Development of a diagnostic online tool system for Swiss sport psychology practitioners

Psychological assessment is a central component of applied sport psychology. Despite obvious and well-documented advantages of diagnostic online tools, there is a lack of a system for such tools for sport psychologists so far in Switzerland. Having the most frequently used questionnaires available online in one single tool for all listed Swiss sport psychologists would make the work of practitioners a lot easier and less time consuming. Therefore, the main goal of this project is to develop a diagnostic online tool system with the possibility to make available different questionnaires often used in sport psychology. Furthermore, we intend to survey status and use of this diagnostic online tool system and the questionnaires by Swiss sport psychologists. A specific challenge is to limit the access to qualified sport psychologists and to secure the confidentiality for the client. In particular, approved sport psychologists get an individual code for each of their athletes for the required questionnaire. With the help of this code, athletes can access the test via a secure website at any place of the world. As soon as they complete and submit the online questionnaire, analysed and interpreted data reach the sport psychologist via E-Mail, which is timesaving and easy applicable for the sport psychologist. Furthermore, data are available for interpretation with athletes and documentation of individual development over time is possible. Later on, completed and anonymised questionnaires will be collected and analysed. Bigger number of collected data give more insight in the psychometric properties, thus helping to improve and further develop the questionnaires. In this presentation, we demonstrate the tool and its feasibility using the German version of the Test of Performance Strategies (TOPS, Schmid et al., 2010). To conclude, this diagnostic online tool system offers new possibilities for sport psychologists working as practitioner.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.