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Cross-sectional and longitudinal factors influencing physical activity of 65 to 75-year-olds: a pan European cohort study based on the survey of health, ageing and retirement in Europe (SHARE)

BMC Geriatrics volume 18, Article number: 94 (2018) Cite this article

Abstract

Background

The promotion of physical activity (PA) plays a major role for healthy ageing even in older age. There is a lack of cross-sectional and longitudinal studies explicitly dealing with barriers and drivers to PA in older adults. Therefore the aims of this study are a) to determine the prevalence of insufficient physical activity (IPA) in 65 to 75-year-olds in Europe and to identify factors associated with IPA in cross-section and b) to identify longitudinal risk factors for IPA in prior active persons.

Methods

This study is using data of the Survey of Health, Ageing and Retirement in Europe (SHARE). SHARE is a cross-national panel database including individual data of the non-institutionalised population aged 50+ from 27 European countries. For the present paper, we included a cohort that participated in all first four waves of SHARE (2004–2011) aged 65-to-75–years at wave four (male n = 1761, female n = 2085) from 10 European countries. To identify cross-sectional and longitudinal associations, we calculated prevalence odds ratios and hazard ratios with 95% confidence intervals.

Results

The prevalence of IPA in 65–75-year-olds varied widely between countries, ranging from 55.4% to 83.3% in women and from 46.6% to 73.7% in men. IPA was associated with several intrapersonal factors and strength of association was similar for men and women for almost all investigated factors. Statistically significant associated with IPA were socioeconomic factors as low educational level (own and parental) and financial difficulties (male: POR: 1.60: 95%-CI: 1.26–2.03; female: POR: 1.58; 95%-CI: 1.26–1.97) and health-related factors as e.g. number of chronic diseases (male: POR: 1.34: 95%-CI: 1.23–1.45; female: POR: 1.31; 95%-CI: 1.21–1.42). Interpersonal only the size of social network was associated with IPA (male and female: POR: 0.88, 95%-CI: 0.81–0.95). Longitudinally in a fully adjusted model, only grip strength (HR: 0.99; CI-95%: 0.98–0.99) and BMI (HR: 1.02; CI-95%: 1.00–1.04) were statistically significant risk factors for IPA.

Conclusions

PA promotion programs for older adults should incorporate the heterogeneity of health status and physical condition that can typically occur in this age group.

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Background

Demographic change is a challenging development for the social systems of all member states in the European Union (EU) [1, 2]. The feared impact of demographic change could be mitigated by healthy ageing, as older adults in good health remain longer at work or can play an active role in society through volunteer activities [3]. Physical activity (PA) is essential for the skeletal, muscular- and digestive systems and also for circulation [4]. In this context, PA plays a major role as it can increase life expectancy, daily living skills, overall well-being and quality of life [5,6,7,8]. An estimated amount of three million premature deaths can be attributed to lack of PA, which could have been avoided through prevention and health promotion [5, 6, 9, 10].

In the EU, the main causes of death are diseases of the cardiovascular system for which lack of PA is one of the major risk factors [5, 6, 9, 11, 12]. Thus, lack of PA is related to one of the major cost factors in the EU health systems [4, 6]. Sufficiently active people have previously shown to carry a lower risk of poor health or development of chronic diseases in old age [5, 13]. Even in older adults, regular PA can still improve mental and physical health and positively affect the general ageing process [4, 5].

The WHO recommends at least 2.5 h/week of moderate or 75 min/week of vigorous PA [13]. In Europe, 35% of adults are considered as physically inactive and this proportion increases with age to 45% of the 60 + −year-olds [4]. Prevalence of PA is different in the European countries, people of southern Europe are less active compared to other areas [14]. In average, women are less active than men [15].

Particularly with regard to increasing life expectancy in the EU, PA opportunities in everyday life of older adults require special attention in terms of promoting PA. The transition to retirement is a promising starting point for interventions promoting an active lifestyle, because people tend to establish new routines and give up previous ones. For example occupational PA opportunities, such as PA at work or active transport to workplace, are no longer relevant. Van Dyck et al. showed that this opportunity not only lasts during the actual transition to retirement, but also during the first years of retirement [16]. In order to pursue a holistic approach to health promotion, variables of all levels should be included in accordance to Bronfenbrenners ecological systems theory [17, 18].

Based on the three functional domains proposed by Livneh [19] and on the classification Bauman et al. (2012) used, determinants of PA can be categorised into intrapersonal, interpersonal and extrapersonal factors. Intrapersonal factors comprise factors related to a person’s mind or self, such as health and psychological well-being. Intrapersonal factors that are negatively associated with PA are age and female gender [20,21,22,23], poor health status [20, 22, 24, 25], perceived frailty [22, 23, 26], low socio-economic status [20, 22], low parental socio-economic position [27, 28] and high Body Mass Index (BMI) [20, 22, 23]. Positively associated are sufficient PA during the life course [20, 22, 23, 26], as well as self-efficacy and the belief in the benefits of PA [20, 22,23,24, 26].

Interpersonal factors refer to family and marital life as well as peer and social relations. It has been shown that social support of family members, friends, sports partners and trainers are important positive factors for PA [20, 22, 26, 29,30,31,32]. Additionally, social contact and a social network in neighbourhood enhance PA in older adults [22, 33, 34]. McNeil et al. stated that social networks can influence PA positively by providing social support and establishing social norms that enable health-promoting behaviours [35].

