Background Some guidelines state that in-person weight management interventions are more efficacious than those delivered digitally. However, digital programs are more scalable and accessible. We hypothesized that one-on-one health coaching via app-based video chat would simulate an in-person experience and help achieve outcomes comparable to those of in-person interventions. Methods A 12-month digital weight management intervention was provided to overweight or obese adults recruited from a large technology company. One-on-one health coaching sessions were offered during a 24-week intensive phase as well as subsequent maintenance phase. Focused on sustainable changes in activity and diet, the intervention incorporates SMART goals, in-app food and activity logs, Fitbit integration, as well as optional sleep and stress modules. Self-Determination Theory and the Transtheoretical Model are incorporated to drive behavior change. Multilevel mixed-effects models were used to analyze weight changes retrospectively. Results Six hundred eighty-three participants reported 29,051 weights. At 12 months, mean percent changes in body weight were-7.2% and-7.6% for overweight and obese groups, respectively. A weight change of-5% is commonly targeted for in-person weight management interventions. Observed weight loss exceeded this target by 2.2% (95% CI, 0.7% to 3.8%; P < .01) for the overweight group and 2.6% (95% CI, 1.4% to 3.9%; P < .01) for the obese group. Conclusions Further research is needed with randomization to in-person or digital interventions. Though limited by an observational, retrospective design, preliminary results suggest that some digital weight management programs with one-on-one coaching may achieve outcomes comparable to those of robust, in-person interventions.

BackgroundOnline social media offer great potential for research participant recruitment and data collection. We conducted synchronous (real-time) online focus groups (OFGs) through Facebook with the target population of young adult substance users to inform development of Facebook health behavior change interventions. In this paper we report methods and lessons learned for future studies.MethodsIn the context of two research studies participants were recruited through Facebook and assigned to one of five 90-min private Facebook OFGs. Study 1 recruited for two OFGs with young adult sexual and/or gender minority (SGM) smokers (range: 9 to 18 participants per group); Study 2 recruited for three groups of young adult smokers who also engage in risky drinking (range: 5 to 11 participants per group).ResultsOver a period of 11 (Study 1) and 22 days (Study 2), respectively, we recruited, assessed eligibility, collected baseline data, and assigned a diverse sample of participants from all over the US to Facebook groups. For Study 1, 27 of 35 (77%) participants invited attended the OFGs and 25 of 32 (78%) for Study 2. Participants in Study 1 contributed an average of 30.9 (SD = 8.9) comments with an average word count of 20.1 (SD = 21.7) words, and 36.0 (SD = 12.3) comments with 11.9 (SD = 13.5) words on average in Study 2. Participants generally provided positive feedback on the study procedures.ConclusionsFacebook can be a feasible and efficient medium to conduct synchronous OFGs with young adults. This data collection strategy has the potential to inform health behavior change intervention development.

Gender and years of university education influence sleep among university students both separately and concomitantly.

Second hand smoke (SHS) exposure during pregnancy is associated with poor pregnancy and fetal outcomes. To design interventions to reduce exposure, an in depth understanding of social and cultural factors of smoking behavior at home is important, especially in South Asia where SHS exposure is high. This study aimed to explore pregnant women’s, their husbands’ and other family members’ knowledge, attitudes and practices regarding home SHS exposure. Semi-structured interviews were conducted with 33 participants in Comilla, Bangladesh and 31 in Bangalore, India (36 pregnant women, 18 husbands, and 10 family members). Data were analyzed using the Framework approach. Husbands smoked in various living areas inside the home, often in the presence of their pregnant wives. Most had never tried to stop smoking at home. Knowledge of the risks was generally poor. Most women had repeatedly asked their husband to smoke outside with little success and only few family members had reprimanded the husbands. Husbands who had stopped did so because of requests from children and their mother. Potential strategies to decrease SHS exposure at home were educating the husband about risks and supporting the pregnant women in negotiation. Interventions must also enlist family support to enhance the woman’s self-efficacy.

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Coccidioidomycosis, also known as Valley fever, has been reported among military personnel in Coccidioides-endemic areas of the southwestern United States since World War II. In this study, the prevalence of Coccidioides was confirmed in different soil and dust samples collected near three military bases in California using DNA extraction and Polymerase Chain Reaction (PCR) methods. Analyses of physical and chemical parameters revealed no significant differences between Coccidioides-positive and -negative sites. Soil samples collected in the Mojave Desert (near Twentynine Palms MCAGCC) showed the highest percentage of Coccidioides-positive soil and dust samples. Samples from the San Joaquin Valley (near NAS Lemoore) showed the lowest percentage of positive samples and were restricted to remnants of semi-natural areas between agricultural fields. Our results suggest that soil disturbance around all three military bases investigated poses a potential Coccidioides exposure risk for military personnel and the public. We conclude that once lands have been severely disturbed from their original state, they become less suitable for Coccidioides growth. We propose a conceptual framework for understanding exposure where disturbance of soils that exhibit natural or remnants of native vegetation (Creosote and Salt Bush) generate a high risk of exposure to the pathogen, likely during dry periods. In contrast, Coccidioides-positive sites, when undisturbed, will not pose a high risk of exposure.

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In agriculture crop price analysis, Data mining is emerging as an important research field. In this paper, we will discuss about the applications and techniques of Data mining in agriculture. There are various data mining techniques such as K-Means, K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN) and Support Vector Machines (SVM) which are used for very recent applications of Data Mining techniques. This paper will consider the problem of price prediction of crops. Price Prediction, nowadays, has become very important agricultural problem which is to be solved only based on the available data. Data Mining techniques can be used to solve this problem. This work is based on finding suitable data models that helps in achieving high accuracy and generality for price prediction. For solving this problem, different Data Mining techniques were evaluated on different data sets.