Seeing the wood for the trees. Carer related research and knowledge: A scoping review

This NIHR-SSCR funded scoping review provides a comprehensive mapping of what is known about carers and caring, and aims to help inform policy, practice and research in relation to carers. The review was undertaken by searching 10 electronic bibliographic databases, supplemented by additional web searches to identify academic research, grey literature and wider knowledge. The analysis adopts a selective thematic approach covering: carer variables - the characteristics of different types of carer and different caring situations; types of care - the nature of needs of the cared for person and the features of the care situation; the impact of caring – resilience and coping, employment and health; and carer support and needs assessment. The final section highlights key messages identified from the review. It found that caring involves all sections and age groups of the population, with people are likely to experience one or more periods of caregiving over their lifetime. The uniqueness of each caring relationship is also highlighted. In relation to types of carers, knowledge about ‘hard to reach’ groups, such as BAME and LGBT carers, remains sparse. Older carers are also relatively invisible in policy and research terms. It found that much of the knowledge about carers identified in the review relates to their characteristics, their lived experience and the nature of their caregiving, with relatively less being known about the effectiveness of interventions to support them. The report concludes by offering suggestions for policy and practice. An appendix provides a bibliography of the 3,434 items identified in review, classified into 17 types of reference

Feasibility and Initial Efficacy of Home-Based Cardiac Telerehabilitation— A Pilot Study

Background: Cardiovascular Disease (CVD) is the top health problem all over the world, including China. Home-based rehabilitation after cardiac surgery has been shown to be beneficial. In our study, a clinical study has been carried out to investigate the feasibility and effectiveness of using emergent mobile and information technology to deliver monitoring and feedback function in Home-Based Cardiac Telerehabilitation program (HBCTR). The main purpose of this study is to assess the feasibility and acceptance of the HBCTR program in low risk patients post Percutaneous Coronary Intervention (PCI). The secondary purpose is to assess the initial efficacy of the HBCTR program. Method: A single-blinded parallel two-arm Randomized Controlled Trial (RCT) has been conducted at the First Affiliated Hospital of Shantou University Medical College, China. A total of 24 post PCI patients were recruited and randomly divided into two equal groups. The control group (Usual Care (UC) program) received paper-based CVD educational booklets and biweekly outpatient review. The experiment group (HBCTR program) carried out outdoor walking/jogging exercise with real time physiological monitoring along with CVD education materials. Feasibility and acceptance of the HBCTR program were evaluated by using an acceptance questionnaire, a satisfaction questionnaire, patients’ adherence evaluation, system abnormalities analysis, and safety evaluation. The effectiveness of this program was measured by using 6 Minutes Walking Test (6MWT), Fagerstrom Test for Nicotine Dependence (FTND), Cardiac Depression Scale (CDS)), and SF-36 Health Survey. Results: A total of 53 respondents completed the HBCTR patient acceptance questionnaire, and 22 participants completed the RCT. One experiment group participant withdrew, and one control group participant lost contact during the RCT. 67.9% of participants deemed the HBCTR program acceptable due to real time exercise monitoring and emergency alert function. Features including real time exercise monitoring and emergency alerts are attributed to the high acceptance of the HBCTR program. The HBCTR program is perceived to allow patients to exercise in a safer and more independent manner compared to the UC method, and 81.8% of participants (n=9) felt satisfied with the HBCTR program. The average adherence rates of HBCTR in terms of exercise trainings, self-reporting, and medication intake are 92.9%, 88.4%, and 90.0% respectively. No serious adverse event was reported in the study; Out of the 184 exercise trainings the HBCTR remote monitoring system had only 7(3.8%) temporary system failures. After the six- week intervention, both groups resulted in statistically significant improvements in SF-36 physical component summary (PCS), SF-36 mental component summary (MCS), 6MWT, FTND, DASI, and CDS. Furthermore, patients in the experimental group had better improvements compared to patients in the control group in PCS scores in SF-36 (HBCTR: Δ12.5±7.8 vs UC: Δ 4.6±5.7), DASI (HBCTR: Δ0.7±0.5 vs UC: Δ 0.3±0.4), and 6 MWT (HBCTR: Δ45.5±17.4 vs UC: Δ27.6±14.7). Conclusion: The proposed HBCTR program is feasible and safe for low-risk post PCI patients. Although improvements were observed in both groups, the physical indicators of PCS and 6MWT of HBCTR patients exceeded those of the UC patients. The patients in the HBCTR program showed high satisfaction and decreased fears in performing rehabilitation exercise “at home” with remote monitoring. With this decreased fears in exercise, the adherence among the HBCTR patients was high which resulted in their benefit physical outcomes. Future study with multiple centers and a large-scale randomized controlled trial can be carried out to further assess the efficiency of the HBCTR program in long term. Cost analysis also can be added into the further study to compare the cost effective between HBCTR and traditional center-based CR

