Characteristics of Smartphone Applications for Nutrition Improvement in Community Settings: A Scoping Review

Reproduced by permission of Oxford University Press https://academic.oup.com Copyright © 2019 American Society for NutritionSmartphone applications are increasingly being used to support nutrition improvement in community settings. However, there is a scarcity of practical literature to support researchers and practitioners in choosing or developing health applications. This work maps the features, key content, theoretical approaches, and methods of consumer testing of applications intended for nutrition improvement in community settings. A systematic, scoping review methodology was used to map published, peer-reviewed literature reporting on applications with a specific nutrition-improvement focus intended for use in the community setting. After screening, articles were grouped into 4 categories: dietary self-monitoring trials, nutrition improvement trials, application description articles, and qualitative application development studies. For mapping, studies were also grouped into categories based on the target population and aim of the application or program. Of the 4818 titles identified from the database search, 64 articles were included. The broad categories of features found to be included in applications generally corresponded to different behavior change support strategies common to many classic behavioral change models. Key content of applications generally focused on food composition, with tailored feedback most commonly used to deliver educational content. Consumer testing before application deployment was reported in just over half of the studies. Collaboration between practitioners and application developers promotes an appropriate balance of evidence-based content and functionality. This work provides a unique resource for program development teams and practitioners seeking to use an application for nutrition improvement in community settings

Artifact Rejection Methodology Enables Continuous, Noninvasive Measurement of Gastric Myoelectric Activity in Ambulatory Subjects.

The increasing prevalence of functional and motility gastrointestinal (GI) disorders is at odds with bottlenecks in their diagnosis, treatment, and follow-up. Lack of noninvasive approaches means that only specialized centers can perform objective assessment procedures. Abnormal GI muscular activity, which is coordinated by electrical slow-waves, may play a key role in symptoms. As such, the electrogastrogram (EGG), a noninvasive means to continuously monitor gastric electrical activity, can be used to inform diagnoses over broader populations. However, it is seldom used due to technical issues: inconsistent results from single-channel measurements and signal artifacts that make interpretation difficult and limit prolonged monitoring. Here, we overcome these limitations with a wearable multi-channel system and artifact removal signal processing methods. Our approach yields an increase of 0.56 in the mean correlation coefficient between EGG and the clinical "gold standard", gastric manometry, across 11 subjects (p < 0.001). We also demonstrate this system's usage for ambulatory monitoring, which reveals myoelectric dynamics in response to meals akin to gastric emptying patterns and circadian-related oscillations. Our approach is noninvasive, easy to administer, and has promise to widen the scope of populations with GI disorders for which clinicians can screen patients, diagnose disorders, and refine treatments objectively

App-based feedback on safety to novice drivers: learning and monetary incentives

An over-proportionally large number of car crashes is caused by novice drivers. In a field experiment, we investigated whether and how car drivers who had recently obtained their driving license reacted to app-based feedback on their safety-relevant driving behavior (speeding, phone usage, cornering, acceleration and braking). Participants went through a pre-measurement phase during which they did not receive app-based feedback but driving behavior was recorded, a treatment phase during which they received app-based feedback, and a post-measurement phase during which they did not receive app-based feedback but driving behavior was recorded. Before the start of the treatment phase, we randomly assigned participants to two possible treatment groups. In addition to receiving app-based feedback, the participants of one group received monetary incentives to improve their safety-relevant driving behavior, while the participants of the other group did not. At the beginning and at the end of experiment, each participant had to fill out a questionnaire to elicit socio-economic and attitudinal information. We conducted regression analyses to identify socio-economic, attitudinal, and driving-behavior-related variables that explain safety-relevant driving behavior during the pre-measurement phase and the self-chosen intensity of app usage during the treatment phase. For the main objective of our study, we applied regression analyses to identify those variables that explain the potential effect of providing app-based feedback during the treatment phase on safety-relevant driving behavior. Last, we applied statistical tests of differences to identify self-selection and attrition biases in our field experiment. For a sample of 130 novice Austrian drivers, we found moderate improvements in safety-relevant driving skills due to app-based feedback. The improvements were more pronounced under the treatment with monetary incentives, and for participants choosing higher feedback intensities. Moreover, drivers who drove relatively safer before receiving app-based feedback used the app more intensely and, ceteris paribus, higher app use intensity led to improvements in safety-related driving skills. Last, we provide empirical evidence for both self-selection and attrition biases