Extrapersonal factors are community-based and therefore beyond the personal or individual dimension, such as policies, physical and social environments. In this regard, economic conditions and societal norms are important determinants for PA in adults [20, 22]. Likewise, built environment and walkability of a neighbourhood can influence PA in older adults negatively and also positively [31, 36,37,38,39]. Moreover, a familiar neighbourhood [40], security in the neighbourhood in terms of traffic and crime as well as access to a PA-promoting infrastructure affect PA positively [31, 39, 40]. In this study, no extra-personal factors are included because the SHARE dataset only collected the resident of the participants and no extra-personal factors at community level.

PA-influencing factors vary between age groups. Therefore, transferability of results from other age groups is limited. There is a lack of studies explicitly dealing with factors influencing PA in older adults in Europe. This especially holds for high-quality longitudinal studies [41, 42] and for studies using objective PA measurements [20]. In this paper, we re-analyse the data of the Survey of Health, Ageing and Retirement in Europe (SHARE). SHARE is a longitudinal study that includes several countries using a common standard and thus allows inter-country comparisons. Although SHARE does not use objective PA measurement, but self-reported PA, the broad range of factors, the high degree of standardisation and the longitudinal nature of the data makes it a valuable resource for research.

The aims of this study are a) to determine the prevalence IPA in 65 to 75-year-olds in Europe and to identify factors associated with IPA in this age group and b) to identify longitudinal risk factors for IPA in prior active persons using the data of the Survey of Health, Ageing and Retirement in Europe (SHARE).

Methods

Study design and population

This study is conducted using data of the interdisciplinary panel Survey of Health, Ageing and Retirement in Europe (SHARE), which is performed in 19 countries of the European Union and Israel. The aim of the SHARE-project is to provide an overall picture of ageing in Europe and it gathered data on health, socio-economic status and social as well as family networks [43]. In SHARE, non-institutionalised people aged 50+ underwent a short physical examination and were interviewed with computer assisted personal interviews (CAPI). Wave one took place in 2004/2005, wave two in 2006/2007, wave three in 2008/2009 and wave four in 2010/2011. SHARE has data of more than 60,000 individuals, the response rate in wave one was around 62%, in waves two, three and four 73%, 77% und 56% [43]. The ten countries participating in all first four waves of SHARE were Belgium, Denmark, Germany, the Netherlands, France, Austria, Italy, Switzerland, Sweden and Spain. For further methodological details of SHARE see Börsch-Supan et al. (2013) [43]. This study is following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement [44]. An additional file shows this in more detail [see Additional file 1]. Inclusion criteria for this study were participation in all four waves of SHARE and belonging to the age group of 65-to-75–year-olds at the time of wave four. This resulted in a sample size of n = 3846 (male n = 1761, female n = 2085).

Outcome and country of residence

Physical activity was assessed based on the following two questions: ‘How often do you engage in vigorous physical activity, such as sports, heavy housework, or a job that involves physical labour?’ and ‘How often do you engage in activities that require a low or moderate level of energy such as gardening, cleaning the car or doing a walk?’. Given possible answers were ‘More than once a week’, ‘once a week’, ‘one to three times a month’ and ‘hardly ever’ or ‘never’. Participants, who stated to engage in vigorous physical activity once a week or less were defined as insufficient physical activity (IPA) (wave 4: n = 2582; 67.2%). Participants engaging in vigorous physical activity more than once a week are categorized as sufficient PA and serve as control group. The WHO used IPA as measurement and defined it as less than 150 min of moderate physical activity or less than 75 min of vigorous physical activity per week [45]. The variable country is defined as the country of residence of the survey participants.

Covariables – Intrapersonal factors

Age was calculated from birth month and year of the participants and time of the interview in wave four. The exact age in months was divided by 12 to generate age in years. Level of education was classified by the International Standard Classification of Education (ISCED) [46]. For parental education, the number of books present in the household that the participants lived in at the age of ten was used [47].The variable was dichotomised. Zero to eleven books were classified as ‘low parental education’ and more than eleven books as ‘higher parental education’. The financial situation was assessed with the question whether a household has trouble or not to make ends meet with the available monthly income. Four answer categories were used: ‘with great difficulty’, ‘with some difficulty’, ‘fairly easily’ and ‘easily’. The variable was dichotomised. All participants answering ‘with great difficulty’ and ‘with some difficulty’ were graded as ‘without difficulty’ and ‘fairly easily’ and ‘easily’ as without difficulties. The number of chronic diseases was based on a multiple answer question. The numeric variable was truncated at a number of three chronic diseases. Depression was examined with the EURO-D symptom scale, an index consisting of twelve items: depressed mood, pessimism, suicidality, guilt, sleep, interest, irritability, appetite, fatigue, concentration, enjoyment and tearfulness. The scale ranges from zero ‘not depressed’ to twelve ‘very depressed’. The variable was dichotomised according to Dewey and Prince into ‘no depression’ (0–3) and ‘depression’ (4–12) [48]. Hospitalization in the last twelve months shows whether participants were in a medical, surgical, psychiatric or any other specialized hospital overnight during the last twelve months before the interview. Two grip strength measurements on each hand were recorded using a dynamometer (Smedley, S Dynamometer, TTM, Tokyo, 100 kg). The variable shows the maximum grip strength of all four measurements. To describe limitations in activities of daily living (ADL) the activities of daily living index (ADLI) was used [49]. The ADLI is the sum of the five tasks dressing, bathing or showering, eating, cutting up food, walking across a room and getting into or out of bed. The higher the index the more difficulties exist with these activities. The variable was dichotomised. All participants with the index value zero were graded as ‘without limitations’ and with the index value one to five as with limitations. Body Mass Index (BMI) is based on self-reported values and is calculated as: BMI = weight in kilogram (kg) / (height in meter (m)) 2.

