Health Promotion for Childhood Obesity: An Approach Based on Self-Tracking of Data

At present, obesity and overweight are a global health epidemic. Traditional interventions for promoting healthy habits do not appear to be effective. However, emerging technological solutions based on wearables and mobile devices can be useful in promoting healthy habits. These applications generate a considerable amount of tracked activity data. Consequently, our approach is based on the quantified-self model for recommending healthy activities. Gamification can also be used as a mechanism to enhance personalization, increasing user motivation. This paper describes the quantified-self model and its data sources, the activity recommender system, and the PROVITAO App user experience model. Furthermore, it presents the results of a gamified program applied for three years in children with obesity and the process of evaluating the quantified-self model with experts. Positive outcomes were obtained in children’s medical parameters and health habits.

[1]  D. Nafus Quantified: Biosensing Technologies in Everyday Life , 2016 .

[2]  A. Must,et al.  Risks and consequences of childhood and adolescent obesity , 1999, International Journal of Obesity.

[3]  Jennifer R. Whitson Gaming the Quantified Self , 2013 .

[4]  Tayfun Keskin Introduction to the Minitrack on Internet of Things: Providing Services Using Smart Devices, Wearables, and Quantified Self , 2018, HICSS.

[5]  Federica Cena,et al.  Quantified self and modeling of human cognition , 2015, UbiComp/ISWC Adjunct.

[6]  José Joaquín Mira,et al.  Mobile Apps for Increasing Treatment Adherence: Systematic Review , 2019, Journal of medical Internet research.

[7]  J. Bruce German,et al.  A Feasibility Study of Wearable Activity Monitors for Pre-Adolescent School-Age Children , 2014, Preventing chronic disease.

[8]  G. Fasano,et al.  A multidimensional version of the Kolmogorov–Smirnov test , 1987 .

[9]  A. Delamater,et al.  Obesity and Type 2 Diabetes in Children: Epidemiology and Treatment , 2014, Current Diabetes Reports.

[10]  Faustine Régnier,et al.  Digital Inequalities in the Use of Self-Tracking Diet and Fitness Apps: Interview Study on the Influence of Social, Economic, and Cultural Factors , 2018, JMIR mHealth and uHealth.

[11]  Ben Williamson The digitised future of physical education : Activity trackers, biosensors and algorithmic biopedagogies , 2017 .

[12]  Mark W. Newman,et al.  When fitness trackers don't 'fit': end-user difficulties in the assessment of personal tracking device accuracy , 2015, UbiComp.

[13]  Panos Markopoulos,et al.  Exploring Quantified Self Attitudes , 2018, HEALTHINF.

[14]  David L. Gast,et al.  Single Case Research Methodology : Applications in Special Education and Behavioral Sciences , 2014 .

[15]  L. Harvey,et al.  An app with remote support achieves better adherence to home exercise programs than paper handouts in people with musculoskeletal conditions: a randomised trial. , 2017, Journal of physiotherapy.

[16]  Rong Hu,et al.  Acceptance issues of personality-based recommender systems , 2009, RecSys '09.

[17]  Frank Biocca,et al.  Health experience model of personal informatics: The case of a quantified self , 2017, Comput. Hum. Behav..

[18]  Francisco J. García-Peñalvo,et al.  Treatment of children obesity and diabetes through gamification: a case of study , 2019, TEEM.

[19]  Sean A. Munson,et al.  Exploring goal-setting, rewards, self-monitoring, and sharing to motivate physical activity , 2012, 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[20]  Mark Hoogendoorn,et al.  Machine Learning for the Quantified Self - On the Art of Learning from Sensory Data , 2017, Cognitive Systems Monographs.

[21]  James H. Rimmer,et al.  A Call to Action: Building a Translational Inclusion Team Science in Physical Activity, Nutrition, and Obesity Management for Children with Disabilities , 2016, Front. Public Health.

[22]  Ara Darzi,et al.  Medication Adherence Apps: Review and Content Analysis , 2018, JMIR mHealth and uHealth.

[23]  Yulin Hswen,et al.  VIRTUAL AVATARS, GAMING, AND SOCIAL MEDIA: DESIGNING A MOBILE HEALTH APP TO HELP CHILDREN CHOOSE HEALTHIER FOOD OPTIONS. , 2013, Journal of mobile technology in medicine.

[24]  Li Chen,et al.  A user-centric evaluation framework for recommender systems , 2011, RecSys '11.

[25]  Lennart E. Nacke,et al.  The maturing of gamification research , 2017, Comput. Hum. Behav..

[26]  Jamie Yea Eun Park,et al.  Mobile Phone Apps Targeting Medication Adherence: Quality Assessment and Content Analysis of User Reviews , 2019, JMIR mHealth and uHealth.

