Out-of-home behavior and cognitive impairment in older adults: findings of the SenTra Project.

459373 urnal of Applied GerontologyWettstein et al.

JAG34110.1177/0733464812459373Jo

Original Article

Out-of-Home Behavior and Cognitive Impairment in Older Adults: Findings of the SenTra Project

Journal of Applied Gerontology 2015, V ol. 34(1) 3–25 © The Author(s) 2012 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0733464812459373 jag.sagepub.com

Markus Wettstein1, Hans-Werner Wahl1, Noam Shoval2, Frank Oswald3, Elke Voss4, Ulrich Seidl5, Lutz Frölich4, Gail Auslander2, Jeremia Heinik6, and Ruth Landau2

Abstract This study explores differences in the out-of-home behavior of communitydwelling older adults with different cognitive impairment. Three levels of complexity of out-of-home behavior were distinguished: (a) mostly automatized walking behavior (low complexity), (b) global out-of-home mobility (medium complexity), and (c) defined units of concrete out-of-home activities, particularly cognitively demanding activities (high complexity). A sample of 257 older adults aged 59 to 91 years (M = 72.9 years, SD = 6.4 years) included 35 persons with Manuscript received: April 6, 2012; final revision received: June 15, 2012; accepted: July 20, 2012. 1

Heidelberg University, Heidelberg, Germany Hebrew University Jerusalem, Israel 3 Goethe University, Frankfurt, Germany 4 Central Institute of Mental Health, Mannheim, Germany 5 Klinikum Stuttgart, Department of Psychiatry, Stuttgart, Germany 6 Tel Aviv University, Tel Aviv, Israel 2

Corresponding Author: Hans-Werner Wahl, Bergheimer Str. 20, Heidelberg, 69115, Germany. Email: [email protected]

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early-stage Alzheimer’s disease (AD), 76 persons with mild cognitive impairment (MCI), and 146 cognitively healthy persons (CH). Mobility data were gathered by using a GPS tracking device as well as by questionnaire. Predicting cognitive impairment status by out-of-home behavior and a range of confounders by means of multinomial logistic regression revealed that only cognitively demanding activities showed at least a marginally significant difference between MCI and CH and were highly significant between AD and CH. Keywords out-of-home behavior, Alzheimer’s disease, mild cognitive impairment, old age, GPS tracking technology Research shows that out-of-home behavior is critical for quality of life, independence, and well-being in old age (Mollenkopf, Hieber, & Wahl, 2011; Mollenkopf et al., 2004; Schaie, Wahl, Mollenkopf, & Oswald, 2003). We use in this article the term out-of-home behavior to address the full range of activities of moving from one location (particularly the home) to another (i.e., all forms of being mobile out of home) as well as activities that are conducted outside the home (Webber, Porter, & Menec, 2010). It is a clinically well-established fact that cognitive impairment is accompanied by changes of out-of-home mobility. In particular, cognitive impairments, such as some of the early manifestations of dementia (particularly, Alzheimer’s disease), but also types of MCI (Petersen et al., 2001; Petersen & Morris, 2003) include problems with out-of-home orientation, way finding, and spatial navigation (Hort et al., 2007; Tippett et al., 2009), which may result in a significant reduction of the use of out-of-home space. Problems with out-of-home mobility are also the single most frequently mentioned challenges of caring for people with dementia in the community; they are also among the difficult-tomanage behaviors (Silverstein, Flaherty, & Tobin, 2002). Change in out-of-home mobility may indeed be an early sign of mild cognitive impairment. The primary reason for this assumption is that out-of-home mobility is complex and resource intensive. For example, it demands continued cognitive attention, sensory motor coordination, and adjustment, due to issues such as changing weather conditions or unexpected occurrences in the physical environment (James, Boyle, Buchman, Barnes, & Bennett, 2011). Seen from a person–environment perspective, particularly the competence-press model of Lawton and Nahemow (1973; see also Wahl & Gitlin, 2007), the likelihood of a lack of fit between person competencies and environmental demands can be expected to substantially increase with the occurrence of cognitive impairment and this may cause unfavorable outcomes such as falls, getting lost, or even a transition to long-term care.


