Detection of Walk Tests in Free-Living Activities Using a Wrist-Worn Device

Sokas, Daivaras; Paliakaitė‬‬‬, Birutė; Rapalis, Andrius; Marozas, Vaidotas; Bailón, Raquel; Petrėnas, Andrius

doi:10.3389/fphys.2021.706545

Cited by 13 publications

(9 citation statements)

References 71 publications

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“…Small pauses in walking and changes of posture lasting up to 3 s were ignored ( Del Din et al, 2016 ). However, a minimum average cadence of 60 steps/min was required ( Sokas et al, 2021 ). For the 1-min resting periods, we required 100% heart rate coverage and zero taken steps.…”

Section: Methodsmentioning

confidence: 99%

Assessing fatigue and sleep in chronic diseases using physiological signals from wearables: A pilot study

Antikainen

Njoum²,

Kudelka³

et al. 2022

Front. Physiol.

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Problems with fatigue and sleep are highly prevalent in patients with chronic diseases and often rated among the most disabling symptoms, impairing their activities of daily living and the health-related quality of life (HRQoL). Currently, they are evaluated primarily via Patient Reported Outcomes (PROs), which can suffer from recall biases and have limited sensitivity to temporal variations. Objective measurements from wearable sensors allow to reliably quantify disease state, changes in the HRQoL, and evaluate therapeutic outcomes. This work investigates the feasibility of capturing continuous physiological signals from an electrocardiography-based wearable device for remote monitoring of fatigue and sleep and quantifies the relationship of objective digital measures to self-reported fatigue and sleep disturbances. 136 individuals were followed for a total of 1,297 recording days in a longitudinal multi-site study conducted in free-living settings and registered with the German Clinical Trial Registry (DRKS00021693). Participants comprised healthy individuals (N = 39) and patients with neurodegenerative disorders (NDD, N = 31) and immune mediated inflammatory diseases (IMID, N = 66). Objective physiological measures correlated with fatigue and sleep PROs, while demonstrating reasonable signal quality. Furthermore, analysis of heart rate recovery estimated during activities of daily living showed significant differences between healthy and patient groups. This work underscores the promise and sensitivity of novel digital measures from multimodal sensor time-series to differentiate chronic patients from healthy individuals and monitor their HRQoL. The presented work provides clinicians with realistic insights of continuous at home patient monitoring and its practical value in quantitative assessment of fatigue and sleep, an area of unmet need.

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Section: Methodsmentioning

confidence: 99%

Assessing fatigue and sleep in chronic diseases using physiological signals from wearables: A pilot study

Antikainen

Njoum²,

Kudelka³

et al. 2022

Front. Physiol.

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“…Today, measuring physical activity at home using wearable devices has become mainstream and is feasible [ 12 , 26 , 27 ]. More recently, also measuring physical capacity using wearables has been shown to be feasible but the clinimetric properties have been investigated to a very limited extent as yet [ 8 , 28 - 31 ].…”

Section: Introductionmentioning

confidence: 99%

Measuring Physical Functioning Using Wearable Sensors in Parkinson Disease and Chronic Obstructive Pulmonary Disease (the Accuracy of Digital Assessment of Performance Trial Study): Protocol for a Prospective Observational Study

