Evaluating physical function and activity in the elderly patient using wearable motion sensors

"Fall Risk Scoring" in Outpatient Gait Analysis: Validation of a New Fall Risk Assessment for Nursing Home Residents

Falls in senior home residents are common. Individual preventive training can lower the fall risk. To detect the need for training, a systematic assessment of the individual fall risk is needed. The aim of this study was thus to assess whether a fall risk score based on free field insole measurements can distinguish between an at-risk group of senior home residents and a healthy young control group. A published fall risk score was used in senior home residents over the age of 75 and a young (< 40 years) control group to determine the individual fall risk. In addition, the fall events over 12 months were assessed. Statistical analysis including ROC analysis was performed to determine the ability of the score to detect participants at heightened fall risk. In total, 18 nursing home residents and 9 young control participants were included. Of the nursing home residents, 15 had at least one fall, with a total of 37 falls recorded over 12 months. In the control group, no falls were recorded. The fall risk score was significantly different between nursing home residents and the control group (9.2 + 3.2 vs. 5.7 ± 2.2). Furthermore, the score significantly differentiated fallers from non-fallers (10.3 ± 1.8 vs. 5.2 ± 2.5), with a cut-off > 7.5 (AUC: 0.95) and a sensitivity of 86.7% (specificity 83.3%). The fall risk score is able to detect the difference between senior nursing home residents and young, healthy controls, as well as between fallers and non-fallers. Its main proof of concept is demonstrated, as based on movement data outside special gait labs, and it can simplify the risk of fall determination in geriatric nursing home residents and can now be used in further, prospective studies.

Evaluation of Upper Body and Lower Limbs Kinematics through an IMU-Based Medical System: A Comparative Study with the Optoelectronic System

In recent years, the use of inertial-based systems has been applied to remote rehabilitation, opening new perspectives for outpatient assessment. In this study, we assessed the accuracy and the concurrent validity of the angular measurements provided by an inertial-based device for rehabilitation with respect to the state-of-the-art system for motion tracking. Data were simultaneously collected with the two systems across a set of exercises for trunk and lower limbs, performed by 21 healthy participants. Additionally, the sensitivity of the inertial measurement unit (IMU)-based system to its malpositioning was assessed. Root mean square error (RMSE) was used to explore the differences in the outputs of the two systems in terms of range of motion (ROM), and their agreement was assessed via Pearson's correlation coefficient (PCC) and Lin's concordance correlation coefficient (CCC). The results showed that the IMU-based system was able to assess upper-body and lower-limb kinematics with a mean error in general lower than 5° and that its measurements were moderately biased by its mispositioning. Although the system does not seem to be suitable for analysis requiring a high level of detail, the findings of this study support the application of the device in rehabilitation programs in unsupervised settings, providing reliable data to remotely monitor the progress of the rehabilitation pathway and change in patient's motor function.

Patterns of physical activity over time in older patients rehabilitating after hip fracture surgery: a preliminary observational study

BACKGROUND

To investigate patterns of continuously monitored physical activity in older patients rehabilitating after hip fracture surgery and the association with patient characteristics.

METHODS

Physical activity of surgically treated hip fracture patients aged 70 years or older, who were rehabilitating at a skilled nursing home, was continuously monitored using a tri-axial accelerometer. The intensity of physical activity per day was calculated from the accelerometer signals to describe the daily physical activity levels of the enrolled patients. The patterns of three different aspects of physical activity were investigated: overall physical activity, overall variability, and day-to-day variability. Two experts in the geriatric rehabilitation field helped identifying unique physical activity patterns for each aspect based on visual analysis. Eighteen healthcare professionals independently classified each patient in one of the predefined patterns for each aspect. Differences between physical activity patterns and patient characteristics were assessed using a Kruskal-Wallis or Fisher's Exact Test.

RESULTS

Physical activity data from 66 older patients were used in this preliminary study. A total of six unique patterns were identified for overall physical activity and overall variability, and five unique patterns for the day-to-day variability. The most common pattern found for the overall physical activity and day-to-day variability had a S-shape, which first slowly increased, then steeply increased, and subsequently flattened (n = 23, 34.8%). A N-shape pattern was found the most common pattern for overall variability, which first slowly increased, then steeply increased, then decreased and lastly increased (n = 14, 21.2%). The functionality at admission to rehabilitation, measured with the Barthel Index, and the duration of rehabilitation stay differed between the patterns of physical activity.

CONCLUSIONS

Multiple patterns of physical activity among older patients during hip fracture rehabilitation were found in this preliminary study. The functionality at admission to rehabilitation and the duration of rehabilitation stay were associated with the different patterns found in this study. Results of this study highlight the importance of personalized hip fracture treatment.

Toward Enhanced Clinical Decision Support for Patients Undergoing a Hip or Knee Replacement: Focus Group and Interview Study With Surgeons

BACKGROUND

The current assessment of recovery after total hip or knee replacement is largely based on the measurement of health outcomes through self-report and clinical observations at follow-up appointments in clinical settings. Home activity-based monitoring may improve assessment of recovery by enabling the collection of more holistic information on a continuous basis.

OBJECTIVE

This study aimed to introduce orthopedic surgeons to time-series analyses of patient activity data generated from a platform of sensors deployed in the homes of patients who have undergone primary total hip or knee replacement and understand the potential role of these data in postoperative clinical decision-making.

METHODS

Orthopedic surgeons and registrars were recruited through a combination of convenience and snowball sampling. Inclusion criteria were a minimum required experience in total joint replacement surgery specific to the hip or knee or familiarity with postoperative recovery assessment. Exclusion criteria included a lack of specific experience in the field. Of the 9 approached participants, 6 (67%) orthopedic surgeons and 3 (33%) registrars took part in either 1 of 3 focus groups or 1 of 2 interviews. Data were collected using an action-based approach in which stimulus materials (mock data visualizations) provided imaginative and creative interactions with the data. The data were analyzed using a thematic analysis approach.

