Life Space in Older Adults
Summary and Keywords
When referring to life space, researchers usually mean the area in which individuals move in their everyday lives. Life space can be measured based on different approaches, by means of self-reports (i.e., questionnaires or diaries) or by more recent approaches of technology-based objective assessment (e.g., via Global Positioning System [GPS] devices or smartphones). Life space is an important indicator of older adults’ out-of-home mobility and is meaningfully associated with autonomy, well-being, and quality of life. Substantial relationships between life space and socio-demographic indicators, health, and cognitive abilities have been reported in previous research. Future research on life space in old age will benefit from a more comprehensive and stronger interdisciplinary perspective, from taking into account different time scales (i.e., short- and long-term variability), and from considering life space as a multidimensional measure that can be best assessed based on multi-method approaches with multiple indicators.
Definitions of Life Space
The life space construct describes the area(s) in which older adults move as part of their everyday lives and the distances they usually cover from home. Life space refers to a mobility component that is defined by distances, action ranges, or activity spaces (Brusilovskiy, Klein, & Salzer, 2016; Hirsch, Winters, Clarke, & McKay, 2014; Wettstein, Wahl, & Diehl, 2014). According to Barnes et al. (2007), life space corresponds to the “spatial extent of movement of older persons” (p. 78). Depending on the specific life space operationalization, life space can range from one’s own home to another town (or even another country). Life space can be considered as “one aspect of environmental complexity for older adults” (Crowe et al., 2008, p. 1241); it constitutes a crucial part of the broader concept of mobility, which refers to “a person’s purposeful movement through the environment from one place to another” (Stalvey, Owsley, Sloane, & Ball, 1999, p. 460) or to the ability to move oneself (e.g., by walking, by using assistive devices, or by using transportation) within community environments that expand from one‘s home, to the neighborhood, and to regions beyond” (Webber, Porter, & Menec, 2010, p. 443).
Measurement of Life Space
Researchers have used various methods and instruments to assess life space. Generally, two major approaches for the assessment of life space exist, namely self-report measures and technology-based assessments.
Life Space Assessment Based on Self-Reports
As one of the first to investigate life space among older adults, May, Nayak, and Isaacs (1985) introduced a life-space diary. Participants reported about their daily movement in five concentric zones (bedroom; rest of the dwelling; garden, courtyard, or grounds surrounding the dwelling; “block” in which the dwelling was located; area across a traffic-bearing street). Based on the diary information, May et al. divided their sample into life-space diameter groups. They also computed a mobility index, which corresponded to the percentage of days spent in one of the three closest concentric zones.
A similar self-report assessment tool developed by Stalvey et al. (1999) is the “Life Space Questionnaire” (LSQ). Nine LSQ items indicate specific hierarchical environmental zones (ranging from “other rooms of your home besides the room where you sleep” to “places outside this region of the United States”), and individuals have to specify whether they have been in these zones within the past three days.
The LSQ has repeatedly been used in empirical research (Barnes et al., 2007; Crowe et al., 2008), either in its original version or slightly modified with regard to the definition of zones and to the specification of the time frame. For instance, the Life-Space Assessment (LSA; Baker, Bodner, & Allman, 2003) refers to life space across a 4-week period and, additionally, assesses the frequency of reaching a specific life space level (i.e., the number of days on which a certain concentric zone was attained) as well as the degree of independence (i.e., whether help from other persons or assistive devices was needed to attain a specific life space area). Based on these life space dimensions, different life space outcomes can be distinguished, namely maximal life space, highest life space using equipment (but without help from persons), independent life space (i.e., highest life space without any help from persons or equipment), and a dichotomous measure of restricted versus unrestricted independent life space. Moreover, a composite measure of life space can be computed by taking into account all information including greatest life space levels, degree of independence, and frequency of attainment.
Self-report measures of life space are easy and quick to administer, so they can easily be embedded into large-scale studies. However—like all assessments that are based on self-reports (e.g., Schwarz, 1999)—they may be inaccurate and biased due to factors such as social desirability or retrospection biases.
Technology-Based Life Space Assessment
More recent approaches to assess life space (and mobility in general) overcome such problems of self-report instruments by using technology, such as location sensors (e.g., Jansen, Diegelmann, Schnabel, Wahl, & Hauer, 2017) or GPS devices (e.g., Shoval et al., 2010, 2011; Webber & Porter, 2009) that can also be embedded into cell phones/smartphones (e.g., Brusilovskiy et al., 2016; Hirsch et al., 2014; Tung et al., 2014; Wan & Lin, 2013). These tools allow for an objective and accurate assessment of life space; they produce metrical, continuous data (e.g., each day’s maximal distance from home in miles) rather than the less fine-grained ordinal data of self-reported concentric zones. However, they are more expensive than self-report instruments, and data collection (as well as data cleaning and analysis) can be time-consuming and complex.
