Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Smartphone diet-tracking applications may help individuals lose weight, manage chronic conditions, and understand dietary patterns, however, the usabilities and functionalities of these apps have not been well studied. Objective: The aim of this study was to review the usability of current iOS and Android diet-tracking apps, the degree to which app features align with behavior change constructs, and to assess variations between apps in nutrient coding. Methods: The top seven diet-tracking apps were identified from the iOS iTunes and Android Play online stores, downloaded and used over a 2-week period. Each app was independently scored by researchers using the System of Usability Scale (SUS), and features were compared to the domains in an integrated behavior change theory framework: the Theoretical Domains Framework (TDF). An estimated 3-day food diary was completed using each app and food items were entered into USDA Food Composition Databases to evaluate their differences in nutrient data against the USDA reference. Results: Of the apps that were reviewed, LifeSum scored the highest average SUS score of 89.2, while MyDietCoach scored the lowest of 46.7. There were some variations in features noted between Android and iOS versions of the same apps, mainly for MyDietCoach, which affected SUS scoring. App features varied considerably, yet all of the apps had features consistent with Beliefs about Capabilities, and thus have the potential to promote self-efficacy by helping individuals track their diet and progress towards goals. None of the apps allowed for tracking of emotional factors that may be associated with diet patterns. The presence of behavior change domain features tended to be weakly correlated with greater usability (R2 ranging from 0 to 0.396). The exception to this was features related to the Reinforcement domain, which were correlated with less usability. Comparing the apps to the USDA reference for a 3-day diet, the average differences were 1.4 % for calories, 1.0 % for carbohydrates, 10.4% for protein, and -6.5% for fat. Conclusions: Almost all reviewed diet tracking apps score well in usability, utilize a variety of behavior change constructs, and accurately code calories and carbohydrates, allowing them to play a potential role in dietary intervention studies.

Smartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline patterns and for monitoring progress in interventions aimed at promoting healthier lifestyles. Individual-based models can be used to examine whether behavior, such as diet, corresponds to certain typological patterns. The objectives of this paper are to demonstrate individual-based modeling methods relevant to a person’s eating behavior, and the value of such approach compared to typical regression models. Using a mobile app, 2 weeks of physical activity and ecological momentary assessment (EMA) data, and 6 days of diet data were collected from 12 university students recruited from a university in Kunming, a rapidly developing city in southwest China. Phone GPS data were collected for the entire 2-week period, from which exposure to various food environments along each subject’s activity space was determined. Physical activity was measured using phone accelerometry. Mobile phone EMA was used to assess self-reported emotion/feelings. The portion size of meals and food groups was determined from voice-annotated videos of meals. Individual-based regression models were used to characterize subjects as following one of 4 diet typologies: those with a routine portion sizes determined by time of day, those with portion sizes that balance physical activity (energy balance), those with portion sizes influenced by emotion, and those with portion sizes associated with food environments. Ample compliance with the phone-based behavioral assessment was observed for all participants. Across all individuals, 868 consumed food items were recorded, with fruits, grains and dairy foods dominating the portion sizes. On average, 218 hours of accelerometry and 35 EMA responses were recorded for each participant. For some subjects, the routine model was able to explain up to 47% of the variation in portion sizes, and the energy balance model was able to explain over 88% of the variation in portion sizes. Across all our subjects, the food environment was an important predictor of eating patterns. Generally, grouping all subjects into a pooled model performed worse than modeling each individual separately. A typological modeling approach was useful in understanding individual dietary behaviors in our cohort. This approach may be applicable to the study of other human behaviors, particularly those that collect repeated measures on individuals, and those involving smartphone-based behavioral measurement.

Inorganic arsenic (iAs) is carcinogenic and highly concentrated in rice. Dietary exposure to iAs is concerning among adolescents due to their developmental stage and iAs’s long-latency effects. This paper aimed to assess iAs exposure from rice and related lifetime cancer risks (LCR) among adolescents in Kunming, China. A comprehensive literature review of iAs levels in rice and LCR in humans was also conducted. Average daily consumption of rice (ADC) was estimated from 267 adolescents (15–18 years). Rice samples obtained from 6 markets were analyzed for iAs concentration (AC). Estimated daily intake (EDI) of iAs was calculated using ADC, AC, and average body weight (BW). Lifetime Cancer Risk (LCR) was calculated using EDI and U.S. EPA derived iAs oral slope factor. The AC was 0.058 mg/kg and the average BW and ADC were 67.5 kg and 410 g/day for males and 55.5 kg and 337 g/day for females. The EDI and LCR were 3.52 × 10⁻⁴ mg/kg-BW/day and 5.28 × 10⁻⁴ for both males and females, with LCR 5 times above the U.S. LCR upper limit of 1.0 × 10⁻⁴. While the AC was below the Chinese maximum contaminant level of 0.2 mg/kg, study results indicated that Kunming adolescents may be at increased risk for iAs-related cancers.

While low-income midlife and older adults are disproportionately affected by non-communicable diseases that can be alleviated by regular physical activity, few physical activity programs have been developed specifically with their needs in mind. Those programs that are available typically do not address the recognized local environmental factors that can impact physical activity. The specific aim of the Steps for Change cluster-randomized controlled trial is to compare systematically the initial (one-year) and sustained (two-year) multi-level impacts of an evidence-based person-level physical activity intervention (Active Living Every Day [ALED] and age-relevant health education information), versus the ALED program in combination with a novel neighborhood-level citizen science intervention called Our Voice. The study sample (N = 300) consists of insufficiently active adults ages 40 years and over living in or around affordable senior public housing settings. Major study assessments occur at baseline, 12, and 24 months. The primary outcome is 12-month change in walking, and secondary outcomes include other forms of physical activity, assessed via validated self-report measures supported by accelerometry, and physical function and well-being variables. Additional intervention impacts are assessed at 24 months. Potential mediators and moderators of intervention success will be explored to better determine which subgroups do best with which type of intervention. Here we present the study design and methods, including recruitment strategies and yields. TRIAL REGISTRATION: clinicaltrial.gov Identifier = NCT03041415.

Given the rapid pace of urbanization and Westernization and the increasing prevalence of obesity, there is a need for research to better understand the influence of the built environment on overweight and obesity in world’s developing regions. Culturally-specific food environment survey instruments are important tools for studying changing food availability and pricing. Here, we present findings from an effort to develop and evaluate food environment survey instruments for use in a rapidly developing city in southwest China.

Background: Healthy aging requires support from local built and social environments. Using latent profile analysis, this study captured the multidimensionality of the built environment and examined relations between objective and perceived built environment profiles, neighborhood social cohesion and quality of life among seniors.Methods: In total, 693 participants aged 66–97 were sampled from two US locales in 2005–2008 as part of the Senior Neighborhood Quality of Life Study (SNQLS). Perceived social cohesion and quality of life were assessed using validated surveys. Six objective (geographic information system (GIS)-based) and seven perceived built environment latent profiles generated in previous SNQLS publications were used for analyses. Mixed-effects models estimated social cohesion and quality of life separately as a function of the built environment profiles.Results: More walkable and destination-rich perceived built environment profiles were associated with higher social cohesion and quality of life. Objective built environment profiles were not associated with social cohesion and only positively associated with quality of life in only one locale (Baltimore/DC).Conclusions: Latent profile analysis offered a comprehensive approach to assessing the built environment. Seniors who perceived their neighborhoods to be highly walkable and recreationally dense experienced higher neighborhood social cohesion and quality of life, which may set the stage for healthier aging.