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Self-management interventions for patients with a chronic disease are considered an essential part of care for those patients. Studies evaluating the effectiveness of this type of interventions have shown heterogeneous results. This leaves healthcare professionals oblivious with regard to implementation of self-management strategies in practice. This thesis aimed to identify which subgroups of patients benefit from self-management interventions and what type of intervention works best. An individual patient data (IPD) meta-analysis was conducted in which the raw data of prior trials in this field of research were requested and merged in one large database. To investigate if observed effects were disease-specific or transcend the specific disease, this IPD meta-analysis was performed parallel in patients with heart failure (n=5624) and patients with COPD (n=3282). First, overall effects were estimated. Self-management interventions were particularly effective in improving outcomes directly related to the disease. There was a small improvement in disease-related quality of life and reduction of disease-related hospitalization or death in both patients groups. The analyses further showed that self-management interventions reduced time to death in heart failure patients with depressive symptoms. In COPD patients, better outcomes were observed for males, patients with more severe airflow obstruction, obesity, and patients with moderate self-efficacy, but results were inconsistent across outcomes. The data were also used to identify characteristics of effective self-management interventions. This analysis revealed that interventions with a longer duration (in months) could lead to better health outcomes in patients with heart failure and patients with COPD.

Introduction Self-management interventions in patients with chronic conditions have received increasing attention over the past few years, yet the meta-analyses encountered considerable heterogeneity in results. This suggests that the effectiveness of self-management interventions must be assessed in the context of which components are responsible for eliciting the effect and in which subgroups of patients the intervention works best. The aim of the present study is to identify condition-transcending determinants of success of self-management interventions in two parallel individual patient data meta-analyses of self-management trials in patients with congestive heart failure (CHF) and in patients with chronic obstructive pulmonary disease (COPD). Methods and analysis Investigators of 53 randomised trials (32 in CHF and 21 in COPD) will be requested to share their de-identified individual patient data. Data will be analysed using random effects models, taking clustering within studies into account. Effect modification by age, sex, disease severity, symptom status, comorbid conditions and level of education will be assessed. Sensitivity analyses will be conducted to assess the robustness of the findings. Ethics and dissemination The de-identified individual patient data are used only for the purpose for which they were originally collected and for which ethical approval has been obtained by the original investigators. Knowledge on the effective ingredients of self-management programmes and identification of subgroups of patients in which those interventions are most effective will guide the development of evidence-based personalised self-management interventions for patients with CHF and COPD as well as with other chronic diseases.

Background Identifying those people at increased risk of early functional decline in activities of daily living (ADL) is essential for initiating preventive interventions. The aim of this study is to develop and validate a clinical prediction model for onset of functional decline in ADL in three years of follow-up in older people of 65–75 years old. Methods Four population-based cohort studies were pooled for the analysis: ActiFE-ULM (Germany), ELSA (United Kingdom), InCHIANTI (Italy), LASA (Netherlands). Included participants were 65–75 years old at baseline and reported no limitations in functional ability in ADL at baseline. Functional decline was assessed with two items on basic ADL and three items on instrumental ADL. Participants who reported at least some limitations at three-year follow-up on any of the five items were classified as experiencing functional decline. Multiple logistic regression analysis was used to develop a prediction model, with subsequent bootstrapping for optimism-correction. We applied internal-external cross-validation by alternating the data from the four cohort studies to assess the discrimination and calibration across the cohorts. Results Two thousand five hundred sixty community-dwelling people were included in the analyses (mean age 69.7 ± 3.0 years old, 47.4% female) of whom 572 (22.3%) reported functional decline at three-year follow-up. The final prediction model included 10 out of 22 predictors: age, handgrip strength, gait speed, five-repeated chair stands time (non-linear association), body mass index, cardiovascular disease, diabetes, chronic obstructive pulmonary disease, arthritis, and depressive symptoms. The optimism-corrected model showed good discrimination with a C statistic of 0.72. The calibration intercept was 0.06 and the calibration slope was 1.05. Internal-external cross-validation showed consistent performance of the model across the four cohorts. Conclusions Based on pooled cohort data analyses we were able to show that the onset of functional decline in ADL in three years in older people aged 65–75 years can be predicted by specific physical performance measures, age, body mass index, presence of depressive symptoms, and chronic conditions. The prediction model showed good discrimination and calibration, which remained stable across the four cohorts, supporting external validity of our findings. Electronic supplementary material The online version of this article (10.1186/s12877-019-1192-1) contains supplementary material, which is available to authorized users.