Diet guidelines recommend increasing dietary diversity. Yet, metrics for dietary diversity have neither been well-defined nor evaluated for impact on metabolic health. Also, whether diversity has effects independent of diet quality is unknown. We characterized and evaluated associations of diet diversity and quality with abdominal obesity and type II diabetes (T2D) in the Multi-Ethnic Study of Atherosclerosis. At baseline (2000–02), diet was assessed among 5,160 Whites, Hispanic, Blacks, and Chinese age 45–84 y and free of T2D, using a validated questionnaire. Three different aspects of diet diversity were characterized including count (number of different food items eaten more than once/week, a broad measure of diversity), evenness (Berry index, a measure of the spread of the diversity), and dissimilarity (Jaccard distance, a measure of the diversity of the attributes of the foods consumed). Diet quality was characterized using aHEI, DASH, and a priori pattern. Count and evenness were weakly positively correlated with diet quality (r with AHEI: 0.20, 0.04), while dissimilarity was moderately inversely correlated (r = -0.34). In multivariate models, neither count nor evenness was associated with change in waist circumference (WC) or incident T2D. Greater food dissimilarity was associated with higher gain in WC (p-trend<0.01), with 120% higher gain in participants in the highest quintile of dissimilarity scores. Diet diversity was not associated with incident T2D. Also, none of the diversity metrics were associated with change in WC or incident T2D when restricted to only healthier or less healthy foods. Higher diet quality was associated with lower risk of T2D. Our findings provide little evidence for benefits of diet diversity for either abdominal obesity or diabetes. Greater dissimilarity among foods was actually associated with gain in WC. These results do not support the notion that “eating everything in moderation” leads to greater diet quality or better metabolic health.

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

Background. Alcohol and drug use is associated with significant morbidity and mortality and is highly prevalent among homeless youth. Ecological Momentary Assessments (EMA) have been used to examine the effect of urges on drug use, though not among homeless youth. Objectives. We assessed the patterns of drug use and the correlation between real-time contextual factors and drug use using EMA collected daily. We identified predictors of drug use among a sample of homeless youth 18–25 years old in Houston, Texas. Methods. Homeless youth (n = 66, 62% male) were recruited from a drop-in center between September 2015 and May 2016. We used generalized linear mixed models and cross-validation methods to determine the best predictive model of drug use. Results. The overall drug use was high: 61% and 32% of participants reported using drugs or alcohol at least one day, respectively. Marijuana and synthetic marijuana use (i.e., Kush, K2, incense packs) were reported most frequently; 86% and 13% of the total drug use EMAs, respectfully. Drug use urge was reported on 26% of days and was the highest on drug use days. Drug use was predicted by discrimination, pornography use, alcohol use, and urges for drugs, alcohol, and to steal. Conclusions. EMA can be used to predict drug use among homeless youth. Drug use treatment among homeless youth should address the role of experiencing discrimination, pornography and alcohol use, and urge management strategies on drug use. Research is needed to determine if EMA informed just-in-time interventions targeting these predictors can reduce use.

Background: There is limited research in advanced cancer patients (ACP) regarding association between objectively measured daytime activity and sleep (as measured by actigraphy), patient characteristics, and cancer symptoms (fatigue, sleep, anxiety, depression, cachexia, and symptom distress scores [SDSs]). Objectives: Our aim of the study was to determine the association between mean daytime activity (MDTA) and the following items: fatigue (FACIT-F), SDSs (Edmonton Symptom Assessment Scale [ESAS]), sleep quality (Pittsburg Sleep Quality Index [PSQI]), objective sleep variables (OSV) (sleep onset, sleep efficacy, wake after sleep onset, total sleep time), anxiety and depression (Hospital Anxiety and Depression Scale [HADS]), body composition scores, and overall survival (OS). We also examined the association between sleep [PSQI and OSV scores] and FACIT-F, HADS, and ESAS. Methods: Secondary analysis of a recent clinical trial of cancer-related fatigue in advanced cancer (NCT00424099). Association between MDTA and OSV (measured by actigraphy) during the first week of the study and patient characteristics, symptoms (FACIT-F, ESAS, HADS, and PSQI), and OS were analyzed. Results: Seventy-nine eligible patients were evaluable. The median age was 57 years. Median MDTA was 248.43 counts/minute. Multivariate analysis shows that low MDTA was significantly associated with age, gender, Functional Assessment of Cancer Therapy (FACT)-Functional Well-Being (FWB), ESAS dyspnea, HADS-anxiety, and total sleep time. MDTA was not associated with FACIT-F ( p = 0.997) and OS ( p = 0.18). Sleep quality (PSQI) was significantly associated with FACIT-F, HADS, ESAS anxiety, and depression, but none of these variables was associated with OSV. Conclusion: In ACP, lower MDTA was significantly associated with age, gender, FACT-FWB, ESAS dyspnea, HADS-anxiety, and total sleep time. Both sleep quality and cancer-related fatigue scores were strongly associated with depression and anxiety. More research is needed.