Accelerometry: A feasible method to monitor physical activity during sub-acute rehabilitation of persons with stroke

Abstract

Objective: To investigate the feasibility of using accelerometers to monitor physical activity in persons with stroke admitted to inpatient rehabilitation. Design: Longitudinal observational study. Participants: Persons with stroke admitted to a specialized rehabilitation centre for sub-acute rehabilitation were recruited between August and December 2016. Methods: Volume and intensity of physical activity were assessed with accelerometers throughout the rehabilitation period. Indicators of feasibility included processes (recruitment, protocol adherence and participants' experiences) and scientific feasibility, which assessed the accelerometers' ability to detect change in physical activity among stroke survivors who ambulate independently and those who are dependent on a mobility device. Results: Twenty-seven out of 31 eligible individuals took part in this study, with 23 (85%) completing it. In total, 432 days of rehabilitation were monitored and valid physical activity data were obtained for 408 days (94%). There were no indications that the measurement interfered with participants' ability to participate in rehabilitation. Despite the subjects' ambulation status, the number of steps and time spent in moderate-to-vigorous physical activity increased significantly across the first 18 days of rehabilitation, whereas sedentary time was unchanged. Conclusion: This study supports the feasibility of using accelerometers to capture physical activity behaviour in survivors of stroke during inpatient rehabilitation.

Levels and patterns of physical activity in stroke survivors with different ambulation status living in low-income areas of Cape Town, South Africa

“…In line with our findings, previous literature affirms that physical factors, gait and balance control, impact on stroke survivors' ability to participate in PA. 12 Our findings corroborate with previous studies in the subacute phase demonstrating that stroke survivors with a gait speed of approximately ≥0.9 m/s took more steps per day and spent more time in MVPA than survivors with more severe walking limitations. 34,35 We further deducted that even the community ambulation group in our study had a lower PA level (3524 steps/day) compared to previous observations of chronic stroke survivors in more developed countries (4078 steps/day). 7 We believe that the contextual setting where the present study was situated, areas of Figure 1.…”

Using an Accelerometer-Based Step Counter in Post-Stroke Patients: Validation of a Low-Cost Tool

“…Step counters have been identified as a useful device for measuring the mobility of post-stroke patients in an ecological way [15]. Even though some authors have demonstrated the possibility of quantifying the level of activity and gait pattern of healthy subjects with a single wearable sensor, this functionality has yet to be confirmed in stroke patients [25]. The quantification of stroke mobility is a new and very important concept because changes in steps per day seem to strongly influence health and global functioning outcomes [13].…”

“…Step counters can be used to detect post-stroke patients' activity levels [25][26][27], assess gait and balance parameters [28,29], and guide the patients in performing exercises [30], as well as acting as a stimulus to increase in-and out-patient activity levels [14]. The literature shows that the monitoring of patients' mobility in an inpatient setting can give information similar to advanced and time-consuming techniques, such as behavioral mapping, and it can be useful to collect the activity levels of hospitalized patients [31].…”

Patient generated health data use in clinical practice: A systematic review

“…Connected medication dispensing unit (Brath et al, 2013;Forni Ogna et al, 2013) Biometric tracking Blood pressure Wireless blood pressure cuff/Bluetooth to Smartphone application (Ciemins et al, 2018;Evans et al, 2016) Weight Digital weight scale (Demiris et al, 2013) Body temperature Digital thermometer (Ask, Ekstrand, Hult, Lindén, & Pettersson, 2012) Oxygen saturation Wireless pulse oximeter (Velardo et al, 2017) Blood glucose level Digital glucose monitor (Lee et al, 2017) Lung function Digital spirometer (Shakkottai, Kaciroti, Kasmikha, & Nasr, 2018) Heart rate Wrist-worn activity tracking device (Thiebaud et al, 2018) Behavioral tracking Activity level Pedometer watch/Accelerometer (Actigraph) (Hooke, Gilchrist, Tanner, Hart, & Withycombe, 2016;Joseph, Stromback, Hagstromer, & Conradsson, 2018) Calorie burning Fitness tracker with calorie burning calculator (Franco, Fallaize, Lovegrove, & Hwang, 2016) Sleep quality Bed sensor strip with ballistocardiography sensor (Kortelainen, van Gils, & Pärkkä, 2012) Daily hygiene routine Water sensors, motion sensors (J. Chung et al, 2017) Environmental tracking Room temperature Temperature sensor (Bock et al, 2016) Noise Indoor sound level sensor (Risojević , Rozman, Pilipović, Češnovar, & Bulić, 2018) Luminosity Home digital luminosity sensor (Bock et al, 2016) Humidity Indoor air quality sensor (Bock et al, 2016) Social interactions tracking Number of visitors Door sensor (Skubic, Guevara, & Rantz, 2015) Time spent outside the home…”