RESULTS

Each data visualization was presented sequentially followed by a discussion of key illustrative commentary from participants, ending with a summary of key themes emerging across the focus group and interview data set.

CONCLUSIONS

The limitations of the evidence are as follows. The data presented are from 1 English hospital. However, all data reflect the views of surgeons following standard national approaches and training. Although convenience sampling was used, participants' background, skills, and experience were considered heterogeneous. Passively collected home monitoring data offered a real opportunity to more objectively characterize patients' recovery from surgery. However, orthopedic surgeons highlighted the considerable difficulty in navigating large amounts of complex data within short medical consultations with patients. Orthopedic surgeons thought that a proposed dashboard presenting information and decision support alerts would fit best with existing clinical workflows. From this, the following guidelines for system design were developed: minimize the risk of misinterpreting data, express a level of confidence in the data, support clinicians in developing relevant skills as time-series data are often unfamiliar, and consider the impact of patient engagement with data in the future.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2018-021862.

The relationship between muscle power, functional performance, accelerometer-based measurement of physical activity and patient-reported outcomes in patients with hip osteoarthritis: A cross-sectional study

BACKGROUND

Patients with unilateral hip osteoarthritis appear to have between-leg differences in leg extension power (LEP). The Nottingham Leg Extensor Power Rig provides reliable and valid results but requires sensitive equipment. It would be relevant to identify measures closely associated with this test.

OBJECTIVE

(i) To investigate if LEP is lower in the affected leg compared to the non-affected leg. Furthermore, to investigate the associations between LEP and the measures: (ii) Functional performance, (iii) accelerometer-based measurement of physical activity and (iv) patient-reported outcome measures (PROM).

DESIGN

Cross-sectional study including 60 patients (30 men, 30 women) with hip osteoarthritis scheduled for hip replacement.

METHOD

The counter movement jump and 10-m sprint tests were used to determine functional performance, accelerometer-sensors were used to determine physical activity and the Copenhagen Hip and Groin Outcome Score (HAGOS) was used to determine PROM.

RESULTS

(i) LEP in the affected leg corresponded to 79% [95% CI 74%; 85%] of the non-affected leg, (ii) LEP was positively associated with functional performance tests (β 0.63 to 0.78, p < 0.05), (iii) positively associated although non-significantly with physical activity (β 0.16 to 0.23, p > 0.05) and (iv) positively associated with the six HAGOS subscales (β 0.25 to 0.54, p < 0.05).

CONCLUSION

Functional performance tests may be used as feasible, inexpensive and fast ways to assess LEP in clinical settings. These results may suggest that interventions aimed at improving LEP can improve functional performance and PROM, but not physical activity. Future research is needed to confirm the causality of these cross-sectional findings.

THE CLINICAL TRIAL REGISTRATION NUMBERS

Danish Data Protection Agency (1-16-02-640-14), ClinicalTrials.gov (NTC02301182) and approved by the Danish Biomedical Research Ethics Committee (1-10-72- 343-14) prior to data collection.

Head movement kinematics are altered during balance stability exercises in individuals with vestibular schwannoma

Background

Balance stabilization exercises are often prescribed to facilitate compensation in individuals with vestibular schwannoma (VS). However, both the assessment and prescription of these exercises are reliant on clinical observations and expert opinion rather than on quantitative evidence. The aim of this study was to quantify head motion kinematics in individuals with vestibular loss while they performed commonly prescribed balance stability exercises.

Methods

Using inertial measurement units, head movements of individuals with vestibular schwannoma were measured before and after surgical deafferentation and compared with age-matched controls.

Results

We found that individuals with vestibular schwannoma experienced more variable head motion compared to healthy controls both pre- and postoperatively, particularly in absence of visual input, but that there was little difference between preoperative and postoperative kinematic measurements for our vestibular schwannoma group. We further found correlations between head motion kinematic measures during balance exercises, performed in the absence of visual input, and multiple clinical measurements for preoperative VS subjects. Subjects with higher head motion variability also had worse DVA scores, moved more slowly during the Timed up and Go and gait speed tests, and had lower scores on the functional gait assessment. In contrast, we did not find strong correlations between clinical measures and postoperative head kinematics for the same VS subjects.

Conclusions

Our data suggest that further development of such metrics based on the quantification of head motion has merit for the assessment and prescription of balance exercises, as demonstrated by the calculation of a “kinematic score” for identifying the most informative balance exercise (i.e., “Standing on foam eyes closed”).

Wearable technology in orthopedic trauma surgery - An AO trauma survey and review of current and future applications

The use of wearable sensors to track activity is increasing. Therefore, a survey among AO Trauma members was conducted to provide an overview of their current utilization and determine future needs and directions. A cross sectional expert opinion survey was administered to members of AO Trauma. Respondents were surveyed concerning their experience, subspeciality, current use characteristics, as well as future needs concerning wearable technology. Three hundred and thirty-three survey sets were available for analysis (Response Rate 16.2%). 20.7% of respondents already use wearable technology as part of their clinical treatment. The most prevalent technology was accelerometry combined with smartphones (75.4%) to measure general patient activity. To facilitate the use of wearable technology in the future, the most pressing issues were cost, patient compliance and validity of results. Wearable activity monitors are currently being used in trauma surgery. Surgeons employing these technologies mostly measure simple activity or activity associated parameters. Cost was the greatest perceived barrier to implementation. Further research, especially concerning the interpretation of the outcome values obtained, is required to facilitate wearable activity monitoring as an objective patient outcome measurement tool.

