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

ABSTRACT: 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.In this study, we explored daily postural transition quantified using a chest-worn wearable technology to identify frailty in community-dwelling older adults.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.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.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.

SUBMITTER: Parvaneh S

PROVIDER: S-EPMC5561495 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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

Parvaneh Saman S Mohler Jane J Toosizadeh Nima N Grewal Gurtej Singh GS Najafi Bijan B

Gerontology 20170311 5

<h4>Background</h4>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.<h4>Objective</h4>In this study, we explored daily postural transition quantified using a chest-worn wearable technology to identify frailty in community-dwelling older ad ...[more]

PMID: 28285311

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Project description:Physical activity levels are related through algorithms to the energetic demand, with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V?o2) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., V?o2 kinetics). This study evaluated aerobic system dynamics based on predicted V?o2 data obtained from wearable sensors during unsupervised activities of daily living (?ADL). Thirteen healthy men performed a laboratory-controlled moderate exercise protocol and were monitored for ?6 h/day for 4 days (?ADL data). Variables derived from hip accelerometer (ACCHIP), heart rate monitor, and respiratory bands during ?ADL were extracted and processed by a validated random forest regression model to predict V?o2. The aerobic system analysis was based on the frequency-domain analysis of ACCHIP and predicted V?o2 data obtained during ?ADL. Optimal samples for frequency domain analysis (constrained to ?0.01 Hz) were selected when ACCHIP was higher than 0.05 g at a given frequency (i.e., participants were active). The temporal characteristics of predicted V?o2 data during ?ADL correlated with the temporal characteristics of measured V?o2 data during laboratory-controlled protocol ([Formula: see text]?=?0.82, P < 0.001, n?=?13). In conclusion, aerobic system dynamics can be investigated during unsupervised activities of daily living by wearable sensors. Although speculative, these algorithms have the potential to be incorporated into wearable systems for early detection of changes in health status in realistic environments by detecting changes in aerobic response dynamics. NEW & NOTEWORTHY The early detection of subclinical aerobic system impairments might be indicative of impaired physiological reserves that impact the capacity for physical activity. This study is the first to use wearable sensors in unsupervised activities of daily living in combination with novel machine learning algorithms to investigate the aerobic system dynamics with the potential to contribute to models of functional health status and guide future individualized health care in the normal population.

Project description:BackgroundThere are considerable gaps between the need for assistive technologies and the actual adoption of these technologies among older adults, although older adults are among the groups that most need assistive technologies. Consequently, research is needed in this area because older adults' technology acceptance and influencing factors may differ depending on their level of frailty.ObjectiveThe objective of this study was to compare frail, prefrail, and robust groups of South Korean adults regarding their behavioral intention to use daily living assistive technologies and the affecting factors-namely, technological context factors, health contexts and abilities, and attitudinal factors-based on a comprehensive senior technology acceptance model.MethodsA nationwide sample of 500 older South Korean adults (aged 55-92 years) was analyzed, and multivariate linear regression analyses of the robust, prefrail, and frail groups were performed. The independent and dependent variables consisted of 3 factors based on previous studies. First, technological context factors consisted of gerontechnology self-efficacy, gerontechnology anxiety, and facilitating conditions. Second, health contexts and abilities consisted of self-reported health conditions, cognitive ability, social relationships, psychological function, and physical function. Third and last, attitudinal factors consisted of behavioral intention to use assistive technologies, attitude toward use, perceived usefulness (PU), and perceived ease of use (PEOU).ResultsThe results of the analyses showed that technological context factors such as gerontechnology self-efficacy, health contexts and abilities such as self-reported health conditions and psychological function, and attitudinal factors such as attitude toward use, PU, and PEOU had significant effects on behavioral intention to use daily living assistive technologies. In particular, gerontechnology self-efficacy had a significant relationship with behavioral intention to use these technologies in the robust (r=0.120; P=.03) and prefrail (r=0.331; P<.001) groups. Psychological function (life satisfaction) had a significant relationship with behavioral intention to use these technologies in the robust group (r=-0.040; P=.02). Self-reported health conditions had a significant relationship with behavioral intention to use these technologies in the prefrail group (r=-0.169; P=.01). Although each group had a different significant relationship with the variables, attitudinal factors such as attitude toward use affected all groups (robust group: r=0.190; P=.03; prefrail group: r=0.235; P=.006; and frail group: r=0.526; P=.002). In addition, PU and PEOU in the attitudinal factors had a significant relationship with behavioral intention to use assistive technologies in the robust (PU: r=0.160; P=.01; and PEOU: r=0.350; P<.001) and prefrail (PU: r=0.265; P<.001; and PEOU: r=0.120; P=.04) groups.ConclusionsThis study found that the comprehensive senior technology acceptance model of daily living assistive technologies had different associations according to the frailty group. These findings provided insights into the consideration of interventions with daily living assistive technologies for older adults with varying levels of frailty.

