Validation of a Body-Worn Accelerometer to Measure Activity Patterns in Octogenarians

Accelerometer techniques for capturing human movement validated against direct observation: a scoping review

Objective.Accelerometers are devices commonly used to measure human physical activity and sedentary time. Accelerometer capabilities and analytical techniques have evolved rapidly, making it difficult for researchers to keep track of advances and best practices for data processing and analysis. The objective of this scoping review is to determine the existing methods for analyzing accelerometer data for capturing human movement which have been validated against the criterion measure of direct observation.Approach.This scoping review searched 14 academic and 5 grey databases. Two independent raters screened by title and abstract, then full text. Data were extracted using Microsoft Excel and checked by an independent reviewer.Mainresults.The search yielded 1039 papers and the final analysis included 115 papers. A total of 71 unique accelerometer models were used across a total of 4217 participants. While all studies underwent validation from direct observation, most direct observation occurred live (55%) or using recordings (42%). Analysis techniques included machine learning (ML) approaches (22%), the use of existing cut-points (18%), receiver operating characteristic curves to determine cut-points (14%), and other strategies including regressions and non-ML algorithms (8%).Significance.ML techniques are becoming more prevalent and are often used for activity identification. Cut-point methods are still frequently used. Activity intensity is the most assessed activity outcome; however, both the analyses and outcomes assessed vary by wear location. This scoping review provides a comprehensive overview of accelerometer analysis and validation techniques using direct observation and is a useful tool for researchers using accelerometers.

Artificial intelligence for dementia-Applied models and digital health

INTRODUCTION

The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available.

METHODS

This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors.

RESULTS

This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health.

DISCUSSION

Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).

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.

Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults

Accurate measurement of sedentary behaviour in older adults is informative and relevant. Yet, activities such as sitting are not accurately distinguished from non-sedentary activities (e.g., upright activities), especially in real-world conditions. This study examines the accuracy of a novel algorithm to identify sitting, lying, and upright activities in community-dwelling older people in real-world conditions. Eighteen older adults wore a single triaxial accelerometer with an onboard triaxial gyroscope on their lower back and performed a range of scripted and non-scripted activities in their homes/retirement villages whilst being videoed. A novel algorithm was developed to identify sitting, lying, and upright activities. The algorithm's sensitivity, specificity, positive predictive value, and negative predictive value for identifying scripted sitting activities ranged from 76.9% to 94.8%. For scripted lying activities: 70.4% to 95.7%. For scripted upright activities: 75.9% to 93.1%. For non-scripted sitting activities: 92.3% to 99.5%. No non-scripted lying activities were captured. For non-scripted upright activities: 94.3% to 99.5%. The algorithm could, at worst, overestimate or underestimate sedentary behaviour bouts by ±40 s, which is within a 5% error for sedentary behaviour bouts. These results indicate good to excellent agreement for the novel algorithm, providing a valid measure of sedentary behaviour in community-dwelling older adults.

Physical activity levels in cognitively normal and cognitively impaired oldest-old and the association with dementia risk factors: a pilot study

BACKGROUND

Research assessing the relationship of physical activity and dementia is usually based on studies with individuals younger than 90 years of age. The primary aim of this study was to determine physical activity levels of cognitively normal and cognitively impaired adults older than 90 years of age (oldest-old). Our secondary aim was to assess if physical activity is associated with risk factors for dementia and brain pathology biomarkers.

METHODS

Physical activity was assessed in cognitively normal (N = 49) and cognitively impaired (N = 12) oldest-old by trunk accelerometry for a 7-day period. We tested physical performance parameters and nutritional status as dementia risk factors, and brain pathology biomarkers. Linear regression models were used to examine the associations, correcting for age, sex and years of education.

RESULTS

Cognitively normal oldest-old were on average active for a total duration of 45 (SD 27) minutes per day, while cognitively impaired oldest-old seemed less physically active with 33 (SD 21) minutes per day with a lower movement intensity. Higher active duration and lower sedentary duration were related to better nutritional status and better physical performance. Higher movement intensities were related to better nutritional status, better physical performance and less white matter hyperintensities. Longer maximum walking bout duration associated with more amyloid binding.

