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

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.

Mobilizing Patient and Public Involvement in the Development of Real-World Digital Technology Solutions: Tutorial.

Although the value of patient and public involvement and engagement (PPIE) activities in the development of new interventions and tools is well known, little guidance exists on how to perform these activities in a meaningful way. This is particularly true within large research consortia that target multiple objectives, include multiple patient groups, and work across many countries. Without clear guidance, there is a risk that PPIE may not capture patient opinions and needs correctly, thereby reducing the usefulness and effectiveness of new tools. Mobilise-D is an example of a large research consortium that aims to develop new digital outcome measures for real-world walking in 4 patient cohorts. Mobility is an important indicator of physical health. As such, there is potential clinical value in being able to accurately measure a person's mobility in their daily life environment to help researchers and clinicians better track changes and patterns in a person's daily life and activities. To achieve this, there is a need to create new ways of measuring walking. Recent advancements in digital technology help researchers meet this need. However, before any new measure can be used, researchers, health care professionals, and regulators need to know that the digital method is accurate and both accepted by and produces meaningful outcomes for patients and clinicians. Therefore, this paper outlines how PPIE structures were developed in the Mobilise-D consortium, providing details about the steps taken to implement PPIE, the experiences PPIE contributors had within this process, the lessons learned from the experiences, and recommendations for others who may want to do similar work in the future. The work outlined in this paper provided the Mobilise-D consortium with a foundation from which future PPIE tasks can be created and managed with clearly defined collaboration between researchers and patient representatives across Europe. This paper provides guidance on the work required to set up PPIE structures within a large consortium to promote and support the creation of meaningful and efficient PPIE related to the development of digital mobility outcomes.

Physical Activity in Community-Dwelling Older Adults: Which Real-World Accelerometry Measures Are Robust? A Systematic Review.

Measurement of real-world physical activity (PA) data using accelerometry in older adults is informative and clinically relevant, but not without challenges. This review appraises the reliability and validity of accelerometry-based PA measures of older adults collected in real-world conditions. Eight electronic databases were systematically searched, with 13 manuscripts included. Intraclass correlation coefficient (ICC) for inter-rater reliability were: walking duration (0.94 to 0.95), lying duration (0.98 to 0.99), sitting duration (0.78 to 0.99) and standing duration (0.98 to 0.99). ICCs for relative reliability ranged from 0.24 to 0.82 for step counts and 0.48 to 0.86 for active calories. Absolute reliability ranged from 5864 to 10,832 steps and for active calories from 289 to 597 kcal. ICCs for responsiveness for step count were 0.02 to 0.41, and for active calories 0.07 to 0.93. Criterion validity for step count ranged from 0.83 to 0.98. Percentage of agreement for walking ranged from 63.6% to 94.5%; for lying 35.6% to 100%, sitting 79.2% to 100%, and standing 38.6% to 96.1%. Construct validity between step count and criteria for moderate-to-vigorous PA was rs = 0.68 and 0.72. Inter-rater reliability and criterion validity for walking, lying, sitting and standing duration are established. Criterion validity of step count is also established. Clinicians and researchers may use these measures with a limited degree of confidence. Further work is required to establish these properties and to extend the repertoire of PA measures beyond "volume" counts to include more nuanced outcomes such as intensity of movement and duration of postural transitions.

Key Considerations When Developing and Implementing Digital Technology for Early Detection of Dementia-Causing Diseases Among Health Care Professionals: Qualitative Study.

