One More Bite? Inferring Food Consumption Level of College Students Using Smartphone Sensing and Self-Reports

Meegahapola, Lakmal; Ruíz-Correa, Salvador; Robledo-Valero, Viridiana del Carmen; Hernandez-Huerfano, Emilio Ernesto; Álvarez-Rivera, Leonardo; Chenu-Abente, Ronald; Gática-Pérez, Daniel

doi:10.1145/3448120

Cited by 30 publications

(47 citation statements)

References 130 publications

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“…53 Some of the key challenges in detecting events include wearability and comfort, differentiation between eating and drinking episodes, and mobility confounds (wearability necessitates real-world use which inevitably leads to non-food-related head movements). [56][57][58] Sensor-based approaches can either be employed as a form of semiautomatic data collection 59 (i.e., to log instances of eating that are intended to be annotated using human computation approaches), 60 or retrospectively recorded in more detail (manually) by the patient (e.g., 54,[61][62][63] ); or as fully automatic food intake sensing platforms (i.e., as a complete substitute for manual data entry). Automatic journaling at a coarse level of granularity (e.g., logging instances or durations of eating episodes) is still much more robust and reliable than attempting to infer specific food content and portion size.…”

Section: Sensing Food Practicementioning

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: Sensing Food Practicementioning

confidence: 99%

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

2022

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“…The city is home to several universities. Together with our local collaborators, we recruited students from two universities [46]. The recruitment campaign was launched in June 2019.…”

Section: Exploratory Data Collectionmentioning

confidence: 99%

“…Results from the initial data analysis showed that smartphone sensing can be used to infer self-perceived levels of eating behavior with an accuracy of 87% in a three-class inference task [46]. In addition, sensing can be used to analyze the social contexts in which students eat during the day, and infer a basic classification of social eating (eating alone or with others) with an accuracy of 84% [45].…”

Section: Exploratory Data Collectionmentioning

confidence: 99%

The Theory, Practice, and Ethical Challenges of Designing a Diversity-Aware Platform for Social Relations

Schelenz

Bison

Busso

et al. 2021

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society

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Diversity-aware platform design is a paradigm that responds to the ethical challenges of existing social media platforms. Available platforms have been criticized for minimizing users' autonomy, marginalizing minorities, and exploiting users' data for profit maximization. This paper presents a design solution that centers the well-being of users. It presents the theory and practice of designing a diversity-aware platform for social relations. In this approach, the diversity of users is leveraged in a way that allows like-minded individuals to pursue similar interests or diverse individuals to complement each other in a complex activity. The end users of the envisioned platform are students, who participate in the design process. Diversity-aware platform design involves numerous steps, of which two are highlighted in this paper: 1) defining a framework and operationalizing the "diversity" of students, 2) collecting "diversity" data to build diversity-aware algorithms. The paper further reflects on the ethical challenges encountered during the design of a diversity-aware platform.

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“…Smartphones allow sensing health and well-being aspects via continuous and interaction sensing techniques, both of which are generally called passive sensing [71]. This capability has been used in areas such as stress [13,67], mood [62,102], depression [14,112], well-being [57,63], and eating behavior [12,72,74]. If we consider drinking related research in mobile sensing, Bae et al [6] conducted an experiment with 30 young adults for 28 days, and used smartphone sensor data to infer non-drinking, drinking, and heavy-drinking episodes with an accuracy of 96.6%.…”

Section: Smartphonementioning

confidence: 99%

“…Even though gaining a holistic understanding regarding eating or drinking behavior is impossible without capturing contextual aspects regarding such behaviors, prior work has shown that people tend to reduce the usage of apps that require a large number of self-reports, and tend to use health and well-being applications that function passively [71]. Mobile sensing offers the opportunity to infer attributes that otherwise require user self-reports, hence reducing user burden [71,72,74]. In addition, mobile sensing could infer attributes to facilitate search acceleration in food/drink logging apps [39].…”

Section: Introductionmentioning

confidence: 99%

Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing

Meegahapola,

Labhart,

Phan

et al. 2021

Preprint

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According to prior work, the type of relationship between a person consuming alcohol and others in the surrounding (friends, family, spouse, etc.), and the number of those people (alone, with one person, with a group) are related to many aspects of alcohol consumption, such as the drinking amount, location, motives, and mood. Even though the social context is recognized as an important aspect that influences the drinking behavior of young adults in alcohol research, relatively little work has been conducted in smartphone sensing research on this topic. In this study, we analyze the weekend nightlife drinking behavior of 241 young adults in a European country, using a dataset consisting of self-reports and passive smartphone sensing data over a period of three months. Using multiple statistical analyses, we show that features from modalities such as accelerometer, location, application usage, bluetooth, and proximity could be informative about different social contexts of drinking. We define and evaluate seven social context inference tasks using smartphone sensing data, obtaining accuracies of the range 75%-86% in four two-class and three three-class inferences. Further, we discuss the possibility of identifying the sex composition of a group of friends using smartphone sensor data with accuracies over 70%. The results are encouraging towards supporting future interventions on alcohol consumption that incorporate users' social context more meaningfully and reducing the need for user self-reports when creating drink logs for self-tracking tools and public health studies. CCS Concepts: • Human-centered computing → Empirical studies in HCI; Empirical studies in ubiquitous and mobile computing; Smartphones; Mobile phones; Mobile devices; Empirical studies in collaborative and social computing; • Computer systems organization → Sensors and actuators; • Applied computing → Consumer health; Health informatics; Sociology; Psychology.

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One More Bite? Inferring Food Consumption Level of College Students Using Smartphone Sensing and Self-Reports

Cited by 30 publications

References 130 publications

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

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

The Theory, Practice, and Ethical Challenges of Designing a Diversity-Aware Platform for Social Relations

Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing

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