CONCLUSION

We found that cognitively impaired oldest-old are active at a lower movement intensity than cognitively normal oldest-old individuals. In the oldest-old, physical activity is related to physical parameters, nutritional status, and moderately to brain pathology biomarkers.

Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model

Activity monitoring combined with machine learning (ML) methods can contribute to detailed knowledge about daily physical behavior in older adults. The current study (1) evaluated the performance of an existing activity type recognition ML model (HARTH), based on data from healthy young adults, for classifying daily physical behavior in fit-to-frail older adults, (2) compared the performance with a ML model (HAR70+) that included training data from older adults, and (3) evaluated the ML models on older adults with and without walking aids. Eighteen older adults aged 70-95 years who ranged widely in physical function, including usage of walking aids, were equipped with a chest-mounted camera and two accelerometers during a semi-structured free-living protocol. Labeled accelerometer data from video analysis was used as ground truth for the classification of walking, standing, sitting, and lying identified by the ML models. Overall accuracy was high for both the HARTH model (91%) and the HAR70+ model (94%). The performance was lower for those using walking aids in both models, however, the overall accuracy improved from 87% to 93% in the HAR70+ model. The validated HAR70+ model contributes to more accurate classification of daily physical behavior in older adults that is essential for future research.

Closing the loop for patients with Parkinson disease: where are we?

Although levodopa remains the most efficacious symptomatic therapy for Parkinson disease (PD), management of levodopa treatment during the advanced stages of the disease is extremely challenging. This difficulty is a result of levodopa's short half-life, a progressive narrowing of the therapeutic window, and major inter-patient and intra-patient variations in the dose-response relationship. Therefore, a suitable alternative to repeated oral administration of levodopa is being sought. Recent research efforts have focused on the development of novel levodopa delivery strategies and wearable physical sensors that track symptoms and disease progression. However, the need for methods to monitor the levels of levodopa present in the body in real time has been overlooked. Advances in chemical sensor technology mean that the development of wearable and mobile biosensors for continuous or frequent levodopa measurements is now possible. Such levodopa monitoring could help to deliver personalized and timely medication dosing to alleviate treatment-related fluctuations in the symptoms of PD. Therefore, with the aim of optimizing therapeutic management of PD and improving the quality of life of patients, we share our vision of a future closed-loop autonomous wearable 'sense-and-act' system. This system consists of a network of physical and chemical sensors coupled with a levodopa delivery device and is guided by effective big data fusion algorithms and machine learning methods.

Wearables in rugby union: A protocol for multimodal digital sports-related concussion assessment

Background

Pragmatic challenges remain in the monitoring and return to play (RTP) decisions following suspected Sports Related Concussion (SRC). Reliance on traditional approaches (pen and paper) means players readiness for RTP is often based on self-reported symptom recognition as a marker for full physiological recovery. Non-digital approaches also limit opportunity for robust data analysis which may hinder understanding of the interconnected nature and relationships in deficit recovery. Digital approaches may provide more objectivity to measure and monitor impairments in SRC. Crucially, there is dearth of protocols for SRC assessment and digital devices have yet to be tested concurrently (multimodal) in SRC rugby union assessment. Here we propose a multimodal protocol for digital assessment in SRC, which could be used to enhance traditional sports concussion assessment approaches.

Methods

We aim to use a repeated measures observational study utilising a battery of multimodal assessment tools (symptom, cognitive, visual, motor). We aim to recruit 200 rugby players (male n≈100 and female n≈100) from University Rugby Union teams and local amateur rugby clubs in the North East of England. The multimodal battery assessment used in this study will compare metrics between digital methods and against traditional assessment.

Conclusion

This paper outlines a protocol for a multimodal approach for the use of digital technologies to augment traditional approaches to SRC, which may better inform RTP in rugby union. Findings may shed light on new ways of working with digital tools in SRC. Multimodal approaches may enhance understanding of the interconnected nature of impairments and provide insightful, more objective assessment and RTP in SRC.