Ability of Wearable Accelerometers-Based Measures to Assess the Stability of Working Postures

With the rapid development and widespread application of wearable inertial sensors in the field of human motion capture, the low-cost and non-invasive accelerometer (ACC) based measures have been widely used for working postural stability assessment. This study systematically investigated the abilities of ACC-based measures to assess the stability of working postures in terms of the ability to detect the effects of work-related factors and the ability to classify stable and unstable working postures. Thirty young males participated in this study and performed twenty-four load-holding tasks (six working postures × two standing surfaces × two holding loads), and forty-three ACC-based measures were derived from the ACC data obtained by using a 17 inertial sensors-based motion capture system. ANOVAs, t-tests and machine learning (ML) methods were adopted to study the factors’ effects detection ability and the postural stability classification ability. The results show that almost all forty-three ACC-based measures could (p < 0.05) detect the main effects of Working Posture and Load Carriage, and their interaction effects. However, most of them failed in (p ≥ 0.05) detecting Standing Surface’s main or interaction effects. Five measures could detect both main and interaction effects of all the three factors, which are recommended for working postural stability assessment. The performance in postural stability classification based on ML was also good, and the feature set exerted a greater influence on the classification accuracy than sensor configuration (i.e., sensor placement locations). The results show that the pelvis and lower legs are recommended locations overall, in which the pelvis is the first choice. The findings of this study have proved that wearable ACC-based measures could assess the stability of working postures, including the work-related factors’ effects detection ability and stable-unstable working postures classification ability. However, researchers should pay more attention to the measure selection, sensors placement, feature selection and extraction in practical applications.

Use of Wearable Technology to Measure Activity in Orthopaedic Trauma Patients: A Systematic Review

BACKGROUND

Patient-Reported Outcome Measures (PROMs) are widely used for measurement of functional outcomes after orthopaedic trauma. However, PROMs rely on patient collaboration and suffer from various types of bias. Wearable Activity Monitors (WAMs) are increasingly used to objectify functional assessment. The objectives of this systematic review were to identify and characterise the WAMs technology and metrics currently used for orthopaedic trauma research.

METHODS

PubMed and Embase biomedical literature search engines were queried. Eligibility criteria included: Human clinical studies published in the English language between 2010 and 2019 involving fracture management and WAMs. Variables collected from each article included: Technology used, vendor/product, WAM body location, metrics measured, measurement time period, year of publication, study geographic location, phase of treatment studied, fractures studied, number of patients studied, sex and age of the study subjects, and study level of evidence. Six investigators reviewed the resulting papers. Descriptive statistics of variables of interest were used to analyse the data.

RESULTS

One hundred and thirty-six papers were available for analysis, showing an increasing trend of publications per year. Accelerometry followed by plantar pressure insoles were the most commonly employed technologies. The most common location for WAM placement was insoles, followed by the waist. The most commonly studied fracture type was hip fractures followed by fragility fractures in general, ankle, "lower extremity", and tibial fractures. The rehabilitation phase following surgery was the most commonly studied period. Sleep duration, activity time or step counts were the most commonly reported WAM metrics. A preferred, clinically validated WAM metric was not identified.

CONCLUSIONS

WAMs have an increasing presence in the orthopaedic trauma literature. The optimal implementation of this technology and its use to understand patients' pre-injury and post-injury functions is currently insufficiently explored and represents an area that will benefit from future study.

SYSTEMATIC REVIEW REGISTRATION NUMBER

PROSPERO ID:210344.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s43465-022-00629-0.

Human activity recognition of children with wearable devices using LightGBM machine learning

Human activity recognition (HAR) using machine learning (ML) methods has been a continuously developed method for collecting and analyzing large amounts of human behavioral data using special wearable sensors in the past decade. Our main goal was to find a reliable method that could automatically detect various playful and daily routine activities in children. We defined 40 activities for ML recognition, and we collected activity motion data by means of wearable smartwatches with a special SensKid software. We analyzed the data of 34 children (19 girls, 15 boys; age range: 6.59–8.38; median age = 7.47). All children were typically developing first graders from three elementary schools. The activity recognition was a binary classification task which was evaluated with a Light Gradient Boosted Machine (LGBM) learning algorithm, a decision tree based method with a threefold cross validation. We used the sliding window technique during the signal processing, and we aimed at finding the best window size for the analysis of each behavior element to achieve the most effective settings. Seventeen activities out of 40 were successfully recognized with AUC values above 0.8. The window size had no significant effect. In summary, the LGBM is a very promising solution for HAR. In line with previous findings, our results provide a firm basis for a more precise and effective recognition system that can make human behavioral analysis faster and more objective.

The grade of instability in fragility fractures of the pelvis correlates with impaired early mobilization

PURPOSE

This study aimed to investigate whether gait patterns of patients with fragility fractures of the pelvis (FFP) comply with the grade of fracture instability, defined by radiological patterns.

PATIENTS AND METHODS

This prospective, single-center, observational study included 39 patients with an FFP. Gait analysis was performed with a wearable insole force sensor (Loadsol® by Novel, Munich, Germany) 4-7 days after admission. Patients were divided in two groups: Group A included FFP type 1 fractures, which affect the anterior pelvic ring only, Group B contained FFP type 2-4 fractures with an involvement of the posterior pelvic ring. Primary outcome parameter was the FTI ratio (force-time integral (N*s)).

RESULTS

The mean age was 85.08 years (SD ± 6.45), 94.9% (37/39) of the patients were female. The most common fracture type was an FFP 2b (64.1%, 25/39). Group A showed a significantly higher FTI ratio (45.12%, SD ± 4.19%) than Group B (38.45%, SD ± 5.97%, p = 0.002). Further, a significant correlation of the FTI ratio and the average (r = 0.570, p < 0.001) and maximum (r = 0.394, p = 0.013) peak force was observed.

CONCLUSION

The gait pattern of patients with an FFP type 2-4 was more imbalanced than of patients with an FFP type 1 fracture. These findings match with the radiological classification of FFP, which indicates higher instability, when the posterior pelvis is affected. Gait analysis might offer earlier functional diagnostics and may accelerate the treatment decision with shorter periods of immobility in future. Especially in cross-border cases, early gait analysis could be beneficial to clarify the indication for or against surgery.

