Free-Living Gait Cadence Measured by Wearable Accelerometer: A Promising Alternative to Traditional Measures of Mobility for Assessing Fall Risk

Wearable Sensors in Other Medical Domains with Application Potential for Orthopedic Trauma Surgery-A Narrative Review

The use of wearable technology is steadily increasing. In orthopedic trauma surgery, where the musculoskeletal system is directly affected, focus has been directed towards assessing aspects of physical functioning, activity behavior, and mobility/disability. This includes sensors and algorithms to monitor real-world walking speed, daily step counts, ground reaction forces, or range of motion. Several specific reviews have focused on this domain. In other medical fields, wearable sensors and algorithms to monitor digital biometrics have been used with a focus on domain-specific health aspects such as heart rate, sleep, blood oxygen saturation, or fall risk. This review explores the most common clinical and research use cases of wearable sensors in other medical domains and, from it, derives suggestions for the meaningful transfer and application in an orthopedic trauma context.

Daily steps and stepping cadence increase one-year following prosthesis osseointegration in people with lower-limb amputation

PURPOSE

People with lower-limb loss participate in less physical activity than able-bodied individuals, which increases the mortality risk and incidence of metabolic syndromes. This study evaluated the effect of lower-limb prosthesis osseointegration on physical activity, including daily steps and stepping cadence.

METHODS

Free-living walking activity was assessed from 14 patients scheduled to undergo prosthesis osseointegration at two time points (within 2 weeks prior to osseointegration surgery and 12-months following). Daily step count, stepping time, number of walking bouts, average step cadence per bout, maximum step cadence per bout, and time spent in bands of step cadence were compared before and after osseointegration.

RESULTS

Twelve months after prosthesis osseointegration, participants increased daily steps, daily stepping time, average step cadence, and maximum cadence per walking bout compared to pre-osseointegration.

CONCLUSIONS

Participants engaged in more daily steps, higher stepping cadence, and longer bouts at higher cadence one year following osseointegration compared to when using a socket prosthesis. As a novel intervention that is becoming more common, it is important to understand walking activity outcomes as these are critical for long-term health.

Measurement properties of the usual and fast gait speed tests in community-dwelling older adults: a COSMIN-based systematic review

OBJECTIVE

The gait speed test is one of the most widely used mobility assessments for older adults. We conducted a systematic review to evaluate and compare the measurement properties of the usual and fast gait speed tests in community-dwelling older adults.

METHODS

Three databases were searched: MEDLINE, EMBASE and CINAHL. Peer-reviewed articles evaluating the gait speed test's measurement properties or interpretability in community-dwelling older adults were included. The Consensus-based Standards for the selection of health Measurement Instruments guidelines were followed for data synthesis and quality assessment.

RESULTS

Ninety-five articles met our inclusion criteria, with 79 evaluating a measurement property and 16 reporting on interpretability. There was sufficient reliability for both tests, with intraclass correlation coefficients (ICC) generally ranging from 0.72 to 0.98, but overall quality of evidence was low. For convergent/discriminant validity, an overall sufficient rating with moderate quality of evidence was found for both tests. Concurrent validity of the usual gait speed test was sufficient (ICCs = 0.79-0.93 with longer distances) with moderate quality of evidence; however, there were insufficient results for the fast gait speed test (e.g. low agreement with longer distances) supported by high-quality studies. Responsiveness was only evaluated in three articles, with low quality of evidence.

CONCLUSION

Findings from this review demonstrated evidence in support of the reliability and validity of the usual and fast gait speed tests in community-dwelling older adults. However, future validation studies should employ rigorous methodology and evaluate the tests' responsiveness.

Associations between performance-based and patient-reported physical functioning and real-world mobile sensor metrics in older cancer survivors: A pilot study

INTRODUCTION

Older cancer survivors are at increased risk for impaired physical functioning, but current assessments of function are difficult to implement in busy oncology clinics. Mobile devices measuring continuous activity and mobility in daily life may be useful for estimating physical functioning. The goal of this pilot study was to examine the associations between consumer wearable device (a wrist-worn activity tracker) and smartphone sensor data and commonly used clinical measures of physical function in cancer survivors aged 65 and older.

MATERIALS AND METHODS

Older adults within five years of completing primary treatment for any cancer completed standardized questionnaires and performance-based tests to measure physical functioning. Continuous passive data from smartphones and consumer wearable devices were collected for four weeks and linked to patient-reported and performance-based physical functioning as well as patient-reported falls or near falls at the end of the four-week monitoring period. To examine associations between sensor variables and physical functioning, we conducted bivariate Pearson correlations as well as multivariable linear regression analyses. To examine associations between sensor variables and falls, we conducted exploratory receiver operating characteristic curve and multivariable logistic regression analyses.

RESULTS

We enrolled 40 participants (mean age 73 years old, range 65-83; 98% White; 50% female). In bivariate analyses, consumer wearable device features reflecting greater amount and speed and lower fragmentation of walking in daily life were significantly related to better patient-reported function (r= 0.43-0.65) and performance-based physical function (r = 0.56-0.72), while smartphone features reflecting more geographic mobility were related to better performance-based physical function (r = 0.40-0.42) but not patient-reported function. After adjusting for age and comorbidities, only consumer wearable device features remained associated with performance-based physical functioning. In exploratory analyses, peak gait cadence was associated with fall risk even after covariate adjustment.

DISCUSSION

This study provides preliminary evidence that real-world data from consumer devices may be useful for estimating functional performance among older cancer survivors and potentially for remotely and longitudinally monitoring functioning in older patients during and after cancer treatment.

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

BACKGROUND

Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates.

METHODS

Twenty healthy older adults, 20 people with Parkinson's disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated.

RESULTS

We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms' performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms.

CONCLUSIONS

Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms' performances. Trial registration ISRCTN - 12246987.

Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson's Disease

Remote assessment of the gait of older adults (OAs) during daily living using wrist-worn sensors has the potential to augment clinical care and mobility research. However, hand movements can degrade gait detection from wrist-sensor recordings. To address this challenge, we developed an anomaly detection algorithm and compared its performance to four previously published gait detection algorithms. Multiday accelerometer recordings from a wrist-worn and lower-back sensor (i.e., the “gold-standard” reference) were obtained in 30 OAs, 60% with Parkinson’s disease (PD). The area under the receiver operator curve (AUC) and the area under the precision−recall curve (AUPRC) were used to evaluate the performance of the algorithms. The anomaly detection algorithm obtained AUCs of 0.80 and 0.74 for OAs and PD, respectively, but AUPRCs of 0.23 and 0.31 for OAs and PD, respectively. The best performing detection algorithm, a deep convolutional neural network (DCNN), exhibited high AUCs (i.e., 0.94 for OAs and 0.89 for PD) but lower AUPRCs (i.e., 0.66 for OAs and 0.60 for PD), indicating trade-offs between precision and recall. When choosing a classification threshold of 0.9 (i.e., opting for high precision) for the DCNN algorithm, strong correlations (r > 0.8) were observed between daily living walking time estimates based on the lower-back (reference) sensor and the wrist sensor. Further, gait quality measures were significantly different in OAs and PD compared to healthy adults. These results demonstrate that daily living gait can be quantified using a wrist-worn sensor.