Effectiveness and Cost-Effectiveness of Self-Management Interventions for Adults Living with Heart Failure to Improve Patient-Important Outcomes: An Evidence Map of Randomized Controlled Trials.

Self-management interventions (SMIs) may enhance heart failure (HF) outcomes and address challenges associated with disease management. This study aims to review randomized evidence and identify knowledge gaps in SMIs for adult HF patients. Within the COMPAR-EU project, from 2010 to 2018, we conducted searches in the databases MEDLINE, CINAHL, Embase, Cochrane, and PsycINFO. We performed a descriptive analysis using predefined categories and developed an evidence map of randomized controlled trials (RCTs). We found 282 RCTs examining SMIs for HF patients, comparing two to four interventions, primarily targeting individual patients (97%) globally (34 countries, only 31% from an European country). These interventions involved support techniques such as information sharing (95%) and self-monitoring (62%), often through a mix of in-person and remote sessions (43%). Commonly assessed outcomes included quality of life, hospital admissions, mortality, exercise capacity, and self-efficacy. Few studies have focused on lower socio-economic or minority groups. Nurses (68%) and physicians (30%) were the primary providers, and most studies were at low risk of bias in generating a random sequence for participant allocation; however, the reporting was noticeably unclear of methods used to conceal the allocation process. Our analysis has revealed prevalent support techniques and delivery methods while highlighting methodological challenges. These findings provide valuable insights for researchers, clinicians, and policymakers striving to optimize SMIs for individuals living with HF.

Contextual factors for the successful implementation of self-management interventions for chronic diseases: A qualitative review of reviews.

OBJECTIVES: To identify and describe the most relevant contextual factors (CFs) from the literature that influence the successful implementation of self-management interventions (SMIs) for patients living with type 2 diabetes mellitus, obesity, COPD and/or heart failure. METHODS: We conducted a qualitative review of reviews. Four databases were searched, 929 reviews were identified, 460 screened and 61 reviews met the inclusion criteria. CFs in this paper are categorized according to the Tailored Implementation for Chronic Diseases framework. RESULTS: A great variety of CFs was identified on several levels, across all four chronic diseases. Most CFs were on the level of the patient, the professional and the interaction level, while less CFs were obtained on the level of the intervention, organization, setting and national level. No differences in main themes of CFs across all four diseases were found. DISCUSSION: For the successful implementation of SMIs, it is crucial to take CFs on several levels into account simultaneously. Person-centered care, by tailoring SMIs to patients' needs and circumstances, may increase the successful uptake, application and implementation of SMIs in real-life practice. The next step will be to identify the most important CFs according to various stakeholders through a group consensus process.

Self-Management Interventions for Adults Living with Type II Diabetes to Improve Patient-Important Outcomes: An Evidence Map.

Self-management interventions (SMIs) may be promising in the treatment of Diabetes Mellitus Type 2 (T2DM). However, accurate comparisons of their relative effectiveness are challenging, partly due to a lack of clarity and detail regarding the intervention content being evaluated. This study summarizes intervention components and characteristics in randomized controlled trials (RCTs) related to T2DM using a taxonomy for SMIs as a framework and identifies components that are insufficiently incorporated into the design of the intervention or insufficiently reported. Following evidence mapping methodology, we searched MEDLINE, CINAHL, Embase, Cochrane, and PsycINFO from 2010 to 2018 for randomized controlled trials (RCTs) on SMIs for T2DM. We used the terms 'self-management', 'adult' and 'T2DM' for content. For data extraction, we used an online platform based on the taxonomy for SMIs. Two independent reviewers assessed eligible references; one reviewer extracted data, and a second checked accuracy. We identified 665 RCTs for SMIs (34% US, 21% Europe) including 164,437 (median 123, range 10-14,559) adults with T2DM. SMIs highly differed in design and content, and characteristics such as mode of delivery, intensity, location and providers involved were poorly described. The majority of interventions aimed to improve clinical outcomes like HbA1c (83%), weight (53%), lipid profile (45%) or blood pressure (42%); 27% (also) targeted quality of life. Improved knowledge, health literacy, patient activation or satisfaction with care were hardly used as outcomes (<16%). SMIs most often used education (98%), self-monitoring (56%), goal-setting (48%) and skills training (42%) to improve outcomes. Management of emotions (17%) and shared decision-making (5%) were almost never mentioned. Although diabetes is highly prevalent in some minority groups, in only 13% of the SMIs, these groups were included. Our findings highlight the large heterogeneity that exists in the design of SMIs for T2DM and the way studies are reported, making accurate comparisons of their relative effectiveness challenging. In addition, SMIs pay limited attention to outcomes other than clinical, despite the importance attached to these outcomes by patients. More standardized and streamlined research is needed to better understand the effectiveness and cost-effectiveness of SMIs of T2DM and benefit patient care.

