Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis

Evaluation of a Virtual Tai Chi Program for Older Veterans at Risk of Loneliness or Physical Deconditioning: A Quality Improvement Project

This Quality Improvement project evaluated the implementation of a virtual Tai Chi program for older Veterans (OVs) at risk of loneliness and/or physical deconditioning. A 12-week Tai Chi course was conducted virtually at three Veterans Affairs sites using VA Video Connect (VVC). Changes in physical function based on the 30-Second Chair Stand (30CST) and loneliness based on the De Jong Gierveld Loneliness Scale (DJGS) were measured, as were the OVs' satisfaction and adherence. Of 109 OVs who enrolled, 74 completed the program with a mean attendance rate of 84%. Completers demonstrated a statistically significant improvement in the 30CST, and those who were moderately or severely lonely at baseline saw a statistically significant improvement in the DJGS. Course evaluations were generally very positive. Results suggest that a virtual Tai Chi program is an effective and very satisfying intervention for OVs at risk of loneliness or physical deconditioning.

30-Second Chair Stand and 5-Times Sit-to-Stand Tests Are Interesting Tools for Assessing Disability and Ability to Ambulate among Patients with Multiple Sclerosis

Multiple Sclerosis (MS) is a demyelinating and chronic disease with variable neurological symptoms. There are different scales that score the level of disability, but only few papers have taken into consideration the 5-times sit-to-stand (5STS) test and the 30 s chair stand test (30CST), which are valid and easily obtainable indicators of other neurological diseases. The aim of our research is to verify the validity, reproducibility, and responsiveness of these tests. Patients afflicted with MS were enrolled in the AISM outpatient facility. The inclusion criterion was an EDSS score less than 6.5. We performed the 5STS, 30CST, and timed 25-foot walk (T25-FW) tests and recorded EDSS scores in the first evaluation. Then, we recorded the performance after 5 days (conducted by a second blind operator to ensure test-retest reproducibility), and the last evaluation was made after 12 sessions of physiotherapy. We recruited 38 patients diagnosed with MS. The results show significant data regarding validity, reproducibility, and responsiveness for both scales. The data argue in favor of adding these tests to the relevant clinical assessments. These two tests are simple, reliable, and easy to administer, and the data confirm that they can be included in the evaluation of patients with MS.

Risky movement: Assessing fall risk in people with multiple sclerosis with wearable sensors and beacon-based smart-home monitoring

BACKGROUND

People with multiple sclerosis (PwMS) fall frequently causing injury, social isolation, and decreased quality of life. Identifying locations and behaviors associated with high fall risk could help direct fall prevention interventions. Here we describe a smart-home system for assessing how mobility metrics relate to real-world fall risk in PwMS.

METHODS

We performed a secondary analysis of a dataset of real-world falls collected from PwMS to identify patterns associated with increased fall risk. Thirty-four individuals were tracked over eight weeks with an inertial sensor comprising a triaxial accelerometer and time-of-flight radio transmitter, which communicated with beacons positioned throughout the home. We evaluated associations between locations in the home and movement behaviors prior to a fall compared with time periods when no falls occurred using metrics including gait initiation, time-spent-moving, movement length, and an entropy-based metric that quantifies movement complexity using transitions between rooms in the home. We also explored how fall risk may be related to the percent of times that a participant paused while walking (pauses-while-walking).

RESULTS

Seventeen of the participants monitored sustained a total of 105 falls that were recorded. More falls occurred while walking (52%) than when stationary despite participants being largely sedentary, only walking 1.5±3.3% (median ± IQR) of the time that they were in their home. A total of 28% of falls occurred within one second of gait initiation. As the percentage of pauses-while-walking increased from 20 to 60%, the likelihood of a fall increased by nearly 3 times from 0.06 to 0.16%. Movement complexity, which was quantified using the entropy of room transitions, was significantly higher in the 10 min preceding falls compared with other 10-min time segments not preceding falls (1.15 ± 0.47 vs. 0.96 ± 0.24, P = 0.02). Path length was significantly longer (151.3 ± 156.1 m vs. 95.0 ± 157.2 m, P = 0.003) in the ten minutes preceding a fall compared with non-fall periods. Fall risk also varied among rooms but not consistently across participants.

CONCLUSIONS

Movement metrics derived from wearable sensors and smart-home tracking systems are associated with fall risk in PwMS. More pauses-while-walking, and more complex, longer movement trajectories are associated with increased fall risk.

FUNDING

Department of Veterans Affairs (RX001831-01A1). National Science Foundation (#2030859).

