Heart failure with preserved ejection fraction (HFpEF) accounts for >50% of heart failure cases and is associated with significant morbidity and health system burden. To date, there have been limited treatment options proven to improve outcomes in these patients, with sodium glucose co-transporter 2 (SGLT2) inhibitors the first class of drug to demonstrate significant clinical benefits, including reductions in heart failure hospitalisation. Obesity is associated with all forms of heart failure and has been linked with worse clinical outcomes. Numerous reviews support the benefits of weight loss in heart failure, more specifically in patients with heart failure with reduced ejection fraction. However, the evidence in HFpEF patients is less clear. With limited pharmacotherapy options and growing support for weight loss in patients with HFpEF, this systematic review and meta-analysis aims to examine the effects of lifestyle interventions on weight loss and other health outcomes in patients with HFpEF. Web of Science, Embase, Scopus, and PubMed databases were searched to identify relevant studies up to February 2023. Included studies were randomised controlled trials (with a duration of four weeks or more) of lifestyle interventions conducted in adults with HFpEF that reported weight loss. Outcomes of interest were body weight, body mass index (BMI), blood pressure (systolic and diastolic), aerobic capacity (6-minute walk distance), New York Heart Association (NYHA) Functional Classification, self-reported health quality of life (Minnesota Living with Heart Failure Questionnaire; MLHFQ), and N-terminal pro B-Type Natriuretic Peptide (NT-proBNP) levels. Review Manager software was used to conduct random effect meta-analyses, forest plots were generated for each outcome, and between-study heterogeneity was estimated using the I² test statistic. Risk-of-bias assessment used the Cochrane risk-of-bias tool, and the certainty of the evidence was assessed using GRADE. From 2,282 records identified, six studies with a total of 375 participants, between three to six months in duration, were included in this systematic review and meta-analysis. Lifestyle interventions consisted of diet only, exercise only, combination of diet and exercise, and education and exercise. Over a mean follow-up of 4.5 months, pooled effects of the interventions were associated with a reduction in body weight of >5kg (weight mean difference (WMD): -5.30 kg; 95% CI: -8.72 to -1.87; p=0.002), and a reduction in resting systolic (WMD: -2.98 mmHg; 95% CI: -4.20 to -1.76; p<0.001) and diastolic blood pressure (WMD: -4.51 mmHg; 95% CI: -8.39 to -0.64; p=0.02) compared with those who received usual care. Interventions also improved 6-minute walk distance (WMD: 43.63 m; 95% CI: 22.28 to 64.97; p<0.001), NYHA class (WMD: -0.54; 95% CI: -0.75 to -0.33; p<0.001), and MLHFQ score (WMD: -17.77; 95% CL: -19.00 to -16.53; p<0.001). In patients with HFpEF, lifestyle intervention was associated with a significant reduction in body weight and had favourable effects on blood pressure, aerobic capacity, NYHA class, and health-related quality of life. Further research is needed in this population to examine the feasibility and durability of weight loss interventions and to examine the potential impact on hard clinical endpoints.

Background Clinical trials are an important research method for improving medical knowledge and patient care. Multiple international and national guidelines stipulate the need for data quality and assurance. Many strategies and interventions are developed to reduce error in trials, including standard operating procedures, personnel training, data monitoring, and design of case report forms. However, guidelines are nonspecific in the nature and extent of necessary methods. Objective This article gathers information about current data quality tools and procedures used within Australian clinical trial sites, with the aim to develop standard data quality monitoring procedures to ensure data integrity. Methods Relevant information about data quality management methods and procedures, error levels, data monitoring, staff training, and development were collected. Staff members from 142 clinical trials listed on the National Health and Medical Research Council (NHMRC) clinical trials Web site were invited to complete a short self-reported semiquantitative anonymous online survey. Results Twenty (14%) clinical trials completed the survey. Results from the survey indicate that procedures to ensure data quality varies among clinical trial sites. Centralized monitoring (65%) was the most common procedure to ensure high-quality data. Ten (50%) trials reported having a data management plan in place and two sites utilized an error acceptance level to minimize discrepancy, set at <5% and 5 to 10%, respectively. The quantity of data variables checked (10–100%), the frequency of visits (once-a-month to annually), and types of variables (100%, critical data or critical and noncritical data audits) for data monitoring varied among respondents. The average time spent on staff training per person was 11.58 hours over a 12-month period and the type of training was diverse. Conclusion Clinical trial sites are implementing ad hoc methods pragmatically to ensure data quality. Findings highlight the necessity for further research into “standard practice” focusing on developing and implementing publicly available data quality monitoring procedures.

