Dataset Information

Diurnal cardio-respiratory changes in ambulatory individuals deciphered using a multi-parameter wearable device.

ABSTRACT:

Background

Recent technological developments enable big data-driven insights on diurnal changes. This study aimed to describe the trajectory of multiple and advanced parameters using a medical-grade wearable remote patient monitor.

Methods

Parameters were monitored for 24 h in 256 ambulatory participants who kept living their normal life. Parameters included heart rate, blood pressure, stroke volume, cardiac index, systemic vascular resistance, blood oxygen saturation, and respiratory rate. Diurnal variations were evaluated, and analyses were stratified based on sex, age, and body mass index.

Results

All parameters showed diurnal changes (p < 0.001). Females demonstrated higher heart rate and cardiac index with lower systemic vascular resistance. Obese participants had a higher blood pressure, and lower stroke volume and cardiac index. Systemic vascular resistance was higher among the elderly. Diurnal changes corresponded with awake-sleep hours and differed between sex, age, and body mass index groups.

Conclusion

Wearable monitoring platforms could decipher hemodynamic changes in subgroups of individuals, and might help with efforts to provide personalized medicine, pre-symptomatic diagnosis and prevention, and drug development.

SUBMITTER: Nachman D

PROVIDER: S-EPMC10695076 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature

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Publications

Diurnal cardio-respiratory changes in ambulatory individuals deciphered using a multi-parameter wearable device.

Nachman Dean D Eisenkraft Arik A Kolben Yotam Y Carmon Erez E Hazan Esther E Goldstein Nir N Ben Ishay Arik A Hershkovitz Mor M Fons Meir M Merin Roei R Amir Offer O Asleh Rabea R Gepner Yftach Y

Digital health 20230101

<h4>Background</h4>Recent technological developments enable big data-driven insights on diurnal changes. This study aimed to describe the trajectory of multiple and advanced parameters using a medical-grade wearable remote patient monitor.<h4>Methods</h4>Parameters were monitored for 24 h in 256 ambulatory participants who kept living their normal life. Parameters included heart rate, blood pressure, stroke volume, cardiac index, systemic vascular resistance, blood oxygen saturation, and respira ...[more]

PMID: 38053733

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Project description:Inappropriate physical inactivity is a global health problem increasing the risk of cardiometabolic diseases. Wearable sensors show great potential to promote physical activity and thus a healthier lifestyle. While commercial activity trackers are available to estimate energy expenditure (EE) in non-disabled individuals, they are not designed for reliable assessments in individuals with an incomplete spinal cord injury (iSCI). Furthermore, activity recommendations for this population are currently rather vague and not tailored to their individual needs, and activity guidelines provided for the non-disabled population may not be easily translated for this population. However, especially in iSCI individuals with impaired abilities to stand and walk, the assessment of physical activities and appropriate recommendations for a healthy lifestyle are challenging. Therefore, the study aimed at developing an EE estimation model for iSCI individuals able to walk based on wearable sensor data. Additionally, the data collected within this study was used to translate common activity recommendations for the non-disabled population to easily understandable activity goals for ambulatory individuals with an iSCI. In total, 30 ambulatory individuals with an iSCI were equipped with wearable sensors while performing 12 different physical activities. EE was measured continuously and demographic and anthropometric variables, clinical assessment scores as well as wearable-sensor-derived features were used to develop different EE estimation models. The best EE estimation model comprised the estimation of resting EE using the updated Harris-Benedict equation, classifying activities using a k-nearest neighbor algorithm, and applying a multiple linear regression-based EE estimation model for each activity class. The mean absolute estimation error of this model was 15.2 ± 6.3% and the corresponding mean signed error was -3.4 ± 8.9%. Translating activity recommendations of global health institutions, we suggest a minimum of 2,000-3,000 steps per day for ambulatory individuals with an iSCI. If ambulatory individuals with an iSCI targeted the popular 10,000 steps a day recommendation for the non-disabled population, their equivalent would be around 8,000 steps a day. The combination of the presented dedicated EE estimation model for ambulatory individuals with an iSCI and the translated activity recommendations is an important step toward promoting an active lifestyle in this population.

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Dataset Information

Diurnal cardio-respiratory changes in ambulatory individuals deciphered using a multi-parameter wearable device.

Background

Methods

Results

Conclusion

Publications

Diurnal cardio-respiratory changes in ambulatory individuals deciphered using a multi-parameter wearable device.

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