Estimation of manual wheelchair-based activities in the free-living environment using a neural network model with inertial body-worn sensors.

Shoulder pain is common in manual wheelchair (MWC) users. Overuse is thought to be a major cause, but little is known about exposure to activities of daily living (ADLs). The study goal was to develop a method to estimate three conditions in the field: (1) non-propulsion activity, (2) MWC propulsion, and (3) static time using an inertial measurement unit (IMU). Upper arm IMU data were collected as ten MWC users performed lab-based MWC-related ADLs. A neural network model was developed to classify data as non-propulsion activity, propulsion, or static, and validated for the lab-based data collection by video comparison. Six of the participants' free-living IMU data were collected and the lab-based model was applied to estimate daily non-propulsion activity, propulsion, and static time. The neural network model yielded lab-based validity measures ≥0.87 for differentiating non-propulsion activity, propulsion, and static time. A quasi-validation of one participant's field-based data yielded validity measures ≥0.66 for identifying propulsion. Participants' estimated mean daily non-propulsion activity, propulsion, and static time ranged from 158 to 409, 13 to 25, and 367 to 609 min, respectively. The preliminary results suggest the model may be able to accurately identify MWC users' field-based activities. The inclusion of field-based IMU data in the model could further improve field-based classification.

Self-Monitoring of Balance Performance can Reduce the Rate of Falls Among Older Adults.

Background: 29% of older adults fall annually, resulting in the leading cause of accidental death. Fall prevention programs typically include exercise training and self-monitoring of physical activity has a positive effect on the self-efficacy and self-regulation of exercise behaviors. We assessed if self-monitoring of fall risk, without an intervention, impacts fall rates. Methods: Fifty-three older adults had open access to a balance measuring platform which allowed them to self-monitor their postural stability and fall risk using a simple 1-min standing balance test. 12-month retrospective fall history was collected and a monthly/bimonthly fall log captured prospective falls. Participants had access to self-monitoring for up to 2.2 years. Fall history and fall incidence rate ratios and their confidence intervals were compared between the periods of time with and without access to self-monitoring. Results: A 54% reduction in the number of people who fell and a 74% reduction in the number of falls was observed when participants were able to self-monitor their postural stability and fall risk, after normalizing for participation length. Further, 42.9% of individuals identified as having high fall risk at baseline shifted to a lower risk category at a median 34 days and voluntarily measured themselves for a longer period of time. Discussion: We attribute this reduction in falls to changes in health behaviors achieved through empowerment from improved self-efficacy and self-regulation. Providing older adults with the ability to self-monitor their postural stability and intuit their risk of falling appears to have modified their health behaviors to successfully reduce fall rates.

Application and Reliability of Accelerometer-Based Arm Use Intensities in the Free-Living Environment for Manual Wheelchair Users and Able-Bodied Individuals.

Arm use in manual wheelchair (MWC) users is characterized by a combination of overuse and a sedentary lifestyle. This study aimed to describe the percentage of daily time MWC users and able-bodied individuals spend in each arm use intensity level utilizing accelerometers. Arm use intensity levels of the upper arms were defined as stationary, low, mid, and high from the signal magnitude area (SMA) of the segment accelerations based on in-lab MWC activities performed by eight MWC users. Accelerometry data were collected in the free-living environments from forty MWC users and 40 sex- and age-matched able-bodied individuals. The SMA intensity levels were applied to the free-living data and the percentage of time spent in each level was calculated. The SMA intensity levels were defined as, stationary: ≤0.67 g, low: 0.671-3.27 g, mid: 3.27-5.87 g, and high: >5.871 g. The dominant arm of both MWC users and able-bodied individuals was stationary for most of the day and less than one percent of the day was spent in high intensity arm activities. Increased MWC user age correlated with increased stationary arm time (R = 0.368, p = 0.019). Five and eight days of data are needed from MWC users and able-bodied individuals, respectively, to achieve reliable representation of their daily arm use intensities.

Comparing supraspinatus to acromion proximity and kinematics of the shoulder and thorax between manual wheelchair propulsion styles: A pilot study.

BACKGROUND: Repetitive glenohumeral joint movement during manual wheelchair propulsion has been associated with shoulder pain in individuals with spinal cord injury. Clinical guidelines for shoulder health maintenance encourage semi-circular over arc propulsion to reduce loading frequency. This study aimed to determine the difference in estimated supraspinatus to acromion compression risk, and shoulder, thorax kinematics between (1) arc and semi-circular propulsion; and (2) self-selected and coached strategies. METHODS: Shoulder and thorax kinematics were captured during wheelchair propulsion, noting individually self-selected styles. Participants were then coached to perform the other style(s) of interest, arc and/or semi-circular. CT bone models of the humerus and scapula were animated using glenohumeral kinematics to estimate the minimum distance between the supraspinatus humeral attachment and the acromion. Compression risk was defined as the proportion of each propulsion phase where the minimum distance fell below 5 mm. Comparisons were made between conditions evaluating compression risk, minimum distances and kinematics at events throughout propulsion. FINDINGS: Ten individuals with spinal cord injury (9 male) participated. Arc and semi-circular propulsion did not significantly differ in compression risk or minimum distance across propulsion phases. Self-selected styles yielded lower compression risk and larger proximity values compared to coached styles. Glenohumeral horizontal abduction and thorax flexion differed between arc and semi-circular propulsion. Multiple glenohumeral and humerothoracic differences emerged between self-selected and coached conditions. INTERPRETATION: Supraspinatus compression was observed during both arc and semi-circular propulsion, suggesting risk may be unavoidable in this task. Self-selected styles yield less risk, likely related to coached style unfamiliarity.

