Author(s):

  • Anderson, Kevin
  • Burford, Oksana
  • Emmerton, Lynne

Abstract:

Objective: Consumers are living longer, creating more pressure on the health system and increasing their requirement for self-care of chronic conditions. Despite rapidly-increasing numbers of mobile health applications (‘apps’) for consumers’ self-care, there is a paucity of research into consumer engagement with electronic self-monitoring. This paper presents a qualitative exploration of how health consumers use apps for health monitoring, their perceived benefits from use of health apps, and suggestions for improvement of health apps.

Materials and Methods: ‘Health app’ was defined as any commercially-available health or fitness app with capacity for self-monitoring. English-speaking consumers aged 18 years and older using any health app for self-monitoring were recruited for interview from the metropolitan area of Perth, Australia. The semi-structured interview guide comprised questions based on the Technology Acceptance Model, Health Information Technology Acceptance Model, and the Mobile Application Rating Scale, and is the only study to do so. These models also facilitated deductive thematic analysis of interview transcripts. Implicit and explicit responses not aligned to these models were analyzed inductively.

Results: Twenty-two consumers (15 female, seven male) participated, 13 of whom were aged 26-35 years. Eighteen participants reported on apps used on iPhones. Apps were used to monitor diabetes, asthma, depression, celiac disease, blood pressure, chronic migraine, pain management, menstrual cycle irregularity, and fitness. Most were used approximately weekly for several minutes per session, and prior to meeting initial milestones, with significantly decreased usage thereafter. Deductive and inductive thematic analysis reduced the data to four dominant themes: engagement in use of the app; technical functionality of the app; ease of use and design features; and management of consumers’ data.

Conclusions: The semi-structured interviews provided insight into usage, benefits and challenges of health monitoring using apps. Understanding the range of consumer experiences and expectations can inform design of health apps to encourage persistence in self-monitoring.

Document:

