Semi-automatic falls risk estimation of elderly adults using single wrist worn accelerometer

PhD ThesisThe population of the oldest old (aged 85 years and over) is growing. It is estimated that 30% of the adults over the age of 65 years experience falls at least once a year. This figure rises to 50% per annum for adults over 80 years living either at home or in care home. Currently older people are the fastest growing segment of the population. In the UK alone, the proportion of people aged 85 years old has increased from 2% to 4% in the past six decades. This marked increase in growth of population aged over 85 years is expected to have substantial impact on overall falls rate and pose serious issues to meet care needs for social and health care departments. In the light of such negative consequences for the faller and the associated costs to society, simple and quantitative techniques for falls risk screening can contribute significantly. This study describes a semi-automated technique to estimate falls risk of community dwelling elderly adults (aged 85 and over). This study presents the detailed analysis of tri-axial accelerometer movement data recorded from the right wrist of individuals undertaking the Timed Up and Go (TUG) test. The semi-automated assessment is evaluated here on 394 subjects’ data collected in their home environment. The study compares logistic regression models developed using accelerometer derived features against the traditional TUG measure ‘time taken to complete the test’. Gender based models were built separately across two groups of participants- with and without walking aid. The accelerometer derived feature model yielded a mean sensitivity of 63.95%, specificity of 63.51% and accuracy of 66.24% based on leave one-out cross validation compared to manually timed TUG (mean sensitivity of 52.64%, specificity of 45.41% and accuracy of 55.22%). Results show that accelerometer derived models offer improvement over traditional falls assessment. This automated method enables identification of older people at risk of falls residing both at home and in care homes and to monitor intervention effectiveness of falls management

Characterizing Movement Patterns of Older Individuals with T2D in Free-Living Environments Using Wearable Accelerometers

(1) Background: Type 2 Diabetes (T2D) is associated with reduced muscle mass, strength, and function, leading to frailty. This study aims to analyze the movement patterns (MPs) of older individuals with T2D across varying levels of physical capacity (PC). (2) Methods: A cross-sectional study was conducted among individuals aged 60 or older with T2D. Participants (n = 103) were equipped with a blinded continuous glucose monitoring (CGM) system and an activity monitoring device for one week. PC tests were performed at the beginning and end of the week, and participants were categorized into three groups: low PC (LPC), medium PC (MPC), and normal PC (NPC). Group differences in MPs and physical activity were analyzed using non-parametric Kruskal-Wallis tests for both categorical and continuous variables. Dunn post-hoc statistical tests were subsequently carried out for pairwise comparisons. For data analysis, we utilized pandas, a Python-based data analysis tool, and conducted the statistical analyses using the scipy.stats package in Python. The significance level was set at p < 0.05. (3) Results: Participants in the LPC group showed lower medio-lateral acceleration and higher vertical and antero-posterior acceleration compared to the NPC group. LPC participants also had higher root mean square values (1.017 m/s2). Moreover, the LPC group spent less time performing in moderate to vigorous physical activity (MVPA) and had fewer daily steps than the MPC and NPC groups. (4) Conclusions: The LPC group exhibited distinct movement patterns and lower activity levels compared to the NPC group. This study is the first to characterize the MPs of older individuals with T2D in their free-living environment. Several accelerometer-derived features were identified that could differentiate between PC groups. This novel approach offers a manpower-free alternative to identify physical deterioration and detect low PC in individuals with T2D based on real free-living physical behavior

A review of activity trackers for senior citizens: research perspectives, commercial landscape and the role of the insurance industry

