Ambient activity monitoring systems produce large amounts of data, which can be used for health monitoring. The problem is that patterns in this data reflecting health status are not identified yet. In this paper the possibility is explored of predicting the functional health status (the motor score of AMPS = Assessment of Motor and Process Skills) of a person from data of binary ambient sensors. Data is collected of five independently living elderly people. Based on expert knowledge, features are extracted from the sensor data and several subsets are selected. We use standard linear regression and Gaussian processes for mapping the features to the functional status and predict the status of a test person using a leave-oneperson-out cross validation. The results show that Gaussian processes perform better than the linear regression model, and that both models perform better with the basic feature set than with location or transition based features. Some suggestions are provided for better feature extraction and selection for the purpose of health monitoring. These results indicate that automated functional health assessment is possible, but some challenges lie ahead. The most important challenge is eliciting expert knowledge and translating that into quantifiable features.
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Background: Geriatric rehabilitation positively influences health outcomes in older adults after acute events. Integrating mobile health (mHealth) technologies with geriatric rehabilitation may further improve outcomes by increasing therapy time and independence, potentially enhancing functional recovery. Previous reviews have highlighted positive outcomes but also the need for further investigation of populations receiving geriatric rehabilitation. Objective: Our main objective was to assess the effects of mHealth applications on the health status of older adults after acute events. A secondary objective was to examine the structure and process elements reported in these studies. Methods: Systematic review, including studies from 2010 to January 2024. Studies were eligible if they involved older adults’ post-acute care and used mHealth interventions, measured health outcomes and compared intervention and control groups. The adjusted Donabedian Structure-Process-Outcome (SPO) framework was used to present reported intervention processes and structures. Results: After initial and secondary screenings of the literature, a total of nine studies reporting 26 health outcomes were included. mHealth interventions ranged from mobile apps to wearables to web platforms. While most outcomes showed improvement in both the intervention and control groups, a majority favored the intervention groups. Reporting of integration into daily practice was minimal. Conclusion: While mHealth shows positive effects on health status in geriatric rehabilitation, the variability in outcomes and methodologies among studies, along with a generally high risk of bias, suggest cautious interpretation. Standardized measurement approaches and co-created interventions are needed to enhance successful uptake into blended care and keep geriatric rehabilitation accessible and affordable.
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Aims: In-hospital prescribing errors may result in patient harm, such as prolonged hospitalisation and hospital (re)admission, and may be an emotional burden for the prescribers and healthcare professionals involved. Despite efforts, in-hospital prescribing errors and related harm still occur, necessitating an innovative approach. We therefore propose a novel approach, in-hospital pharmacotherapeutic stewardship (IPS). The aim of this study was to reach consensus on a set of quality indicators (QIs) as a basis for IPS. Methods: A three-round modified Delphi procedure was performed. Potential QIs were retrieved from two systematic searches of the literature, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. In two written questionnaires and a focus meeting (held between the written questionnaire rounds), potential QIs were appraised by an international, multidisciplinary expert panel composed of members of the European Association for Clinical Pharmacology and Therapeutics (EACPT). Results: The expert panel rated 59 QIs and four general statements, of which 35 QIs were accepted with consensus rates ranging between 79% and 97%. These QIs describe the activities of an IPS programme, the team delivering IPS, the patients eligible for the programme and the outcome measures that should be used to evaluate the care delivered. Conclusions: A framework of 35 QIs for an IPS programme was systematically developed. These QIs can guide hospitals in setting up a pharmacotherapeutic stewardship programme to reduce in-hospital prescribing errors and improve in-hospital medication safety.
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