Examining the Use of Autonomous Systems for Home Health Support Using a Smart Mirror

The home is becoming a key location for healthcare delivery, including the use of technology driven by autonomous systems (AS) to monitor and support healthcare plans. Using the example of a smart mirror, this paper describes the outcomes of focus groups with people with multiple sclerosis (MS; n = 6) and people who have had a stroke (n = 15) to understand their attitudes towards the use of AS for healthcare in the home. Qualitative data were analysed using a thematic analysis. The results indicate that the use of such technology depends on the level of adaptability and responsiveness to users' specific circumstances, including their relationships with the healthcare system. A smart mirror would need to support manual entry, responsive goal setting, the effective aggregation of data sources and integration with other technology, have a range of input methods, be supportive rather than prescriptive in messaging, and give the user full control of their data. The barriers to its adoption include a perceived lack of portability and practicality, a lack of accessibility and inclusivity, a sense of redundancy, feeling overwhelmed by multiple technological devices, and a lack of trust in data sharing. These results inform the development and deployment of future health technologies based on the lived experiences of people with health conditions who require ongoing care.

Ecological Fall Prediction Sensitivity, Specificity, and Accuracy in Patients with Mild Cognitive Impairment at a High Risk of Falls

While falls among patients with mild cognitive impairment (MCI) have been closely associated with an increased postural sway during ecological activities of daily living, there is a dearth of postural sway detection (PSD) research in ecological environments. The present study aimed to investigate the fall sensitivity, specificity, and accuracy of our PSD system. Forty healthy young and older adults with MCI at a high risk of falls underwent the sensitivity, specificity, and accuracy tests for PSD by simultaneously recording the Berg Balance Scale and Timed Up and Go in ecological environments, and the data were analyzed using the receiver operating characteristic curve and area under the curve. The fall prediction sensitivity ranged from 0.82 to 0.99, specificity ranged from 0.69 to 0.90, and accuracy ranged from 0.53 to 0.81. The PSD system's fall prediction sensitivity, specificity, and accuracy data suggest a reasonable discriminative capacity for distinguishing between fallers and non-fallers as well as predicting falls in older adults with MCI in ecological testing environments.

Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective

BACKGROUND

Wearable sensor technologies have the potential to improve monitoring in people with multiple sclerosis (MS) and inform timely disease management decisions. Evidence of the utility of wearable sensor technologies in people with MS is accumulating but is generally limited to specific subgroups of patients, clinical or laboratory settings, and functional domains.

OBJECTIVE

This review aims to provide a comprehensive overview of all studies that have used wearable sensors to assess, monitor, and quantify motor function in people with MS during daily activities or in a controlled laboratory setting and to shed light on the technological advances over the past decades.

METHODS

We systematically reviewed studies on wearable sensors to assess the motor performance of people with MS. We scanned PubMed, Scopus, Embase, and Web of Science databases until December 31, 2022, considering search terms "multiple sclerosis" and those associated with wearable technologies and included all studies assessing motor functions. The types of results from relevant studies were systematically mapped into 9 predefined categories (association with clinical scores or other measures; test-retest reliability; group differences, 3 types; responsiveness to change or intervention; and acceptability to study participants), and the reporting quality was determined through 9 questions. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines.

RESULTS

Of the 1251 identified publications, 308 were included: 176 (57.1%) in a real-world context, 107 (34.7%) in a laboratory context, and 25 (8.1%) in a mixed context. Most publications studied physical activity (196/308, 63.6%), followed by gait (81/308, 26.3%), dexterity or tremor (38/308, 12.3%), and balance (34/308, 11%). In the laboratory setting, outcome measures included (in addition to clinical severity scores) 2- and 6-minute walking tests, timed 25-foot walking test, timed up and go, stair climbing, balance tests, and finger-to-nose test, among others. The most popular anatomical landmarks for wearable placement were the waist, wrist, and lower back. Triaxial accelerometers were most commonly used (229/308, 74.4%). A surge in the number of sensors embedded in smartphones and smartwatches has been observed. Overall, the reporting quality was good.

CONCLUSIONS

Continuous monitoring with wearable sensors could optimize the management of people with MS, but some hurdles still exist to full clinical adoption of digital monitoring. Despite a possible publication bias and vast heterogeneity in the outcomes reported, our review provides an overview of the current literature on wearable sensor technologies used for people with MS and highlights shortcomings, such as the lack of harmonization, transparency in reporting methods and results, and limited data availability for the research community. These limitations need to be addressed for the growing implementation of wearable sensor technologies in clinical routine and clinical trials, which is of utmost importance for further progress in clinical research and daily management of people with MS.

TRIAL REGISTRATION

PROSPERO CRD42021243249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=243249.

