Evaluation of a smartwatch-based intervention providing feedback of daily activity within a research-naive stroke ward: a pilot randomised controlled trial.

BACKGROUND: The majority of stroke patients are inactive outside formal therapy sessions. Tailored activity feedback via a smartwatch has the potential to increase inpatient activity. The aim of the study was to identify the challenges and support needed by ward staff and researchers and to examine the feasibility of conducting a randomised controlled trial (RCT) using smartwatch activity monitors in research-naive rehabilitation wards. Objectives (Phase 1 and 2) were to report any challenges and support needed and determine the recruitment and retention rate, completion of outcome measures, smartwatch adherence rate, (Phase 2 only) readiness to randomise, adherence to protocol (intervention fidelity) and potential for effect. METHODS: First admission, stroke patients (onset < 4 months) aged 40-75, able to walk 10 m prior to stroke and follow a two-stage command with sufficient cognition and vision (clinically judged) were recruited within the Second Affiliated Hospital of Anhui University of Traditional Chinese Medicine. Phase 1: a non-randomised observation phase (to allow practice of protocol)-patients received no activity feedback. Phase 2: a parallel single-blind pilot RCT. Patients were randomised into one of two groups: to receive daily activity feedback over a 9-h period or to receive no activity feedback. EQ-5D-5L, WHODAS and RMI were conducted at baseline, discharge and 3 months post-discharge. Descriptive statistics were performed on recruitment, retention, completion and activity counts as well as adherence to protocol. RESULTS: Out of 470 ward admissions, 11% were recruited across the two phases, over a 30-week period. Retention rate at 3 months post-discharge was 48%. Twenty-two percent of patients dropped out post-baseline assessment, 78% completed baseline and discharge admissions, from which 62% were assessed 3 months post-discharge. Smartwatch data were received from all patients. Patients were correctly randomised into each RCT group. RCT adherence rate to wearing the smartwatch was 80%. Baseline activity was exceeded for 65% of days in the feedback group compared to 55% of days in the no feedback group. CONCLUSIONS: Delivery of a smartwatch RCT is feasible in a research-naive rehabilitation ward. However, frequent support and guidance of research-naive staff are required to ensure completeness of clinical assessment data and protocol adherence. TRIALS REGISTRATION: ClinicalTrials.gov Identifier, NCT02587585-30th September 2015.

Validation of a Commercial Android Smartwatch as an Activity Monitoring Platform.

Activity monitoring (AM) is an established technique for the assessment of a person's physical activity. With the rapid rise of smartwatch technology, this platform presents an interesting opportunity to use a device for AM that has both the ability to monitor activity and also the ability to interface seamlessly with other healthcare systems. There are questions however around the suitability of smartwatches as monitoring devices. This paper presents a validation of one smartwatch, the ZGPAX S8, for use as an activity monitor. Two experiments are presented: a physical manipulation test and a co-location test. In the physical manipulation test, three S8s are compared to a reference accelerometer under human physical manipulation. In the co-location test, the smartwatch is used alongside a reference device for a period of three hours by four participants to assess both the accelerometer data and the results of processing on data from both devices. Findings from these experiments show that the S8 accelerometer has a good correlation and limits of agreement in the physical manipulation test (r2 ∼ 0.95, CR ∼ 2.5 m/s 2), and excellent correlation and limits of agreement in the analysis of processed data from the co-location experiment (r2 ∼ 0.99, CR ∼ 0.23). From these results, the S8 is evaluated to be a suitable device for AM. Some specific limitations in the S8 are identified such as data range clipping, time drift and sample rate consistency, but these are not found to impact on the suitability of the device once algorithmic processing is applied to the data.

Does feedback on daily activity level from a Smart watch during inpatient stroke rehabilitation increase physical activity levels? Study protocol for a randomized controlled trial.

