GOAL-SETTING CHARACTERISTICS OF NUTRITION-RELATED MHEALTH SYSTEMS: A MORPHOLOGICAL ANALYSIS

Setting and pursuing goals plays a major role in health behavior change. As an elementary component of interventions and mHealth apps, goals can support people in pursuing and attaining a desired outcome or behavior. Despite its widespread use and indicated effects on behavioral performance, there is no guidance on how to design and implement goal setting, planning and evaluation for mHealth nutrition promotion. We investigate goal setting and related components in the context of mHealth and health behavior change in a structured literature review. By utilizing morphological analysis, we develop a design framework for goal setting and pursuit. We validate and refine the framework by application on popular commercial nutrition apps. As a result, we identify the coverage of goal-setting and -pursuit characteristics as well as current gaps in the implementation of commercial mHealth nutrition apps, which offer the potential for future effective development of goal setting in mHealth nutrition promotion

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

A Conversational & Reflective Approach for Dietary Logging with the Food Pyramid: Chatbot RAINA

Mobile health (mHealth) applications have the potential to assist users in tracking and monitoring their behavior with the goal of raising awareness and finally supporting a healthier lifestyle. However, current nutrition-related applications mainly support weight loss through calorie counting rather than focusing on a balanced, healthy diet. In this work, we develop a chatbot that follows a more holistic approach to tracking and monitoring one’s diet. It incorporates the principles of a food pyramid to track a personal diet via free-text input and reflect on it via visual feedback. In a seven-day user study with 18 participants, we assess the user engagement and experience by analyzing usage data and results from a pre-/post-study questionnaire. Our results provide first promising insights into the user perceptions of this alternative approach regarding usability and usefulness, nutrition self-efficacy, reflective thinking, health consciousness, as well as intentions and recommendations for future use

Can an Automated Personalized Nutrition Assistance System Successfully Change Nutrition Behavior? - Study Design

Despite a multitude of existing dietary guidelines, the rise of the number of people suffering from a diet-related disease occurs on a yearly basis. Studies show that the response to different diets varies individually, calling for more personalized measures available at any time in any context. Therefore, this paper proposes a research design based on a smartphone app, that delivers automated, personalized dietary recommendations, to encourage a healthier nutrition lifestyle. Founding on previous research in computer and nutritional science, we propose 6 different intervention factors: (1) type of dietary recommendations, (2) dietary assessment, (3) tracking of physical activity via smartphones or smart activity trackers, (4) feedback with visualization of personal nutritional data, (5) feedback with textual explanations behind recommendations, and (6) dietary recommendations including blood values. In an extensive 6-month field study, we plan to examine which of the factors influence a healthier behavior change and long-term app engagement most