Identifying most important contextual factors for the implementation of self-management interventions: A Delphi study.

OBJECTIVE: To reach consensus amongst stakeholders about the most important contextual factors (CFs) that may influence the successful implementation of (components of) self-management interventions (SMIs) for type 2 diabetes, obesity, COPD and heart failure. METHODS: Building on our literature review that identified 31 CFs on different levels we conducted a Delphi with 44 stakeholders to identify which of these CFs, or additional ones, contribute most to successful implementation of SMIs. The Delphi consisted of three rounds in which the CFs were scored, prioritized and discussed. RESULTS: The most important CFs overlapped to a great extent across components of SMIs and diseases. Overall, stakeholders identified 'HCP's ability to adapt the advice, communication or intervention to patients' situation and level of knowledge' as most important CF. CONCLUSION: CFs need to be taken into account when implementing promising SMIs. According to stakeholders, the most important CFs are patient-, HCP- or interaction related. 'Tailoring' was selected as the most crucial aspect for HCPs. PRACTICE IMPLICATIONS: Stakeholders can make informed decisions on the adoption of the most suitable SMIs in a given context. These CFs are available through a self-management platform. Suggestions to implement self-management behaviour and to close the research-to-practice gap are made.

Self-management interventions for adults living with obesity to improve patient-relevant outcomes: An evidence map.

OBJECTIVES: To conduct an evidence map on self-management interventions and patient-relevant outcomes for adults living with overweight/obesity. METHODS: Following Arksey and O'Malley methodology, we searched in five electronical databases including randomized controlled trials (RCTs) on SMIs for overweight/obesity. We used the terms "self-management", "adult" and "obesity" for content. Two independent reviewers assessed eligible references; one reviewer extracted data, a second checked accuracy. RESULTS: We identified 497 RCTs (58% US, 20% Europe) including 99,741 (median 112, range 11-5145) adults living with overweight/obesity. Most research evaluated clinical outcomes (617, 55%) and behaviors adherence (255, 23%). Empowerment skills, quality of life and satisfaction were less targeted (8%, 7%, 0.2%, respectively). The most frequent techniques included sharing information (858, 99%), goal setting (619, 72%) and self-monitoring training (614, 71%), provided face-to-face (386, 45%) or in combination with remote techniques (256, 30%). Emotional management, social support and shared-decision were less frequent (18%, 26%, 4%). Socio-economic status, minorities or health literacy were seldom reported. CONCLUSION: There is a need of widening the scope of research by focusing on outcomes important to patients, assessing emotional/social/share-decision support, exploring remote techniques and including vulnerable populations.

Exploring the Effectiveness of Self-Management Interventions in Type 2 Diabetes: A Systematic Review and Network Meta-Analysis.

BACKGROUND: Chronic diseases are a leading cause of global morbidity and mortality. In response to this challenge, self-management interventions (SMIs) have emerged as an essential tool in improving patient outcomes. However, the diverse and complex nature of SMIs pose significant challenges in measuring their effectiveness. This work aims to investigate the comparative effectiveness of SMIs on Type 2 diabetes mellitus (T2DM) outcomes. METHODS: A rigorous analytical framework was employed to assess the relative effectiveness of different SMIs, encompassing both pairwise and network meta-analysis (NMA), as well as component network meta-analysis (CNMA). Various outcomes were considered, including glycated hemoglobin (HbA1c) control, body mass index (BMI) reduction and low-density lipoprotein (LDL) cholesterol. Visualization tools were also utilized to enhance the interpretation of results. RESULTS: SMIs were found promising in improving clinical outcomes and patient-reported measures. However, considerable heterogeneity and inconsistency across studies challenged the validity of NMA results. CNMA along with various visualization tools offered insights into the contributions of individual SMI components, highlighting the complexity of these interventions. DISCUSSION/CONCLUSIONS: SMIs represent a valuable approach to managing chronic conditions, but their effectiveness is context-dependent. Further research is needed to elucidate the contextual factors influencing SMI outcomes. This work contributes to a comprehensive understanding of SMIs' role in T2DM management, aiming to aid decision-makers, clinicians, and patients in selecting tailored interventions.