The instrumented single leg stance test detects early balance impairment in people with multiple sclerosis

Balance impairment is frequent in people with multiple sclerosis (pwMS) and affects risk of falls and quality of life. By using inertial measurement units (IMUs) on the Single Leg Stance Test (SLS) we aimed to discriminate healthy controls (HC) from pwMS and detect differences in balance endurance and quality. Thirdly, we wanted to test the correlation between instrumented SLS parameters and self-reported measures of gait and balance. Fifty-five pwMS with mild (EDSS<4) and moderate disability (EDSS≥4) and 20 HC performed the SLS with 3 IMUs placed on the feet and sacrum and filled the Twelve Item Multiple Sclerosis Walking Scale (MSWS-12) questionnaire. A linear mixed model was used to compare differences in the automated balance measures. Balance duration was significantly longer in HC compared to pwMS (p < 0.001) and between the two disability groups (p < 0.001). Instrumented measures identified that trunk stability (normalized mediolateral and antero-posterior center of mass stability) had the strongest association with disability (R2 marginal 0.30, p < 0.001) and correlated well with MSWS-12 (R = 0.650, p < 0.001). PwMS tended to overestimate own balance compared to measured balance duration. The use of both self-reported and objective assessments from IMUs can secure the follow-up of balance in pwMS.

Digital Phenotypes of Instability and Fatigue Derived From Daily Standing Transitions in Persons With Multiple Sclerosis

Impairment in persons with multiple sclerosis (PwMS) can often be attributed to symptoms of motor instability and fatigue. Symptom monitoring and queued interventions often target these symptoms. Clinical metrics are currently limited to objective physician assessments or subjective patient reported measures. Recent research has turned to wearables for improving the objectivity and temporal resolution of assessment. Our group has previously observed wearable assessment of supervised and unsupervised standing transitions to be predictive of fall-risk in PwMS. Here we extend the application of standing transition quantification to longitudinal home monitoring of symptoms. Subjects (N=23) with varying degrees of MS impairment were recruited and monitored with accelerometry for a total of  ∼  6 weeks each. These data were processed using a preexisting framework, applying a deep learning activity classifier to isolate periods of standing transition from which descriptive features were extracted for analysis. Participants completed daily and biweekly assessments describing their symptoms. From these data, Canonical Correlation Analysis was used to derive digital phenotypes of MS instability and fatigue. We find these phenotypes capable of distinguishing fallers from non-fallers, and further that they demonstrate a capacity to characterize symptoms at both daily and sub-daily resolutions. These results represent promising support for future applications of wearables, which may soon augment or replace current metrics in longitudinal monitoring of PwMS.

Arterial stiffness expressed as brachial-ankle pulse wave velocity and gait assessment independent of lower extremity strength: a cross-sectional study in the older men population

BACKGROUND

Older men are more vulnerable to fatal falls than women, and gait disturbances contribute to the risk of falls. Studies have assessed the association between arterial stiffness and gait dysfunction, but the results have been inconclusive. This study aimed to conduct a cross-sectional analysis to evaluate the association between brachial-ankle pulse wave velocity (baPWV) and gait assessment in older men.

METHODS

Data from the 2014-2015 Korea Institute of Sport Science Fitness Standards project were used for the analysis. The inclusion criteria were men aged > 65 years with gait assessment [the 30-s chair stand test (30s-CST), the timed up and go (TUG) test, the figure-of-8 walk (F8W) test, the 2-min step test (2MST), and the 6-min walk test (6MWT)] and baPWV measurement data. Generalized linear regression analysis was conducted with multiple confounding factor adjustments, including lower extremity isometric strength.

RESULTS

A total of 291 participants were included in the analysis. The mean age was 71.38 ± 4.40 years. The mean values were as follows: (1) 30s-CST, 17.48 ± 5.00; (2) TUG test, 6.01 ± 1.10 s; (3) F8W test, 25.65 ± 4.71 s; (4) 2MST, 102.40 ± 18.83 per 2 min; and (5) 6MWT, 500.02 ± 85.65 m. After multivariable adjustment, baPWV was associated with the 6MWT (β = -0.037, 95% CI: -0.072--0.002), TUG test (β = 0, 95% CI: 0.000-0.001), and F8W test (β = 0.002, 95% CI: 0.000-0.004). baPWV was not associated with the 30s-CST and 2MST.

CONCLUSIONS

The current study showed a statistically significant association between gait assessments and arterial stiffness, independent of lower extremity strength. However, this association was modest. Future prospective studies are needed to elucidate the complex relationship between arterial stiffness and gait dysfunction.

Validity of the 30-Second Sit-to-Stand test as a measure of lower extremity function in persons with multiple sclerosis: Preliminary evidence

BACKGROUND

The 30-Second Sit-To-Stand (30SSTS) is a quick, inexpensive, safe, and widely used clinical measure of lower extremity function. To date, there is limited evidence regarding the use of 30SSTS in multiple sclerosis (MS). The purpose of this study was to examine the construct validity of the 30SSTS test in persons with MS compared with non-MS healthy controls.

