Estimation of Average and Maximum Daily-Life Mobility Performance Using the Timed Up-and-Go: Exploring the Added Value of an Instrumented Timed Up-and-Go.

INTRODUCTION: The association between specific motor capacity variables obtained in a laboratory and parameters of daily-life mobility performance (MP) obtained via wearables is still unclear. The Timed Up-and-Go (TUG) test is a widely used motor capacity tests available either as traditional hand-stopped TUG or as instrumented TUG (iTUG), providing specific information about its subphases. This study aimed to: (1) estimate the association between the TUG and specific parameters reflecting average and maximum daily-life MP, (2) estimate the benefits of the iTUG in terms of explaining MP in daily life compared to the TUG. METHODS: The present study was a cross-sectional analysis using baseline data of 294 older persons (mean age: 76.7 ± 5.3 years). Univariate linear regression analysis was performed to delineate the coefficient of determination between TUG time and participants' MP. MP variables containing mean cadence (MCA) to represent average performance and the 95th percentile of mean cadence of walks with more than three steps (p95>3stepsMCA) to represent maximum performance. To determine whether the iTUG variables give more information about MP, a stepwise multivariate regression analysis between iTUG variables and the p95>3stepsMCA variable to represent maximum performance was conducted. RESULTS: The univariate regression models revealed associations of the TUG with MCA (adjusted R2 = 0.078, p < 0.001) and p95>3stepsMCA (adjusted R2 = 0.199, p < 0.001). The multivariate stepwise regression models revealed a total explanation of maximum daily-life MP (p95>3stepsMCA) of the TUG (adjusted R2 = 0.199, p < 0.001) versus iTUG (adjusted R2 = 0.278, p < 0.010). DISCUSSION/CONCLUSION: This study shows that the TUG better reflects maximum daily-life MP than average daily-life MP. Moreover, we demonstrate the added value of the iTUG for a more accurate estimation of daily MP compared to the traditional TUG. The iTUG is recommended to estimate maximum daily-life MP in fall-prone older adults. The study is a step toward a specific assessment paradigm using capacity variables from the iTUG to estimate maximum daily-life MP.

Using Smartphone TechnolOGy to Support an EffecTive Home ExeRcise Intervention to Prevent Falls amongst Community-Dwelling Older Adults: The TOGETHER Feasibility RCT.

INTRODUCTION: Falls have major implications for quality of life, independence, and cost of health services. Strength and balance training has been found to be effective in reducing the rate/risk of falls, as long as there is adequate fidelity to the evidence-based programme. The aims of this study were to (1) assess the feasibility of using the "Motivate Me" and "My Activity Programme" interventions to support falls rehabilitation when delivered in practice and (2) assess study design and trial procedures for the evaluation of the intervention. METHODS: A two-arm pragmatic feasibility randomized controlled trial was conducted with five health service providers in the UK. Patients aged 50+ years eligible for a falls rehabilitation exercise programme from community services were recruited and received either (1) standard service with a smartphone for outcome measurement only or (2) standard service plus the "Motivate Me" and "My Activity Programme" apps. The primary outcome was feasibility of the intervention, study design, and procedures (including recruitment rate, adherence, and dropout). Outcome measures include balance, function, falls, strength, fear of falling, health-related quality of life, resource use, and adherence, measured at baseline, three-month, and six-month post-randomization. Blinded assessors collected the outcome measures. RESULTS: Twenty four patients were randomized to control group and 26 to intervention group, with a mean age of 77.6 (range 62-92) years. We recruited 37.5% of eligible participants across the five clinical sites. 77% in the intervention group completed their full exercise programme (including the use of the app). Response rates for outcome measures at 6 months were 77-80% across outcome measures, but this was affected by the COVID-19 pandemic. There was a mean 2.6 ± 1.9 point difference between groups in change in Berg balance score from baseline to 3 months and mean 4.4 ± 2.7 point difference from baseline to 6 months in favour of the intervention group. Less falls (1.8 ± 2.8 vs. 9.1 ± 32.6) and less injurious falls (0.1 ± 0.5 vs. 0.4 ± 0.6) in the intervention group and higher adherence scores at three (17.7 ± 6.8 vs. 13.1 ± 6.5) and 6 months (15.2 ± 7.8 vs. 14.9 ± 6.1). There were no related adverse events. Health professionals and patients had few technical issues with the apps. CONCLUSIONS: The motivational apps and trial procedures were feasible for health professionals and patients. There are positive indications from outcome measures in the feasibility trial, and key criteria for progression to full trial were met.

mCrutch: A Novel m-Health Approach Supporting Continuity of Care.

