Background: People aged 50 years and older are regularly identified as the most sedentary group in the population. However, even within this group, there are considerable interindividual variations in physical activity (PA) levels. They have been the subject of many studies. Based on single studies, no clear picture as to which characteristics are important has emerged. Objective: The goal of our contribution was to identify which individual characteristics are consistently linked to high PA levels in older adults. Methods: We conducted a systematic review of the literature considering demographic characteristics (gender, education, marital status, employment), health (subjective, health problems), and psychological factors (motivation, self-efficacy, locus of control). A systematic search of abstracts in the database Web of Science and a thorough screening process according to a priori specified criteria yielded 63 studies for inclusion in this review. Results: Two psychological factors - motivation and self-efficacy - and the perception of one's health seem to be consistently linked to higher PA levels in older adults. Selected demographic variables - gender and education - may be important for some types of PA. Conclusion: Our review suggests that differentiation of PA by domains is important for identifying and understanding which individual characteristics are associated with PA levels and how. Pinpointing what reliably distinguishes older adults who are active from those who are not is essential for designing effective interventions to promote PA in later life.

Active lifestyles contribute to the maintenance and improvement of health and well-being and to the prevention of diseases among older adults [1]. In particular, physical activity (PA) reduces the risk of cardiovascular disease [2] and osteoporosis [3] and improves cognitive functioning [4] and subjective well-being [5]. Properties of PA such as gait speed predict survival probabilities over up to 10 years [6], and population-level estimates suggest that if inactivity were eliminated, the average life expectancy would increase by 0.68 years worldwide [7]. Per the most widely used definition that describes PA as “any bodily movement produced by skeletal muscles that results in energy expenditure” [8], benefits can be derived from PA performed for a variety of purposes (i.e., domains), including structured exercise, walking for transportation, working in a physically demanding job, and housework. Importantly, activities like walking that are particularly accessible to older adults [9] allow people to meet the World Health Organization's (WHO) recommendation to complete at least 150 minutes of moderate-intensity PA per week [10,11]. These recommendations were made based on the growing evidence for PA's health-promoting properties and the rising importance of physical inactivity as a risk factor for mortality [11].

The PA levels of older adults are too low worldwide, with most studies reporting that between 40 and 80% of older people do not meet PA guidelines [12,13]. The findings on historical trends in PA engagement are inconsistent. Regular PA participation - defined as meeting the WHO PA recommendations [11] or being physically active at least five times per week - seems to have decreased in several countries [12,14], probably because of reduced work-related PA [13], whereas PA levels in other countries have risen, possibly due to increases in leisure-time PA [12,13,14]. Consistently, people aged 55 years and older are the most sedentary group in the population [14], even when one considers walking, an activity that is popular among and easily accessible for older adults [15]. PA levels are often even lower in still older age groups, e.g., those over 65 years [15]. However, within this age group, there are considerable interindividual differences in PA levels. In an effort to pinpoint opportunities for intervention, individual characteristics related to higher PA levels have been the subject of many studies. Thus far, a set of attributes that would reliably characterize older adults who engage in higher levels of PA has not yet been identified. Findings from different studies related to a particular attribute frequently do not converge. For example, some reports suggested that married people were more physically active than unmarried people, whereas others suggested the reverse [16,17]. Similarly mixed results were also found for other variables such as gender [18]. This necessitates a systematic review of the literature. Previous endeavors to summarize findings in this area have focused on older adults' adherence to PA in randomized controlled trials [19], have examined determinants of initiation versus maintenance of PA rather than routine PA [20], or have not differentiated domains of PA that did not fall into the structured exercise category, even if structured exercise and other types of PA were distinguished [21].

