Mobile Networks and Applications

, Volume 22, Issue 3, pp 478–492 | Cite as

EHR: a Sensing Technology Readiness Model for Lifestyle Changes

Article
  • 174 Downloads

Abstract

Interest in developing user-centered sensing technologies for personalized behavior change has gained significant momentum. However, very little research work has been done to understand issues relative to user readiness and adoption of the sensing technologies to change their behaviors, especially the motivations as well as the concerns and impediments for adoption. We have developed a model called EHR (e-health readiness), to understand and explain the relationship between user habits, perceived healthiness and beliefs towards sensing technologies, and how these factors influence user readiness to use sensing technologies to manage their wellness. We then validate the model using psychometric methods by a large-scale user study (N = 541). Results show overall readiness to sensing technologies is positively influenced by readiness to monitor health conditions, share data within social networks, and receive recommendations. Additionally, readiness is significantly impacted by perceptions of healthiness, technology satisfaction and usefulness of such technology. Finally, we summarize user motivations and concerns for pervasive sensing tools through qualitative analysis on their comments. We present this model and the results of this survey to shed light on designing future sensing technologies for behavior change.

Keywords

Sensors Pervasive health Technology readiness User modeling 

References

  1. 1.
    Consolvo S, Klasnja P, McDonald DW, Froehlich J, LeGrand L, Libby R, Mosher K, Landay JA (2000) Flowers or a Ro-bot Army? Encouraging awareness & activity with personal, mobile displays. In: Proceedings of 10th international conference ubiquitous computing (Ubicomp’00), pp 54–63Google Scholar
  2. 2.
    Li I, Dey A, Forlizzi J (2010) A stage-based model of personal informatics systems. In: Proceedings of 28th international conference human factors in computing systems (CHI ’10), pp 557–566Google Scholar
  3. 3.
    Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT (2010) A survey of mobile phone sensing. Commun Mag 48(9):140–150CrossRefGoogle Scholar
  4. 4.
    Yumak Z, Pu P (2013) Survey of sensor-based personal wellness management systems. BioNanoScience 3 (3):254–269CrossRefGoogle Scholar
  5. 5.
    Weeks JW, Heimberg RG, Fresco DM, Hart TA, Turk CL, Schneier FR, Liebowitz MR (2005) Empirical validation and psychometric evaluation of the brief fear of negative evaluation scale in patients with social anxiety disorder. Psychol Assess 17(2):179CrossRefGoogle Scholar
  6. 6.
    Hudd S, Dumlao J, Erdmann-Sager D, Murray D, Phan E, Soukas N, Yokozuka N (2000) Stress at college: effects on health habits, health status and self-esteem. College Student JournalGoogle Scholar
  7. 7.
    Hoeger W, Hoeger S (2012) Lifetime physical fitness and wellness: a personalized program. Cengage LearningGoogle Scholar
  8. 8.
    Klasnja P, Consolvo S, Pratt W (2011) How to evaluate technologies for health behavior change in HCI research. In: Proceedings of 29th international conference human factors in computing systems (CHI ’11), pp 3063–3072Google Scholar
  9. 9.
    Lin JJ, Mamykina L, Lindtner S, Delajoux G, Strub HB (2006) Fish’n’steps: encouraging physical activity with an interactive computer game. In: Proceedings of 16th international conference ubiquitous computing (Ubicomp’06), pp 261–278Google Scholar
  10. 10.
    Consolvo S, McDonald DW, Toscos T, Chen MY, Froehlich J, Harrison B, Klasnja P, LaMarca A, LeGrand L, Libby R, Smith I, Landay JA (2008) Activity sensing in the wild a field trial of ubifit garden. In: Proceedings of 26th international conference human factors in computing systems (CHI ’08), pp 1797–1806Google Scholar