This paper reports the architecture of a low-cost smart crutches system for mobile health applications. The prototype is based on a set of sensorized crutches connected to a custom Android application. Crutches were instrumented with a 6-axis inertial measurement unit, a uniaxial load cell, WiFi connectivity, and a microcontroller for data collection and processing. Crutch orientation and applied force were calibrated with a motion capture system and a force platform. Data are processed and visualized in real-time on the Android smartphone and are stored on the local memory for further offline analysis. The prototype's architecture is reported along with the post-calibration accuracy for estimating crutch orientation (5° RMSE in dynamic conditions) and applied force (10 N RMSE). The system is a mobile-health platform enabling the design and development of real-time biofeedback applications and continuity of care scenarios, such as telemonitoring and telerehabilitation.

Robustness of In-Laboratory and Daily-Life Gait Speed Measures over One Year in High Functioning 61- to 70-Year-Old Adults.

INTRODUCTION: Gait speed is a simple and safe measure with strong predictive value for negative health outcomes in clinical practice, yet in-laboratory gait speed seems not representative for daily-life gait speed. This study aimed to investigate the interrelation between and robustness of in-laboratory and daily-life gait speed measures over 12 months in 61- to 70-year-old adults. METHODS: Gait speed was assessed in laboratory through standardized stopwatch tests and in daily life by 7 days of trunk accelerometry in the PreventIT cohort, at baseline, and after 6 and 12 months. The interrelation was investigated using Pearson's correlations between gait speed measures at each time point. For robustness, changes over time and variance components were assessed by ANOVA and measurement agreement over time by Bland-Altman analyses. RESULTS: Included were 189 participants (median age 67 years [interquartile range: 64-68], 52.2% females). In-laboratory and daily-life gait speed measures showed low correlations (Pearson's r = 0.045-0.455) at each time point. Moreover, both in-laboratory and daily-life gait speed measures appeared robust over time, with comparable and smaller within-subject than between-subject variance (range 0.001-0.095 m/s and 0.032-0.397 m/s, respectively) and minimal differences between measurements over time (Bland-Altman) with wide limits of agreement (standard deviation of mean difference range: 0.12-0.34 m/s). DISCUSSION/CONCLUSION: In-laboratory and daily-life gait speed measures show robust assessments of gait speed over 12 months and are distinct constructs in this population of high-functioning adults. This suggests that (a combination of) both measures may have added value in predicting health outcomes.

Template-Based Recognition of Human Locomotion in IMU Sensor Data Using Dynamic Time Warping.

Increased levels of light, moderate and vigorous physical activity (PA) are positively associated with health benefits. Therefore, sensor-based human activity recognition can identify different types and levels of PA. In this paper, we propose a two-layer locomotion recognition method using dynamic time warping applied to inertial sensor data. Based on a video-validated dataset (ADAPT), which included inertial sensor data recorded at the lower back (L5 position) during an unsupervised task-based free-living protocol, the recognition algorithm was developed, validated and tested. As a first step, we focused on the identification of locomotion activities walking, ascending and descending stairs. These activities are difficult to differentiate due to a high similarity. The results showed that walking could be recognized with a sensitivity of 88% and a specificity of 89%. Specificity for stair climbing was higher compared to walking, but sensitivity was noticeably decreased. In most cases of misclassification, stair climbing was falsely detected as walking, with only 0.2-5% not assigned to any of the chosen types of locomotion. Our results demonstrate a promising approach to recognize and differentiate human locomotion within a variety of daily activities.

Will We Do If We Can? Habitual Qualitative and Quantitative Physical Activity in Multi-Morbid, Older Persons with Cognitive Impairment.

This study aimed to identify determinants of quantitative dimensions of physical activity (PA; duration, frequency, and intensity) in community-dwelling, multi-morbid, older persons with cognitive impairment (CI). In addition, qualitative and quantitative aspects of habitual PA have been described. Quantitative PA and qualitative gait characteristics while walking straight and while walking turns were documented by a validated, sensor-based activity monitor. Univariate and multiple linear regression analyses were performed to delineate associations of quantitative PA dimensions with qualitative characteristics of gait performance and further potential influencing factors (motor capacity measures, demographic, and health-related parameters). In 94 multi-morbid, older adults (82.3 ± 5.9 years) with CI (Mini-Mental State Examination score: 23.3 ± 2.4), analyses of quantitative and qualitative PA documented highly inactive behavior (89.6% inactivity) and a high incidence of gait deficits, respectively. The multiple regression models (adjusted R2 = 0.395-0.679, all p < 0.001) identified specific qualitative gait characteristics as independent determinants for all quantitative PA dimensions, whereas motor capacity was an independent determinant only for the PA dimension duration. Demographic and health-related parameters were not identified as independent determinants. High associations between innovative, qualitative, and established, quantitative PA performances may suggest gait quality as a potential target to increase quantity of PA in multi-morbid, older persons.

