Lifespace metrics of older adults with mild cognitive impairment and dementia recorded via geolocation data.

OBJECTIVE: Lifespace, the physical area in which someone conducts life activities, indicates lived community mobility. This study explored the feasibility of technology-based lifespace measurement for older people with dementia and mild cognitive impairment (MCI), including the generation of a range of lifespace metrics, and investigation of relationships with health and mobility status. METHODS: An exploratory study was conducted within a longitudinal observational study. Eighteen older adults (mean age 86.7 years (SD: 3.2); 8 men; 15 MCI), participated. Lifespace metrics were generated from geolocation data (GPS and Bluetooth beacon) collected through a smartphone application for one week (2015-2016). Cognitive and mobility-related outcomes were compared from study data sets at baseline (2005-2007) and 6-year follow-up (2011-2014). RESULTS: Lifespace data could be collected from all participants, and metrics were generated including percentage of time at home, maximum distance from home, episodes of travel in a week, days in a week participants left home, lifespace area (daily, weekly and total), indoor lifespace (regions in the home/hour), and a developed lifespace score that combined time, frequency of travel, distance and area. Results indicated a large range of lifespace areas (0.1 - 97.88 km2 ; median 6.77 km2 ) with similar patterns across lifespace metrics. Significant relationships were found between lifespace metrics and concurrent driving status and anteceding scores on the sit-to-stand test (at baseline and follow-up). CONCLUSIONS: Further longitudinal exploration of lifespace is required to develop an understanding of the nature of lifespace of older community-dwelling people, and its relationship with health, mobility and well-being outcomes.

Technology-assisted quantification of movement to predict infants at high risk of motor disability: A systematic review.

AIM: To systematically review the scientific literature to determine the predictive validity of technology-assisted measures of observable infant movement in infants less than six months of corrected age (CA) to identify high-risk of motor disability. METHOD: A comprehensive search for randomised and non-randomised controlled trials, cohort studies and cross-comparison trials was performed on five electronic databases up to Feb 2021. Studies were included if they quantified infant movement before 6 months CA using some method of technology-assistance and compared the instrumented measure to a diagnostic clinical measure of neurodevelopment. Studies were excluded if they did not report a technology-assisted measure of infant movement. Methodological quality of the included studies was assessed using the Downs and Black scale. RESULTS: 23 studies met the full inclusion and exclusion criteria. Methodological quality of the included papers ranged from 9 to 24 (out of 26) on the Downs and Black scale. Infant movement assessments included the General Movements Assessment (GMA) and domains of the Hammersmith Infant Neurological Assessment (HINE). Studies used 2D video recordings, RGB-Depth recordings, accelerometry, and electromagnetic motion tracking technologies to quantify movement. Analytical approaches and movement features of interest were individual and varied. Technology assisted quantitative assessments identified cases of later diagnosed CP with sensitivity 44-100 %, specificity 59-95 %, Area under the ROC Curve 82-93 %; and typical development with sensitivity range 30-46 %, specificity 88-95 %, Area under the ROC Curve 68 %. INTERPRETATION: Technology-assisted assessments of movement in infants less than 6 months CA using current technologies are feasible. Validation of measurement tools are limited. Although methods and results appear promising clinical uptake of technology-assisted assessments remains limited.

A prospective cohort study of prodromal Alzheimer's disease: Prospective Imaging Study of Ageing: Genes, Brain and Behaviour (PISA).

This prospective cohort study, "Prospective Imaging Study of Ageing: Genes, Brain and Behaviour" (PISA) seeks to characterise the phenotype and natural history of healthy adult Australians at high future risk of Alzheimer's disease (AD). In particular, we are recruiting midlife and older Australians with high and low genetic risk of dementia to discover biological markers of early neuropathology, identify modifiable risk factors, and establish the very earliest phenotypic and neuronal signs of disease onset. PISA utilises genetic prediction to recruit and enrich a prospective cohort and follow them longitudinally. Online surveys and cognitive testing are used to characterise an Australia-wide sample currently totalling over 3800 participants. Participants from a defined at-risk cohort and positive controls (clinical cohort of patients with mild cognitive impairment or early AD) are invited for onsite visits for detailed functional, structural and molecular neuroimaging, lifestyle monitoring, detailed neurocognitive testing, plus blood sample donation. This paper describes recruitment of the PISA cohort, study methodology and baseline demographics.

Physiological Signal Monitoring for Identification of Emotional Dysregulation in Children.