BACKGROUND: Practicing activities improves recovery after stroke, but many people in hospital do little activity. Feedback on activity using an accelerometer is a potential method to increase activity in hospital inpatients. This study's goal is to investigate the effect of feedback, enabled by a Smart watch, on daily physical activity levels during inpatient stroke rehabilitation and the short-term effects on simple functional activities, primarily mobility. METHODS/DESIGN: A randomized controlled trial will be undertaken within the stroke rehabilitation wards of the Second Affiliated hospital of Anhui University of Traditional Chinese Medicine, Hefei, China. The study participants will be stroke survivors who meet inclusion criteria for the study, primarily: able to participate, no more than 4 months after stroke and walking independently before stroke. Participants will all receive standard local rehabilitation and will be randomly assigned either to receive regular feedback about activity levels, relative to a daily goal tailored by the smart watch over five time periods throughout a working day, or to no feedback, but still wearing the Smart watch. The intervention will last up to 3 weeks, ending sooner if discharged. The data to be collected in all participants include measures of daily activity (Smart watch measure); mobility (Rivermead Mobility Index and 10-metre walking time); independence in personal care (Barthel Activities of Daily Living (ADL) Index); overall activities (the World Health Organization (WHO) Disability Assessment Scale, 12-item version); and quality of life (the Euro-Qol 5L5D). Data will be collected by assessors blinded to allocation of the intervention at baseline, 3 weeks or at discharge (whichever is the sooner); and a reduced data set will be collected at 12 weeks by telephone interview. The primary outcome will be change in daily accelerometer activity scores. Secondary outcomes are compliance and adherence to wearing the watch, and changes in mobility, independence in personal care activities, and health-related quality of life. DISCUSSION: This project is being implemented in a large city hospital with limited resources and limited research experience. There has been a pilot feasibility study using the Smart watch, which highlighted some areas needing change and these are incorporated in this protocol. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02587585 . Registered on 30 September 2015. Chinese Clinical Trial Registry, ChiCTR-IOR-15007179 . Registered on 8 August 2015.

Reconstructing physical activity monitoring outcome measures using an interval sampling approach.

Smart-watches and wearables are becoming more and more popular and this popularity is resulting in an increased interest in their use in research and clinical settings for monitoring physical activity. The battery life of these devices is of some concern, especially when they are put under increased duress through the need to constantly sample from their accelerometers. In this paper a novel approach to sampling from the accelerometer is explored whereby the accelerometer is sampled in intervals to avoid constant sampling. Results show that through reducing the sampling time and sampling in discontinuous intervals that outcome measures of time spent in sedentary, moderate and vigorous activity can be reconstructed to within acceptable error levels when compared to the same outcome measures calculated from continually sampled data.

AART-BC: a sensor system for monitoring Assistive Technology use beyond the clinic.

A wide range of assistive and rehabilitative technologies (ART) are available to assist with mobility and upper limb function. However, anecdotal evidence suggests many of the devices prescribed, or purchased, are either poorly used, or rejected entirely. This situation is costly, both for the healthcare provider and the user, and may be leading to secondary consequences, such as falls and/or social isolation. This paper reports on the development and initial feasibility testing of a system for monitoring when and how assistive devices are used outside of the clinic setting, and feeding this information to the device user themselves and/or prescribing clinician (where appropriate). Illustrative data from multiple time-synchronized device and body worn sensors are presented on a wheelchair user and a user of a "rollator" walking frame, moving along a walkway. Observation of the sensor data in both cases showed characteristic signatures corresponding to individual "pushes". In parallel with this work, other project partners are exploring clinician and patient data requirements, as well we sensor set acceptability The initial results highlight the potential for the approach and demonstrate the need for further work to reduce and optimize the sensor set.

Preliminary study on activity monitoring using an android smart-watch.

The global trend for increasing life expectancy is resulting in aging populations in a number of countries. This brings to bear a pressure to provide effective care for the older population with increasing constraints on available resources. Providing care for and maintaining the independence of an older person in their own home is one way that this problem can be addressed. The EU Funded Unobtrusive Smart Environments for Independent Living (USEFIL) project is an assistive technology tool being developed to enhance independent living. As part of USEFIL, a wrist wearable unit (WWU) is being developed to monitor the physical activity (PA) of the user and integrate with the USEFIL system. The WWU is a novel application of an existing technology to the assisted living problem domain. It combines existing technologies and new algorithms to extract PA parameters for activity monitoring. The parameters that are extracted include: activity level, step count and worn state. The WWU, the algorithms that have been developed and a preliminary validation are presented. The results show that activity level can be successfully extracted, that worn state can be correctly identified and that step counts in walking data can be estimated within 3% error, using the controlled dataset.