Predicting Advanced Balance Ability and Mobility with an Instrumented Timed Up and Go Test.

Extensive test batteries are often needed to obtain a comprehensive picture of a person's functional status. Many test batteries are not suitable for active and healthy adults due to ceiling effects, or require a lot of space, time, and training. The Community Balance and Mobility Scale (CBMS) is considered a gold standard for this population, but the test is complex, as well as time- and resource intensive. There is a strong need for a faster, yet sensitive and robust test of physical function in seniors. We sought to investigate whether an instrumented Timed Up and Go (iTUG) could predict the CBMS score in 60 outpatients and healthy community-dwelling seniors, where features of the iTUG were predictive, and how the prediction of CBMS with the iTUG compared to standard clinical tests. A partial least squares regression analysis was used to identify latent components explaining variation in CBMS total score. The model with iTUG features was able to predict the CBMS total score with an accuracy of 85.2% (84.9-85.5%), while standard clinical tests predicted 82.5% (82.2-82.8%) of the score. These findings suggest that a fast and easily administered iTUG could be used to predict CBMS score, providing a valuable tool for research and clinical care.

Prominent physical inactivity in acute dementia care: Psychopathology seems to be more important than the dose of sedative medication.

INTRODUCTION: To objectively quantify patients' physical activity and analyze the relationships between physical activity levels, psychopathology, and sedative medication in acute hospital dementia care. MATERIALS AND METHODS: In this cross-sectional study, we assessed the patients' physical activity based on data collection by hybrid motion sensors attached on their lower back. Daily doses of antipsychotics have been converted to olanzapine-equivalents and daily benzodiazepine medication is reported as diazepam-equivalents. We assessed patients' neuropsychiatric symptoms with the Neuropsychiatric Inventory and the Cohen-Mansfield Agitation Inventory. RESULTS: We analyzed motion sensor data from 64 patients (MMSE M = 18.6). On average, patients were lying for 11.5 hours, sitting/standing sedentary for 10.3 hours, sitting/standing active for 1.0 hours, and walking for 1.2 hours per day. The analysis revealed no correlations between patients' physical activity and antipsychotic or benzodiazepine medication. More severe neuropsychiatric symptoms were associated with a decrease in the patients' physical activity (r = .32, P = .01). In particular, patients with apathy symptoms were less physically active than patients without apathy symptoms. DISCUSSION: The results reveal that most of the patients in acute dementia care had very low levels of physical activity. Their physical inactivity may be due to the severity of their neuropsychiatric symptoms, especially apathy. Antipsychotic and benzodiazepine medication appeared to have less impact on patients' physical activity. Dementia care should pay more attention to prevent physical inactivity in patients.

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.

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.

HABITAT: An IoT Solution for Independent Elderly.

In this work, a flexible and extensive digital platform for Smart Homes is presented, exploiting the most advanced technologies of the Internet of Things, such as Radio Frequency Identification, wearable electronics, Wireless Sensor Networks, and Artificial Intelligence. Thus, the main novelty of the paper is the system-level description of the platform flexibility allowing the interoperability of different smart devices. This research was developed within the framework of the operative project HABITAT (Home Assistance Based on the Internet of Things for the Autonomy of Everybody), aiming at developing smart devices to support elderly people both in their own houses and in retirement homes, and embedding them in everyday life objects, thus reducing the expenses for healthcare due to the lower need for personal assistance, and providing a better life quality to the elderly users.

Complexity of Daily Physical Activity Is More Sensitive Than Conventional Metrics to Assess Functional Change in Younger Older Adults.