Children, particularly those with atypical or delayed development, have a reduced ability to self-regulate their emotions and behaviour. After a number of anxiety or stress provoking events, this reduced regulatory ability can result in a meltdown. Extrinsic signals of an impending meltdown are often recognised and acted on by clinicians or parents. These external indications are also accompanied by internal physiological changes, such as increase in heart rate, skin electrodermal activity, and skin temperature. These physiological signals may be used to predict impending meltdown events and facilitate earlier and effective carer intervention, especially in complex management cases. We present a preliminary study using a wearable sensor system for continuous monitoring of physiological signals to measure and predict emotional changes in school-aged children. Our models are able to correctly classify the behavioural state of a child with 68% mean global model accuracy and up to 85% for person-dependent models. Prediction of emotion and identification of impending meltdowns will potentially assist parents, carers, teachers and clinicians to manage stress and problem behaviours before they escalate, and support self-management strategies throughout the variety of normal daily life.

Use of eHealth in the management of pulmonary arterial hypertension: review of the literature.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a severe chronic condition associated with poor quality of life and high risks of mortality and hospitalisation. The utilisation of novel diagnostic technologies has improved survival rates although the effectiveness of Electronic Health (eHealth) interventions in patients with a chronic cardiopulmonary disease remains controversial. As the effectiveness of eHealth can be established by specific evaluation for different chronic health conditions, the aim of this study was to explore and summarise the utilisation of eHealth in PAH. METHOD: We searched PubMed, CINAHL and Embase for all studies reporting clinical trials on eHealth solutions for the management of PAH. No limitations in terms of study design or date of publication were imposed. RESULTS: 18 studies (6 peer-reviewed journal papers and 12 conference papers) were identified. Seven studies addressed the accuracy, safety or reliability of eHealth technologies such as intra-arterial haemodynamic monitoring of the pulmonary artery pressure, self-administered 6-Minute walk test App, computerised step-pulse oximeter and ambulatory impedance cardiography. Two studies evaluated eHealth as part of the medical management and showed a reduction in hospitalisation rate. CONCLUSIONS: The evidence of eHealth supporting the management of people with PAH is limited and only embraced through a few studies of small sample size and short-term duration. Given the proposed clinical benefits in heart failure, we postulate that the evaluation of eHealth for the clinical management of PAH is highly warranted.

Digital health interventions for chronic diseases: a scoping review of evaluation frameworks.

BACKGROUND: Monitoring and evaluations of digital health (DH) solutions for the management of chronic diseases are quite heterogeneous and evidences around evaluating frameworks are inconsistent. An evidenced-based framework is needed to inform the evaluation process and rationale of such interventions. We aimed to explore the nature, extent and components of existing DH frameworks for chronic diseases. METHODS: This review was conducted based on the five steps of Arksey and O'Malley's scoping review methodology. Out of 172 studies identified from, PubMed, Embase and Web of Science, 11 met our inclusion criteria. The reviewed studies developed DH frameworks for chronic diseases and published between 2010 and 2018. RESULTS: According to WHO guidelines for monitoring and evaluation of DH interventions, we identified seven Conceptual frameworks, two Results frameworks, one Logical framework and one Theory of change. The frameworks developed for providing interventions such as self-management, achieving personal goals and reducing relapse for cardiovascular disease, diabetes, chronic obstructive pulmonary disease and severe mental health. A few studies reported evaluation of the frameworks using randomised clinical trials (n=3) and feasibility testing via Likert scale survey (n=2). A wide range of outcomes were reported including access to care, cost-effectiveness, behavioural outcomes, patient-provider communications, technology acceptance and user experience. CONCLUSION: There is a lack of evidence on the application of consistent DH frameworks. Future research should address the use of evidence-based frameworks into the research design, monitoring and evaluation process. This review explores the nature of DH frameworks for the management of chronic diseases and provides examples to guide monitoring and evaluation of interventions.

Sequence Classification Restricted Boltzmann Machines With Gated Units.