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The relationship between mobility as a major indicator of out-of-home behavior and cognitive impairment has drawn considerable research attention in the past. First, experimental research has demonstrated a robust (negative) relationship between cognitive impairment—particularly, impaired executive function— and indicators of mobility (e.g., gait disturbances), which are more disturbed in those with impaired executive control (Ble et al., 2005; Holtzer, Verghese, Xue, & Lipton, 2006). Second, research related to the out-of-home behavior of older adults with dementia has frequently been conducted with residents of institutional settings (Miskelly, 2005; Silverstein et al., 2002), while much less is known about the out-of-home behavior of community-dwelling older adults. There is, however, some evidence pointing to reduced out-of-home life space and activity range in community-dwelling older adults with more severe cognitive impairment (Crowe et al., 2008; Shoval et al., 2011). In terms of methodology, most research has assessed out-of-home behavior, or rather single aspects of out-ofhome behavior, such as out-of-home mobility and activities that are conducted out of home, via a questionnaire approach. Several shortcomings emerge from these streams and preferred methods of previous research. In most of the studies, only single aspects or indicators of out-ofhome behavior were assessed, failing to emphasize its multidimensionality. However, an integrative and multidimensional understanding of out-of-home behavior needs multiple indicators. It may indeed be the case that different components of out-of-home behavior reveal different strengths of relationship with different cognitive status, and this can only be detected by using a multidimensional approach. Going further, assessing out-of-home behavior via self-reports or in highly controlled experimental designs—as has been done in many cases in previous work—has several disadvantages: Self-reports may be biased particularly regarding highly detailed aspects of out-of-home behavior (e.g., length of walking tracks), and mobility and cognition dynamics assessed in a laboratory context might only partially reflect the dynamics of out-of-home behavior in natural settings. For example, out-of-home behavior in natural settings is embedded in a full range of compensatory means, such as social partners’ impact on behavior, the use of highly familiar environments, and reliance on decade-long routines of walking. In light of these limitations of the previous research, our work builds on prior empirical findings as well as conceptual reasoning supporting the view of out-ofhome behavior as a multidimensional and multifaceted phenomenon that should be analyzed at various levels of complexity (Metz, 2000; Patla & Shumway-Cook, 1999; Webber et al., 2010). In particular, the distinction of different complexity levels allows us, unlike most approaches in previous research, to test for differential relationships between levels of out-of-home behavioral complexity and various degrees of cognitive status (Figure 1). First, at a rather low level of complexity,


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Figure 1. Conceptual framework of out-of-home-behavior.

out-of-home walking indicators, such as walking duration and distance, number of walking trips per day, and walking speed reflect highly automatized out-of-home behavior developed over the life course; this behavior is typically executed in familiar settings in a rather narrow action range around one’s home. Second, global out-of-home mobility indicators, such as total time spent out of home and number of places visited, include the full range of out-of-home behaviors of all kinds of cognitive complexity, but in a rather general and unspecified manner. Therefore, we locate global out-of-home mobility indicators at the medium level of complexity of out-of-home behavior. A third level of complexity uses more content-circumscribed and higher-order units of behavior operating at a higher level of complexity. Here, we will use the term out-of-home activity in contrast to out-of-home mobility to indicate such out-of-home behavior. Going further, the distinction between physically demanding and cognitively demanding out-ofhome activities, as used in the previous research on activities in the time budget and other research arenas (Horgas, Wilms, & Baltes, 1998; Karp et al., 2006;


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Wilson & Bennett, 2003), may be helpful regarding activities. In particular, cognitively demanding out-of-home activities, such as going to a bank for financial purposes or educational activities, such as visiting a library, were expected to be high in complexity; physically demanding out-of-home activities, such as doing gardening, were expected to be less complex. Concerning interdependencies between the derived out-of-home behavior dimensions (out-of-home mobility and out-of-home activity), a certain overlap is plausible as the exertion of a specific out-of-home activity such as shopping requires mobility performances. Therefore, a hierarchy of out-of-home behavior dimensions can be assumed, with out-ofhome mobility on a lower hierarchical level than out-of-home activity.

