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We introduce Experiencer, a newly developed Experience Sampling Method (ESM) software for commodity-level smartwatches. We designed this software mainly to address the compliance-related challenges, such as dropouts of study participants, that generations of ESM software solutions have faced. Dropouts are often caused by the inconvenient frequency and timing of the ESM prompts. This can partly be mitigated by utilizing physiological smartwatch sensors to learn which prompting moments are both convenient to the study participant and also relevant to the ESM study designer. Experiencer enables researchers to configure context-sensitive sampling protocols, providing access to raw sensor data, within the boundaries of European privacy legislation. In this paper, we describe the technical capabilities of our software, compare its features with the state-of-the-art, and showcase its application in studies that used Experiencer.

This document describes the components and technologies of the Ageing@Work system. The aim is to provide an extended overview of the different blocks and components described in the technical deliverables, illustrating how these components interact as a unique system and how they provide the different functionalities that will be validated into the pilots.

This deliverable presents the privacy and security modules of the Ageing@Work platform, in order to protect sensitive data and elicit trust to the end-users.

In this deliverable the Augmented Reality-based (AR) Remote Collaboration Tool of the Ageing@Work project is presented. The AR Remote Collaboration Tool is an augmented reality platform, aiming to assist workers in remote collaboration and training.

The aim of this deliverable is to present the results of the Personalized Reward-Based Worker Motivation Framework included in the Ageing@Work platform.

In this deliverable, the worker behavior and affective traits monitoring methods and infrastructure of Ageing@Work platform are presented.

In this deliverable,the worker behavior and affective traits monitoring methods and infrastructure of Ageing@Work platform are presented.

The purpose of this deliverable is to present the design and development of the Ageing@Work ambient virtual coach (AVC).

This paper presents the development of AR/VR-based collaborative and telepresence tools, along with a web-based platform for knowledge exchange and interaction, as part of the Ageing@Work project, that enable easy transfer of know-how from experienced (possibly older) to novice workers. &nbsp

Acoustic analysis of sustained vowels is typically used to quantify perturbations in fundamental frequency (F0), amplitude, and deviations from periodicity, and associate these with clinical outcomes of interest. Computational and practical constraints suggest that 2-3 seconds are often sufficient to acoustically characterize a sustained vowel phonation. The question then is how to best determine a short quasi-stationary segment from a typical 20-30 seconds speech recording. We computed the F0 contour in 10 millisecond epochs using SWIPE, a state-of-the-art F0 estimation algorithm, which we had previously demonstrated is very competitive in F0 estimation for sustained /a/ vowels. Subsequently, we determined the two second signal segment that exhibits the smallest mean absolute successive F0 difference. We tested the segmentation algorithm on 100 randomly selected sustained vowel /a/ phonations from the Parkinson’s Voice Initiative, where we had hand-labeled the quasi-stationary segments. We found the algorithm correctly identified the quasi-stationary segments in all cases, thus demonstrating it can be deployed at large scale studies automating further processing of sustained vowels. We also demonstrated that this pre-processing step can have a major influence in the acoustic characterization of the phonations.

Background: Machine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals.Objective: Our objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain.Methods: We searched the PubMed and Scopus bibliographic databases with terms related to machine learning, to identify real-life studies of digital health interventions incorporating machine learning algorithms. We grouped those interventions according to their target (ie, target condition), study design, number of enrolled participants, follow-up duration, primary outcome and whether this had been statistically significant, machine learning algorithms used in the intervention, and outcome of the algorithms (eg, prediction).Results: Our literature search identified 8 interventions incorporating machine learning in a real-life research setting, of which 3 (37%) were evaluated in a randomized controlled trial and 5 (63%) in a pilot or experimental single-group study. The interventions targeted depression prediction and management, speech recognition for people with speech disabilities, self-efficacy for weight loss, detection of changes in biopsychosocial condition of patients with multiple morbidity, stress management, treatment of phantom limb pain, smoking cessation, and personalized nutrition based on glycemic response. The average number of enrolled participants in the studies was 71 (range 8-214), and the average follow-up study duration was 69 days (range 3-180). Of the 8 interventions, 6 (75%) showed statistical significance (at the P=.05 level) in health outcomes.Conclusions: This review found that digital health interventions incorporating machine learning algorithms in real-life studies can be useful and effective. Given the low number of studies identified in this review and that they did not follow a rigorous machine learning evaluation methodology, we urge the research community to conduct further studies in intervention settings following evaluation principles and demonstrating the potential of machine learning in clinical practice.

Background Cardiovascular diseases (CVD) are a leading cause of premature death and disability and an economic burden worldwide. International guidelines recommend routine availability and delivery of all phases of cardiac rehabilitation (CR). Uptake of traditional cardiac rehabilitation remains suboptimal, as attendance at formal hospital-based CR programmes is low, with community-based CR rates and individual long-term exercise maintenance even lower. Home-based CR programs have been shown to be equally effective in clinical and health-related quality of life outcomes, and yet are not readily available. Purpose The aim of the current study was to develop the PATHway intervention (Physical Activity Towards Health) for the self-management of cardiovascular disease. Increasing physical activity in individuals with CVD was the primary behaviour. Methods The PATHway intervention was theoretically informed by the Behaviour Change Wheel (BCW) and Social Cognitive Theory (SCT). All relevant intervention functions, behaviour change techniques (BCTs) and policy categories were identified and translated into intervention content. Furthermore, a person-centred approach was adopted involving an iterative co-design process and extensive user-testing. Results Education, enablement, modelling, persuasion, training and social restructuring were selected as appropriate intervention functions. Twenty-two BCTs, linked to the 6 intervention functions and 3 policy categories were identified for inclusion and translated into PATHway intervention content. Conclusions This paper details the use of the BCW and SCT within a person-centred framework to develop an eHealth intervention for the self-management of CVD. The systematic and transparent development of the PATHway intervention will facilitate the evaluation of intervention effectiveness and future replication. The Template for Intervention Description and Replication (TIDieR) checklist was used to specify details of the intervention including the who, what, how and where of proposed intervention delivery.

Rehabilitation is important for patients with cardiovascular diseases (CVD) to improve health outcomes and quality of life. However, adherence to current exercise programmes in cardiac rehabilitation is limited. We present the design and development of a Decision Support System (DSS) for telerehabilitation, aiming to enhance exercise programmes for CVD patients through ensuring their safety, personalising the programme according to their needs and performance, and motivating them toward meeting their physical activity goals. The DSS processes data originated from a Microsoft Kinect camera, a blood pressure monitor, a heart rate sensor and questionnaires, in order to generate a highly individualised exercise programme and improve patient adherence. Initial results within the EU-funded PATHway project show the potential of our approach. ispartof: pages:40-45 ispartof: Studies in Health Technology and Informatics vol:224 pages:40-45 ispartof: 13th International Conference on Wearable Micro and Nano Technologies for Personalised Health location:Heraklion, Greece date:29 May - 31 May 2016 status: published