Life space parameters obtained by accelerometers may therefore complement self-reported life space measures and allow extraction of specific parameters, such as step count or amount of time spent with different physical (and sedentary) activities (Tsai et al., 2015). Contrasting accelerometers with other technological devices, accelerometers allow for a detection of differences in specific ambulation characteristics (e.g., duration of single walking episodes during a day) that are often not accurately measurable by GPS technology, particularly indoors. Also, GPS assessments can have limitations when quantifying short walking trips (Cho, Rodríguez, & Evenson, 2011). On the other hand, determination of specific life space indicators in terms of covered distances and of visited places can be provided only by GPS devices, not based on accelerometers.
Continuous smartphone sensing, combining different sources of data (GPS and accelerometers), represents a promising avenue for simultaneous assessment of life space and mobility characteristics (Hossain & Poellabauer, 2016). Specifically, accelerometers can provide fine-grained data on a micro level (e.g., number of steps, time spent with specific activities of different physical intensity), whereas “macro data” (e.g., distances covered from home, number of out-of-home places visited) can be better obtained based on GPS devices. On the same note, achieving uninterrupted data collection and analysis on smartphones will be essential to the success and widespread use of these applications. Despite enormous progress in technology, building smartphone-sensing applications that operate reliably and efficiently on a continuous basis is still challenging. In particular, determination of the optimal tradeoff between accuracy of data collection and energy consumption has been discussed as a major challenge of this methodology (Balan, Lee, Tan Kiat, & Misra, 2014).
Factors Associated With Life Space
Higher levels of mobility in old age are associated with higher autonomy, higher quality of life and well-being, and better social embeddedness (Metz, 2000; Nordbakke & Schwanen, 2014; Webber & Porter, 2009). Mobility thus seems to play a key role for successful aging, and maintaining high levels of mobility is beneficial for older adults in many regards.
Notably, older adults are remarkably heterogeneous regarding their mobility patterns. For instance, Wettstein, Wahl, Shoval, Auslander, et al. (2015) identified three clusters of out-of-home mobility as assessed by GPS technology in a sample of German and Israeli older adults, with one “mobility-restricted cluster” revealing low levels in all mobility indicators assessed, one “outdoor-oriented cluster,” and a “walkers cluster.” Specifically, older adults of the “walking cluster” walked faster, on average, covered further distances by walking, and spent more time per day walking than both other clusters. However, the cluster with most out-of-home places visited and most time spent out-of-home was the “outdoor-oriented cluster.” Regarding determinants or correlates of mobility profiles, individuals of the “mobility-restricted” cluster were older and in poorer health than both other groups, and the prevalence of cognitive impairment was highest in this cluster. Older adults are thus considerably different regarding individual mobility patterns and preferences (see also Mollenkopf et al., 2004).
Which factors can explain this heterogeneity in the mobility and life space profiles of older adults? So far, conceptual mobility models that systematically specify predictors, correlates, and outcomes of life space-related mobility in a comprehensive way are still rare. One exception is the mobility framework by Webber et al. (2010), who distinguish between six major determinants of mobility (see Figure 1) whose importance could be confirmed by recent empirical research (e.g., Umstattd Meyer, Janke, & Beaujean, 2014). These determinants are essential at all life space areas, ranging from room to world; but with each more distal concentric zone, higher proportions of the six determinants need to be invested and orchestrated to reach the respective area, resulting in a conical model. Major life space determinants specified by Webber et al. are “gender, cultural, and biographical influences,” as well as cognitive, physical, environmental, psychosocial, and financial resources.
In this article, existing empirical evidence of correlates of life space and life space-related mobility are summarized. The Webber et al. framework is used as a starting point by including, modifying, and broadening the determinants proposed in this model. Specifically, the focus is on socio-demographic indicators (such as gender in the original Webber framework), physical factors (i.e., health and physical functioning), cognitive factors (cognitive abilities and cognitive impairment), psychosocial factors (well-being and other psychosocial resources), and environmental factors (such as weekday, season, weather, neighborhood characteristics). Potentially reciprocal associations between mobility and life space and its correlates are also considered.