Day-to-Day Variability and Year-to-Year Reproducibility of Accelerometer-Measured Free-Living Sit-to-Stand Transitions Volume and Intensity among Community-Dwelling Older Adults

(1) Background: The purpose of this study was to evaluate the day-to-day variability and year-to-year reproducibility of an accelerometer-based algorithm for sit-to-stand (STS) transitions in a free-living environment among community-dwelling older adults. (2) Methods: Free-living thigh-worn accelerometry was recorded for three to seven days in 86 (women n = 55) community-dwelling older adults, on two occasions separated by one year, to evaluate the long-term consistency of free-living behavior. (3) Results: Year-to-year intraclass correlation coefficients (ICC) for the number of STS transitions were 0.79 (95% confidence interval, 0.70-0.86, p < 0.001), for mean angular velocity-0.81 (95% ci, 0.72-0.87, p < 0.001), and maximal angular velocity-0.73 (95% ci, 0.61-0.82, p < 0.001), respectively. Day-to-day ICCs were 0.63-0.72 for number of STS transitions (95% ci, 0.49-0.81, p < 0.001) and for mean angular velocity-0.75-0.80 (95% ci, 0.64-0.87, p < 0.001). Minimum detectable change (MDC) was 20.1 transitions/day for volume, 9.7°/s for mean intensity, and 31.7°/s for maximal intensity. (4) Conclusions: The volume and intensity of STS transitions monitored by a thigh-worn accelerometer and a sit-to-stand transitions algorithm are reproducible from day to day and year to year. The accelerometer can be used to reliably study STS transitions in free-living environments, which could add value to identifying individuals at increased risk for functional disability.

Review of Wearable Devices and Data Collection Considerations for Connected Health

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.

Clinical assessment of gait and functional mobility in Italian healthy and cognitively impaired older persons using wearable inertial sensors

AIM

The main purpose of the present study was to verify the feasibility of wearable inertial sensors (IMUs) in a clinical setting to screen gait and functional mobility in Italian older persons. In particular, we intended to verify the capability of IMUs to discriminate individuals with and without cognitive impairments and assess the existence of significant correlations between mobility parameters extracted by processing trunk accelerations and cognitive status.

METHODS

This is a cross-sectional study performed on 213 adults aged over 65 years (mean age 77.0 ± 5.4; 62% female) who underwent cognitive assessment (through Addenbrooke's Cognitive Examination Revised, ACE-R) instrumental gait analysis and the Timed Up and Go (TUG) test carried out using a wearable IMU located in the lower back.

RESULTS

Individuals with cognitive impairments exhibit a peculiar gait pattern, characterized by significant reduction of speed (- 34% vs. healthy individuals), stride length (- 28%), cadence (- 9%), and increase in double support duration (+ 11%). Slight, but significant changes in stance and swing phase duration were also detected. Poorer performances in presence of cognitive impairment were observed in terms of functional mobility as overall and sub-phase TUG times resulted significantly higher with respect to healthy individuals (overall time, + 38%, sub-phases times ranging from + 22 to + 34%), although with some difference associated with age. The severity of mobility alterations was found moderately to strongly correlated with the ACE-R score (Spearman's rho = 0.58 vs. gait speed, 0.54 vs. stride length, 0.66 vs. overall TUG time).

CONCLUSION

The findings obtained in the present study suggest that wearable IMUs appear to be an effective solution for the clinical assessment of mobility parameters of older persons screened for cognitive impairments within a clinical setting. They may represent a useful tool for the clinician in verifying the effectiveness of interventions to alleviate the impact of mobility limitations on daily life in cognitively impaired individuals.

Development of a dynamic fall risk profile in elderly nursing home residents: A free field gait analysis based study

Falls in nursing home residents are associated with a significant individual and socioeconomic burden of disease. To trigger and tailor individual intervention programs, solid early detection measures of residents at risk are needed. Aim of this study was thus to test the capability of a free field gait analysis insole to determine its usefulness in determining fall risk. In an observational study gait data of 22 nursing home residents over the age of 75 years was collected over one week with a measuring insole. Clinical scores were performed at baseline (POMA; DGI, TUG). For 6 months before and after the insole measurement, the fall events per resident were recorded. Correlation analysis as well as receiver operating characteristic curve analysis were performed. The average resident age was 88.2 years (range 78-99), 15 had at least one fall event. There was no significant correlation between clinical assessment and fall risk. Moderate correlations between different temporospatial parameters and fall risk were seen. Pressure distribution during gait was markedly changed in fallers. Differences between fallers and non-fallers as well as cut off values for increased fall risk in the ROC analysis could be determined. The introduced measurement protocol suggests that patients at risk for falling can be detected without any additional office visits. Based on the introduced protocol in a limited patient setting, further large scale studies should now determine the effect of prevention measures triggered by gait analysis, the specific risk reduction and the associated personal and socioeconomic advantages.

Finding NEEMO: towards organizing smart digital solutions in orthopaedic trauma surgery

There are many digital solutions which assist the orthopaedic trauma surgeon. This already broad field is rapidly expanding, making a complete overview of the existing solutions difficult.The AO Foundation has established a task force to address the need for an overview of digital solutions in the field of orthopaedic trauma surgery.Areas of new technology which will help the surgeon gain a greater understanding of these possible solutions are reviewed.We propose a categorization of the current needs in orthopaedic trauma surgery matched with available or potential digital solutions, and provide a narrative overview of this broad topic, including the needs, solutions and basic rules to ensure adequate use in orthopaedic trauma surgery. We seek to make this field more accessible, allowing for technological solutions to be clearly matched to trauma surgeons' needs. Cite this article: EFORT Open Rev 2020;5:408-420. DOI: 10.1302/2058-5241.5.200021.