The goal of this systematic review was to identify individual characteristics that are consistently linked to higher routine PA levels in different domains in older adults. We define individual characteristics as person-specific attributes such as demographic variables (e.g., gender, education), physical health (e.g., subjective health, number of health conditions), and psychological factors (e.g., motivation, locus of control). We chose demographic variables that may be related to knowledge about PA and its health-promoting properties (education), that may be linked to PA in specific contexts, e.g., work (employment status), and that have yielded mixed findings with regards to PA participation in previous research [16,17]. Our approach was aimed at being gender sensitive (inclusion of gender). The consideration of health is important because, on the one hand, it may limit people's ability to be active, but, on the other hand, it may necessitate the participation in PA aimed at prevention or rehabilitation. The chosen psychological variables are incorporated in reputable health behavior theories (motivation: e.g., Theory of Planned Behavior [22], self-determination theory [23]; self-efficacy: Social Cognitive Theory [24]; locus of control: Theory of Planned Behavior [22]). The aim of this review was to answer the question of how specific individual characteristics (gender, education, marital status, employment, physical health, motivation, self-efficacy, life satisfaction, and locus of control) of cognitively intact older adults are related to routine PA in people's daily lives.

The methodological approach was based on the PRISMA guidelines [25] developed to improve and systematize reporting standards in reviews and meta-analyses. Although this review was not comprised of randomized controlled trials, we sought to follow the PRISMA guidelines as closely as possible. A protocol for this systematic review was established and used, but not preregistered.

The a priori specified criteria for study inclusion were as follows. Samples had to include older adults aged ≥60 years, the United Nations cutoff for old age [26]. Whenever samples were comprised of adults across the life span, results had to be reported separately for those at least 60 years of age. We focused on participants with normal cognitive functioning because we assumed that they could understand the health-promoting properties of PA. No restrictions were made for place of residence, i.e., participants could reside in the community, assisted living facilities, or care homes.

Due to our interest in older adult's routine PA, this systematic review does not include intervention studies. To be considered for inclusion, studies had to assess at least one of the individual characteristics of interest. For demographic variables, we included gender (men versus women), education (higher versus lower education), marital status (married versus not married), and employment (employed versus not employed). Physical health referred to both subjective health (better versus worse health) as well as the number of health problems (lower versus higher number) and presence of chronic health conditions (healthy versus chronic health condition). The psychological factors examined were motivation (higher versus lower), self-efficacy (higher versus lower), life satisfaction (higher versus lower), and locus of control (internal versus external).

The outcome was PA as defined earlier [8], comprehensively encompassing different domains: structured exercise and sports; walking; PA in house or garden; work-related PA; transportation-related PA; leisure-time PA; light PA; moderate PA; vigorous PA; a composite consisting of moderate and vigorous PA; meeting the WHO or similar PA guidelines (150 min of moderate aerobic activity per week or 75 min of vigorous aerobic activity per week and strength training twice a week [11]); and total PA that was not specifically classified into any of the previously listed categories or referred to activity from more than one category. PA level was treated on a continuum, whenever possible. When studies dichotomized PA (e.g., meeting PA guidelines: yes versus no), the same classification was adopted. This review focuses on associations between individual characteristics and the level of PA, not the change in PA over time; longitudinal components of the included studies were thus not considered. PA had to be measured over a period of at least 7 days, either with an accelerometer or pedometer or with a questionnaire that referred to a period of ≥7 days. Initial estimates applying a classical measurement error model and requiring the intraclass correlation coefficient to be >0.80 have suggested that the 7-day measurement period sufficiently minimizes intraindividual variations in PA when PA is monitored objectively with pedometers or accelerometers [27,28]. When questionnaires are used to measure PA, recall error can be reduced when participants are asked to report on recent engagement in PA and when recall is clearly structured [29]. The 7-day period is advantageous because it allows assessment of PA on both weekdays and weekend days, which ensures that activities that go beyond daily self-care behaviors are captured [28,29].