The emerging mHealth applications, incorporating wearable sensors, enables continuous monitoring of physical activity (PA). This study aimed at analyzing the relevance of a multivariate complexity metric in assessment of functional change in younger older adults. Thirty individuals (60⁻70 years old) participated in a 4-week home-based exercise intervention. The Community Balance and Mobility Scale (CBMS) was used for clinical assessment of the participants’ functional balance and mobility performance pre- and post- intervention. Accelerometers worn on the low back were used to register PA of one week before and in the third week of the intervention. Changes in conventional univariate PA metrics (percentage of walking and sedentary time, step counts, mean cadence) and complexity were compared to the change as measured by the CBMS. Statistical analyses (21 participants) showed significant rank correlation between the change as measured by complexity and CBMS (ρ = 0.47, p = 0.03). Smoothing the activity output improved the correlation (ρ = 0.58, p = 0.01). In contrast, change in univariate PA metrics did not show correlations. These findings demonstrate the high potential of the complexity metric being useful and more sensitive than conventional PA metrics for assessing functional changes in younger older adults.

Mobile Health Applications to Promote Active and Healthy Ageing.

The European population is ageing, and there is a need for health solutions that keep older adults independent longer. With increasing access to mobile technology, such as smartphones and smartwatches, the development and use of mobile health applications is rapidly growing. To meet the societal challenge of changing demography, mobile health solutions are warranted that support older adults to stay healthy and active and that can prevent or delay functional decline. This paper reviews the literature on mobile technology, in particular wearable technology, such as smartphones, smartwatches, and wristbands, presenting new ideas on how this technology can be used to encourage an active lifestyle, and discusses the way forward in order further to advance development and practice in the field of mobile technology for active, healthy ageing.

Developing the FARSEEING Taxonomy of Technologies: Classification and description of technology use (including ICT) in falls prevention studies.

BACKGROUND: Recent Cochrane reviews on falls and fall prevention have shown that it is possible to prevent falls in older adults living in the community and in care facilities. Technologies aimed at fall detection, assessment, prediction and prevention are emerging, yet there has been no consistency in describing or reporting on interventions using technologies. With the growth of eHealth and data driven interventions, a common language and classification is required. OBJECTIVE: The FARSEEING Taxonomy of Technologies was developed as a tool for those in the field of biomedical informatics to classify and characterise components of studies and interventions. METHODS: The Taxonomy Development Group (TDG) comprised experts from across Europe. Through face-to-face meetings and contributions via email, five domains were developed, modified and agreed: Approach; Base; Components of outcome measures; Descriptors of technologies; and Evaluation. Each domain included sub-domains and categories with accompanying definitions. The classification system was tested against published papers and further amendments undertaken, including development of an online tool. Six papers were classified by the TDG with levels of consensus recorded. RESULTS: Testing the taxonomy with papers highlighted difficulties in definitions across international healthcare systems, together with differences of TDG members' backgrounds. Definitions were clarified and amended accordingly, but some difficulties remained. The taxonomy and manual were large documents leading to a lengthy classification process. The development of the online application enabled a much simpler classification process, as categories and definitions appeared only when relevant. Overall consensus for the classified papers was 70.66%. Consensus scores increased as modifications were made to the taxonomy. CONCLUSION: The FARSEEING Taxonomy of Technologies presents a common language, which should now be adopted in the field of biomedical informatics. In developing the taxonomy as an online tool, it has become possible to continue to develop and modify the classification system to incorporate new technologies and interventions.

Sensor-based assessment of mobility-related behavior in dementia: feasibility and relevance in a hospital context.

BACKGROUND: The assessment of patients' motor behavior is a key challenge in dementia care. Common geriatric assessment questionnaires or actigraphy measurements often lack methodological quality and are unsuitable to individually tailor interventions. Hence, there is a need for developing objective tools to assess patterns of motor behavior. Therefore, the feasibility of a sensor-based assessment of mobility-related behavior in patients with dementia is investigated. METHODS: A cross-sectional investigation on three dementia care wards in a psychiatric hospital was conducted. Forty-five patients with stages of dementia were included. Hybrid motion sensors, recording the sequence of body-postures, were attached on the patients' lower back for 72 consecutive hours. RESULTS: Eighty-nine percent of the assessment periods were completed. On average patients spent 10.9 h/day lying (45%), 9.7 h/day sedentary while sitting or standing (41%), 1.7 h/day active while sitting or standing (7%), 1.7 h/day walking (7%), and reached on average 8,829 steps per day (SD = 7,428). Though overall activity levels were low, the results indicate a wide spectrum of activity patterns - ranging from almost inactive to highly active with general restlessness and wandering behavior. CONCLUSION: The excellent adherence to the assessment protocol compared to wrist-worn actigraphy and the consistency of the sensor-derived analyses with clinical observations are pivotal findings of this study. These results show that it is possible to acquire objective data on individual motor behavior of patients suffering from dementia. This information is essential for tailoring the therapeutic management of these patients in a hospital context.

