An mHealth Tool Suite for Mobility Assessment

My-AHA: Software Platform to Promote Active and Healthy Ageing

The population is getting old, and the use of technology has improved the quality of life of the senior population. This is confirmed by the increasing number of solutions targeting healthy and active ageing. Such solutions keep track of the daily routine of the elderly and combine it with other relevant information (e.g., biosignals, physical activity, social activity, nutrition) to help identify early signs of decline. Caregivers and elders use this information to improve their routine, focusing on improving the current condition. With that in mind, we have developed a software platform to support My-AHA, which is composed of a multi-platform middleware, a decision support system (DSS), and a dashboard. The middleware seamlessly merges data coming from multiple platforms targeting health and active ageing, the DSS performs an intelligent computation on top of the collected data, and the dashboard provides a user’s interaction with the whole system. To show the potential of the proposed My-AHA software platform, we introduce the My Personal Dashboard web-based application over a frailty use case to illustrate how senior well-being can benefit from the use of technology.

Development of an Automated 30 Second Chair Stand Test Using Smartwatch Application

This paper presents development of the smartwatch application for automation of the standard 30 Second Chair Stand Test (30SCST). 30SCST is primarily used to test leg strength and endurance, but also speed and mobility and assess risk of falls. We use inertial signals on smartwatch to detect and count stands during the test. The application notifies the user to start and stop the test using vibration on the smartwatch. Synchronization of notifications and signal acquisition allows assessment of user's response time during the test. Our application monitors baseline heart rate before the test, heart rate increase during the test, and heart rate recovery after the test that might allow assessment of cardiovascular fitness of the user. The application is developed using Wear OS and tested on two smartwatch platforms: Fossil G4 and Polar M600. Pilot test included 12 subjects, six male and six female (mean age 39.1, S.D. 19 years). Overall accuracy of detection of the number of standups is 98.8%. Smartwatch application can be used for automated testing in clinical setups as well as for self-monitoring at home.

Wearables Meet IoT: Synergistic Personal Area Networks (SPANs)

Wearable monitoring and mobile health (mHealth) revolutionized healthcare diagnostics and delivery, while the exponential increase of deployed "things" in the Internet of things (IoT) transforms our homes and industries. "Things" with embedded activity and vital sign sensors that we refer to as "smart stuff" can interact with wearable and ambient sensors. A dynamic, ad-hoc personal area network can span multiple domains and facilitate processing in synergistic personal area networks-SPANs. The synergy of information from multiple sensors can provide: (a) New information that cannot be generated from existing data alone, (b) user identification, (c) more robust assessment of physiological signals, and (d) automatic annotation of events/records. In this paper, we present possible new applications of SPANs and results of feasibility studies. Preliminary tests indicate that users interact with smart stuff-in our case, a smart water bottle-dozens of times a day and sufficiently long to collect vital signs of the users. Synergistic processing of sensors from the smartwatch and objects of everyday use may provide user identification and assessment of new parameters that individual sensors could not generate, such as pulse wave velocity (PWV) and blood pressure. As a result, SPANs facilitate seamless monitoring and annotation of vital signs dozens of times per day, every day, every time the smart object is used, without additional setup of sensors and initiation of measurements. SPANs creates a dynamic "opportunistic bubble" for ad-hoc integration with other sensors of interest around the user, wherever they go. Continuous long-term monitoring of user's activity and vital signs can provide better diagnostic procedures and personalized feedback to motivate a proactive approach to health and wellbeing.

Evaluation of a Rehabilitation System for the Elderly in a Day Care Center

This paper presents a rehabilitation system based on a customizable exergame protocol to prevent falls in the elderly population. The system is based on depth sensors and exergames. The experiments carried out with several seniors, in a day care center, make it possible to evaluate the usability and the efficiency of the system. The outcomes highlight the user-friendliness, the very good usability of the developed system and the significant enhancement of the elderly in maintaining a physical activity. The performance of the postural response is improved by an average of 80%.

Factors Affecting User Acceptance in Overuse of Smartphones in Mobile Health Services: An Empirical Study Testing a Modified Integrated Model in South Korea

Smartphones have become crucial in people's everyday lives, including in the medical field. However, as people become close to their smartphones, this leads easily to overuse. Overuse leads to fatigue due to lack of sleep, depressive symptoms, and social relationship failure, and in the case of adolescents, it hinders academic achievement. Self-control solutions are needed, and effective tools can be developed through behavioral analysis. Therefore, the aim of this study was to investigate the determinants of users' intentions to use m-Health for smartphone overuse interventions. A research model was based on TAM and UTAUT, which were modified to be applied to the case of smartphone overuse. The studied population consisted of 400 randomly selected smartphone users aged from 19 to 60 years in South Korea. Structural equation modeling was conducted between variables to test the hypotheses using a 95% confidence interval. Perceived ease of use had a very strong direct positive association with perceived usefulness, and perceived usefulness had a very strong direct positive association with behavioral intention to use. Resistance to change had a direct positive association with behavioral intention to use and, lastly, social norm had a very strong direct positive association with behavioral intention to use. The findings that perceived ease of use influenced perceived usefulness, that perceived usefulness influenced behavioral intention to use, and social norm influenced behavioral intention to use were in accordance with prior related research. Other results that were not consistent with previous research imply that these are unique behavioral findings regarding smartphone overuse. This research identifies the critical factors that need to be considered when implementing systems or solutions in the future for tackling the issue of smartphone overuse.

