Advertisement

Mobile Networks and Applications

, Volume 19, Issue 3, pp 345–359 | Cite as

BeWell: Sensing Sleep, Physical Activities and Social Interactions to Promote Wellbeing

  • Nicholas D. LaneEmail author
  • Mu Lin
  • Mashfiqui Mohammod
  • Xiaochao Yang
  • Hong Lu
  • Giuseppe Cardone
  • Shahid Ali
  • Afsaneh Doryab
  • Ethan Berke
  • Andrew T. Campbell
  • Tanzeem Choudhury
Article

Abstract

Smartphone sensing and persuasive feedback design is enabling a new generation of wellbeing apps capable of automatically monitoring multiple aspects of physical and mental health. In this article, we present BeWell+ the next generation of the BeWell smartphone wellbeing app, which monitors user behavior along three health dimensions, namely sleep, physical activity, and social interaction. BeWell promotes improved behavioral patterns via feedback rendered as an ambient display on the smartphone’s wallpaper. With BeWell+, we introduce new mechanisms to address key limitations of the original BeWell app; specifically, (1) community adaptive wellbeing feedback, which generalizes to diverse user communities (e.g., elderly, children) by promoting better behavior yet remains realistic to the user’s lifestyle; and, (2) wellbeing adaptive energy allocation, which prioritizes monitoring fidelity and feedback responsiveness on specific health dimensions (e.g., sleep) where the user needs additional help. We evaluate BeWell+ with a 27 person, 19 day field trial. Our findings show that not only can BeWell+ operate successfully on consumer smartphones; but also users understand feedback and respond by taking steps towards leading healthier lifestyles.