Background: Despite the increasing use of mobile health (mHealth) services, such as mHealth apps or SMS text messaging services, that support the patient self-management of chronic conditions, many existing mHealth services lack theoretical guidance. In addition, although often the target audience for requirement acquisition at the initial mHealth app design stage, it is a common challenge for them to fully conceptualize their needs for mHealth services that help self-manage chronic conditions. Objective: This study proposes a novel co-design approach with the initial requirements for mHealth services proposed by clinicians based on their experiences in guiding patients to self-manage chronic conditions. A design case is presented to illustrate our innovative approach to designing an mHealth app that supports the self-management of patients with obesity in their preparation for elective surgery. Methods: We adopted a clinician-led co-design approach. The co-design approach consisted of the following four cyclic phases: understanding user needs, identifying an applicable underlying theory, integrating the theory into the prototype design, and evaluating and refining the prototype mHealth services with patients. Expert panel discussions, a literature review, intervention mapping, and patient focus group discussions were conducted in these four phases. Results: In stage 1, the expert panel proposed the following three common user needs: motivational, educational, and supportive needs. In stage 2, the team selected the Social Cognitive Theory to guide the app design. In stage 3, the team designed and developed the key functions of the mHealth app, including automatic push notifications; web-based resources; goal setting and monitoring; and interactive health-related exchanges that encourage physical activity, healthy eating, psychological preparation, and a positive outlook for elective surgery. Push notifications were designed in response to a patient’s risk level, as informed by the person’s response to a baseline health survey. In stage 4, the prototype mHealth app was used to capture further requirements from patients in the two focus group discussions. Focus group participants affirmed the potential benefits of the app and suggested more requirements for the function, presentation, and personalization needs. The app was improved based on these suggestions. Conclusions: This study reports an innovative co-design approach that was used to leverage the clinical experiences of clinicians to produce the initial prototype app and the approach taken to allow patients to effectively voice their needs and expectations for the mHealth app in a focus group discussion. This approach can be generalized to the design of any mHealth service that aims to support the patient self-management of chronic conditions.

Cardiovascular disease (CVD) is the leading cause of death globally. Habitual consumption of tree nuts and peanuts is associated with cardioprotective benefits. Food-based dietary guidelines globally recommend nuts as a key component of a healthy diet. This systematic review and meta-analysis were conducted to examine the relationship between tree nut and peanut consumption and risk factors for CVD in randomized controlled trials (RCTs) (PROSPERO: CRD42022309156). MEDLINE, PubMed, CINAHL, and Cochrane Central databases were searched up to 26 September, 2021. All RCT studies that assessed the effects of tree nut or peanut consumption of any dose on CVD risk factors were included. Review Manager software was used to conduct a random effect meta-analysis for CVD outcomes from RCTs. Forest plots were generated for each outcome, between-study heterogeneity was estimated using the I² test statistic and funnel plots and Egger's test for outcomes with ≥10 strata. The quality assessment used the Health Canada Quality Appraisal Tool, and the certainty of the evidence was assessed using grading of recommendations assessment, development, and evaluation (GRADE). A total of 153 articles describing 139 studies (81 parallel design and 58 cross-over design) were included in the systematic review, with 129 studies in the meta-analysis. The meta-analysis showed a significant decrease for low-density lipoprotein (LDL) cholesterol, total cholesterol (TC), triglycerides (TG), TC:high-density lipoprotein (HDL) cholesterol, LDL cholesterol:HDL cholesterol, and apolipoprotein B (apoB) following nut consumption. However, the quality of evidence was "low" for only 18 intervention studies. The certainty of the body of evidence for TC:HDL cholesterol, LDL cholesterol:HDL cholesterol, and apoB were "moderate" because of inconsistency, for TG were "low," and for LDL cholesterol and TC were "very low" because of inconsistency and the likelihood of publication bias. The findings of this review provide evidence of a combined effect of tree nuts and peanuts on a range of biomarkers to create an overall CVD risk reduction.