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Innovations in the Use of Interactive Technology to Support Weight Management

  • Health Services and Programs (SFL Kirk, Section Editor)
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Abstract

New and emerging mobile technologies are providing unprecedented possibilities for understanding and intervening on obesity-related behaviors in real time. However, the mobile health (mHealth) field has yet to catch up with the fast-paced development of technology. Current mHealth efforts in weight management still tend to focus mainly on short message systems (SMS) interventions, rather than taking advantage of real-time sensing to develop just-in-time adaptive interventions (JITAIs). This paper will give an overview of the current technology landscape for sensing and intervening on three behaviors that are central to weight management: diet, physical activity, and sleep. Then five studies that really dig into the possibilities that these new technologies afford will be showcased. We conclude with a discussion of hurdles that mHealth obesity research has yet to overcome and a future-facing discussion.

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References

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  1. Kaplan RM, Stone AA. Bringing the laboratory and clinic to the community: mobile technologies for health promotion and disease prevention a. Annu Rev Psychol. 2013;64:471–98.

    Article  PubMed  Google Scholar 

  2. Turner T, Spruijt-Metz D, Wen C, Hingle M. Prevention and treatment of pediatric obesity using mobile and wireless technologies: a systematic review. Pediatr Obes. 2015. doi:10.1111/ijpo.12002.

  3. Thomas JG, Bond DS. Review of innovations in digital health technology to promote weight control. Curr Diab Rep. 2014;14(5):485.

    Article  PubMed  Google Scholar 

  4. Spruijt-Metz D. Etiology, treatment, and prevention of obesity in childhood and adolescence: a decade in review. J Res Adolesc. 2011;21(1):129–52.

    Article  PubMed Central  PubMed  Google Scholar 

  5. Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4:1–32.

    Article  PubMed  Google Scholar 

  6. Nahum-Shani I, Hekler E, Spruijt-Metz D. Building health behavior models to guide the development of just-in-time adaptive interventions: a pragmatic framework. 2015. In press.

  7. Liu F, Kong X, Cao J, Chen S, Li C, Huang J, et al. Mobile phone intervention and weight loss among overweight and obese adults: a meta-analysis of randomized controlled trials. Am J Epidemiol. 2015;181(5):337–48.

    Article  PubMed  Google Scholar 

  8. Hutchesson M, Rollo M, Krukowski R, Ells L, Harvey J, Morgan P, et al. eHealth interventions for the prevention and treatment of overweight and obesity in adults: a systematic review with meta‐analysis. Obes Rev. 2015;16(5):376–92.

    Article  CAS  PubMed  Google Scholar 

  9. Wickham CA, Carbone ET. Who’s calling for weight loss? A systematic review of mobile phone weight loss programs for adolescents. Nutr Rev. 2015;73(6):386–98.

    Article  PubMed  Google Scholar 

  10. Bacigalupo R, Cudd P, Littlewood C, Bissell P, Hawley MS, Buckley Woods H. Interventions employing mobile technology for overweight and obesity: an early systematic review of randomized controlled trials. Obes Rev. 2013;14(4):279–91.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  11. Actigraph. 2015 [cited 2015 July 6]; Available from: http://www.actigraphcorp.com/solutions-and-products/activity-monitors/actigraph-link/.

  12. Johansson E, Ekelund U, Nero H, Marcus C, Hagströmer M. Calibration and cross-validation of a wrist-worn Actigraph in young preschoolers. Pediatr Obes. 2015;10(1):1–6.

    Article  CAS  PubMed  Google Scholar 

  13. Trost SG, Loprinzi PD, Moore R, Pfeiffer KA. Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc. 2011;43(7):1360–8.

    Article  PubMed  Google Scholar 

  14. Li M, Rozgica V, Thatte G, Lee S, Emken A, Annavaram M, et al. Multimodal physical activity recognition by fusing temporal and cepstral information. IEEE Trans Neural Syst Rehabil Eng. 2010;18(4):369–80.

