Personal and Ubiquitous Computing

, Volume 17, Issue 6, pp 1147–1157

Ubiquitous monitoring and assessment of childhood obesity

  • Irene Zaragozá
  • Jaime Guixeres
  • Mariano Alcañiz
  • Ausiás Cebolla
  • Javier Saiz
  • Julio Álvarez
Original Article


Childhood obesity is a significant health problem in current societies that is increasing at an alarming way among population of all ages. To date, studies on the effectiveness of treatments for childhood obesity in the medium and long term suggest a moderate effect on weight loss and maintenance, which has led to suggestions that early interventions have a preventive nature on adult obesity. The long-term recovery of the weight lost is often associated with a lack of adherence to recommendations for changing life habits. Then, obesity becomes a chronic problem, difficult to approach, and the main difficulty lies in promoting and ensuring adherence to a change in lifestyle. A system known as ETIOBE has been developed to improve the treatment adherence, to promote the mechanisms of self-control in patients and to prevent relapses. An important part of the ETIOBE system is the ubiquitous monitoring platform since it enables the clinician to obtain relevant information from patients (contextual, physiological and psychological), which enables treatment customization and adaptation, depending on the patient’s evolution. The aim of this paper is to describe the monitoring platform which is intended to establish a sensor network whose focus is the obese children under clinical treatment, and the various elements that compose it: electronic PDA records to establish diet habits, HAS: home ambulatory system (data integration of biomedical devices; blood pressure to study hypertension; pulse oximeter to detect Sleep Disorders; and electronic t-shirt to detect physical activity). This paper presents the first validations of the electronic PDA records and the electronic t-shirt. These validations suggest that the monitoring platform can help to achieve the goals previously mentioned, by offering constant support and increasing motivation to change.


