Implementing 360° Quantified Self for childhood obesity: Feasibility study and experiences from a weight loss camp in Qatar

by | Apr 13, 2017 | Research Publications

Author(s):

  • Fernandez-Luque, Luis
  • Singh, Meghna
  • Ofli, Ferda
  • Mejova, Yelena A.
  • Weber, Ingmar
  • Aupetit, Michael
  • Jreige, Sahar Karim
  • Elmagarmid, Ahmed
  • Srivastava, Jaideep
  • Ahmedna, Mohamed

Abstract:

Background

The explosion of consumer electronics and social media are facilitating the rise of the Quantified Self (QS) movement where millions of users are tracking various aspects of their daily life using social media, mobile technology, and wearable devices. Data from mobile phones, wearables and social media can facilitate a better understanding of the health behaviors of individuals. At the same time, there is an unprecedented increase in childhood obesity rates worldwide. This is a cause for grave concern due to its potential long-term health consequences (e.g., diabetes or cardiovascular diseases). Childhood obesity is highly prevalent in Qatar and the Gulf Region. In this study we examine the feasibility of capturing quantified-self data from social media, wearables and mobiles within a weight lost camp for overweight children in Qatar.

Methods

Over 50 children (9–12 years old) and parents used a wide range of technologies, including wearable sensors (actigraphy), mobile and social media (WhatsApp and Instagram) to collect data related to physical activity and food, that was then integrated with physiological data to gain insights about their health habits.

In this paper, we report about the acquired data and visualization techniques following the 360° Quantified Self (360QS) methodology (Haddadi et al., ICHI 587–92, 2015).

Results

360QS allows for capturing insights on the behavioral patterns of children and serves as a mechanism to reinforce education of their mothers via social media. We also identified human factors, such as gender and cultural acceptability aspects that can affect the implementation of this technology beyond a feasibility study. Furthermore, technical challenges regarding the visualization and integration of heterogeneous and sparse data sets are described in the paper.

Conclusions

We proved the feasibility of using 360QS in childhood obesity through this pilot study. However, in order to fully implement the 360QS technology careful planning and integration in the health professionals’ workflow is needed.

Document:

