No haste, more taste: An EMA study of the effects of stress, negative and positive emotions on eating behavior.

OBJECTIVES: Stress and emotions alter eating behavior in several ways: While experiencing negative or positive emotions typically leads to increased food intake, stress may result in either over- or undereating. Several participant characteristics, like gender, BMI and restrained, emotional, or external eating styles seem to influence these relationships. Thus far, most research relied on experimental laboratory studies, thereby reducing the complexity of real-life eating episodes. The aim of the present study was to delineate the effects of stress, negative and positive emotions on two key facets of eating behavior, namely taste- and hunger-based eating, in daily life using ecological momentary assessment (EMA). Furthermore, the already mentioned individual differences as well as time pressure during eating, an important but unstudied construct in EMA studies, were examined. METHODS: Fifty-nine participants completed 10days of signal-contingent sampling and data were analyzed using multilevel modeling. RESULTS: Results revealed that higher stress led to decreased taste-eating which is in line with physiological stress-models. Time pressure during eating resulted in less taste- and more hunger-eating. In line with previous research, stronger positive emotions went along with increased taste-eating. Emotional eating style moderated the relationship between negative emotions and taste-eating as well as hunger-eating. BMI moderated the relationship between negative as well as positive emotions and hunger-eating. CONCLUSIONS: These findings emphasize the importance of individual differences for understanding eating behavior in daily life. Experienced time pressure may be an important aspect for future EMA eating studies.

Mobile Phone-Supported Physiotherapy for Frozen Shoulder: Feasibility Assessment Based on a Usability Study.

BACKGROUND: Patients with frozen shoulder show limited shoulder mobility often accompanied by pain. Common treatment methods include physiotherapy, pain medication, administration of corticosteroids, and surgical capsulotomy. Frozen shoulder often lasts from months to years and mostly affects persons in the age group of 40 to 70 years. It severely reduces the quality of life and the ability to work. OBJECTIVE: The objective of this study was to evaluate the feasibility of a mobile health (mHealth) intervention that supports patients affected by "stage two" frozen shoulder. Patients were supported with app-based exercise instructions and tools to monitor their training compliance and progress. These training compliance and progress data supplement the patients' oral reports to the physiotherapists and physicians and can assist them in therapy adjustment. METHODS: In order to assess the feasibility of the mHealth intervention, a pilot study of a newly developed app for frozen shoulder patients was conducted with 5 patients for 3 weeks. The main function of the app was the instruction for exercising at home. Standardized questionnaires on usability such as System Usability Scale (SUS) and USE (Usefulness, Satisfaction, and Ease of use), and Technology Acceptance Model-2 (TAM-2) were completed by the study participants at the end of the study. Additionally, a nonstandardized questionnaire was completed by all patients. The correctness of the exercises as conducted by the patients was assessed by a physiotherapist at the end of the study. The mobility of the shoulder and pain in shoulder movement was assessed by a physiotherapist at the start and the end of the study. RESULTS: The pilot study was successfully conducted, and the app was evaluated by the patients after 3 weeks. The results of the standardized questionnaires showed high acceptance (TAM-2) and high usability (SUS) of the developed app. The overall usability of the system as assessed by the SUS questionnaire was very good (an average score of 88 out of 100). The average score of the TAM-2 questionnaire on the intention to further use the app was 4.2 out of 5, which indicated that most patients would use the app if further available. The results of the USE questionnaires highlighted that the patients learned how to use the app easily (an average score of 4.2 out of 5) and were satisfied with the app (an average score of 4.7 out of 5). The frequency of app usage and training was very high based on patient reports and verified by analysis of the usage data. The patients conducted the exercises almost flawlessly. CONCLUSIONS: Our results indicate the feasibility of the mHealth intervention, as the app was easy to use and frequently used by the patients. The app supported the patients' physiotherapy by providing clear exercising instructions.

Training of carbohydrate estimation for people with diabetes using mobile augmented reality.

BACKGROUND: Imprecise carbohydrate counting as a measure to guide the treatment of diabetes may be a source of errors resulting in problems in glycemic control. Exact measurements can be tedious, leading most patients to estimate their carbohydrate intake. In the presented pilot study a smartphone application (BE(AR)), that guides the estimation of the amounts of carbohydrates, was used by a group of diabetic patients. METHODS: Eight adult patients with diabetes mellitus type 1 were recruited for the study. At the beginning of the study patients were introduced to BE(AR) in sessions lasting 45 minutes per patient. Patients redraw the real food in 3D on the smartphone screen. Based on a selected food type and the 3D form created using BE(AR) an estimation of carbohydrate content is calculated. Patients were supplied with the application on their personal smartphone or a loaner device and were instructed to use the application in real-world context during the study period. For evaluation purpose a test measuring carbohydrate estimation quality was designed and performed at the beginning and the end of the study. RESULTS: In 44% of the estimations performed at the end of the study the error reduced by at least 6 grams of carbohydrate. This improvement occurred albeit several problems with the usage of BE(AR) were reported. CONCLUSIONS: Despite user interaction problems in this group of patients the provided intervention resulted in a reduction in the absolute error of carbohydrate estimation. Intervention with smartphone applications to assist carbohydrate counting apparently results in more accurate estimations.

Can smartphone-based logging support diabetologists in solving glycemic control problems?

Treatment of diabetic patients strongly relies on the continuous logging of parameters relevant to glycemic control. Keeping diabetes diaries can be tedious which can affect the data quality and completeness. Mobile technologies could provide means to overcome these limitations. However, studies analyzing the direct effect on the treatment of patients are rare. In the presented study diabetic patients were supplied with a smartphone application to record various parameters relevant for glycemic control. Questions regarding the completeness of diabetes diaries were answered by the patients before and after the study. The attending diabetologist analyzed the data obtained from the smartphone-based diaries to determine whether these provided solutions for problems in glycemic control. The analysis of the available smartphone data provided the basis for therapeutic recommendations that can improve the daily glycemic control for almost all participants. Importantly, especially the newly developed implicit-activity logging, registering the participants' movements, provided important means to generate these recommendations.