Feasibility of Using Commercially Available Accelerometers to Monitor Upper Extremity Home Practice With Persons Poststroke

PURPOSE: Accelerometers have been used in the literature to measure outcomes related to hand function for persons post-stroke (Bailey et al., 2015; Reiterer et al., 2008; Uswatte et al., 2006). While research previously found accelerometers to be valid and reliable real-time monitors for home rehabilitation programs, challenges regarding data management and translation of accelerometer output information into clinically interpretable results continue to exist (Noorkõiv et al., 2014). The purpose of this secondary data analysis was to explore the feasibility of using a commercially available accelerometer with custom software to collect data corroborating self-reported practice in a repetitive task practice home program.

DESIGN: The original study was a single-subject design intervention study that tracked home practice for upper extremity rehabilitation and the effects of adherence with and without music. Participants were adults with chronic stroke resulting in aphasia and hemi-paresis in one upper extremity.

METHOD: Primary data collection (n = 7) involved wearing commercially available accelerometers on both upper extremities while completing several sets of two to three pre-determined repetitive, occupation-based exercises. Participants were asked to practice twice per day for four weeks. Accelerometer data were downloaded and imported into Excel and exercises were cut into individual practice sessions according to participant reported practice times. Angular velocity and acceleration data were graphed in a custom Matlab program and the start/stop of each set was visually identified. Key outputs included duration, average angular velocity, and average acceleration for total practice and individual sets of practice. Comparisons were made between the following: 1) the number of self-reported sessions and the number of sessions captured via accelerometry, 2) self-reported duration of practice and duration captured via accelerometry, and 3) average angular velocity captured from total practice sessions and individual sets within a practice session. Descriptive statistics have been completed as an initial analysis.

RESULTS: Preliminary data analysis from four participants indicated key findings for data capture, accuracy of self-reported time, and trends in angular velocity. Across practice weeks, the average percent of self-reported data captured via the accelerometer ranged from 37.39% to 71.04% (Median = 49.68%). The average percent of active time during practices, calculated by accelerometer individual set time over the total practice time, ranged from 63.34% to 91.01% (Median = 80.73%). Additionally, three out of four participants on average had higher self-reported total practice times, with the difference between self-reported and accelerometer captured ranging from 26 to 107 seconds. In all four participants, individual set average angular velocity was higher than total practice angular velocity.

CONCLUSION: Only 50% of self-reported data were accounted for via accelerometers, despite trainings and weekly check-ins to facilitate data capture. In future work, the use of cloud-based data monitoring of accelerometer data will be essential to increase amount of data captured and support real-time technology related problem solving. When accelerometer data was captured, it was reflective of self-reported data for duration and the differences observed were likely due to elapsed time between writing the start/stop time and when practice began/ended.

IMPACT STATEMENT: This project highlighted the challenges and the potential parameters for clinical interpretation of accelerometer data for use in home programs which will support the extension of occupational therapy practice into the community.

References

Bailey, R. R., Birkenmeier, R. L., & Lang, C. E. (2015). Real-world affected upper limb activity in chronic stroke: An examination of potential modifying factors. Topics in Stroke Rehabilitation, 22(1), 26-33. https://doi.org/10.1179/1074935714Z.0000000040

Noorkõiv, M., Rodgers, H., & Price, C. I. (2014). Accelerometer measurement of upper extremity movement after stroke: A systematic review of clinical studies. Journal of Neuroengineering and Rehabilitation, 11(1), 144. https://doi.org/10.1186/1743-0003-11-144

Reiterer, V., Sauter, C., Klösch, G., Lalouschek, W., & Zeitlhofer, J. (2008). Actigraphy–a useful tool for motor activity monitoring in stroke patients. European Neurology, 60(6), 285-291. https://doi.org/10.1159/000157882

Uswatte, G., Giuliani, C., Winstein, C., Zeringue, A., Hobbs, L., & Wolf, S. L. (2006). Validity of accelerometry for monitoring real-world arm activity in patients with subacute stroke: Evidence from the extremity constraint-induced therapy evaluation trial. Archives of Physical Medicine and Rehabilitation, 87(10), 1340-1345. https://doi.org/10.1016/j.apmr.2006.06.006