Inferring Physical Function From Wearable Activity Monitors: Analysis of Free-Living Activity Data From Patients With Knee Osteoarthritis

Development of a Conceptual Model of Physical Functioning Limitations Experienced by Patients with Late-Stage Chronic Kidney Disease: A Qualitative Interview Study

INTRODUCTION

Limitations in physical functioning are common in patients with late-stage chronic kidney disease (CKD) and can greatly affect their lives. Using patient interviews, this study reports experiences associated with physical functioning limitations for patients with late-stage CKD.

METHODS

A preliminary conceptual model on concepts relevant to physical functioning limitations in patients with CKD was developed using data from a targeted literature review (patients with CKD stages IV-V) and previous interviews (patients with CKD stages IIIa-IIIb). The preliminary conceptual model informed a semi-structured interview guide designed to capture experiences of physical functioning limitations in patients with CKD. Patients with CKD stages IV-V who were not receiving dialysis were interviewed; their responses were used to develop a comprehensive conceptual model summarizing their experiences associated with physical functioning limitations.

RESULTS

A total of 25 patients with CKD stage IV (n = 19) or V (n = 6) were interviewed. Based on patient responses, the reported concepts were grouped into one of six categories: physical functioning limitations/difficulties, behavioural impacts, activity participation restrictions, symptoms attributed to physical functioning limitations, impacts on sleep and emotional functioning impacts related to physical functioning limitations. Twenty-three patients reported concepts associated with physical functioning limitations, most frequently 'walking up and down stairs' (83%) and 'walking distances' (74%). All 23 patients also reported behavioural impacts, including 'need to rest/subsequent periods of rest' (100%) and 'participation in fewer activities' (91%). As well as summarizing the reported concepts, the comprehensive conceptual model shows how concepts may relate to one another; for example, challenging symptoms or difficulty completing tasks can lead to changes in patient behaviour such as purposely reducing or avoiding activities.

CONCLUSIONS

This study found that patients with late-stage CKD not receiving dialysis who experience physical functioning limitations report a range of impacts on their daily lives. The comprehensive conceptual model summarizes the concepts reported and the relationships between them, providing a holistic understanding of how patients with late-stage CKD are affected by physical functioning limitations. Infographic available for this article. INFOGRAPHIC.

Inertial measurement units and application for remote healthcare in hip and knee osteoarthritis: a narrative review (Preprint)

Background

Measuring and modifying movement-related joint loading is integral to the management of lower extremity osteoarthritis (OA). Although traditional approaches rely on measurements made within the laboratory or clinical environments, inertial sensors provide an opportunity to quantify these outcomes in patients’ natural environments, providing greater ecological validity and opportunities to develop large data sets of movement data for the development of OA interventions.

Objective

This narrative review aimed to discuss and summarize recent developments in the use of inertial sensors for assessing movement during daily activities in individuals with hip and knee OA and to identify how this may translate to improved remote health care for this population.

Methods

A literature search was performed in November 2018 and repeated in July 2019 and March 2021 using the PubMed and Embase databases for publications on inertial sensors in hip and knee OA published in English within the previous 5 years. The search terms encompassed both OA and wearable sensors. Duplicate studies, systematic reviews, conference abstracts, and study protocols were also excluded. One reviewer screened the search result titles by removing irrelevant studies, and 2 reviewers screened study abstracts to identify studies using inertial sensors as the main sensing technology and a primary outcome related to movement quality. In addition, after the March 2021 search, 2 reviewers rescreened all previously included studies to confirm their relevance to this review.

Results

From the search process, 43 studies were determined to be relevant and subsequently included in this review. Inertial sensors have been successfully implemented for assessing the presence and severity of OA (n=11), assessing disease progression risk and providing feedback for gait retraining (n=7), and remotely monitoring intervention outcomes and identifying potential responders and nonresponders to interventions (n=14). In addition, studies have validated the use of inertial sensors for these applications (n=8) and analyzed the optimal sensor placement combinations and data input analysis for measuring different metrics of interest (n=3). These studies show promise for remote health care monitoring and intervention delivery in hip and knee OA, but many studies have focused on walking rather than a range of activities of daily living and have been performed in small samples (<100 participants) and in a laboratory rather than in a real-world environment.

Conclusions

Inertial sensors show promise for remote monitoring, risk assessment, and intervention delivery in individuals with hip and knee OA. Future opportunities remain to validate these sensors in real-world settings across a range of activities of daily living and to optimize sensor placement and data analysis approaches.

Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation

Background

For rehabilitation training systems, it is essential to automatically record and recognize exercises, especially when more than one type of exercise is performed without a predefined sequence. Most motion recognition methods are based on feature engineering and machine learning algorithms. Time-domain and frequency-domain features are extracted from original time series data collected by sensor nodes. For high-dimensional data, feature selection plays an important role in improving the performance of motion recognition. Existing feature selection methods can be categorized into filter and wrapper methods. Wrapper methods usually achieve better performance than filter methods; however, in most cases, they are computationally intensive, and the feature subset obtained is usually optimized only for the specific learning algorithm.