Clinical trial registration

NCT04938570. https://clinicaltrials.gov/ct2/results?cond=NCT04938570&term=&cntry=&state=&city=&dist=

Free-Living Standing Activity as Assessed by Seismic Accelerometers and Cognitive Function in Community-Dwelling Older Adults: The MIND Trial

BACKGROUND

Few older adults are able to achieve recommended levels of moderate-vigorous physical activity despite known cognitive benefits. Alternatively, less intense activities such as standing can be easily integrated into daily life. No existing study has examined the impact of free-living standing activity during daily life as measured by a device on cognition in older adults. Our purpose was to examine the association between free-living standing activity and cognitive function in cognitively healthy older adults.

METHOD

Participants were 98 adults aged 65 years or older from the ongoing MIND trial (NCT02817074) without diagnoses or symptoms of mild cognitive impairment or dementia. Linear regression analyses tested cross-sectional associations between standing activity (duration and intensity from the MoveMonitor+ accelerometer/gyroscope) and cognition (4 cognitive domains constructed from 12 cognitive performance tests).

RESULTS

Participants were on average 69.7 years old (SD = 3.7), 69.4% women, and 73.5% had a college degree or higher. Higher mean intensity of standing activity was significantly associated with higher levels of perceptual speed when adjusting for age, gender, and education level. Each log unit increase in standing activity intensity was associated with 0.72 units higher of perceptual speed (p = .023). When we additionally adjusted for cognitive activities and moderate-vigorous physical activity, and then also for body mass index, depressive symptoms, prescription medication use, and device wear time, the positive association remained.

CONCLUSIONS

These findings should be further explored in longitudinal analyses and interventions for cognition that incorporate small changes to free-living activity in addition to promoting moderate-vigorous physical activity.

When Will My Patient Fall? Sensor-Based In-Home Walking Speed Identifies Future Falls in Older Adults

BACKGROUND

Although there are known clinical measures that may be associated with risk of future falls in older adults, we are still unable to predict when the fall will happen. Our objective was to determine whether unobtrusive in-home assessment of walking speed can detect a future fall.

METHOD

In both ISAAC and ORCATECH Living Laboratory studies, a sensor-based monitoring system has been deployed in the homes of older adults. Longitudinal mixed-effects regression models were used to explore trajectories of sensor-based walking speed metrics in those destined to fall versus controls over time. Falls were captured during a 3-year period.

RESULTS

We observed no major differences between those destined to fall (n = 55) and controls (n = 70) at baseline in clinical functional tests. There was a longitudinal decline in median daily walking speed over the 3 months before a fall in those destined to fall when compared with controls, p < .01 (ie, mean walking speed declined 0.1 cm s-1 per week). We also found prefall differences in sensor-based walking speed metrics in individuals who experienced a fall: walking speed variability was lower the month and the week just before the fall compared with 3 months before the fall, both p < .01.

CONCLUSIONS

While basic clinical tests were not able to differentiate who will prospectively fall, we found that significant variations in walking speed metrics before a fall were measurable. These results provide evidence of a potential sensor-based risk biomarker of prospective falls in community living older adults.

Quantifying Reliable Walking Activity with a Wearable Device in Aged Residential Care: How Many Days Are Enough?

Strong associations exist between quality of life and physical activity for those living in aged residential care (ARC). Suitable and reliable tools are required to quantify physical activity for descriptive and evaluative purposes. We calculated the number of days required for reliable walking outcomes indicative of physical activity in an ARC population using a trunk-worn device. ARC participants (n = 257) wore the device for up to 7 days. Reasons for data loss were also recorded. The volume, pattern, and variability of walking was calculated. For 197 participants who wore the device for at least 3 days, linear mixed models determined the impact of week structure and number of days required to achieve reliable outcomes, collectively and then stratified by care level. The average days recorded by the wearable device was 5.2 days. Day of the week did not impact walking activity. Depending on the outcome and level of care, 2–5 days was sufficient for reliable estimates. This study provides informative evidence for future studies aiming to use a wearable device located on the trunk to quantify physical activity walking out in the ARC population.