Insights from Machine-Learned Diet Success Prediction

To support people trying to lose weight and stay healthy, more and more fitness apps have sprung up including the ability to track both calories intake and expenditure. Users of such apps are part of a wider ``quantified self'' movement and many opt-in to publicly share their logged data. In this paper, we use public food diaries of more than 4,000 long-term active MyFitnessPal users to study the characteristics of a (un-)successful diet. Concretely, we train a machine learning model to predict repeatedly being over or under self-set daily calories goals and then look at which features contribute to the model's prediction. Our findings include both expected results, such as the token ``mcdonalds'' or the category ``dessert'' being indicative for being over the calories goal, but also less obvious ones such as the difference between pork and poultry concerning dieting success, or the use of the ``quick added calories'' functionality being indicative of over-shooting calorie-wise. This study also hints at the feasibility of using such data for more in-depth data mining, e.g., looking at the interaction between consumed foods such as mixing protein- and carbohydrate-rich foods. To the best of our knowledge, this is the first systematic study of public food diaries.Comment: Preprint of an article appearing at the Pacific Symposium on Biocomputing (PSB) 2016 in the Social Media Mining for Public Health Monitoring and Surveillance trac

Multi-Device Nutrition Control

Precision nutrition is a popular eHealth topic among several groups, such as athletes, 1 people with dementia, rare diseases, diabetes, and overweight. Its implementation demands tight 2 nutrition control, starting with nutritionists who build up food plans for specific groups or individuals. 3 Each person then follows the food plan by preparing meals and logging all food and water intake. 4 However, the discipline demanded to follow food plans and log food intake turns out into high 5 dropout rates. This article presents the concepts, requirements, and architecture of a solution that 6 assists the nutritionist in building up and revising food plans and the user following them. It does 7 so by minimizing human-computer interaction by integrating the nutritionist and user systems 8 and introducing off-the-shelf IoT devices in the system, such as temperature sensors, smartwatches, 9 smartphones, and smart bottles. An interaction time analysis using the Keystroke Level Model 10 provides a baseline for comparison in future work addressing both the use of machine learning and 11 IoT devices to reduce the interaction effort of users.info:eu-repo/semantics/publishedVersio

Data, Data Everywhere, and Still Too Hard to Link: Insights from User Interactions with Diabetes Apps

For those with chronic conditions, such as Type 1 diabetes, smartphone apps offer the promise of an affordable, convenient, and personalized disease management tool. How- ever, despite significant academic research and commercial development in this area, diabetes apps still show low adoption rates and underwhelming clinical outcomes. Through user-interaction sessions with 16 people with Type 1 diabetes, we provide evidence that commonly used interfaces for diabetes self-management apps, while providing certain benefits, can fail to explicitly address the cognitive and emotional requirements of users. From analysis of these sessions with eight such user interface designs, we report on user requirements, as well as interface benefits, limitations, and then discuss the implications of these findings. Finally, with the goal of improving these apps, we identify 3 questions for designers, and review for each in turn: current shortcomings, relevant approaches, exposed challenges, and potential solutions

SysMART Indoor Services: A System of Smart and Connected Supermarkets

Smart gadgets are being embedded almost in every aspect of our lives. From smart cities to smart watches, modern industries are increasingly supporting the Internet of Things (IoT). SysMART aims at making supermarkets smart, productive, and with a touch of modern lifestyle. While similar implementations to improve the shopping experience exists, they tend mainly to replace the shopping activity at the store with online shopping. Although online shopping reduces time and effort, it deprives customers from enjoying the experience. SysMART relies on cutting-edge devices and technology to simplify and reduce the time required during grocery shopping inside the supermarket. In addition, the system monitors and maintains perishable products in good condition suitable for human consumption. SysMART is built using state-of-the-art technologies that support rapid prototyping and precision data acquisition. The selected development environment is LabVIEW with its world-class interfacing libraries. The paper comprises a detailed system description, development strategy, interface design, software engineering, and a thorough analysis and evaluation.Comment: 7 pages, 11 figur