Quality over quantity: skeletal loading intensity plays a key role in understanding the relationship between physical activity and bone density in postmenopausal women.

OBJECTIVE: Increasing physical activity (PA) is regularly cited as a modifiable target to improve health outcomes and quality of life in the aging population, especially postmenopausal women who exhibit low bone mineral density (BMD) and high fracture risk. In this cross-sectional study, we aimed to quantify real-world PA and its association with BMD in postmenopausal women. METHODS: Seventy postmenopausal women, aged 46 to 79 years, received a dual-energy X-ray absorptiometry scan measuring total hip BMD and wore bilateral triaxial accelerometers on the ankles for 7 days to measure PA in their free-living environment. Custom step detection and peak vertical ground reaction force estimation algorithms, sensitive to both quantity and intensity of PA, were used to calculate a daily bone density index (BDI) for each participant. Multiple regression was used to quantify the relationship between total hip BMD, age, step counts, and mean BDI over the span of 7 days of data collection. RESULTS: All participants completed the full 7 days of PA monitoring, totaling more than 7 million detected steps. Participants averaged 14,485 ±â€Š4,334 steps daily with mean peak vertical ground reaction force stepping loads of 675 ±â€Š121 N during daily living. Across the population, total hip BMD was found to be significantly correlated with objective estimates of mean BDI (r = 0.44), as well as participant age (r = 0.285). CONCLUSION: Despite having higher-than-expected PA, the low stepping loads observed in this cohort, along with half of the participants having low BMD measures, underscores the need for PA intensity to be considered in the management of postmenopausal bone health.

A Postural Assessment Utilizing Machine Learning Prospectively Identifies Older Adults at a High Risk of Falling.

Introduction: Falls are the leading cause of accidental death in older adults. Each year, 28.7% of US adults over 65 years experience a fall resulting in over 300,000 hip fractures and $50 billion in medical costs. Annual fall risk assessments have become part of the standard care plan for older adults. However, the effectiveness of these assessments in identifying at-risk individuals remains limited. This study characterizes the performance of a commercially available, automated method, for assessing fall risk using machine learning. Methods: Participants (N = 209) were recruited from eight senior living facilities and from adults living in the community (five local community centers in Houston, TX) to participate in a 12-month retrospective and a 12-month prospective cohort study. Upon enrollment, each participant stood for 60 s, with eyes open, on a commercial balance measurement platform which uses force-plate technology to capture center-of-pressure (60 Hz frequency). Linear and non-linear components of the center-of-pressure were analyzed using a machine-learning algorithm resulting in a postural stability (PS) score (range 1-10). A higher PS score indicated greater stability. Participants were contacted monthly for a year to track fall events and determine fall circumstances. Reliability among repeated trials, past and future fall prediction, as well as survival analyses, were assessed. Results: Measurement reliability was found to be high (ICC(2,1) [95% CI]=0.78 [0.76-0.81]). Individuals in the high-risk range (1-3) were three times more likely to fall within a year than those in low-risk (7-10). They were also an order of magnitude more likely (12/104 vs. 1/105) to suffer a spontaneous fall i.e., a fall where no cause was self-reported. Survival analyses suggests a fall event within 9 months (median) for high risk individuals. Conclusions: We demonstrate that an easy-to-use, automated method for assessing fall risk can reliably predict falls a year in advance. Objective identification of at-risk patients will aid clinicians in providing individualized fall prevention care.

Assessment of gait kinetics in post-menopausal women using tri-axial ankle accelerometers during barefoot walking.

BACKGROUND: Physical activity (PA) interventions, designed to increase exposure to ground reaction force (GRF) loading, are a common target for reducing fracture risk in post-menopausal women with low bone mineral density (BMD). Unfortunately, accurate tracking of PA in free-living environments and the ability to translate this activity into evaluations of bone health is currently limited. RESEARCH QUESTION: This study evaluates the effectiveness of ankle-worn accelerometers to estimate the vertical GRFs responsible for bone and joint loading in post-menopausal women at a range of self-selected walking speeds during barefoot walking. METHODS: Seventy women, at least one year post-menopause, wore Actigraph GT3X + on both ankles and completed walking trials at self-selected speeds (a minimum of five each at fast, normal and slow walking) along a 30 m instrumented walkway with force plates and photocells to measure loading and estimate gait velocity. Repeated measures correlation analysis and step-wise mixed-effects modelling were performed to evaluate significant predictors of peak vertical GRFs normalized to body weight (pVGRFbw), including peak vertical ankle accelerations (pVacc), walking velocity (Velw) and age. RESULTS: A strong repeated measures correlation of r = 0.75 (95%CI [0.71-0.76] via 1000 bootstrap passes) between pVacc and pVGRFbw was observed. Five-fold cross-validation of mixed-model predictions yielded an average mean-absolute-error (MAE[95%CI]) and root-mean-square-error (RMSE) rate of 5.98%[5.61-6.42] and 0.076 [0.069-0.082] with a more complex model (including Velw,) and 6.80%[6.37-7.54] and 0.087BW[0.081-0.095] with a simpler model (including only pVacc), when comparing accelerometer-based estimations of pVGRFbw to force plate measures of pVGRFbw. Age was not found to be significant. SIGNIFICANCE: This study is the first to show a strong relationship among ankle accelerometry data and high fidelity lower-limb loading approximations in post-menopausal women. The results provide the first steps necessary for estimation of real-world limb and joint loading supporting the goals of accurate PA tracking and improved individualization of clinical interventions.