Objective

This study aimed to provide a feature selection method for motion recognition of upper-limb exercises and improve the recognition performance.

Methods

Motion data from 5 types of upper-limb exercises performed by 21 participants were collected by a customized inertial measurement unit (IMU) node. A total of 60 time-domain and frequency-domain features were extracted from the original sensor data. A hybrid feature selection method by combining filter and wrapper methods (FESCOM) was proposed to eliminate irrelevant features for motion recognition of upper-limb exercises. In the filter stage, candidate features were first selected from the original feature set according to the significance for motion recognition. In the wrapper stage, k-nearest neighbors (kNN), Naïve Bayes (NB), and random forest (RF) were evaluated as the wrapping components to further refine the features from the candidate feature set. The performance of the proposed FESCOM method was verified using experiments on motion recognition of upper-limb exercises and compared with the traditional wrapper method.

Results

Using kNN, NB, and RF as the wrapping components, the classification error rates of the proposed FESCOM method were 1.7%, 8.9%, and 7.4%, respectively, and the feature selection time in each iteration was 13 seconds, 71 seconds, and 541 seconds, respectively.

Conclusions

The experimental results demonstrated that, in the case of 5 motion types performed by 21 healthy participants, the proposed FESCOM method using kNN and NB as the wrapping components achieved better recognition performance than the traditional wrapper method. The FESCOM method dramatically reduces the search time in the feature selection process. The results also demonstrated that the optimal number of features depends on the classifier. This approach serves to improve feature selection and classification algorithm selection for upper-limb motion recognition based on wearable sensor data, which can be extended to motion recognition of more motion types and participants.

Monitoring work-related physical activity and estimating lower-limb loading: a proof-of-concept study

BACKGROUND

Physical activity (PA) is important to general health and knee osteoarthritis (OA). Excessive workplace PA is an established risk factor for knee OA however, appropriate methods of measurement are unclear. There is a need to examine and assess the utility of new methods of measuring workplace PA and estimating knee load prior to application to large-scale, knee OA cohorts. Our aims, therefore, were to monitor workplace PA and estimate lower-limb loading across different occupations in health participants.

METHODS

Twenty-four healthy adults, currently working full-time in a single occupation (≥ 35 h/week) and free of musculoskeletal disease, comorbidity and had no history of lower-limb injury/surgery (past 12-months) were recruited across New South Wales (Australia). A convenience sample was recruited with occupations assigned to levels of workload; sedentary, light manual and heavy manual. Metrics of workplace PA including tasks performed (i.e., sitting), step-count and lower-limb loading were monitored over 10 working days using a daily survey, smartwatch, and a smartphone.

RESULTS

Participants of light manual occupations had the greatest between-person variations in mean lower-limb load (from 2 to 59 kg*m/s3). Lower-limb load for most participants of the light manual group was similar to a single participant in heavy manual work (30 kg*m/s3) and was at least three times greater than the sedentary group (2 kg*m/s3). The trends of workplace PA over working hours were largely consistent, per individual, but rare events of extreme loads were observed across all participants (up to 760 kg*m/s3).

CONCLUSIONS

There are large interpersonal variations in metrics of workplace PA, particularly among light and heavy manual occupations. Our estimates of lower-limb loading were largely consistent with pre-conceived levels of physical demand. We present a new approach to monitoring PA and estimating lower-limb loading, which could be applied to future occupational studies of knee OA.

Digital systems for improving outcomes in patients with primary immune defects

PURPOSE OF REVIEW

Healthcare has already been impacted by the fourth industrial revolution exemplified by tip of spear technology, such as artificial intelligence and quantum computing. Yet, there is much to be accomplished as systems remain suboptimal, and full interoperability of digital records is not realized. Given the footprint of technology in healthcare, the field of clinical immunology will certainly see improvements related to these tools.

RECENT FINDINGS

Biomedical informatics spans the gamut of technology in biomedicine. Within this distinct field, advances are being made, which allow for engineering of systems to automate disease detection, create computable phenotypes and improve record portability. Within clinical immunology, technologies are emerging along these lines and are expected to continue.

SUMMARY

This review highlights advancements in digital health including learning health systems, electronic phenotyping, artificial intelligence and use of registries. Technological advancements for improving diagnosis and care of patients with primary immunodeficiency diseases is also highlighted.

Assessing Motor Function in Congenital Muscular Dystrophy Patients Using Accelerometry

BACKGROUND

When tested in a controlled clinic environment, individuals with neuromuscular-related symptoms may complete motor tasks within normal predicted ranges. However, measuring activity at home may better reflect typical motor performance. The accuracy of accelerometry measurements in individuals with congenital muscular dystrophy (CMD) is unknown. We aimed to compare accelerometry and manual step counts and assess free-living physical activity intensity in individuals with CMD using accelerometry.