BACKGROUND: The World Health Organization (WHO) promotes using digital technologies to accelerate global attainment of health and well-being. This has led to a growth in research exploring the use of digital technology to aid early detection and preventative interventions for dementia-causing diseases such as Alzheimer disease. The opinions and perspectives of health care professionals must be incorporated into the development and implementation of technology to promote its successful adoption in clinical practice. OBJECTIVE: This study aimed to explore health care professionals' perspectives on the key considerations of developing and implementing digital technologies for the early detection of dementia-causing diseases in the National Health Service (NHS). METHODS: Health care professionals with patient-facing roles in primary or secondary care settings in the NHS were recruited through various web-based NHS clinical networks. Participants were interviewed to explore their experiences of the current dementia diagnostic practices, views on early detection and use of digital technology to aid these practices, and the challenges of implementing such interventions in health care. An inductive thematic analysis approach was applied to identify central concepts and themes in the interviews, allowing the data to determine our themes. A list of central concepts and themes was applied systematically to the whole data set using NVivo (version 1.6.1; QSR International). Using the constant comparison technique, the researchers moved backward and forward between these data and evolving explanations until a fit was made. RESULTS: Eighteen semistructured interviews were conducted, with 11 primary and 7 secondary care health care professionals. We identified 3 main categories of considerations relevant to health care service users, health care professionals, and the digital health technology itself. Health care professionals recognized the potential of using digital technology to collect real-time data and the possible benefits of detecting dementia-causing diseases earlier if an effective intervention were available. However, some were concerned about postdetection management, questioning the point of an early detection of dementia-causing diseases if an effective intervention cannot be provided and feared this would only lead to increased anxiety in patients. Health care professionals also expressed mixed opinions on who should be screened for early detection. Some suggested it should be available to everyone to mitigate the chance of excluding those who are not in touch with their health care or are digitally excluded. Others were concerned about the resources that would be required to make the technology available to everyone. CONCLUSIONS: This study highlights the need to design digital health technology in a way that is accessible to all and does not add burden to health care professionals. Further work is needed to ensure inclusive strategies are used in digital research to promote health equity.

Associations Between Local Area Deprivation and Physical Activity Participation in People with Cognitive Impairment in the North East of England.

BACKGROUND: Promoting physical activity, such as habitual walking behaviors, in people with cognitive impairment may support their ability to remain independent with a good quality of life for longer. However, people with cognitive impairment participate in less physical activity compared to cognitively unimpaired older adults. The local area in which people live may significantly impact abilities to participate in physical activity. For example, people who live in more deprived areas may have less safe and walkable routes. OBJECTIVE: To examine this further, this study aimed to explore associations between local area deprivation and physical activity in people with cognitive impairment and cognitively unimpaired older adults (controls). METHODS: 87 participants with cognitive impairment (mild cognitive impairment or dementia) and 27 older adult controls from the North East of England were included in this analysis. Participants wore a tri-axial wearable accelerometer (AX3, Axivity) on their lower backs continuously for seven days. The primary physical activity outcome was daily step count. Individuals' neighborhoods were linked to UK government area deprivation statistics. Hierarchical Bayesian models assessed the association between local area deprivation and daily step count in people with cognitive impairment and controls. RESULTS: Key findings indicated that there was no association between local area deprivation and daily step count in people with cognitive impairment, but higher deprivation was associated with lower daily steps for controls. CONCLUSION: These findings suggest that cognitive impairment may be associated with lower participation in physical activity which supersedes the influence of local area deprivation observed in normal aging.

Digital Endpoints for Assessing Instrumental Activities of Daily Living in Mild Cognitive Impairment: Systematic Review.