Smoothness of Gait in Healthy and Cognitively Impaired Individuals: A Study on Italian Elderly Using Wearable Inertial Sensor

The main purpose of the present study was to compare the smoothness of gait in older adults with and without cognitive impairments, using the harmonic ratio (HR), a metric derived from trunk accelerations. Ninety older adults aged over 65 (age: 78.9 ± 4.8 years; 62% female) underwent instrumental gait analysis, performed using a wearable inertial sensor and cognitive assessment with the Mini Mental State Examination (MMSE) and Addenbrooke’s Cognitive Examination Revised (ACE-R). They were stratified into three groups based on their MMSE performance: healthy controls (HC), early and advanced cognitive decline (ECD, ACD). The spatio-temporal and smoothness of gait parameters, the latter expressed through HR in anteroposterior (AP), vertical (V) and mediolateral (ML) directions, were derived from trunk acceleration data. The existence of a relationship between gait parameters and degree of cognitive impairment was also explored. The results show that individuals with ECD and ACD exhibited significantly slower speed and shorter stride length, as well as reduced values of HR in the AP and V directions compared to HC, while no significant differences were found between ECD and ACD in any of the investigated parameters. Gait speed, stride length and HR in all directions were found to be moderately correlated with both MMSE and ACE-R scores. Such findings suggest that, in addition to the known changes in gait speed and stride length, important reductions in smoothness of gait are likely to occur in older adults, owing to early/prodromal stages of cognitive impairment. Given the peculiar nature of these metrics, which refers to overall body stability during gait, the calculation of HR may result in being useful in improving the characterization of gait patterns in older adults with cognitive impairments.

Fifteen Years of Wireless Sensors for Balance Assessment in Neurological Disorders

Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined.

High density muscle size and muscle power are associated with both gait and sit-to-stand kinematic parameters in frail nonagenarians

Frailty is an important concept in clinical and demographic research in the elderly because of its incidence level and its relationship with adverse outcomes. Functional ability declines with advanced age, likely due to changes in muscle function. This study aimed to examine the relationship between muscle quality and muscle power with kinematics from functional tests in a population of 21 institutionalized frail nonagenarian (91.3 ± 3.1 years). Here, muscle quality was measured by segmenting areas of high- and low-density fibers with computerized tomography. In addition, muscle strength and muscle power were obtained through maximal strength and power tests using resistance exercises. Finally, functional capacity outcomes (i.e., balance, gait velocity and sit-to-stand ability), as well as kinematic parameters, were evaluated from a tri-axial sensor used during a battery of functional tests. Our results show that lower limb muscle quality, maximal strength and power output present statistically significant relationships with different kinematic parameters, especially during the sit-to-stand and gait tests (e.g. leg power and maximum power during sit-to-stand (r = 0.80) as well as quadriceps muscle mass and step asymmetry (r = -0,71). In particular, frail individuals with greater muscle quality needed less trunk range of motion to make the transition between sitting and standing, took less time to stand up, and exerted a major peak power of force. As a conclusion, a loss of muscle quality and power may lead to motor control impairments such as gait, sit-to-stand and balance that can be the cause of adverse events such as falls.

Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors

Spasticity is a frequently observed symptom in patients with neurological impairments. Spastic movements of their upper and lower limbs are periodically measured to evaluate functional outcomes of physical rehabilitation, and they are quantified by clinical outcome measures such as the modified Ashworth scale (MAS). This study proposes a method to determine the severity of elbow spasticity, by analyzing the acceleration and rotation attributes collected from the elbow of the affected side of patients and machine-learning algorithms to classify the degree of spastic movement; this approach is comparable to assigning an MAS score. We collected inertial data from participants using a wearable device incorporating inertial measurement units during a passive stretch test. Machine-learning algorithms-including decision tree, random forests (RFs), support vector machine, linear discriminant analysis, and multilayer perceptrons-were evaluated in combinations of two segmentation techniques and feature sets. A RF performed well, achieving up to 95.4% accuracy. This work not only successfully demonstrates how wearable technology and machine learning can be used to generate a clinically meaningful index but also offers rehabilitation patients an opportunity to monitor the degree of spasticity, even in nonhealthcare institutions where the help of clinical professionals is unavailable.

Use of accelerometer-based activity monitoring in orthopaedics: benefits, impact and practical considerations

Studies of the effectiveness of orthopaedic interventions do not generally measure physical activity (PA). Applying accelerometer-based activity monitoring in orthopaedic studies will add relevant information to the generally examined physical function and pain assessment.Accelerometer-based activity monitoring is practically feasible in orthopaedic patient populations, since current day activity sensors have battery time and memory to measure continuously for several weeks without requiring technical expertise.The ongoing development in sensor technology has made it possible to combine functional tests with activity monitoring.For clinicians, the application of accelerometer-based activity monitoring can provide a measure of PA and can be used for clinical comparisons before and after interventions.In orthopaedic rehabilitation, accelerometer-based activity monitoring may be used to help patients reach their targets for PA and to coach patients towards a more active lifestyle through direct feedback. Cite this article: EFORT Open Rev 2019;4:678-685. DOI: 10.1302/2058-5241.4.180041.

Optimized scoring tool to quantify the functional performance during the sit-to-stand transition with a magneto-inertial measurement unit

BACKGROUND

Sit-to-stand is used as a qualitative test to evaluate functional performance, especially to detect fall risks and frail individuals. The use of various quantitative criteria would enable a better understanding of musculoskeletal deficits and movement strategy modifications. This quantification was proven possible with a magneto-inertial unit which provides a compatible wearable device for clinical routine motion analysis.

METHODS

Sit-to-stand movements were recorded using a single magneto-inertial measurement unit fixed on the chest for 74 subjects in three groups healthy young, healthy senior and frail. MIMU data was used to compute 15 spatiotemporal, kinematic and energetic parameters. Nonparametric statistical test showed a significant influence of age and frailness. After reducing the number of parameters by a principal component analysis, an AgingScore and a FrailtyScore were computed.

