Step-Counting Accuracy of a Commercial Smartwatch in Mild-to-Moderate PD Patients and Effect of Spatiotemporal Gait Parameters, Laterality of Symptoms, Pharmacological State, and Clinical Variables

Abstract

Commercial smartwatches could be useful for step counting and monitoring ambulatory activity. However, in Parkinson’s disease (PD) patients, an altered gait, pharmacological condition, and symptoms lateralization may affect their accuracy and potential usefulness in research and clinical routine. Steps were counted during a 6 min walk in 47 patients with PD and 47 healthy subjects (HS) wearing a Garmin Vivosmart 4 (GV4) on each wrist. Manual step counting was used as a reference. An inertial sensor (BTS G-Walk), placed on the lower back, was used to compute spatial-temporal gait parameters. Intraclass correlation coefficient (ICC) and mean absolute percentage error (MAPE) were used for accuracy evaluation and the Spearman test was used to assess the correlations between variables. The GV4 overestimated steps in PD patients with only a poor-to-moderate agreement. The OFF pharmacological state and wearing the device on the most-affected body side led to an unacceptable accuracy. The GV4 showed an excellent agreement and MAPE in HS at a self-selected speed, but an unacceptable performance at a slow speed. In PD patients, MAPE was not associated with gait parameters and clinical variables. The accuracy of commercial smartwatches for monitoring step counting might be reduced in PD patients and further influenced by the pharmacological condition and placement of the device.

Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist- Worn Accelerometer Data: Development and Validation of ElderNet

“…These non-wear periods were de ned as consistent low movement (low STD) across all acceleration axes for at least 30 minutes 51,52 . For each participant, we extracted data from four full (24-hour-long) days, as a recent study has shown that this duration provides reliable gait quantity measures 53 . Ten participants were excluded, due to an insu cient amount of activity (less than 96 hours of data), resulting in a nal number of 157 participants who were included in this stage.…”

Reliability of average daily steps measured through a consumer smartwatch in Parkinson’s disease phenotypes, stages and severities: a cross-sectional study. (Preprint)

“…In PD, motor and gait manifestations could render step detection and step count challenging and hence significantly diminish the validity and reliability of device algorithms [20][21][22]. Nevertheless, a prior study from our group involving 47 PwPD demonstrated a strong criterion validity in step counting using a consumer smartwatch (Garmin Vivosmart 4), when worn on the side least affected by the disease and under well-controlled pharmacological conditions [20].…”

“…In PD, motor and gait manifestations could render step detection and step count challenging and hence significantly diminish the validity and reliability of device algorithms [20][21][22]. Nevertheless, a prior study from our group involving 47 PwPD demonstrated a strong criterion validity in step counting using a consumer smartwatch (Garmin Vivosmart 4), when worn on the side least affected by the disease and under well-controlled pharmacological conditions [20]. Similar results were obtained in real-life setting by Ginis and colleagues [23] who demonstrated the criterion validity in estimating average daily steps (avDS) of two Fitbit devices (Fitbit Alta and Fitbit Inspire 3) in 28 PwPD at home, compared to a research-grade device (Dynaport Movemonitor, McRoberts, NL).…”

“…Besides criterion validity, we recently demonstrated that an optimal reliability of avDS recorded by Garmin Vivosmart 4 in real-life conditions could be achieved if the smartwatch is worn for a minimum of 4 days [24]. However, the clinical heterogeneity of PD when evaluating metrological characteristics of stepcounting devices has been neglected so far.…”

“…This classification could be relevant when measuring avDS in PwPD since tremor could increase noise-to-signal ratio, making step detection more challenging [21]. Similarly, in PIGD individuals, the higher degree of gait alterations could alter the performance of step detection algorithms [20,22]. Symptoms severity and disease stage could also represent other relevant parameters to be considered when assessing reliability of any wearable devices in counting avDS.…”