Validity, reliability, and feasibility of the uSense activity monitor to register physical activity and gait performance in habitual settings of geriatric patients

OBJECTIVE

The aim of the study was to investigate the psychometric quality of a newly developed activity monitor (uSense) to document established physical activity parameters as well as innovative qualitative and quantitative gait characteristics in geriatric patients.

APPROACH

Construct and concurrent validity, test-retest reliability, and feasibility of established as well as innovative characteristics for qualitative gait analysis were analyzed in multi-morbid, geriatric patients with cognitive impairment (CI) (n  =  110), recently discharged from geriatric rehabilitation.

MAIN RESULTS

Spearman correlations of established and innovative uSense parameters reflecting active behavior with clinically relevant construct parameters were on average moderate to high for motor performance and life-space and low to moderate for other parameters, while correlations with uSense parameters reflecting inactive behavior were predominantly low. Concurrent validity of established physical activity parameters showed consistently high correlations between the uSense and an established comparator system (PAMSys^™), but the absolute agreement between both sensor systems was low. On average excellent test-retest reliability for all uSense parameters and good feasibility could be documented.

SIGNIFICANCE

The uSense monitor allows the assessment of established and-for the first time-a semi-qualitative gait assessment of habitual activity behavior in older persons most affected by motor and CI and activity restrictions. On average moderate to good construct validity, high test-retest reliability, and good feasibility indicated a sound psychometric quality of most measures, while the results of concurrent validity as measured by a comparable system indicated high correlation but low absolute agreement based on different algorithms used.

Development of a gold-standard method for the identification of sedentary, light and moderate physical activities in older adults: Definitions for video annotation

OBJECTIVES

The development of a reliable method for the identification of sedentary, light and moderate physical activities in older adults. The method consists of a validated set of definitions for the identification of the initiation and termination of physical activities performed by older adult participants, video recorded during free-living and a laboratory setting.

DESIGN

Inter-rater reliability assessment in a fully crossed design.

METHODS

An iterative consensus process was used to define the initiation and termination of common activities of daily living. These definitions were then tested using videos recorded in two scenarios (1) by 9 raters who annotated a video recording, of a free-living protocol in a home environment, recorded in a first person view, using a body-worn camera and (2) by 7 raters who annotated a video recording, of older adults performing a semi-structured protocol in a living-lab environment, recorded in a third person view, using wall mounted cameras.

RESULTS

Inter-rater reliability was excellent for all items, with Krippendorff's alpha and Fleiss' kappa all above 0.84 and a percentage of agreement above 88%. All ICC(C,1) inter-rater values for the activity quantity and duration were all above 0.9.

CONCLUSIONS

This set of physical activity initiation and termination definitions offers independent researchers a gold standard method to allow for the consistent annotation of high-frequency video footage (25fps), in both a free-living and laboratory setting. When synchronised with body-worn or ambient sensors, this annotation will allow for the development and validation of physical activity classification systems to a higher resolution than before.

Inertial sensors versus standard systems in gait analysis: a systematic review and meta-analysis

INTRODUCTION

The increasing popularity of inertial sensors in clinical practice is not supported by precise information on their reliability or guidelines for their use in rehabilitation. The authors investigated the state of the literature concerning the use of inertial sensors for gait analysis in both healthy and pathological adults comparing traditional systems. Furthermore, trying to define directions for clinicians.

EVIDENCE ACQUISITION

In accordance with the PRISMA statement, authors searched in PubMed, Web of Science and Scopus all paper published from January 1st, 2005 until December 31st, 2017. They included both healthy and pathological adults' subjects as population, wearable or inertial sensors used for gait analysis and compared with classical gait analysis performed in a Motion Lab as intervention and comparison, gait parameters as outcomes. Considering the methodological quality, authors focused on: sample; description of the study; type of gait analysis used for comparison; type of sensor; sensor placement on the body; gait task requested.

