BackgroundDiet is an important determinant of health, and food purchasing is a key antecedent to consumption.ObjectiveWe set out to evaluate the effectiveness of grocery store interventions to change food purchasing, and to examine whether effectiveness varied based on intervention components, setting, or socioeconomic status.DesignWe conducted a systematic review of randomized controlled trials (search performed June 2017). Studies must have: aimed to change food purchasing; been implemented in grocery stores (real or simulated); reported purchasing; and had a minimal control or compared interventions fulfilling our criteria. Searching, screening, bias assessment, and data extraction followed Cochrane methods. We grouped studies by intervention type (economic, environmental, swaps, and/or education), synthesized results narratively, and conducted an exploratory qualitative comparative analysis.ResultsWe included 35 studies representing 89 interventions, >20,000 participants, and >800 stores. Risk of bias was mixed. Economic interventions showed the most promise, with 8 of the 9 studies in real stores and all 6 in simulated environments detecting an effect on purchasing. Swap interventions appeared promising in the 2 studies based in real stores. Store environment interventions showed mixed effects. Education-only interventions appeared effective in simulated environments but not in real stores. Available data suggested that effects of economic interventions did not differ by socioeconomic status, whereas for other interventions impact was variable. In our qualitative comparative analysis, economic interventions (regardless of setting) and environmental and swap interventions in real stores were associated with statistically significant changes in purchasing in the desired direction for ≥1 of the foods targeted by the intervention, whereas education-only interventions in real stores were not.ConclusionsFindings suggest that interventions implemented in grocery stores—particularly ones that manipulate price, suggest swaps, and perhaps manipulate item availability—have an impact on purchasing and could play a role in public health strategies to improve health. Review protocol registered at https://www.crd.york.ac.uk/PROSPERO/ as CRD42017068809.

Background: On the 11 March 2020, the World Health Organization (WHO) declared that COVID-19 was a pandemic. To date, there are no medical treatments for COVID-19 with proven effectiveness. Novel treatments and/or vaccines will take time to be developed and distributed to patients. In light of this, there has been growing interest in the use of existing medications, such as chloroquine (CQ) and hydroxychloroquine (HCQ), as potential treatments of this disease.Aim: To establish the current evidence for the effectiveness of CQ and HCQ in treating COVID-19. Design & setting:A rapid review of the literature was conducted.Method: Electronic searches in PubMed and Google Scholar were conducted on 21 March 2020. A further search was conducted in Google for relevant literature on 28 March 2020.Results: There is limited evidence of in vitro activity of CQ/HCQ against SARS-CoV-2. A number of in vivo clinical trials are underway. The empirical data available from two of these trials reveal conflicting results. Both trials are characterised by small numbers of participants (n = 30 and n = 36) and suffer methodological limitations. No medium or long-term follow-up data is available.Conclusion: At present, there is insufficient evidence to determine whether CQ/HCQ are safe and effective treatments for COVID-19. High quality, adequately powered randomised clinical trials in primary and secondary care settings are urgently required to guide policymakers and clinicians. These studies should report medium-and long-term follow-up results, and safety data. How this fits inChloroquine (CQ) and hydroxychloroquine (HCQ) have been used in the treatment and prophylaxis of a number of conditions, such as malaria, for several years. As novel treatments for COVID-19 are likely to take time to develop, a number of clinical trials have been registered to investigate the effectiveness of existing medications such as CQ or HCQ. At present, there is insufficient evidence to recommend their use for the current pandemic outside of clinical trials. Further, high quality studies are urgently needed to provide timely guidance for clinicians and policymakers alike.

This review assessed the effects of environmental labels on consumers’ demand for more sustainable food products. Six electronic databases were searched for experimental studies of ecolabels and food choices. We followed standard Cochrane methods and results were synthesized using vote counting. Fifty-six studies ( N = 42,768 participants, 76 interventions) were included. Outcomes comprised selection ( n = 14), purchase ( n = 40) and consumption ( n = 2). The ecolabel was presented as text ( n = 36), logo ( n = 13) or combination ( n = 27). Message types included: organic ( n = 25), environmentally sustainable ( n = 27), greenhouse gas emissions ( n = 17), and assorted “other” message types ( n = 7). Ecolabels were tested in actual ( n = 15) and hypothetical ( n = 41) environments. Thirty-nine studies received an unclear or high RoB rating. Sixty comparisons favored the intervention and 16 favored control. Ecolabeling with a variety of messages and formats was associated with the selection and purchase of more sustainable food products.

Background: There are no established effective treatments for COVID-19. While novel drugs are being developed, azithromycin has been identified as a candidate treatment in the interim.Aim: To review the evidence for the effectiveness and safety of azithromycin in treating COVID-19.

