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403 results on '"Mars, Monica"'

1. The SPLENDID Eating Detection Sensor: Development and Feasibility Study

Author

van den Boer, Janet, van der Lee, Annemiek, Zhou, Lingchuan, Papapanagiotou, Vasileios, Diou, Christos, Delopoulos, Anastasios, and Mars, Monica

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundThe available methods for monitoring food intake—which for a great part rely on self-report—often provide biased and incomplete data. Currently, no good technological solutions are available. Hence, the SPLENDID eating detection sensor (an ear-worn device with an air microphone and a photoplethysmogram [PPG] sensor) was developed to enable complete and objective measurements of eating events. The technical performance of this device has been described before. To date, literature is lacking a description of how such a device is perceived and experienced by potential users. ObjectiveThe objective of our study was to explore how potential users perceive and experience the SPLENDID eating detection sensor. MethodsPotential users evaluated the eating detection sensor at different stages of its development: (1) At the start, 12 health professionals (eg, dieticians, personal trainers) were interviewed and a focus group was held with 5 potential end users to find out their thoughts on the concept of the eating detection sensor. (2) Then, preliminary prototypes of the eating detection sensor were tested in a laboratory setting where 23 young adults reported their experiences. (3) Next, the first wearable version of the eating detection sensor was tested in a semicontrolled study where 22 young, overweight adults used the sensor on 2 separate days (from lunch till dinner) and reported their experiences. (4) The final version of the sensor was tested in a 4-week feasibility study by 20 young, overweight adults who reported their experiences. ResultsThroughout all the development stages, most individuals were enthusiastic about the eating detection sensor. However, it was stressed multiple times that it was critical that the device be discreet and comfortable to wear for a longer period. In the final study, the eating detection sensor received an average grade of 3.7 for wearer comfort on a scale of 1 to 10. Moreover, experienced discomfort was the main reason for wearing the eating detection sensor

Published
2018
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4. Recognition of food-texture attributes using an in-ear microphone

Author

Papapanagiotou, Vasileios, Diou, Christos, Boer, Janet van den, Mars, Monica, and Delopoulos, Anastasios

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Food texture is a complex property; various sensory attributes such as perceived crispiness and wetness have been identified as ways to quantify it. Objective and automatic recognition of these attributes has applications in multiple fields, including health sciences and food engineering. In this work we use an in-ear microphone, commonly used for chewing detection, and propose algorithms for recognizing three food-texture attributes, specifically crispiness, wetness (moisture), and chewiness. We use binary SVMs, one for each attribute, and propose two algorithms: one that recognizes each texture attribute at the chew level and one at the chewing-bout level. We evaluate the proposed algorithms using leave-one-subject-out cross-validation on a dataset with 9 subjects. We also evaluate them using leave-one-food-type-out cross-validation, in order to examine the generalization of our approach to new, unknown food types. Our approach performs very well in recognizing crispiness (0.95 weighted accuracy on new subjects and 0.93 on new food types) and demonstrates promising results for objective and automatic recognition of wetness and chewiness.

Published
2021
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6. BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment

Author

Diou, Christos, Sarafis, Ioannis, Papapanagiotou, Vasileios, Alagialoglou, Leonidas, Lekka, Irini, Filos, Dimitrios, Stefanopoulos, Leandros, Kilintzis, Vasileios, Maramis, Christos, Karavidopoulou, Youla, Maglaveras, Nikos, Ioakimidis, Ioannis, Charmandari, Evangelia, Kassari, Penio, Tragomalou, Athanasia, Mars, Monica, Nguyen, Thien-An Ngoc, Kechadi, Tahar, Donnell, Shane O', Doyle, Gerardine, Browne, Sarah, Malley, Grace O', Heimeier, Rachel, Riviou, Katerina, Koukoula, Evangelia, Filis, Konstantinos, Hassapidou, Maria, Pagkalos, Ioannis, Ferri, Daniel, Pérez, Isabel, and Delopoulos, Anastasios

Subjects
Computer Science - Computers and Society
Abstract

Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO (http://bigoprogram.eu), a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants., Comment: Accepted version to be published in 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, Canada

