Enabling active and healthy ageing decision support systems with the smart collection of TV usage patterns.

Smart monitoring of seniors behavioural patterns and more specifically activities of daily living have attracted immense research interest in recent years. Development of smart decision support systems to support the promotion of health smart homes has also emerged taking advantage of the plethora of smart, inexpensive and unobtrusive monitoring sensors, devices and software tools. To this end, a smart monitoring system has been used in order to extract meaningful information about television (TV) usage patterns and subsequently associate them with clinical findings of experts. The smart TV operating state remote monitoring system was installed in four elderly women homes and gathered data for more than 11 months. Results suggest that TV daily usage (time the TV is turned on) can predict mental health change. Conclusively, the authors suggest that collection of smart device usage patterns could strengthen the inference capabilities of existing health DSSs applied in uncontrolled settings such as real senior homes.

Design, Implementation, and Wide Pilot Deployment of FitForAll: An Easy to use Exergaming Platform Improving Physical Fitness and Life Quality of Senior Citizens.

Many platforms have emerged as response to the call for technology supporting active and healthy aging. Key requirements for any such e-health systems and any subsequent business exploitation are tailor-made design and proper evaluation. This paper presents the design, implementation, wide deployment, and evaluation of the low cost, physical exercise, and gaming (exergaming) FitForAll (FFA) platform system usability, user adherence to exercise, and efficacy are explored. The design of FFA is tailored to elderly populations, distilling literature guidelines and recommendations. The FFA architecture introduces standard physical exercise protocols in exergaming software engineering, as well as, standard physical assessment tests for augmented adaptability through adjustable exercise intensity. This opens up the way to next generation exergaming software, which may be more automatically/smartly adaptive. 116 elderly users piloted FFA five times/week, during an eight-week controlled intervention. Usability evaluation was formally conducted (SUS, SUMI questionnaires). Control group consisted of a size-matched elderly group following cognitive training. Efficacy was assessed objectively through the senior fitness (Fullerton) test, and subjectively, through WHOQoL-BREF comparisons of pre-postintervention between groups. Adherence to schedule was measured by attendance logs. The global SUMI score was 68.33±5.85%, while SUS was 77.7. Good usability perception is reflected in relatively high adherence of 82% for a daily two months pilot schedule. Compared to control group, elderly using FFA improved significantly strength, flexibility, endurance, and balance while presenting a significant trend in quality of life improvements. This is the first elderly focused exergaming platform intensively evaluated with more than 100 participants. The use of formal tools makes the findings comparable to other studies and forms an elderly exergaming corpus.

Gains in cognition through combined cognitive and physical training: the role of training dosage and severity of neurocognitive disorder.

Physical as well as cognitive training interventions improve specific cognitive functions but effects barely generalize on global cognition. Combined physical and cognitive training may overcome this shortcoming as physical training may facilitate the neuroplastic potential which, in turn, may be guided by cognitive training. This study aimed at investigating the benefits of combined training on global cognition while assessing the effect of training dosage and exploring the role of several potential effect modifiers. In this multi-center study, 322 older adults with or without neurocognitive disorders (NCDs) were allocated to a computerized, game-based, combined physical and cognitive training group (n = 237) or a passive control group (n = 85). Training group participants were allocated to different training dosages ranging from 24 to 110 potential sessions. In a pre-post-test design, global cognition was assessed by averaging standardized performance in working memory, episodic memory and executive function tests. The intervention group increased in global cognition compared to the control group, p = 0.002, Cohen's d = 0.31. Exploratory analysis revealed a trend for less benefits in participants with more severe NCD, p = 0.08 (cognitively healthy: d = 0.54; mild cognitive impairment: d = 0.19; dementia: d = 0.04). In participants without dementia, we found a dose-response effect of the potential number and of the completed number of training sessions on global cognition, p = 0.008 and p = 0.04, respectively. The results indicate that combined physical and cognitive training improves global cognition in a dose-responsive manner but these benefits may be less pronounced in older adults with more severe NCD. The long-lasting impact of combined training on the incidence and trajectory of NCDs in relation to its severity should be assessed in future long-term trials.

A decision-support framework for promoting independent living and ageing well.

Artificial intelligence and decision support systems offer a plethora of health monitoring capabilities in ambient assisted living environment. Continuous assessment of health indicators for elderly people living on their own is of utmost importance, so as to prolong their independence and quality of life. Slow varying, long-term deteriorating health trends are not easily identifiable in seniors. Thus, early sign detection of a specific condition, as well as, any likely transition from a healthy state to a pathological one are key problems that the herein proposed framework aims at resolving. Statistical process control concepts offer a personalized approach toward identification of trends that are away from the atypical behavior or state of the seniors, while fuzzy cognitive maps knowledge representation and inference schema have proved to be efficient in terms of disease classification. Geriatric depression is used as a case study throughout the paper, so to prove the validity of the framework, which is planned to be pilot tested with a series of lone-living seniors in their own homes.