We think it's important to share our findings

Authors

Ekerete, I.F, Garcia-Constantino, Diaz, Y, Giggins, O.M, Mustafa, M.A, Konios, A, Pouliet, P, Nugent, C.D, McLaughlin, J.

This paper proposes the fusion of data from unobtrusive sensing solutions for the recognition and classification of activities in home environments. The ability to recognize and classify activities can help in the objective monitoring of health and wellness trends in ageing adults. While the use of video and stereo cameras for monitoring activities provides an adequate insight, the privacy of users is not fully protected (i.e., users can easily be recognized from the images). Another concern is that widely used wearable sensors, such as accelerometers, have some disadvantages, such as limited battery life, adoption issues and wearability. This study investigates the use of low-cost thermal sensing solutions capable of generating distinct thermal blobs with timestamps to recognize the activities of study participants. More than 11,000 thermal blobs were recorded from 10 healthy participants with two thermal sensors placed in a laboratory kitchen: (i) one mounted on the ceiling, and (ii) the other positioned on a mini tripod stand in the corner of the room. Furthermore, data from the ceiling thermal sensor were fused with data gleaned from the lateral thermal sensor. Contact sensors were used at each stage as the gold standard for timestamp approximation during data acquisition, which allowed the attainment of: (i) the time at which each activity took place, (ii) the type of activity performed, and (iii) the location of each participant. Experimental results demonstrated successful cluster-based activity recognition and classification with an average regression co-efficient of 0.95 for tested clusters and features. Also, an average accuracy of 95% was obtained for data mining models such as k-nearest neighbor, logistic regression, neural network and random forest on Evaluation Test.Clinical Relevance-This study presents an unobtrusive (i.e., privacy-friendly) solution for activity recognition and classification, for the purposes of profiling trends in health and wellbeing.

Authors

Diaz-Skeete, Yohanca; Giggins, Oonagh M; McQuaid, David; Beaney, Paul

An ageing population and chronic disease are putting pressure on the Irish health system. The field of eHealth is rapidly evolving and has the potential to become an important component of healthcare, but there appears to be a gap currently between research in this field and the integration of eHealth technology into clinical practice. During the eHealth Ireland Ecosystem Conference held in April 2018, a workshop was conducted to explore the barriers and facilitators to the adoption of eHealth technology, particularly remote monitoring systems in community and home cardiac care. Participants included clinicians, academic researchers, technologists, patient advocates, policy makers, and representatives from the health service. The conversations in the workshop pivoted around why technology systems in cardiac care rarely moved beyond the research project stage and what can be done to address this issue. The discussions in the workshop focused around the lack of funding available, the need for reimbursement models, the lack of awareness about remote monitoring, the angst about who is responsible for the data generated, the design of systems, regulatory standards, and the increasing demand on services, education, and patient empowerment.

Authors

Smith, S

Age-friendly community projects have been contributing to initiatives across the spectrum of ‘healthy ageing’, ‘active ageing’, ‘productive ageing’ and the many other approaches to support better ageing in all its forms. Louth Age Friendly County Office and the Health Service Executive (HSE) have been supporting and nurturing community groups in this regard since the first Age-Friendly County alliance was set up in Louth in 2008. One such community group is Drogheda District Support 4 Older People (DDS4OP). This report outlines findings from an evaluation of DDS4OP as part of the EU Smart Health Age-Friendly Environments (SHAFE) project, which aims to improve policies and practices in seven European countries through learning and sharing best practices.

Authors

Smith, S., Carragher, L.

This paper examines elements of recorded GP answering machine messages that may impact on older persons seeking to access out-of-hours GP services. A content analysis approach was used to examine audio recordings of all outgoing answering machine messages from GP practices in two rural counties in Ireland. Both technical and interpretive elements of outgoing answering machine recordings present barriers for older people in accessing doctor-on-call. The information processing ability of older people, often in urgent need when seeking out-of-hours care, may be compromised due to stress, as well as illness or age-related related physical challenges. Answering machine messages, providing care directions, should be created to maximise the potential for older patients to effectively acquire the necessary details to access Doctor-on-Call.

Authors

Moran, O., McHugh, L.

The importance of a healthy sense of self for adolescent mental health is well documented. According to Relational Frame Theory there are three distinct self-discriminations, as well as three corresponding senses of other. Recent evidence suggests that in naturally occurring speech these discriminations are predictive of lower distress, as well as increased well-being, and psychological flexibility. The present study investigates these self and other discriminations in a sample of 76 adolescents using a mixed methods design with opened-ended questions and quantitative measures of mental health, well-being, and experiential avoidance. Participants’ responses to the open ended questions were coded for occurrences of the three senses of self, other, and rule governed behavior using the Functional Self-Discrimination Measure (Atkins & Styles, 2016). The findings indicated that different patterns of relating to the self and others were associated with higher levels of well being, better mental health and higher psychological flexibility. The findings are considered in relation to the benefits of using a behavioral measure of self-relating in adolescents.

Authors

Giggins, OM., Doyle, J., Hogan, K., Geroge, M.

Achieving adequate levels of illumination to stimulate the circadian system can be difficult in a nursing home. The aim of this study was to examine the impact that a 4-week cycled lighting intervention had on activity, sleep, and mood in older adults living in a nursing home.

Authors

Desparis, S., Tommasi, P., Pascale, A., Rifai, H., Doyle, J., Dinsmore, J.

With the increase of multimorbidity due to population ageing, managing multiple chronic health conditions is a rising challenge. Machine-learning can contribute to a better understanding of persons with multimorbidity (PwMs) and how to design an effective framework of care and support for them. We present a risk model of older PwMs that was derived from the TILDA dataset, a longitudinal study of the ageing Irish population. This model is based on a 26-nodes Bayesian network that represents patients possibly having one or more chronic conditions among diabetes, chronic obstructive pulmonary disease and arthritis, through a joint probability distribution of demographic, symptomatic and behavioral dimensions. We describe our method, give an exploratory analysis of the risk model, and assess its prediction accuracy in a cross-validation experiment. Finally we discuss its use in supporting management of care for PwMs, drawing on comments from health practitioners on the model.