Covariables – Interpersonal factors

To determine marital status the existing response categories ‘married and living together with spouse’, ‘married, living separated from spouse’ and ‘registered partnership’ were summarised in the category ‘married and registered partnership’. The dichotomous variable partner in household indicates whether a participant was living together with his partner in a household. To measure the size of family network, a score was calculated from the number of children alive, the number of parents alive and the number of siblings alive. The numeric variable number of children was truncated at a number of four children. For four or more children, four children were included into the score. A high score value indicates a large family network. From wave four of SHARE on, information on up to seven individuals with whom respondents discussed important things most often during the 12 months before the interview is available. The size of social network may include family members, friends, neighbours and others and was truncated at three network partners. The dichotomous variable perceived social support shows whether the respondent had received instrumental social support within the last 12 month. The perceived quality of social network was assessed on a scale ranging from ‘0’ (completely dissatisfied) to ‘10’ (completely satisfied), including respondents who reported to have no social network.

Statistical analyses

To identify cross-sectional associations of intra- and interpersonal factors with IPA in wave four, prevalence odds ratios (POR) and 95%-confidence intervals (95%-CI) were calculated using binary logistic regression models.

To analyse the longitudinal association over time, we calculated hazard rations (HR) with 95% confidence intervals (95%-CI) using Cox regression models in the group of sufficient PA in wave 1. IPA at wave 4 served as the outcome. To account for heterogeneity between the observed countries, we included country as random effect in our models. We omitted stratification by sex for intrapersonal factors, because in the cross-sectional analyses the effects were similar for men and women. For interpersonal factors this was not the case, therefore we stratified the analyses by sex. Model building was based on the Wald statistic. But previously we calculated all other inclusion methods (backward, forward and expected inclusion) with the result that the method did not affect the result of regression after adjustment.

All analyses were performed using the statistical analysis software IBM SPSS statistics for Windows, version 20.0 (IBM Corp. Released 2011. Armonk, NY).

Results

A high proportion of our study group completed an upper secondary education or higher (women: 45.5%; men: 58.4%, Table 1). Most participants were married (female: 68.5%; male: 84.4%). A considerably higher proportion of women than men was widowed (female: 18.2%; male: 5.2%) and lived in single households (female: 29.2%; male: 13.1%). The majority of the sample had at least one child (female: 92.1%; male: 90.9%).

Table 1 Description of the study sample
Full size table

Within the 65–75 year-olds, 67.2% reported IPA with women being less active than men and an increasing trend by age group in both genders (Table 2). At baseline, prevalence of IPA in the different countries ranged from 42.1% (Germany) to 63.5% (Italy) in men and from 48.5% (Denmark) to 75.9% (Belgium) in women. The steepest increase in IPA within seven years were seen in Danish women (wave 1: 48.5%; wave 4: 66.2%) and in German men (wave 1: 42.1%; wave 4: 55.2%).

Table 2 Longitudinal development of prevalence rates (PR) and 95%-confidence interval (95%-CI) of insufficient physical activity (IPA)
Full size table

All intrapersonal factors showed a significant cross-sectional association with IPA in both sexes. Strength of association was similar for men and women for almost all investigated factors. Protective factors for IPA were higher education (male: POR: 0.57: 95%-Ci: 0.46–0.70; female: POR: 0.51; 95%-CI: 0.41–0.64), education of the parents (male: POR: 0.57: 95%-Ci: 0.47–0.70; female: POR: 0.53; 95%-CI: 0.44–0.66) and grip strength in kg (male: POR: 0.95: 95%-Ci: 0.94–0.96; female: POR: 0.96; 95%-CI: 0.94–0.97). Factors increasing the risk in older adults for IPA are age (male: POR: 1.04: 95%-Ci: 1.01–1.07; female: POR: 1.05; 95%-CI: 1.02–1.09), a difficult financial situation of the household (male: POR: 1.60: 95%-Ci: 1.26–2.03; female: POR: 1.58; 95%-CI: 1.26–1.97), a higher BMI (male: POR: 1.05: 95%-Ci: 1.03–1.08; female: POR: 1.05; 95%-CI: 1.03–1.08), number of chronic diseases (male: POR: 1.34: 95%-Ci: 1.23–1.45; female: POR: 1.31; 95%-CI: 1.21–1.42), depression (male: POR: 1.24: 95%-Ci: 1.13–1.35; female: POR: 1.17; 95%-CI: 1.09–1.25) and limitations in the activities of daily living (male: POR: 2.88: 95%-Ci: 1.88–4.39; female: POR: 2.45; 95%-CI: 1.64–3.68). The greatest gender-related difference was seen for hospitalisation within the last 12 months, that was a risk factor for IPA in both sexes, but stronger in women (POR: 2.18; 95%-CI: 1.59–2.98) than in men (POR: 1.36; 95%-CI: 1.05–1.77).