Children obesity E-therapy Physical activity detection Wireless monitoring 


  1. 1.
    Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM (2004) Overweight and obesity among US children, adolescents and adults, 1999–2002. JAMA 291:2847–2850CrossRefGoogle Scholar
  2. 2.
    Srinvasan SR, Bao W, Wattigney WA, Berenson GS (1995) Adolescent overweight is associated with adult overweight and related multiple cardiovascular risk factors: the Bogalusa heart study. Metab Clin Exp 107:782–787Google Scholar
  3. 3.
    World Health Organization (1998) Obesity: preventing and managing the global epidemic. WHO, GenevaGoogle Scholar
  4. 4.
    World Health Organization (2003) Global strategy on diet; physical activity and health. WHO, GenevaGoogle Scholar
  5. 5.
    Franco C, Bengtsson B, Johannsson G (2006) The GH/IGF-1 axis in obesity: physiological and pathological aspects. Metab Syndr Relat Disord 4:51–56CrossRefGoogle Scholar
  6. 6.
    Aranceta J, Pérez C, Foz M, Mantilla T, Serra L, Moreno B et al (2004) Tablas de evaluación del riesgo coronario adaptadas a la población española. Estudio DORICA. Med Clínic 123:686–691CrossRefGoogle Scholar
  7. 7.
    Salas-Salvadó J, Rubio MA, Barbany M, Moreno B (2007) Consenso SEEDO 2007 para la evaluación del sobrepeso y la obesidad y el establecimiento de criterios de intervención terapéutico. Med Clínic 128(5):184–196CrossRefGoogle Scholar
  8. 8.
    Wadden TA, Berkowitz RI, Womble LG, Sarwer DB, Phelan S, Cato RK et al (2005) Randomized trial of lifestyle modification and pharmacotherapy for obesity. N Engl J Med 353:2111–20CrossRefGoogle Scholar
  9. 9.
    Perri MG, Nezu AM, McKelvey WF, Shermer RL, Renjilian DA, Viegener BJ (2001) Relapse prevention training and problem-solving therapy in the longterm management of obesity. J Consult Clin Psychol 69:722–6CrossRefGoogle Scholar
  10. 10.
    Wadden TA, Butryn ML, Byrne KJ (2004) Efficacy of lifestyle modification for long-term weight control. Obes Res 12(l):151–62CrossRefGoogle Scholar
  11. 11.
    Wadden TA, Brownell KD, Foster GD (2002) Obesity: managing the global epidemic. J Consult Clin Psychol 70:510–525CrossRefGoogle Scholar
  12. 12.
    US Department of Health and Human Services: Office of Disease Prevention and Health Promotion (2000) Healthy People 2010. Nasnewsletter 15(3):3Google Scholar
  13. 13.
    Twisk JW, Kemper HC, van Mechelen W (2002) Prediction of cardiovascular disease risk factors later in life by physical activity and physical fitness in youth: general comments and conclusions. Int J Sports Med 23(1):S44–S49CrossRefGoogle Scholar
  14. 14.
    Kohl HW, Fulton JE, Caspersen C (2000) Assessment of physical activity among children and adolescents: a review and synthesis. Prev Med 31:s54–s76CrossRefGoogle Scholar
  15. 15.
    Alcañiz M, Rey B (2005) New technologies for ambient intelligence. In: Riva G, Vatalaro F, Davide F, Alcañiz M (eds), Ambient intelligence. IOS Press, Amsterdam, pp 3–15Google Scholar
  16. 16.
    Bravo J, Alamán X, Riesgo T (2006) Ubiquitous computing and ambient intelligence: new challenges for computing. J Univ Comput Sci 12(3):233–235Google Scholar
  17. 17.
    José R, Rodrigues H, Otero N (2010) Ambient intelligence: beyond the inspiring vision. J Univ Comput Sci 16(2):1480–1499Google Scholar
  18. 18.
    Alexandros P, Nikolaos GB (2010) A survey on wearable sensorbased systems for health monitoring and prognosis. IEEE Trans Syst Man Cybern Part C 40:1–12Google Scholar
  19. 19.
    Varshney U (2007) Pervasive healthcare and wireless health monitoring. Mobile Netw Appl 12:113–127CrossRefGoogle Scholar
  20. 20.
    Toscos T, Faber A, Connelly K, Upoma AM (2008) Encouraging physical activity in teens: can technology help reduce barriers to physical activity in adolescent girls? Proceedings of Pervasive Computing Technologies for Healthcare (PervasiveHealth’08), pp 218–221Google Scholar
  21. 21.
    Fujiki Y, Kazakos K, Puri C, Buddharaju P, Pavlidis I, Levine J (2008) NEAT-o-Games: blending physical activity and fun in the daily routine. Comput Entertain (CIE) 6(2):21:1–21:22Google Scholar
  22. 22.
    Klasnja Predrag, Consolvo Sunny, McDonald David W, Landay James A, Pratt Wanda (2009) Using mobile and personal sensing technologies to support health behavior change in everyday life: lessons learned. Annual Conference of the American Medical Informatics AssociationGoogle Scholar
  23. 23.
  24. 24.
    FitBit. Avalaible:
  25. 25.
    Jawbone. Avalaible:
  26. 26.
    Zaragozá I, Guixeres J, Alcañiz M (2009) Ontologies for intelligent e-therapy: application to obesity. Lect Notes Comput Sci: Distrib Comput Artif Intell Bioinform Soft Comput Ambient Assist Living 5518: 894–901Google Scholar
  27. 27.
    Piasecki TM, MSolhan MR~Hufford, Trull TJ (2007) Assessing clients in their natural environments with electronic diaries: rationale, benefits, limitations, and barriers. Psychol Assess 19:25–43CrossRefGoogle Scholar