Research Aims and Hypotheses The goal of this study is to analyze the relationship between out-of-home behavior and cognitive impairment status, referring to different levels of complexity of out-of-home behavior and a multimethods approach (GPS tracking technology and questionnaire data). In regard to the lowest level of complexity of out-ofhome behavior, reflected in out-of-home walking indicators, we expect no difference between cognitively healthy older adults (CH) and those with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). With respect to the medium level of cognitive complexity of out-of-home behavior, expressed in indicators of global out-of-home mobility indicators, we expect differences between AD and both MCI and CH, whereas no differences between MCI and CH are expected. With regard to the highest level of complexity of out-of-home behavior, reflected in cognitively demanding out-of-home activities, we expect differences not only between AD and MCI and CH but also between MCI and CH. We will also examine whether such predictions will hold after controlling for covariates such as age, gender, education, household constellation, and physical functioning, all of which have been found to be related with out-ofhome behavior (Murata, Kondo, Tamakoshi, Yatsuya, & Toyoshima, 2006; Shumway-Cook et al., 2007; Webber et al., 2010).

Method Project Design, Study Samples, and Recruitment Strategy Data were gathered within the project “The Use of Advanced Tracking Technologies for the Analysis of Mobility in Alzheimer’s Disease and Related Cognitive Diseases” (“Senior Tracking”; SenTra), an interdisciplinary study of German and Israeli psychologists, psychiatrists, geographers, and social workers


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(Oswald et al., 2010; Shoval et al., 2008). Potential participants with AD and MCI were identified in Germany, through the memory clinics of the Department of General Psychiatry, Heidelberg University, Heidelberg (Germany), and the Central Institute of Mental Health, Mannheim (Germany), and in Israel, through the psychogeriatric center of the Tel Aviv Sourasky Medical Center. The participants were part of the normal work-up of outpatients with cognitive disorders at either institution. A comprehensive medical, neuropsychological, and neuropsychiatric assessment, including the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) standardized procedure for the evaluation and diagnosis of patients with cognitive impairments (Morris, Heyman, Mohs, & Hughes, 1989; German version by Thalmann et al., 2000) in Germany and the Camdex-R (Roth, Huppert, Tym, & Mountjoy, 1998) in Israel, was carried out by a multidisciplinary team (including experienced [neuro-]psychologists, neurologists, and geriatric psychiatrists) in the local memory clinics. CH individuals were drawn in Germany at random from official local public registers. In Israel, CH individuals were identified via senior centers and other organizations because public registers are not readily available. CH individuals underwent the same assessment procedure as the other participants. Participants with cognitive impairment fulfilled either the diagnostic criteria for AD or MCI (Levy, 1994; Winblad et al., 2004). Because of our intention to use questionnaires as well as the use of GPS technology across a period of 4 weeks (see below), we only included individuals with early-stage AD, based on a global deterioration scale (GDS; Reisberg, Ferris, de Leon, & Crook, 1982) score of 4 or a Clinical Dementia Rating (CDR) score of 0.5 or 1.0. Exclusion criteria were other types of dementia (e.g., vascular, frontotemporal), other severe psychiatric disorders (e.g., major depression, schizophrenia, or severe personality disorders), substance abuse, severe motor disturbances (e.g., caused by Parkinson’s disease), sensory deficits potentially affecting mobility, severe somatic illness (e.g., cancer), and use of prescription drugs that could potentially affect cognition and functioning (e.g., neuroleptics). Inclusion criteria for the CH included no subjective cognitive complaints, no activities of daily living (ADL) impairments, and performance within 1 standard deviation—according to norm scores—in all cognitive domains. We also strived to enlist participants with a caregiver or significant other available in the household because we were also interested in their experience regarding the out-of-home behavior of the target person (we are not using this information in the current paper). All participants were informed about the project and the assessment procedure by means of individual invitation letters, followed by personal telephone calls. If respondents agreed to participate, they were enrolled in the study after informed consent, following the ethical guidelines and procedures for formal ethical