Very few of the investigated interpersonal factors had a statistically significant association with the prevalence of IPA (Table 3). A significant influence on both sexes was shown by the size of social network (POR: 0.88, 95%-CI: 0.81–0.95). All other interpersonal factors differed between sexes. In women but not in men, divorce was a protective factor for IPA (POR: 0.65; 95%-CI: 0.46–0.91). Single and widowed men had a higher risk of IPA (never married: POR: 1.92: 95%-Ci: 1.17–3.15; widowed: POR: 2.07; 95%-CI: 1.27–3.39). In men, a lower risk of IPA was seen for living together with a partner in household (POR: 0.59; 95%-CI: 0.44–0.79), the number of grandchildren (POR: 0.96; 95%-CI: 0.94–0.98), the size of family network without partner (POR: 0.94; 95%-CI: 0.90–0.99) and the perceived social support (POR: 1.44, 95%-CI: 1.01–2.07). None of these variables had a cross-sectional association with IPA in women.

Table 3 Cross-sectional association of intra- and interpersonal factors for insufficient physical activity (IPA) in 65–75-years-olds
Full size table

Longitudinally, we found a protective effect on IPA for higher education (HR: 0.80; 95%-CI: 0.67–0.95) and grip strength (HR: 0.99; 95%-CI: 0.98–0.99) at baseline (Table 4). Whereas the number of chronic diseases (HR: 0.80; 95%-CI: 0.67–0.95), depression (HR: 1.31; 95%-CI: 1.10–1.56) and BMI (HR: 1.02; 95%-CI: 1.00–1.04) at baseline were statistically significant risk factors for IPA seven years later. In a fully adjusted model only grip strength (HR: 0.99; 95%-CI: 0.98–0.99) and BMI (HR: 1.03; 95%-CI: 1.01–1.05) remained statistically significant.

Table 4 Longitudinal intrapersonal factors of insufficient physical activity (IPA) at age 65–75 in prior active persons1
Full size table

For the interpersonal factors in the longitudinal analyses, it was found that the only factor showing an influence on IPA in previously active 65–75 year-olds was living together with the partner in one household at baseline (Table 5, HR: 0.74; 95%-CI: 0.56–0.97). This influence was only shown in men.

Table 5 Longitudinal interpersonal factors of insufficient physical activity (IPA) at age 65–75 in prior active persons1
Full size table

Discussion

We investigated cross-sectional and longitudinal associations of a broad range of intra- and interpersonal factors with IPA.

Cross-sectional associations

Regarding to the intrapersonal level, women were less physically active than men. These results are in line with previous research: A cross-sectional study using objective measurements of PA showed that 31% of older men and 20% of older women meet the WHO recommendations of 2.5 h moderate PA per week [50]; in questionnaire-based studies 16–21% in men and 11–21% in women were categorized as sufficiently physically active [15, 51]. In the SHARE data, we found an increasing proportion of IPA with age in both sexes. The influence of age on PA was confirmed in several previous studies [20,21,22,23]. Other intrapersonal factors of IPA that reached statistical significance were low educational level and financial difficulties. There is ample evidence for an association of socio-economic status with PA in the general population and this is as well true for older adults [20, 22]. Interestingly, we were also able to establish an association between parental education and IPA in older adults. Even in adulthood, the influence of parental education on several lifestyle factors was confirmed in studies [27, 28]. A previous study reported an association between childhood socioeconomic position and physical capability levels in adulthood [52]. Likewise, a number of health-related and fitness-related factors were associated to IPA. Many previous studies showed that good health in general is positively associated to PA in older adults [20, 22, 24, 25]. There are fewer studies on fitness-related factors in this age group. However, our findings of a positive association between higher grip strength and PA in older adults are consistent with a previous study [53].

The investigated interpersonal factors display several statistically significant associations with IPA, especially in men. The size of the social network as well as the size of the family network are both negatively associated with IPA, i.e. the larger these networks are the less likely it is to perform IPA. This is in line with previous research, which stated social networks and social support from friends and peers as influential factors for PA in older adults [20, 29]. However, we found that the perceived quality of the social network has no association with IPA. The perceived social support is even positively associated with IPA. One reason for this result might be that the SHARE data only includes received instrumental social support. Additional analyses showed that this result is rather a statistical artefact that can be fully explained by partner in household in men and by frailty status in women (data not shown). A recent Australian study showed that the association between social network and PA might be more complex and dependent on the type of activity [54], e.g. household activities and leisure-time activities. Distinctive gender patterns were present regarding family indicators, marital status, partner in household and number of grandchildren. In men, being married, presence of a partner in household and number of grandchildren showed all a statistically significant positive association with IPA. In women, the only interpersonal factor being statistically significant associated with IPA was being divorced. A clear gender difference of the health effects of divorce or being single was reported before [55].

In this study, the prevalence of IPA in 65–75-year-olds varied widely between countries, ranging from 55.4% to 83.3% in women and from 46.6% to 73.7% in men. A comparison of the IPA prevalence of the sub set used in this study and all participants of SHARE in this age group showed that most prevalence rates are in line with each other. Only the IPA prevalence for Germany, Austria and Denmark only in women differed (see Additional file 1). A German health survey showed an IPA prevalence in Germany of 60% in men and 63% in women in the age group of 60 to 69 years, which are higher values compared to those presented in this study [56]. For Austria and Denmark no prevalence rates of IPA where found for the age group 65 to 75 years. Therefore, the prevalence rates of the mentioned countries must be considered with caution. Women and men were more likely to meet the WHO recommendations of PA in northern and central European countries, as Sweden or Germany, and compared to southern European countries, as Italy and Spain. The reported increase in IPA from Northern and Central Europe to Southern Europe are in line with findings of a study of the European Commission in 2014 [14]. Climate, environmental, infrastructural and policy conditions for PA can differ highly within countries such as weather, topography, culture, street layout and crime rate. In our study, older adults from the Netherland, Germany, Denmark and Switzerland showed lower rates of overall IPA. A possible explanation is that these countries are all good practice examples for high levels of cycling and street safety [57], setting the ground for physical activity in older adults.