BACKGROUND: Subtle impairments in instrumental activities of daily living (IADLs) can be a key predictor of disease progression and are considered central to functional independence. Mild cognitive impairment (MCI) is a syndrome associated with significant changes in cognitive function and mild impairment in complex functional abilities. The early detection of functional decline through the identification of IADL impairments can aid early intervention strategies. Digital health technology is an objective method of capturing IADL-related behaviors. However, it is unclear how these IADL-related behaviors have been digitally assessed in the literature and what differences can be observed between MCI and normal aging. OBJECTIVE: This review aimed to identify the digital methods and metrics used to assess IADL-related behaviors in people with MCI and report any statistically significant differences in digital endpoints between MCI and normal aging and how these digital endpoints change over time. METHODS: A total of 16,099 articles were identified from 8 databases (CINAHL, Embase, MEDLINE, ProQuest, PsycINFO, PubMed, Web of Science, and Scopus), out of which 15 were included in this review. The included studies must have used continuous remote digital measures to assess IADL-related behaviors in adults characterized as having MCI by clinical diagnosis or assessment. This review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS: Ambient technology was the most commonly used digital method to assess IADL-related behaviors in the included studies (14/15, 93%), with passive infrared motion sensors (5/15, 33%) and contact sensors (5/15, 33%) being the most prevalent types of methods. Digital technologies were used to assess IADL-related behaviors across 5 domains: activities outside of the home, everyday technology use, household and personal management, medication management, and orientation. Other recognized domains-culturally specific tasks and socialization and communication-were not assessed. Of the 79 metrics recorded among 11 types of technologies, 65 (82%) were used only once. There were inconsistent findings around differences in digital IADL endpoints across the cognitive spectrum, with limited longitudinal assessment of how they changed over time. CONCLUSIONS: Despite the broad range of metrics and methods used to digitally assess IADL-related behaviors in people with MCI, several IADLs relevant to functional decline were not studied. Measuring multiple IADL-related digital endpoints could offer more value than the measurement of discrete IADL outcomes alone to observe functional decline. Key recommendations include the development of suitable core metrics relevant to IADL-related behaviors that are based on clinically meaningful outcomes to aid the standardization and further validation of digital technologies against existing IADL measures. Increased longitudinal monitoring is necessary to capture changes in digital IADL endpoints over time in people with MCI. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42022326861; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=326861.

Using Digital Technology to Quantify Habitual Physical Activity in Community Dwellers With Cognitive Impairment: Systematic Review.

BACKGROUND: Participating in habitual physical activity (HPA) can support people with dementia and mild cognitive impairment (MCI) to maintain functional independence. Digital technology can continuously measure HPA objectively, capturing nuanced measures relating to its volume, intensity, pattern, and variability. OBJECTIVE: To understand HPA participation in people with cognitive impairment, this systematic review aims to (1) identify digital methods and protocols; (2) identify metrics used to assess HPA; (3) describe differences in HPA between people with dementia, MCI, and controls; and (4) make recommendations for measuring and reporting HPA in people with cognitive impairment. METHODS: Key search terms were input into 6 databases: Scopus, Web of Science, Psych Articles, PsychInfo, MEDLINE, and Embase. Articles were included if they included community dwellers with dementia or MCI, reported HPA metrics derived from digital technology, were published in English, and were peer reviewed. Articles were excluded if they considered populations without dementia or MCI diagnoses, were based in aged care settings, did not concern digitally derived HPA metrics, or were only concerned with physical activity interventions. Key outcomes extracted included the methods and metrics used to assess HPA and differences in HPA outcomes across the cognitive spectrum. Data were synthesized narratively. An adapted version of the National Institute of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies was used to assess the quality of articles. Due to significant heterogeneity, a meta-analysis was not feasible. RESULTS: A total of 3394 titles were identified, with 33 articles included following the systematic review. The quality assessment suggested that studies were moderate-to-good quality. Accelerometers worn on the wrist or lower back were the most prevalent methods, while metrics relating to volume (eg, daily steps) were most common for measuring HPA. People with dementia had lower volumes, intensities, and variability with different daytime patterns of HPA than controls. Findings in people with MCI varied, but they demonstrated different patterns of HPA compared to controls. CONCLUSIONS: This review highlights limitations in the current literature, including lack of standardization in methods, protocols, and metrics; limited information on validity and acceptability of methods; lack of longitudinal research; and limited associations between HPA metrics and clinically meaningful outcomes. Limitations of this review include the exclusion of functional physical activity metrics (eg, sitting/standing) and non-English articles. Recommendations from this review include suggestions for measuring and reporting HPA in people with cognitive impairment and for future research including validation of methods, development of a core set of clinically meaningful HPA outcomes, and further investigation of socioecological factors that may influence HPA participation. TRIAL REGISTRATION: PROSPERO CRD42020216744; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=216744 .

