Published on in Vol 7 (2024)

Preprints (earlier versions) of this paper are available at, first published .
Characterizing Walking Behaviors in Aged Residential Care Using Accelerometry, With Comparison Across Care Levels, Cognitive Status, and Physical Function: Cross-Sectional Study

Characterizing Walking Behaviors in Aged Residential Care Using Accelerometry, With Comparison Across Care Levels, Cognitive Status, and Physical Function: Cross-Sectional Study

Characterizing Walking Behaviors in Aged Residential Care Using Accelerometry, With Comparison Across Care Levels, Cognitive Status, and Physical Function: Cross-Sectional Study

1Translational and Clinical Research Institute, Newcastle University, , Newcastle Upon Tyne, , United Kingdom

2National Institute for Health and Care Research Biomedical Research Centre, Newcastle University and the Newcastle Upon Tyne Hospitals National Health Service Foundation Trust, , Newcastle Upon Tyne, , United Kingdom

3School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, , Auckland, , New Zealand

4The Newcastle Upon Tyne Hospitals National Health Institute Foundation Trust, , Newcastle Upon Tyne, , United Kingdom

*these authors contributed equally

Corresponding Author:

Ríona Mc Ardle, PhD

Background: Walking is important for maintaining physical and mental well-being in aged residential care (ARC). Walking behaviors are not well characterized in ARC due to inconsistencies in assessment methods and metrics as well as limited research regarding the impact of care environment, cognition, or physical function on these behaviors. It is recommended that walking behaviors in ARC are assessed using validated digital methods that can capture low volumes of walking activity.

Objective: This study aims to characterize and compare accelerometry-derived walking behaviors in ARC residents across different care levels, cognitive abilities, and physical capacities.

Methods: A total of 306 ARC residents were recruited from the Staying UpRight randomized controlled trial from 3 care levels: rest home (n=164), hospital (n=117), and dementia care (n=25). Participants’ cognitive status was classified as mild (n=87), moderate (n=128), or severe impairment (n=61); physical function was classified as high-moderate (n=74) and low-very low (n=222) using the Montreal Cognitive Assessment and the Short Physical Performance Battery cutoff scores, respectively. To assess walking, participants wore an accelerometer (Axivity AX3; dimensions: 23×32.5×7.6 mm; weight: 11 g; sampling rate: 100 Hz; range: ±8 g; and memory: 512 MB) on their lower back for 7 days. Outcomes included volume (ie, daily time spent walking, steps, and bouts), pattern (ie, mean walking bout duration and alpha), and variability (of bout length) of walking. Analysis of covariance was used to assess differences in walking behaviors between groups categorized by level of care, cognition, or physical function while controlling for age and sex. Tukey honest significant difference tests for multiple comparisons were used to determine where significant differences occurred. The effect sizes of group differences were calculated using Hedges g (0.2-0.4: small, 0.5-0.7: medium, and 0.8: large).

Results: Dementia care residents showed greater volumes of walking (P<.001; Hedges g=1.0-2.0), with longer (P<.001; Hedges g=0.7-0.8), more variable (P=.008 vs hospital; P<.001 vs rest home; Hedges g=0.6-0.9) bouts compared to other care levels with a lower alpha score (vs hospital: P<.001; Hedges g=0.9, vs rest home: P=.004; Hedges g=0.8). Residents with severe cognitive impairment took longer (P<.001; Hedges g=0.5-0.6), more variable (P<.001; Hedges g=0.4-0.6) bouts, compared to those with mild and moderate cognitive impairment. Residents with low-very low physical function had lower walking volumes (total walk time and bouts per day: P<.001; steps per day: P=.005; Hedges g=0.4-0.5) and higher variability (P=.04; Hedges g=0.2) compared to those with high-moderate capacity.

Conclusions: ARC residents across different levels of care, cognition, and physical function demonstrate different walking behaviors. However, ARC residents often present with varying levels of both cognitive and physical abilities, reflecting their complex multimorbid nature, which should be considered in further work. This work has demonstrated the importance of considering a nuanced framework of digital outcomes relating to volume, pattern, and variability of walking behaviors among ARC residents.

Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12618001827224;

JMIR Aging 2024;7:e53020



Physical mobility, such as walking, is a key predictor of health [1] and is considered a multifaceted experience that interconnects the physical, mental, social, and emotional needs of an individual with their sense of self [2,3]. Loss of physical mobility (eg, reduced volume of walking) is associated with increased safety risks (eg, falls), social withdrawal, and poorer well-being [4,5]. Supporting residents’ physical mobility in aged residential care (ARC) can decelerate the progression of disabilities and dependency [6]. ARC refers to long-term full-time residential care, which provides multiple levels of care depending on an individual’s needs. Other common terms for ARC include assisted living facilities, care homes, and nursing homes. It is recommended that all residents who can ambulate, regardless of cognitive abilities, should increase their activity levels to support their functional independence [7]. Continuous remote digital monitoring of mobility outcomes has been proposed as a method to objectively quantify changes in walking behaviors. This approach will inform the development of interventions aimed at better supporting mobility, which is a key factor in influencing well-being and function in the older population [2].