Keywords

Smartphone sensing mHealth Wellbeing apps 

References

  1. 1.
    Center for Disease Control and Prevention. http://www.cdc.gov
  2. 2.
  3. 3.
    National Sleep Foundation. http://www.sleepfoundation.org
  4. 4.
  5. 5.
  6. 6.
    SF-36.org. A Community for Measuring Health Outcoming using SF tools. http://www.sf-36.org/tools/SF36.shtml
  7. 7.
  8. 8.
  9. 9.
    Ahtinen A, Mattila E, Vaatanen A, Hynninen L, Salminen J, Koskinen E, Laine K (2009) User experiences of mobile wellness applications in health promotion: user study of wellness diary, mobile coach and selfrelax. In: Proceedings of Pervasive Health ’09Google Scholar
  10. 10.
    Ali A, Hossain S, Hovsepian K, Rahman M, Plarre K, Kumar S (2012) mPuff: automated detection of cigarette smoking puffs from respiration measurements. In: Proceedings of IPSN ’12Google Scholar
  11. 11.
    Alvarez G, Ayas N (2004) The impact of daily sleep duration on health: a review of the literature. Prog Cardiovasc Nurs 19(2):56CrossRefGoogle Scholar
  12. 12.
    Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, O’Brien WL, Bassett DR, Schmitz KH, Emplaincourt PO, Jacobs DR, Leon AS (2000) Compendium of physical activities: An update of activity codes and MET intensities. Med Sci Sports Exerc 32:9CrossRefGoogle Scholar
  13. 13.
    Balan R, Satyanarayanan M, Park S, Okoshi T (2003) Tactics-based Remote execution for mobile computing. In: Proceedings of Mobisys ’03Google Scholar
  14. 14.
    Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: Proceedings of Pervasive ’04Google Scholar
  15. 15.
    Bishop CM (2006) Pattern recognition and machine learning (Information Science and Statistics). SpringerGoogle Scholar
  16. 16.
    Consolvo S, Klasnja P, McDonald DW, Avrahami D, Froehlich J, LeGrand L, Libby R, Mosher K, Landay JA (2008) Flowers or a Robot Army?: encouraging awareness and activity with personal, mobile displays. In: Proceedings of UbiComp ’08Google Scholar
  17. 17.
    Consolvo S, Klasnja P, McDonald DW, Landay J (2010) Goal-setting considerations for persuasive technologies that encourage physical activity. In: Proceedings of Persuasive ’09Google Scholar
  18. 18.
    Denning T, Andrew A, Chaudhri R, Hartung C, Lester J, Borriello G, Duncan G (2009) BALANCE: towards a usable pervasive wellness application with accurate activity inference. In: Proceedings of HotMobile ’09Google Scholar
  19. 19.
    Dipietro L, Caspersen C, Ostfeld A, Nadel E (1993) A survey for assessing physical activity among older adults. Med Sci Sports Exerc 25:628–628CrossRefGoogle Scholar
  20. 20.
    Ferreira P, Sanches P, Höök K, Jaensson T (2008) License to chill!: how to empower users to cope with stress. In: Proc of NordiCHI ’08Google Scholar
  21. 21.
    Fogg BJ (2002) Persuasive technology: using Computers to change what we think and do. Ubiquity 2002 (December):2Google Scholar
  22. 22.
    Fox K (1999) The influence of physical activity on mental well-being. Public Health Nutr 2(3a):411–418CrossRefGoogle Scholar
  23. 23.
    Halko S, Kientz J (2010) Personality and persuasive technology: an exploratory study on health-promoting mobile applications. In: Proceedings of Persuasive ’10Google Scholar
  24. 24.
    Hareva DH, Okada H, Kitawaki T, Oka H (2009) Supportive Intervention using a mobile phone in behavior modification. Acta Med Okayama 63(2):113–20Google Scholar
  25. 25.
    Penedo F, Dahn J (2005) Exercise and well-being: a review of mental and physical health benefits associated with physical activity. Curr Opin Psychiatr 18(2):189CrossRefGoogle Scholar
  26. 26.
    Rozin P (2005) The meaning of food in our lives: a cross-cultural perspective on eating and well-being. J Nutr Educ Behav 37:S107–S112CrossRefGoogle Scholar
  27. 27.
    George L, Blazer D, Hughes D, Fowler N (1989) Social support and the outcome of major depression. Brit J Psychiatry 154(4):478CrossRefGoogle Scholar
  28. 28.
    Knutson JF, Lansing CR (1990) The relationship between communication problems and psychological difficulties in persons with profound acquired hearing loss. J Speech Hear Disord 55(4):656CrossRefGoogle Scholar
  29. 29.
    Blazer DG (1982) Social support and mortality in an elderly community population. Am J Epidemiol 115(5):684Google Scholar
  30. 30.
    Hicks J, Ramanathan N, Kim D, Monibi M, Selsky J, Hansen M, Estrin D (2010) Andwellness: an open mobile system for activity and experience sampling. In: Proceedings of wireless health ’10Google Scholar
  31. 31.
    Paffenbarger R, Hyde R, Wing A, Hsieh C (1986) Physical activity, all-cause mortality, and longevity of college alumni. N Engl J Med 314(10):605–613CrossRefGoogle Scholar
  32. 32.
