Home-based monitoring of falls using wearable sensors in Parkinson's disease.

INTRODUCTION: Falling is among the most serious clinical problems in Parkinson's disease (PD). We used body-worn sensors (falls detector worn as a necklace) to quantify the hazard ratio of falls in PD patients in real life. METHODS: We matched all 2063 elderly individuals with self-reported PD to 2063 elderly individuals without PD based on age, gender, comorbidity, and living conditions. We analyzed fall events collected at home via a wearable sensor. Fall events were collected either automatically using the wearable falls detector or were registered by a button push on the same device. We extracted fall events from a 2.5-year window, with an average follow-up of 1.1 years. All falls included were confirmed immediately by a subsequent telephone call. The outcomes evaluated were (1) incidence rate of any fall, (2) incidence rate of a new fall after enrollment (ie, hazard ratio), and (3) 1-year cumulative incidence of falling. RESULTS: The incidence rate of any fall was higher among self-reported PD patients than controls (2.1 vs. 0.7 falls/person, respectively; P < .0001). The incidence rate of a new fall after enrollment (ie, hazard ratio) was 1.8 times higher for self-reported PD patients than controls (95% confidence interval, 1.6-2.0). CONCLUSION: Having PD nearly doubles the incidence of falling in real life. These findings highlight PD as a prime "falling disease." The results also point to the feasibility of using body-worn sensors to monitor falls in daily life. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease.

Wearable devices can capture objective day-to-day data about Parkinson's Disease (PD). This study aims to assess the feasibility of implementing wearable technology to collect data from multiple sensors during the daily lives of PD patients. The Parkinson@home study is an observational, two-cohort (North America, NAM; The Netherlands, NL) study. To recruit participants, different strategies were used between sites. Main enrolment criteria were self-reported diagnosis of PD, possession of a smartphone and age≥18 years. Participants used the Fox Wearable Companion app on a smartwatch and smartphone for a minimum of 6 weeks (NAM) or 13 weeks (NL). Sensor-derived measures estimated information about movement. Additionally, medication intake and symptoms were collected via self-reports in the app. A total of 953 participants were included (NL: 304, NAM: 649). Enrolment rate was 88% in the NL (n = 304) and 51% (n = 649) in NAM. Overall, 84% (n = 805) of participants contributed sensor data. Participants were compliant for 68% (16.3 hours/participant/day) of the study period in NL and for 62% (14.8 hours/participant/day) in NAM. Daily accelerometer data collection decreased 23% in the NL after 13 weeks, and 27% in NAM after 6 weeks. Data contribution was not affected by demographics, clinical characteristics or attitude towards technology, but was by the platform usability score in the NL (χ2 (2) = 32.014, p<0.001), and self-reported depression in NAM (χ2(2) = 6.397, p = .04). The Parkinson@home study shows that it is feasible to collect objective data using multiple wearable sensors in PD during daily life in a large cohort.

A Home-Based Exercise Program Driven by Tablet Application and Mobility Monitoring for Frail Older Adults: Feasibility and Practical Implications.

INTRODUCTION: Stimulation of a physically active lifestyle among older adults is essential to health and well-being. The objective of this study was to evaluate the feasibility and user opinion of a home-based exercise program supported by a sensor and tablet application for frail older adults. METHODS: Community-dwelling older adults (aged ≥70 y) living in The Netherlands were recruited in 2014. Participants exercised 3 months with and 3 months without supervision from a remote coach. Feasibility was operationalized as adherence to exercise (percentage of 5 exercise bouts per week completed) and to wearing the sensor (with 70% defined as sufficient adherence) and the number of problems reported. User opinion was measured with a questionnaire addressing ease of use of the technology and opinion on the program. RESULTS: Twenty-one of 40 enrolled participants completed the trial. Adherence overall was 60.9% (average of 3 bouts per week). Adherence among completers (69.2%) was significantly higher than adherence among dropouts (49.9%). Adherence was sufficient among completers during the 3 months of supervision (75.8%). Adherence to wearing the sensor was 66.7% and was significantly higher among completers than among dropouts (75.7% vs 54.2%). The rate of incidents was significantly lower among completers than among dropouts (0.4 vs 1.2 incidents per participant per week). Connectivity-related incidents were prominent. On a scale of 1 to 5, completers gave ratings of 4.3 (after 3 months) and 4.2 (after 6 months). CONCLUSION: A home-based exercise program using novel technology seems feasible when participants are given a stable internet connection. This program shows promise for stimulating physical activity among older frail adults, especially if it offers regular coaching.