EVIDENCE SYNTHESIS

From a total of 888 articles, 16 manuscripts were selected and 7 of them were considered for meta-analysis for different gait parameters. Demographic data, tested devices, reference systems, test procedures and outcomes were analyzed.

CONCLUSIONS

Our results show a good agreement between inertial sensors and classical gait analysis for some gait parameters, supporting their use as a solution for capturing kinematic information over an extended space and time and even outside a laboratory in real-life conditions. Authors can support the use of portable inertial sensors for a practical gait analysis in clinical setting with good reliability. It will then be the experience of the clinician to direct the decision-making process.

Body-worn triaxial accelerometer coherence and reliability related to static posturography in unilateral vestibular failure

Since changes in vestibular function may be one cause of disequilibrium, major advances in measuring postural control and sensory integration in vestibular impairments have been achieved by using posturography. However, in order to overcome problems related to this type of technology, body-worn accelerometers (ACC) have been proposed as a portable, low-cost alternative to posturography for measurements of postural sway in a friendly and ecologic environment. Due to the fact that no study to date has shown the experimental validity of ACC-based measures of body sway with respect to posturography for subjects with vestibular deficits, the aim of the present study was: i) to develop and validate a practical tool that can allow clinicians to measure postural sway derangements in an otoneurological setting by ACC, and ii) to provide reliable, sensitive and accurate automatic analysis of sway that could help in discriminating unilateral vestibular failure (UVF) patients. Thus, a group of 13 patients (seven females, 6 males; mean age 48.6 ± 6.4 years) affected for at least 6 months by UVF and 13 matched healthy subjects were instructed to maintain an upright position during a static forceplate-based posturography (FBP) acquisition while wearing a Movit® sensor (by Captiks) with 3-D accelerometers mounted on the posterior trunk near the body centre of mass. Pearson product moment correlation demonstrated a high level of correspondence of four time-domain and three frequency-domain measures extracted by ACC and FBP testing; in addition, t-test demonstrated that two ACC-based time- and frequency-domain parameters were reliable measures in discriminating UVF subjects. These aspects, overall, should further highlight the attention of clinicians and researchers to this kind of sway recording technique in the field of otoneurological disorders by considering the possibility to enrich the amount of quantitative and qualitative information useful for discrimination, diagnosis and treatment of UVF. In conclusion, we believe the present ACC-based measurement of sway offers a patient-friendly, reliable, inexpensive and efficient alternative recording technique that is useful - together with clinical balance and mobility tests - in various circumstances, as well as in outcome studies involving diagnosis, follow-up and rehabilitation of UVF patients.

A Physical Activity Reference Data-Set Recorded from Older Adults Using Body-Worn Inertial Sensors and Video Technology-The ADAPT Study Data-Set

Physical activity monitoring algorithms are often developed using conditions that do not represent real-life activities, not developed using the target population, or not labelled to a high enough resolution to capture the true detail of human movement. We have designed a semi-structured supervised laboratory-based activity protocol and an unsupervised free-living activity protocol and recorded 20 older adults performing both protocols while wearing up to 12 body-worn sensors. Subjects’ movements were recorded using synchronised cameras (≥25 fps), both deployed in a laboratory environment to capture the in-lab portion of the protocol and a body-worn camera for out-of-lab activities. Video labelling of the subjects’ movements was performed by five raters using 11 different category labels. The overall level of agreement was high (percentage of agreement >90.05%, and Cohen’s Kappa, corrected kappa, Krippendorff’s alpha and Fleiss’ kappa >0.86). A total of 43.92 h of activities were recorded, including 9.52 h of in-lab and 34.41 h of out-of-lab activities. A total of 88.37% and 152.01% of planned transitions were recorded during the in-lab and out-of-lab scenarios, respectively. This study has produced the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate (≥25 fps) video labelled data recorded in a free-living environment from older adults living independently. This dataset is suitable for validation of existing activity classification systems and development of new activity classification algorithms.