FINDINGS

The fraction of variance explained by the first principal component was 77.48 ± 2.80% for principal component analysis with healthy young and healthy senior groups, and 74.94 ± 2.24% with healthy and frail senior groups. By receiver operating characteristic curve analysis of this score, we were able to refine the analysis to differentiate between healthy young and healthy senior subjects as well as healthy senior and frail subjects. By radar plot of the most discriminate parameters, the motion's strategy could be characterized and be used to detect premature functional deficit or frail subjects.

INTERPRETATION

Sit-to-stand measured by a single magneto-inertial unit and dedicated post processing is able to quantify subject's musculoskeletal performance and will allow longitudinal investigation of aging population.

Wearable Inertial Sensors to Assess Standing Balance: A Systematic Review

Wearable sensors are de facto revolutionizing the assessment of standing balance. The aim of this work is to review the state-of-the-art literature that adopts this new posturographic paradigm, i.e., to analyse human postural sway through inertial sensors directly worn on the subject body. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 73 full-text articles, selecting 47 high-quality contributions. A good inter-rater reliability was obtained (Cohen's kappa = 0.79). This selection of papers was used to summarize the available knowledge on the types of sensors used and their positioning, the data acquisition protocols and the main applications in this field (e.g., "active aging", biofeedback-based rehabilitation for fall prevention, and the management of Parkinson's disease and other balance-related pathologies), as well as the most adopted outcome measures. A critical discussion on the validation of wearable systems against gold standards is also presented.

Validity of accelerometry in step detection and gait speed measurement in orthogeriatric patients

BACKGROUND

Mobile accelerometry is a powerful and promising option to capture long-term changes in gait in both clinical and real-world scenarios. Increasingly, gait parameters have demonstrated their value as clinical outcome parameters, but validation of these parameters in elderly patients is still limited.

OBJECTIVE

The aim of this study was to implement a validation framework appropriate for elderly patients and representative of real-world settings, and to use this framework to test and improve algorithms for mobile accelerometry data in an orthogeriatric population.

METHODS

Twenty elderly subjects wearing a 3D-accelerometer completed a parcours imitating a real-world scenario. High-definition video and mobile reference speed capture served to validate different algorithms.

RESULTS

Particularly at slow gait speeds, relevant improvements in accuracy have been achieved. Compared to the reference the deviation was less than 1% in step detection and less than 0.05 m/s in gait speed measurements, even for slow walking subjects (< 0.8 m/s).

CONCLUSION

With the described setup, algorithms for step and gait speed detection have successfully been validated in an elderly population and demonstrated to have improved performance versus previously published algorithms. These results are promising that long-term and/or real-world measurements are possible with an acceptable accuracy even in elderly frail patients with slow gait speeds.

An Exploratory Factor Analysis of Sensor-Based Physical Capability Assessment

Physical capability (PC) is conventionally evaluated through performance-based clinical assessments. We aimed to transform a battery of sensor-based functional tests into a clinically applicable assessment tool. We used Exploratory Factor Analysis (EFA) to uncover the underlying latent structure within sensor-based measures obtained in a population-based study. Three hundred four community-dwelling older adults (163 females, 80.9 ± 6.4 years), underwent three functional tests (Quiet Stand, QS, 7-meter Walk, 7MW and Chair Stand, CST) wearing a smartphone at the lower back. Instrumented tests provided 73 sensor-based measures, out of which EFA identified a fifteen-factor model. A priori knowledge and the associations with health-related measures supported the functional interpretation and construct validity analysis of the factors, and provided the basis for developing a conceptual model of PC. For example, the “Walking Impairment” domain obtained from the 7MW test was significantly associated with measures of leg muscle power, gait speed, and overall lower extremity function. To the best of our knowledge, this is the first time that a battery of functional tests, instrumented through a smartphone, is used for outlining a sensor-based conceptual model, which could be suitable for assessing PC in older adults and tracking its changes over time.

Exploring the clinical context of adopting an instrumented insole: a qualitative study of clinicians' preferences in England

OBJECTIVES

This study explores clinicians' views of the clinical uptake of a smart pressure-sensing insole, named Flexifoot, to enhance the care and management of patients with osteoarthritis (OA). Clinicians are key users of wearable technologies, and can provide appropriate feedback for a specific device for successful clinical implementation.

DESIGN

Qualitative study with in-depth, semi-structured interviews, analysed using inductive analysis to generate key themes.

SETTING

Conducted in a University setting.

PARTICIPANTS

30 clinicians were interviewed (11 physiotherapists, 11 orthopaedic surgeons, 5 general practitioners, 3 podiatrists).

RESULTS

All clinicians regarded Flexifoot to be useful for the care and management of patients in adjunction to current methods. Responses revealed four main themes: use, data presentation, barriers to use and future development. Flexifoot data were recognised as capable of enhancing information exchange between clinicians and patients, and also between clinicians themselves. Participants supported the use of feedback for rehabilitation, screening and evaluation of treatment progress/success purposes. Flexifoot use by patients was encouraged as a self-management tool that may motivate them by setting attainment goals. The data interface should be secure, concise and visually appealing. The measured parameters of Flexifoot, its duration of wear and frequency of data output would all depend on the rationale for its use. The clinicians and patients must collaborate to optimise the use of Flexifoot for long-term monitoring of disease for patient care in clinical practice. Many identified potential other uses for Flexifoot.

CONCLUSIONS

Clinicians thought that Flexifoot may complement and improve current methods of long-term patient management for OA or other conditions in clinical settings. Flexifoot was recognised to be useful for objective measures and should be tailored carefully for each person and condition to maximise compliance. Adopting the device, and other similar technologies, requires reducing the main barriers to use (time, cost, patient compliance) before its successful implementation.