Longitudinal analyses

Longitudinally, educational level, number of chronic diseases, presence of depression, grip strength and BMI were statistically significant predictors of IPA at the age of 65 to 75 in previously active persons. Of the investigated interpersonal factors, only living together with a partner in one household showed a statistically significant influence in men, but not in women.

In the multivariable model, only grip strength and BMI remained statistically significant predictors for IPA in older adults. Previous cross-sectional studies that were not able to distinguish between cause and effect showed that BMI is associated with PA in older adults [20, 23, 50, 58].

Limitations and strengths

There are several limitations to the study. One limitation is the measurement of PA by questionnaire. Objective methods using accelerometers are more valid for PA measurements [59], but many large population studies did not use accelerometers due to high costs. In SHARE, only information on the frequency but not on the duration of moderate and vigorous PA is available. Thus, it can only be determined with some certainty that the WHO recommendations are not met, if persons engage in PA at most once a week. Moreover, there is a certain risk of social desirability bias due to the interview method (computer assisted personal interview) and the interviewee knowing that sufficient PA is desirable [60]. Previous studies have found that questionnaire-based surveys are likely to overestimate duration and intensity of PA compared to objective measurements [59]. A further limitation is the restricted set of variables that leaves out further putative interesting factors from investigation as e.g. social support as noted above or extrapersonal variables, which could help explaining cross-country differences, as built environment, weather conditions, cultural or policy factors [31, 36,37,38,39]. Finally, we restricted our analyses to a subset of the SHARE participants (participation in all 4 waves) to be able to analyse longitudinal effects. A questionnaire survey of non-responder in Germany showed only small indication for a non-response bias with regard to household composition and health status [43, 61]. Additionally, subgroup analyses indicated only small differences in sex and age in survey participation and retention [43, 53, 62].

The study also has several strengths. SHARE is conducted in several European countries with a highly standardized study protocol involving physical measurements as e.g. grip strength and BMI [43]. SHARE provides panel data of high quality and representativeness due to the high degree of standardization in data collection. It allows comparing several European countries and the sample size is sufficient to detect smaller effects and has high participation rates as well as only small evidence for attrition bias [53]. The longitudinal nature of the data enables identification of temporality of associations as e.g. BMI and physical activity [63].

Conclusions

The detected variations in IPA in the northern and southern countries indicate an association between PA in older adults and extrapersonal factors. Therefore, further studies are needed, looking at extrapersonal factors in detail. Also, more studies are needed reflecting the complex nature of interpersonal factors in older adults on the one hand and PA on the other hand. We found a predominant influence of intrapersonal factors on meeting physical activity recommendations in older age for men and women. Longitudinally, grip strength and BMI were the influential factors for IPA emphasizing the importance of health as prerequisite to PA participation. Intervention programs promoting PA in older adults could focus on this fact by providing PA opportunities for all capability levels recognizing limitations that can typically occur at this age group.

Abbreviations

ADL:

Activities of daily living

ADLI:

Activities of daily living index

BMI:

Body mass index

CAPI:

Computer assisted personal interviews

CI:

Confidence intervals

EU:

European Union

HR:

Hazard ratio

IPA:

Insufficient physical activity

ISCED:

International Standard Classification of Education

PA:

Physical activity

POR:

Prevalence odds ratio

SHARE:

Survey of Health, Ageing and Retirement in Europe; STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

References

  1. Linz K, Stula S. Demographic change in Europe - an overview. In: Working paper no 4 of the Observatory for Sociopolitical Developments in Europe: Observatory for Sociopolitical Developments in Europe; 2010.

    Google Scholar 

  2. Wait S, Harding E. The state of ageing and health in Europe. In: International longevity Centre-UK and the Merck Company Foundation; 2006.

    Google Scholar 

  3. Sanderson WC, Scherbov S. Demography. Remeasuring aging. Science. 2010;329:1287–8.

    Article  CAS  PubMed  Google Scholar 

  4. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U, Lancet Physical Activity Series Working G. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380:247–57.

    Article  PubMed  Google Scholar 

  5. Holme I, Anderssen SA. Increases in physical activity is as important as smoking cessation for reduction in total mortality in elderly men: 12 years of follow-up of the Oslo II study. Br J Sports Med. 2015;49:743–8.

    Article  CAS  PubMed  Google Scholar 

  6. Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT, Lancet Physical Activity Series Working G. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380:219–29.

    Article  PubMed  PubMed Central  Google Scholar 

  7. RKI. Gesundheit im Alter. In: Gesundheitsberichterstattung des Bundes, vol. Heft 10: Robert Koch-Institut; 2005.

    Google Scholar 

  8. Warburton DE, Nicol CW, Bredin SS. Health benefits of physical activity: the evidence. CMAJ. 2006;174:801–9.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kohl HW 3rd, Craig CL, Lambert EV, Inoue S, Alkandari JR, Leetongin G, Kahlmeier S, Lancet Physical Activity Series Working G. The pandemic of physical inactivity: global action for public health. Lancet. 2012;380:294–305.