To identify studies, we systematically searched abstracts in the scientific database Web of Science using the following search terms: physical activity; exercise; walking; sports; older adults; seniors; elderly; old age; gender; education; marital status; occupation; health; motivation; self-efficacy; life satisfaction; satisfaction with life; locus of control; control beliefs. The search was restricted by excluding studies containing the following search terms: intervention; cognitive impairment; cognitively impaired; dementia; Alzheimer. The detailed search strategy and combination of search terms is presented in the online supplementary material (see Only studies published in peer-reviewed journals in English or German between January 1995 and September 2016 were considered. The literature search was conducted between April 2016 and September 2016 by the first and second author.

Eligibility was assessed by the first and second author; interrater reliability was κ = 0.98; in cases of disagreement, the first author made the final call. Data were extracted from the selected full-text articles by giving a score of “+” when the association between the examined individual characteristic and PA was positive, a score of “0” when there was no association, and a score of “-” when the association was negative. For the majority of the variables, associations between individual characteristics and PA levels were understood to lie on a continuum. Most individual characteristics were defined as continuous variables in the original studies. For example, a significant positive association between motivation and PA means that higher levels of motivation were associated with higher levels of PA; it does not refer to a comparison of PA engagement for “motivated” and “unmotivated” people. For the categorical variables, we adopted the categories from the original studies. Scores were assigned as follows: For gender, a score of “+” indicated more activity in men than in women, “0” equal activity in both genders, and “-” less activity in men than in women. For marital status, “+” referred to more activity in married than in unmarried (e.g., never married, widowed, divorced, separated) individuals, “0” to equal amounts of activity in married and unmarried individuals, and “-” to less activity in married than unmarried individuals. With regards to employment, “+” was given when employed individuals were more active than not employed individuals (e.g., retired, unemployed), “0” when employed and not employed individuals were equally active, and “-” when employed individuals were less active than not employed individuals. For example, a study showing that higher levels of self-efficacy were related to more leisure-time PA would have received a score of “+”; a study showing that participants with osteoarthritis performed as much PA in the house and garden as healthy participants would have received a score of “0”; and a study showing that men walked less than women would have received a score of “-.” Scores for each predictor-outcome pair were summarized by summing the “+,” “0,” and “-” scores, respectively. With this approach, we calculated how frequently a given effect was found and thus went beyond a narrative summary of the evidence, making use of a means to quantitatively summarize study findings. We acknowledge that this strategy did not allow us to grasp the size of a given effect.

The risk of bias for each study was rated by the first and second author. Interrater agreement was κ = 0.62. According to Landis and Koch [30], this corresponds to substantial agreement; conservative approaches consider it moderate agreement [31]. In cases of disagreement, the final call was made by the first author. Since the studies comprising this review were not randomized controlled trials, the rating items recommended in the PRISMA guidelines could not be used. Instead, we developed rating items based on the American Psychological Association's recommendations for reporting research findings [32]. The system for rating study quality together with the study information provided in Table 1[33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120] meet the recommendations regarding the assessment of study quality made in the MOOSE guidelines [121] and the STROBE statement [122]. We rated each study on its research methodology (items: “Was the study sample adequately described?”; “Was the sample representative of the population being studied?”; “Was a rationale for the sample size given?”; “Were eligibility criteria for participant selection used?”; “Were withdrawals reported and explained?”; “Were measurement instruments clearly described?”; “Was a standardized study protocol used?”), statistical analyses (“Were appropriate statistical methods used?”; “Were confounding variables controlled?”), and discussion of the results (“Were limitations acknowledged?”). Each item was scored on a three-point scale with 1 = yes, 0 = unclear, and -1 = no. These ratings provided some insight into the quality of the included studies.

Table 1

Study information

Study information
Study information

The study selection process is illustrated in Figure 1. We identified 21,992 records through database searching. We screened all 21,992 abstracts. Of these, 21,027 were excluded because they did not meet the eligibility criteria, resulting in 965 abstracts for further screening. After the removal of 664 duplicates, 301 articles were selected for full-text screening. One full-text could not be obtained, not even by contacting the authors; thus, we screened 300 full-text articles. Of these, 7 were excluded because they were review articles and would have led to duplicate inclusion of studies; 2 were overview papers and not quantitative studies; 1 was a dissertation that was not published in a peer-reviewed journal; 7 were intervention studies; 109 did not report results separately for participants aged at least 60 years; 3 included participants with cognitive impairments; in 75, the measurement period for PA was shorter than 7 days; 45 did not examine the association between any of the individual characteristics of interest and PA; and 137 did not include information on the amount of PA participants engaged in. Notably, multiple reasons for exclusion applied for many articles. A total of 63 full-text articles were included in the review. Such a ratio of studies identified initially and eligible for inclusion in a review is common [123,124].