The quality of turning in Parkinson's disease: a compensatory strategy to prevent postural instability?

BACKGROUND: The ability to turn while walking is essential for daily living activities. Turning is slower and more steps are required to complete a turn in people with Parkinson's disease (PD) compared to control subjects but it is unclear whether this altered strategy is pathological or compensatory. The aim of our study is to characterize the dynamics of postural stability during continuous series of turns while walking at various speeds in subjects with PD compared to control subjects. We hypothesize that people with PD slow their turns to compensate for impaired postural stability. METHOD: Motion analysis was used to compare gait kinematics between 12 subjects with PD in their ON state and 19 control subjects while walking continuously on a route composed of short, straight paths interspersed with eleven right and left turns between 30 and 180°. We asked subjects to perform the route at three different speeds: preferred, faster, and slower. Features describing gait spatio-temporal parameters and turning characteristics were extracted from marker trajectories. In addition, to quantify dynamic stability during turns we calculated the distance between the lateral edge of the base of support and the body center of mass, as well as the extrapolated body center of mass. RESULTS: Subjects with PD had slower turns and did not widen the distance between their feet for turning, compared to control subjects. Subjects with PD tended to cut short their turns compared to control subjects, resulting in a shorter walking path. Dynamic stability was smaller in the PD, compared to the healthy group, particularly for fast turning angles of 90°. CONCLUSIONS: The slower turning speeds and larger turning angles in people with PD might reflect a compensatory strategy to prevent dynamic postural instability given their narrow base of support.

Dyskinesia detection and monitoring by a single sensor in patients with Parkinson's disease.

OBJECTIVE: In current clinical practice, assessment of levodopa-induced dyskinesias (LIDs) in Parkinson's disease (PD) is based on semiquantitative scales or patients' diaries. We aimed to assess the feasibility, clinical validity, and usability of a waist-worn inertial sensor for discriminating between LIDs and physiological sway in both supervised and unsupervised settings. METHODS: Forty-six PD patients on L-dopa therapy, 18 de novo PD patients, and 18 healthy controls were enrolled. Patients underwent clinical assessment of motor signs and dyskinesias and kinetic-dynamic L-dopa monitoring, tracked by serial measurements of plasma drug concentrations and motor and postural tests. RESULTS: A subset of features was selected, which showed excellent reliability. Sensitivity and specificity of the selected features for dyskinesia recognition were assessed in both supervised and unsupervised settings with an accuracy of 95% and 86%, respectively. CONCLUSIONS: Our preliminary findings suggest that it is feasible to design a reliable sensor-based application for dyskinesia monitoring at home.

Corrigendum: CONNECTED: leveraging digital twins and personal knowledge graphs in healthcare digitalization.

[This corrects the article DOI: 10.3389/fdgth.2023.1322428.].

Real-World Balance Assessment While Standing for Fall Prediction in Older Adults.

OBJECTIVE: Postural control naturally declines with age, leading to an increased risk of falling. Within clinical settings, the deployment of balance assessments has become commonplace, facilitating the identification of postural instability and targeted interventions to forestall falls among older adults. Some studies have ventured beyond the controlled laboratory, leaving, however, a gap in our understanding of balance in real-world scenarios. METHODS: Previously reported algorithms were used to build a finite-state machine (FSM) with four states: walking, turning, sitting, and standing. The FSM was validated against video annotations (gold standard) in an independent dataset with data collected on 20 older adults. Later, the FSM was applied to data from 168 community-dwelling older people in the InCHIANTI cohort who were evaluated both in the laboratory and then remotely in real-world conditions for a week. A 70/30 data split with recursive feature selection and resampling techniques was used to train and test four machine-learning models. RESULTS: In identifying fallers, duration, distance, and mean frequency computed during standing in real-world settings revealed significant relationships with fall risk. Also, the best-performing model (Lasso Regression) built on real-world balance features had a higher area under the curve (AUC, 0.76) than one built on lab-based assessments (0.57). CONCLUSION: Real-world balance features differ considerably from laboratory balance assessments (Romberg test) and have a higher predictive capacity for identifying patients at high risk of falling. SIGNIFICANCE: These findings highlight the need to move beyond traditional laboratory-based balance measures and develop more sensitive and accurate methods for predicting falls.