Comparison of Standard Clinical and Instrumented Physical Performance Tests in Discriminating Functional Status of High-Functioning People Aged 61⁻70 Years Old

Assessment of physical performance by standard clinical tests such as the 30-sec Chair Stand (30CST) and the Timed Up and Go (TUG) may allow early detection of functional decline, even in high-functioning populations, and facilitate preventive interventions. Inertial sensors are emerging to obtain instrumented measures that can provide subtle details regarding the quality of the movement while performing such tests. We compared standard clinical with instrumented measures of physical performance in their ability to distinguish between high and very high functional status, stratified by the Late-Life Function and Disability Instrument (LLFDI). We assessed 160 participants from the PreventIT study (66.3 ± 2.4 years, 87 females, median LLFDI 72.31, range: 44.33⁻100) performing the 30CST and TUG while a smartphone was attached to their lower back. The number of 30CST repetitions and the stopwatch-based TUG duration were recorded. Instrumented features were computed from the smartphone embedded inertial sensors. Four logistic regression models were fitted and the Areas Under the Receiver Operating Curve (AUC) were calculated and compared using the DeLong test. Standard clinical and instrumented measures of 30CST both showed equal moderate discriminative ability of 0.68 (95%CI 0.60⁻0.76), p = 0.97. Similarly, for TUG: AUC was 0.68 (95%CI 0.60⁻0.77) and 0.65 (95%CI 0.56⁻0.73), respectively, p = 0.26. In conclusion, both clinical and instrumented measures, recorded through a smartphone, can discriminate early functional decline in healthy adults aged 61⁻70 years.

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

Background

Clinical assessments for physical function do not objectively quantify routine daily activities. Wearable activity monitors (WAMs) enable objective measurement of daily activities, but it remains unclear how these map to clinically measured physical function measures.

Objective

This study aims to derive a representation of physical function from daily measurements of free-living activity obtained through a WAM. In addition, we evaluate our derived measure against objectively measured function using an ordinal classification setup.

Methods

We defined function profiles representing average time spent in a set of pattern classes over consecutive days. We constructed a function profile using minute-level activity data from a WAM available from the Osteoarthritis Initiative. Using the function profile as input, we trained statistical models that classified subjects into quartiles of objective measurements of physical function as measured through the 400-m walk test, 20-m walk test, and 5 times sit-stand test. Furthermore, we evaluated model performance on held-out data.

Results

The function profile derived from minute-level activity data can accurately predict physical performance as measured through clinical assessments. Using held-out data, the Goodman-Kruskal Gamma statistic obtained in classifying performance values in the first quartile, interquartile range, and the fourth quartile was 0.62, 0.53, and 0.51 for the 400-m walk, 20-m walk, and 5 times sit-stand tests, respectively.

Conclusions

Function profiles accurately represent physical function, as demonstrated by the relationship between the profiles and clinically measured physical performance. The estimation of physical performance through function profiles derived from free-living activity data may enable remote functional monitoring of patients.

Research methodology for orthopaedic surgeons, with a focus on outcome

Since improving the patient's condition is the ultimate goal of clinical care and research, this review of research methodology focuses on outcomes in the musculoskeletal field.This paper provides an overview of conceptual models, different types of outcomes and commonly assessed outcomes in orthopaedics as well as epidemiological and statistical aspects of outcomes determination, measurement and interpretation.Clinicians should determine the outcome(s) most important to patients and/or public health in collaboration with the patients, epidemiologists/statisticians and other stakeholders.Key points in outcome choice are to evaluate both the benefit and harm of a health intervention, and to consider short- and longer-term outcomes including patient-reported outcomes.Outcome estimation should aim at identifying a clinically important difference (not the same as a statistically significant difference), at presenting measures of effects with confidence intervals and at taking the necessary steps to minimize bias. Cite this article: EFORT Open Rev 2018;3 DOI: 10.1302/2058-5241.3.170064.

Are patients with knee osteoarthritis and patients with knee joint replacement as physically active as healthy persons?

Background

It is well documented that patients with knee osteoarthritis (KOA) suffer from reduced physical function and that function of the affected knee is improved after knee joint replacement (KJR). However, it remains uncertain whether patients with KOA are less physically active than healthy people and whether patients increase their level of physical activity after surgery to a level comparable with that of healthy people. The aim of this study was to examine whether patients with KOA are less physically active than healthy participants and whether patients who have undergone KJR show an increased activity and achieve the same level of physical activity as healthy participants 5 years postoperatively.

Methods

Fifty-four patients with KOA (29 women; mean age 62 ± 8.6; mean body mass index (BMI) 27 ± 5), 52 patients who had KJR due to KOA 5 years earlier (26 women; mean age 66 ± 7.2; mean BMI 30 ± 5) and 171 healthy participants (76 women, mean age 64 ± 9.7, mean BMI 26 ± 5) were included in this cross-sectional study. The level of physical activity was measured over a mean period of 5.5 days with a triaxial accelerometer mounted on the thigh. Number of daily steps, number of daily short walking bouts of <10 s duration and number of daily transfers from sitting to standing position were recorded. Data were analysed using two sample t tests and were adjusted for age, gender and BMI.

Results

Patients with KOA did not differ significantly from healthy participants regarding daily steps (+321, p = 0.50) or daily transfers from sitting to standing (+1.9, p = 0.52) but performed significantly less daily short walking bouts <10 s (-11.9, p = 0.02). Patients after KJR did not differ significantly from healthy participants regarding daily steps (-281, p = 0.60) of transfers from sitting to standing position (-3.2, p = 0.32) but performed significantly less daily short walking bouts <10 s (-21.7, p = 0.001).

Conclusion

Patients with KOA and KJR showed no significant reduction in number of daily step counts and transfers from sitting to standing position when compared with matched healthy controls. However, the number of short walking bouts was reduced in patients with KOA and by twice as much in patients with KJR. This indicates that KOA and treatment with KJR hardly affect health-related general activity but do affect specific physical activity behaviour potentially indicative of KOA or post-KJR functional limitations.

