Physical Activity Behavior of Patients at a Skilled Nursing Facility: Longitudinal Cohort Study

Abstract

Background On-body wearable sensors have been used to predict adverse outcomes such as hospitalizations or fall, thereby enabling clinicians to develop better intervention guidelines and personalized models of care to prevent harmful outcomes. In our previous work, we introduced a generic remote patient monitoring framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and the extraction of indoor localization using Bluetooth low energy beacons, in concert. Using the same framework, this paper addresses the longitudinal analyses of a group of patients in a skilled nursing facility. We try to investigate if the metrics derived from a remote patient monitoring system comprised of physical activity and indoor localization sensors, as well as their association with therapist assessments, provide additional insight into the recovery process of patients receiving rehabilitation. Objective The aim of this paper is twofold: (1) to observe longitudinal changes of sensor-based physical activity and indoor localization features of patients receiving rehabilitation at a skilled nursing facility and (2) to investigate if the sensor-based longitudinal changes can complement patients’ changes captured by therapist assessments over the course of rehabilitation in the skilled nursing facility. Methods From June 2016 to November 2017, patients were recruited after admission to a subacute rehabilitation center in Los Angeles, CA. Longitudinal cohort study of patients at a skilled nursing facility was followed over the course of 21 days. At the time of discharge from the skilled nursing facility, the patients were either readmitted to the hospital for continued care or discharged to a community setting. A longitudinal study of the physical therapy, occupational therapy, and sensor-based data assessments was performed. A generalized linear mixed model was used to find associations between functional measures with sensor-based features. Occupational therapy and physical therapy assessments were performed at the time of admission and once a week during the skilled nursing facility admission. Results Of the 110 individuals in the analytic sample with mean age of 79.4 (SD 5.9) years, 79 (72%) were female and 31 (28%) were male participants. The energy intensity of an individual while in the therapy area was positively associated with transfer activities (β=.22; SE 0.08; P=.02). Sitting energy intensity showed positive association with transfer activities (β=.16; SE 0.07; P=.02). Lying down energy intensity was negatively associated with hygiene activities (β=–.27; SE 0.14; P=.04). The interaction of sitting energy intensity with time (β=–.13; SE 0.06; P=.04) was associated with toileting activities. Conclusions This study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features, a subset of which can provide crucial information to the story line of daily and longitudinal activity patterns of patients receiving rehabilitation at a skilled nursing facility. The findings suggest that detecting physical activity changes within locations may offer some insight into better characterizing patients’ progress or decline.

Developing a Smartwatch-Based Healthcare Application: Notes to Consider

“…In our application, the accelerometer is utilized to continuously capture data in the background, determining whether the wearer is in motion or stationary using a pretrained classifier embedded on the watch. When the user is stationary, the data collection application conserves battery power by excluding the reading of location-related sensors, focusing solely on the accelerometer to identify potential changes in physical activity [ 13 ]. In instances where the classification result lacks confidence, supplementary data from the gyroscope sensor is collected to enhance the accuracy of the classification.…”

“…These patients face the risk of experiencing a decline in overall well-being, increased dependency, and a higher susceptibility to fall-related injuries due to deteriorating motor performance. Leveraging noninvasive health tracking devices, especially during the postsurgery recovery period, offers substantial benefits and is well received by patients for extended monitoring purposes [ 12 , 13 , 14 , 15 ].…”

Mobile healthcare application on Android OS using cloud computing

“…Owing to declining motor function, these patients run the risk of suffering a decline in general well-being, increasing reliance, and a higher vulnerability to fall-related injuries. Noninvasive health monitoring tools offer significant advantages and are wellappreciated by patients for prolonged monitoring, particularly during the postoperative recovery phase [7,8] . This slowdown was caused by a lack of knowledge about whether the data were fully secure.…”