[27]  Juho Hamari,et al.  Gamification, quantified-self or social networking? Matching users’ goals with motivational technology , 2018, User Modeling and User-Adapted Interaction.

[28]  Bermingham-McDonogh The Data Science of the Quantified Self , 2015 .

[29]  Mohamed Elhoseny,et al.  Quantified Self Using IoT Wearable Devices , 2017, AISI.

[30]  M. Jung,et al.  A Theory-Based Exercise App to Enhance Exercise Adherence: A Pilot Study , 2016, JMIR mHealth and uHealth.

[31]  Kathleen Dracup,et al.  A quantitative systematic review of the efficacy of mobile phone interventions to improve medication adherence. , 2014, Journal of advanced nursing.

[32]  M. Gard,et al.  Physical education’s grand convergence: Fitnessgram®, big-data and the digital commerce of children’s health , 2018 .

[33]  M. Swan Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking , 2009, International journal of environmental research and public health.

[34]  Dawn Nafus,et al.  This One Does Not Go Up to 11: The Quantified Self Movement as an Alternative Big Data Practice , 2014 .

[35]  J. Fleiss,et al.  The measurement of interrater agreement , 2004 .

[36]  Nazaret Gómez-del-Río,et al.  Gamified educational programme for childhood obesity , 2018, 2018 IEEE Global Engineering Education Conference (EDUCON).

[37]  Amon Rapp,et al.  Visualization of Human Behavior Data: The Quantified Self , 2014 .

[38]  Christian Greiffenhagen,et al.  The mundane experience of everyday calorie trackers: Beyond the metaphor of Quantified Self , 2018, New Media Soc..

[39]  ShinDong-Hee,et al.  Health experience model of personal informatics , 2017 .

[40]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[41]  Bart P. Knijnenburg,et al.  Explaining the user experience of recommender systems , 2012, User Modeling and User-Adapted Interaction.

[42]  Tiffany Champagne-Langabeer,et al.  Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs , 2017, Health Information Science and Systems.

[43]  Andreas Menychtas,et al.  On the Integration of Wearable Sensors in IoT Enabled mHealth and Quantified-self Applications , 2017, IMCL.

[44]  Melanie Swan,et al.  Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0 , 2012, J. Sens. Actuator Networks.

[45]  Melanie Swan,et al.  The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery , 2013, Big Data.

[46]  B. Swinburn,et al.  The global obesity pandemic: shaped by global drivers and local environments , 2011, The Lancet.

[47]  Henner Gimpel,et al.  Quantifying the Quantified Self: A Study on the Motivations of Patients to Track Their Own Health , 2013, ICIS.

[48]  Peter F. Merenda,et al.  BASC: Behavior Assessment System for Children. , 1996 .

[49]  Javier Aranceta,et al.  Food, youth and the Mediterranean diet in Spain. Development of KIDMED, Mediterranean Diet Quality Index in children and adolescents , 2004, Public Health Nutrition.

[50]  Dong-Hee Shin,et al.  Cross-Platform Users’ Experiences Toward Designing Interusable Systems , 2016, Int. J. Hum. Comput. Interact..

[51]  Celementina R. Russo,et al.  The Quantified Self , 2015, HCI.

[52]  H. D. de Vries,et al.  Do activity monitors increase physical activity in adults with overweight or obesity? A systematic review and meta‐analysis , 2016, Obesity.

[53]  Tamar Sharon Self-Tracking for Health and the Quantified Self: Re-Articulating Autonomy, Solidarity, and Authenticity in an Age of Personalized Healthcare , 2017 .

[54]  Li Chen,et al.  Evaluating recommender systems from the user’s perspective: survey of the state of the art , 2012, User Modeling and User-Adapted Interaction.

[55]  Mariana Porto Zambon,et al.  Reasons for non-adherence to obesity treatment in children and adolescents , 2013, Revista paulista de pediatria : orgao oficial da Sociedade de Pediatria de Sao Paulo.

[56]  Francisco J. García-Peñalvo,et al.  Effects of a Gamified Educational Program in the Nutrition of Children with Obesity , 2019, Journal of Medical Systems.

[57]  Wanda Pratt,et al.  Understanding quantified-selfers' practices in collecting and exploring personal data , 2014, CHI.

[58]  Andreas Menychtas,et al.  Advancing Quantified-Self Applications Utilizing Visual Data Analytics and the Internet of Things , 2018, AIAI.

[59]  Federica Cena,et al.  Engaging Users in Self-Reporting Their Data: A Tangible Interface for Quantified Self , 2015, HCI.

[60]  M. Delgado-Rodríguez,et al.  Systematic review and meta-analysis. , 2017, Medicina intensiva.