Feasibility and usability of a digital health technology system to monitor mobility and assess medication adherence in mild-to-moderate Parkinson's disease.

Introduction: Parkinson's disease (PD) is a neurodegenerative disorder which requires complex medication regimens to mitigate motor symptoms. The use of digital health technology systems (DHTSs) to collect mobility and medication data provides an opportunity to objectively quantify the effect of medication on motor performance during day-to-day activities. This insight could inform clinical decision-making, personalise care, and aid self-management. This study investigates the feasibility and usability of a multi-component DHTS to remotely assess self-reported medication adherence and monitor mobility in people with Parkinson's (PwP). Methods: Thirty participants with PD [Hoehn and Yahr stage I (n = 1) and II (n = 29)] were recruited for this cross-sectional study. Participants were required to wear, and where appropriate, interact with a DHTS (smartwatch, inertial measurement unit, and smartphone) for seven consecutive days to assess medication adherence and monitor digital mobility outcomes and contextual factors. Participants reported their daily motor complications [motor fluctuations and dyskinesias (i.e., involuntary movements)] in a diary. Following the monitoring period, participants completed a questionnaire to gauge the usability of the DHTS. Feasibility was assessed through the percentage of data collected, and usability through analysis of qualitative questionnaire feedback. Results: Adherence to each device exceeded 70% and ranged from 73 to 97%. Overall, the DHTS was well tolerated with 17/30 participants giving a score > 75% [average score for these participants = 89%, from 0 (worst) to 100 (best)] for its usability. Usability of the DHTS was significantly associated with age (ρ = -0.560, BCa 95% CI [-0.791, -0.207]). This study identified means to improve usability of the DHTS by addressing technical and design issues of the smartwatch. Feasibility, usability and acceptability were identified as key themes from PwP qualitative feedback on the DHTS. Conclusion: This study highlighted the feasibility and usability of our integrated DHTS to remotely assess medication adherence and monitor mobility in people with mild-to-moderate Parkinson's disease. Further work is necessary to determine whether this DHTS can be implemented for clinical decision-making to optimise management of PwP.

Diagnostic test accuracy of remote, multidomain cognitive assessment (telephone and video call) for dementia.

BACKGROUND: Remote cognitive assessments are increasingly needed to assist in the detection of cognitive disorders, but the diagnostic accuracy of telephone- and video-based cognitive screening remains unclear. OBJECTIVES: To assess the test accuracy of any multidomain cognitive test delivered remotely for the diagnosis of any form of dementia. To assess for potential differences in cognitive test scoring when using a remote platform, and where a remote screener was compared to the equivalent face-to-face test. SEARCH METHODS: We searched ALOIS, the Cochrane Dementia and Cognitive Improvement Group Specialized Register, CENTRAL, MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, LILACS, and ClinicalTrials.gov (www. CLINICALTRIALS: gov/) databases on 2 June 2021. We performed forward and backward searching of included citations. SELECTION CRITERIA: We included cross-sectional studies, where a remote, multidomain assessment was administered alongside a clinical diagnosis of dementia or equivalent face-to-face test. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed risk of bias and extracted data; a third review author moderated disagreements. Our primary analysis was the accuracy of remote assessments against a clinical diagnosis of dementia. Where data were available, we reported test accuracy as sensitivity and specificity. We did not perform quantitative meta-analysis as there were too few studies at individual test level. For those studies comparing remote versus in-person use of an equivalent screening test, if data allowed, we described correlations, reliability, differences in scores and the proportion classified as having cognitive impairment for each test. MAIN RESULTS: The review contains 31 studies (19 differing tests, 3075 participants), of which seven studies (six telephone, one video call, 756 participants) were relevant to our primary objective of describing test accuracy against a clinical diagnosis of dementia. All studies were at unclear or high risk of bias in at least one domain, but were low risk in applicability to the review question. Overall, sensitivity of remote tools varied with values between 26% and 100%, and specificity between 65% and 100%, with no clearly superior test. Across the 24 papers comparing equivalent remote and in-person tests (14 telephone, 10 video call), agreement between tests was good, but rarely perfect (correlation coefficient range: 0.48 to 0.98). AUTHORS' CONCLUSIONS: Despite the common and increasing use of remote cognitive assessment, supporting evidence on test accuracy is limited. Available data do not allow us to suggest a preferred test. Remote testing is complex, and this is reflected in the heterogeneity seen in tests used, their application, and their analysis. More research is needed to describe accuracy of contemporary approaches to remote cognitive assessment. While data comparing remote and in-person use of a test were reassuring, thresholds and scoring rules derived from in-person testing may not be applicable when the equivalent test is adapted for remote use.

