Interventions to Promote Healthy Eating, Physical Activity and Smoking in Low-Income Groups: a Systematic Review with Meta-Analysis of Behavior Change Techniques and Delivery/Context

Purpose Healthy eating, physical activity and smoking interventions for low-income groups may have small, positive effects. Identifying effective intervention components could guide intervention development. This study investigated which content and delivery components of interventions were associated with increased healthy behavior in randomised controlled trials (RCTs) for low-income adults. Method Data from a review showing intervention effects in 35 RCTs containing 45 interventions with 17,000 participants were analysed to assess associations with behavior change techniques (BCTs) and delivery/context components from the template for intervention description and replication (TIDieR) checklist. The associations of 46 BCTs and 14 delivery/context components with behavior change (measures of healthy eating, physical activity and smoking cessation) were examined using random effects subgroup meta-analyses. Synergistic effects of components were examined using classification and regression trees (meta-CART) analyses based on both fixed and random effects assumptions. Results For healthy eating, self-monitoring, delivery through personal contact, and targeting multiple behaviors were associated with increased effectiveness. Providing feedback, information about emotional consequences, or using prompts and cues were associated with reduced effectiveness. In synergistic analyses, interventions were most effective without feedback, or with selfmonitoring excluding feedback. More effective physical activity interventions included behavioral practice/rehearsal or instruction, focussed solely on physical activity or took place in home/community settings. Information about antecedents was associated with reduced effectiveness. In synergistic analyses, interventions were most effective in home/community settings with instruction. No associations were identified for smoking. Conclusion This study identified BCTs and delivery/context components, individually and synergistically, linked to increased and reduced effectiveness of healthy eating and physical activity interventions. The identified components should be subject to further experimental study to help inform the development effective behavior change interventions for low-income groups to reduce health inequalities

Serosurveillance for Livestock Pathogens in Free-Ranging Mule Deer (Odocoileus hemionus)

Routine disease surveillance has been conducted for decades in mule deer (Odocoileus hemionus) in California for pathogens shared between wildlife and domestic ruminants that may have implications for the animal production industry and wildlife health. Deer sampled from 1990 to 2007 (n = 2,619) were tested for exposure to six pathogens: bluetongue virus (BTV), epizootic hemorrhagic disease virus (EHDV), bovine viral diarrhea virus (BVDV), Leptospira spp., Anaplasma spp. and Brucella spp. We evaluated the relationship between exposure to these pathogens and demographic risk factors to identify broad patterns in seroprevalence across a large temporal and spatial scale. The overall seroprevalence for the entire study period was 13.4% for BTV, 16.8% for EHDV, 17.1% for BVDV, 6.5% for Leptospira spp., 0.2% for Brucella spp., and 17% for Anaplasma spp. Antibodies against BTV and EHDV were most prevalent in the deer populations of southern California. Antibodies against Leptospira spp. and Anaplasma spp. were most prevalent in coastal and central northern California whereas antibodies against BVDV were most prevalent in central-eastern and northeastern California. The overall seroprevalence for Anaplasma spp. was slightly lower than detected in previous studies. North and central eastern California contains large tracts of federal land grazed by livestock; therefore, possible contact between deer and livestock could explain the high BVDV seroprevalence found in these areas. Findings from this study will help to establish baseline values for future comparisons of pathogen exposure in deer, inform on long-term trends in deer population health and provide relevant information on the distribution of diseases that are shared between wildlife and livestock