Estimated physical activity in Bavaria, Germany, and its implications for obesity risk: Results from the BVS-II Study

BACKGROUND: Adequate physical activity (PA) is considered as a key factor in the fight against the obesity epidemic. Therefore, detailed description of the actual PA and its components in the population is necessary. Additionally, this study aims to investigate the association between PA and obesity risk in a representative population sample in Bavaria, Germany. METHODS: Data from 893 participants (age 13–80 years) of the Bavarian Food Consumption Survey II (BVS II) were used. In each participant, three computer-based 24-hour recalls were conducted by telephone assessing type and duration of PA in the domains occupation, sports, other strenuous leisure time activities (of mostly moderate intensity) as well as TV/PC use in leisure time and duration of sleeping. After assigning metabolic equivalents (METs) to each activity, estimates of energy expenditure (MET*h) and total daily PA level (PAL(est.)) were calculated. In a subgroup of adults (n = 568) with anthropometric measurements logistic regression models were used to quantify the impact of PA on obesity risk. RESULTS: Estimated average PA in women and men was 38.5 ± 5.0 and 40.6 ± 9.3 MET*h/d, respectively, corresponding to PAL(est. )values of 1.66 ± 0.22 and 1.75 ± 0.40. Obese subjects showed lower energy expenditure in the categories sports, occupation, and sleeping, while the time spent with TV/PC during leisure time was highest. This is confirmed in logistic regression analyses revealing a statistically significant association between obesity and TV/PC use during leisure time, while sports activity was inversely related to obesity risk. Overall, less than 1/3 of the study participants reached the recommended PAL of ≥ 1.75. Subjects within the recommended range of PA had an about 60 % (odds ratio = 0.43; 95% CI: 0.21–0.85) reduced risk of obesity as compared to inactive subjects with a PAL(est. )<1.5. CONCLUSION: Based on the results of short-term PA patterns, a major part of the Bavarian adult population does not reach the recommendations (PAL>1.75; moderate PA of > 30 min/d). Despite the limitations of the study design, the existing associations between sports activity, TV/PC use and obesity risk in this population give further support to the recommendation of increasing sports activity and reducing sedentary behaviour in order to prevent rising rates of obesity

Theory-driven Visual Design to Support Reflective Dietary Practice via mHealth: A Design Science Approach

Design for reflection in human-computer interaction (HCI) has evolved from focusing on an abstract and outcome-driven design subject towards exposing procedural or structural reflection characteristics. Although HCI research has recognized that an individual\u27s reflection is a long-lasting, multi-layered process that can be supported by meaningful design, researchers have made few efforts to derive insights from a theoretical perspective about appropriate translation into end-user visual means. Therefore, we synthesize theoretical knowledge from reflective practice and learning and argue for a differentiation between time contexts of reflection that design needs to address differently. In an interdisciplinary design-science-research project in the mHealth nutrition promotion context, we developed theory-driven guidelines for “reflection-in-action” and “reflection-on-action”. Our final design guidelines emerged from prior demonstrations and a final utility evaluation with mockup artifacts in a laboratory experiment with 64 users. Our iterative design and the resulting design guidelines offer assistance for addressing reflection design by answering reflective practice’s respective contextual requirements. Based on our user study, we show that reflection in terms of “reflection- in-action” benefits from offering actionable choice criteria in an instant timeframe, while “reflection-on-action” profits from the structured classification of behavior-related criteria from a longer, still memorable timeframe

Effects and challenges of using a nutrition assistance system: results of a long-term mixed-method study

Healthy nutrition contributes to preventing non-communicable and diet-related diseases. Recommender systems, as an integral part of mHealth technologies, address this task by supporting users with healthy food recommendations. However, knowledge about the effects of the long-term provision of health-aware recommendations in real-life situations is limited. This study investigates the impact of a mobile, personalized recommender system named Nutrilize. Our system offers automated personalized visual feedback and recommendations based on individual dietary behaviour, phenotype, and preferences. By using quantitative and qualitative measures of 34 participants during a study of 2–3 months, we provide a deeper understanding of how our nutrition application affects the users’ physique, nutrition behaviour, system interactions and system perception. Our results show that Nutrilize positively affects nutritional behaviour (conditional R2=. 342) measured by the optimal intake of each nutrient. The analysis of different application features shows that reflective visual feedback has a more substantial impact on healthy behaviour than the recommender (conditional R2=. 354). We further identify system limitations influencing this result, such as a lack of diversity, mistrust in healthiness and personalization, real-life contexts, and personal user characteristics with a qualitative analysis of semi-structured in-depth interviews. Finally, we discuss general knowledge acquired on the design of personalized mobile nutrition recommendations by identifying important factors, such as the users’ acceptance of the recommender’s taste, health, and personalization

Perspective: a conceptual framework for adaptive personalized nutrition advice systems (APNASs)

