Current Obesity Reports

, Volume 4, Issue 4, pp 510–519 | Cite as

Innovations in the Use of Interactive Technology to Support Weight Management

  • 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
Health Services and Programs (SFL Kirk, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Health Services and Programs


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.


Obesity mHealth Sensors Real-time Just-in-time Adaptive interventions 



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

Compliance with Ethics Guidelines

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.


Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 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.CrossRefPubMedGoogle Scholar
  2. 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. 3.
    Thomas JG, Bond DS. Review of innovations in digital health technology to promote weight control. Curr Diab Rep. 2014;14(5):485.CrossRefPubMedGoogle Scholar
  4. 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.PubMedCentralCrossRefPubMedGoogle Scholar
  5. 5.
    Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4:1–32.CrossRefPubMedGoogle Scholar
  6. 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.Google Scholar
  7. 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.CrossRefPubMedGoogle Scholar
  8. 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.CrossRefPubMedGoogle Scholar
  9. 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.CrossRefPubMedGoogle Scholar
  10. 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.PubMedCentralCrossRefPubMedGoogle Scholar
  11. 11.
  12. 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.CrossRefPubMedGoogle Scholar
  13. 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.CrossRefPubMedGoogle Scholar
  14. 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.PubMedCentralCrossRefPubMedGoogle Scholar
  15. 15.
    Incel OD, Kose M, Ersoy C. A review and taxonomy of activity recognition on mobile phones. BioNanoScience. 2013;3(2):145–71.CrossRefGoogle Scholar
  16. 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.PubMedCentralCrossRefPubMedGoogle Scholar
  17. 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.CrossRefPubMedGoogle Scholar
  18. 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.PubMedGoogle Scholar
  19. 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.PubMedCentralCrossRefPubMedGoogle Scholar
  20. 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.Google Scholar
  21. 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.CrossRefGoogle Scholar
  22. 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.PubMedCentralCrossRefPubMedGoogle Scholar
  23. 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.Google Scholar
  24. 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.CrossRefPubMedGoogle Scholar
  25. 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.PubMedCentralCrossRefPubMedGoogle Scholar
  26. 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.PubMedCentralPubMedGoogle Scholar
  27. 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. 28.
    Lee J, Finkelstein J. Consumer sleep tracking devices: a critical review. Stud Health Technol Inform. 2015;210:458–60.Google Scholar
  29. 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.Google Scholar
  30. 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.Google Scholar
  31. 31.
    Beddit. Beddit. 2015 [cited 2015 July 7]; Available from:
  32. 32.
    Hello. Sense. 2015 [cited 2015 July 8]; Available from:
  33. 33.
    Estrin D, Sim I. Open mHealth architecture: an engine for health care innovation. Science. 2010;330(6005):759.CrossRefPubMedGoogle Scholar
  34. 34.
    Volpp K, Loewenstein G, Asch D. Behavioral economics and health. Health Behav: Theory Res Pract. 2015: 389.Google Scholar
  35. 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.CrossRefPubMedGoogle Scholar
  36. 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.CrossRefPubMedGoogle Scholar
  37. 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.Google Scholar
  38. 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.CrossRefGoogle Scholar
  39. 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. 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.CrossRefGoogle Scholar
  41. 41.
    Pew Research Center. The smartphone difference: U.S. Smartphone Ownership 2015; 2015.Google Scholar
  42. 42.
    International Telecommunication Union. Measuring the Information Society Report 2014. Geneva, Switzerland; 2014Google Scholar
  43. 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.Google Scholar
  44. 44.
    Smith JM. The doctor will see you ALWAYS. IEEE Spectr. 2011;48(10):56–62.CrossRefGoogle Scholar
  45. 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.PubMedCentralCrossRefPubMedGoogle Scholar
  46. 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. 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.Google Scholar
  48. 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.PubMedCentralCrossRefPubMedGoogle Scholar
  49. 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; 2014Google Scholar
  50. 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.Google Scholar
  51. 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.CrossRefPubMedGoogle Scholar
  52. 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.PubMedCentralCrossRefPubMedGoogle Scholar
  53. 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.Google Scholar
  54. 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. 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.PubMedCentralCrossRefPubMedGoogle Scholar
  56. 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.Google Scholar
  57. 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.CrossRefGoogle Scholar
  58. 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.Google Scholar
  59. 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.PubMedCentralCrossRefPubMedGoogle Scholar
  60. 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.PubMedCentralCrossRefPubMedGoogle Scholar
  61. 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.PubMedCentralCrossRefPubMedGoogle Scholar
  62. 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.CrossRefPubMedGoogle Scholar
  63. 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.CrossRefPubMedGoogle Scholar
  64. 64.
    Carver CS, Scheier MF. On the self-regulation of behavior. Cambridge University Press; 2001.Google Scholar
  65. 65.
    Bandura A. Toward a psychology of human agency. Perspect Psychol Sci. 2006;1(2):164–80.CrossRefPubMedGoogle Scholar
  66. 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.CrossRefPubMedGoogle Scholar
  67. 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.CrossRefGoogle Scholar
  68. 68.
    Skinner BF. Science and human behavior. Simon and Schuster; 1953.Google Scholar
  69. 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. 70.
    Epstein LH. Integrating theoretical approaches to promote physical activity. Am J Prev Med. 1998;15(4):257–65.CrossRefPubMedGoogle Scholar
  71. 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.Google Scholar
  72. 72.
    Alive technologies. Alive heart and activity monitor. 2015 [cited 2015 July 10]; Available from:
  73. 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.Google Scholar
  74. 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.PubMedCentralPubMedGoogle Scholar
  75. 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.PubMedCentralPubMedGoogle Scholar
  76. 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.PubMedCentralCrossRefPubMedGoogle Scholar
  77. 77.
    Baron NS. Shall we talk? Conversing with humans and robots. Inf Soc. 2015;31(3):257–64.CrossRefGoogle Scholar
  78. 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.

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • D. Spruijt-Metz
    • 1
  • C. K. F. Wen
    • 1
  • G. O’Reilly
    • 1
  • M. Li
    • 1
    • 2
  • S Lee
    • 1
  • B. A. Emken
    • 1
  • U. Mitra
    • 1
  • M. Annavaram
    • 1
  • G. Ragusa
    • 1
  • S. Narayanan
    • 1
  1. 1.University of Southern CaliforniaLos AngelesUSA
  2. 2.SYSU-CMU Joint Institute of EngineeringSun Yat-sen UniversityGuangzhouChina

Personalised recommendations