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consent. In particular, expected ethical considerations regarding the study’s use of GPS/GIS technology (e.g., fear of being observed, intrusion into privacy) were discussed during the informed consent. Major reasons to refuse participation in the study were distrust, fear, lack of interest and/or time, general health problems, and occurrences with significant others (e.g., recent death of partner). We generally recruited a to some extent positively selected group of CH, MCI, and AD participants regarding outcome variables such as health. Written informed consent was received from all participants, and the Ethics Board Review of the Universities of Heidelberg and the Helsinki Committee (IRB) at the Tel Aviv Sourasky Medical Center approved the study. Because we expected difficulties in recruiting participants and their family members who would commit themselves for 4 weeks of GPS tracking as well as interviewing and cognitive testing, our sampling goal was to gather as large as possible groups in the 2-year period available for the recruitment process. Specifically, we strived to enroll at least 30 individuals in the AD group. On the other hand, we purposefully planned to collect at least 100 CH older adults for additional analysis with a sole focus on those without cognitive impairment (not reported in this article). In total, data were obtained from 35 AD participants (15 from Israel), 76 MCI participants (39 from Israel), and 146 CH participants (46 from Israel). In all three groups, the majority (66 to 75%) reported the availability of a car in their household, with no significant group difference in car availability. Nearly all study participants (84%) were retired (or currently not working). Table 1 provides a description of the sample. As can be seen in the Table 1, no differences were observed between the three groups regarding age, gender, number of persons in household composition (statistical tendency that individuals with MCI and AD more frequently lived with another person), perceived health, physical functioning, and income satisfaction. However, the MCI and AD groups revealed a significantly lower level of education. Education is considered as one of several influences on the cognitive reserve (Snowdon, 2003; Stern, 2003), which may contribute to prevention or delayed onset of cognitive impairment. This may be an explanation why most of the highly educated persons belonged to the CH group. Groups also varied by MMSE (Folstein, Folstein, & McHugh, 1975) and two common tests of executive functioning, Trail Making Tests A and B (Reitan, 1958; Spreen & Strauss, 1991).

Measurement of Out-of-Home Behavior Measurement of out-of-home walking and global out-of-home mobility indicators. This part of the assessment was done via a GPS data collection approach. Participants received a GPS tracking kit and instructions concerning its use. The kit


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Table 1. Group Comparison (CH, MCI, and AD) in Sociodemographic and Cognitive Measures. Variable N

Total, n = 257

Sociodemographic measures Age (M, SD, range) 72.9, 6.4, 59-91 Gender (%) male 51.0 Education (years; 13.6, 4.2, 2-26 M, SD, Range) 1.8, 0.7, 1-5 Number of persons in household Perceived healtha 2.8, 0.8, 1-5 Physical 80.5, 19-7, functioningb 0-100 Income 7.1,2.2, 0-10 satisfaction Cognitive measures MMSEc 27.5, 2.3, 19-30 Trail-Making Test A (sec)c Trail-Making Test B (sec)c

60.8, 34.5, 19-300 141.6, 80.1, 26-416

CH, n = 146 72.5, 6.1, 61-91 50.0 14.5, 4.2, 2-26 1.7, 0.6, 1-5

2.8, 0.7, 1-5 82.6, 16.9, 30-100 7.3,2.0, 2-10

28.6, 1.3, 26-30 51.2, 21.8, 19-129 121.7, 69.2, 26-370

MCI, n = 76

AD, n = 35

Statistical test

72.9, 6.5, 59-88 74.1, 7.1, 59-86 ns 48.7 60.0 ns 12.3, 4.2, 4-23 12.5, 3.2, 9.5-21 *** (CH > MCI, AD) 1.9, 0.7, 1-5 1.9, 0.6, 1-3 *

