Intakes and sources of isoflavones, lignans, enterolignans, coumestrol and soya-containing foods in the Norfolk arm of the European Prospective Investigation into Cancer and Nutrition (EPIC-Norfolk), from 7-day food diaries, using a newly updated database

Objective: A phytoestrogen-rich diet has been suggested to protect against a variety of common diseases but UK intake data on phytoestrogens or their food sources is sparse. This study aims to estimate the average intake of isoflavones, lignans, enterolignans and coumestrol from 7-day food diaries (7dFD), and to provide data on total isoflavone, lignan and phytoestrogen consumption by food group. Design: Development of a food composition database for twelve phytoestrogens and analysis of soya food and phytoestrogen consumption in a population-based study. Setting: Men and women, aged 40-79 years from the general population participating in EPIC-Norfolk between 1993 and 1997, with nutrient and food data from 7dFD. Subjects: A subset of 20 437 participants. Results: The median daily phytoestrogen intake for men was 1.20mg (interquartile range (IQR) 0.93-1.54 mg; mean 1.50 mg, SD 1.50 mg) and 0.89 mg for women (IQR 0.71-1.14 mg; mean 1.20 mg, SD 1.70 mg). In soya-consumers (SC), median daily intakes were higher: 2.86 mg in men (IQR – 1.30-7.27mg; mean 5.05 mg, SD 5.03 mg) and 3.14 mg in women (IQR – 1.09-7.33mg; mean 5.40 mg, SD 6.09 mg). In both men and women, bread made the greatest contribution to phytoestrogen intake – 40.7% and 35.7% respectively. In SC men and women, vegetable dishes and soya/goat’s/sheep’s milks were the main contributors – 42.6% and 18.9% in men and 38.8% and 29.1% in women, respectively. Conclusions: The ability to estimate phytoestrogen intake in Western populations more accurately will aid investigations into their suggested effects on health.

Targeting medication non-adherence behavior in selected autoimmune diseases: a systematic approach to digital health program development

Background 29 autoimmune diseases, including Rheumatoid Arthritis, gout, Crohn’s Disease, and Systematic Lupus Erythematosus affect 7.6-9.4% of the population. While effective therapy is available, many patients do not follow treatment or use medications as directed. Digital health and Web 2.0 interventions have demonstrated much promise in increasing medication and treatment adherence, but to date many Internet tools have proven disappointing. In fact, most digital interventions continue to suffer from high attrition in patient populations, are burdensome for healthcare professionals, and have relatively short life spans. Objective Digital health tools have traditionally centered on the transformation of existing interventions (such as diaries, trackers, stage-based or cognitive behavioral therapy programs, coupons, or symptom checklists) to electronic format. Advanced digital interventions have also incorporated attributes of Web 2.0 such as social networking, text messaging, and the use of video. Despite these efforts, there has not been little measurable impact in non-adherence for illnesses that require medical interventions, and research must look to other strategies or development methodologies. As a first step in investigating the feasibility of developing such a tool, the objective of the current study is to systematically rate factors of non-adherence that have been reported in past research studies. Methods Grounded Theory, recognized as a rigorous method that facilitates the emergence of new themes through systematic analysis, data collection and coding, was used to analyze quantitative, qualitative and mixed method studies addressing the following autoimmune diseases: Rheumatoid Arthritis, gout, Crohn’s Disease, Systematic Lupus Erythematosus, and inflammatory bowel disease. Studies were only included if they contained primary data addressing the relationship with non-adherence. Results Out of the 27 studies, four non-modifiable and 11 modifiable risk factors were discovered. Over one third of articles identified the following risk factors as common contributors to medication non-adherence (percent of studies reporting): patients not understanding treatment (44%), side effects (41%), age (37%), dose regimen (33%), and perceived medication ineffectiveness (33%). An unanticipated finding that emerged was the need for risk stratification tools (81%) with patient-centric approaches (67%). Conclusions This study systematically identifies and categorizes medication non-adherence risk factors in select autoimmune diseases. Findings indicate that patients understanding of their disease and the role of medication are paramount. An unexpected finding was that the majority of research articles called for the creation of tailored, patient-centric interventions that dispel personal misconceptions about disease, pharmacotherapy, and how the body responds to treatment. To our knowledge, these interventions do not yet exist in digital format. Rather than adopting a systems level approach, digital health programs should focus on cohorts with heterogeneous needs, and develop tailored interventions based on individual non-adherence patterns.