    Article  PubMed Central  PubMed  Google Scholar 

  15. Incel OD, Kose M, Ersoy C. A review and taxonomy of activity recognition on mobile phones. BioNanoScience. 2013;3(2):145–71.

    Article  Google Scholar 

  16. Mannini A, Intille SS, Rosenberger M, Sabatini AM, Haskell W. Activity recognition using a single accelerometer placed at the wrist or ankle. Med Sci Sports Exerc. 2013;45(11):2193–203.

    Article  PubMed Central  PubMed  Google Scholar 

  17. Case MA, Burwick HA, Volpp KG, Patel MS. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA. 2015;313(6):625–6.

    Article  CAS  PubMed  Google Scholar 

  18. Intille SS, Albinali F, Mota S, Kuris B, Botana P, Haskell WL. Design of a wearable physical activity monitoring system using mobile phones and accelerometers. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:3636–9.

    PubMed  Google Scholar 

  19. Hekler EB, Buman MP, Grieco L, Rosenberger M, Winter SJ, Haskell W, et al. Validation of physical activity tracking via android smartphones compared to ActiGraph accelerometer: laboratory-based and free-living validation studies. JMIR mHealth uHealth. 2015;3(2):e36.

    Article  PubMed Central  PubMed  Google Scholar 

  20. Scisco JL, Muth ER, Dong Y, Hoover AW, O’Neil P, Fishel-Brown SR. Usability and Acceptability of the “Bite Counter” Device. Proceedings of the Human Factors and Ergonomics Society Annual Meeting; 2011: SAGE Publications; 2011. p. 1967–9.

  21. Sazonov ES, Fontana JM. A sensor system for automatic detection of food intake through non-invasive monitoring of chewing. IEEE Sensors J. 2012;12(5):1340–8.

    Article  Google Scholar 

  22. Boushey CJ, Harray AJ, Kerr DA, Schap TE, Paterson S, Aflague T, et al. How willing Are adolescents to record their dietary intake? the mobile food record. JMIR mHealth uHealth. 2015;3(2):e47.

    Article  PubMed Central  PubMed  Google Scholar 

  23. Sun M, Burke LE, Mao ZH, Chen Y, Chen HC, Bai Y, et al. eButton: a wearable computer for health monitoring and personal assistance. Proc Des Autom Conf 2014. 2014;1–6.

  24. Desendorf J, Bassett DR, Raynor HA, Coe DP. Validity of the bite counter device in a controlled laboratory setting. Eat Behav. 2014;15(3):502–4.

    Article  PubMed  Google Scholar 

  25. Dong Y, Hoover A, Scisco J, Muth E. A new method for measuring meal intake in humans via automated wrist motion tracking. Appl Psychophysiol Biofeedback. 2012;37(3):205–15.

    Article  PubMed Central  PubMed  Google Scholar 

  26. Johnson NL, Kirchner HL, Rosen CL, Storfer-lsser A, Cartar LN, Ancoli-Israel S, et al. Sleep estimation using wrist actigraphy in adolescents with and without sleep disordered breathing: a comparison of three data modes. Sleep. 2007;30(7):899.

    PubMed Central  PubMed  Google Scholar 

  27. Blackwell T, Yaffe K, Laffan A, Ancoli-Israel S, Redline S, Ensrud KE, et al. Associations of objectively and subjectively measured sleep quality with subsequent cognitive decline in older community-dwelling men: the MrOS sleep study. Sleep. 2013;37(4):655–63.

    Google Scholar 

  28. Lee J, Finkelstein J. Consumer sleep tracking devices: a critical review. Stud Health Technol Inform. 2015;210:458–60.

  29. Kay M, Choe EK, Shepherd J, Greenstein B, Watson N, Consolvo S, et al. Lullaby: a capture & access system for understanding the sleep environment. Proceedings of the 2012 ACM Conference on Ubiquitous Computing; 2012: ACM; 2012. p. 226–34.