Socio-Demographic Correlates of Life Space
Life space of older adults is meaningfully associated with socio-demographic factors such as age, gender, or education (Barnes et al., 2007; Crowe et al., 2008; Eronen et al., 2016; Hirsch et al., 2014; Sartori et al., 2012; Shoval et al., 2011; Umstattd Meyer et al., 2014). Individuals with higher levels of education usually have larger life spaces than those with less education, which may be due to higher socioeconomic resources that enable individuals to attain more distal life space areas (e.g., by means of car ownership, or being able to afford a cab ride). Indeed, lower financial resources have been found to be associated with more restricted mobility levels in older adults (e.g., Golant, 1984; Mollenkopf et al., 2004). In addition, education is meaningfully associated with cognitive abilities (e.g., Farfel et al., 2013) and health (e.g., Lynch, 2003), and these factors may mediate education-life space associations. Another mechanism could be that more years of education are associated with higher cognitive abilities, which are in turn related with certain personality traits, such as higher openness to experience (e.g., Curtis, Windsor, & Soubelet, 2014), and these traits have been found to be meaningful predictors of life space and related mobility outcomes (Barnes et al., 2007; Tolea, Costa, et al., 2012; Tolea, Ferrucci, et al., 2012).
In addition, life space and related measures, such as time spent out of home or the number of places visited (Harada et al., 2016; Wettstein et al., 2014) seem to decrease with advancing age. This may be due to age-associated loss in biological plasticity (Baltes, 2006) and age-related declines in physical, cognitive, and sensory functioning (Bainbridge & Wallhagen, 2014; Jacobs et al., 2012; McArdle, Ferrer-Caja, Hamagami, & Woodcock, 2002) which lead to increasingly restricted life spaces.
Older women reveal more restricted life spaces than men, which may be due to higher rates of multimorbidity, functional limitations, and of mobility disability in older women (Berlau, Corrada, & Kawas, 2009; Melzer, Izmirlian, Leveille, & Guralnik, 2001). In addition, fewer older women than men have drivers licences and access to a car, and older women drive less frequently than men (Anstey, Windsor, Luszcz, & Andrews, 2006; Mollenkopf et al., 2002; Viljanen, Mikkola, Rantakokko, Portegijs, & Rantanen, 2016). Notably, and not surprisingly, car drivers usually have larger life spaces than individuals who do not drive (Stalvey et al., 1999; Viljanen, Mikkola, Rantakokko, Portegijs, & Rantanen, 2016). However, this gender difference in car availability and driving habits—as well as life space in general and the importance of its determinants—may be subject to societal change across aging cohorts in the future.
Cognitive Correlates of Life Space
It is a plausible assumption that acquiring distal life spaces requires cognitive abilities. Specifically, older adults with extended life spaces need to anticipate and plan how to get to their desired places, which requires cognitive and, particularly, executive functions. Moreover, individuals have to remember how to get to specific destinations, which requires intact memory functions. Finally, to attain unknown places, orientation skills and spatial abilities are needed. Given that cognitive abilities, particularly “fluid” ones such as memory, information processing speed or executive functions, typically decline with advancing age (Lövdén, Ghisletta, & Lindenberger, 2004; Royall, Palmer, Chiodo, & Polk, 2005), the reported age differences in life space may be due, to some extent, to such age-associated cognitive declines.
Indeed, positive associations between life space measures and cognitive abilities have been reported (Barnes et al., 2007; Giannouli, Bock, & Zijlstra, 2017; Hoang et al., 2016; Jansen, Diegelmann, Schnabel, et al., 2017; Stalvey et al., 1999); however, only very few studies have compared the associations of different specific cognitive functions with life space rather than considering exclusively global cognitive functioning. Wettstein et al. (2014) observed, in a sample of cognitively healthy older adults, that individuals who covered larger distances in their everyday lives (as assessed by GPS technology) scored higher on episodic memory. A study by Sartori et al. (2012) reported significant positive associations of self-reported life space with memory, reasoning and processing speed.
Importantly, relationships between life space and cognitive abilities may be complex and reciprocal rather than unidirectional. Specifically, maintaining large life spaces in old age may require cognitive resources, but moving in more extended life spaces may as well have an impact on cognitive abilities, because complex movement in space could stimulate cognitive functioning. The “use it or lose it” assumption (Hertzog, 2009) states that engagement in (complex) activities, particularly in physical, cognitive, and social ones, may buffer late-life cognitive decline. Though this issue is still controversial to some extent (e.g., Salthouse, 2006), there is accumulating evidence for beneficial effects of activity engagement on cognitive abilities and on dementia risk in old age (Colcombe & Kramer, 2003; Hertzog, Kramer, Wilson, & Lindenberger, 2008; Karp et al., 2006; Lövdén, Ghisletta, & Lindenberger, 2005). Given that individuals usually attain zones within their life space to engage in such activities, and given that traveling within one’s life space may per se correspond to a cognitive and—depending on the transport mode chosen—possibly a physical activity, a larger life space may be associated with less cognitive decline in older adults. Indeed, Crowe et al. (2008) observed that a greater life space was associated with reduced 4-year cognitive decline in older adults, and this effect remained significant when potential confounding factors such as age, gender, or education were controlled for. This association between life space and cognitive change was partly, but not fully, accounted for by physical functioning.