For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (RNNs) are the preferred models. While the former can explicitly model the temporal dependences between the variables, and the latter have the capability of learning representations. The recurrent temporal restricted Boltzmann machine (RTRBM) is a model that combines these two features. However, learning and inference in RTRBMs can be difficult because of the exponential nature of its gradient computations when maximizing log likelihoods. In this article, first, we address this intractability by optimizing a conditional rather than a joint probability distribution when performing sequence classification. This results in the "sequence classification restricted Boltzmann machine" (SCRBM). Second, we introduce gated SCRBMs (gSCRBMs), which use an information processing gate, as an integration of SCRBMs with long short-term memory (LSTM) models. In the experiments reported in this article, we evaluate the proposed models on optical character recognition, chunking, and multiresident activity recognition in smart homes. The experimental results show that gSCRBMs achieve the performance comparable to that of the state of the art in all three tasks. gSCRBMs require far fewer parameters in comparison with other recurrent networks with memory gates, in particular, LSTMs and gated recurrent units (GRUs).

Outcomes of a feasibility trial using an innovative mobile health programme to assist in insulin dose adjustment.

OBJECTIVE: Intensification of diabetes therapy with insulin is often delayed for people with suboptimal glycaemic control. This paper reports on the feasibility of using an innovative mobile health (mHealth) programme to assist a diabetes insulin dose adjustment (IDA) service. METHODS: Twenty adults with diabetes referred to a tertiary hospital IDA service were recruited. They were provided with a cloud-based mobile remote monitoring system-the mobile diabetes management system (MDMS). The credentialled diabetes educator (CDE) recorded the time taken to perform IDA utilising the MDMS versus the conventional method-which is a weekly adjustment of insulin doses by a CDE through telephone contact based on three or more daily blood glucose readings. Participants and staff completed a feedback questionnaire. RESULTS: The CDE spent 55% less time performing IDA using MDMS than using the conventional method. The participants were satisfied with MDMS use and the CDEs reported improved efficiency. CONCLUSION: Incorporating a mHealth programme for an IDA service has the potential to improve service delivery efficiencies while simultaneously improving the patient experience.

International feasibility trial on the use of an interactive mobile health platform for cardiac rehabilitation: protocol of the Diversity 1 study.

INTRODUCTION: The implementation of home-based cardiac rehabilitation has demonstrated potential to increase patient participation, but the content and the delivering of the programmes varies across countries. The objective of this study is to investigate whether an Australian-validated mobile health (mHealth) platform for cardiac rehabilitation will be accepted and adopted irrespectively from the existing organisational and contextual factors in five different European countries. METHODS AND ANALYSIS: This international multicentre feasibility study will use surveys, preliminary observations and analysis to evaluate the use and the user's perceptions (satisfaction) of a validated mHealth platform in different contextual settings. ETHICS AND DISSEMINATION: This study protocol has been approved by the Australian research organisation CSIRO and the respective ethical committees of the European sites. The dissemination of this trial will serve as a ground for the further implementation of an international large randomised controlled trial which will contribute to an effective global introduction of mHealth into daily clinical practice.

Mobile-based insulin dose adjustment for type 2 diabetes in community and rural populations: study protocol for a pilot randomized controlled trial.

BACKGROUND: Insulin initiation and/or titration for type 2 diabetes (T2DM) is often delayed as it is a resource-intensive process, often requiring frequent exchange of information between a patient and their diabetes healthcare professional, such as a credentialed diabetes educator (CDE) for insulin dose adjustment (IDA). Existing models of IDA are unlikely to meet the increasing service demand unless efficiencies are increased. Mobile health (mHealth), a subset of Ehealth, has been shown to improve glycaemic control through enhanced self-management and feedback leading to improved patient satisfaction and could simultaneously reduce costs. Considering the potential benefits of mHealth, we have developed an innovative mHealth-based care model to support patients and clinicians in diabetes specialist community outreach and telehealth clinics, that is, REthinking Model of Outpatient Diabetes care utilizing EheaLth - Insulin Dose Adjustment (REMODEL-IDA). This model primarily aims to improve the glycaemic management of patients with T2DM on insulin, with the secondary aims of improving healthcare service delivery efficiency and the patients' experience. METHODS/DESIGN: A two-arm pilot randomized controlled trial (RCT) will be conducted for 3 months with 44 participants, randomized at a 1:1 ratio to receive either the mHealth-based model of care (intervention) or routine care (control), in diabetes specialist community outreach and telehealth clinics. The intervention arm will exchange information related to blood glucose levels via the Mobile Diabetes Management System developed for outpatients with T2DM. They will receive advice on insulin titration from the CDE via the mobile-app and receive automated text-message prompts for better self-management based on their blood glucose levels and frequency of blood glucose testing. The routine care arm will be followed up via telephone calls by the CDE as per usual practice. The primary outcome is change in glycated haemoglobin, a marker of glycaemic management, at 3 months. Patient and healthcare provider satisfaction, and time required to perform IDA by healthcare providers in both arms will be collected. This pilot study will guide the conduct of a large-scale pragmatic RCT in regional Australia.