  30. Paalasmaa J, Waris M, Toivonen H, Leppakorpi L, Partinen M. Unobtrusive online monitoring of sleep at home. Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE; 2012: IEEE; 2012. p. 3784–8.

  31. Beddit. Beddit. 2015 [cited 2015 July 7]; Available from: http://www.beddit.com.

  32. Hello. Sense. 2015 [cited 2015 July 8]; Available from: https://hello.is.

  33. Estrin D, Sim I. Open mHealth architecture: an engine for health care innovation. Science. 2010;330(6005):759.

    Article  CAS  PubMed  Google Scholar 

  34. Volpp K, Loewenstein G, Asch D. Behavioral economics and health. Health Behav: Theory Res Pract. 2015: 389.

  35. King AC, Glanz K, Patrick K. Technologies to measure and modify physical activity and eating environments. Am J Prev Med. 2015;48(5):630–8.

    Article  PubMed  Google Scholar 

  36. Kerr J, Marshall SJ, Godbole S, Chen J, Legge A, Doherty AR, et al. Using the SenseCam to improve classifications of sedentary behavior in free-living settings. Am J Prev Med. 2013;44(3):290–6.

    Article  PubMed  Google Scholar 

  37. Madan A, Moturu ST, Lazer D, Pentland AS. Social sensing: obesity, unhealthy eating and exercise in face-to-face networks. Wireless Health 2010; 2010: ACM; 2010. p. 104–10.

  38. Lane ND, Lin M, Mohammod M, Yang X, Lu H, Cardone G, et al. Bewell: sensing sleep, physical activities and social interactions to promote wellbeing. Mob Netw Appl. 2014;19(3):345–59.

    Article  Google Scholar 

  39. Spruijt-Metz D, Berrigan D, Kelly LA, McConnell R, Dueker D, Lindsey G, et al. Measures of physical activity and exercise. In: Allison DB, Baskin ML, editors. Handbook of assessment methods for eating behaviors and weight-related problems: measures, theory, and research. 2nd ed. Los Angeles: Sage; 2009. p. 187–254.

    Google Scholar 

  40. Ko J, Lu C, Srivastava MB, Stankovic J, Terzis A, Welsh M. Wireless sensor networks for healthcare. Proc IEEE. 2010;98(11):1947–60.

    Article  Google Scholar 

  41. Pew Research Center. The smartphone difference: U.S. Smartphone Ownership 2015; 2015.

  42. International Telecommunication Union. Measuring the Information Society Report 2014. Geneva, Switzerland; 2014

  43. Kim H, Jin Z, Oh S, Lee M. An information provider for exercise data using IoT techniques. Int J. 2015;4(2):31–5.

  44. Smith JM. The doctor will see you ALWAYS. IEEE Spectr. 2011;48(10):56–62.

    Article  Google Scholar 

  45. King AC, Hekler EB, Grieco LA, Winter SJ, Sheats JL, Buman MP, et al. Harnessing different motivational frames via mobile phones to promote daily physical activity and reduce sedentary behavior in aging adults. PLoS One. 2013;8(4):e62613.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  46. Ritter S. Apple’s Research Kit development framework for iphone apps enables innovative approaches to medical research data collection. J Clin Trials. 2015;5:e120.

    Google Scholar 

  47. Hekler E, Klasnja P, Riley WT, Buman MP, Huberty J. Agile science: creating useful products for sustained behavior change in the real-world. under review.

  48. Almirall D, Nahum-Shani I, Sherwood NE, Murphy SA. Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research. Transl Behav Med. 2014;4(3):260–74.

    Article  PubMed Central  PubMed  Google Scholar 

  49. Nebeker C. Examining the ethical dimensions of wearable and sensing technologies in mHealth research. 142nd APHA Annual Meeting and Exposition (November 15-November 19, 2014); 2014: APHA; 2014

  50. Spruijt-Metz D, Hekler E, Saranummi N, Intille S, Korhonen I, Nilsen W, et al. Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Transl Behav Med. 2015;5(3):335–46.