Factors Influencing Habitual Physical Activity in Parkinson's Disease: Considering the Psychosocial State and Wellbeing of People with Parkinson's and Their Carers.

Participating in habitual physical activity (HPA) may slow onset of dependency and disability for people with Parkinson's disease (PwP). While cognitive and physical determinants of HPA are well understood, psychosocial influences are not. This pilot study aimed to identify psychosocial factors associated with HPA to guide future intervention development. Sixty-four PwP participated in this study; forty had carer informants. PwP participants wore a tri-axial accelerometer on the lower back continuously for seven days at two timepoints (18 months apart), measuring volume, pattern and variability of HPA. Linear mixed effects analysis identified relationships between demographic, clinical and psychosocial data and HPA from baseline to 18 months. Key results in PwP with carers indicated that carer anxiety and depression were associated with increased HPA volume (p < 0.01), while poorer carer self-care was associated with reduced volume of HPA over 18 months (p < 0.01). Greater carer strain was associated with taking longer walking bouts after 18 months (p < 0.01). Greater carer depression was associated with lower variability of HPA cross-sectionally (p = 0.009). This pilot study provides preliminary novel evidence that psychosocial outcomes from PwP's carers may impact HPA in Parkinson's disease. Interventions to improve HPA could target both PwP and carers and consider approaches that also support psychosocial wellbeing.

Instrumental Activities of Daily Living Scales to Detect Cognitive Impairment and Dementia in Low- and Middle-Income Countries: A Systematic Review.

BACKGROUND: The largest proportion of people with dementia worldwide live in low- and middle- income countries (LMICs), with dementia prevalence continuing to rise. Assessment and diagnosis of dementia involves identifying the impact of cognitive decline on function, usually measured by instrumental activities of daily living (IADLs). OBJECTIVE: This review aimed to identify IADL measures which are specifically developed, validated, or adapted for use in LMICs to guide selection of such tools. METHODS: A systematic search was conducted (fourteen databases) up to April 2020. Only studies reporting on development, validation, or adaptation of IADL measures for dementia or cognitive impairment among older adults (aged over 50) in LMICs were included. The QUADAS 2 was used to assess quality of diagnostic accuracy studies. RESULTS: 22 papers met inclusion criteria; identifying 19 discrete IADL tools across 11 LMICs. These were either translated from IADL measures used in high-income countries (n = 6), translated and adapted for cultural differences (n = 6), or newly developed for target LMIC populations (n = 7). Seven measures were investigated in multiple studies; overall quality of diagnostic accuracy was moderate to good. CONCLUSION: Reliability, validity, and accuracy of IADL measures for supporting dementia diagnosis within LMICs was reported. Key components to consider when selecting an IADL tool for such settings were highlighted, including choosing culturally appropriate, time-efficient tools that account for gender- and literacy-bias, and can be conducted by any volunteer with appropriate training. There is a need for greater technical and external validation of IADL tools across different regions, countries, populations, and cultures.

Balance Impairments as Differential Markers of Dementia Disease Subtype.