Accelerometers are the most common method to continuously assess walking behaviors in ARC residents [8], with outcomes relating to volume (eg, steps per day) and intensity (eg, moderate-vigorous physical activity) of walking activities most frequently reported. Based on the current literature, ARC residents primarily participate in low volumes of light-intensity walking and show little variation in their walking behaviors [8]. Based on prevailing gaps in the literature, current recommendations for the assessment of walking behaviors in ARC include the use of validated digital methods that can capture very low volumes of activity, using low cutoff thresholds (eg, any walking activity ≥3 steps), and derive standardized outcomes relating to volume (ie, the amount or duration of walking activity), pattern (ie, the distribution of walking activity across a time period), and variability (ie, changes in walking activities—either within-person or group activities—and over time) of walking behaviors [8,9].

Using this nuanced framework, we can consider how different attributes impact discrete walking behaviors. For example, we previously found that better physical function was associated with higher walking volumes in ARC residents in intermediate (ie, rest homes) and high-level (ie, hospitals) care, while surprisingly, moderate dementia, mild depression, and pain had no effect on walking volumes [10]. In contrast, people with mild cognitive impairment in the community show no differences in walking volumes compared to people who undergo normal aging, but they do demonstrate different patterns and greater variability in their walking behaviors [9]. By looking beyond the volume of walking activities to pattern and variability, we may garner information about people’s routines and the time they spend indoors and outdoors (based on walking bout lengths) [8,9,11-14]; we can then examine the impact of cognitive and physical impairments on these behaviors [14]. This information can contribute toward the development of more holistic interventions to support mobility in ARC.

Notably, ARC residents are a complex multimorbid population with significant variation in cognitive and physical function, often reflected in the level of care provided. These heterogeneities are not reflected in the literature regarding walking behaviors, highlighting a clear gap [8,15,16]. For example, Mc Ardle et al’s [8] review on the quantification of ambulatory activities in ARC reported that 26% of studies excluded people with cognitive impairment and only 17% explicitly characterized walking activities in people with cognitive impairment, despite a 65%-70% prevalence of cognitive impairment in ARC residents [17,18]. Additionally, no studies compared different levels of care. As such, Mc Ardle et al [8] recommended that we must characterize and compare the volumes, patterns, and variability of walking behaviors in ARC residents across different care levels, with different cognitive and physical abilities. By characterizing walking behaviors in a representative group of ambulatory ARC residents, we can gain a better understanding of physical mobility in ARC, which will inform future interventions and policies to promote walking activities and support mobility and function in ARC residents.

To address the highlighted gaps and recommendations, the primary aim of this study was to digitally characterize and compare walking behaviors across different levels of ARC using a validated and standardized framework, encompassing volume, pattern, and variability of walking. Secondary aims of this study were to characterize and compare walking behaviors in ARC residents according to their cognitive status and physical function.


Residents from 24 ARC facilities in New Zealand were recruited as part of the Staying UpRight randomized controlled trial (RCT), which evaluated an exercise intervention to reduce fall risk [19]. Only baseline data are included in this study. Participants were included if they were aged ≥65 years and mobile (ie, able to walk and transfer independently or with supervisory assistance). Participants were receiving one of the following levels of care: hospital-level care (24-hour care by, or under the supervision of, a nurse), rest home–level care (24-hour health-related care but not nursing care), or dementia-level care (rest-home level care in a secure environment to minimize the risks associated with dementia).

We excluded residents in psychogeriatric, respite, or palliative care; residents unable to undertake the assessment or the exercise intervention in the main RCT because they were acutely unwell (eg, gastroenteritis), or immobile (ie, unable to mobilize without 2-person assistance or bed bound) were also excluded.

Ethical Considerations

Participants who were able to give informed written consent did so before enrollment, and the facility clinical lead provided written consent for residents unable to provide their own informed consent because of cognitive impairment. The study was conducted according to the guidelines of the Declaration of Helsinki. Ethics approval was provided by the New Zealand Health and Disability Ethics Committee on October 31, 2018 (NZHDEC 18/NTB/151).

Clinical and Cognitive Outcomes

Demographic information for ARC residents included the following: age, sex, and years spent in the ARC facility. Physical function was measured using Short Physical Performance Battery (SPPB) [20] and the Timed Up and Go test [21]. Cognitive ability was assessed using the Montreal Cognitive Assessment (MoCA) [22].

Assessment of Walking Behaviors

ARC residents were asked to wear a small body-worn accelerometer (Axivity AX3; dimensions: 23×32.5×7.6 mm; weight: 11 g; sampling rate: 100 Hz; range: ±8 g; and memory: 512 MB) on the fifth lumber vertebra on the lower back. The accelerometer was affixed onto the skin using a double-sided hydrogel adhesive and a hypoallergenic plaster (Hypafix BSN Medical Limited). This particular protocol has been found to be feasible for multisite studies [23] in different aging cohorts [11,13,24]. Of particular note, algorithms used in this study for walking bout detection have been validated in ARC residents, with high accuracy for start and end time [25].

Participants were asked to wear the accelerometer continuously for 7 days, including in the shower and to bed. Once the assessment was complete, data were downloaded to a computer and processed via a validated analytical pipeline in MATLAB.

Data Processing and Walking Behavior Outcomes

Signals from the accelerometer were transformed to a horizontal-vertical co-ordinate system. Walking bouts were identified by filtering raw acceleration data using a second-order low-pass Butterworth two-pass digital filter, with a cutoff frequency of 17 Hz, and by applying selective thresholds on the vector magnitude and standard deviations of triaxial acceleration signals [11,23,26,27]. Once walking bouts were identified, for detecting steps, raw acceleration signals were filtered with low-pass, fourth-order Butterworth filter with cutoff frequency of 20 Hz. A Gaussian continuous wavelet transform of vertical acceleration was then applied to identify initial and final contacts, allowing the identification of steps. For each walking bout, total steps per bout and bout length were calculated. Sleep, lying, and sitting data were excluded based on the thresholds applied on the magnitude and standard of the accelerometry signal used to identify walking (eg, vertical acceleration, in a vertical position, needs to be −1 g and acceleration magnitude or standard needs to exceed these thresholds to be classified as walking). For sleep, the magnitude and standard of acceleration would be lower and the vertical acceleration would not be −1 g, so the position (orientation) excludes sleep, lying, or sitting.