METHODS

Twenty ambulatory persons with MS and twenty age- and sex-matched healthy controls completed the 30SSTS, Timed 25-Foot Walk (T25FW), Timed Up and Go (TUG), Six-Minute Walk (6MW), and Godin Leisure-Time Exercise Questionnaire (GLTEQ). Persons with MS also completed the Patient Determined Disease Steps (PDDS) and 12-item MS Walking Scale (MSWS-12).

RESULTS

Persons with MS had significantly worse performance on the TUG (mean difference [95% confidence interval] = 1.4 [0.5, 2.3] sec) and 6MW (-259.2 [-450.8, -67.6] ft), but not on the 30SSTS (-1.6 [-1.5, 4.6] reps) and T25FW (-0.59 [-0.1, 1.2] ft/sec) compared with controls. There were significant moderate-to-strong correlations between the 30SSTS with T25FW, TUG, and 6MW scores in persons with MS (r = 0.48, -0.65 and 0.61, respectively), whereas the 30SSTS was only significantly associated with 6MW scores (r = 0.43) in controls. The 30SSTS was negatively associated with MS-related walking disability assessed by the PDDS and MSWS-12 (r_s = -0.52 and -0.64, respectively), but was not significantly associated with the GLTEQ in MS and controls (r = 0.30 and 0.17, respectively).

CONCLUSION

This study provides initial support for the construct validity of the 30SSTS as a measure of lower extremity function in persons with MS. Our findings warrant the inclusion of the 30SSTS as a feasible and valid measure of physical function in clinical research and practice involving persons with MS.

Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis

Falls are frequent and associated with morbidity in persons with multiple sclerosis (PwMS). Symptoms of MS fluctuate, and standard biannual clinical visits cannot capture these fluctuations. Remote monitoring techniques that leverage wearable sensors have recently emerged as an approach sensitive to disease variability. Previous research has shown that fall risk can be identified from walking data collected by wearable sensors in controlled laboratory conditions however this data may not be generalizable to variable home environments. To investigate fall risk and daily activity performance from remote data, we introduce a new open-source dataset featuring data collected from 38 PwMS, 21 of whom are identified as fallers and 17 as non-fallers based on their six-month fall history. This dataset contains inertial-measurement-unit data from eleven body locations collected in the laboratory, patient-reported surveys and neurological assessments, and two days of free-living sensor data from the chest and right thigh. Six-month (n = 28) and one-year repeat assessment (n = 15) data are also available for some patients. To demonstrate the utility of these data, we explore the use of free-living walking bouts for characterizing fall risk in PwMS, compare these data to those collected in controlled environments, and examine the impact of bout duration on gait parameters and fall risk estimates. Both gait parameters and fall risk classification performance were found to change with bout duration. Deep learning models outperformed feature-based models using home data; the best performance was observed with all bouts for deep-learning and short bouts for feature-based models when evaluating performance on individual bouts. Overall, short duration free-living walking bouts were found to be the least similar to laboratory walking, longer duration free-living walking bouts provided more significant differences between fallers and non-fallers, and an aggregation of all free-living walking bouts yields the best performance in fall risk classification.

How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway

Wearable sensors facilitate the evaluation of gait and balance impairment in the free-living environment, often with observation periods spanning weeks, months, and even years. Data supporting the minimal duration of sensor wear, which is necessary to capture representative variability in impairment measures, are needed to balance patient burden, data quality, and study cost. Prior investigations have examined the duration required for resolving a variety of movement variables (e.g., gait speed, sit-to-stand tests), but these studies use differing methodologies and have only examined a small subset of potential measures of gait and balance impairment. Notably, postural sway measures have not yet been considered in these analyses. Here, we propose a three-level framework for examining this problem. Difference testing and intra-class correlations (ICC) are used to examine the agreement in features computed from potential wear durations (levels one and two). The association between features and established patient reported outcomes at each wear duration is also considered (level three) for determining the necessary wear duration. Utilizing wearable accelerometer data continuously collected from 22 persons with multiple sclerosis (PwMS) for 6 weeks, this framework suggests that 2 to 3 days of monitoring may be sufficient to capture most of the variability in gait and sway; however, longer periods (e.g., 3 to 6 days) may be needed to establish strong correlations to patient-reported clinical measures. Regression analysis indicates that the required wear duration depends on both the observation frequency and variability of the measure being considered. This approach provides a framework for evaluating wear duration as one aspect of the comprehensive assessment, which is necessary to ensure that wearable sensor-based methods for capturing gait and balance impairment in the free-living environment are fit for purpose.