Fig. 1

Adapted PRISMA flow diagram illustrating the study selection process. PA, physical activity.

Fig. 1

Adapted PRISMA flow diagram illustrating the study selection process. PA, physical activity.

Close modal

The study characteristics are presented in Table 1. Most included studies examined the association between demographic variables and PA; only few considered psychological factors. Of the demographic variables, gender was examined most frequently, namely in 40 of the included studies, followed by education in 26, marital status in 12, and employment in 5. Health was also examined in many included studies, subjective health in 21 and health problems in 14. Of the psychological factors, self-efficacy was considered most frequently, namely in 7 included studies, followed by locus of control in 3 and motivation in 2. Life satisfaction was not examined in the included studies. PA was most frequently categorized as total PA (30 included studies), followed by meeting PA guidelines (11 studies), walking (9 studies), sports/structured exercise and leisure-time PA (7 studies for both), PA in house or garden (4 studies), work-related PA (1 study), PA for transportation (1 study), and vigorous PA (1 study). Light PA, moderate PA, and moderate and vigorous PA were not assessed in the included studies. Four studies measured PA with accelerometers only, 2 with accelerometers and questionnaires, and 59 relied on questionnaires only.

The risk of bias ratings for all studies included in the present paper are provided in Table 2. Considering all ten items, studies had between 2 and 9 “yes” ratings (M = 6.71, SD = 1.68, Md = 7.00), between 0 and 4 “no” ratings (M = 0.75, SD = 1.00, Md = 0.00), and between 1 and 5 “unclear” ratings (M = 2.54, SD = 1.22, Md = 3.00). When “yes,” “no,” and “unclear” ratings were summed, total scores ranged from -2 to 9 (M = 5.97, SD = 2.48).

Table 2

Risk of bias ratings

Risk of bias ratings
Risk of bias ratings

The results of the data extraction are presented in Table 3. For every pair of individual characteristic and PA outcome that was examined in the included studies, we recorded the number of positive associations, the number of negative associations, and the number of times when no association was found. For each individual characteristic, we also summed the number of positive, negative, and no associations across PA domains. Categorical variables were treated in a dichotomous fashion, as described earlier.

Table 3

Results of the data extraction

Results of the data extraction
Results of the data extraction


Although possible gender differences in PA levels were examined in many studies, the results are somewhat inconclusive. Across all PA types, men were more active than women in 27 instances and less active in 7; no association between gender and PA level was found in 19 instances. Whether or not an association between gender and PA level can be observed may depend on the PA domain. Men had higher PA levels than women when vigorous PA, work-related PA, and PA for transportation (1 instance, respectively) were concerned, and in the majority of instances for the domains sports/exercise (5 instances versus 2 instances with no association) and leisure-time PA (3 instances versus 1 instance with no association). Women, on the other hand, performed more PA in house/garden (3 instances). The picture is somewhat less clear for walking and meeting PA guidelines, and particularly for total PA. Thus, to truly understand gender effects, it seems important to compare activity levels between men and women within specific PA domains.


Across all PA types, higher levels of education were associated with more PA participation in 21 instances, but in another 17, there was no association between education and PA. Only positive associations between education and PA level were found for the domains walking (1), PA in house/garden (1), and vigorous PA (1). More than half of the associations between education and PA level were positive for sports/exercise and meeting PA guidelines (4 positive associations versus 2 instances with no associations for both PA domains). For the pairing education and leisure-time PA, the majority of the associations were positive as well (6 versus 4 instances with no association). The picture looked different for total PA, where education was positively associated with PA in less than half (4) of the cases and not associated in 9 instances. Higher levels of education seem to be associated with more PA in domains in which PA is voluntary or something that individuals seek out. Further details on the activities included under total PA could help to further explain if this is the case.