A framework of walking behaviors was derived to remain consistent with previous literature [11,12,23,26], including volume, pattern, and variability of walking. Volume characteristics included total minutes spent walking as well as steps and bouts per day. Pattern characteristics included mean bout duration and alpha, which is derived by logarithmic transformation of bout density and length and is based on shape and power-law distribution [28,29]; alpha refers to the ratio of short to long walking bouts, which are scaled relative to an individual’s shortest walking bout. A high alpha score indicates that an individual’s total walking time is composed of proportionally shorter walking bouts compared to long walking bouts. Variability (S2) refers to the variability of bout duration between walking bouts, estimating how much an individual’s bout duration changes over the time period of data collection, and it was estimated using the maximum likelihood technique (previously described by Mc Ardle et al and Del Din [9,13]. The proportion of walking bouts taken in very short (<10 s), short (10-30 s), medium (30-60 s), and prolonged (>60 s) walks were calculated. These walking bout thresholds have been used commonly in other studies of a similar nature and provide contextual information regarding how walking takes place [13,30,31].

Considerations for Inclusion of Data

Given that most habitual walking takes place in <10-second bouts [13,32,33], we applied a minimum bout duration of 3 consecutive steps, and any period of rest that was ≥2.5 seconds was considered resting time [32]. Additionally, we included participants if they had ≥2 days of continuous walking activity data collected, as this is the minimum number of days required to reliably quantify our primary outcomes (ie, the volume of walking) across different care levels, based on Buckley et al [27].

Data Analysis

For demographic variables, chi-square tests and Fisher exact test were used to determine differences between groups for nominal variables, while one-way ANOVA was used to determine between-group differences for continuous variables; post hoc Tukey honest significant difference (HSD) tests determined where the differences lay.

Prior to statistical analysis relating to our primary and secondary aims, walking activity data were inspected visually using box plots, and outliers were identified. Separate analyses of covariance were used to assess differences in walking behaviors between groups categorized by level of care, cognition, or physical function while controlling for age and sex. Tukey HSD tests for multiple comparisons were used to determine where significant differences occurred. Sensitivity analysis was conducted by removing outliers more than 1.5 times above the third quartile or below the first quartile and by conducting the analysis of covariance and subsequent post hoc tests for each discrete grouping separately (eg, level of care, cognition, or physical function).

The effect size of group differences was calculated using the Hedges g formula to account for disparities between groups’ sample sizes [34]. Effect sizes are interpreted as follows: 0.2-0.4: small, 0.5-0.7: medium, and ≥0.8: large. Assumptions were evaluated (eg, normality of residuals) for all models, and statistical significance was defined as a P<.05.

Cognitive levels were assessed and categorized using MoCA cutoff scores, as follows: cognitively intact (MoCA ≥26), mild cognitive impairment (MoCA 18-25), moderate cognitive impairment (MoCA 10-17), and severe cognitive impairment (MoCA <10) [35]. Cognitively intact participants were excluded from the cognitive impairment severity analysis due to the small sample size but retained for illustrative purposes in Figures. Physical function levels were assessed and categorized using the SPPB cutoff scores, as follows: high-moderate function (SPPB 12-7) or low-very low function (SPPB <7) [36].

Demographic Information

A total of 306 ARC residents were included in this analysis and were primarily grouped according to their level of care (Table 1). Figure 1 outlines reasons for exclusion and inclusion of participants for this secondary analysis from the Staying UpRight RCT. Hospital-level care residents had lower physical function compared to rest home–level care residents (P=.01) and took a longer time to complete the Timed Up and Go test compared to rest home–level (P<.001) and dementia-level (P=.03) residents. MoCA scores were significantly lower in dementia-level residents compared to rest home–level and hospital-level care residents (P<.001).

Table 1. Demographic information for participants categorized by levels of care. Italicized P values indicate significance.
CharacteristicsHospital (n=117)Rest home (n=164)Dementia care (n=25)Overall P valuea
Age (years; n=306), mean (SD)84 (7)84 (7)81 (8).20
Sex (n=306), n (%).90
Female70 (60)101 (62)16 (64)
Male47 (40)63 (38)9 (36)
Years in facilityb (n=304), mean (SD)0.4 (0.2)0.4 (0.2)0.4 (0.1).60
SPPBc score (0-12; n=296), mean (SD)4.3 (2.6)d5.2 (2.6)d4.1 (2.3).008
Physical function levele(n=296), n (%)f
High physical function (SPPB 10-12)5 (4.3)9 (5.6)0 (0)
Moderate physical function (SPPB 7-9)19 (17)38 (24)3 (15)
Low physical function (SPPB 4-6)37 (32)70 (43)8 (40)
Very low physical function (SPPB <4)54 (47)44 (27)9 (45)
TUGh (s; n=289), mean (SD)37 (22)d,i27 (15)d25 (18)i<.001
MoCAj score (0-30; n=285), mean (SD)15 (6)i15 (6)k4 (6)i,k<.001
Cognitive levele(n=285), n (%)
Cognitively intact (MoCA ≥26)5 (4.6)4 (2.5)0 (0)
Mild cognitive impairment (MoCA 18-25)28 (26)58 (36)1 (5.6)
Moderate cognitive impairment (MoCA 10-17)60 (56)66 (42)2 (11)
Severe cognitive impairment (MoCA <10)15 (14)31 (19)15 (83)
Not testedl957
Days wearing the activity monitor (n=306), mean (SD)6.5 (1)6.4 (1)6.3 (1).60

aOne-way ANOVA, Pearson chi-square test, and Fisher exact test.