Characterization of wrist-wearable activity measurement using whole body calorimetry in semi-free living conditions.

Physical activity (PA) is a significant factor in a number of health conditions and monitoring PA can play a significant role in the treatment of, or research into, these conditions. For longitudinal monitoring of PA, unobtrusive devices are often used and there is a need for the development of energy expenditure (EE) estimation techniques from single-device systems. This paper presents an experiment designed to characterize the relationship between a previously described technique, the activity score (AS) and EE obtained from whole-room indirect calorimetry. The study used 8 participants over a 24-hr period with interspersed exercise periods to observe physical movement with wearable devices and EE in 5 minute epochs. Results show that AS and EE are correlated with a Spearman's rank correlation coefficient of 0.775 with p <; 0.001.

Setting the scene: Mobile and wearable technology for managing healthcare and wellbeing.

The growing proliferation of mobile and wearable technology (MWT) offers interesting use cases when applied to health and wellness management. Current trends towards more longer term health and wellness management coupled with global challenges around the provision of healthcare to aging populations with tighter budget constraints, create rich opportunities to exploit this new technology to maintain health and wellness. This paper provides an overview of commonly available MWT and examines how it can be used in health and wellness systems. Case studies are given from two recent research projects and the issues and challenges that arise in the use of MWT are discussed. We conclude that MWT offers some key advantages in some healthcare situations, but that care must be taken to select appropriate technology for each use.

Monitoring changes in behaviour from multi-sensor systems.

Behavioural patterns are important indicators of health status in a number of conditions and changes in behaviour can often indicate a change in health status. Currently, limited behaviour monitoring is carried out using paper-based assessment techniques. As technology becomes more prevalent and low-cost, there is an increasing movement towards automated behaviour-monitoring systems. These systems typically make use of a multi-sensor environment to gather data. Large data volumes are produced in this way, which poses a significant problem in terms of extracting useful indicators. Presented is a novel method for detecting behavioural patterns and calculating a metric for quantifying behavioural change in multi-sensor environments. The data analysis method is shown and an experimental validation of the method is presented which shows that it is possible to detect the difference between weekdays and weekend days. Two participants are analysed, with different sensor configurations and test environments and in both cases, the results show that the behavioural change metric for weekdays and weekend days is significantly different at 95% confidence level, using the methods presented.

A novel method for the automatic segmentation of activity data from a wrist worn device: Preliminary results.

Activity monitoring is used in a number of fields in order to assess the physical activity of the user. Applications include health and well-being, rehabilitation and enhancing independent living. Data are often gathered from multiple accelerometers and analysis focuses on multi-parametric classification. For longer term monitoring this is unsuitable and it is desirable to develop a method for the precise analysis of activity data with respect to time. This paper presents the initial results of a novel approach to this problem which is capable of segmenting activity data collected from a single accelerometer recording naturalized activity.

Behavioral pattern detection from Personalized Ambient Monitoring.

Bipolar disorder (BD) is a serious psychiatric condition that affects a large number of people. Many people with BD self-monitor their condition in order to try and keep the disturbances from affective episodes to a minimum. The Personalized Ambient Monitoring (PAM) project has developed a system that performs behavioral monitoring in an unobtrusive manner and can detect changes in a person's behavior. The system uses a variety of discreet sensors to gather data on the parson's behavior and this data is processed to extract behavioral patterns and detect changes in those patterns. In this paper we present one method of data processing that takes 24hr long data-streams from the sensors, pre-processes them and uses the Continuous Profile Model to align and extract the underlying patterns from the data-streams. We present some preliminary results from a technical trial.