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0156164

References:
  1. 1. Wiederhold BK, Riva G, Graffigna G. Ensuring the best care for our increasing aging population: health engagement and positive technology can help patients achieve a more active role in future healthcare. Cyberpsychol Behav Soc Netw. 2013;16(6):411–12. pmid:23751102
  2. 2. Lorig KR, Sobel DS, Stewart AL, Brown BW Jr, Bandura A, Ritter P, et al. Evidence suggesting that a chronic disease self-management program can improve health status while reducing hospitalization: a randomized trial. Med Care. 1999;37(1):5–14. pmid:10413387
  3. 3. Holman H, Lorig K. Patient self-management: a key to effectiveness and efficiency in care of chronic disease. Public Health Rep. 2004;119(3):239. pmid:15158102
  4. 4. Chodosh J, Morton SC, Mojica W, Maglione M, Suttorp MJ, Hilton L, et al. Meta-analysis: chronic disease self-management programs for older adults. Ann Intern Med. 2005;143(6):427–38. pmid:16172441
  5. 5. Williams MV, Baker DW, Honig EG, Lee TM, Nowlan A. Inadequate literacy is a barrier to asthma knowledge and self-care. Chest. 1998;114(4):1008–15. pmid:9792569.
  6. 6. Baker DW, Gazmararian JA, Williams MV, Scott T, Parker RM, Green D, et al. Functional health literacy and the risk of hospital admission among Medicare managed care enrollees. Am J Public Health. 2002;92(8):1278–83. pmid:12144984.
  7. 7. Baker DW, Parker RM, Williams MV, Clark WS. Health literacy and the risk of hospital admission. J Gen Intern Med. 1998;13(12):791–8. pmid:9844076.
  8. 8. Williams MV, Parker RM, Baker DW, Parikh NS, Pitkin K, Coates WC, et al. Inadequate functional health literacy among patients at two public hospitals. JAMA. 1995;274(21):1677–82. pmid:7474271.
  9. 9. Gill PS, Kamath A, Gill TS. Distraction: an assessment of smartphone usage in health care work settings. Risk Manag Healthc Policy. 2012;5(1):105–14.
  10. 10. Dumas RA. Health App completely buggy? [Internet]. c2014. Available: https://discussions.apple.com/thread/6680914. Accessed 2 November 2015.
  11. 11. Thomas O. Apple’s health app is an embarrassment [Internet]. c2014. Available: http://readwrite.com/2014/10/02/apple-health-app. Accessed 2 November 2015.
  12. 12. Finkelstein EA, Chay J, Bajpai S. The economic burden of self-reported and undiagnosed cardiovascular diseases and diabetes on Indonesian households. PLoS ONE. 2014;9(6):e99572. pmid:24915510; PubMed Central PMCID: PMC4051736.
  13. 13. Miller KM, Beck RW, Bergenstal RM, Goland RS, Haller MJ, McGill JB, et al. Evidence of a strong association between frequency of self-monitoring of blood glucose and hemoglobin A1c levels in T1D exchange clinic registry participants. Diabetes Care. 2013;36(7):2009–14. pmid:23378621.
  14. 14. Host T, Person C, Lewis P. Health Market Validation Program (Health MVP) call for proposal application form for SMEs. [Internet]. c2014. Available: http://www.business.vic.gov.au/grants-and-assistance/programs/health-market-validation-program. Accessed 20 February 2015.
  15. 15. Blödt S, Pach D, Roll S, Witt CM. Effectiveness of app-based relaxation for patients with chronic low back pain (RelaxBack) and chronic neck pain (RelaxNeck): study protocol for two randomized pragmatic trials. Trials. 2014;15(1):490–99.
  16. 16. Nilges P, Köster B, Schmidt CO. Pain acceptance—concept and validation of a German version of the chronic pain acceptance questionnaire. Schmerz. 2007;21(1):57–8. pmid:17111168
  17. 17. Morrison LG, Hargood C, Lin SX, Dennison L, Joseph J, Hughes S, et al. Understanding usage of a hybrid website and smartphone app for weight management: a mixed-methods study. J Med Internet Res. 2014;16(10):e201. PMC4259922. pmid:25355131
  18. 18. Cooper S, Foster K, Naughton F, Leonardi-Bee J, Sutton S, Ussher M, et al. Pilot study to evaluate a tailored text message intervention for pregnant smokers (MiQuit): study protocol for a randomised controlled trial. Trials. 2015;16(1):s13063-014-0546-4. pmid:25622639; PubMed Central PMCID: PMCPmc4318454.
  19. 19. Haug S, Castro RP, Filler A, Kowatsch T, Fleisch E, Schaub MP. Efficacy of an Internet and SMS-based integrated smoking cessation and alcohol intervention for smoking cessation in young people: study protocol of a two-arm cluster randomised controlled trial. BMC Public Health. 2014;14(1):1140–48. pmid:25369857; PubMed Central PMCID: PMCPmc4228117.
  20. 20. Proudfoot J, Clarke J, Birch M, Whitton AE, Parker G, Manicavasagar V, et al. Impact of a mobile phone and web program on symptom and functional outcomes for people with mild-to-moderate depression, anxiety and stress: a randomised controlled trial. BMC Psychiatry. 2013;13(1):312–24.
  21. 21. Eyles H, McLean R, Neal B, Doughty R, Jiang Y, Mhurchu C. Using mobile technology to support lower-salt food choices for people with cardiovascular disease: protocol for the SaltSwitch randomized controlled trial. BMC Public Health. 2014;14(1):950–8. pmid:25217039.
  22. 22. Hasford J, Uricher J, Tauscher M, Bramlage P, Virchow JC. Persistence with asthma treatment is low in Germany especially for controller medication—a population based study of 483051 patients. Allergy. 2010;65(3):347–54. pmid:19712117
  23. 23. Zichermann G. Gamification by design: implementing game mechanics in web and mobile apps. Cunningham C, editor. Sebastopol: O’Reilly Media; 2011.
  24. 24. Pandey A, Hasan S, Dubey D, Sarangi S. Smartphone apps as a source of cancer information: changing trends in health information-seeking behavior. J Cancer Educ. 2013;28(1):138–42. pmid:23275239
  25. 25. Krebs P D D. Health app use among US mobile phone owners: a national survey. JMIR mHealth uHealth. 2015;3(4):e101. pmid:26537656
  26. 26. Licskai CJ, Sands T, Ferrone M. Development and pilot testing of a mobile health solution for asthma self-management: Asthma action plan smartphone application pilot study. Can Respir J. 2013;20(4):301–6. pmid:23936890