  51. Hufford MR, Shields AL, Shiffman S, Paty J, Balabanis M. Reactivity to ecological momentary assessment: an example using undergraduate problem drinkers. Psychol Addict Behav. 2002;16(3):205.

    Article  PubMed  Google Scholar 

  52. Intille SS, Lester J, Sallis JF, Duncan G. New horizons in sensor development. Med Sci Sports Exerc. 2012;44(1 Suppl 1):S24–31.

    Article  PubMed Central  PubMed  Google Scholar 

  53. Bond DS, Thomas JG, Raynor HA, Moon J, Sieling J, Trautvetter J, et al. B-mobile-a smartphone-based intervention to reduce sedentary time in overweight/obese individuals: a within-subjects experimental trial. 2014.

  54. Sarker H, Sharmin M, Ali AA, Rahman MM, Bari R, Hossain SM, et al. Assessing the availability of users to engage in just-in-time intervention in the natural environment. UbiComp `14 Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2014. p. 909–20. doi:10.1145/2632048.2636082

  55. Adams MA, Sallis JF, Norman GJ, Hovell MF, Hekler EB, Perata E. An adaptive physical activity intervention for overweight adults: a randomized controlled trial. PLoS One. 2013;8(12):e82901. This paper gives an excellent example of a Just-in-Time, Adaptive Intervention.

    Article  PubMed Central  PubMed  Google Scholar 

  56. Consolvo S, McDonald DW, Toscos T, Chen MY, Froehlich J, Harrison B, et al. Activity sensing in the wild: a field trial of ubifit garden. 2008: ACM; 2008. p. 1797–806.

  57. Anderson I, Maitland J, Sherwood S, Barkhuus L, Chalmers M, Hall M, et al. Shakra: tracking and sharing daily activity levels with unaugmented mobile phones. Mob Netw Appl. 2007;12(2):185–99.

    Article  Google Scholar 

  58. Lin J, Mamykina L, Lindtner S, Delajoux G, Strub H. Fish’n’Steps: Encouraging physical activity with an interactive computer game. UbiComp 2006: Ubiquitous Computing. 2006: 261–78.

  59. O’Reilly GA, Spruijt-Metz D. Current mHealth technologies for physical activity assessment and promotion. Am J Prev Med. 2013;45(4):501–7.

    Article  PubMed Central  PubMed  Google Scholar 

  60. Pellegrini CA, Duncan JM, Moller AC, Buscemi J, Sularz A, DeMott A, et al. A smartphone-supported weight loss program: design of the ENGAGED randomized controlled trial. BMC Public Health. 2012;12(1):1041. Nice overview of how to design a Just-in-Time, Adaptive Intervention.

    Article  PubMed Central  PubMed  Google Scholar 

  61. Spring B, Duncan JM, Janke EA, Kozak AT, McFadden HG, DeMott A, et al. Integrating technology into standard weight loss treatment: a randomized controlled trial. JAMA Intern Med. 2013;173(2):105–11.

    Article  PubMed Central  PubMed  Google Scholar 

  62. Spring B, Gotsis M, Paiva A, Spruijt-Metz D. Healthy apps: mobile devices for continuous monitoring and intervention. IEEE Pulse. 2013;4(6):34–40.

    Article  PubMed  Google Scholar 

  63. Knowler WC, Fowler SE, Hamman RF, Christophi CA, Hoffman HJ, Brenneman AT, et al. 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet. 2009;374(9702):1677–86.

    Article  PubMed  Google Scholar 

  64. Carver CS, Scheier MF. On the self-regulation of behavior. Cambridge University Press; 2001.

  65. Bandura A. Toward a psychology of human agency. Perspect Psychol Sci. 2006;1(2):164–80.

    Article  PubMed  Google Scholar 

  66. Umstattd MR, Wilcox S, Saunders R, Watkins K, Dowda M. Self-regulation and physical activity: the relationship in older adults. Am J Health Behav. 2008;32(2):115–24.