The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future

Physiol Meas

Actigraphy has attracted much attention for assessing physical activity in the past decade. Many algorithms have been developed to automate the analysis process, but none has targeted a general model to discover related features for detecting or predicting mobility function, or more specifically, mobility impairment and major mobility disability (MMD). Men (N\u2009\u2009=\u2009\u2009357) and women (N\u2009\u2009=\u2009\u2009778) aged 70-89 years wore a tri-axial accelerometer (Actigraph GT3X) on the right hip during free-living conditions for 8.4\u2009\u2009\ub1\u2009\u20093.0 d. One-second epoch data were summarized into 67 features. Several machine learning techniques were used to select features from the free-living condition to predict mobility impairment, defined as 400 m walking speed\u2009\u2009<0.80 m s|. Selected features were also included in a model to predict the first occurrence of MMD-defined as the loss in the ability to walk 400 m. Each method yielded a similar estimate of 400 m walking speed with a root mean square error of ~0.07 m s| and an R-squared values ranging from 0.37-0.41. Sensitivity and specificity of identifying slow walkers was approximately 70% and 80% for all methods, respectively. The top five features, which were related to movement pace and amount (activity counts and steps), length in activity engagement (bout length), accumulation patterns of activity, and movement variability significantly improved the prediction of MMD beyond that found with common covariates (age, diseases, anthropometry, etc). This study identified a subset of actigraphy features collected in free-living conditions that are moderately accurate in identifying persons with clinically-assessed mobility impaired and significantly improve the prediction of MMD. These findings suggest that the combination of features as opposed to a specific feature is important to consider when choosing features and/or combinations of features for prediction of mobility phenotypes in older adults.P30 AG024827/AG/NIA NIH HHS/United StatesR01 HL121023/HL/NHLBI NIH HHS/United StatesP30 AG031679/AG/NIA NIH HHS/United StatesK07 CA154862/CA/NCI NIH HHS/United StatesU54 EB020404/EB/NIBIB NIH HHS/United StatesR01 AG049024/AG/NIA NIH HHS/United StatesR01 HL075451/HL/NHLBI NIH HHS/United StatesP30 AG028740/AG/NIA NIH HHS/United StatesR01 DK097364/DK/NIDDK NIH HHS/United StatesP30 AG021342/AG/NIA NIH HHS/United StatesR21 OH010785/OH/NIOSH CDC HHS/United StatesR01 AG042525/AG/NIA NIH HHS/United StatesUL1 RR025744/RR/NCRR NIH HHS/United StatesR24 HD065688/HD/NICHD NIH HHS/United StatesR21 HD073807/HD/NICHD NIH HHS/United StatesU01 AG022376/AG/NIA NIH HHS/United StatesP30 AG021332/AG/NIA NIH HHS/United States2018-03-21T00:00:00Z27653966PMC5360536vault:2171

Classification of Frailty among Community Dwelling Older Adults Using Parameters of Physical Activity Obtained Independently and Unsupervised

The global population is ageing at an unprecedented rate, with the percentage of those aged over 65 years expected to double and those aged over 80 years expected to treble by the year 2050. With ageing comes biological and physiological changes that affect functional capacity. Frailty is a potentially avoidable, reversible biopsychosocial condition associated with biological but not chronological age, affecting a quarter of all community-dwelling older adults. Frailty results in disability, increased dependency and institutionalisation. Screening for frailty could help reduce its prevalence and mitigate the adverse outcomes however, traditional screening tools are time-consuming to perform, require clinician input and by their subjective nature are flawed. The use of wearable sensors has been proposed as a means of screening for frailty and parameters of mobility and physical activity have been identified as being associated with frailty. The goal of this thesis was to examine if community-dwelling older adults could capture parameters of mobility and physical activity independently in their own home and if these parameters could discriminate between frail and non-frail status. This work provides evidence that a single parameter of mobility and physical activity obtained from a single body-worn sensor correlates with frailty. It also provides evidence that community-dwelling older adults can independently capture parameters of mobility and physical activity, unsupervised in their own home using a consumer-grade wearable device, and that these data can predict pre-frailty and frailty with acceptable accuracy. Thresholds for parameters of physical activity predictive of frailty have been identified. The results of this thesis will guide future work to focus community-dwelling older adults on the importance of frailty screening and guide the development of a user-friendly device or sensor system suitable for use by older adults for continuous data collection relevant to frailty

A Comparison of Commonly Used Accelerometer Based Activity Monitors in Controlled and Free-Living Environment