bFor years in facility, 1 partcipant’s data were missing from both the “hospital” and “rest home” groups.

cSPPB: Short Physical Performance Battery (2 participants in the “hospital” group, 3 participants in the “rest home” group, and 5 participants in the “dementia care” group were not tested for SPPB).

dHospital vs rest home.

eDescriptive variable only (no statistical testing performed).

fNot applicable.

g“Unknown” indicates participant data missing in each group, so percentages are not applicable.

hTUG: Timed Up and Go (6 participants in the “hospital” group, 3 participants in the “rest home” group, and 8 participants in the “dementia care” group were not tested).

iHospital vs dementia care.

jMoCA: Montreal Cognitive Assessment (9 participants in the “hospital” group, 5 participants in the “rest home” group, and 7 participants in the “dementia care” group were not tested).

kRest home vs dementia care.

lIndicates the number of participants not tested in each group, so percentages are not applicable.

Figure 1. Flowchart for inclusion of participants in this analysis.

Walking Behaviors Across Care Levels

Dementia care residents demonstrated higher volumes of walking, with longer, more variable bout durations and lower alpha scores compared to residents in both rest homes and hospitals (moderate to large effect sizes). Residents in rest homes also showed higher volumes of walking compared to those in hospitals (small effect sizes). Table 2 provides further details; Multimedia Appendix 1 provides P values for post hoc tests and details regarding effect sizes. Notably, sensitivity analysis indicated that variability of walking bout length did not differ between groups following the removal of outliers (P=.16).

Table 2. Characterization of walking behaviors, categorized by level of care (N=306).
CharacteristicsHospital (n=117)Rest home (n=164)Dementia care (n=25)Overall P valuea
Walk time per day (min), mean (SD)58 (37)b,c74 (39)b,d137 (59)c,d<.001
Steps per day, mean (SD)4138 (2766)b,c5216 (2925)b,d10,886 (5453)c,d<.001
Bouts per day, mean (SD)256 (165)b,c321 (160)b,d496 (238)c,d<.001
Mean bout duration (s), mean (SD)13.9 (3.6)c14.1 (3.3)d20.1 (20.4)c,d<.001
Variability, mean (SD)0.81 (0.11)c0.80 (0.09)d0.89 (0.18)c,d.002
Alpha score, mean (SD)1.68 (0.08)c1.67 (0.07)d1.61 (0.09)c,d<.001
Distribution of walking bouts by discrete walking bout length (%), mean (SD)
<10-second bouts65 (8)c64 (8)d56 (10)c,d<.001
10- to 30-second bouts25.9 (5.5)c27.6 (5.5)d30.8 (5.8)c,d<.001
30- to 60-second bouts5.81 (3.05)c5.51 (2.42)d8.64 (3.64)c,d<.001
>60-second bouts3.20 (2.13)c3.15 (2.06)d4.82 (4.92)c,d.01

aOne-way ANOVA, controlling for age and sex.

bHospital vs rest home.

cHospital vs dementia care.

dRest home vs dementia care.

Additionally, dementia care residents spent a significantly lower percentage of their walking bouts in very short bouts (eg, <10 s) and a greater percentage in short, medium, and prolonged walking bouts compared to residents in other care levels (moderate to large effect sizes; Table 2).

Walking Behaviors Across Cognitive Impairment Severities

There were no significant differences between cognitive groups for any volume characteristics (Figure 2 and Multimedia Appendix 2). People with severe cognitive impairment took longer, more variable walking bouts with a lower alpha score compared to those with mild (moderate to large effect sizes) and moderate cognitive impairment (small to moderate effect sizes). Figure 2 and Multimedia Appendix 2 provide further details.

Figure 2. Volume, pattern, and variability of walking behaviors across cognitive groups. ***P≤.001; **P≤.01.

Walking Behaviors Across Physical Function Levels

ARC residents with high-moderate physical function spent more time walking and took more steps and bouts per day, with less variability for bout length, compared to those with low-very low physical function (Figure 3 and Multimedia Appendix 3 present further details).

Figure 3. Volume, pattern, and variability of walking behaviors across physical function groups. ***P≤.001; **P≤.01.

Principal Findings

This is the first study to describe the volume, pattern, and variability of walking behaviors in ARC, captured by an accelerometer, with consideration of different care levels, cognitive abilities, and physical function, reflecting the typical population of residents. Key findings highlight that dementia care residents have significantly higher volumes of walking, take longer and more variable walking bouts on average, and spend proportionately more of their walking time in prolonged bouts of walking compared to rest home and hospital levels of care. Although the volume of walking is similar across different levels of cognitive impairment severity, people with more severe cognitive impairment show different patterns (eg, longer walking bouts) and greater variability compared to those who are less cognitively impaired. In contrast, people with lower physical function have significantly lower volumes of walking and higher variability of walking bout lengths but do not differ in terms of the pattern of this activity. ARC residents are a complex multimorbid population who often present with varying levels of both cognitive and physical abilities, and these nuances should be considered in further research aiming to improve mobility and reduce fall risk.