The translational potential of this article

Activity monitoring with an accelerometer-based method gives insights into health-related general activity levels such as total daily steps and specific parameters such as short walking bouts, which may serve as an objective outcome measure in clinical practice.

Objective assessment of physical activity and sedentary behaviour in knee osteoarthritis patients - beyond daily steps and total sedentary time

BACKGROUND

Knee osteoarthritis patients may become physically inactive due to pain and functional limitations. Whether physical activity exerts a protective or harmful effect depends on the frequency, intensity, time and type (F.I.T.T.). The F.I.T.T. dimensions should therefore be assessed during daily life, which so far has hardly been feasible. Furthermore, physical activity should be assessed within subgroups of patients, as they might experience different activity limitations. Therefore, this study aimed to objectively describe physical activity, by assessing the F.I.T.T. dimensions, and sedentary behaviour of knee osteoarthritis patients during daily life. An additional goal was to determine whether activity events, based on different types and durations of physical activity, were able to discriminate between subgroups of KOA patients based on risk factors.

METHODS

Clinically diagnosed knee osteoarthritis patients (according to American College of Rheumatology criteria) were monitored for 1 week with a tri-axial accelerometer. Furthermore, they performed three functional tests and completed the Knee Osteoarthritis Outcome Score. Physical activity levels were described for knee osteoarthritis patients and compared between subgroups.

RESULTS

Sixty-one patients performed 7303 mean level steps, 319 ascending and 312 descending steps and 601 bicycle crank revolutions per day. Most waking hours were spent sedentary (61%), with 4.6 bouts of long duration (> 30 min). Specific events, particularly ascending and descending stairs/slopes, brief walking and sedentary bouts and prolonged walking bouts, varied between subgroups.

CONCLUSIONS

From this sample of KOA patients, the most common form of activity was level walking, although cycling and stair climbing activities occurred frequently, highlighting the relevance of distinguishing between these types of PA. The total active time encompassed a small portion of their waking hours, as they spent most of their time sedentary, which was exacerbated by frequently occurring prolonged bouts. In this study, event-based parameters, such as stair climbing or short bouts of walking or sedentary time, were found more capable of discriminating between subgroups of KOA patients compared to overall levels of PA and sedentary time. Thereby, subtle limitations in physical behaviour of KOA-subgroups were revealed, which might ultimately be targeted in rehabilitation programs.

TRIAL REGISTRATION

German Clinical Trials Registry under ' DRKS00008735 ' at 02.12.2015.

Telehealth in older adults with cancer in the United States: The emerging use of wearable sensors

As the aging and cancer populations in the world continue to increase, the need for complements to traditional geriatric assessments and the logical incorporation of fast and reliable telehealth tools have become interlinked. In the United States, studies examining the use of telehealth for chronic disease management have shown promising results in small groups. The implementation of health technology on a broader scale requires older adults to both accept and adapt such innovation into routine medical care. Though the commercial and recreational use of new technology has increased in older individuals, the transition into creating a smart and connected home that can interface with both patients and healthcare professionals is in its early phases. Current limitations include an inherent digital divide, as well as concerns regarding privacy, data volume, rapid change, cost and reimbursement. The emergence of low-cost, high-fidelity wearable sensors with a spectrum of clinical utility may be the key to increased use and adaptation by older adults. An opportunity to utilize wearable sensors for objective and real-time assessment of older patients with cancer for baseline functional status and treatment toxicity may be on the horizon.

Wearables for independent living in older adults: Gait and falls

Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) is one approach that offers a viable means for ubiquitous, sustainable and scalable monitoring of the health of older adults in habitual free-living environments. Gait has been presented as a relevant (bio)marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research-grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic, with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of the recent literature on gait assessment through the use of wearables, focusing on the need for an algorithm fusion approach to measurement, culminating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Suggestions for how this domain needs to progress are also summarised.

Clinical validation of a body-fixed 3D accelerometer and algorithm for activity monitoring in orthopaedic patients

Background/Objective

Activity is increasingly being recognized as a highly relevant parameter in all areas of healthcare for diagnosis, treatment, or outcome assessment, especially in orthopaedics where the movement apparatus is directly affected. Therefore, the aim of this study was to develop, describe, and clinically validate a generic activity-monitoring algorithm, satisfying a combination of three criteria. The algorithm must be able to identify, count, and time a large set of relevant daily activities. It must be validated for orthopaedic patients as well as healthy individuals, and the validation must be in a setting that mimics free-living conditions.

Methods

Using various technical solutions, such as a dual-axis approach, dynamic inclinometry (hip flexion), and semiautomatic calibration (gait speed), the algorithms were designed to count and time the following postures, transfers, and activities of daily living: resting/sitting, standing, walking, ascending and descending stairs, sit-stand transitions, and cycling. In addition, the number of steps per walking bout was determined. Validation was performed with healthy individuals and patients who had undergone unilateral total joint arthroplasty, representing a wide spectrum of functional capacity. Video observation was used as the gold standard to count and time activities in a validation protocol approaching free-living conditions.

Results

In total 992 and 390 events (activities or postures) were recorded in the healthy group and patient group, respectively. The mean error varied between 0% and 2.8% for the healthy group and between 0% and 7.5% for the patient group. The error expressed in percentage of time varied between 2.0% and 3.0% for both groups.

Conclusion

Activity monitoring of orthopaedic patients by counting and timing a large set of relevant daily life events is feasible in a user- and patient-friendly way and at high clinical validity using a generic three-dimensional accelerometer and algorithms based on empirical and physical methods. The algorithms performed well for healthy individuals as well as patients recovering after total joint replacement in a challenging validation set-up. With such a simple and transparent method real-life activity parameters can be collected in orthopaedic practice for diagnostics, treatments, outcome assessment, or biofeedback.