https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-017-0432-6#Abs1

References:
  1. de Onis M, Blossner M, Borghi E. Global prevalence and trends of overweight and obesity among preschool children. Am J Clin Nutr. 2010;92(5):1257–64.
  2. Article  PubMed  Google Scholar  2.
  3. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the united states, 2011–2012. JAMA. 2014;311(8):806–14.
  4. CAS  Article  PubMed  PubMed Central  Google Scholar  3.
  5. Haddadi H, Ofli F, Mejova Y, Weber I, Srivastava J. 360° Quantified Self. IEEE International Conference on Health Informatics (ICHI). 2015;587–92. doi:10.1109/ICHI.2015.95.4.
  6. Taylor MJ, Vlaev I, Taylor D, Gately P, Ahmedna M, Kerkadi A, Lothian J, Alsaadi A, Al-Kuwari M, Gholoum S, Al-Kuwari H, Darzi A. A weight-management camp followed by weekly after-school lifestyle education sessions as an obesity intervention for Qatari children: a prospective cohort study. Lancet. 2015;386:72.
  7. Article  Google Scholar  5.
  8. Zhang X, Han X, Dang Y, Meng F, Guo X, Lin J. User acceptance of mobile health services from users’ perspectives: The role of self-efficacy and response-efficacy in technology acceptance. Informatics for Health and Social Care. 2017;42(2):194–206.6.
  9. Daniels SR, Arnett DK, Eckel RH, Gidding SS, Hayman LL, Kumanyika S, Robinson TN, Scott BJ, StJeor S, Williams CL. Overweight in children and adolescents. Circulation. 2005;111(15):1999–2012.
  10. Article  PubMed  Google Scholar  7.
  11. Freedman DS, Mei Z, Srinivasan SR, Berenson GS, Dietz WH. Cardiovascular risk factors and excess adiposity among overweight children and adolescents: the Bogalusa Heart Study. J Pediatr. 2007;150(1):12–172.
  12. Article  PubMed  Google Scholar  8.
  13. Li C, Ford ES, Zhao G, Mokdad AH. Prevalence of pre-diabetes and its association with clustering of cardiometabolic risk factors and hyperinsulinemia among U.S. adolescents: National Health and Nutrition Examination Survey 2005–2006. Diabetes Care. 2009;32(2):342–7.
  14. Article  PubMed  PubMed Central  Google Scholar  9.
  15. Dietz WH, Robinson TN. Overweight Children and Adolescents. N Engl J Med. 2005;352(20):2100–9.
  16. CAS  Article  PubMed  Google Scholar  10.
  17. Arora, T., Broglia, E., Pushpakumar, e.a.: An Investigation into the Strength of the Association and Agreement Levels between Subjective and Objective Sleep Duration in Adolescents. PLoS ONE 8(8), 72406 (2013)11.
  18. Ho M, Garnett SP, Baur L, Burrows T, Stewart L, Neve M, Collins C. Effectiveness of lifestyle interventions in child obesity: systematic review with meta-analysis. Pediatrics. 2012;130(6):1647–71.
  19. Article  Google Scholar  12.
  20. Christakis NA, Fowler JH. The spread of obesity in a large social network over 32 year. N Engl J Med. 2007;357(4):370–9.
  21. CAS  Article  PubMed  Google Scholar  13.
  22. Heron KE, Smyth JM. Ecological momentary interventions: incorporating mobile technology into psychosocial and health behavior treatments. Br J Health Psychol. 2010;15(Pt 1):1–39.
  23. Article  PubMed  Google Scholar  14.
  24. Fox S, Duggan M. Tracking for Health. Technical report, Pew Research Center (2013). http://pewinternet.org/Reports/2013/Tracking-for-Health.aspx. Accessed 2 Apr 2017.15.
  25. Swan M. Melanie: emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int J Environ Res Public Health. 2009;6(2):492–525.
  26. Article  PubMed  PubMed Central  Google Scholar  16.
  27. Wang, Y., Weber, I., Mitra, P.: Quantified Self Meets Social Media: Sharing of Weight Updates on Twitter (2016)17.
  28. Fatema Akbar IW. #sleep as android: Feasibility of using sleep logs on twitter for sleep studies. International Conference on Health Informatics (ICHI). 2016. p. 227–233. doi:10.1109/ICHI.2016.32.18.
  29. Harris JK, Mart A, Moreland-Russell S, Caburnay CA. Diabetes topics associated with engagement on twitter. Prev Chronic Dis. 2015;12:62.
  30. Article  Google Scholar  19.
  31. Abbar, S., Mejova, Y., Weber, I.: You Tweet What You Eat: Studying Food Consumption Through Twitter. Conference on Human Factors in Computing Systems (ACM CHI) (2015)20.
  32. Gimpel H, Nißen M, Gorlitz RA. Quantifying the quantified self: A study on the motivation of patients to track their own health. In: ICIS 2013. 2013. pp. 128–133.21.