Evaluation of an innovative mobile health programme for the self-management of chronic obstructive pulmonary disease (MH-COPD): protocol of a randomised controlled trial.

INTRODUCTION: Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death globally. In outpatient care, the self-management of COPD is essential, but patient adherence to this remains suboptimal. The objective of this study is to examine whether an innovative mobile health (mHealth)-enabled care programme (MH-COPD) will improve the patient self-management and relevant health outcomes. METHODS AND ANALYSIS: A prospective open randomised controlled trial has been designed. In the trial, patients with COPD will be recruited from The Prince Charles Hospital, Brisbane, Australia. They will then be randomised to participate in either the MH-COPD intervention group (n=50 patients), or usual care control group (UC-COPD) (n=50 patients) for 6 months. The MH-COPD programme has been designed to integrate an mHealth system within a clinical COPD care service. In the programme, participants will use a mHealth application at home to review educational videos, monitor COPD symptoms, use an electronic action plan, modify the risk factors of cigarette smoking and regular physical activity, and learn to use inhalers optimally. All participants will be assessed at baseline, 3 months and 6 months. The primary outcomes will be COPD symptoms and quality of life. The secondary outcomes will be patient adherence, physical activity, smoking cessation, use of COPD medicines, frequency of COPD exacerbations and hospital readmissions, and user experience of the mobile app. ETHICS AND DISSEMINATION: The clinical trial has been approved by The Prince Charles Hospital Human Research Ethics Committee (HREC/16/QPCH/252). The recruitment and follow-up of the trial will be from January 2019 to December 2020. The study outcomes will be disseminated according to the Consolidated Standards of Reporting Trials statement through a journal publication, approximately 6 months after finishing data collection. TRIAL REGISTRATION NUMBER: ACTRN12618001091291.

Rethinking Models of Outpatient Specialist Care in Type 2 Diabetes Using eHealth: Study Protocol for a Pilot Randomised Controlled Trial.

Conventional outpatient services are unlikely to meet burgeoning demand for diabetes services given increasing prevalence of diabetes, and resultant impact on the healthcare workforce and healthcare costs. Disruptive technologies (such as smartphone and wireless sensors) create an opportunity to redesign outpatient services. In collaboration, the Department of Diabetes and Endocrinology at Brisbane Princess Alexandra Hospital, the University of Queensland Centre for Health Services Research and the Australian e-Health Research Centre developed a mobile diabetes management system (MDMS) to support the management of complex outpatient type 2 diabetes mellitus (T2DM) adults. The system comprises of a mobile App, an automated text-messaging feedback and a clinician portal. Blood glucose levels (BGL) data are automatically transferred by Bluetooth-enabled glucose meter to the clinician portal via the mobile App. The primary aim of the study described here is to examine improvement in glycaemic control of a new model of care employing MDMS for patients with complex T2DM attending a tertiary level outpatient service. A two-group, 12-month, pilot pragmatic randomised control trial will recruit 44 T2DM patients. The control group will receive routine care. The intervention group will be supported by the MDMS enabling the participants to potentially better self-manage their diabetes, and the endocrinologists to remotely monitor BGL and to interact with patients through a variety of eHealth modalities. Intervention participants will be encouraged to complete relevant pathology tests, and report on current diabetes management through an online questionnaire. Using this information, the endocrinologist may choose to reschedule the appointment or substitute it with a telephone or video-consultation. This pilot study will guide the conduct of a large-scale study regarding the capacity for a new model of care. This model utilises multimodal eHealth strategies via the MDMS to primarily improve glycaemic control with secondary aims to improve patient experience, reduce reliance on physical clinics, and decrease service delivery cost.

Development of a Wearable Sensor Network for Quantification of Infant General Movements for the Diagnosis of Cerebral Palsy.

Qualitative assessments of infant spontaneous movements can be performed to measure neurodevelopmental status and provide early insight into the presence of any abnormalities. Clinical assessments of infant movements at 12 weeks post term age are up to 98% predictive of the eventual development of Cerebral Palsy, but their reach is often limited to infants already identified as high-risk within the traditional healthcare system. We present the development of a network of wearable sensors designed to noninvasively measure spontaneous movements in infants from 12-20 weeks post-term- age both within the clinic and for future home use. Pilot data on a single healthy term infant is presented to demonstrate clinical functionality towards future validation studies in infants at high-risk of Cerebral Palsy. Using this system for tele- delivered assessments in the home could enhance screening of neurodevelopmental disorders for infants and families in rural and remote areas, a population with reduced health services.