Using a Taxonomy to Systematically Identify and Describe Self-Management Interventions Components in Randomized Trials for COPD.

Self-management interventions (SMIs) may improve outcomes in Chronic Obstructive Pulmonary Disease (COPD). However, accurate comparisons of their relative effectiveness are challenging, partly due to a lack of clarity and detail regarding the intervention content being evaluated. This study systematically describes intervention components and characteristics in randomized controlled trials (RCTs) related to COPD self-management using the COMPAR-EU taxonomy as a framework, identifying components that are insufficiently incorporated into the design of the intervention or insufficiently reported. Overall, 235 RCTs published between 2010 and 2018, from a systematic review were coded using the taxonomy, which includes 132 components across four domains: intervention characteristics, expected patient (or caregiver) self-management behaviours, patient relevant outcomes, and target population characteristics. Risk of bias was also assessed. Interventions mainly focused on physical activity (67.4%), and condition-specific behaviours like breathing exercise (63.5%), self-monitoring (50.8%), and medication use (33.9%). Support techniques like education and skills-training, self-monitoring, and goal setting (over 35% of the RCTs) were mostly used for this. Emotional-based techniques, problem-solving, and shared decision-making were less frequently reported (less than 15% of the studies). Numerous SMIs components were insufficiently incorporated into the design of COPD SMIs or insufficiently reported. Characteristics like mode of delivery, intensity, location, and providers involved were often not described. Only 8% of the interventions were tailored to the target population's characteristics. Outcomes that are considered important by patients were hardly taken into account. There is still a lot to improve in both the design and description of SMIs for COPD. Using a framework such as the COMPAR-EU SMI taxonomy may contribute to better reporting and to better informing of replication efforts. In addition, prospective use of the taxonomy for developing and reporting intervention content would further aid in building a cumulative science of effective SMIs in COPD.

Applying machine learning on health record data from general practitioners to predict suicidality.

BACKGROUND: Suicidal behaviour is difficult to detect in the general practice. Machine learning (ML) algorithms using routinely collected data might support General Practitioners (GPs) in the detection of suicidal behaviour. In this paper, we applied machine learning techniques to support GPs recognizing suicidal behaviour in primary care patients using routinely collected general practice data. METHODS: This case-control study used data from a national representative primary care database including over 1.5 million patients (Nivel Primary Care Database). Patients with a suicide (attempt) in 2017 were selected as cases (N = 574) and an at risk control group (N = 207,308) was selected from patients with psychological vulnerability but without a suicide attempt in 2017. RandomForest was trained on a small subsample of the data (training set), and evaluated on unseen data (test set). RESULTS: Almost two-third (65%) of the cases visited their GP within the last 30 days before the suicide (attempt). RandomForest showed a positive predictive value (PPV) of 0.05 (0.04-0.06), with a sensitivity of 0.39 (0.32-0.47) and area under the curve (AUC) of 0.85 (0.81-0.88). Almost all controls were accurately labeled as controls (specificity = 0.98 (0.97-0.98)). Among a sample of 650 at-risk primary care patients, the algorithm would label 20 patients as high-risk. Of those, one would be an actual case and additionally, one case would be missed. CONCLUSION: In this study, we applied machine learning to predict suicidal behaviour using general practice data. Our results showed that these techniques can be used as a complementary step in the identification and stratification of patients at risk of suicidal behaviour. The results are encouraging and provide a first step to use automated screening directly in clinical practice. Additional data from different social domains, such as employment and education, might improve accuracy.