  33. Swan M. Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0. J Sensor Actuator Netw. 2012;1(3):217–53.
  34. Article  Google Scholar  22.
  35. Rodgers MM, Pai VM, Conroy RS. Recent advances in wearable sensors for health monitoring. IEEE Sensors J. 2015;15(6):3119–26.
  36. Article  Google Scholar  23.
  37. Swan M. Health 2050: the realization of personalized medicine through crowdsourcing, the quantified self, and the participatory biocitizen. J Personalized Med. 2012;2(3):93–118.
  38. Article  Google Scholar  24.
  39. Azmak O, Bayer H, Caplin A, Chun M, Glimcher P, Koonin S, Patrinos A. Using Big data to understand the human condition: the kavli HUMAN project. Big data. 2015;3(3):173–88.
  40. Article  PubMed  PubMed Central  Google Scholar  25.
  41. Godinho C, Domingos J, Cunha G, Santos AT, Fernandes RM, Abreu D, Goncalves N, Matthews H, Isaacs T, Duffen J, Al-Jawad A, Larsen F, Serrano A, Weber P, Thoms A, Sollinger S, Graessner H, Maetzler W, Ferreira JJ. A systematic review of the characteristics and validity of monitoring technologies to assess Parkinson’s disease. J Neuroeng Rehabil. 2016;13(1):24.
  42. Article  PubMed  PubMed Central  Google Scholar  26.
  43. Tsamardinos I, Triantafillou S, Lagani V. Towards integrative causal analysis of heterogeneous data sets and studies. J Mach Learn Res. 2012;13(Apr):1097–157.
  44. Google Scholar  27.
  45. Hendler J. Data integration for heterogenous datasets. Big data. 2014;2(4):205–15.
  46. Article  PubMed  PubMed Central  Google Scholar  28.
  47. Shneiderman, B.: The eyes have it: A task by data type taxonomy for information visualizations. In: VL ’96: Proceedings of the 1996 IEEE Symposium on Visual Languages, p. 336. IEEE Computer Society (1996)29.
  48. Alotaibi MM, Istepanian RSH, Sungoor A, Philip N. An intelligent mobile diabetes management and educational system for Saudi Arabia: System architecture. In: 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics. BHI. 2014;2014:29–32.
  49. Google Scholar  30.
  50. Alhazbi, S., Alkhateeb, M.: Mobile application for diabetes control in Qatar. Computing Technology and Information Management (ICCM), 2012 8th International Conference on, Volume: 2 (1), 763–766 (2012)31.
  51. Mansar, S.L., Kekre, S.: A founding framework for addressing obesity in Qatar using mobile technologies. In: Communications in Computer and Information Science, vol. 221 CCIS, pp. 402–412 (2011)32.
  52. Choe EK, Lee B, Schraefel MC. Characterizing visualization insights from quantified Selfers’ personal data presentations. IEEE Comput Graph Appl. 2015;35(4):28–37.
  53. Article  Google Scholar  33.
  54. Larsen, J.E., Cuttone, A., Lehmann, S.: QS Spiral : Visualizing Periodic Quantified Self Data. Personal Informatics in the Wild: Hacking Habits for Health & Happiness — CHI 2013 Workshop, 5–8 (2013)34.
  55. West, V.L., Borland, D., Hammond, W.E.: Innovative information visualization of electronic health record data: a systematic review. Journal of the American Medical Informatics Association : JAMIA, 1–7 (2014)35.
  56. Badgeley MA, Shameer K, Glicksberg BS, Tomlinson MS, Levin MA, McCormick PJ, Kasarskis A, Reich DL, Dudley JT. EHDViz: clinical dashboard development using open-source technologies. BMJ Open. 2016;6(3):010579.
  57. Article  Google Scholar  36.
  58. de Folter, J., Gokalp, H., Fursse, J., Sharma, U., Clarke, M., Crepeau, e.a.: Designing effective visualizations of habits data to aid clinical decision making. BMC Medical Informatics and Decision Making 2014 14:1 14(1), 942–945 (2014)37.
  59. Ledesma A, Al-Musawi M, Nieminen H, Hood L, Flores C, Shneiderman B. Health figures: an open source JavaScript library for health data visualization. BMC Med Inform Decis Mak. 2016;16(1):38.
  60. Article  PubMed  PubMed Central  Google Scholar  38.
  61. Peters DH, Tran NT, Adam T. Implementation Research in Health: A Practical Guide. WHO; 2013. p. 1–69.39.
  62. Bakken S, Ruland CM. Translating clinical informatics interventions into routine clinical care: How Can the RE-AIM framework help? J Am Med Inform Assoc. 2009;16(6):889–97.
  63. Article  PubMed  PubMed Central  Google Scholar  40.
  64. Abbott, P.A., Foster, J., Marin, H.d.F., Dykes, P.C.: Complexity and the science of implementation in health IT–knowledge gaps and future visions. International journal of medical informatics 83(7), 12–22 (2014)