Chair Rise Peak Power in Daily Life Measured With a Pendant Sensor Associates With Mobility, Limitation in Activities, and Frailty in Old People.

The aim of this study was to analyze the clinical relevance of sensor-based daily life chair rise performance measured in old people. A pendant-sensor was worn during standardized tests and in daily life to detect chair rise transfers and analyze transfer peak power. Linear correlations between mean, median, 25th, and 75th percentile transfer peak powers in daily life and mean peak power in standardized tests were evaluated with Pearson correlation ( r). Associations between transfer peak powers in different experiments and outcomes of a clinical mobility test [timed-up-and-go (TUG)], a test of limitation in activities [Groningen activity restriction scale (GARS)], and a frailty test [Groningen frailty indicator (GFI)] were evaluated with Spearman correlation (ρ). Twenty-five old people (70-85 years) participated in the study. The results showed that chair rise peak powers assessed based upon one-week of daily life activities significantly correlated with peak power measured in standardized tests (r: [0.66, 0.74], p < 0.01). Chair rise peak power in daily life significantly associated with TUG scores (ρ: [-0.71, -0.58], ), GARS (ρ: [-0.62, -0.48], ), and GFI (ρ: [-0.52, -0.43], ). Chair rise peak powers in daily life had stronger associations with clinical measurements than standardized tests. In addition, chair rise peak powers measured in old people using assistive devices was significantly lower compared to those not using assistive devices. These results indicate usefulness of the pendant-sensor-based chair rise performance analysis in continuous monitoring and assessment of mobility, limitations in activities and frailty associated variables in old people's daily life.

Large-Scale Wearable Sensor Deployment in Parkinson's Patients: The Parkinson@Home Study Protocol.

BACKGROUND: Long-term management of Parkinson's disease does not reach its full potential because we lack knowledge about individual variations in clinical presentation and disease progression. Continuous and longitudinal assessments in real-life (ie, within the patients' own home environment) might fill this knowledge gap. OBJECTIVE: The primary aim of the Parkinson@Home study is to evaluate the feasibility and compliance of using multiple wearable sensors to collect clinically relevant data. Our second aim is to address the usability of these data for answering clinical research questions. Finally, we aim to build a database for future validation of novel algorithms applied to sensor-derived data from Parkinson's patients during daily functioning. METHODS: The Parkinson@Home study is a two-phase observational study involving 1000 Parkinson's patients and 250 physiotherapists. Disease status is assessed using a short version of the Parkinson's Progression Markers Initiative protocol, performed by certified physiotherapists. Additionally, participants will wear a set of sensors (smartwatch, smartphone, and fall detector), and use these together with a customized smartphone app (Fox Insight), 24/7 for 3 months. The sensors embedded within the smartwatch and fall detector may be used to estimate physical activity, tremor, sleep quality, and falls. Medication intake and fall incidents will be measured via patients' self-reports in the smartphone app. Phase one will address the feasibility of the study protocol. In phase two, mathematicians will distill relevant summary statistics from the raw sensor signals, which will be compared against the clinical outcomes. RESULTS: Recruitment of 300 participants for phase one was concluded in March, 2016, and the follow-up period will end in June, 2016. Phase two will include the remaining participants, and will commence in September, 2016. CONCLUSIONS: The Parkinson@Home study is expected to generate new insights into the feasibility of integrating self-collected information from wearable sensors into both daily routines and clinical practices for Parkinson's patients. This study represents an important step towards building a reliable system that translates and integrates real-life information into clinical decisions, with the long-term aim of delivering personalized disease management support. CLINICALTRIAL: ClinicalTrials.gov NCT02474329; https://clinicaltrials.gov/ct2/show/NCT02474329 (Archived at http://www.webcitation.org/6joEc5P1v).

Accuracy and concurrent validity of a sensor-based analysis of sit-to-stand movements in older adults.