Walking Behaviors Across Different Care Environments

This is the first study to show that people living in a dementia unit participate in higher volumes of walking, with different patterns (ie, longer walking bouts) and greater variability compared to other ARC environments, with medium to large effect sizes. However, differences between groups for the variability of walking bout length disappeared following the removal of significant outliers, therefore, results should be interpreted with caution. As physical function scores are comparable between the dementia care and rest home residents (Table 1), the differences in walking behaviors may illustrate a behavioral component of dementia (eg, wandering—a dementia-related locomotor behavior involving frequent and repetitive movements, such as pacing). Objective remote monitoring of wandering behaviors using digital methods has previously been proposed to detect and monitor wandering behaviors [37,38]. We propose that variability of walking bout lengths should be considered in future research in this area, as it may reflect wandering behaviors[39]; clinical validation is required to investigate this hypothesis.

Contrary to our findings, baseline results from one previous RCT reported very low volumes of walking activity in dementia care units, showing <400 steps in one 24-hour period assessed via an activity armband [40]. However, these studies are difficult to compare due to differences in device location (ie, lower back compared to arm) and data collection periods. Moyle et al [40] noted that these activity armbands were unreliable and resulted in large amounts of missing data. Additionally, high volumes of walking activity reported here (eg, >4000 steps per day) are likely due to our low cutoff thresholds for defining walking activity, as most walking takes place in very short walking bouts in this population. The cutoff threshold applied to characterize walking behaviors can make significant differences in the volume of walking captured—with differences ranging from 2000 to 10,000 steps in previous literature [30]. This study has expanded beyond simple volume metrics and highlighted the importance of selecting validated and sensitive digital methods when assessing walking behaviors in this population [8].

Walking Behavior Across Cognitive Impairment Severity and Physical Capacities

This is the first study to demonstrate that people with severe cognitive impairment have similar volumes but significantly different patterns (ie, longer bouts) and greater variability of walking compared to less cognitively impaired groups, suggesting that although cognition does not influence the amount of activity, it may change the way this activity is carried out. This finding is supported by and extends our previous work, which excluded dementia care residents and highlighted that while worse physical function is associated with lower volumes of walking in ARC, cognitive impairment showed no effect on walking volume [10]. Perhaps this indicates that pattern and variability of walking behaviors are cognitively mediated outcomes and may be useful to monitor in ARC as a proxy for cognitive decline. For example, in line with our results from the dementia care unit, the literature indicates that people with severe dementia are more likely to wander [41] and we propose that this is reflected in the pattern and variability of walking. Longer walking bouts and higher variability of bout length are considered positive outcomes in cognitively healthy individuals, indicative of dynamic and varied routines [11], but perhaps higher variability in tandem with significant cognitive impairment is more reflective of repetitive lapping behaviors (ie, wandering). Clinical validation is required to address this speculation. Differences in pattern of walking behaviors have previously been reported between community-dwellers with mild cognitive impairment and normal aging [42], supporting the hypothesis that cognitive decline may influence these behaviors.

In contrast, people with worse physical function have significantly lower volumes of walking but show no differences in pattern or variability compared to those with better physical function. The association between higher walking volumes and better physical function confirms the findings of previous studies [43,44]. However, the cross-sectional design of this study precludes commentary on the direction of causality. Although from our results, we cannot determine if encouraging walking as part of a resident’s daily activities can result in clinically meaningful improvements in function, previous research demonstrated that function-focused care (ie, increasing routine activities) leads to increased activity volumes and improved functional outcomes in ARC residents with moderate functional dependency [44] but not in dementia residents with severe functional dependency [45]. However, pattern and variability of walking are considered to reflect daily routines, and the effects of function-focused care may be more readily observed in these outcomes rather than in volume, especially in individuals with severe cognitive impairment. Additionally, marginal increases in the duration and variability of walking bouts may lead to significant improvements in function [28] and should be considered in very frail residents. These hypotheses could be considered in future intervention studies, with consideration for the multimorbid nature and varying levels of both cognitive and physical issues inherent in ARC residents.

Strengths and Limitations

Strengths of this study were the large sample, drawn from multiple facilities, distributed across 3 levels of care and encompassing a broad spectrum of cognitive and physical capacities. This is particularly notable, as there can be significant difficulties in collecting data using wearable technology from people with dementia in ARC facilities [29]. We used a technically appropriate digital method to collect low volumes of walking data¸ meeting the recommendations from Mc Ardle et al [8]. Additionally, we used a standardized framework to characterize walking behaviors, making our findings comparable to multiple other cohorts and enhancing our understanding of walking behaviors across the spectrum of care and cognition [11,12]. We addressed the reliability of our primary outcomes based on previous empirical evidence [27].