Increasing Health Care Adherence Through Gamification, Video Feedback, and Real-World Rewards.

Treatment non-adherence poses a sizeable and persistent challenge to health professionals. In the US alone, it is estimated that at least $100 billion per year is spent on avoidable health care costs with an additional $230 billion per year forfeited due to lost productivity. Efforts to increase adherence have yielded mixed results. We present an adaptable, theoretical framework that uses established gamification methods coupled with a means of motivating patients using real-world rewards. The framework presented herein is implemented via user interface modifications to a clinically validated health tracking app, as well as a means of delivering video feedback for viewing a variety of potential reward outcomes.

User Experience of an Innovative Mobile Health Program to Assist in Insulin Dose Adjustment: Outcomes of a Proof-Of-Concept Trial.

BACKGROUND: Many patients with diabetes require insulin therapy to achieve optimal glycemic control. Initiation and titration of insulin often require an insulin dose adjustment (IDA) program, involving frequent exchange of blood glucose levels (BGLs) and insulin prescription advice between the patient and healthcare team. This process is time consuming with logistical barriers. OBJECTIVE: To develop an innovative mobile health (m-Health) mobile-based IDA program (mIDA) and evaluate the user adherence and experience through a proof-of-concept trial. METHODS: In the program, an m-Health system was designed to be integrated within a clinical IDA service, comprising a Bluetooth-enabled glucose meter, smartphone application, and clinician portal. Insulin-requiring patients with type-2 diabetes mellitus and stable BGL were recruited to use the m-Health system to record and exchange BGL entries, insulin dosages, and clinical messages for 2 weeks. The user experience was evaluated by a Likert scale questionnaire. RESULTS: Nine participants, aged 58 ± 14 years (mean ± SD), completed the trial with average daily records of 3.1 BGL entries and 1.2 insulin dosage entries. The participants recognized the potential value of the clinical messages. They felt confident about managing their diabetes and were positive regarding ease of use and family support of the system, but disagreed that there were no technical issues. Finally, they were satisfied with the program and would continue to use it if possible. CONCLUSIONS: The m-Health system for IDA showed promising levels of adherence, usability, perception of usefulness, and satisfaction. Further research is required to assess the feasibility and cost-effectiveness of using this system in outpatient settings.

Predicting food nutrition facts using pocket-size near-infrared sensor.

Diet monitoring is one of the most important aspects in preventative health care that aims to reduce various health risks. Manual recording has been a prevalence among all approaches yet it is tedious and often end up with a low adherence rate. Several existing techniques that have been developed to monitor food intake suffer too with accuracy, efficiency, and user acceptance rate. In this paper we propose a novel approach on measuring food nutrition facts, through a pocket-size non-intrusive near-infrared (NIR) scanner. We build efficient regression models that can make quantitative prediction on food nutrition contents, such as energy and carbohydrate. Our extensive experiments on off-the-shelf liquid foods demonstrates the accuracy of these regression models and proves the applicability of using NIR spectra that are collected by small hand-held scanner, on food nutrition prediction.

Innovative Telemonitoring Enhanced Care Programme for Chronic Heart Failure (ITEC-CHF) to improve guideline compliance and collaborative care: protocol of a multicentre randomised controlled trial.

INTRODUCTION: Chronic heart failure (CHF) is a life-threatening chronic disease characterised by periodic exacerbations and recurrent hospitalisations. In the management of CHF, patient compliance with evidence-based clinical guidelines is essential, but remains difficult practically. The objective of this study is to examine whether an Innovative Telemonitoring Enhanced Care Programme for CHF (ITEC-CHF) improves patients' compliance, and associated health and economic outcomes. METHODS AND ANALYSIS: An open multicentre randomised controlled trial has been designed. Patients will be recruited and randomised to receive either ITEC-CHF (n=150) or usual care CHF (n=150) for at least 6 months. ITEC-CHF combines usual care and an additional telemonitoring service including remote weight monitoring, structured telephone support and nurse-led collaborative care. The primary outcomes are the compliance rates with the best-practice guidelines for daily weight monitoring. The secondary outcomes include the compliance with other guideline recommendations (health maintenance, medication, diet and exercise), health (health-related quality of life, risk factors, functional capacity and psychological states) and economic outcomes related to the use of healthcare resources such as hospital readmissions and general practitioner/emergency department visits. ETHICS AND DISSEMINATION: The clinical trial has been approved by Peninsula Health Human Research Ethics Committee (HREC Reference: HREC/14/PH/27), Royal Perth Hospital Human Research Ethics Committee (Reference: 15-081) and the Curtin University Human Research Ethics Committee (Reference: HR 181/2014). We will disseminate the final results to the public via conferences and journal publications. A final study report will also be provided to the ethics committees. TRIAL REGISTRATION NUMBER: Registered with Australian New Zealand Clinical Trial Registry (ACTRN12614000916640).