    Klasnja P, Consolvo S, McDonald D, Landay J, Pratt W (2009) Using mobile and personal sensing technologies to support health behavior change in everyday life: lessons learned. In: Proceedings of AMIA 09Google Scholar
  33. 33.
    Lamminmaki E, Parkka J, Hermersdorf J, Kaasinen J, Samposalo K, Vainio J, Kolari J, Kulju M, Lappalainen R, Korhonen I (2005) Wellness diary for mobile phones. In: Proceedings of EMBEC ’05Google Scholar
  34. 34.
    Lane ND, Mohammod M, Lin M, Yang X, Lu H, Ali S, Doryab A, Berke E, Choudhury T, Campbell A (2011) BeWell: a smartphone application to monitor, model and promote wellbeing. In: Proceedings of Pervasive Health ’11Google Scholar
  35. 35.
    Lane ND, Xu Y, Lu H, Hu S, Choudhury T, Campbell AT, Zhao F (2011) Enabling large-scale human activity inference on smartphones using community similarity networks (CSN). In: Proceedings of Ubicomp ’11Google Scholar
  36. 36.
    Lane ND, Xu Y, Lu H, Campbell A, Choudhury T, Eisenman S (2011) Exploiting social networks for large-scale human behavior modeling. Pervasive Comput IEEE 10(4):45–53CrossRefGoogle Scholar
  37. 37.
    Lester J, Choudhury T, Kern N, Borriello G, Hannaford B (2005) A Hybrid discriminative/generative approach for modeling human activities. In: Proceedings of the IJCAI ’05Google Scholar
  38. 38.
    LiKamWa R, Liu Y, Lane N, Zhong L (2011) Can Your Smartphone Infer Your Mood? In: Proceedings of PhoneSense ’11Google Scholar
  39. 39.
    Pilcher J, Ginter D, Sadowsky B (1997) Sleep quality versus sleep quantity: relationships between sleep and measures of health, well-being and sleepiness in college students. J Psychosom Res 42(6):583–596CrossRefGoogle Scholar
  40. 40.
    Lin JJ, Mamykina L, Lindtner S, Delajoux G, Strub HB (2006) Fish’n’steps: encouraging physical activity with an interactive computer game. In: Proc. of UbiComp ’06Google Scholar
  41. 41.
    Lin M, Lane ND, Mohammod M, Yang X, Lu H, Cardone G, Ali S, Doryab A, Berke E, Campbell A, Choudhury T (2012) BeWell+: multi-dimensional wellbeing monitoring with community-guided user feedback and energy optimization. In: Proceedings of wireless health ’12Google Scholar
  42. 42.
    Lu H, Pan W, Lane ND, Choudhury T, Campbell AT (2009) Soundsense: scalable sound sensing for people-centric applications on mobile phones. In: Proceedings of MobiSys ’09Google Scholar
  43. 43.
    Lu H, Yang J, Liu Z, Lane ND, Choudhury T, Campbell AT (2010) The jigsaw continuous sensing engine for mobile phone applications. In: Proceedings of Sensys ’10Google Scholar
  44. 44.
    Miluzzo E, Oakley J, Lu H, Lane N, Peterson R, Campbell A (2008) Evaluating the iphone as a mobile platform for people-centric sensing applications. In: Proceedings of UrbanSense ’08Google Scholar
  45. 45.
    Norris R, Carroll D, Cochrane R (1992) The effects of physical activity and exercise training on psychological stress and well-being in an adolescent population. J Psychosom Res 36(1):55–65CrossRefGoogle Scholar
  46. 46.
    Patrick K, Raab F, Adams M, Dillon L, Zabinski M, Rock C, Griswold W, Norman G (2009) A text message–based intervention for weight loss: randomized controlled trial. J Med Internet Res 11(1)Google Scholar
  47. 47.
    Wang Y, Lin J, Annavaram M, Jacobson Q, Hong J, Krishnamachari B, Sadeh N (2009) A framework of energy efficient mobile sensing for automatic user state recognition. In: Proceedings of Mobisys ’09Google Scholar
  48. 48.
    Yaggi H, Araujo A, McKinlay J (2006) Sleep duration as a risk factor for the development of type 2 diabetes. Diabetes Care 29(3):657CrossRefGoogle Scholar
  49. 49.
    Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell A (2010) A survey of mobile phone sensing. IEEE Commun Mag 48:140–150CrossRefGoogle Scholar
  50. 50.
    Rachuri KK, Mascolo C, Musolesi M, Rentfrow P (2011) SociableSense: exploring the trade-offs of adaptive sampling and computation offloading for social sensing. In: Proceedings of Mobicom ’11Google Scholar
  51. 51.
    Chu D, Lane ND, Lai TT, Pang C, Meng X, Guo Q, Li F, Zhao F (2011) Balancing energy, latency and accuracy for mobile sensor data classification. In: Proceedings of Sensys ’Google Scholar
  52. 52.
    Estrin D, Sim I (2011) Open mHealth architecture: an engine for health care innovation. ScienceGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Nicholas D. Lane
    • 1
    Email author
  • Mu Lin
    • 2
  • Mashfiqui Mohammod
    • 6
  • Xiaochao Yang
    • 2
  • Hong Lu
    • 3
  • Giuseppe Cardone
    • 4
  • Shahid Ali
    • 2
  • Afsaneh Doryab
    • 5
  • Ethan Berke
    • 2
  • Andrew T. Campbell
    • 2
  • Tanzeem Choudhury
    • 6
  1. 1.Microsoft Research AsiaBeijingChina
  2. 2.Dartmouth CollegeHanoverUSA
  3. 3.Intel LabSanta ClaraUSA
  4. 4.University of BolognaBolognaItaly
  5. 5.Carnegie Mellon UniversityPittsburghUSA
  6. 6.Cornell UniversityIthacaUSA

Personalised recommendations