3.0, 0.9, 1-5 77.2, 21.8, 0-100 6.8,2.3, 0-10

2.8, 1.0, 1-5 78.7, 24.8, 15-100 7.1,2.4, 0-10

ns ns ns

27.0, 2.1, 21-30 24.1, 2.4, 19-30 *** (CH > MCI > AD) 66.5, 33.9, 91.1, 56.4, *** 25-171 36-300 (CH AD)

*** (CH > MCI > AD)

*(CH, MCI > AD)

** (CH, MCI > AD) *** (CH, MCI > AD)

ns ns ns

Statistical test

Partial η2 = .10

Partial η2 = .04

Partial η2 = .16

Partial η2 = .03

Partial η2 = .05 Partial η2 = .08

Partial η2 = .00 Partial η2 = .01 Partial η2 = .00

Effect size

Note: CH = cognitively healthy control persons; MCI = persons with mild cognitive impairment; AD = persons with early-stage Alzheimer‘s disease. Statistical test for differences: Analysis of variance (ANOVA) F test (means). a 1 = never, 2 = less than once/month, 3 = once/month, 4 = more than once/month, 5 = once/week, 6 = more than once/week, 7 = daily. ns=not significant. *p < .05. **p < .01. ***p < .001.

Out-of-home walking indicators Walking distance (km) 0.7, 0.6, 0.1-3.7 Walking duration (h) 0.2, 0.2, 0.0-3.2 Walking speed (km/h) 3.7, 0.6, 1.6-5.7 Global out-of-home mobility indicators Time out-of-home (hours) 4.0, 2.3, 0.0-12.8 Number of nodes visited 4.5, 1.4, 1.1-9.4 Out-of-home activities Number of physically 2.1, 0.8, 0-3 demanding activities Number of cognitively 1.8, 1.0, 0-5 demanding activities Frequency of physically 4.2, 1.3, 1.0-7.0 demanding activitiesa Frequency of cognitively 2.2, 0.8, 1.0-5.5 demanding activitiesa

Variable, N

Table 2. Differences Between Groups (CH, MCI, and AD) in Out-of-Home Behavior Indicators.

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Table 3. Multinomial Logistic Regression Model: Predicting Cognitive Impairment Status by Out-of-Home Behavior Indicators. Predictors (odds ratio) [95% confidence interval] CH versus MCI Country Age Education Physical functioning Income satisfaction Number of persons in household Walking distance Walking speed Mean number of nodes Number of cognitively demanding activities Number of physically demanding activities CH versus AD Country Age Education Physical functioning Income satisfaction Number of persons in household Walking distance Walking speed Mean number of nodes Number of cognitively demanding activities Number of physically demanding activities R2 Cox & Snell R2 Nagelkerke

2.94** [1.32, 6.56] 1.01 [0.95, 1.08] 0.89** [0.82, 0.97] 0.99 [0.97, 1.01] 0.90 [0.78, 1.04] 1.67(*) [0.97, 2.88] 1.43 [0.73, 2.80] 1.02 [0.55, 1.89] 1.16 [0.89, 1.53] 0.73 (*) [0.51, 1.05] 1.29 [0.78, 2.16] 1.11* [0.29, 4.09] 1.02 [0.93, 1.12] 0.98* [0.87, 1.11] 1.01 [0.98, 1.03] 0.99 [0.80, 1.23] 1.68 [0.78, 3.59] 1.63 [0.70, 3.79] 1.02 [0.42, 2.46] 0.52** [0.31, 0.88] 0.27*** [0.14, 0.51] 0.92 [0.48, 1.77] .30 .35

Note: N = 257. CH = cognitively healthy control persons; MCI = persons with mild cognitive impairment; AD = persons with early-stage Alzheimer’s disease. (*)p < .10. *p < .05. **p < .01. ***p < .001.

to the CH group when exerting more cognitively demanding activities. Furthermore, a higher number of visited nodes out-of-home was related with a higher likelihood of CH group membership. In addition, country (due to sample composition; see above) and a higher education level were also shown to be significant in contributing to CH membership.