Marital Status

Marital status was not associated with PA participation in most instances (11) across all PA categories; the ones considered included sports/exercise (2), walking (2), vigorous PA (1), total PA (2), leisure-time PA (3), and meeting PA guidelines (1). In 1 instance (total PA), married older adults had higher PA levels than those who were not married, and in 1 instance (leisure-time PA), married older adults had lower PA levels than those who were not married.


Employment status does not seem to have a strong association with PA levels. No association was found with meeting PA guidelines (2 instances). Employment status was positively associated with total PA once and negatively associated with vigorous PA once.

Subjective Health

In most instances (19), a positive association was observed between subjective health and PA levels when all domains were considered together. For the outcomes PA in house/garden and meeting PA guidelines, only positive associations with subjective health were found, but these pairings were examined in very few studies (1 and 2, respectively). For walking, total PA, and leisure-time PA, a positive association with subjective health was found in the majority of instances, but for sports/exercise only one-third of the time. Overall, no association between subjective health and PA was found in 8 instances; these referred to the domains sports/exercise (2), walking (1), total PA (4), and leisure-time PA (1).

Health Problems

The role of health problems (presence of a chronic condition or number of health problems) for PA engagement is less clear than that of subjective health. Across all PA domains, fewer health problems or the absence of a chronic condition were associated with higher PA levels in 19 instances, but in another 19, no association was found. Reading of the included studies suggested that chronic conditions differ in whether or not they affect PA participation or which PA domains they affect. However, the data were insufficient to make formal comparisons.


Higher levels of motivation were associated with more PA across the two examined PA domains walking (1 instance) and total PA (1 instance). Motivation was measured as reasons that motivated participants to walk in the past week in the study focusing on walking and as intention in the study with total PA as an outcome.


Higher self-efficacy was associated with more PA across domains in most instances (7). Of these, 1 referred to the association with sports/exercise, 2 to walking, and 4 to total PA. Only in 1 instance was self-efficacy not associated with PA level; this was the case for total PA.

Locus of Control

An external locus of control was associated with more PA in 2 instances across PA domains, 1 referring to sports/exercise and 1 to walking. However, in another 2 also referring to sports/exercise and walking, there was no association between locus of control and walking. Reading of the included studies suggested that the presence or absence of an association may depend on the type of control measure used (e.g., general locus of control versus health locus of control), but the data were too sparse to allow for formal comparisons.

The goal of this systematic review was to identify individual characteristics that are consistently linked to higher PA levels in older adults. Specifically, we set out to answer the question of how specific individual characteristics (gender, education, marital status, employment, physical health, motivation, self-efficacy, life satisfaction, and locus of control) of cognitively intact older adults are related to routine PA in their daily lives. We applied an innovative approach by considering various types of PA separately. One main finding of this review was that only a relatively small number of the included studies had measured PA in one of these domains or specified clearly in which domain PA had been measured. For the vast majority, the outcome PA had to be listed in the “total PA” category.

Although few included studies examined associations between the psychological factors (motivation, self-efficacy, locus of control) and PA, the picture is clearest for this group of variables. Of course, given the small number of studies linking psychological factors and routine PA in older adults, the results have to be interpreted with caution. Across PA domains, higher levels of motivation and self-efficacy were associated with more PA in almost all instances. Findings regarding the association between locus of control and PA are inconclusive, possibly because the operationalization of control differed between studies. In the future, a differentiation between control domains could clarify these results.