Life Space and Cognitive Impairment
Some studies have also investigated associations between life space and cognitive impairment, such as dementia. The prevalence of dementia increases with advancing age, with particularly high rates in very old age (Brookmeyer, Johnson, Ziegler-Graham, & Arrighi, 2007). Tung et al. (2014) observed that life space, assessed by GPS technology, was significantly narrower in older adults with Alzheimer’s disease compared to cognitively healthy controls. Differences between older adults with and without dementia, regarding their everyday activities, mobility levels, and life spaces, have been reported in other studies that used GPS assessment (Shoval et al., 2010, 2011; Wettstein, Seidl, Wahl, Shoval, & Heinik, 2014; Wettstein, Wahl, Shoval, Oswald, et al., 2015). This may be different for mild cognitive impairment (MCI), a condition that is associated with a higher risk of dementia, but that does not necessarily lead to dementia and may even be reversible in some cases (Albert et al., 2011; Sachdev et al., 2013). Though MCI individuals reported less extended life space compared to cognitively healthy controls, the life space trajectories of both groups over five years were comparable in one study (O’Connor, Edwards, Wadley, & Crowe, 2010). When considering mobility indicators beyond life space, MCI does not seem to be associated with reduced mobility. Only engagement in more complex out-of-home activities was found to be lower in older adults with MCI than in cognitively unimpaired individuals (Wettstein, Seidl, et al., 2014; Wettstein, Wahl, Shoval, Oswald, et al., 2015). Notably, slightly closer cognition-mobility associations have been found in MCI individuals compared to cognitively healthy controls (Wahl et al., 2013). These closer relationships may indicate that once cognitive impairment has set in, the remaining cognitive resources may become more important for the maintenance of everyday mobility.
To our knowledge, the role of life space as a predictor of cognitive impairment has so far been investigated in only one study, by James, Boyle, Buchman, Barnes, and Bennett (2011). They found that, over a mean follow-up period of 4.4 years, a more constricted life space (assessed by self-reports) was associated with a significantly increased risk of dementia and MCI. Specifically, individuals whose life space was constricted to their home were nearly twice as likely to develop Alzheimer’s disease as individuals who revealed the largest life space. Moreover, this relationship persisted after adjustment for performance-based physical function, disability, depressive symptoms, social network size, vascular disease burden, and vascular risk factors.
Based on these empirical findings, it seems clear that more research is needed to disentangle the complex interplay between life space and cognitive abilities in late life. Future research should consider the reciprocity of associations by using longitudinal study designs. Moreover, including cognitive indicators from different domains is necessary to identify those specific cognitive functions most closely associated with life space. Such insights will be helpful to derive interventions that promote these mental functions and that may also facilitate maintenance of life space in old and very old age. Finally, the reported findings by James et al. (2011) suggest that life space assessment may have the potential to contribute to better detection and early diagnosis of cognitive disorders such as MCI.
Physical Factors and Life Space
Among the determinants of life space and mobility specified by Webber et al. (2010) and others, physical health may be the most crucial one (Umstattd Meyer et al., 2014): Attaining distal life space zones requires movement, so physical functioning needs to be intact. However, limitations in health and functional ability can be compensated for to some extent by the assistance of others or by the use of transport modes that are less physically challenging (e.g., using public transportation rather than walking).
Physical correlates of life space that have been identified so far are functional limitations as assessed by performance-based tests (Barnes et al., 2007; Stalvey et al., 1999) and self-reports of disability (e.g., Baker et al., 2003). Additionally, sensory functioning is systematically associated with life space (Barnes et al., 2007), with poorer vision (and also poorer hearing; Polku, Mikkola, Rantakokko, et al., 2015) predicting more restricted life space levels. This association deserves particular attention, given the high prevalence of sensory impairment in old and very old age (Hong Mitchell, Rochtchina, Fong, Chia, & Wang, 2013; Wettstein & Wahl, 2016). Life space may be reduced in older adults with impaired vision because these individuals tend to avoid driving (or at least driving situations they consider difficult; Ball et al., 1998). Moreover, impaired vision is associated with lower cognitive abilities and reduced everyday competence, which in turn are related to reduced engagement in out-of-home leisure activities (Heyl, Wahl, & Mollenkopf, 2005).
However, in analogy to the other life space correlates, considering health exclusively as a determinant of life space may be too simplistic. For instance, Xue, Fried, Glass, Laffan, and Chaves (2008) found that older women with life space constrictions were more likely to develop frailty (which is a clinical syndrome of poor health, characterized by weakness, slowness, low physical activity, weight loss, and exhaustion) over a 3-year period than women with unrestricted life spaces. According to the authors, life space could be a “marker of declines in physiologic reserve,” thus preceding decline in health. A restricted life space is also associated with a higher mortality risk in both older women (Xue et al., 2008) and men (Mackey et al., 2014), and this association persisted even when chronic disease burden and other covariates were adjusted for. Poor health and functioning may thus not only predict life space restrictions; they may as well result from constrained life spaces. Furthermore, physical functioning does not only represent an important prerequisite of life space; it is also closely associated with other life space correlates (such as cognitive abilities; Umstattd Meyer et al., 2014) and may therefore mediate associations of these correlates with life space. For instance, Crowe et al. (2008) found that life space was a significant predictor of cognitive changes in older adults, and physical functioning accounted for a large proportion of this association.