Body-fixed motion sensors have been applied for the assessment of sit-to-stand (STS) performance. However, the accuracy and concurrent validity of sensor-based estimations of the body's center of mass (CoM) motion during STS are unclear. Therefore, this study investigated the accuracy and concurrent validity of sensor-based measures of CoM motion during STS in older adults. Accuracy and concurrent validity were investigated by comparing the sensor-based method to a force plate method. Twenty-seven older adults (20 females, 7 males; age: 72-94 years) performed five STS movements while data were collected with force plates and motion sensors on the hip and chest. Hip maximal acceleration provided an accurate estimation of the center of mass (CoM) maximal acceleration (limits of agreement (LOA) smaller than 5% of the CoM maximal acceleration; estimated and real CoM maximal acceleration did not differ (p=0.823)). Other hip STS measures and the chest STS measures did not provide accurate estimations of CoM motion (LOA ranged from -155.6% to 333.3% of the CoM value; sensor-based measures overestimated CoM motion (range p: <0.001 to 0.01)). However, the hip sensor did not overestimate maximal jerk of the CoM (p=0.679). Moderate to very strong associations were observed between sensor-based estimations and actual CoM motion (range r=0.64-0.94, p<0.001). Hence, sensor-based estimations of CoM motion during STS are possible, but accuracy is limited. The sensor-based method cannot replace laboratory methods for a mechanical analysis of CoM motion during STS but it may be a practical alternative for the clinical assessment of STS performance in older persons.

Sensor-based monitoring of sit-to-stand performance is indicative of objective and self-reported aspects of functional status in older adults.

Studies show that body-fixed motion sensors can be used for long-term monitoring of sit-to-stand (STS) performance in older persons. However, it is unclear how sensor-based measures of STS performance relate to functional status in older adults. Therefore, this study investigated the associations between sensor-based STS measures and standard clinical measures of functional status in older adults. Participants (24 females, 12 males; 72-94 years) performed five normal STS movements while wearing motion sensors on the hip and chest. Objective measures were used to assess mobility (Timed-Up-and-Go Test, Five-Times-Sit-to-Stand Test, Stair Walk Test) and quadriceps strength. Self-reported questionnaires were used to assess limitations in activities of daily living (Groningen Activity Restriction Scale) and frailty (Groningen Frailty Indicator). In general, chest STS measures showed a larger number of significant associations and stronger associations with clinical measures than hip STS measures. Chest maximal velocity, chest peak power, chest scaled peak power and chest stabilization phase SD demonstrated significant associations (weak to strong) with all six clinical measures. Noteworthy is that hip stabilization phase SD showed significant associations (weak to moderate) with five clinical measures. In particular chest peak power and chest scaled peak power demonstrated a moderate ability to discriminate between higher and lower functioning individuals (area under the receiver-operating characteristic curve: 0.75-0.90). This study shows that in particular chest STS measures are indicative of objective and self-reported aspects of functional status in older adults. These findings support the clinical relevance of sensor-based monitoring of STS performance in older persons.

Test-retest reliability of a pendant-worn sensor device in measuring chair rise performance in older persons.

Chair rise performance is incorporated in clinical assessments to indicate fall risk status in older persons. This study investigated the test-retest reliability of a pendant-sensor-based assessment of chair rise performance. Forty-one older persons (28 females, 13 males, age: 72-94) were assessed in two sessions with 3 to 8 days in between. Repeated chair rise transfers were measured after different instructions. Relative and absolute test-retest reliability of chair rise measurements in individual tests and average over all tests were evaluated by means of intra-class correlation coefficients (ICCs) and standard error of measurement (SEM) as a percentage of the measurement mean. Systematic bias between the measurements in test and retest was examined with paired t-tests. Heteroscedasticity of the measurements was visually checked with Bland-Altman plots. In the different test conditions, the ICCs ranged between 0.63 and 0.93, and the SEM% ranged between 5.7% and 21.2%. The relative and absolute reliability of the average over all tests were ICC = 0.86 and SEM% = 9.5% for transfer duration, ICC = 0.93 and SEM% = 9.2% for maximum vertical acceleration, and ICC = 0.89 and SEM% = 10.0% for peak power. The results over all tests indicated that a fall risk assessment application based on pendant-worn-sensor measured chair rise performance in daily life might be feasible.

Test-retest reliability of sensor-based sit-to-stand measures in young and older adults.