Published
2020

11. Toward Systems Models for Obesity Prevention: A Big Role for Big Data

Author

Tufford, Adele R, Diou, Christos, Lucassen, Desiree A, Ioakimidis, Ioannis, O'Malley, Grace, Alagialoglou, Leonidas, Charmandari, Evangelia, Doyle, Gerardine, Filis, Konstantinos, Kassari, Penio, Kechadi, Tahar, Kilintzis, Vassilis, Kok, Esther, Lekka, Irini, Maglaveras, Nicos, Pagkalos, Ioannis, Papapanagiotou, Vasileios, Sarafis, Ioannis, Shahid, Arsalan, van ’t Veer, Pieter, Delopoulos, Anastasios, and Mars, Monica

Published
2022
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34. Objective and Subjective Meal Registration via a Smartphone Application

Author

Moulos, Ioannis, Maramis, Christos, Ioakimidis, Ioannis, van den Boer, Janet, Nolstam, Jenny, Mars, Monica, Bergh, Cecilia, Maglaveras, Nicos, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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35. Fractal Nature of Chewing Sounds

Author

Papapanagiotou, Vasileios, Diou, Christos, Lingchuan, Zhou, van den Boer, Janet, Mars, Monica, Delopoulos, Anastasios, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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39. Validation of the smartphone-based dietary assessment tool 'Traqq' for assessing actual dietary intake by repeated 2-hour recalls in adults: comparison with 24h recalls and urinary biomarkers

Author

Lucassen, Desiree A, Brouwer-Brolsma, Elske M, Boshuizen, Hendriek C, Mars, Monica, de Vogel-Van den Bosch, Johan, and Feskens, Edith J M

Subjects
Technology, Healthy adults, Ecological Momentary Assessment, Validation, (5-10): Dietary Assessment, Recall, Innovation, Nutritional biomarkers
Published
2023

49. Toward Systems Models for Obesity Prevention: A Big Role for Big Data

Author

Urban Futures, Tufford, Adele R, Diou, Christos, Lucassen, Desiree A, Ioakimidis, Ioannis, O'Malley, Grace, Alagialoglou, Leonidas, Charmandari, Evangelia, Doyle, Gerardine, Filis, Konstantinos, Kassari, Penio, Kechadi, Tahar, Kilintzis, Vassilis, Kok, Esther, Lekka, Irini, Maglaveras, Nicos, Pagkalos, Ioannis, Papapanagiotou, Vasileios, Sarafis, Ioannis, Shahid, Arsalan, van ’t Veer, Pieter, Delopoulos, Anastasios, Mars, Monica, Urban Futures, Tufford, Adele R, Diou, Christos, Lucassen, Desiree A, Ioakimidis, Ioannis, O'Malley, Grace, Alagialoglou, Leonidas, Charmandari, Evangelia, Doyle, Gerardine, Filis, Konstantinos, Kassari, Penio, Kechadi, Tahar, Kilintzis, Vassilis, Kok, Esther, Lekka, Irini, Maglaveras, Nicos, Pagkalos, Ioannis, Papapanagiotou, Vasileios, Sarafis, Ioannis, Shahid, Arsalan, van ’t Veer, Pieter, Delopoulos, Anastasios, and Mars, Monica

Published
2022

50. Toward Systems Models for Obesity Prevention: A Big Role for Big Data

Author

Tufford, Adele R., Diou, Christos, Lucassen, Desiree A., Ioakimidis, Ioannis, O'Malley, Grace, Alagialoglou, Leonidas, Charmandari, Evangelia, Doyle, Gerardine, Filis, Konstantinos, Kassari, Penio, Kechadi, Tahar, Kilintzis, Vassilis, Kok, Esther, Lekka, Irini, Maglaveras, Nicos, Pagkalos, Ioannis, Papapanagiotou, Vasileios, Sarafis, Ioannis, Shahid, Arsalan, van 't Veer, Pieter, Delopoulos, Anastasios, Mars, Monica, Tufford, Adele R., Diou, Christos, Lucassen, Desiree A., Ioakimidis, Ioannis, O'Malley, Grace, Alagialoglou, Leonidas, Charmandari, Evangelia, Doyle, Gerardine, Filis, Konstantinos, Kassari, Penio, Kechadi, Tahar, Kilintzis, Vassilis, Kok, Esther, Lekka, Irini, Maglaveras, Nicos, Pagkalos, Ioannis, Papapanagiotou, Vasileios, Sarafis, Ioannis, Shahid, Arsalan, van 't Veer, Pieter, Delopoulos, Anastasios, and Mars, Monica

Abstract

The relation among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, the traditional epidemiologic and emerging big data approaches used in obesity research are compared, describing the research questions, needs, and outcomes of 3 broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project “BigO: Big data against childhood obesity” used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learning on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions.

Published
2022

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