Happyinu: exploring how to use games and extrinsic rewards for consistent food tracking behavior

Luhanga, Edith Talina; Hippocrate, Akpa Akpro Elder; Suwa, Hirohiko; Arakawa, Yutaka; Yasumoto, Keiichi

doi:10.1109/icmu.2016.7742088

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…Instead of focusing solely on streamlining and automation, some food journaling applications have increased user engagement by incorporating techniques associated with gamification, such as providing challenges to motivate continued adherence to data collection 75 or rewards for successfully meeting food journaling benchmarks. 76 Nudges 77 can be a powerful behavioral modification and habit formation technique which is potentially useful for helping patients internalize food journaling practice. In contrast, the implicit or inadvertent use of "negative nudges" in the design of popular food-tracking applications, have been shown to deter longterm use and lower adherence to computer-assisted data collection of food intake.…”

Section: Capturing Food Practice Datamentioning

confidence: 99%

Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making

2022

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Background Food practice plays an important role in health. Food practice data collected in daily living settings can inform clinical decisions. However, integrating such data into clinical decision-making is burdensome for both clinicians and patients, resulting in poor adherence and limited utilization. Automation offers benefits in this regard, minimizing this burden resulting in a better fit with a patient's daily living routines, and creating opportunities for better integration into clinical workflow. Although the literature on patient-generated health data (PGHD) can serve as a starting point for the automation of food practice data, more diverse characteristics of food practice data provide additional challenges. Objectives We describe a series of steps for integrating food practices into clinical decision-making. These steps include the following: (1) sensing food practice; (2) capturing food practice data; (3) representing food practice; (4) reflecting the information to the patient; (5) incorporating data into the EHR; (6) presenting contextualized food practice information to clinicians; and (7) integrating food practice into clinical decision-making. Methods We elaborate on automation opportunities and challenges in each step, providing a summary visualization of the flow of food practice-related data from daily living settings to clinical settings. Results We propose four implications of automating food practice hereinafter. First, there are multiple ways of automating workflow related to food practice. Second, steps may occur in daily living and others in clinical settings. Food practice data and the necessary contextual information should be integrated into clinical decision-making to enable action. Third, as accuracy becomes important for food practice data, macrolevel data may have advantages over microlevel data in some situations. Fourth, relevant systems should be designed to eliminate disparities in leveraging food practice data. Conclusion Our work confirms previously developed recommendations in the context of PGHD work and provides additional specificity on how these recommendations apply to food practice.

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Section: Capturing Food Practice Datamentioning

confidence: 99%

Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making

2022

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“…We also considered incorporation of gamification techniques [10], [11], [55], [56] into the workplace. For example, gamification and sensing techniques can be used to solve reservation problems in offices.…”

Section: Changing Workplace Behavior With Nudgesmentioning

confidence: 99%

Sensing and Changing Human Behavior for Workplace Wellness

Arakawa

2019

Journal of Information Processing

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Recently, companies have begun to care more about the well-being of their employees. With the spread of sensors, the Internet of Things, and artificial intelligence, the movement to build a better working environment by utilizing these technologies has been spreading. Especially, research on behavior that can change lifestyle habits is becoming popular. In this paper, we summarize workplace behavior research and projects for sensing and changing human behavior in a workplace and aim to improve the productivity and wellness of employees. Also, we introduce concepts for future workplaces and some of our related achievements. For physical state sensing, we have developed a continuous posture-sensing chair, which will soon be available commercially. For internal state sensing, we propose a method for estimating quality of life with wearable sensors. Our system have already achieved to estimate QoL (Quality of Life) around 90% with only 9 questions. In addition, we propose interactive digital signage to provide habit-changing reminders. Through one month experiment, we confirmed that our system can be feasible in daily life.

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“…Over three weeks, the participants used this app to collect food images for testing the estimation method proposed in this study. The application is also utilized in another research study, in which we assess the consistency of meal-tracking and behavior change using a game-based approach [30]. Figure 9 shows some screenshots of the application relevant to the food images collection and the proposed estimation method.…”

Section: Image Collection Through Smartphone Applicationmentioning

confidence: 99%

Smartphone-Based Food Weight and Calorie Estimation Method for Effective Food Journaling

Akpa

Suwa

Arakawa

et al. 2017

SICE Journal of Control, Measurement, and System Integration

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: World health statistics about overweight and obesity show that overweight, obesity and diet-related diseases still remain major health risks. According to the World Health Organization, most of the world's population live in countries where overweight and obesity kill more people than underweight. Recently, many studies have been driven by the motivation of elaborating the "ideal" solutions to prevent and/or monitor overweight, obesity and diet-related diseases, and to encourage healthy diet and lifestyle. In this work, we present our idea to automatically measure food weight and calories, from food photo using ordinary chopsticks as a measurement reference. The analysis of the obtained results show that the use of near-by eating utensils combined with computer vision techniques is a great and exploitable approach to ubiquitously help in diet assessment and obesity treatment.

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Happyinu: exploring how to use games and extrinsic rewards for consistent food tracking behavior

Cited by 9 publications

References 16 publications

Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making

Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making

Sensing and Changing Human Behavior for Workplace Wellness

Smartphone-Based Food Weight and Calorie Estimation Method for Effective Food Journaling

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