This study investigated test-retest reliability of sensor-based sit-to-stand (STS) peak power and other STS measures in young and older adults. In addition, test-retest reliability of the sensor method was compared to test-retest reliability of the Timed Up and Go Test (TUGT) and Five-Times-Sit-to-Stand Test (FTSST) in older adults. Ten healthy young female adults (20-23 years) and 31 older adults (21 females; 73-94 years) participated in two assessment sessions separated by 3-8 days. Vertical peak power was assessed during three (young adults) and five (older adults) normal and fast STS trials with a hybrid motion sensor worn on the hip. Older adults also performed the FTSST and TUGT. The average sensor-based STS peak power of the normal STS trials and the average sensor-based STS peak power of the fast STS trials showed excellent test-retest reliability in young adults (intra-class correlation (ICC)≥0.90; zero in 95% confidence interval of mean difference between test and retest (95%CI of D); standard error of measurement (SEM)≤6.7% of mean peak power) and older adults (ICC≥0.91; zero in 95%CI of D; SEM≤9.9%). Test-retest reliability of sensor-based STS peak power and TUGT (ICC=0.98; zero in 95%CI of D; SEM=8.5%) was comparable in older adults, test-retest reliability of the FTSST was lower (ICC=0.73; zero outside 95%CI of D; SEM=14.4%). Sensor-based STS peak power demonstrated excellent test-retest reliability and may therefore be useful for clinical assessment of functional status and fall risk.

Sensitivity of sensor-based sit-to-stand peak power to the effects of training leg strength, leg power and balance in older adults.

Increasing leg strength, leg power and overall balance can improve mobility and reduce fall risk. Sensor-based assessment of peak power during the sit-to-stand (STS) transfer may be useful for detecting changes in mobility and fall risk. Therefore, this study investigated whether sensor-based STS peak power and related measures are sensitive to the effects of increasing leg strength, leg power and overall balance in older adults. A further aim was to compare sensitivity between sensor-based STS measures and standard clinical measures of leg strength, leg power, balance, mobility and fall risk, following an exercise-based intervention. To achieve these aims, 26 older adults (age: 70-84 years) participated in an eight-week exercise program aimed at improving leg strength, leg power and balance. Before and after the intervention, performance on normal and fast STS transfers was evaluated with a hybrid motion sensor worn on the hip. In addition, standard clinical tests (isometric quadriceps strength, Timed Up and Go test, Berg Balance Scale) were performed. Standard clinical tests as well as sensor-based measures of peak power, maximal velocity and duration of normal and fast STS showed significant improvements. Sensor-based measurement of peak power, maximal velocity and duration of normal STS demonstrated a higher sensitivity (absolute standardized response mean (SRM): ≥ 0.69) to the effects of training leg strength, leg power and balance than standard clinical measures (absolute SRM: ≤ 0.61). Therefore, the presented sensor-based method appears to be useful for detecting changes in mobility and fall risk.

Chair rise transfer detection and analysis using a pendant sensor: an algorithm for fall risk assessment in older people.

Falls result in substantial disability, morbidity, and mortality among older people. Early detection of fall risks and timely intervention can prevent falls and injuries due to falls. Simple field tests, such as repeated chair rise, are used in clinical assessment of fall risks in older people. Development of on-body sensors introduces potential beneficial alternatives for traditional clinical methods. In this article, we present a pendant sensor based chair rise detection and analysis algorithm for fall risk assessment in older people. The recall and the precision of the transfer detection were 85% and 87% in standard protocol, and 61% and 89% in daily life activities. Estimation errors of chair rise performance indicators: duration, maximum acceleration, peak power and maximum jerk were tested in over 800 transfers. Median estimation error in transfer peak power ranged from 1.9% to 4.6% in various tests. Among all the performance indicators, maximum acceleration had the lowest median estimation error of 0% and duration had the highest median estimation error of 24% over all tests. The developed algorithm might be feasible for continuous fall risk assessment in older people.

A body-fixed-sensor-based analysis of power during sit-to-stand movements.

This study presents an analysis of power exertion for lifting the body's centre of mass (CoM) during rising from a chair. Five healthy young (21-44 years) and 12 healthy older (70-79 years) subjects performed sit-to-stand (STS) movements while data were measured with force-plates underneath chair and feet and motion sensors attached to different locations on the upper and lower trunk. Force-plate-data were used to determine the timing of STS movements and the vertical power for lifting the CoM from a sitting to a standing position. Data of three-dimensional hybrid motion sensors, consisting of accelerometers, gyroscopes and earth-magnetic-field sensors, were used to determine vertical accelerations and power. The comparison of sensor-based estimations of peak power with peak power calculated from force-plate-data demonstrated fair to excellent linear relationships for all sensor locations on the trunk. The best approximation of peak power was obtained by a weighted combination of data measured at different trunk locations. Results of the older subjects were consistent with those of the young subjects performing slow, normal and fast STS movements. The presented approach is relevant for monitoring fall risk and assessment of mobility in older people. Similar approaches for assessing power may be developed for other mobility related activities, such as stair walking, or sports related activities such as jumping.