Search

Objectives: We conducted a systematic review that aimed to document and describe how (1) expectation of benefit from treatment (response expectancies) were measured and reported in acupuncture trials, and (2) examine any effect on outcomes.Design: We searched MEDLINE, EMBASE, AMED, CIHAHL, CENTRAL and Science and Technology Proceedings up to November 2007 for randomised (RCT) and quasi-randomised (CCT) controlled trials and prospective controlled cohorts of acupuncture as treatment for a medical or psychological condition in adults. An update citation search was conducted in April 2010. We included studies that mentioned soliciting response expectancies.Results: We found 58 RCTs that fulfilled our inclusion criteria. Around half referenced one of five published instruments, most of which were designed to measure sham credibility and included one question on response expectancy. A wide range of question phrasing and response scales was used. There was some evidence that response scales may influence the measurement of expectations. Eight trials analysed the association between pre-randomisation expectations for assigned treatment and outcomes, and six the effect of pre-randomisation expectations across all patients independent of treatment allocation. Some showed associations but others did not.Conclusions: There is some evidence that response expectancies interact with outcomes in acupuncture trials however the variety of question phrasing and analysis methods precludes drawing a firm conclusion about for whom and under which circumstance. To further our understanding of expectations, more methodological work is needed to standardise the questions and response scales that are used. [ABSTRACT FROM AUTHOR]

Background: The average life expectancy for people with a severe mental illness (SMI) such as schizophrenia or bipolar disorder is 15 to 20 years less than that for the population as a whole. Diabetes contributes significantly to this inequality, being 2 to 3 times more prevalent in people with SMI. Various risk factors have been implicated, including side effects of antipsychotic medication and unhealthy lifestyles, which often occur in the context of socioeconomic disadvantage and health care inequality. However, little is known about how these factors may interact to influence the risk of developing diabetes and poor diabetic outcomes, or how the organization and provision of health care may contribute. Objective: This study aims to identify the determinants of diabetes and to explore variation in diabetes outcomes for people with SMI. Methods: This study will employ a concurrent mixed methods design combining the interrogation of electronic primary care health records from the Clinical Practice Research Datalink (CPRD GOLD) with qualitative interviews with adults with SMI and diabetes, their relatives and friends, and health care staff. The study has been funded for 2 years, from September 2017 to September 2019, and data collection has recently ended. Results: CPRD and linked health data will be used to explore the association of sociodemographics, illness, and health care–related factors with both the development and outcomes of type 2 diabetes in people with SMI. Experiences of managing the comorbidity and accessing health care will be explored through qualitative interviews using topic guides informed by evidence synthesis and expert consultation. Findings from both datasets will be merged to develop a more comprehensive understanding of diabetes risks, interventions, and outcomes for people with SMI. Findings will be translated into recommendations for interventions and services using co-design workshops. Conclusions: Improving diabetes outcomes for people with SMI is a high-priority area nationally and globally. Understanding how risk factors combine to generate high prevalence of diabetes and poor diabetic outcomes for this population is a necessary first step in developing health care interventions to improve outcomes for people with diabetes and SMI. Trial Registration: ClinicalTrials.gov NCT03534921; https://clinicaltrials.gov/ct2/show/NCT03534921 We help JMIR researchers to raise funds to pursue their research and development aimed at tackling important health and technology challenges. If you would like to show your support for this author, please donate using the button below. The funds raised will directly benefit the corresponding author of this article (minus 8% admin fees). Your donations will help this author to continue publishing open access papers in JMIR journals. Donations of over $100 may also be acknowledged in future publications. Suggested contribution levels: $20/$50/$100 [ABSTRACT FROM AUTHOR]