  28. 28.
    Net Compact Framework 3.5 Redistributable. Available: Last access 05-05-2009
  29. 29.
    Baños R, Cebolla A, Zaragoza I, Botella C, Alcañiz M Electronic PDA dietary and physical activity registers in weight loss treatment program for children: a descriprion of the ETIOBE personal Digital Assistant System. J Cyberther Rehabil 2(3):235–241Google Scholar
  30. 30.
    Stergiou GS, Yiannes NG, Rarra VC (2006) Validation of the Omron 705 IT oscillometric device for home blood pressure measurement in children and adolescents: the Arsakion School Study. Blood Press Monit 11:229–234CrossRefGoogle Scholar
  31. 31.
    Nigro CA, Aimaretti S, Gonzalez S, Rhodius E (2009) Validation of the WristOx 3100 oximeter for the diagnosis of sleep apnea/hypopnea syndrome. Sleep Breath 13(2):127–136CrossRefGoogle Scholar
  32. 32.
    Lymberis A, De Rossi D (eds) (2004) Wearable eHealth systems for personalised health management-state of the art and future challenges, vol 108. Studies in Health Technology and Informatics. IOS Press, AmsterdamGoogle Scholar
  33. 33.
    De Rossi D, Lymberis A (2005) Guest editorial New Generation of Smart Wearable health Systems and Application. IEEE Trans Inf Technol Biomed 9(3):293–294Google Scholar
  34. 34.
    NUUBO. Availabe:
  35. 35.
    Payne PR, Wheleer EF, Salvosa CB (1971) Prediction of daily energy expenditure from average pulse rate. Am J Clinic Nutr 24(9):1164–1170Google Scholar
  36. 36.
    Mathie MJ, Celler BG, Lovell NH, Coster AC (2004) Classification of basic daily movements using a triaxial accelerometer. Med Biol Eng Comput 42:670–687CrossRefGoogle Scholar
  37. 37.
    Freedson P, Pober D, Janz KF (2005) Calibration of accelerometer output for children. Med Sci Sports Exerc 37(11):S523–S530Google Scholar
  38. 38.
    Pate RR, Almeida MJ, McIver Kl, Pfeiffer KA, Dowda M (2006) Validation and calibration of an accelerometer in preschool children. Obesity 14(11):2000–2006CrossRefGoogle Scholar
  39. 39.
    Esliger DW, Trembaly MS (2006) Technical reliability assesment of three accelerometer models in a mechanical setup. Med Sci Sports Exerc 38(12):2173–2181CrossRefGoogle Scholar
  40. 40.
    Nilsson A (2008) Physical activity assessed bt accelerometry in children. Örebo University, ÖreboGoogle Scholar
  41. 41.
    Trost SG, Way R, Oakley AD (2006) Predictive validity of three actigraph energy expenditure equations for children. Med Sci Sports Exerc 38(2):380–387CrossRefGoogle Scholar
  42. 42.
    Crouter SE (2006) A novel method for using accelerometer data to predict energy expenditure. J Appl Physiol 100(4):1324–1331CrossRefGoogle Scholar
  43. 43.
    Treuth MS (2004) Defining accelerometer thresholds for activity intensities in adolescent girls. Med Sci Sports Exerc 36(7):1259–1266Google Scholar
  44. 44.
    Fitmate PRO. Cosmed Technologies. Availabe :
  45. 45.
    Puyau MR, Adolph AL, Vohra FA, Zakeri I, Butte NF (2004) Prediction of activity energy expenditure using accelerometer in children. Med Sci Sports Exerc 36(9):1625–31Google Scholar
  46. 46.
    Reilly JJ, Kelly LA, Montgomery C, Jackson DM, SlaterC Grant S, Paton JY (2006) Validation of actigraph accelerometer estimates of total energy expenditure in young children. Int J Pediatr Obes 1(3):161–167CrossRefGoogle Scholar
  47. 47.
    Norman J (January 2006) Accelerometry as an estimation of energy expenditure in healthy children and children with cerebral palsy during self-paced ambulation. Int J Alli Health Sci Practice 4(1):1–5Google Scholar
  48. 48.
    Corder K, Brage S, Mattoks C, Ness A, Riddoch C, Wareham NJ, Ekelund U (2007) Comparison of two methods to assess PAEE during six activities in children. Med Sci Sports Exerc 39(12):2180–2188CrossRefGoogle Scholar
  49. 49.
    Corder K, Brage S, Wareham NJ, Ekelund U (2005) Comparison of PAEE from Combined and Separate Heart Rate and Movement Models in Children. Med Sci Sports Exerc 37(10):1761–1767CrossRefGoogle Scholar
  50. 50.
    Brage S, Wedderkopp N, Franks PW, Andersen LB, Froberg K (2003) Reexamination of validity and reliability of the CSA monitor in walking and running. Med Sci Sports Exerc 35(8):1447–54CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Irene Zaragozá
    • 1
    • 2
  • Jaime Guixeres
    • 3
  • Mariano Alcañiz
    • 1
    • 2
  • Ausiás Cebolla
    • 4
  • Javier Saiz
    • 3
  • Julio Álvarez
    • 5
  1. 1.Universidad Politécnica de Valencia, I3BH/LabHumanValenciaSpain
  2. 2.Ciber, Fisiopatología Obesidad y Nutrición, CB06/03, Instituto de Salud Carlos IIIMadridSpain
  3. 3.Universidad Politécnica de Valencia, I3BH/GBIOValenciaSpain
  4. 4.Labpsitec, Universitat Jaume ICastellónSpain
  5. 5.Cardiovascular Risk Unit. Pediatrics ServiceGeneral University Hospital of ValenciaValenciaSpain

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