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Background Research has shown the feasibility of human activity recognition using wearable accelerometer devices. Different studies have used varying numbers and placements for data collection using sensors. Objective This study aims to compare accuracy performance between multiple and variable placements of accelerometer devices in categorizing the type of physical activity and corresponding energy expenditure in older adults. Methods In total, 93 participants (mean age 72.2 years, SD 7.1) completed a total of 32 activities of daily life in a laboratory setting. Activities were classified as sedentary versus nonsedentary, locomotion versus nonlocomotion, and lifestyle versus nonlifestyle activities (eg, leisure walk vs computer work). A portable metabolic unit was worn during each activity to measure metabolic equivalents (METs). Accelerometers were placed on 5 different body positions: wrist, hip, ankle, upper arm, and thigh. Accelerometer data from each body position and combinations of positions were used to develop random forest models to assess activity category recognition accuracy and MET estimation. Results Model performance for both MET estimation and activity category recognition were strengthened with the use of additional accelerometer devices. However, a single accelerometer on the ankle, upper arm, hip, thigh, or wrist had only a 0.03-0.09 MET increase in prediction error compared with wearing all 5 devices. Balanced accuracy showed similar trends with slight decreases in balanced accuracy for the detection of locomotion (balanced accuracy decrease range 0-0.01), sedentary (balanced accuracy decrease range 0.05-0.13), and lifestyle activities (balanced accuracy decrease range 0.04-0.08) compared with all 5 placements. The accuracy of recognizing activity categories increased with additional placements (accuracy decrease range 0.15-0.29). Notably, the hip was the best single body position for MET estimation and activity category recognition. Conclusions Additional accelerometer devices slightly enhance activity recognition accuracy and MET estimation in older adults. However, given the extra burden of wearing additional devices, single accelerometers with appropriate placement appear to be sufficient for estimating energy expenditure and activity category recognition in older adults. 

Older citizens experience a large number of falls and hospitalizations per year throughout the world. These intervening health events (IHEs) such as falls/injuries, illnesses, hospitalizations, are strong precipitants of disability in older adults. They are episodic in nature, extremely difficult to study, and require continuous and long-term monitoring. Wearable technologies with remote capabilities are an ideal solution for capturing information before and after such events. This work presents the ROAMM campaign platform for harnessing sensor and interface capabilities on smart wearables to provide customizable, affordable, and versatile health monitoring that leads to practical remote-based interventions. The platform is flexible, efficient, and scalable for concurrently running multiple studies, each of which consists of patient-reported outcomes, ecological momentary assessments and mental health-related patient responses. Additionally, the system is able to capture and derive ecological, momentary assessments of pain with concurrent mobility tracking that allows life-space mobility ascertainment. The platform supports multiple watches, and we show implementations on both the Samsung Galaxy and Apple series of smartwatches. 

Background Older adults who experience pain are more likely to reduce their community and life-space mobility (ie, the usual range of places in an environment in which a person engages). However, there is significant day-to-day variability in pain experiences that offer unique insights into the consequences on life-space mobility, which are not well understood. This variability is complex and cannot be captured with traditional recall-based pain surveys. As a solution, ecological momentary assessments record repeated pain experiences throughout the day in the natural environment. Objective The aim of this study was to examine the temporal association between ecological momentary assessments of pain and GPS metrics in older adults with symptomatic knee osteoarthritis by using a smartwatch platform called Real-time Online Assessment and Mobility Monitor. Methods Participants (n=19, mean 73.1 years, SD 4.8; female: 13/19, 68%; male: 6/19, 32%) wore a smartwatch for a mean period of 13.16 days (SD 2.94). Participants were prompted in their natural environment about their pain intensity (range 0-10) at random time windows in the morning, afternoon, and evening. GPS coordinates were collected at 15-minute intervals and aggregated each day into excursion, ellipsoid, clustering, and trip frequency features. Pain intensity ratings were averaged across time windows for each day. A random effects model was used to investigate the within and between-person effects. Results The daily mean pain intensities reported by participants ranged between 0 and 8 with 40% reporting intensities ≥2. The within-person associations between pain intensity and GPS features were more likely to be statistically significant than those observed between persons. Within-person pain intensity was significantly associated with excursion size, and others (excursion span, total distance, and ellipse major axis) showed a statistical trend (excursion span: P=.08; total distance: P=.07; ellipse major axis: P=.07). Each point increase in the mean pain intensity was associated with a 3.06 km decrease in excursion size, 2.89 km decrease in excursion span, 5.71 km decrease total distance travelled per day, 31.4 km2 decrease in ellipse area, 0.47 km decrease ellipse minor axis, and 3.64 km decrease in ellipse major axis. While not statistically significant, the point estimates for number of clusters (P=.73), frequency of trips (P=.81), and homestay (P=.15) were positively associated with pain intensity, and entropy (P=.99) was negatively associated with pain intensity. Conclusions In this demonstration study, higher intensity knee pain in older adults was associated with lower life-space mobility. Results demonstrate that a custom-designed smartwatch platform is effective at simultaneously collecting rich information about ecological pain and life-space mobility. Such smart tools are expected to be important for remote health interventions that harness the variability in pain symptoms while understanding their impact on life-space mobility. 

Background Wearable technology, such as smartwatches, can capture valuable patient-generated data and help inform patient care. Electronic health records provide logical and practical platforms for including such data, but it is necessary to evaluate the way the data are presented and visualized. Objective The aim of this study is to evaluate a graphical interface that displays patients’ health data from smartwatches, mimicking the integration within the environment of electronic health records. Methods A total of 12 health care professionals evaluated a simulated interface using a usability scale questionnaire, testing the clarity of the interface, colors, usefulness of information, navigation, and readability of text. Results The interface was positively received, with 14 out of the 16 questions generating a score of 5 or greater among at least 75% of participants (9/12). On an 8-point Likert scale, the highest rated features of the interface were quick turnaround times (mean score 7.1), readability of the text (mean score 6.8), and use of terminology/abbreviations (mean score 6.75). Conclusions Collaborating with health care professionals to develop and refine a graphical interface for visualizing patients’ health data from smartwatches revealed that the key elements of the interface were acceptable. The implementation of such data from smartwatches and other mobile devices within electronic health records should consider the opinions of key stakeholders as the development of this platform progresses.