BACKGROUND: Accurately differentiating dementia subtypes, such as Alzheimer's disease (AD) and Lewy body disease [including dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD)] is important to ensure appropriate management and treatment of the disease. Similarities in clinical presentation create difficulties for differential diagnosis. Simple supportive markers, such as balance assessments, may be useful to the diagnostic toolkit. This study aimed to identify differences in balance impairments between different dementia disease subtypes and normal aging using a single triaxial accelerometer. METHODS: Ninety-seven participants were recruited, forming four groups: cognitive impairment due to Alzheimer's disease (AD group; n = 31), dementia with Lewy bodies (DLB group; n = 26), Parkinson's disease dementia (PDD group; n = 13), and normal aging controls (n = 27). Participants were asked to stand still for 2 minutes in a standardized position with their eyes open while wearing a single triaxial accelerometer on their lower back. Seven balance characteristics were derived, including jerk (combined, mediolateral, and anterior-posterior), root mean square (RMS; combined, mediolateral, and anterior-posterior), and ellipsis. Mann-Whitney U tests identified the balance differences between groups. Receiver operating characteristics and area under the curve (AUC) determined the overall accuracy of the selected balance characteristics. RESULTS: The PDD group demonstrated higher RMS [combined (p = 0.001), mediolateral (p = 0.005), and anterior-posterior (p = 0.001)] and ellipsis scores (p < 0.002) than the AD group (AUC = 0.71-0.82). The PDD group also demonstrated significantly impaired balance across all characteristics (p ≤ 0.001) compared to the controls (AUC = 0.79-0.83). Balance differences were not significant between PDD and DLB (AUC = 0.69-0.74), DLB and AD (AUC = 0.50-0.65), DLB and controls (AUC = 0.62-0.68), or AD and controls (AUC = 0.55-0.67) following Bonferroni correction. DISCUSSION: Although feasible and quick to conduct, key findings suggest that an accelerometer-based balance during quiet standing does not differentiate dementia disease subtypes accurately. Assessments that challenge balance more, such as gait or standing with eyes closed, may prove more effective to support differential diagnosis.

The Impact of Environment on Gait Assessment: Considerations from Real-World Gait Analysis in Dementia Subtypes.

Laboratory-based gait assessments are indicative of clinical outcomes (e.g., disease identification). Real-world gait may be more sensitive to clinical outcomes, as impairments may be exaggerated in complex environments. This study aims to investigate how different environments (e.g., lab, real world) impact gait. Different walking bout lengths in the real world will be considered proxy measures of context. Data collected in different dementia disease subtypes will be analysed as disease-specific gait impairments are reported between these groups. Thirty-two people with cognitive impairment due to Alzheimer's disease (AD), 28 due to dementia with Lewy bodies (DLB) and 25 controls were recruited. Participants wore a tri-axial accelerometer for six 10 m walks in lab settings, and continuously for seven days in the real world. Fourteen gait characteristics across five domains were measured (i.e., pace, variability, rhythm, asymmetry, postural control). In the lab, the DLB group showed greater step length variability (p = 0.008) compared to AD. Both subtypes demonstrated significant gait impairments (p < 0.01) compared to controls. In the real world, only very short walking bouts (<10 s) demonstrated different gait impairments between subtypes. The context where walking occurs impacts signatures of gait impairment in dementia subtypes. To develop real-world gait assessment as a clinical tool, algorithms and metrics must accommodate for changes in context.

Quantifying physical activity in aged residential care facilities: A structured review.

BACKGROUND: Engaging aged residential care (ARC) residents with physical activity (PA) may be a useful strategy to decelerate dependence and disability. It is unclear what volume, intensity and patterns of PA ARC residents participate in. This review aims to synthesize the literature to quantify the volume, intensity and pattern of PA that ARC residents participate in across differing care levels (e.g. low, intermediate, high, mixed), and make recommendations for future research. METHODS: 30 studies of 48,760 yielded were reviewed using systematic review strategies. RESULTS: Questionnaires and technological tools were used to assess PA, with accelerometers employed in 70% of studies. Overall, studies reported low volumes and intensities of PA across all care levels, and suggested limited variation in patterns of PA (e.g. little day-to-day variation in total PA). There was limited inclusion of people with cognitive impairment, potentially causing representativeness bias. Findings were limited by lack of consistency in methodological approaches and PA outcomes. DISCUSSION: Based on findings and limitations of current research, we recommend that total volume or low-light intensity PA are more useful interventional outcomes than higher-intensity PA. Researchers also need to consider which methodology and PA outcomes are most useful to quantify PA in ARC residents.