BackgroundSignificant weight loss takes several months to achieve, and behavioral support can enhance weight loss success. Weight loss apps could provide ongoing support and deliver innovative interventions, but to do so, developers must ensure user satisfaction.ObjectiveThe aim of this study was to conduct a review of Google Play Store apps to explore what users like and dislike about weight loss and weight-tracking apps and to examine qualitative feedback through analysis of user reviews.MethodsThe Google Play Store was searched and screened for weight loss apps using the search terms weight loss and weight track*, resulting in 179 mobile apps. A content analysis was conducted based on the Oxford Food and Activity Behaviors taxonomy. Correlational analyses were used to assess the association between complexity of mobile health (mHealth) apps and popularity indicators. The sample was then screened for popular apps that primarily focus on weight-tracking. For the resulting subset of 15 weight-tracking apps, 569 user reviews were sampled from the Google Play Store. Framework and thematic analysis of user reviews was conducted to assess which features users valued and how design influenced users’ responses.ResultsThe complexity (number of components) of weight loss apps was significantly positively correlated with the rating (r=.25; P=.001), number of reviews (r=.28; P<.001), and number of downloads (r=.48; P<.001) of the app. In contrast, in the qualitative analysis of weight-tracking apps, users expressed preference for simplicity and ease of use. In addition, we found that positive reinforcement through detailed feedback fostered users’ motivation for further weight loss. Smooth functioning and reliable data storage emerged as critical prerequisites for long-term app usage.ConclusionsUsers of weight-tracking apps valued simplicity, whereas users of comprehensive weight loss apps appreciated availability of more features, indicating that complexity demands are specific to different target populations. The provision of feedback on progress can motivate users to continue their weight loss attempts. Users value seamless functioning and reliable data storage.

Background Self-regulation for weight loss requires regular self-monitoring of weight, but the frequency of weight tracking commonly declines over time. Objective This study aimed to investigate whether it is a decline in weight loss or a drop in motivation to lose weight (using physical activity tracking as a proxy) that may be prompting a stop in weight monitoring. Methods We analyzed weight and physical activity data from 1605 Withings Health Mate app users, who had set a weight loss goal and stopped tracking their weight for at least six weeks after a minimum of 16 weeks of continuous tracking. Mixed effects models compared weight change, average daily steps, and physical activity tracking frequency between a 4-week period of continuous tracking and a 4-week period preceding the stop in weight tracking. Additional mixed effects models investigated subsequent changes in physical activity data during 4 weeks of the 6-week long stop in weight tracking. Results People lost weight during continuous tracking (mean −0.47 kg, SD 1.73) but gained weight preceding the stop in weight tracking (mean 0.25 kg, SD 1.62; difference 0.71 kg; 95% CI 0.60 to 0.81). Average daily steps (beta=−220 daily steps per time period; 95% CI −320 to −120) and physical activity tracking frequency (beta=−3.4 days per time period; 95% CI −3.8 to −3.1) significantly declined from the continuous tracking to the pre-stop period. From pre-stop to post-stop, physical activity tracking frequency further decreased (beta=−6.6 days per time period; 95% CI −7.12 to −6.16), whereas daily step count on the day’s activity was measured increased (beta=110 daily steps per time period; 95% CI 50 to 170). Conclusions In the weeks before people stop tracking their weight, their physical activity and physical activity monitoring frequency decline. At the same time, weight increases, suggesting that declining motivation for weight control and difficulties with making use of negative weight feedback might explain why people stop tracking their weight. The increase in daily steps but decrease in physical activity tracking frequency post-stop might result from selective measurement of more active days.

Reliable and valid measurements of meat intake are needed to advance understanding of its health effects and to evaluate interventions to reduce meat consumption. Here, we describe the development and reliability of the Oxford Meat Frequency Questionnaire (MFQ). It asks individuals to report the number of servings of meat and seafood products they consumed in the last 24 h and is administered daily over 7 days. The MFQ combines food portion size data from the UK Food Standards Agency with estimates of meat content in composite dishes from the UK’s National Diet and Nutrition Survey. Adults who self-reported to eat meat (n = 129) completed a 3-week web-based test–retest reliability study assessing the MFQ twice, with a wash-out week in the middle. Two-way random intraclass correlation coefficients (ICC) revealed moderate to good agreement on all meat outcomes (total meat ICC = 0.716; minimum–maximum individual components = 0.531–0.680), except for fish and seafood (ICC = 0.257). Participants reported finding the questionnaire easy to use and, on average, completed it in less than 2 min. This new MFQ offers a quick, acceptable, and reliable method to measure changes in an individual’s meat intake in a UK population.

Objectives. To evaluate effectiveness and acceptability of a novel intervention, based on self-regulation theory, for weight loss. Design. A two-arm parallel group design was employed. Methods. Adult participants with a BMI ≥ 30 kg/m 2 and the aim to lose weight were recruited and randomized to either a control or intervention group. Both groups were asked to weigh themselves daily for eight weeks. The intervention group was encouraged to use a weight tracking app, and complete daily and weekly questionnaires to prompt action planning, reflection, and evaluation of actions. Participants chose daily actions from a menu of 53 behaviours. The primary outcome was weight change after 8 weeks, assessed using linear mixed effects models. At follow-up, 20 intervention group participants were interviewed regarding their experiences in the trial. Results. 100 participants were recruited, and 98% were followed up at 8 weeks. Mean weight loss was À4.18 kg (SD = 3.84) in the intervention compared to À1.01 kg (SD = 2.67) in the control group; the adjusted difference was À3.20 kg (95% CI À4.49, À1.92). Participants rated the intervention's usefulness as 8.25 (SD = 2.04) on a scale from 1 to 10. Adherence was a significant independent predictor of weight loss success (À1.54 kg per one SD, 95% CI À2.16, À0.93), but not a mediator of the intervention effect. Participants reported that the intervention enabled them to experiment with and identify effective weight loss actions. Conclusions. Guiding participants through the self-regulation process was feasible, acceptable to participants, and led to significantly greater short-term weight loss than unguided self-weighing. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.