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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“…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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“…Unintentional walk testing, which does not modify casual activity habits, may increase the number of pwMS who are less motivated to perform such tests for extended periods. According to a recent study, unintentional walk testing has been shown to be practical and useful for assessing walking capacity in free-living activities ( Sokas et al., 2021 ). Another important limitation is the interpretation of SB during walk test.…”

Section: Discussionmentioning

confidence: 99%

Autonomic response to walk tests is useful for assessing outcome measures in people with multiple sclerosis

et al. 2023

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Objective: The aim of this study was to evaluate the association between changes in the autonomic control of cardiorespiratory system induced by walk tests and outcome measures in people with Multiple Sclerosis (pwMS).Methods: Electrocardiogram (ECG) recordings of 148 people with Relapsing-Remitting MS (RRMS) and 58 with Secondary Progressive MS (SPMS) were acquired using a wearable device before, during, and after walk test performance from a total of 386 periodical clinical visits. A subset of 90 participants repeated a walk test at home. Various MS-related symptoms, including fatigue, disability, and walking capacity were evaluated at each clinical visit, while heart rate variability (HRV) and ECG-derived respiration (EDR) were analyzed to assess autonomic nervous system (ANS) function. Statistical tests were conducted to assess differences in ANS control between pwMS grouped based on the phenotype or the severity of MS-related symptoms. Furthermore, correlation coefficients (r) were calculated to assess the association between the most significant ANS parameters and MS-outcome measures.Results: People with SPMS, compared to RRMS, reached higher mean heart rate (HRM) values during walk test, and larger sympathovagal balance after test performance. Furthermore, pwMS who were able to adjust their HRM and ventilatory values, such as respiratory rate and standard deviation of the ECG-derived respiration, were associated with better clinical outcomes. Correlation analyses showed weak associations between ANS parameters and clinical outcomes when the Multiple Sclerosis phenotype is not taken into account. Blunted autonomic response, in particular HRM reactivity, was related with worse walking capacity, yielding r = 0.36 r = 0.29 (RRMS) and r > 0.5 (SPMS). A positive strong correlation r > 0.7 r > 0.65 between cardiorespiratory parameters derived at hospital and at home was also found.Conclusion: Autonomic function, as measured by HRV, differs according to MS phenotype. Autonomic response to walk tests may be useful for assessing clinical outcomes, mainly in the progressive stage of MS. Participants with larger changes in HRM are able to walk longer distance, while reduced ventilatory function during and after walk test performance is associated with higher fatigue and disability severity scores. Monitoring of disorder severity could also be feasible using ECG-derived cardiac and respiratory parameters recorded with a wearable device at home.

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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

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

Autonomic response to walk tests is useful for assessing outcome measures in people with multiple sclerosis

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