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Pseudo R2 values ranged from .30 to .35, depending on different calculation modalities indicating an appropriate fit of the multinomial logistic regression model.

Discussion Although out-of-home behavior of older adults is closely linked with quality of life and likely associated with cognitive status, research in this area has been limited. Specifically, previous research has focused frequently on older adults in long-term care settings, tended to neglect adults with mild cognitive impairment, relied heavily on experimental methodology with the consequence of lowered external validity, and has mostly used a rather narrow operationalization of out-of-home behavior, solely based on questionnaire data. In the current study, we aimed to fill this gap in the literature by considering various forms of cognitive impairment in community-dwelling older adults. In addition, we have argued for the analysis of out-of-home behavior and cognitive status in natural settings based on a multidimensional understanding of out-of-home behavior, various levels of complexity, as well as a multimethods approach, including GPS and questionnaire assessment. Although links between cognitive status and out-of-home behavior have consistently been found in the previous literature (Ble et al., 2005; Crowe et al., 2008; Holtzer et al., 2006; James et al., 2011), we found that differentiation is needed. Consistent with our hypotheses, the CH, MCI, and AD did not significantly differ in out-of-home walking indicators; these indicators reflect highly routinized out-of-home behaviors of a rather low complexity level, typically happening in familiar settings near to one’s home. In some contrast to our findings, however, previous studies have reported substantial relationships between walking speed and cognitive abilities or cognitive status (Ble et al., 2005; Holtzer et al., 2006). One reason for these incongruent findings could be differences in the operationalization of walking speed. In this study, walking speed was not assessed in an experimental setting under controlled context conditions. Therefore, the nonsignificant difference in walking speed between the three groups could be due to a differential choice of walking environments by the groups (see Prohaska et al., 2009), with cognitively impaired persons possibly preferring less complex and more familiar environments where maintaining a normal walking speed was still possible. Global out-of-home mobility indicators with a medium complexity (time spent out of home, number of nodes visited), as well as highly complex behaviors—assessed by physically but particularly cognitively demanding out-of-home activities—were significantly reduced in AD, as compared to both other groups. The number of exerted cognitively demanding out-of-home activities was also lower in MCI, as compared to CH participants.


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Controlling for potentially confounding variables, such as age, sex, education, number of persons in the household, and physical functioning, did not change the picture that cognitively demanding out-of-home activities best discriminated among the three groups out of all out-of-home indicators used. However, this was less the case for the number of visited out-of-home nodes, a GPS indicator of global out-of-home mobility, and definitely was not the case for out-of-home walking indicators. Results thus rather consistently underscore that older adults with AD engage in less out-of-home behavior of high and medium complexity as compared to MCI and CH, and the only, at least marginally significant difference, between MCI and CH appeared in terms of cognitively demanding out-of-home activities. Specifically, older persons with AD seem to visit fewer places in their environment, possibly avoiding complex and cognitively demanding locations and preferring familiar, less demanding places. Explanations of the observed differences in out-of-home behavior between AD and the remaining groups are tempted to solely refer to the role of lowered cognitive performance. However, as seen from a wider perspective of human adaptation in case of constraining developmental conditions, older adults with early-stage AD, as included in the present study—in cooperation with their caregivers—may reduce their out-of-home behavior due to an awareness of lowered cognitive resources; thus, they may attempt to avoid risky and potentially dangerous situations. On the other hand, highly routinized behaviors, such as out-of-home walking patterns in safe and familiar settings, are still feasible and executed in a similar magnitude, as in MCI and CH. This would reflect an efficient strategy in terms of compensation via simplification and reduction of out-of-home behavior (Dixon & Bäckman, 1995), which may preserve quality of life for a considerable period of time. Indeed, referring to a classic distinction in lifespan psychology (Baltes, Lindenberger, & Staudinger, 2006), some pronounced loss in terms of a lowered life space would lead to the gain of maintaining out-of-home autonomy to some extent. However, the effectiveness of such compensation strategies may be reduced as cognitive impairment progresses. Specifically, older adults with early-stage AD may still have some awareness of their impaired cognitive capacity, which supports their ability to use adaptational strategies. This sort of awareness may be absent in advanced stages of AD and may lead to out-of-home behaviors of high safety risk, such as wandering and severe spatial disorientation (Colombo et al., 2001), which can result in severe injury or even death due to falls or traffic accidents (Klein et al., 1999; Koester & Stooksbury, 1995). Overall, the increasing lack of fit between lowered cognitive competencies and high demands of the external environment coming along with dementia-related disorders (Lawton & Nahemow, 1973; Wahl & Gitlin, 2007) may lead to increased restriction of life space in these individuals and may indeed also heighten their risk of social isolation.