Differentiating dementia disease subtypes with gait analysis: feasibility of wearable sensors?

BACKGROUND: There are unique signatures of gait impairments in different dementia disease subtypes, such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease (PDD). This suggests gait analysis is a useful differential marker for dementia disease subtypes, but this has yet to be assessed using inexpensive wearable technology. RESEARCH QUESTION: This study aimed to assess whether a single accelerometer-based wearable could differentiate dementia disease subtypes through gait analysis. METHODS: 80 people with mild cognitive impairment or dementia due to AD, DLB or PD performed six ten-metre walks. An accelerometer-based wearable (Axivity) assessed gait. Data was processed using algorithms validated in other neurological disorders and older adults. Fourteen spatiotemporal characteristic were computed, that broadly represent pace, variability, rhythm, asymmetry and postural control features of gait. One way analysis of variance and Kruskall Wallis tests identified significant between-group differences, and post-hoc independent t-tests and Mann Whitney U's established where differences lay. Receiver Operating Characteristics and Area Under the Curve (AUC) demonstrated overall accuracy for single gait characteristics. RESULTS: The wearable was able to differentiate dementia disease subtypes (p ≤ .05) and demonstrated significant differences between the groups in 7 gait characteristics with modest accuracy. For reference the instrumented walkway showed 2 between-group differences in gait characteristics. SIGNIFICANCE: This study found that a wearable device can be used to differentiate dementia disease subtypes. This provides a foundation for future research to investigate the application of wearable technology as a clinical tool to aid diagnostic accuracy, allowing the correct treatment and care to be applied. Wearable technology may be particularly useful as its use is less restricted to context, making it easier to implement.

Factors That Influence Habitual Activity in Mild Cognitive Impairment and Dementia.

BACKGROUND: Reduced engagement with habitual activity (HA) is associated with greater risk and progression of cognitive decline and falls in older adults and people with dementia. Understanding external and intrinsic factors that affect HA may provide novel targets for non-pharmacologic interventions. OBJECTIVE: This study primarily aims to identify factors that influence HA in normal ageing and cognitive impairment, such as cognitive and motor problems and disease subtype. METHODS: 108 older adults participated in this study; 36 with cognitive impairment due to Alzheimer's disease (AD), 30 with dementia with Lewy bodies (DLB), 16 with Parkinson's disease dementia (PDD), and 26 controls. A tri-axial accelerometer recorded continuous data of volume, variability, and pattern of HA over 7 days. Participants undertook a battery of cognitive and neuropsychological assessments. RESULTS: One-way analysis of variance controlling for age and gender shows that people with DLB and PDD engage less with HA than controls (p ≤ 0.01), but there were no significant differences between AD and controls (p ≥ 0.01). Multivariate analysis demonstrated motor disease and impairments in activities of daily living (ADLs) independently explained 10-26% of volume, variability, and pattern of HA in people with cognitive impairment. CONCLUSION: People with cognitive impairment have reduced HA engagement compared to controls. Motor disease and impairments in ADLs most strongly contribute to these findings and may be important to consider for disease management. Wearable technology can provide a personalised picture of an individual's daily behaviours and may be a useful tool for person-centred care.

Do Alzheimer's and Lewy body disease have discrete pathological signatures of gait?