Understanding Smart Home Sensor Data for Ageing in Place Through Everyday Household Routines: A Mixed Method Case Study.

BACKGROUND: An ongoing challenge for smart homes research for aging-in-place is how to make sense of the large amounts of data from in-home sensors to facilitate real-time monitoring and develop reliable alerts. OBJECTIVE: The objective of our study was to explore the usefulness of a routine-based approach for making sense of smart home data for the elderly. METHODS: Maximum variation sampling was used to select three cases for an in-depth mixed methods exploration of the daily routines of three elderly participants in a smart home trial using 180 days of power use and motion sensor data and longitudinal interview data. RESULTS: Sensor data accurately matched self-reported routines. By comparing daily movement data with personal routines, it was possible to identify changes in routine that signaled illness, recovery from bereavement, and gradual deterioration of sleep quality and daily movement. Interview and sensor data also identified changes in routine with variations in temperature and daylight hours. CONCLUSIONS: The findings demonstrated that a routine-based approach makes interpreting sensor data easy, intuitive, and transparent. They highlighted the importance of understanding and accounting for individual differences in preferences for routinization and the influence of the cyclical nature of daily routines, social or cultural rhythms, and seasonal changes in temperature and daylight hours when interpreting information based on sensor data. This research has demonstrated the usefulness of a routine-based approach for making sense of smart home data, which has furthered the understanding of the challenges that need to be addressed in order to make real-time monitoring and effective alerts a reality.

Examining an Australian physical activity and nutrition intervention using RE-AIM.

Translating evidence-based interventions into community practice is vital to health promotion. This study used the RE-AIM framework to evaluate the larger dissemination of the ManUp intervention, an intervention which utilized interactive web-based technologies to improve the physical activity and nutrition behaviors of residents in Central Queensland, Australia. Data were collected for each RE-AIM measure (Reach, Effectiveness, Adoption, Implementation, Maintenance) using (i) computer-assisted telephone interview survey (N = 312) with adults (18 years and over) from Central Queensland, (ii) interviews with key stakeholders from local organizations (n = 12) and (iii) examination of project-related statistics and findings. In terms of Reach, 47% of participants were aware of the intervention; Effectiveness, there were no significant differences between physical activity and healthy nutrition levels in those aware and unaware; Adoption, 73 participants registered for the intervention and 25% of organizations adopted some part of the intervention; Implementation, 26% of participants initially logged onto the website, 29 and 17% started the web-based physical activity and nutrition challenges, 33% of organizations implemented the intervention, 42% considered implementation and 25% reported difficulties; Maintenance, an average of 0.57 logins and 1.35 entries per week during the 12 week dissemination and 0.27 logins and 0.63 entries per week during the 9-month follow-up were achieved, 22 and 0% of participants completed the web-based physical activity and nutrition challenges and 33.3% of organizations intended to continue utilizing components of the intervention. While this intervention demonstrated good reach, effectiveness, adoption and implementation warrant further investigation.

Feasibility of unobstrusive ambient sensors for fall detections in home environment.

Falls are the leading threats of death, injury and hospital admissions of seniors. About one third of seniors fall every year. Real time fall detection is thus critical to support independent livings of seniors by getting timely interventions from carers/family members. Recent years along with the IoT booming, many wearable sensor based fall detection systems have been developed. However privacy concerns or simply forgot-to-wear make wearable sensors not very accepted by seniors in a home environment seeking long term monitoring solutions. This motivates the development of unobtrusive ambient fall detection system. In this paper, we reviewed studies in this area and categorised them into two types of approaches, namely active and passive. We also evaluated their feasibilities within five domains: obtrusiveness, power connectivity, affordability, complexity of installation and being tested in field trials. The evaluation results could be used as guidance in designing new unobtrusive ambient fall detection systems.