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In contrast, the sole significant difference between CH and MCI with regards to out-of-home behavior was in cognitively demanding out-of-home activities, which MCI participants conducted to a lower extent than cognitively healthy participants. In contrast to those with AD, it seems that cognitive resources in those with MCI are still sufficient to maintain previous patterns of out-of-home behavior. However, this out-of-home competence seems to find its limits in highly cognitively demanding activities, such as banking, accompanying and supervising another person (e.g., a grandchild), or doing educational activities. Indeed, it may be the case that early signals for MCI in the everyday context may be seen in the reduction or relinquishing of cognitively demanding activities; this may even eventually, as future research may show, provide earlier indications of MCI than assessed cognitive performance. It should be mentioned that we have also checked for differences in intraindividual variability of out-of-home behavior based on our 4-week GPS data, because such variability, for example, in the area of cognitive performance, has been found to predict potential for long-term cognitive trajectories (Hultsch, MacDonald, Hunter, Levy-Bencheton, & Strauss, 2000). However, in our data, the analysis of intraindividual variability, based on a selection of out-of-home mobility indicators, revealed only minor group differences regarding mobility fluctuation measures. Moreover, these fluctuation measures were not relevant for the prediction of cognitive impairment status in a respective multinomial logistic regression model. In terms of practical implications of our results, we see these at different levels. First, the finding that older adults with AD engage in less out-of-home behavior is an important insight for city planners and stakeholders at large; public places, spaces, and buildings may undergo optimization—such as including orientation tools, reducing environmental hazards, and alerting professionals (such as police, cab drivers, professionals of public transport)—to accommodate the needs and problems of older adults with dementia. That is, there is a still little planning for creating “age-friendly” livable community settings based on a clear idea of the needs of those with cognitive impairment, not only in terms of objective barriers but also when it comes to social participation and “normal” navigation through one’s environments as a senior citizen. Technology such as GPS technology, used in our study only as a research tool, may also become a major practical aid here in the future (Topo, 2009). The societal relevance of such strategies is obvious, given that a high number of people in the community are currently living with cognitive impairment, and even more people are expected to do so in the future. Thus, there is a pressing need to move toward what some have coined the “open city” of the future, with more investment in safety and leisure activity options in public areas that better accommodate the needs of those with various cognitive impairments