Our study has several limitations. Residents were only included if they could ambulate, and residents who could not complete the MoCA or SPPB were not included in our secondary analysis; therefore, we may have reduced representation of different levels of cognitive and physical capacities. Although this is only the second study to specifically characterize walking activity in a dementia care unit [8], it should be noted that our sample size for this group was low and likely to have limited statistical power; therefore, statistical analysis was exploratory and results should be considered with that in mind. This is a cross-sectional study; therefore, assessing changes in walking activity over time or establishing causality of influences on walking activity is not possible; in the future, a longitudinal study may offer valuable insights into predictors of walking behaviors in ARC. Although we adjusted for multiple corrections within statistical models (ie, Tukey HSD tests), we did not adjust for multiple comparisons for multiple outcomes, and there may be a risk of type I error. We also included participants with ≥2 days of walking activity data, as this is the number of days required to obtain reliable volume outcomes (ie, our primary outcome) in ARC [27]; however, our secondary outcomes of pattern and variability require 2-5 days of data to ensure reliability, pending on the discrete variable, and thus, results should be interpreted with caution. Although commonly assessed in ARC [8], we did not include outcomes relating to the intensity of walking activity, as this has been suggested to be inappropriate to characterize in this population, given that ARC residents primarily engage only in light-intensity activities [8]. Additionally, the ARC facilities included in this study reflect a New Zealand context, and findings may be different in other countries due to alternative organizational features and policies [46]. We recognize that apart from resident-related factors of physical function and cognition, walking activity may be influenced by the physical and organizational environment [46]—aspects that were not measured in our study. As previously noted, ARC residents may have varying levels of both cognitive and physical impairments, and the combined spectrum should be considered in future research. Finally, digital outcomes beyond those described in this analysis can provide important clinical information about ARC residents and should be considered in future research. For example, sleep disturbances can be measured using actigraphy. Sleep disturbances are common in people living in ARC and are associated with neuropsychiatric symptoms and prescription of psychotropic drugs, which can enhance fall risks and greater staff distress [47]. Although it is beyond the scope of this study, further research may consider using qualitative approaches to complement current findings and the wider literature [8], which would allow us to garner rich insights from ARC residents regarding which digital outcomes relate to their lived experiences and are meaningful to assess.


This is the first study to show the influence of care environment, cognitive status, and physical function on walking behaviors in ARC residents. Our results indicate that cognitive and physical abilities may discretely impact the volumes, pattern, and variability of walking. This work has addressed a significant gap in the literature and has generated new hypotheses regarding which digitally derived walking outcomes are meaningful to assess in ARC residents.


The study has been funded by a Health Research Council of New Zealand project grant (reference 18/414). The study funder plays no role in study design, data collection, or analyses.

RMA received funding from the National Institute for Health and Care Research (NIHR) for her fellowship (NIHR 301677). SDD and LR were supported by the Innovative Medicines Initiative 2 Joint Undertaking (IMI2 JU) project IDEA-FAST (grant agreement 853981). This joint undertaking (JU) receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). SDD and LR were also supported by the Mobilise-D project, which has received funding from the Innovative Medicines Initiative 2 JU (grant agreement 820820). RMA, SDD, and LR were supported by the NIHR Newcastle Biomedical Research Centre (BRC) based at The Newcastle Upon Tyne Hospital National Health Service (NHS) Foundation Trust, Newcastle University, and the Cumbria, Northumberland and Tyne and Wear (CNTW) NHS Foundation Trust. SDD and LR were supported by the NIHR/Wellcome Trust Clinical Research Facility (CRF) infrastructure at Newcastle Upon Tyne Hospitals NHS Foundation Trust.

All opinions are those of the authors and not the funders.

The authors would like to acknowledge the care home residents who participated in this study and the study research assistants for their time in data collection and analysis.

Authors' Contributions

RMA, LT, LR, SDD, and NK contributed to the conceptualization and design of the study as well as data analysis and interpretation. RMA, LT, and SDD were in charge of data curation. RMA conducted data analysis and statistical work. AC provided support for statistical work. RMA and LT wrote the original draft. RMA, LT, LR, AC, SDD, and NK wrote the draft, reviewed, and edited it. All authors approved the final version of this manuscript. RMA and LT contributed equally to this manuscript; SDD and NK also contributed equally to this manuscript.

Conflicts of Interest

SDD reports consultancy activity with Hoffmann-La Roche Ltd. LR consults for MJ Fox Foundation for service on Endpoints Advisory Committee.

Multimedia Appendix 1

Detailed description of between-group analysis results for different care levels.

DOCX File, 13 KB

Multimedia Appendix 2

Walking behaviors categorized by cognitive impairment severity.

DOCX File, 19 KB

Multimedia Appendix 3

Walking behaviors categorized by physical function.