    Article  PubMed  Google Scholar 

  67. Basic Behavioral Science Task Force of the National Advisory Mental Health Council. Basic behavioral science research for mental health: social influence and social cognition. Am Psychol. 1996;51(5):478–84.

    Article  Google Scholar 

  68. Skinner BF. Science and human behavior. Simon and Schuster; 1953.

  69. Sallis JF, Owen N, Fisher EB. Ecological models of health behavior. Health Behav Health Educ: Theory Res Pract. 2008;4:465–86.

    Google Scholar 

  70. Epstein LH. Integrating theoretical approaches to promote physical activity. Am J Prev Med. 1998;15(4):257–65.

    Article  CAS  PubMed  Google Scholar 

  71. Toscos T, Faber A, An S, Gandhi MP. Chick clique: persuasive technology to motivate teenage girls to exercise. 2006: ACM; 2006. p. 1873–8.

  72. Alive technologies. Alive heart and activity monitor. 2015 [cited 2015 July 10]; Available from: http://www.alivetec.com/alive-bluetooth-heart-activity-monitor/.

  73. Lee S, Annavaram M, Thatte G, Rozgic V, Li M, Mitra U, et al. Sensing for obesity: KNOWME Implementation and Lessons for an Architect. Proceedings of the Workshop on Biomedicine in Computing: Systems, Architectures, and Circuits (BiC2009). Austin, TX; 2009.

  74. Emken BA, Li M, Thatte G, Lee S, Annavaram M, Mitra U, et al. Recognition of physical activities in overweight Hispanic youth using KNOWME Networks. J Phys Act Health. 2012;9(3):432–41.

    PubMed Central  PubMed  Google Scholar 

  75. Rubak S, Sandbaek A, Lauritzen T, Christensen B. Motivational interviewing: a systematic review and meta-analysis. Br J Gen Pract. 2005;55(513):305–12.

    PubMed Central  PubMed  Google Scholar 

  76. Collins LM, Murphy SA, Strecher V. The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. Am J Prev Med. 2007;32(5 Suppl):S112–8. This is a key paper on new agile research designs.

    Article  PubMed Central  PubMed  Google Scholar 

  77. Baron NS. Shall we talk? Conversing with humans and robots. Inf Soc. 2015;31(3):257–64.

    Article  Google Scholar 

  78. Baker TB, Gustafson DH, Shah D. How can research keep up with eHealth? Ten strategies for increasing the timeliness and usefulness of eHealth research. J Med Internet Res. 2014;16(2). doi:10.2196/jmir.2925.

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Acknowledgments

Dr. Spruijt-Metz reports grants from National Institutes of Health (NIMHD 3P60MD002254-02S1).

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Conflict of Interest

D. Spruijt-Metz, C.K.F. Wen, G. O’Reilly, M. Li, S Lee, B.A. Emken, U. Mitra, M. Annavaram, G. Ragusa, and S. Narayanan declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

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Authors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    D. Spruijt-Metz, C. K. F. Wen, G. O’Reilly, M. Li, S Lee, B. A. Emken, U. Mitra, M. Annavaram, G. Ragusa & S. Narayanan

  2. SYSU-CMU Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, China

    M. Li

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  1. D. Spruijt-Metz
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  2. C. K. F. Wen
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  3. G. O’Reilly
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  5. S Lee
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  9. G. Ragusa
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Correspondence to D. Spruijt-Metz.

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Spruijt-Metz, D., Wen, C.K.F., O’Reilly, G. et al. Innovations in the Use of Interactive Technology to Support Weight Management. Curr Obes Rep 4, 510–519 (2015). https://doi.org/10.1007/s13679-015-0183-6

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