Moreover, apart from a direct association between physical functioning and life space, there may also be an indirect one that is mediated by psychological factors such as sense of autonomy (Portegijs, Rantakokko, Mikkola, Viljanen, & Rantanen, 2014). Therefore, just like all other correlates of life space, physical functioning and health seem to be interrelated with life space in multiple, complex ways that need to be further explored, and they interact with other life space correlates such as cognitive or psychosocial factors.
Life space is not only a matter of “intra-personal” resources; it also depends on contextual and environmental factors. A conceptual framework of dimensions of mobility, explicitly including environmental factors, has been proposed by Patla and Shumway-Cook (1999). They define “ambient conditions” (such as light levels or weather conditions) as well as “terrain characteristics” (stairs, curbs) and traffic levels as key environmental factors that constitute the degree of complexity and difficulty of community mobility.
Empirical studies have demonstrated that life space and mobility levels of older adults indeed vary as a function of weather conditions and season, but also of weekday—for instance, life spaces among Israeli older adults are less extended on Friday and Saturday, which corresponds to “weekend” (Sabbat) in Israel (Shoval et al., 2011). With regard to season and weather, Petersen, Austin, Mattek, and Kaye (2015) found that older adults spend more time out of home on days without (or with less) precipitation, with lower wind speeds, and with later sunsets (i.e., in summer).
In addition, certain neighborhood characteristics (Fisher, Li, Michael, & Cleveland, 2004; King et al., 2005; Li, Fisher, Brownson, & Bosworth, 2005) such as socioeconomic status, neighborhood safety (Umstattd Meyer et al., 2014), social cohesion, or “walkability” of a neighbourhood (King et al., 2011) play a crucial role for older adults’ physical activity levels, particularly walking activities. Moreover, environmental barriers within and outside of an individual’s home, such as slippery floors, can considerably limit an older individual’s mobility and physical activity (Benzinger et al., 2014).
Psychosocial Factors and Life Space
Mobility is substantially associated with well-being and quality of life (Metz, 2000; Nordbakke & Schwanen, 2014). This is also true for life space, with a larger life space being associated with fewer depressive symptoms and better mental health (Baker et al., 2003; Jansen, Diegelmann, Schnabel, et al., 2017; Polku, Mikkola, Portegijs, et al., 2015; Stalvey et al., 1999; Tung et al., 2014; Umstattd Meyer et al., 2014), as well as with higher levels of purpose in life (Barnes et al., 2007). Rantakokko, Portegijs, Viljanen, Iwarsson, and Rantanen (2013) investigated physical, psychological, social, and environmental quality of life (measured by the WHOQOL-BREF Quality of Life Assessment; The WHO QOLGroup, 1998) in a sample of Finnish older adults. They found meaningful associations between a life space composite score (comprising self-reported distance, frequency, and independence of movement) and all four quality of life dimensions. In contrast, maximal life space (i.e., the greatest distance attained with the help of others or of assistive devices, if needed) and independent life space (i.e., life space attained without others’ help or assistive devices) were significantly associated with all dimensions except social well being. These associations were also observed longitudinally over 2 years, with decline in life space being associated with decline in quality of life (Rantakokko et al., 2016).
Most of the research on the interrelations between life space and well being has been cross-sectional so far. Therefore, the causality of these associations is still unclear. Specifically, facing restrictions in one’s life space may cause dissatisfaction and lower well being, such as heightened depressive symptoms. However, depressive symptoms may also lead to a reduced life space because of a generally reduced motivation to be out of home and to engage in out-of-home activities. Associations between life space and well being thus may be complex and reciprocal, and more (longitudinal) research is needed to explore these associations as well as to identify meaningful mediating and moderating factors.
Extending the focus on further psychosocial correlates of life space beyond well being, Wahl et al. (2013) compared the associations of cognitive (general cognitive ability, information processing speed) versus psychosocial resources (such as environmental mastery or depression) with different GPS-assessed mobility indicators (time spent out of home, number of visited places) as well as with self-reported activity indicators (number of conducted physically and cognitively demanding activities) in a sample of older adults. Cognitive indicators were more closely associated with mobility and particularly with activity than psychosocial resources.