DOCX File, 17 KB

  1. Fritz S, Lusardi M. White paper: "walking speed: the sixth vital sign". J Geriatr Phys Ther. 2009;32(2):46-49. [Medline]
  2. Polhemus A, Ortiz LD, Brittain G, et al. Walking on common ground: a cross-disciplinary scoping review on the clinical utility of digital mobility outcomes. NPJ Digit Med. Oct 14, 2021;4(1):149. [CrossRef] [Medline]
  3. Delgado-Ortiz L, Polhemus A, Keogh A, et al. Listening to the patients' voice: a conceptual framework of the walking experience. Age Ageing. Jan 8, 2023;52(1):afac233. [CrossRef] [Medline]
  4. Sherrington C, Fairhall N, Kwok W, et al. Evidence on physical activity and falls prevention for people aged 65+ years: systematic review to inform the WHO guidelines on physical activity and sedentary behaviour. Int J Behav Nutr Phys Act. Nov 26, 2020;17(1):144. [CrossRef] [Medline]
  5. Cunningham C, O’Sullivan R, Caserotti P, Tully MA. Consequences of physical inactivity in older adults: a systematic review of reviews and meta-analyses. Scand J Med Sci Sports. May 2020;30(5):816-827. [CrossRef] [Medline]
  6. Baldelli G, De Santi M, De Felice F, Brandi G. Physical activity interventions to improve the quality of life of older adults living in residential care facilities: a systematic review. Geriatr Nurs. 2021;42(4):806-815. [CrossRef] [Medline]
  7. de Souto Barreto P, Morley JE, Chodzko-Zajko W, et al. Recommendations on physical activity and exercise for older adults living in long-term care facilities: a taskforce report. J Am Med Dir Assoc. May 1, 2016;17(5):381-392. [CrossRef] [Medline]
  8. Mc Ardle R, Sverdrup K, Del Din S, et al. Quantifying physical activity in aged residential care facilities: a structured review. Ageing Res Rev. May 2021;67:101298. [CrossRef] [Medline]
  9. Mc Ardle R, Jabbar KA, Del Din S, et al. Using digital technology to quantify habitual physical activity in community dwellers with cognitive impairment: systematic review. J Med Internet Res. May 18, 2023;25:e44352. [CrossRef] [Medline]
  10. Taylor LM, Lord S, Parsons J, et al. Walking is associated with physical capacity and fatigue but not cognition in long-term care residents. J Am Med Dir Assoc. Nov 2022;23(11):e1-e2. [CrossRef] [Medline]
  11. Mc Ardle R, Del Din S, Donaghy P, Galna B, Thomas A, Rochester L. Factors that influence habitual activity in mild cognitive impairment and dementia. Gerontology. 2020;66(2):197-208. [CrossRef] [Medline]
  12. Mc Ardle R, Del Din S, Morris R, et al. Factors influencing habitual physical activity in Parkinson's disease: considering the psychosocial state and wellbeing of people with Parkinson's and their carers. Sensors (Basel). Jan 24, 2022;22(3):871. [CrossRef] [Medline]
  13. Del Din S, Godfrey A, Galna B, Lord S, Rochester L. Free-living gait characteristics in ageing and Parkinson's disease: impact of environment and ambulatory bout length. J Neuroeng Rehabil. May 12, 2016;13(1):46. [CrossRef] [Medline]
  14. Mc Ardle R, Hamilton C, Del Din S, et al. Associations between local area deprivation and physical activity participation in people with cognitive impairment in the north east of England. J Alzheimers Dis. 2023;95(1):265-273. [CrossRef] [Medline]
  15. Sverdrup K, Bergh S, Selbæk G, Røen I, Kirkevold Ø, Tangen GG. Mobility and cognition at admission to the nursing home - a cross-sectional study. BMC Geriatr. Jan 30, 2018;18(1):30. [CrossRef] [Medline]
  16. Barker RO, Hanratty B, Kingston A, Ramsay SE, Matthews FE. Changes in health and functioning of care home residents over two decades: what can we learn from population-based studies? Age Ageing. May 5, 2021;50(3):921-927. [CrossRef] [Medline]
  17. Zimmerman S, Sloane PD, Reed D. Dementia prevalence and care in assisted living. Health Aff (Millwood). Apr 2014;33(4):658-666. [CrossRef] [Medline]
  18. Kijowska V, Szczerbińska K. Prevalence of cognitive impairment among long-term care residents: a comparison between nursing homes and residential homes in Poland. Eur Geriatr Med. Aug 2018;9(4):467-476. [CrossRef] [Medline]
  19. Taylor L, Parsons J, Taylor D, et al. Evaluating the effects of an exercise program (Staying UpRight) for older adults in long-term care on rates of falls: study protocol for a randomised controlled trial. Trials. Jan 8, 2020;21(1):46. [CrossRef] [Medline]
  20. Guralnik JM, Simonsick EM, Ferrucci L, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. Mar 1994;49(2):M85-M94. [CrossRef] [Medline]
  21. Podsiadlo D, Richardson S. The timed "Up & Go": a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. Feb 1991;39(2):142-148. [CrossRef] [Medline]
  22. Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCa: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. Apr 2005;53(4):695-699. [CrossRef] [Medline]
  23. Mc Ardle R, Morris R, Hickey A, et al. Gait in mild Alzheimer's disease: feasibility of multi-center measurement in the clinic and home with body-worn sensors: a pilot study. J Alzheimers Dis. 2018;63(1):331-341. [CrossRef] [Medline]
  24. Moore SA, Hickey A, Lord S, Del Din S, Godfrey A, Rochester L. Comprehensive measurement of stroke gait characteristics with a single accelerometer in the laboratory and community: a feasibility, validity and reliability study. J Neuroeng Rehabil. Dec 29, 2017;14(1):130. [CrossRef] [Medline]
  25. MacLean MK, Rehman RZU, Kerse N, Taylor L, Rochester L, Del Din S. Walking bout detection for people living in long residential care: a computationally efficient algorithm for a 3-axis accelerometer on the lower back. Sensors (Basel). Nov 4, 2023;23(21):8973. [CrossRef] [Medline]