    Article  PubMed  Google Scholar 

  10. World Health Organization (WHO). Global health risks: mortality and burden of disease attributable selected major risks. Geneva: World Health Organization; 2009.

  11. Nichols M, Townsend N, Scarborough P, Rayner M. Cardiovascular disease in Europe 2014: epidemiological update. Eur Heart J. 2014;35:2950–9.

    Article  CAS  PubMed  Google Scholar 

  12. Eurostat: Eurostat regional yearbook 2014. 2014.

    Google Scholar 

  13. World Health Organization (WHO). Global Recommendations on Physical Activity for Health. Geneva: World Health Organization; 2010.

  14. EuropeanCommission. Sport and physical activity. In: Special Eurobarometer 412; 2014.

    Google Scholar 

  15. Krug S, Jordan S, Mensink GB, Muters S, Finger J, Lampert T. Physical activity: results of the German health interview and examination survey for adults (DEGS1). Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2013;56:765–71.

    Article  CAS  PubMed  Google Scholar 

  16. Van Dyck D, Cardon G, De Bourdeaudhuij I. Longitudinal changes in physical activity and sedentary time in adults around retirement age: what is the moderating role of retirement status, gender and educational level? BMC Public Health. 2016;16:1125.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Bronfenbrenner U. The ecology of human development: experiments by nature and design. Cambridge: Harvard University Press; 1979.

    Google Scholar 

  18. WHO. Ottawa Charta for health promotion. In: First international conference on health promotion. Ottawa: World Health Organization; 1986.

  19. Livneh H. Psychosocial adaptation to chronic illness and disability: a conceptual framework. Rehabilitation Counseling Bulletin. 2001;44:151–60.

    Article  Google Scholar 

  20. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJ, Martin BW, Lancet Physical Activity Series Working G. Correlates of physical activity: why are some people physically active and others not? Lancet. 2012;380:258–71.

    Article  PubMed  Google Scholar 

  21. Shimada H, Lord SR, Yoshida H, Kim H, Suzuki T. Predictors of cessation of regular leisure-time physical activity in community-dwelling elderly people. Gerontology. 2007;53:293–7.

    Article  PubMed  Google Scholar 

  22. Van Stralen M, de Vries H, Mudde A, Bolman C, Lechner L. Determinants of initiation and maintenance of physical activity among older adults: a literature review. Health Psychol Rev. 2009;3:147–207.

    Article  Google Scholar 

  23. Koeneman MA, Verheijden MW, Chinapaw MJ, Hopman-Rock M. Determinants of physical activity and exercise in healthy older adults: a systematic review. Int J Behav Nutr Phys Act. 2011;8:142.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Martin K, Sinden A. Who will stay and who will go? A review of older adults’ adherence to randomized controlled trials of exercise. J Aging Phys Act. 2001;9:91–114.

    Article  Google Scholar 

  25. Nelson ME, Rejeski WJ, Blair SN, Duncan PW, Judge JO, King AC, Macera CA, Castaneda-Sceppa C. Physical activity and public health in older adults: recommendation from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc. 2007;39:1435–45.

    Article  PubMed  Google Scholar 

  26. Rhodes RE, Martin AD, Taunton JE, Rhodes EC, Donnelly M, Elliot J. Factors associated with exercise adherence among older adults. An individual perspective. Sports Med. 1999;28:397–411.

    Article  CAS  PubMed  Google Scholar 

  27. Savelieva K, Pulkki-Raback L, Jokela M, Kubzansky LD, Elovainio M, Mikkila V, Tammelin T, Juonala M, Raitakari OT, Keltikangas-Jarvinen L. Intergenerational transmission of socioeconomic position and ideal cardiovascular health: 32-year follow-up study. Health Psychol. 2017;36:270–9.

    Article  PubMed  Google Scholar 

  28. Elhakeem A, Hardy R, Bann D, Caleyachetty R, Cosco TD, Hayhoe RP, Muthuri SG, Wilson R, Cooper R. Intergenerational social mobility and leisure-time physical activity in adulthood: a systematic review. J Epidemiol Community Health. 2017;71:673–80.

    Article  PubMed  Google Scholar 

  29. Carlson JA, Sallis JF, Conway TL, Saelens BE, Frank LD, Kerr J, Cain KL, King AC. Interactions between psychosocial and built environment factors in explaining older adults’ physical activity. Prev Med. 2012;54:68–73.

    Article  PubMed  Google Scholar 

  30. Pettee KK, Brach JS, Kriska AM, Boudreau R, Richardson CR, Colbert LH, Satterfield S, Visser M, Harris TB, Ayonayon HN, Newman AB. Influence of marital status on physical activity levels among older adults. Med Sci Sports Exerc. 2006;38:541–6.

    Article  PubMed  Google Scholar 

  31. Cunningham GO, Michael YL. Concepts guiding the study of the impact of the built environment on physical activity for older adults: a review of the literature. Am J Health Promot. 2004;18:435–43.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Seguin R, Connor L, Nelson M, LaCroix A, Eldridge G. Understanding barriers and facilitators to healthy eating and active living in rural communities. J Nutr Metab. 2014;2014:146502.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Lager D, Van Hoven B, Huigen P. Understanding older adults’ social capital in place: obstacles to and opportunities for social contacts in the neighborhood. Geoforum. 2014;59:87–97.