Nearly all approaches to personalized nutrition (PN) use information such as the gene variants of individuals to deliver advice that is more beneficial than a generic one-size-fits-all recommendation. Despite great enthusiasm and the increased availability of commercial services, thus far, scientific studies have only revealed small to negligible effects on the efficacy and effectiveness of personalized dietary recommendations, even when using genetic or other individual information. In addition, from a public health perspective, scholars are critical of PN because it primarily targets socially privileged groups rather than the general population, thereby potentially widening health inequality. Therefore, in this perspective, we propose to extend current PN approaches by creating adaptive personalized nutrition advice systems (APNASs) that are tailored to the type and timing of personalized advice for individual needs, capacities, and receptivity in real-life food environments. These systems encompass a broadening of current PN goals (i.e., what should be achieved) to incorporate individual goal preferences beyond currently advocated biomedical targets (e.g., making sustainable food choices). Moreover, they cover the personalization processes of behavior change by providing in situ, just-in-time information in real-life environments (how and when to change), which accounts for individual capacities and constraints (e.g., economic resources). Finally, they are concerned with a participatory dialogue between individuals and experts (e.g., actual or virtual dieticians, nutritionists, and advisors), when setting goals and deriving measures of adaption. Within this framework, emerging digital nutrition ecosystems enable continuous, real-time monitoring, advice, and support in food environments from exposure to consumption. We present this vision of a novel PN framework along with scenarios and arguments that describe its potential to efficiently address individual and population needs and target groups that would benefit most from its implementation

Dietary quality in vegetarian and omnivorous female students in Germany: a retrospective study

Vegetarian diets have gained in popularity, especially among highly educated women, and are considered beneficial to health. Comparative studies assessing the diet of vegetarians against omnivores are rather limited and often provide ambivalent results. Therefore, this study examined the nutrient intake and nutritional quality of vegetarian and omnivorous diets in a group of 61 female students in Germany. Habitual dietary intake was evaluated using a validated graphical online food frequency questionnaire (FFQ). Differences in nutrient intakes were analyzed by Mann–Whitney-U-Tests. Odds Ratios (OR) were calculated for vegetarians exceeding dietary reference values (DRV) compared to omnivores. The overall nutritional quality was assessed using the Healthy-Eating-Index-2015 (HEI-2015). In omnivores, intakes of total energy from saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), long-chain omega-3 polyunsaturated fatty acids (LC-n3-PUFA), cholesterol, sucrose, lactose, retinol, and cobalamin were significantly higher than in vegetarians. Significantly lower intakes were observed for fiber, magnesium, and beta-carotene. Significant OR were detected for total fat (OR = 0.29), SFA (OR = 0.04), beta-carotene (OR = 4.55), and cobalamin (OR = 0.32). HEI-2015 scores were higher for vegetarians than for omnivores (79 points versus 74 points) and significant differences were recorded for the HEI-2015 components dairy, seafood plant proteins, fatty acids, added sugars, and saturated fatty acids

Eating out is different from eating at home among individuals who occasionally eat out. A cross-sectional study among middle-aged adults from eleven European countries

Eating out has been linked to the current obesity epidemic, but the evaluation of the extent to which out of home (OH) dietary intakes are different from those at home (AH) is limited. Data collected among 8849 men and 14 277 women aged 35–64 years from the general population of eleven European countries through 24-h dietary recalls or food diaries were analysed to: (1) compare food consumption OH to those AH; (2) describe the characteristics of substantial OH eaters, defined as those who consumed 25 % or more of their total daily energy intake at OH locations. Logistic regression models were fit to identify personal characteristics associated with eating out. In both sexes, beverages, sugar, desserts, sweet and savoury bakery products were consumed more OH than AH. In some countries, men reported higher intakes of fish OH than AH. Overall, substantial OH eating was more common among men, the younger and the more educated participants, but was weakly associated with total energy intake. The substantial OH eaters reported similar dietary intakes OH and AH. Individuals who were not identified as substantial OH eaters reported consuming proportionally higher quantities of sweet and savoury bakery products, soft drinks, juices and other non-alcoholic beverages OH than AH. The OH intakes were different from the AH ones, only among individuals who reported a relatively small contribution of OH eating to their daily intakes and this may partly explain the inconsistent findings relating eating out to the current obesity epidemic

FUZZIFIED DUMMY VARIABLES HAVE A BETTER FIT THAN REGULAR DUMMY VARIABLES IN STATISTICAL MODELS - AN EXEMPLARY APPROACH BASED ON DATA FROM A GERMAN NUTRITION SURVEY

Introduction: In statistical analyses, the classification of quantitative dependent variables grants substantial flexibility when modelling their effect on an outcome variable. An observation`s membership to a specific class (e.g. a subject`s age group) is traditionally coded by dummy variables taking only values of either zero or one. This, however, leads to undesired breaks at the intersection of two classes (staircase function).Materials and Methods: With regular dummy variables (rDV), an observation can only be a member of a single class (e.g. a subject can only be assigned to one age group). With fuzzified dummy variables (fDV), however, an observation is simultaneously assigned to two neighboring classes. The closer the value of the quantitative variable to the center of a class, the higher the respective degree of membership (with a maximum value of one). At the intersection of two classes, the degree of membership is 0.5 to both of these classes.As with rDV, fDVs sum up to exactly one. Since fuzzification does not affect the number of dummy variables required for classifying a quantitative variable, the degrees of freedom of the respective statistical analysis remain unchanged.The effect of fuzzifying dummy variables is exemplarily analyzed for the association of age and body mass index (BMI) in a subsample of a German nutrition survey (n=1500).Results: Models with fDV show consistently better fits (i.e. higher R² values) than those with rDV. The difference is the more pronounced, the lower the number of age groups.In contrast to rDV, fDVs do not show staircase functions in scatterplots of mean values of age and predicted BMI, but rather smooth transitions across age groups.Conclusion: It seems that statistical modelling with fDVs leads to a better fit than rDV. They might contribute to avoiding artifacts in data analyses possibly evoked by inappropriate staircase functions