  11. 11.
    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: ICST conference on pervasive computing technologies for healthcare, pp 23–26Google Scholar
  12. 12.
    McDu D, Karlson A, Kapoor A, Roseway A, Czerwinski M (2012) AffectAura: an intelligent system for emotional memory. In: Proceedings of 30th international conference human factors in computing systems (CHI’12), pp 849–858Google Scholar
  13. 13.
    Yan Z, Liu C, Niemi V, Yu G (2013) Exploring the impact of trust information visualization on mobile application usage. Pers Ubiquit Comput 17(6):1295–1313CrossRefGoogle Scholar
  14. 14.
    Yan Z, Dong Y, Niemi V, Yu G (2013) Exploring trust of mobile applications based on user behaviors: an empirical study. J Appl Soc Psychol 43(3):638–659CrossRefGoogle Scholar
  15. 15.
    Consolvo S, McDonald DW, Landay JA (2009) Theory-driven design strategies for technologies that support behavior change in everyday life. In: Proceedings 27th international conference human factors in computing systems (CHI ’09), pp 405–414Google Scholar
  16. 16.
    Fogg BJ (2003) Persuasive technology: using computers to change what we think and do. Morgan KaufmannGoogle Scholar
  17. 17.
    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? In: Proceedings of pervasive computing technologies for healthcare (PervasiveHealth’08), pp 218–221Google Scholar
  18. 18.
    Choe EK, Consolvo S, Watson N, Kientz J (2011) Opportunities for computing technologies to support healthy sleep behaviors. In: Proceedings of 29th international conference human factors in com-puting systems (CHI’11), pp 3053–3062Google Scholar
  19. 19.
    Totter A, Bonaldi D, Majoe D (2011) A human-centered approach to the design and evaluation of wearable sensors - framework and case study. In: Proceedings 6th international conference on pervasive computing and applications (ICPCA’11), pp 233–241Google Scholar
  20. 20.
    Cherubini M, De Oliveira R, Hiltunen A, Oliver N (2011) barriers and bridges the adoption of today’s mobile phone contextual services. In: Proceedings of 13th international conference on human computer interaction with mobile devices and services (MobileHCI ’11), pp 167–176Google Scholar
  21. 21.
    Pu P, Chen L, Hu R (2011) A user-centric evaluation framework for recommender systems. In: Proceedings of 5th ACM conference on recommender systems (RecSys ’11), pp 157–164Google Scholar
  22. 22.
    Holden RJ, Karsh BT (2010) The technology acceptance model: Its past and its future in healthcare. J Biomed Inform 43(1): 159–172CrossRefGoogle Scholar
  23. 23.
    Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q, pp 319–340Google Scholar
  24. 24.
    Wu JH, Wang SC, Lin LM (2007) Mobile computing acceptance factors in the healthcare industry: a structural equation model. Intl J Med Inform 76(1):66–77CrossRefGoogle Scholar
  25. 25.
    Steele R, Lo A, Secombe C, Wong YK (2009) Elderly persons’ perception and acceptance of using wireless sensor networks to assist healthcare. Intl J Med Inform 78(12):788–801CrossRefGoogle Scholar
  26. 26.
    Fensli R, Pedersen PE, Gundersen T, Hejlesen O (2008) Sensor acceptance model–measuring patient acceptance of wearable sensors. Methods Inf Med 47:89–95Google Scholar
  27. 27.
    Heerink M, Kröse B, Evers V, Wielinga B (2010) Assessing acceptance of assistive social agent technology by older adults: the almere model. Intl J Social Robot 2(4):361–375CrossRefGoogle Scholar
  28. 28.
    Grimes A, Tan D, Morris D (2009) Toward technologies that support family reflections on health. In: Proceedings of international conference supporting group work (CSCW ’09), pp 311–320Google Scholar
  29. 29.