Behavioural modelling for ambient assisted living

Tese de doutoramento - MAP-i (University of Minho, Aveiro, and Porto)A mudança incomum na rotina diária ao nível da mobilidade de um idoso em sua casa, pode ser um sinal ou sintoma precoce para a possibilidade de vir a desenvolver um problema de saúde. O recurso a diferentes sensores pode ser um meio para complementar os sistemas de cuidados de saúde tradicionais, de forma a obter uma visão mais detalhada da mobilidade diária do individuo em sua casa, enquanto realiza as suas tarefas diárias. Acreditamos, que os dados recolhidos a partir de sensores de baixo custo, como sensores de presença e ocupação, podem ser utilizados para fornecer evidências sobre os hábitos diários de mobilidade dos idosos que vivem sozinhos em casa e detetar desta forma mudanças nas suas rotinas. Neste trabalho, validamos esta hipótese, desenvolvendo um sistema que aprende automaticamente as transições diárias entre divisões da habitação e hábitos de estadia em cada uma dessas divisões em cada momento do dia e consequentemente gera alarmes sempre que os desvios são detetados. Apresentamos neste trabalho um algoritmo que processa os fluxos de dados dos diferentes sensores e identifica características que descrevem a rotina diária de mobilidade de um idoso que vive sozinho em casa. Para isso foi definido um conjunto de dimensões baseadas nos dados extraídos dos sensores, como parte do nosso Behaviour Monitoring System (BMS). Fomos capazes de detetar com um atraso mínimo os comportamentos incomuns e ao mesmo tempo, durações de confirmação da deteção elevadas, de tal modo suficientes para um conjunto comum de situações anormais. Apresentamos e avaliamos o BMS com dados sintetizados, produzidos por um gerador de dados desenvolvido para este efeito e projetado para simular diferentes perfis de mobilidade de indivíduos em casa, e também com dados reais obtidos de trabalhos de investigação anteriores. Os resultados indicam que o BMS deteta várias mudanças de mobilidade que podem ser sintomas para problemas de saúde comuns. O sistema proposto é uma abordagem útil para a aprendizagem dos hábitos de mobilidade em ambientes domésticos, com potencial para detetar alterações comportamentais que ocorrem devido a problemas de saúde, e assim encorajar a monitorização dos comportamentos e dos cuidados de saúde dos idosos.Unusual changes in the regular daily mobility routine of an elderly at home can be an indicator or early symptoms for developing a health problem. Sensor technology can be utilised to complement the traditional healthcare systems to gain a more detailed view of the daily mobility of a person at home when performing everyday tasks. We hypothesise that data collected from low-cost sensors such as presence and occupancy sensors can be analysed to provide insights on the daily mobility habits of the elderly living alone at home and to detect routine changes. We validate this hypothesis by designing a system that automatically learns the daily room-to-room transitions and stays habits in each room at each time of the day and generates alarm notifications when deviations are detected. We present an algorithm to process the sensor data streams and compute features that describe the daily mobility routine of an elderly living alone at home. This was done by defining a set of sensor-driven dimensions extracted from the sensor data as part of our Behaviour Monitoring System (BMS). We are able to achieve low detection delay with confirmation time that is high enough to convey the detection of a set of common abnormal situations. We illustrate and evaluate BMS with synthetic data, generated by a developed data generator that was designed to mimic different users’ mobility profiles at home, and also with real-life dataset collected from prior research work. Results indicate BMS detects several mobility changes that can be symptoms of common health problems. The proposed system is a useful approach for learning the mobility habits at home environments, with the potential to detect behaviour changes that occur due to health problems, and therefore, motivating progress toward behaviour monitoring and elder’s care