Postural Transitions during Activities of Daily Living Could Identify Frailty Status: Application of Wearable Technology to Identify Frailty during Unsupervised Condition

Abstract

Background: Impairment of physical function is a major indicator of frailty. Functional performance tests have been shown to be useful for identification of frailty in older adults. However, these tests are often not translatable into unsupervised and remote monitoring of frailty status at home and/or community settings. Objective: In this study, we explored daily postural transition quantified using a chest-worn wearable technology to identify frailty in community-dwelling older adults. Methods: Spontaneous daily physical activity was monitored over 24 h in 120 community-dwelling elderly (age: 78 ± 8 years) using an unobtrusive wearable sensor (PAMSys™, BioSensics LLC, Watertown, MA, USA). Participants were classified as non-frail and pre-frail/frail using Fried's criteria. A validated software package was used to identify body postures and postural transition between each independent postural activity such as sit-to-stand, stand-to-sit, stand-to-walk, and walk-to-stand. The transition from walking to sitting was further classified as quick sitting and cautious sitting based on presence/absence of a standing posture pause between sitting and walking. A general linear model univariate test was used for between-group comparison. Pearson's correlation was used to determine the association between sensor-derived parameters and age. Logistic regression model was used to identify independent predictors of frailty. Results: According to Fried's criteria, 63% of participants were pre-frail/frail. The total number of postural transitions, stand-to-walk, and walk-to-stand were, respectively, 25.2, 30.2, and 30.6% lower in the pre-frail/frail group when compared to the non-frail group (p < 0.05, Cohen's d = 0.73-0.79). Furthermore, the ratio of cautious sitting was significantly higher by 6.2% in pre-frail/frail compared to non-frail (p = 0.025, Cohen's d = 0.22). Total number of postural transitions and the ratio of cautious sitting also showed significant negative and positive correlations with age, respectively (r = -0.51 and 0.29, p < 0.05). After applying a logistic regression model, among tested parameters, walk-to-stand (odds ratio [OR] = 0.997 p = 0.013), quick sitting (OR = 1.036, p = 0.05), and age (OR = 1.073, p = 0.016) were recognized as independent variables to identify frailty status. Conclusions: This study demonstrated that daily number of specific postural transitions such as walk-to-stand and quick sitting could be used for monitoring frailty status by unsupervised monitoring of daily physical activity. Further study is warranted to explore whether tracking the daily number of specific postural transitions is also sensitive to track change in the status of frailty over time.

Sensor-Based Daily Physical Activity: Towards Prediction of the Level of Concern about Falling in Peripheral Neuropathy

“…Commercial software (PAMWare TM , BioSensics LLC, Newton, MA, USA) was used to calculate duration of walking bouts, standing and sedentary postures, the number of walking bouts, and total step counts over the 48-hour period. The algorithm and validity of the sensor for physical activity measurements has been described elsewhere [29][30][31][32][33][34]. Briefly, durations for standing, sitting, and lying cumulative postures were estimated based on a simple biomechanical model of the chest and determination of the type of transitions between two postures (e.g., from sitting to standing) as well as a series of biomechanical rules described elsewhere (e.g., prolonged leaning backward of the chest is unlikely during standing, and walking is impossible during sitting) [29][30][31].…”

Harnessing digital health to objectively assess cognitive impairment in people undergoing hemodialysis process: The Impact of cognitive impairment on mobility performance measured by wearables

“…Mobility performance was characterized by 1) cumulated posture duration, including percentage of sitting, lying, standing, and walking postures of 24-hour; 2) daily walking performance, including step count and number of unbroken walking bout (an unbroken walking bout was defined as at least three consecutive steps within 5 seconds interval [38]); and 3) postural-transition, including total number of postural-transition such as sit-to-stand, stand-to-sit, walk-tostand, stand-to-walk, walk-to-sit (direct transition from walking to sitting with standing pause less than 1 seconds [39]), and sit-to-walk (direct transition from sitting to walking with standing pause less than 1 seconds [39]), as well as average duration of postural-transition (time needed for rising from a chair or sitting on a chair [40]). Mobility performance was recorded for a continuous period of 24-hour using a validated pendant sensor (PAMSys TM , BioSensics LLC., MA, USA, Fig 1) worn during a non-dialysis day.…”

“…The PAMSys TM sensor contains a 3-axis accelerometer (sampling frequency of 50 Hz) and built-in memory for recording long-term data. The description of methods to extract metrics of interest was described in details in our previous studies [38][39][40][41][42].…”

“…Additionally, we found the cognitive-impaired HD patients have less number of postural-transition than cognitive-intact HD patients during daily living. The limited number of postural-transition has been identified as a factor which may contribute to the muscle weakness and activity limitations, causing physical frailty [39,45]. Frailty together with cognitive impairment (known as 'cognitive frailty') has been shown to be a strong and independent predictor of further cognitive decline over time [46,47].…”

Could Wearable and Mobile Technology Improve the Management of Essential Tremor?

“…This is a much more complicated task as it is more subjective, and defining task “success” criteria is problematic at best. Nonetheless, some groups have started considering this by identifying wearable sensor features from daily functional tasks that are associated with the overall frailty level of older individuals ( 103 , 104 ). While much work remains to be done, mobile and wearable technology could provide important information regarding when ADLs become impaired.…”