Validation and User Evaluation of a Sensor-Based Method for Detecting Mobility-Related Activities in Older Adults

Regular physical activity is essential for older adults to stay healthy and independent. However, daily physical activity is generally low among older adults and mainly consists of activities such as standing and shuffling around indoors. Accurate measurement of this low-energy expenditure daily physical activity is crucial for stimulation of activity. The objective of this study was to assess the validity of a necklace-worn sensor-based method for detecting time-on-legs and daily life mobility related postures in older adults. In addition user opinion about the practical use of the sensor was evaluated. Twenty frail and non-frail older adults performed a standardized and free movement protocol in their own home. Results of the sensor-based method were compared to video observation. Sensitivity, specificity and overall agreement of sensor outcomes compared to video observation were calculated. Mobility was assessed based on time-on-legs. Further assessment included the categories standing, sitting, walking and lying. Time-on-legs based sensitivity, specificity and percentage agreement were good to excellent and comparable to laboratory outcomes in other studies. Category-based sensitivity, specificity and overall agreement were moderate to excellent. The necklace-worn sensor is considered an acceptable valid instrument for assessing home-based physical activity based upon time-on-legs in frail and non-frail older adults, but category-based assessment of gait and postures could be further developed.

Use of Accelerometry to Monitor Physical Activity in Critically Ill Subjects: A Systematic Review

Medical management of critically ill patients often incorporates prolonged bed rest, which, in combination with the underlying illness, results in global muscle weakness and atrophy. Recent evidence has demonstrated improvements in clinical and functional outcomes when exercise and physical activity are incorporated early in the management of ICU patients. Accurate monitoring of ICU patients' physical activity is essential for proper prescription and escalation of activity levels. Accelerometry is a technique used to measure physical activity and has been validated in several ambulatory populations. However, its use in critically ill, hospitalized patients with poor functional mobility is limited. In this review, we focus on the few studies assessing the use of accelerometry to measure physical activity in the care of mechanically ventilated adult ICU patients. The selected literature demonstrates that accelerometry correlates well with direct observation in reporting frequency and duration of various types of physical activity (rolling, sitting up, transferring, walking), but cannot differentiate various intensities of activity or whether movements are voluntary or involuntary with respect to effort. Thus, although accelerometry may serve as a useful adjunct in reporting temporality of physical activity in critically ill patients, other objective information may be needed to accurately record frequency, duration, and intensity of activity in this population.

Measuring physical activity in older adults: calibrating cut-points for the MotionWatch 8(©)

Given the world's aging population, the staggering economic impact of dementia, the lack of effective treatments, and the fact a cure for dementia is likely many years away - there is an urgent need to develop interventions to prevent or at least delay dementia's progression. Thus, lifestyle approaches to promote healthy aging are an important line of scientific inquiry. Good sleep quality and physical activity (PA) are pillars of healthy aging, and as such, are an increasing focus for intervention studies aimed at promoting health and cognitive function in older adults. However, PA and sleep quality are difficult constructs to evaluate empirically. Wrist-worn actigraphy (WWA) is currently accepted as a valid objective measure of sleep quality. The MotionWatch 8(©) (MW8) is the latest WWA, replacing the discontinued Actiwatch 4 and Actiwatch 7. In the current study, concurrent measurement of WWA and indirect calorimetry was performed during 10 different activities of daily living for 23 healthy older adults (aged 57-80 years) to determine cut-points for sedentary and moderate-vigorous PA - using receiver operating characteristic curves - with the cut-point for light activity being the boundaries between sedentary and moderate to vigorous PA. In addition, simultaneous multi-unit reliability was determined for the MW8 using inter-class correlations. The current study is the first to validate MW8 activity count cut-points - for sedentary, light, and moderate to vigorous PA - specifically for use with healthy older adults. These cut-points provide important context for better interpretation of MW8 activity counts, and a greater understanding of what these counts mean in terms of PA. Hence, our results validate another level of analysis for researchers using the MW8 in studies aiming to examine PA and sleep quality concurrently in older adults.