Background: The increase in life expectancy has brought about a higher prevalence of chronic illnesses among older people. Objectives: To identify common chronic illnesses among older adults, to examine the influence of such conditions on their Health-Related Quality of Life (HRQoL), and to determine factors predicting their HRQoL. Method: A population-based cross-sectional study was conducted involving 377 individuals aged 60 years and above who were selected using multi-stage sampling techniques in Olorunda Local Government, Osun State, Nigeria. Data were collected using an interviewer-administered questionnaire comprising socio-demographic characteristics, chronic illnesses, and the World Health Organization quality of life instrument (WHOQOL-BREF) containing physical health, psychological, social relationships, and environmental domains. Results: About half (51.5%) of the respondents reported at least one chronic illness which has lasted for 1–5 years (43.3%). The prevalence of hypertension was 36.1%, diabetes 13.9% and arthritis 13.4%. Respondents with chronic illness had significantly lower HRQoL overall and in the physical health, social relationships and the environmental domains (all p<0.05) compared to those without a chronic illness. Factors that predicted HRQoL include age, marital status, level of education, the presence of chronic illness and prognosis of the condition. Conclusion: This study concluded that chronic illness is prevalent in Nigerian older people and significantly influence their HRQoL. Age, marital status, and level of education were associated with HRQoL in this group.
In this thesis, a Dutch version of the Brief IPQ is presented to assess IPs in daily physiotherapy practice in The Netherlands. Further, a literature overview of the existing associations and prognosis of IPs on MSP and functioning is presented, and these associations in primary physiotherapy care in The Netherlands are explored. The impact of a matched care physiotherapy package, matched to dysfunctional IPs, and MSP and physical functioning is studied. In this thesis, three themes (ie. measurement, association / prediction and treatment) are explored for their contribution to physiotherapy management of MSP in general, and especially for low back pain
The adaptation of urbanised areas to climate change is currently one of the key challenges in the domain of urban policy. The diversity of environmental determinants requires the formulation of individual plans dedicated to the most significant local issues. This article serves as a methodic proposition for the stage of retrieving data (with the PESTEL and the Delphi method), systemic diagnosis (evaluation of risk and susceptibility), prognosis (goal trees, goal intensity map) and the formulation of urban adaptation plans. The suggested solution complies with the Polish guidelines for establishing adaptation plans. The proposed methodological approach guarantees the participation of various groups of stakeholders in the process of working on urban adaptation plans, which is in accordance with the current tendencies to strengthen the role of public participation in spatial management. https://doi.org/10.12911/22998993/81658
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Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