OBJECTIVE: We aimed to refine the hypothesis that dementia has a unique signature of gait impairment reflective of underlying pathology by considering two dementia subtypes, Alzheimer's disease (AD) and Lewy body disease (LBD), and exploring the role of cognition in disease-specific gait impairments. BACKGROUND: Accurately differentiating AD and LBD is important for treatment and disease management. Early evidence suggests gait could be a marker of dementia due to associations between discrete gait characteristics and cognitive domains. UPDATED HYPOTHESIS: We hypothesize that AD and LBD have unique signatures of gait, reflecting disease-specific cognitive profiles and underlying pathologies. An exploratory study included individuals with mild cognitive impairment or dementia due to LBD (n = 45) and AD (n = 36) and 29 older adult controls. An instrumented walkway quantified 16 gait characteristics reflecting five independent domains of locomotion (pace, rhythm, variability, asymmetry, and postural control). The LBD group demonstrated greater impairments in asymmetry and variability compared with AD; both groups were more impaired in pace and variability domains than controls. Executive dysfunction explained 11% of variance for gait variability in LBD, whereas global cognitive impairment explained 13.5% of variance in AD; therefore, gait impairments may reflect disease-specific cognitive profiles. With a refined hypothesis that AD- and LBD-specific signatures of gait reflect discrete pathologies, future studies must examine the relationship between a validated model of gait with neural networks, using recognized biomarkers and postmortem follow-up. MAJOR CHALLENGES FOR HYPOTHESIS: Differential diagnosis of AD and LBD used appropriate criteria and required consensus from an expert diagnostic panel to improve diagnostic accuracy. Future work should follow the framework set out in Parkinson's disease to establish unique signatures of gait as proxy measures of disease-specific pathology; that is, use a validated gait model to explore the progressive relationship between gait, cognition, and pathology. LINKAGE TO OTHER MAJOR THEORIES: These exploratory findings support the theory of interacting cognitive-motor networks, as the gait-cognition relationship may reflect cognitive control over motor networks. Unique signatures of gait may reflect different temporal patterns of pathological burden in neural areas related to cognitive and motor function.

The neural correlates of discrete gait characteristics in ageing: A structured review.

Gait is complex, described by diverse characteristics underpinned by widespread central nervous system networks including motor and cognitive functions. Despite this, neural substrates of discrete gait characteristics are poorly understood, limiting understanding of gait impairment in ageing and disease. This structured review aims to map gait characteristics, defined from a pre-specified model reflecting independent gait domains, to brain imaging parameters in older adults. Fifty-two studies of 38,029 yielded were reviewed. Studies showed inconsistent approaches when mapping gait assessment to neural substrates, limiting conclusions. Gait impairments typically associated with brain deterioration, specifically grey matter atrophy and white matter integrity loss. Gait velocity, a global measure of gait control, was most frequently associated with these imaging markers within frontal and basal ganglia regions, and its decline predicted from white matter volume and integrity measurements. Fewer studies assessed additional gait measures or functional imaging parameters. Future studies mapping regional neuroanatomical and functional correlates of gait are needed, including those which take a multi-process network perspective to better understand mobility in health and disease.

Gait in Mild Alzheimer's Disease: Feasibility of Multi-Center Measurement in the Clinic and Home with Body-Worn Sensors: A Pilot Study.

Gait is emerging as a potential diagnostic tool for cognitive decline. The 'Deep and Frequent Phenotyping for Experimental Medicine in Dementia Study' (D&FP) is a multicenter feasibility study embedded in the United Kingdom Dementia Platform designed to determine participant acceptability and feasibility of extensive and repeated phenotyping to determine the optimal combination of biomarkers to detect disease progression and identify early risk of Alzheimer's disease (AD). Gait is included as a clinical biomarker. The tools to quantify gait in the clinic and home, and suitability for multi-center application have not been examined. Six centers from the National Institute for Health Research Translational Research Collaboration in Dementia initiative recruited 20 individuals with early onset AD. Participants wore a single wearable (tri-axial accelerometer) and completed both clinic-based and free-living gait assessment. A series of macro (behavioral) and micro (spatiotemporal) characteristics were derived from the resultant data using previously validated algorithms. Results indicate good participant acceptability, and potential for use of body-worn sensors in both the clinic and the home. Recommendations for future studies have been provided. Gait has been demonstrated to be a feasible and suitable measure, and future research should examine its suitability as a biomarker in AD.