Project description:INTRODUCTION:Unobtrusive metrics that can auto-assess performance during clinical procedures are of value. Three approaches to deriving wearable technology-based metrics are explored: (1) eye tracking, (2) psychophysiological measurements [e.g. electrodermal activity (EDA)] and (3) arm and hand movement via accelerometry. We also measure attentional capacity by tasking the operator with an additional task to track an unrelated object during the procedure. METHODS:Two aspects of performance are measured: (1) using eye gaze and psychophysiology metrics and (2) measuring attentional capacity via an additional unrelated task (to monitor a visual stimulus/playing cards). The aim was to identify metrics that can be used to automatically discriminate between levels of performance or at least between novices and experts. The study was conducted using two groups: (1) novice operators and (2) expert operators. Both groups made two attempts at a coronary angiography procedure using a full-physics virtual reality simulator. Participants wore eye tracking glasses and an E4 wearable wristband. Areas of interest were defined to track visual attention on display screens, including: (1) X-ray, (2) vital signs, (3) instruments and (4) the stimulus screen (for measuring attentional capacity). RESULTS:Experts provided greater dwell time (63% vs 42%, p?=?0.03) and fixations (50% vs 34%, p?=?0.04) on display screens. They also provided greater dwell time (11% vs 5%, p?=?0.006) and fixations (9% vs 4%, p?=?0.007) when selecting instruments. The experts' performance for tracking the unrelated object during the visual stimulus task negatively correlated with total errors (r?=?-?0.95, p?=?0.0009). Experts also had a higher standard deviation of EDA (2.52 µS vs 0.89 µS, p?=?0.04). CONCLUSIONS:Eye tracking metrics may help discriminate between a novice and expert operator, by showing that experts maintain greater visual attention on the display screens. In addition, the visual stimulus study shows that an unrelated task can measure attentional capacity. Trial registration This work is registered through clinicaltrials.gov, a service of the U.S. National Health Institute, and is identified by the trial reference: NCT02928796.

Project description:BackgroundTo experience external objects in such a way that they are perceived as an integral part of one's own body is called embodiment. Wearable technology is a category of objects, which, due to its intrinsic properties (eg, close to the body, inviting frequent interaction, and access to personal information), is likely to be embodied. This phenomenon, which is referred to in this paper as wearable technology embodiment, has led to extensive conceptual considerations in various research fields. These considerations and further possibilities with regard to quantifying wearable technology embodiment are of particular value to the mobile health (mHealth) field. For example, the ability to predict the effectiveness of mHealth interventions and knowing the extent to which people embody the technology might be crucial for improving mHealth adherence. To facilitate examining wearable technology embodiment, we developed a measurement scale for this construct.ObjectiveThis study aimed to conceptualize wearable technology embodiment, create an instrument to measure it, and test the predictive validity of the scale using well-known constructs related to technology adoption. The introduced instrument has 3 dimensions and includes 9 measurement items. The items are distributed evenly between the 3 dimensions, which include body extension, cognitive extension, and self-extension.MethodsData were collected through a vignette-based survey (n=182). Each respondent was given 3 different vignettes, describing a hypothetical situation using a different type of wearable technology (a smart phone, a smart wristband, or a smart watch) with the purpose of tracking daily activities. Scale dimensions and item reliability were tested for their validity and Goodness of Fit Index (GFI).ResultsConvergent validity of the 3 dimensions and their reliability were established as confirmatory factor analysis factor loadings (>0.70), average variance extracted values (>0.50), and minimum item to total correlations (>0.40) exceeded established threshold values. The reliability of the dimensions was also confirmed as Cronbach alpha and composite reliability exceeded 0.70. GFI testing confirmed that the 3 dimensions function as intercorrelated first-order factors. Predictive validity testing showed that these dimensions significantly add to multiple constructs associated with predicting the adoption of new technologies (ie, trust, perceived usefulness, involvement, attitude, and continuous intention).ConclusionsThe wearable technology embodiment measurement instrument has shown promise as a tool to measure the extension of an individual's body, cognition, and self, as well as predict certain aspects of technology adoption. This 3-dimensional instrument can be applied to mixed method research and used by wearable technology developers to improve future versions through such things as fit, improved accuracy of biofeedback data, and customizable features or fashion to connect to the users' personal identity. Further research is recommended to apply this measurement instrument to multiple scenarios and technologies, and more diverse user groups.

Dataset Information

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

Publications

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

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