This dissertation was designed to determine the effects of body mass index (BMI) and walking speed on activity monitor outputs. A secondary purpose was to compare the activity monitors’ performance in a free-living environment. In the first experiment, 71 participants wore three waist-mounted activity monitors (Actical, ActiGraph, and NL-2000) and an ankle-mounted device (StepWatch 3) while walking on a treadmill (40, 67 and 94 m/min). The tilt angle of each device was measured. The Actical recorded 26% higher activity counts (P \u3c 0.01) in obese persons with a tilt \u3c10 degrees, compared to normal weight persons. The ActiGraph was unaffected by BMI or tilt angle. In the second experiment, the steps recorded by the devices were compared to actual steps. Speed had the greatest influence on the accuracy these devices. At 40 m/min, the ActiGraph was the least accurate device for normal weight (38%), overweight (46%) and obese (48%) individuals. The Actical, NL-2000 and StepWatch averaged 65%, 73% and 99% of steps taken, respectively. Lastly, several generations of the ActiGraph (7164, GT1M, and GT3X), and other research grade activity monitors (Actical; ActivPAL; and Digi-Walker) were compared to a criterion measure of steps. Fifty-six participants performed treadmill walking (40, 54, 67, 80 and 94 m/min) and wore the devices for 24-hours under free-living conditions. BMI did not affect step count accuracy during treadmill walking. The StepWatch, PAL, and the AG7164 were the most accurate across all speeds; the other devices were only accurate at the faster speeds. In the free-living environment, all devices recorded about 75% of StepWatch-determined steps, except the AG7164 (99%). Based on these findings, we conclude that BMI does not affect the output of these activity monitors. However, waist-borne activity monitors are highly susceptible to under-counting steps at walking speeds below 67 m/min, or stepping rates below 100 steps/min. An activity monitor worn on the ankle is less susceptible to these speed effects and provides the greatest accuracy for step counting

Incident Depression and Daily-life Mobility in Middle-aged and Older Adults

Depression is among the most prevalent mental disorders in middle-aged and older adults, with a global prevalence of up to 11%. Effective preventive measures for depression are often costly and labour-intensive and therefore require risk screenings to be practical. Recent studies suggested that clinically measured walking speed is a risk factor for depression, while little is known about whether other aspects of mobility are also predictive. To explore the temporal association between mobility, in particular daily-life mobility, and incident depression in older adults, one systematic review, one study on method development and validation, and three large-scale cohort studies were conducted. Significant findings include: • The Timed Up and Go Test, which incorporates multiple aspects of mobility (i.e., gait initiation, turning, and sit-to-stand time), is more predictive of depressive trajectories than the Six-Metre Walk Test and Five Times Sit to Stand Test. • Duration of the longest daily walking bout, measured with a waist-worn sensor, independently and significantly predicts incident depression over two years. • Daily-life walking speed, quality, quantity, and distribution can be reliably and validly measured with a wrist-worn sensor. • Daily-life gait quality and quantity, measured with a wrist-worn sensor, independently and significantly predict incident depression over nine years of follow-up. These findings add to the understanding of the association between human locomotion and depression. Gait quality and daily-life gait performances are independent and potentially modifiable predictors of depression. These measures, therefore, may have value for upcoming screening program development. Future research should investigate whether interventions addressing daily-life gait can play a role in preventing depression in middle-aged and older adults

Replacing Sedentary Behavior with a Light Intensity Physical Activity in the Homes of Older Adults

