year Gendera Deprivation per decile TPB PBC Behavioural intention Beliefs Behavioural Normative Perceived barriers Health/age Access Interest Safety Adjusted R 2 ΔR 2
0.17–0.21
0.27–0.28 0.07–0.08
0.28–0.29 0.01
Significant values in bold. N = 583 based on five imputed data sets; higher barrier values refer to higher levels of perceived barriers. PBC, perceived behavioural control. a 0, men; 1, women. *P < 0.05. **P < 0.01. ***P < 0.001.
Model 3 does not explain significant additional variance compared with Model 2. The inclusion of the four perceived barriers to being physically active in Model 4—the main hypothesised model— improved the prediction, explaining an additional 2–3% of variance in physical activity over and above the first two blocks of variables. Barriers regarding poor health/age as
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well as lack of interest are significant in the final model, and the effect of gender became non-significant.
Discussion This study examined the factorial structure of barriers to being physically active in a large sample of older adults
Perceived barriers to physical activity stratified by age and deprivation status. It was hypothesised that perceived barriers to physical activity would be predictive of objectively measured physical activity levels, in addition to demographics and social cognitions (Hypothesis 1). Further, perceived barriers related to the individual (e.g. poor health and lack of interest) were hypothesised to be superior in predicting physical activity compared with constraints related to the physical environment (Hypothesis 2). Factorial structure
The factor analyses suggested four separate groups of barriers to physical activity, namely poor health/age and general lack of interest in physical activity, as well as lack of access and concerns about safety. The first two factors might reflect person-related factors ( poor health and lack of interest), whereas the later ones (concerns about safety and lack of access) might represent interpersonal or environment-related factors. This detected factorial structure is in line with findings of previous research such as a meta-analysis of Baert et al. [7] who identified perceived barriers to physical activity in old age and grouped them into (intra)personal perceived barriers (56% of all barriers such as poor health or lack of interest) and constraints related to the physical environment and barriers on a interpersonal or community level (44%). Testing the sufficiency hypothesis
Findings from the current study dispute the sufficiency hypothesis of the TPB (Model 2) which proposes that variables external to the model (i.e. the co-variates of age, gender and deprivation) influence behaviour only indirectly through the model constructs. Looking into the results of Model 4, the sufficiency hypothesis is challenged by the introduction of the four perceived barrier factors as these perceived barriers have an additional effect on subsequent physical activity and bypass the TPB constructs. Perceived person-related barriers: poor health and lack of interest are predictive in addition to TPB variables
Regressing physical activity onto the four factors of perceived barriers simultaneously, results have shown poor health/age and lack of interest to be associated with physical activity levels (confirming Hypothesis 2), which is not in accordance with the sufficiency hypothesis of the TPB (confirming Hypothesis 1). Ayotte et al. [20] outlined an indirect effect of perceived barriers on physical activity via more proximal social-cognitive factors, such as goals and self-regulation, confirming the sufficiency hypothesis. In contrast, the current study has shown a strong direct effect of two of the four barrier factors in addition to social cognitive factors which doubts the sufficiency hypothesis. Ayotte et al. [20] used a unidimensional scale that incorporates personal and environmental perceived barriers, whereas in the present study environmental but not personal perceived barriers were in line with sufficiency hypothesis. As our study has
shown that the differential operationalization of personal and environmental perceived barriers might give further insights about which type of barriers confirm the sufficiency hypothesis, yet further research is needed. In line with previous studies [8, 10, 21], poor health/age had the highest mean value out of all four perceived barrier factors in the present study. Physical activity interventions should target these perceived health and age barriers by including coping planning how to overcome these perceived health barriers [22, 23]. Since lack of interest in physical activity was a major factor, interventions should target increasing motivation but also adapt intervention strategies to potential motivational changes in old age. For instance, some research has shown that older adults prefer the process of being active itself rather than the outcomes of physical activity [24]. Furthermore, older adults’ motivation tends to be driven more by affective rather than cognitive beliefs about consequences of physical activity [25]. Perceived environmental barriers: concerns about safety and lack of access are not independent predictors of physical activity once PBC and behavioural intention have been taken into account
In line with other research [8, 26], perceived environmental barriers (concerns about safety and perceived lack access) did not have any direct effect on physical activity levels, compared with more person-related barriers, such as lack of interest and poor health, which is in line with the sufficiency hypothesis (as there is no direct effect of these barriers on physical activity when TPB predictors are in the model). Salmon et al. [26] also found that personal barriers, such as lack of time and other priorities, were predictive, whereas environmental barriers were not strongly related to participation in physical activity. Also Giles-Corti and Donovan [27] found the physical environment to be secondary to individual and social environmental barriers. A supportive physical environment is necessary, but it may be insufficient to increase recommended levels of physical activity. Complementary strategies are required that aim to influence individual and social environmental factors. Limitations
Despite objective measures, the relationships under study were tested cross-sectionally and need replication in longitudinal and experimental designs. Also the four empirically detected barriers need cross-validation by means of confirmatory factor analysis in a follow-up study and in other samples. To reduce potential selection bias due to age or deprivation status, the present study sample was stratified by these variables. Additionally, the present results were based on missing value imputation to account for selective study missingness, yet bias cannot be ruled out completely. The use of accelerometry overcomes various sources of bias, for example bias due to problems with recall. The PACS cohort used one of the few accelerometers specifically validated for older adults, but bias due to wear time or deviation from the protocol for wearing the devices is possible. The TPB
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P. Gellert et al. measures in the PACS cohort were following standard procedures for assessment [15], but the research justifying these measures has predominantly been based on younger adults.
Supplementary data Supplementary data mentioned in the text are available to subscribers in Age and Ageing online.
Implications for future studies
Future studies might assess whether the perceived barriers vary with increasing age. They should also focus on further disentangling actual barriers and perceived ones, as well as increasing insight into the causal impact of perceived barriers by means of ecological experimental designs. An aim of future research might be to inform intervention development in changing perceived and actual barriers as an additional factor in intervention designs and to adopt interventions specific to the barriers and needs of older adults [23, 25].
Conclusion In conclusion, four types of perceived barriers emerged: poor health, lack of interest, lack of access and lack of safety. Person-related barriers (poor health and lack of interest) had a direct effect on physical activity, even after accounting for demographic variables and social-cognitive factors proposed by TPB disputing the theory’s sufficiency hypothesis. Conversely, perceived environmental barriers (lack of safety and lack of access) did not have any direct effect on physical activity alongside TPB.
Key points • Perceived barriers to physical activity cluster around the themes of perceived health, interest, access and safety. • Perceived poor health and lack of interest are associated with objective physical activity. These effects are not accounted for by the Theory of Planned Behaviour.
Conflicts of interest None declared.
Funding This study was funded by Scottish Executive grant CZH/4/ 518. The sponsor was the University of Dundee. Neither funder nor sponsor had a role in the design, conduct or interpretation of the study. F.F.S. is funded by Fuse, the Centre for Translational Research in Public Health, a UKCRC Public Health Research Centre of Excellence. Funding for Fuse from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council and the National Institute for Health Research, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged.
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