de Graaf,

de Vries,

van de Zande

et al. 2024

JMIR Res Protoc

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Background Physical capacity and physical activity are important aspects of physical functioning and quality of life in people with a chronic disease such as Parkinson disease (PD) or chronic obstructive pulmonary disease (COPD). Both physical capacity and physical activity are currently measured in the clinic using standardized questionnaires and tests, such as the 6-minute walk test (6MWT) and the Timed Up and Go test (TUG). However, relying only on in-clinic tests is suboptimal since they offer limited information on how a person functions in daily life and how functioning fluctuates throughout the day. Wearable sensor technology may offer a solution that enables us to better understand true physical functioning in daily life. Objective We aim to study whether device-assisted versions of 6MWT and TUG, such that the tests can be performed independently at home using a smartwatch, is a valid and reliable way to measure the performance compared to a supervised, in-clinic test. Methods This is a decentralized, prospective, observational study including 100 people with PD and 100 with COPD. The inclusion criteria are broad: age ≥18 years, able to walk independently, and no co-occurrence of PD and COPD. Participants are followed for 15 weeks with 4 in-clinic visits, once every 5 weeks. Outcomes include several walking tests, cognitive tests, and disease-specific questionnaires accompanied by data collection using wearable devices (the Verily Study Watch and Modus StepWatch). Additionally, during the last 10 weeks of this study, participants will follow an aerobic exercise training program aiming to increase physical capacity, creating the opportunity to study the responsiveness of the remote 6MWT. Results In total, 89 people with PD and 65 people with COPD were included in this study. Data analysis will start in April 2024. Conclusions The results of this study will provide information on the measurement properties of the device-assisted 6MWT and TUG in the clinic and at home. When reliable and valid, this can contribute to a better understanding of a person’s physical capacity in real life, which makes it possible to personalize treatment options. Trial Registration ClinicalTrials.gov NCT05756075; https://clinicaltrials.gov/study/NCT05756075 International Registered Report Identifier (IRRID) DERR1-10.2196/55452

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“…The previous studies showed good correlation between P6MC cadence and the official P6MC distance; however, the direct transformation from cadence to distance is still challenging [ 38 ]. The sensitivity of this outcome in detecting older individuals with functional capacity has been demonstrated [ 51 ].…”

Section: Discussionmentioning

confidence: 99%

“…The highest continuous activity during 6 min is detected in step data, using a sliding 6 min window with 1 min overlap [ 38 ]. The one with the largest number of steps is chosen as the most representative in order to obtain the highest intensity reached during 6 consecutives minutes.…”

Section: Methodsmentioning

confidence: 99%

Incorporating Wearable Technology for Enhanced Rehabilitation Monitoring after Hip and Knee Replacement

Lebleu,

Daniels,

Pauwels

et al. 2024

Sensors

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Osteoarthritis (OA) poses a growing challenge for the aging population, especially in the hip and knee joints, contributing significantly to disability and societal costs. Exploring the integration of wearable technology, this study addresses the limitations of traditional rehabilitation assessments in capturing real-world experiences and dynamic variations. Specifically, it focuses on continuously monitoring physical activity in hip and knee OA patients using automated unsupervised evaluations within the rehabilitation process. We analyzed data from 1144 patients who used a mobile health application after surgery; the activity data were collected using the Garmin Vivofit 4. Several parameters, such as the total number of steps per day, the peak 6-minute consecutive cadence (P6MC) and peak 1-minute cadence (P1M), were computed and analyzed on a daily basis. The results indicated that cadence-based measurements can effectively, and earlier, differ among patients with hip and knee conditions, as well as in the recovery process. Comparisons based on recovery status and type of surgery reveal distinctive trajectories, emphasizing the effectiveness of P6MC and P1M in detecting variations earlier than total steps per day. Furthermore, cadence-based measurements showed a lower inter-day variability (40%) compared to the total number of steps per day (80%). Automated assessments, including P1M and P6MC, offer nuanced insights into the patients’ dynamic activity profiles.

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Detection of Walk Tests in Free-Living Activities Using a Wrist-Worn Device

Cited by 13 publications

References 71 publications

Assessing fatigue and sleep in chronic diseases using physiological signals from wearables: A pilot study

Assessing fatigue and sleep in chronic diseases using physiological signals from wearables: A pilot study

Measuring Physical Functioning Using Wearable Sensors in Parkinson Disease and Chronic Obstructive Pulmonary Disease (the Accuracy of Digital Assessment of Performance Trial Study): Protocol for a Prospective Observational Study

Incorporating Wearable Technology for Enhanced Rehabilitation Monitoring after Hip and Knee Replacement

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