The picture regarding the association between individual characteristics and PA levels is less clear for the demographic variables, although they were examined in many of the included studies. Considering all types of PA together, it remained relatively unclear whether there are gender differences in activity levels. It seemed that overall PA levels may not differ between men and women. However, men may be more active than women in some domains (e.g., vigorous PA, leisure-time PA), whereas women may be more active than men in others (e.g., PA in house/garden). Some of these results may be due to today's older adults' still adopting relatively traditional gender roles, and patterns may change as new cohorts reach old age. The effects of education also seem to depend on domain. Higher levels of education may be associated with higher PA levels whenever individuals specifically need to seek out PA (e.g., sports/exercise) or when knowledge is relevant (e.g., meeting PA guidelines). Further specification of activities in studies currently labeled as total PA could clarify this idea. Marital and employment status, on the other hand, seem to contribute little to explaining individual differences in PA levels of older adults.

With regards to health, it may be more important how someone is feeling subjectively than whether or not he or she suffers from one or more health problems. We observed that better subjective health was associated with more PA in the majority of cases and across PA domains. Health problems, even chronic ones, do not necessarily prevent older adults from being active. In this review, we saw that in some cases, they even resulted in greater PA participation, whereas in others, they had no effect. This may also depend on PA domain.

Strengths, Limitations, and Future Directions

One of the main strengths of this review was its application of the broad definition of PA by Caspersen et al. [14] and the differentiation of the outcome PA by domains. However, in many of the included studies, the outcome had to be categorized as total PA because the studies did not differentiate between domains or lacked specific details on the context in which PA was measured. It is possible that this contributed to the inconclusive results concerning the association of several variables and PA. We also included studies applying objective and self-report measures of PA. Due to the low number of studies that measured PA objectively, it was not possible to compare whether effects differed by measurement method. Furthermore, the self-report measures utilized in the included studies differed; it is thus possible that inconsistencies in associations between individual characteristics and PA are - to some degree - due to differences in measurement instruments. One issue of studies with objective PA measures was that the majority did not differentiate PA by domains and only reported total PA.

We took a comprehensive look at the association between individual characteristics and PA by considering characteristics from three domains: demographic variables, health, and psychological factors. Our findings suggest that psychological factors are relatively reliably associated with PA, although this conclusion should be viewed with some caution because only a relatively small number of included studies examined psychological variables. Two of the demographic variables, marital and employment status, on the other hand, seemed to have little relevance for PA. It is possible that their effects were somewhat obscured by the dichotomous coding (married versus not married, employed versus not employed) that we applied in order to be able to utilize the same data extraction method as for the other variables; in the future, it would be useful to consider other levels of these variables.

Our quantitative data extraction approach went beyond a simple narrative summary. A future meta-analytic approach taking into consideration effect sizes would be a useful next step. It has the potential to shed more light onto some of the inconclusive results. Additionally, effect sizes could lead to further insights into how influential the different individual characteristics are relative to each other. With meta-analytic techniques, it would be possible to examine associations of several individual characteristics with PA levels simultaneously. Furthermore, one could account for heterogeneity in associations due to the different measurement instruments that were used in the included studies, and determine to what extent study quality is linked to the likelihood of reporting a significant result.


The findings from this review have a number of important theoretical and practical implications. Individual characteristics seem to be differentially associated with PA levels in distinct domains; thus, explicit measurement and promotion of PA in various domains seems necessary. In intervention studies and practical settings, it may make sense to target those activities that people may be predisposed to engage in based on their individual characteristics, or specifically those that they may engage in to a lesser degree. A clear description of PA measurement instruments would facilitate a better comparison of study results as well as conclusions regarding whether participants are close to meeting the WHO PA guidelines.

In this systematic review, we observed that two psychological factors - motivation and self-efficacy - and the perception of one's health seem to be consistently linked to higher PA levels in older adults in a small number of studies that met the inclusion criteria. Selected demographic variables - gender and education - may be important for some types of PA. Perhaps most importantly, this review suggests that differentiation of PA by domains is crucial for identifying and understanding which individual characteristics are associated with PA levels and how.

This work was funded by grant H2020-MSCA-IF-2014 661555 from the European Commission.

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