However, well being, psychosocial factors, and cognitive abilities should not be considered separately; they are interrelated (Umstattd Meyer et al., 2014), and there seems to be a complex interplay between them: Sartori et al. (2012) observed an interaction of cognitive resources and control beliefs in their association with life space. Specifically, those older adults who scored high on memory tests and low on external control beliefs (i.e., lower beliefs in the control of external forces on aging outcomes) had the largest self-reported life spaces. However, their analyses were based on a cross-sectional study design. Low external control beliefs are not necessarily a resource for attaining larger life spaces; alternatively, a restricted life space may lead to feeling more dependent, and consequently to higher external control beliefs.
Wettstein, Wahl, Shoval, et al. (2014) found significant positive associations between less complex mobility dimensions (e.g., walking) and environmental mastery in older adults with dementia, but not in cognitively unimpaired older adults. However, more complex out-of-home activities were associated with higher negative affect levels in individuals with dementia or with MCI, but not in individuals without cognitive impairment. Though these findings were cross-sectional and therefore require cautious interpretation, this pattern of associations might support the competence-environmental press model developed by Lawton and Nahemow (1973): As long as environmental demands do not exceed an individual’s competence (which may be compromised in individuals with MCI and dementia), positive affect and adaptive behavior result, and the individual operates in a “zone of comfort.” However, once demands are no longer manageable by an individual’s competence (after the onset of cognitive impairment, for example), the individual ends up with negative affect and maladaptive behavior.
One of the very few studies that addressed the interplay between psychosocial factors and life space longitudinally was conducted by Saajanaho et al. (2015). They found that personal goals were meaningfully associated with self-reported life space and with change in life space over 2 years. Specifically, their results indicated that goals to be active in daily life, to stay mentally alert, and to exercise were related with larger life spaces, and this relationship persisted over 2 years. In addition, personal goals referring to maintenance of one’s own functioning predicted a more extended life space after 2 years. In contrast, goals related to improvement of one’s own poor physical functioning were predictive of a more restricted life space (which may to some extent be due to the impaired physical functioning of individuals who pursue such a goal).
To summarize, psychosocial factors are meaningfully related with older adults’ life space. They may, on the one hand, represent determinants of life space mobility, because moving in distal areas depends on psychosocial resources. On the other hand, psychosocial factors may also benefit from an individual’s experience of independently moving in unrestricted life space areas, which may promote feelings of autonomy, mastery, and self-efficacy. Finally, psychosocial factors may also mediate associations of other determinants with life space (e.g., autonomy as a mediator of the relation between physical functioning and life space; Portegijs et al., 2014).
Interventions to Promote Life Space in Old Age
Given the strong link between life space and quality of life in old age, older adults would certainly benefit from interventions to maintain or increase life space. However, there is a remarkable lack of intervention studies that have focused directly on life space; rather, most existing interventions have targeted other mobility domains (such as physical activity), or mobility in general.
As reported before, life space is associated with multiple factors and requires cognitive, physical, psychosocial, and environmental resources. In the following, intervention research that has focused on these different factors will be summarized.
In their review on the effects of cognitive training on mobility in old age, O’Connor, Hudak, and Edwards (2011) report beneficial effects of speed of processing trainings on outcomes related to driving (driving exposure, driving difficulties, driving space, occurrence of accidents, and driving cessation). However, whether these effects are generalizable to life space outcomes beyond driving still needs to be investigated.
As pointed out, factors associated with life space should not be regarded as separate, independent entities, as they interact in multiple, complex ways. This is particularly true for physical and cognitive factors: Ambulating in a complex environment requires “dual tasking,” that is, the execution of motor tasks such as walking while simultaneously cognitively processing other inputs such as visual (e.g., traffic light) or verbal information (e.g., holding a conversation, “walking while talking”; Neider et al., 2011). Older adults—particularly those with cognitive impairment—frequently reveal a so-called “dual-task deficit” (Bahureksa et al., 2017; Beurskens & Bock, 2012; Lindenberger, Marsiske, & Baltes, 2000) and seem to be forced, due to limited resource pool, to prioritize either the motor or the cognitive domain by allocating their resources correspondingly. Specific dual-task task training interventions can improve dual-task performances in older adults, even in those with dementia (Schwenk, Zieschang, Oster, & Hauer, 2010). To date, however, intervention effects have been measured under laboratory conditions only. Future studies need to evaluate whether these interventions lead to lasting increases in everyday physical activity and life space for older adults.
