Dataset Information

Experiences of Health-Promoting Activities among Individuals with Knee Pain: The Halland Osteoarthritis Cohort.

ABSTRACT: Knee pain is an early sign of knee osteoarthritis (KOA) and a risk factor for chronic widespread pain (CWP). Early prevention is vital, and more research is needed to understand health-promoting activities for individuals with knee pain from a patient perspective. This study aimed to explore how individuals with knee pain experienced health-promoting activities. Explorative qualitative design with inductive approach was applied to explore the experiences of 22 individuals (13 women, 9 men; median age: 52). Semi-structured interviews were conducted and analysed using latent qualitative content analysis. The results revealed health-promoting activities in individuals with knee pain and were interpreted in the overall theme, striving for balance in everyday life. Two categories explored the content in health-promoting activities: (1) Caring for the body-being physically active, having a healthy diet, and utilising support; and (2) Managing life stressors-allowing for recovery, promoting vitality, and safeguarding healthy relationships. In conclusion, individuals with knee pain described various health-promoting activities. They strived for balance in everyday life by caring for the body and managing life stressors. We suggest that a broader approach to everyday life can be helpful in treatment plans and health promotion to manage and prevent KOA and CWP, while striving for a healthy lifestyle.

SUBMITTER: Sylwander C

PROVIDER: S-EPMC9518063 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Experiences of Health-Promoting Activities among Individuals with Knee Pain: The Halland Osteoarthritis Cohort.

Sylwander Charlotte C Sunesson Evelina E Andersson Maria L E MLE Haglund Emma E Larsson Ingrid I

International journal of environmental research and public health 20220824 17

Knee pain is an early sign of knee osteoarthritis (KOA) and a risk factor for chronic widespread pain (CWP). Early prevention is vital, and more research is needed to understand health-promoting activities for individuals with knee pain from a patient perspective. This study aimed to explore how individuals with knee pain experienced health-promoting activities. Explorative qualitative design with inductive approach was applied to explore the experiences of 22 individuals (13 women, 9 men; media ...[more]

PMID: 36078245

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Project description:ObjectiveThis study aimed to examine the performance of machine learning models in predicting the progression of knee pain, functional decline, and incidence of knee osteoarthritis (OA) in high-risk individuals, with automated machine learning (AutoML) being used to automate the prediction process.DesignThere were four stages in the process of our AutoML-integrated prediction. Stage 1-Data preparation: The data of 3200 eligible individuals in the Osteoarthritis Initiative (OAI) study who were considered at high risk of knee OA at the baseline visit were extracted and used. Specifically, 1094 variables from the OAI study were used to predict the changes in knee pain, physical function, and incidence of knee OA (i.e. the first occurrence of frequent knee symptoms and definite tibial osteophytes (Kellgren and Lawrence grade ≥2)) over a 9-year period. Stage 2-Model training: The AutoML approach was used to automatically train nine widely used machine learning (ML) models. Stage 3-Model testing: The AutoML approach was used to automatically test the performance of the ML models. Stage 4-Selection of important input variables: The AutoML approach automated the process of computing the importance scores of all input variables and identifying the most important ones, using the technique of permutation feature importance.ResultsUsing the AutoML approach, the weighted ensemble model and the CatBoost model showed the best performance among all nine ML models. For the prediction of each outcome in each year, the five most important input variables were identified, most of which were obtained from self-reported questionnaire surveys and radiographic imaging reports.ConclusionThe AutoML approach has shown potential in automating the process of using ML models to predict long-term changes in knee OA-related outcomes. Its use could support the deployment of ML solutions, facilitating the provision of personalized interventions to prevent the deterioration of knee health and incident knee OA.

Project description:ObjectiveMetabolic factors have been shown to be associated to severe radiographic knee osteoarthritis (RKOA). However, more knowledge is needed in early clinical knee osteoarthritis (KOA). The aim was to study associations between metabolic factors and radiographic knee osteoarthritis (OA) in individuals with knee pain. A second aim was to study associations between metabolic factors and RKOA in those with normal BMI and in those overweight/obese, respectively.MethodThis cross-sectional study included 282 individuals with knee pain (without cruciate ligament injury) and aged 30-67 years, and 70% women. Waist circumference, body mass index (BMI), proportion of fat and visceral fat area (VFA) were assessed. RKOA was defined as Ahlbäck grade 1 in at least one knee. Fasting blood samples were taken and triglycerides, cholesterol (total, low density lipoprotein (LDL) and high density lipoprotein (HDL)), C-reactive protein (CRP), glucose, HbA1C were analysed. Metabolic syndrome was defined in accordance with the International Diabetes Federation (IDF). Associations were analysed by logistic regression.ResultsIndividuals with RKOA were older, had higher BMI, higher VFA, larger waist circumference and had increased total cholesterol, triglycerides and LDL-cholesterol, but not fasting glucose. There was no difference between the group with RKOA vs. non-radiographic group regarding the presence of metabolic syndrome. In a subgroup analysis of individuals with normal BMI (n = 126), those with RKOA had higher VFA, more central obesity, higher levels of CRP and total cholesterol, compared with individuals without RKOA. In individuals with obesity, age was the only outcome associated to RKOA.ConclusionThere were clear associations between metabolic factors and RKOA in individuals with knee pain, also in those with normal BMI. In individuals with obesity age was the only variable associated to RKOA.Trial registrationclinicalTrials.gov Identifier: NCT04928170.

Dataset Information

Experiences of Health-Promoting Activities among Individuals with Knee Pain: The Halland Osteoarthritis Cohort.

Publications

Experiences of Health-Promoting Activities among Individuals with Knee Pain: The Halland Osteoarthritis Cohort.

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