    Article  Google Scholar 

  34. Nogueira H. Healthy communities: the challenge of social capital in the Lisbon metropolitan area. Health Place. 2009;15:133–9.

    Article  PubMed  Google Scholar 

  35. McNeill LH, Kreuter MW, Subramanian SV. Social environment and physical activity: a review of concepts and evidence. Soc Sci Med. 2006;63:1011–22.

    Article  PubMed  Google Scholar 

  36. Stahl A, Horstmann V, Iwarsson S. A five-year follow-up among older people after an outdoor environment intervention. Transp Policy. 2013;27:134–41.

    Article  Google Scholar 

  37. Bucksch J, Schneider S. Walkability - Das Handbuch zur Bewegungsförderung in der Kommune. Bonn: Hans Huber; 2014.

    Google Scholar 

  38. King AC, Sallis JF, Frank LD, Saelens BE, Cain K, Conway TL, Chapman JE, Ahn DK, Kerr J. Aging in neighborhoods differing in walkability and income: associations with physical activity and obesity in older adults. Soc Sci Med. 2011;73:1525–33.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Saelens BE, Sallis JF, Black JB, Chen D. Neighborhood-based differences in physical activity: an environment scale evaluation. Am J Public Health. 2003;93:1552–8.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Bjornsdottir G, Arnadottir SA, Halldorsdottir S. Facilitators of and barriers to physical activity in retirement communities: experiences of older women in urban areas. Phys Ther. 2012;92:551–62.

    Article  PubMed  Google Scholar 

  41. Chastin SFM, Buck C, Freiberger E, Murphy M, Brug J, Cardon G, O’Donoghue G, Pigeot I, Oppert J-M. Systematic literature review of determinants of sedentary behaviour in older adults: a DEDIPAC study. Int J Behav Nutr Phys Act. 2015;12:127.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Van Dyck D, Cardon G, De Bourdeaudhuij I. Which psychological, social and physical environmental characteristics predict changes in physical activity and sedentary behaviors during early retirement? A longitudinal study. PeerJ. 2017;5:e3242.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Borsch-Supan A, Brandt M, Hunkler C, Kneip T, Korbmacher J, Malter F, Schaan B, Stuck S, Zuber S, Team SCC. Data resource profile: the survey of health, ageing and retirement in Europe (SHARE). Int J Epidemiol. 2013;42:992–1001.

    Article  PubMed  PubMed Central  Google Scholar 

  44. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP, Initiative S. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Int J Surg. 2014;12:1495–9.

    Article  PubMed  Google Scholar 

  45. WHO: Global Health Observatory (GHO) data - prevalence of insufficient physical activity. 2018.

    Google Scholar 

  46. UNESCO. International standard classification of education. ISCED 1997. In: Paris; 2006.

    Google Scholar 

  47. Esping-Andersen G. Untying the Gordian knot of social inheritance. In: Inequality: structures, dynamics and mechanism: essays in honor of Aege B Sorensen. Volume 21; 2004. p. 115–38.

    Google Scholar 

  48. Dewey M, Prince M. Mental health. In: Health, Aeging and Retirement in Europe - First Results from the Survey of Health, Aeging and Retirement in Europe. Edited by Börsch-Supan a, Jürges H. Mannheim: MEA Eigenverlag; 2005. p. 108–17.

    Google Scholar 

  49. Katz S. Assessing self-maintenance: activities of daily living, mobility, and instrumental activities of daily living. J Am Geriatr Soc. 1983;31:721–7.

    Article  CAS  PubMed  Google Scholar 

  50. Sabia S, Cogranne P, van Hees VT, Bell JA, Elbaz A, Kivimaki M, Singh-Manoux A. Physical activity and adiposity markers at older ages: accelerometer vs questionnaire data. J Am Med Dir Assoc. 2015;16(438):e437–13.

    Google Scholar 

  51. Godfrey A, Lord S, Galna B, Mathers JC, Burn DJ, Rochester L. The association between retirement and age on physical activity in older adults. Age Ageing. 2014;43:386–93.

    Article  PubMed  Google Scholar 

  52. Birnie K, Cooper R, Martin RM, Kuh D, Sayer AA, Alvarado BE, Bayer A, Christensen K, Cho SI, Cooper C, et al. Childhood socioeconomic position and objectively measured physical capability levels in adulthood: a systematic review and meta-analysis. PLoS One. 2011;6:e15564.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Cooper AJ, Lamb MJ, Sharp SJ, Simmons RK, Griffin SJ. Bidirectional association between physical activity and muscular strength in older adults: results from the UK biobank study. Int J Epidemiol. 2016;46(1):141–8.

    PubMed Central  Google Scholar 

  54. Robins LM, Brown T, Lalor AF, Stolwyk R, McDermott F, Haines T. Social isolation, physical capacity and physical activity in older community-dwelling adults post-hospitalisation. J Aging Phys Act. 2018:1-10. https://doi.org/10.1123/japa.2016-0257. [Epub ahead of print].

  55. Robards J, Evandrou M, Falkingham J, Vlachantoni A. Marital status, health and mortality. Maturitas. 2012;73:295–9.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Rütten A, Abu-Omar K, Lampert T, Ziese T. Körperliche Aktivität. In: Gesundheitsberichterstattung des Bundes, vol. Heft 26: Robert Koch-Institut; 2005.