A strong association between physical functioning and physical activity has been observed in frail older adults whereas this relationship is weaker in high-functioning people (Rapp et al., 2012). Severe limitations in leg strength, postural balance, or walking adversely affect the ability to ambulate in the everyday environment and carry out daily out-of-home tasks such as walking to a store or visiting a neighbor (Aoyagi, Park, Watanabe, Park, & Shephard, 2009). In contrast, in rather high-functioning people, a further increase in physical performance indicators, such as walking speed, is not necessarily associated with an increase in physical activity. In these individuals, other personal, social, or environmental factors account for inter-individual differences in mobility and physical activity levels (Booth, Owen, Bauman, Clavisi, & Leslie, 2000; Dacey, Baltzell, & Zaichkowsky, 2008; Fisher et al., 2004). Therefore, people with low functional status may benefit most from physical “life space interventions.” Numerous studies have shown that specific exercise training programs such as progressive strength and balance training can substantially improve physical performance, even in particularly vulnerable populations, such as frail older adults or cognitively impaired individuals (Chodzko-Zajko et al., 2009; Hauer et al., 2017; Schwenk et al., 2014). However, few studies have evaluated the impact of physical interventions on life space. One exception is the study by Jansen, Diegelmann, Schilling, et al. (2017), who were able to show that a physical activity intervention resulted in an extended life space in a vulnerable sample of older nursing-home residents (see also Jansen, Claßen, Hauer, Diegelmann, & Wahl, 2014).
Changing the physical environmental set-up may also have an impact on life space in various ways. For instance, home modifications such as removal of barriers and decluttering may improve navigation and safe movement and thus extend life space within the home environment (Gitlin, Hodgson, Piersol, Hess, & Hauck, 2014; Wahl, Fänge, Oswald, Gitlin, & Iwarsson, 2009). Similarly, optimizing the physical design of a ward such as a special care unit for older adults with dementia-related disorders may improve way-finding and orientation so that individuals can use available space more competently and safely (Day, Carreon, & Stump, 2000).
Lack of motivation to participate in structured exercise programs has been discussed as a major drawback in intervention studies. For many older adults, engagement in structured exercise or sport is either not appealing or not practically feasible (Costello, Kafchinski, Vrazel, & Sullivan, 2011). This is often due to a lack of transportation, limited access to the facilities, time commitments, or unwillingness to join a group (Schutzer & Graves, 2004). Recent studies highlight older adults’ preference for lifestyle activities such as cleaning or gardening rather than performing specific exercises (Burton, Lewin, & Boldy, 2013). Furthermore, structured exercise programs typically do not include a behavioral change concept for fostering long-term adherence and habituation of exercising. Integrating exercises and physical activity into everyday life has been discussed as one promising alternative to structured programs (Weber et al., 2018). Lifestyle-integrated programs aim to turn daily routines into opportunities for physical activity. Several studies have focused on increasing daily walking time, for instance by fostering everyday activities such as walking to the store rather than taking the bus. This approach has been expanded to integrate various functional exercises designed for improving balance, strength and functional performance (Clemson et al., 2012). A recent review on lifestyle integrated training shows that both physical performance and physical activity can be substantially increased using this training format (Weber et al., 2018). However, no study so far has investigated whether such interventions also result in an increase in life space. A currently ongoing European Union project called PreventIT evaluates a lifestyle-integrated training program delivered by smartphone and smartwatch devices, including assessment of physical activity and life space by accelerometer and GPS technology. Results are expected in early 2018.
With regard to promoting walking in older adults, Notthoff and Carstensen (2014) found that informing older adults about the benefits of walking (e.g., “Walking can have important cardiovascular health benefits”) was more effective in promoting walking activity than pointing out the negative consequences of failing to walk (e.g., “Not walking enough can lead to an increased risk for cardiovascular disease”). These findings are in line with the “age-related positivity effect” (Reed & Carstensen, 2012), which states that older adults generally prefer positive stimuli and information over negative ones. However, it seems that merely pointing out the benefits of walking does not increase walking as long as other (environmental) prerequisites, such as neighborhood walkability, are not met (Notthoff & Carstensen, 2017), which underlines again the importance of considering mobility and life space as constructs that are driven by multiple, interrelated determinants.
Recommendations for Future Research
Life Space Assessment
The Role of Intraindividual Variability
Despite technological advances, many studies still assess life space in a screening-like manner, using just a single indicator or a singular item at one point in time. Such approaches have two major disadvantages, namely a non-consideration of the everyday dynamics in life space as well as a neglect of the multidimensionality of the life space construct.
Life space levels vary considerably from day to day as well as according to the time of a day (Petersen et al., 2015; Shoval et al., 2011). Generally, aspects of short-term variability, such as day-to-day fluctuations, across different developmental domains have been receiving increasing attention from researchers (Diehl, Hooker, & Sliwinski, 2015) as they may reflect far more than just noise or random variation. For instance, higher intraindividual variability in cognitive performance seems to indicate general neurocognitive vulnerability and precedes steeper long-term cognitive decline (MacDonald, Hultsch, & Dixon, 2003). Analogous to this finding, it could be a promising approach to quantify day-to-day variability in life space of older adults, and to identify its correlates, as well, its association with long-term change in life space. Addressing such a research question requires longitudinal, multiple-time-scale designs (Ram & Diehl, 2015) that cover both short- and long-term time spans (e.g., measurement-burst studies, with short assessment intervals of days or weeks embedded in longer intervals of years).