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(Blackman et al., 2003). Second, because studies have shown that caregivers of people with dementia often regard the problems with out-of-home behavior as a major burden and stressor (Colombo et al., 2001), advanced tracking technologies may help caregivers to locate (via the Internet) lost cognitively impaired older adults in the quickest time possible; therefore, they may serve as an efficient emergency- and harm-preventing device (e.g., Brooks & Scarfo, 2009). However, the ethical issues associated with the use of such equipment deserve serious attention, and additional findings from SenTra (Landau et al., 2009) support the view that the perspectives of family caregivers may be different from professional caregivers. In particular, the possibility and risk of “Big Brother” scenarios that accompany lost privacy and “total control” generally seems to be more emphasized by professional caregivers. Third, at the measurement level, an important implication of our findings is that activity and everyday competence-oriented assessment scales able to differentiate between MCI and cognitively healthy adults should put major emphasis on a differentiated assessment of highly cognitively demanding out-of-home activities. Such insights may lead to a new generation of Activities of Daily Living–like scales, able to add to the early prediction and differentiation of MCI and thus serve early intervention. The results of the current research need to be considered relative to a number of limitations. Our research was based on rather limited sample sizes, particularly regarding MCI and AD groups, limiting the statistical power of our analyses. Furthermore, the cross-sectional nature of our data prevents any causal conclusions. For example, although we have implied that cognitive impairment affects out-of-home behavior, it seems to also be the case that certain out-of-home behaviors, such as those connected with social participation, impact the course of cognitive impairment (Lövdén, Ghisletta, & Lindenberger, 2005). Indeed, some studies have demonstrated that out-of-home behavior is protective against cognitive decline and dementia (e.g., Wilson & Bennett, 2003) or is able to predict cognitive impairment at a later point in time (James et al., 2011; Prohaska et al., 2009). Moreover, we could not assess for every single conducted out-of-home activity of each person whether the activity had been exerted alone or accompanied, which probably makes a difference in terms of the amount of cognitive demands posed by the activity. However, we did take into account possible influences of social partners by controlling for the number of household members when predicting group membership based on cognitive predictors. Furthermore, cultural and geographical differences between Germany and Israel may also have been underrated in our study—in merging both samples—although we have controlled for country in our multivariate analyses. Finally, caution is necessary to generalize our findings to those living alone because the majority of our participants lived together with another person.


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There is, nevertheless, reason to assume that we have addressed in our findings rather fundamental relationships, which may be largely independent from cultural and geographic characteristics. Finally, our measurement of out-of-home behavioral variables, including the GPS tracking, may be still too rough (exactness of 4-5 meters) to detect differences in out-of-home behavior among CH, MCI, and AD, both at the mean and intraindividual variability level. Despite these limitations, the current study contributes to the understanding of out-of-home behavior and cognitive impairment. In particular, our findings lend support to the view that research regarding out-of-home behavior of older adults with various degrees of cognitive performance can gain from the use of a multimethod assessment of out-of-home behavior, as well as from an interdisciplinary approach including psychological, geographical, and psychiatric expertise.

Acknowledgment The Study “The Use of Advanced Tracking Technologies for the Analysis of Mobility in Alzheimer’s Disease and Related Cognitive Diseases” (abbreviated as Senior Tracking/SenTra) was supported by the German Research Foundation between 2008 and 2011, based on a grant to Hans-Werner Wahl (WA809/11-1). We would like to thank our geography partners in Germany—in particular, Dr. Tim Freytag—for their valuable support regarding the GPS/GIS data collection. Heike Hercher has provided the expert rating for distinguishing cognitively demanding activities in her Master’s thesis in Psychology. Additionally, we would like to thank Katharina Hager, Heike Hercher, Hannah Schmidt-Friderichs, and Johanna Martinez-Slebi for great support in collecting and processing the data of the project. We are also very thankful to the older adults who have participated in the study.

Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The authors received no financial support for the research, authorship, and/or publication of this article.

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Author Biographies Markus Wettstein is post-doc and a psychologist with interest in cognitive and sensory aging, and out-of-home mobility in later life.


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Hans-Werner Wahl is professor and a psychologist with interest in person–environment interrelations in later life, sensory aging, and the awareness of age-related change. Noam Shoval is professor and a geographer with interest in the relationship between mobility and cognitive impairment in old age. Frank Oswald is professor and a psychologist with interest in person–environment interrelations in later life including out-of-home mobility. Elke Voss is a research associate with interest in out-of-home mobility and cognitive impairment as well as depression in old age. Ulrich Seidl is a psychiatrist with interest in cognitive impairment and mobility in later life as well as autobiographical memory and apathy. Lutz Frölich is professor and a psychiatrist with interest in dementia, mild cognitive impairment, and mobility in later life, including pharmacological and behavioral interventions. Gail Auslander is professor and a social worker with interest in caregiving for demented older adults. Jeremia Heinik is a psychiatrist with interest in dementia, mild cognitive impairment, and mobility in later life, including diagnostic and intervention issues. Ruth Landau is professor and social worker with interest in ethical issues related to the use of technology in demented older adults.


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