    Google Scholar 

  57. Pucher J, Buehler R. Making cycling irresistible: lessons from the Netherlands, Denmark and Germany. Transp Rev. 2008;28:495–528.

    Article  Google Scholar 

  58. Bann D, Hire D, Manini T, Cooper R, Botoseneanu A, McDermott MM, Pahor M, Glynn NW, Fielding R, King AC, et al. Light intensity physical activity and sedentary behavior in relation to body mass index and grip strength in older adults: cross-sectional findings from the lifestyle interventions and independence for elders (LIFE) study. PLoS One. 2015;10:e0116058.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Sabia S, van Hees VT, Shipley MJ, Trenell MI, Hagger-Johnson G, Elbaz A, Kivimaki M, Singh-Manoux A. Association between questionnaire- and accelerometer-assessed physical activity: the role of sociodemographic factors. Am J Epidemiol. 2014;179:781–90.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Adams SA, Matthews CE, Ebbeling CB, Moore CG, Cunningham JE, Fulton J, Hebert JR. The effect of social desirability and social approval on self-reports of physical activity. Am J Epidemiol. 2005;161:389–98.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Borsch-Supan A, Krieger U. Investigating response behavior. In: Malter F, Borsch-Supan A, editors. Share wave 4: Innovations & Methodology. Munich: Munich Center for the Economics of Aging (MEA), Max-Planck-Institute for Social Law and Social Policy; 2013.

    Google Scholar 

  62. De Luca G, Peracchi F. Survey participation in the first wave of SHARE. In: Borsch-Supan A, Jürges H, editors. The survey of health, ageing and retirement in Europe - methodology. Mannheim: Mannheim Research Institute for the Economics of Aging (MEA); 2005.

    Google Scholar 

  63. Batsis JA, Zbehlik AJ, Barre LK, Bynum JP, Pidgeon D, Bartels SJ. Impact of obesity on disability, function, and physical activity: data from the osteoarthritis initiative. Scand J Rheumatol. 2015;44:495–502.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank Nicola Muldoon, University of the West of Scotland, for language editing the manuscript as a native speaker.

Availability of data and material

This paper uses data from SHARE Waves 1, 2, 3 (SHARELIFE) and 4 (DOIs: https://doi.org/10.6103/SHARE.w1.500, https://doi.org/10.6103/SHARE.w2.500, https://doi.org/10.6103/SHARE.w3.500, https://doi.org/10.6103/SHARE.w4.500). Data supporting the conclusions presented here are available in the SHARE repository as “scientific-use files” [http://www.share-project.org/]. Methodological details on the SHARE study design and data collection are presented elsewhere (Börsch-Supan et al., 2013).

Funding

The SHARE data collection has been primarily funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812) and FP7 (SHARE-PREP: N°211909, SHARE-LEAP: N°227822, SHARE M4: N°261982). Additional funding from the German Ministry of Education and Research, the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06–11, OGHA_04–064) and from various national funding sources is gratefully acknowledged (see www.share-project.org).

The conception, the analyses and interpretations as well as the writing process of this study has partly been funded by the German Federal Ministry of Education and Research (01EL1422B). The funding bodies did not participate in the design of the study, acquisition of the data nor in the secondary analysis presented here.

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Authors and Affiliations

  1. Institute for Public Health and Nursing Sciences (IPP), University of Bremen, Bremen, Germany

    Lena Lübs, Jenny Peplies, Carina Drell & Karin Bammann

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  1. Lena Lübs
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Contributions

LL with KB, JP and CD all made substantial contributions to the conception and design of the study. LL and KB designed the study, LL conducted the analyses, LL, KB, JP and CD interpreted the data. LL drafted the paper, and LL, KB, JP and CD revised it critically for important intellectual content. All authors contributed to revisions of the paper. All authors read and approved the final manuscript and participated in the work to take appropriate responsibility for the content.

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Correspondence to Lena Lübs.

Ethics declarations

Ethics approval and consent to participate

The SHARE project is continuously reviewed by both primarily responsible ethics committees (University of Mannheim and Max Planck Society, Germany) as well as national ethics committees in participating countries. The reviews refer to all facets of the project, including study design and the informed consent. The reviews confirm the project to comply with important legal norms and international ethical standards. The owner of the SHARE study is the “Survey of Health, Ageing and Retirement in Europe – European Research Infrastructure Consortium (SHARE-ERIC)”. After registration persons with a scientific background may use the data in the context of “scientific-use files”. The secondary analysis performed here underwent no further ethical approval. Further information on the ethical approval can be found on the SHARE project web page [http://www.share-project.org/].

Competing interests

The authors declare that they have no competing interests.

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Additional file

Additional file 1:

“Prevalence rates (PR) of insufficient physical activity (IPA) of all participants in the age group 65 to 75 years at wave 4”. This file shows the prevalence rates of all participants of SHARE in the age group 65–75 years at wave 4 as a table. (DOCX 24 kb)

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Lübs, L., Peplies, J., Drell, C. et al. Cross-sectional and longitudinal factors influencing physical activity of 65 to 75-year-olds: a pan European cohort study based on the survey of health, ageing and retirement in Europe (SHARE). BMC Geriatr 18, 94 (2018). https://doi.org/10.1186/s12877-018-0781-8

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BMC Geriatrics

ISSN: 1471-2318

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