Aging is positively correlated with time spent in sedentary behavior (SB), which has been found to be linked to premature mortality, cardiovascular disease risk, and functional limitations. Moderate-to-vigorous-intensity physical activity (MVPA) is a potent stimulus for preventing and improving functional limitations in older adults, but less than 17% of the older adult population meets the recommended PA guidelines. While increased time spent in SB is detrimental to health in all, the impact among those that are physically inactive appear to be most pronounced. Recent evidence suggests increasing light-intensity physical activity (LPA) in these populations may provide health benefits and could be a more practical approach for older adults. The purpose of this dissertation project is to identify safe, effective, and practical evidence-based approaches to reduce SB to maintain or improve physical function in late life. Therefore, the aims of this dissertation project are three fold: 1) to identify the impact of replacing time spent in SB with physical activity on measures of physical function in community-dwelling older adults, 2) identify the feasibility of using a seated portable elliptical device (SED) in the homes of older adults, and 3) determine the effectiveness of using a SED to replace time spent in SB with a LPA and explore the impact on measures of physical function in older adults. An isotemporal substitution regression model identified that replacing as little as 30 minutes of SB with LPA led to significant improvements in walking speed. Meanwhile, replacing up to 60 minutes of SB with LPA led to larger magnitudes of improvement which approached clinical relevance. Further, supplementing LPA with MVPA progressively increased the improvements in a battery of functional assessments. Interventions to reduce SB have come with difficulty and methods to purposefully replace SB with PA should be developed to test the validity of the findings from these novel statistical models. A seated elliptical pedaling device (SED) was used to purposefully target reducing SB and replacing with LPA, while allowing participants to maintain the enjoyment of their typically passive activity in their home. A one week trial study identified that there was no difference in the ability of older adults to accumulate between 15 to 60 minutes of pedaling per day. Further, there was high acceptability among all participants that were randomly assigned to either 15, 30, 45, or 60 minutes per day pedaling groups. This led to the development of an 8-week pilot randomized controlled trial using the SED. In this trial, the intervention was effective at replacing SB with LPA as identified by a group by time interaction effect. Specifically, the elliptical group (EG) experienced a significant 7.3% (p = 0.003) reduction in daily SB and 7.1% (p = 0.002) increase in LPA between baseline and follow-up testing compared to no significant difference in the control group (CG). Participants suggested improvements in function, but small effect sizes and sample sizes did not produce significant improvements in measures of physical function. Introducing a SED during passive activities in the home is a feasible and effective approach at reducing daily SB in older adults. While some of the functional tests did exhibit ceiling effects among those that were high functioning at baseline, subjective responses from individuals of lower functioning suggest the potential for impacting the QOL of those that have difficulty performing ambulatory activities. Future investigations using the SED should be directed toward longer intervention periods, with larger sample sizes, and among individuals of various levels of functional ability and life circumstances

Feasibility of Sensor Technology for Balance Assessment in Home Rehabilitation Settings

The increased use of sensor technology has been crucial in releasing the potential for remote rehabilitation. However, it is vital that human factors, that have potential to affect real-world use, are fully considered before sensors are adopted into remote rehabilitation practice. The smart sensor devices for rehabilitation and connected health (SENDoc) project assesses the human factors associated with sensors for remote rehabilitation of elders in the Northern Periphery of Europe. This article conducts a literature review of human factors and puts forward an objective scoring system to evaluate the feasibility of balance assessment technology for adaption into remote rehabilitation settings. The main factors that must be considered are: Deployment constraints, usability, comfort and accuracy. This article shows that improving accuracy, reliability and validity is the main goal of research focusing on developing novel balance assessment technology. However, other aspects of usability related to human factors such as practicality, comfort and ease of use need further consideration by researchers to help advance the technology to a state where it can be applied in remote rehabilitation settings

The effect of aerobic training on healthy elderly women’s walking speed, step length and habitual physical activity

This study’s main aim was to investigate whether training could increase walking speed, step length and habitual physical activity (HPA) in elderly women. This randomized controlled trial was carried out with 22 elderly women, who were first monitored for a week (heart rate and movement), had their chosen and faster walking speed determined, as well as their step rate and length, and underwent sub maximal treadmill tests. Data from monitoring were organized into 7 HPA indices. The women were randomly allocated to a control (73.6±1.7 years) or exercise group (75.5±2.9). For 12 weeks the exercisers walked 3 times a week on a treadmill at 60-65% of their predicted HRR. Controls continued with their normal routine. Post-training, all measurements were repeated. Training was associated with significant increases in step length and walking speeds. However, no significant changes in the HPA indices were found. It is concluded that the three-month aerobic training program by treadmill walking resulted in significant increases in steplength and self-selected walking speeds in these healthy, independent, elderly women, however, it did not lead to increased habitual physical activity