    Consolvo S, Everitt K, Smith I, Landay JA (2006) Design requirements for technologies that encourage physical activity. In: Proceedings 24th international conference human factors in computing systems (CHI ’06), pp 457–466Google Scholar
  30. 30.
    Chen Y, Pu P (2014) Healthytogether: exploring social incentives for mobile fitness applications. In: Proceedings of 2nd international symposium Chinese CHI, pp 25–34Google Scholar
  31. 31.
    Ahtinen A, Isomursu M, Mukhtar M, Mäntyjärvi J, Häk-kilä J, Blom J (2009) Designing social features for mobile and ubiquitous wellness applications. In: Proceedings 8th international conference mobile and ubiquitous multimedia (MUM’09), pp 12–21Google Scholar
  32. 32.
    Maitland J, Chalmers M (2008) Finding a balance: social support v. privacy during weight-management. In: CHI’08 ex-tended abstracts on human factors in computing systems, pp 3015–3020Google Scholar
  33. 33.
    Chiu MC, Chang SP, Chang YC, Chu HH, Chen CCH, Hsiao FH, Ko JC (2009) Playful bottle: a mobile social persuasion system to motivate healthy water in-take. In: Proceedings of 11th international conference ubiquitous computing (Ubicomp’09), pp 185–194Google Scholar
  34. 34.
    Kim S, Kientz JA, Patel SN, Abowd GD (2008) Are you sleeping? sharing portrayed sleeping status within a social network. In: Proceedings 2008 ACM conference computer supported cooperative work (CSCW’08), pp 619–628Google Scholar
  35. 35.
    Munson SA, Consolvo S (2012) Exploring goal-setting, rewards, self-monitoring, and sharing to motivate physical activity. In: Proceedings of IEEE 6th international conference pervasive computing technologies for healthcare (PervasiveHealth’12), pp 25–32Google Scholar
  36. 36.
    Bauer J, Consolvo S, Greenstein B, Schooler J, Wu E, Watson NF, Bauer JS (2012) ShutEye: encouraging awareness of healthy sleep recommendations with a mobile, peripheral display. In: Proceedings 30th international conference human factors in computing systems (CHI ’12), pp 1401–1410Google Scholar
  37. 37.
    Munson S, Krupka E, Richardson C, Resnick P (2015) Effects of public commitments and accountability in a technology-supported physical activity intervention. Proc Intl Conf Human Factors in Computing Systems (CHI ’15), pp 1135–1144Google Scholar
  38. 38.
    Chen Y, Zhang J, Pu P (2014) Exploring social accountability for pervasive fitness apps. Proc 8th Intl Conf Mobile Ubiquitous Computing, Systems, Services and TechnologiesGoogle Scholar
  39. 39.
    Chi PYP, Chen JH, Chu HH, Lo JL (2008) Enabling calorie-aware cooking in a smart kitchen. Persuasive technology, pp 116–127Google Scholar
  40. 40.
    Paolacci G, Chandler J, Ipeirotis P (2010) Running experiments on amazon mechanical turk. Judgment and Decision Making 5(5):411–419Google Scholar
  41. 41.
    Clason DL, Dormody TJ (1994) Analyzing data measured by individual likert-type items. J Agric Educ 35(4)Google Scholar
  42. 42.
    Welkowitz J, Cohen BH, Lea RB (2012) Introductory statistics for the behavioral sciences. Wiley.comGoogle Scholar
  43. 43.
    Van Lente E, Barry MM, Molcho M, Morgan K, Watson D, Harrington J, McGee H (2012) Measuring population mental health and social well-being. Int J Public Health 57 (2):421–430Google Scholar
  44. 44.
    Schreiber JB, Nora A, Stage FK, Barlow EA, King J (2006) Reporting structural equation modeling and confirmatory factor analysis results: a review. J Educ Res 99(6):323– 338CrossRefGoogle Scholar
  45. 45.
    Singh R, Sandhu HS, Metri BA, Kaur R (2011) Organizational performance and retail challenges: a structural equation approach. iBusiness 3(02):159CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.University of California, IrvineIrvineUSA
  2. 2.ELCA SALausanneSwitzerland
  3. 3.Utrech UniversityUtrechtNetherlands
  4. 4.Swiss Federal Institute of Technology at Lausanne (EPFL)LausanneSwitzerland

Personalised recommendations