The Role of Multidimensionality
Measures that only use the information of the furthest distance covered across a specified time period neglect several life space dimensions. Specifically, it may make a difference in terms of resources needed whether such a distance is covered only once or more often over that time period (i.e., life space frequency). Additionally, it may make a difference whether individuals receive help from others or from technical devices when covering that distance (i.e., degree of independence of life space). Indeed, relationships of life space with other domains are different when such additional information is considered (Baker et al., 2003; Rantakokko et al., 2013). Future research should follow the established distinction between different life space indicators, as introduced by Baker et al. (2003) and others.
Finally, technology-based assessment is not necessarily the best way to measure life space, as there may be problems regarding technology acceptance by study participants, data loss, and other (e.g., ethical) issues. A multi-method assessment approach, combining self-reports (questionnaires and diaries) with GPS or other technologies, is recommended. Both sources of information could be validated by this multi-method approach, and the disadvantages of each of the two assessment modes could be compensated to some extent by the other one.
The Need for Interdisciplinary Perspectives on Life Space
Life space research could also benefit from a more interdisciplinary perspective by including and synthetizing expertise from various disciplines. Specifically, experts are needed from geography, geo-informatics, and computer sciences for an in-depth, state-of-the-art assessment, collection, and analysis of mobility data (by means of GPS devices, for example). Innovative approaches are needed to appropriately operationalize concepts such as mobility routines or to differentiate between different degrees of complexity of various mobility performances. Furthermore, sports scientists play an important role in illuminating the interplay of physical functions with different aspects of life space and mobility; and as these functions interact with psychological resources and characteristics (e.g., Portegijs et al., 2014), a close interdisciplinary exchange and collaboration with psychologists is desirable.
The Complex Interplay of Factors Associated With Life Space
Finally, theoretical frameworks that describe determinants of life space and mobility, such as the one by Webber et al. (2010), may need to be refined. Determinants do not influence life space in an isolated way, but interact in multiple ways (Portegijs et al., 2014; Sartori et al., 2012). Apart from postulating direct pathways between various determinants and life space, indirect pathways via mediating mechanisms and moderating factors need to be added and specified in conceptual frameworks. Finally, in addition to determinants, outcomes of life space need to be considered in future life space models as well, and potentially bi-directional associations between life space and other domains need to be examined. Specifically, life space is not only an outcome that is determined by multiple influences; rather, life space also acts as predictor of change in key developmental domains such as cognitive ability (Crowe et al., 2008; James et al., 2011) or physical health/mortality (Mackey et al., 2014; Xue et al., 2008).
Integrative, Multiple-Component Intervention Formats as an Avenue for Future Research
Combining different intervention formats to promote life space among older adults might be particularly promising and effective. According to empirical findings and to the Webber et al. (2010) framework, the life space of older adults depends on many factors. Therefore, an intervention should ideally be multi-modal, consisting of cognitive, physical, and combined (dual task) trainings as well as more educative and psychosocial elements (e.g., by taking into account specific motivations and preferences of older adults).
There may be, of course, other promising approaches to promote life space apart from trainings and interventions. For instance, as long as an older person perceives his or her neighborhood as poorly walkable or insecure, it is rather unlikely that a cognitive, psychosocial or physical intervention alone will contribute to an increase in life space (Notthoff & Carstensen, 2017). There are important means besides interventions that have the potential to additionally promote life space, namely: (a), removing common environmental barriers that restrict life space in old age (e.g., by optimizing light conditions and terrain characteristics in public places, reducing stairs and/or adding stair-rails, creating homes and public transport modes that are barrier-free); (b), improving personal resources such as vision (using all available corrections, cataract surgery, etc.); and (c), providing older adults with all available assistive devices (such as walkers or canes) that facilitate out-of-home mobility.
Life space can be considered a key indicator of out-of-home mobility in older adults and is substantially related with autonomy, well being, and quality of life. Research on life space in old age has been intensified over the past decades. Recent developments of technology-based life space assessment have stimulated and enriched this research area. Future life space research will benefit from further improvement of life space measurement, considering life space as a multidimensional and dynamic construct that varies over both short-term and long-term periods. Future research should also take a perspective on life space that is more interdisciplinary and that pays more attention to the various complex pathways in which the determinants (and consequences) of life space interact. Finally, interventions to increase life space in older adults need to be multi-modal, and they should address and promote more than just one of the key resources that are required for access to distal life spaces.
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