  26. Hickey A, Del Din S, Rochester L, Godfrey A. Detecting free-living steps and walking bouts: validating an algorithm for macro gait analysis. Physiol Meas. Jan 2017;38(1):N1-N15. [CrossRef] [Medline]
  27. Buckley C, Cavadino A, Del Din S, et al. Quantifying reliable walking activity with a wearable device in aged residential care: how many days are enough. Sensors (Basel). Nov 5, 2020;20(21):6314. [CrossRef] [Medline]
  28. Rochester L, Chastin SFM, Lord S, Baker K, Burn DJ. Understanding the impact of deep brain stimulation on ambulatory activity in advanced Parkinson's disease. J Neurol. Jun 2012;259(6):1081-1086. [CrossRef] [Medline]
  29. Teipel S, Heine C, Hein A, et al. Multidimensional assessment of challenging behaviors in advanced stages of dementia in nursing homes-the insideDEM framework. Alzheimers Dement (Amst). 2017;8:36-44. [CrossRef] [Medline]
  30. Del Din S, Godfrey A, Mazzà C, Lord S, Rochester L. Free-living monitoring of Parkinson's disease: lessons from the field. Mov Disord. Sep 2016;31(9):1293-1313. [CrossRef] [Medline]
  31. Mc Ardle R, Del Din S, Donaghy P, Galna B, Thomas AJ, Rochester L. The impact of environment on gait assessment: considerations from real-world gait analysis in dementia subtypes. Sensors (Basel). Jan 26, 2021;21(3):813. [CrossRef] [Medline]
  32. Del Din S, Galna B, Godfrey A, et al. Analysis of free-living gait in older adults with and without Parkinson's disease and with and without a history of falls: identifying generic and disease-specific characteristics. J Gerontol A Biol Sci Med Sci. Mar 14, 2019;74(4):500-506. [CrossRef] [Medline]
  33. Orendurff MS, Schoen JA, Bernatz GC, Segal AD, Klute GK. How humans walk: bout duration, steps per bout, and rest duration. J Rehabil Res Dev. 2008;45(7):1077-1089. [CrossRef] [Medline]
  34. Brydges CR. Effect size guidelines, sample size calculations, and statistical power in gerontology. Innov Aging. Aug 2019;3(4):igz036. [CrossRef] [Medline]
  35. Canadian Task Force on Preventive Health Care, Pottie K, Rahal R, et al. Recommendations on screening for cognitive impairment in older adults. CMAJ. Jan 5, 2016;188(1):37-46. [CrossRef] [Medline]
  36. Guralnik JM, Ferrucci L, Pieper CF, et al. Lower extremity function and subsequent disability: consistency across studies, predictive models, and value of gait speed alone compared with the short physical performance battery. J Gerontol A Biol Sci Med Sci. Apr 2000;55(4):M221-M231. [CrossRef] [Medline]
  37. Lin Q, Zhang D, Chen L, Ni H, Zhou X. Managing elders’ wandering behavior using sensors-based solutions: a survey. Int J Gerontol. Jun 2014;8(2):49-55. [CrossRef]
  38. Kamil RJ, Bakar D, Ehrenburg M, et al. Detection of wandering behaviors using a body-worn inertial sensor in patients with cognitive impairment: a feasibility study. Front Neurol. Mar 11, 2021;12:529661. [CrossRef] [Medline]
  39. Agrawal AK, Gowda M, Achary U, Gowda GS, Harbishettar V. Approach to management of wandering in dementia: ethical and legal issue. Indian J Psychol Med. Sep 2021;43(5 Suppl):S53-S59. [CrossRef] [Medline]
  40. Moyle W, Jones C, Murfield J, et al. Effect of a robotic seal on the motor activity and sleep patterns of older people with dementia, as measured by wearable technology: a cluster-randomised controlled trial. Maturitas. Apr 2018;110:10-17. [CrossRef] [Medline]
  41. Murata S, Takegami M, Ogata S, et al. Joint effect of cognitive decline and walking ability on incidence of wandering behavior in older adults with dementia: a cohort study. Int J Geriatr Psychiatry. May 2022;37(5). [CrossRef] [Medline]
  42. Mc Ardle R, Jabbar KA, Del Din S, et al. Using digital technology to quantify habitual physical activity in community-dwellers with cognitive impairment: a systematic review. J Med Internet Res. May 18, 2023;25:e44352. [CrossRef] [Medline]
  43. Corcoran MP, Chui KKH, White DK, et al. Accelerometer assessment of physical activity and its association with physical function in older adults residing at assisted care facilities. J Nutr Health Aging. 2016;20(7):752-758. [CrossRef] [Medline]
  44. Buckinx F, Mouton A, Reginster JY, et al. Relationship between ambulatory physical activity assessed by activity trackers and physical frailty among nursing home residents. Gait Posture. May 2017;54:56-61. [CrossRef] [Medline]
  45. Galik EM, Resnick B, Holmes SD, et al. A cluster randomized controlled trial testing the impact of function and behavior focused care for nursing home residents with dementia. J Am Med Dir Assoc. Jul 2021;22(7):1421-1428. [CrossRef] [Medline]
  46. Benjamin K, Edwards N, Ploeg J, Legault F. Barriers to physical activity and restorative care for residents in long-term care: a review of the literature. J Aging Phys Act. Jan 2014;22(1):154-165. [CrossRef] [Medline]
  47. Webster L, Costafreda Gonzalez S, Stringer A, et al. Measuring the prevalence of sleep disturbances in people with dementia living in care homes: a systematic review and meta-analysis. Sleep. Apr 15, 2020;43(4):zsz251. [CrossRef] [Medline]

ARC: aged residential care
HSD: honest significant difference
MoCA: Montreal Cognitive Assessment
RCT: randomized controlled trial
SPPB: Short Physical Performance Battery

Edited by Haley LaMonica; submitted 22.09.23; peer-reviewed by Guilherme Balbim, Sonia Lippke; final revised version received 05.03.24; accepted 06.03.24; published 04.06.24.


© Ríona Mc Ardle, Lynne Taylor, Alana Cavadino, Lynn Rochester, Silvia Del Din, Ngaire Kerse. Originally published in JMIR Aging (, 4.6.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Aging, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.