METHODS

Ambulatory pediatric CMD participants (n = 9) performed the 6-minute walk test in clinic while wearing ActiGraph GT3X accelerometer devices. During the test, manual step counting was conducted to assess concurrent validity of the ActiGraph step count in this population using Bland-Altman analysis. In addition, activity intensity of 6 pediatric CMD participants was monitored at home with accelerometer devices for an average of 7 days. Cut-point values previously validated for neuromuscular disorders were used for data analysis.

RESULTS

Bland-Altman and intraclass correlation analyses showed no concurrent validity between manual and ActiGraph-recorded step counts. Fewer steps were recorded by ActiGraph step counts compared with manual step counts (411 ± 74 vs 699 ± 43, respectively; P = .004). Although improved, results were in the same direction with the application of low-frequency extension filters (587 ± 40 vs 699 ± 43, P = .03). ActiGraph step-count data did not correlate with manual step count (Spearman ρ = 0.32, P = .41; with low-frequency extension: Spearman ρ = 0.45, P = .22). Seven-day physical activity monitoring showed that participants spent more than 80% of their time in the sedentary activity level.

CONCLUSIONS

In a controlled clinic setting, step count was significantly lower by ActiGraph GT3X than by manual step counting, possibly because of the abnormal gait in this population. Additional studies using triaxial assessment are needed to validate accelerometry measurement of activity intensity in individuals with CMD. Accelerometry outcomes may provide valuable measures and complement the 6-minute walk test in the assessment of treatment efficacy in CMD.

Gait features for discriminating between mobility-limiting musculoskeletal disorders: Lumbar spinal stenosis and knee osteoarthritis

BACKGROUND

Functional ambulation limitations are features of lumbar spinal stenosis (LSS) and knee osteoarthritis (OA). With numerous validated walking assessment protocols and a vast number of spatiotemporal gait parameters available from sensor-based assessment, there is a critical need for selection of appropriate test protocols and variables for research and clinical applications.

RESEARCH QUESTION

In patients with knee OA and LSS, what are the best sensor-derived gait parameters and the most suitable clinical walking test to discriminate between these patient populations and controls?

METHODS

We collected foot-mounted inertial measurement unit (IMU) data during three walking tests (fast-paced walk test-FPWT, 6-min walk test- 6MWT, self-paced walk test - SPWT) for subjects with LSS, knee OA and matched controls (N = 10 for each group). Spatiotemporal gait characteristics were extracted and pairwise compared (Omega partial squared - ω_p²) between patients and controls.

RESULTS

We found that normal paced walking tests (6MWT, SPWT) are better suited for distinguishing gait characteristics between patients and controls. Among the sensor-based gait parameters, stance and double support phase timing were identified as the best gait characteristics for the OA population discrimination, whereas foot flat ratio, gait speed, stride length and cadence were identified as the best gait characteristics for the LSS population discrimination.

SIGNIFICANCE

These findings provide guidance on the selection of sensor-derived gait parameters and clinical walking tests to detect alterations in mobility for people with LSS and knee OA.

Improvements in Objectively Measured Activity Behaviors Do Not Correlate With Improvements in Patient-Reported Outcome Measures Following Total Knee Arthroplasty

BACKGROUND

Activity monitors have added a new dimension to our ability to objectively measure physical activity in patients undergoing total knee arthroplasty (TKA). The aim of the study is to assess whether changes in the time spent sitting, standing, and stepping were associated with changes in patient-reported outcome measures (PROMs) before and after TKA.

METHODS

Valid activPAL data (>3 days) and PROMs were obtained from 49 men and women (mean [SD] age, 62.8 [8.6] years; body mass index, 33.8 [7.1] kg/m²) who underwent primary TKA, before and at 6 weeks or 6 months after surgery. Patient-reported symptoms of pain, stiffness, and knee function were obtained using the Knee injury and Osteoarthritis Outcome Score and Oxford Knee Score questionnaires.

RESULTS

Mean (SD) Knee injury and Osteoarthritis Outcome Score (80.1 [16.3] to 41.6 [6.5], P < .001) and Oxford Knee Score (12.0 [9.8] to 17.7 [22.8], P < .001) scores improved 6 months after TKA. Walking time (mean [95% confidence interval]; min/d) increased from before (79 [67-91]) to 6 months after TKA (101 [88-114], P = .006). Standing time (318 [276-360] to 321 [291-352], P = .782) and sitting time (545 [491-599] to 509.0 [459.7-558.3], P = .285) did not change from before to 6 months after TKA. Participants took more steps (2559 [2128-2991] to 3515 [2983-4048] steps/day, P = .001) and accumulated more steps (31 [30-34] to 34 [33-35] steps/min, P < .001) after TKA compared to before. There were no associations between changes in activity behaviors and changes in PROMs (P > .05).

CONCLUSION

Despite improvements in self-reported knee pain and functional ability, these changes do not correlate with improvements in objectively measured light-intensity and sedentary activity behaviors.