A model based analysis of optimality of sit-to-stand transition

Objective assessment of mobility and effectiveness of interventions remains an open issue. Timed Up and Go (TUG) and 30 Second Chair Stand (30SCS) tests are routinely used in assessing mobility of subjects, but they provide a single parameter. Instrumenting subjects with wearable sensors enables a detailed mobility assessment. Specifically, we argue that instrumented sit-to-stand (S2ST) posture transitions during the TUG and 30SCS tests can be used to assess the strength and balance of subjects. In this paper we develop a personalized three-segment model that quantifies torques/forces on the body and assesses optimality of each sit-to-stand transition. To characterize a S2ST transition we calculate action defined as an integral of mechanical energy over time. The theoretical optimal transition time can thus be determined for each person by finding the minimum action necessary for a S2ST transition. Our model assesses action during the S2ST transition using inputs from smartphone's inertial sensors, and calculates optimum S2ST transition time for a given body composition of a subject. Our experimental evaluation shows that healthy young subjects have posture transition times close to the optimal transition time generated by the model. We hypothesize that the optimality of posture transition provides an objective and potentially more accurate estimation of the mobility. We tested the model by evaluating optimum action and optimum S2ST transition time for 10 geriatric patients undergoing a mobility improvement program by comparing their performance with the optimum performance generated by the model. This paper presents the model and possible use of the results to assess long-term changes in mobility of users.

Concussion Assessment With Smartglasses: Validation Study of Balance Measurement Toward a Lightweight, Multimodal, Field-Ready Platform

BACKGROUND

Lightweight and portable devices that objectively measure concussion-related impairments could improve injury detection and critical decision-making in contact sports and the military, where brain injuries commonly occur but remain underreported. Current standard assessments often rely heavily on subjective methods such as symptom self-reporting. Head-mounted wearables, such as smartglasses, provide an emerging platform for consideration that could deliver the range of assessments necessary to develop a rapid and objective screen for brain injury. Standing balance assessment, one parameter that may inform a concussion diagnosis, could theoretically be performed quantitatively using current off-the-shelf smartglasses with an internal accelerometer. However, the validity of balance measurement using smartglasses has not been investigated.

OBJECTIVE

This study aimed to perform preliminary validation of a smartglasses-based balance accelerometer measure (BAM) compared with the well-described and characterized waist-based BAM.

METHODS

Forty-two healthy individuals (26 male, 16 female; mean age 23.8 [SD 5.2] years) participated in the study. Following the BAM protocol, each subject performed 2 trials of 6 balance stances while accelerometer and gyroscope data were recorded from smartglasses (Glass Explorer Edition). Test-retest reliability and correlation were determined relative to waist-based BAM as used in the National Institutes of Health's Standing Balance Toolbox.

RESULTS

Balance measurements obtained using a head-mounted wearable were highly correlated with those obtained through a waist-mounted accelerometer (Spearman rho, ρ=.85). Test-retest reliability was high (intraclass correlation coefficient, ICC2,1=0.85, 95% CI 0.81-0.88) and in good agreement with waist balance measurements (ICC2,1=0.84, 95% CI 0.80-0.88). Considering the normalized path length magnitude across all 3 axes improved interdevice correlation (ρ=.90) while maintaining test-retest reliability (ICC2,1=0.87, 95% CI 0.83-0.90). All subjects successfully completed the study, demonstrating the feasibility of using a head-mounted wearable to assess balance in a healthy population.

CONCLUSIONS

Balance measurements derived from the smartglasses-based accelerometer were consistent with those obtained using a waist-mounted accelerometer. Additional research is necessary to determine to what extent smartglasses-based accelerometry measures can detect balance dysfunction associated with concussion. However, given the potential for smartglasses to perform additional concussion-related assessments in an integrated, wearable platform, continued development and validation of a smartglasses-based balance assessment is warranted. This approach could lead to a wearable platform for real-time assessment of concussion-related impairments that could be further augmented with telemedicine capabilities to integrate professional clinical guidance. Smartglasses may be superior to fully immersive virtual reality headsets for this application, given their lighter weight and reduced likelihood of potential safety concerns.

A systematic review of balance and fall risk assessments with mobile phone technology

Falls are a major health concern for older adults. Preventative measures can help reduce the incidence and severity of falls. Methods for assessing balance and fall risk factors are necessary to effectively implement preventative measures. Research groups are currently developing mobile applications to enable seniors, caregivers, and clinicians to monitor balance and fall risk. The following systematic review assesses the current state of mobile health apps for testing balance as a fall risk factor. Thirteen studies were identified and included in the review and analyzed based on study design, population, sample size, measures of balance, main outcome measures, and evaluation of validity and reliability. All studies successfully tested their applications, but only 38% evaluated the validity, and 23% evaluated the reliability of their applications. Of those, all applications were found to accurately and reliably measure balance on select variables. Four of the 13 studies included special populations groups. Out of the 13 studies, 12 reported clinicians as their intended user and seven reported seniors as their intended user. Further research should examine the validity of mobile health applications as well as report on the application's usability.