  27. 27. Ryan D, Price D, Musgrave SD, Malhotra S, Lee AJ, Ayansina D, et al. Clinical and cost effectiveness of mobile phone supported self monitoring of asthma: multicentre randomised controlled trial. BMJ. 2012;296(6614):e1756.
  28. 28. Liu WT, Huang CD, Wang CH, Lee KY, Lin SM, Kuo HP. A mobile telephone-based interactive self-care system improves asthma control. Eur Respir J. 2011;37(2):310–7. pmid:20562122.
  29. 29. Kirwan M, Vandelanotte C, Fenning A, Duncan M. Diabetes self-management smartphone application for adults with type 1 diabetes: randomized controlled trial. J Med Internet Res. 2013;15(11):e235. pmid:24225149.
  30. 30. Jeon E, Park H. Development of a smartphone application for clinical-guideline-based obesity management. Healthc Inform Res. 2015;21(1):10–20. pmid:25705553
  31. 31. McCarroll ML, Armbruster S, Pohle-Krauza RJ, Lyzen AM, Min S, Nash DW, et al. Feasibility of a lifestyle intervention for overweight/obese endometrial and breast cancer survivors using an interactive mobile application. Gynecol Oncol. 2015;137(3):508–15. WOS:000355779000025. pmid:25681782
  32. 32. Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based parkinson’s disease assessment system for home-based monitoring. JMIR mHealth uHealth. 2015;3(1).
  33. 33. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart. 1989;13(3):319–40.
  34. 34. Madden TJ, Ellen PS, Ajzen I. A comparison of the theory of planned behavior and the theory of reasoned action. Pers Soc Psychol B. 1992;18(1):3–9. WOS:A1992HC29000001.
  35. 35. Fishbein M, Ajzen I, Albarracin D, Hornik RC. Prediction and change of health behavior: Applying the reasoned action approach. Mahwah: Lawrence Erlbaum Associates; 2007.
  36. 36. Yarbrough AK, Smith TB. Technology acceptance among physicians: a new take on TAM. Med Care Res Rev. 2007;64(6):650–52. pmid:17717378
  37. 37. Briz-Ponce L, García-Peñalvo F. An empirical assessment of a technology acceptance model for apps in medical education. J Med Syst. 2015;39(11):1–5.
  38. 38. Cho J, Quinlan MM, Park D, Noh GY. Determinants of adoption of smartphone health apps among college students. Am J Health Behav. 2014;38(6):860–70. Epub 2014/09/11. pmid:25207512.
  39. 39. Kim J, Park H-A. Development of a health information technology acceptance model using consumers’ health behavior intention. J Med Internet Res. 2012;14(5):e133. pmid:23026508
  40. 40. Janz N, Becker M. The health belief model: a decade later. Health Educ Behav. 1984;11(1):1–47.
  41. 41. Becker MH, Radius SM, Rosenstock IM, Drachman RH, Schuberth KC, Teets KC. Compliance with a medical regimen for asthma: a test of the health belief model. Public Health Rep. 1978;93(3):268–77. pmid:652949
  42. 42. Cormier DJ, Ferreira DC, Vise KM, Cahalin LP. A pilot study of childhood health behaviour and asthma using the health belief model. J Cardiopulm Rehabil. 2006;26(4):250–74.
  43. 43. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR mHealth uHealth. 2015;3(1):e27. pmid:25760773
  44. 44. Antezana G, Bidargaddi N, Blake V, Schrader G, Kaambwa B, Quinn S, et al. Development of an online well-being intervention for young people: an evaluation protocol. JMIR research protocols. 2015;4(2):e48. pmid:25929201
  45. 45. Kenny R, Dooley B, Fitzgerald A. Feasibility of “CopeSmart”: a telemental health app for adolescents. JMIR mHealth uHealth. 2015;2(3):e22. http://doi.org/10.2196/mental.4370.
  46. 46. Chiang LC, Huang JL, Yeh KW, Lu CM. Effects of a self-management asthma educational program in Taiwan based on PRECEDE-PROCEED model for parents with asthmatic children. J Asthma. 2004;41(2):205–15. pmid:15115173.
  47. 47. Green L. The PRECEDE-PROCEED model of health program planning & evaluation [Internet]. c2014. Available: http://lgreen.net/precede.htm. Accessed 11 December 2014.
  48. 48. Velsor-Friedrich B, Pigott T, Srof B. A practitioner-based asthma intervention program with African American inner-city school children. J Pediatr Health Care. 2005;19(3):163–71. pmid:15867832
  49. 49. Hesse-Biber SN, Leavy P. The Practice of Qualitative Research: SAGE Publications; 2010.
  50. 50. Holden RJ, Karsh B-T. The technology acceptance model: its past and its future in health care. J Biomed Inf. 2010;43(1):159–72.
  51. 51. Scheibe M, Reichelt J, Bellmann M, Kirch W. Acceptance factors of mobile apps for diabetes by patients aged 50 or older: a qualitative study. Med 20. 2015;4(1):e1. Epub 2015/03/04. pmid:25733033.
  52. 52. Doherty G, Coyle D, Matthews M. Design and evaluation guidelines for mental health technologies. Interact Comput. 2010;22(4):243–52.
  53. 53. Jin BS, Ji YG. Usability risk level evaluation for physical user interface of mobile phone. HCC Sys Ind. 2010;61(4):350–63.
  54. 54. Charmaz K. Constructing grounded theory: a practical guide through qualitative analysis / Kathy Charmaz. London: London: SAGE Publications; 2006.
  55. 55. Radcliffe C, Lester H. Perceived stress during undergraduate medical training: a qualitative study. Med Educ. 2003;37(1):32–8. pmid:12535113
  56. 56. Green J, Thorogood N. Qualitative methods for health research. 2nd ed. Los Angeles: SAGE; 2009.
  57. 57. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.
  58. 58. Fereday J, Muir-Cochrane E. Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. Int J Qual Methods. 2008;5(1):80–92.
  59. 59. Underwood B, Birdsall J, Kay E. The use of a mobile app to motivate evidence-based oral hygiene behaviour. Br Dent J. 2015;219(4):7. http://dx.doi.org/10.1038/sj.bdj.2015.660. 1707792818.
  60. 60. Elias P, Rajan NO, McArthur K, Dacso CC. InSpire to promote lung assessment in youth: evolving the self-management paradigms of young people with asthma. Med 20. 2013;2(1):e1. Epub 2013/01/01. pmid:25075232; PubMed Central PMCID: PMCPmc4084766.
  61. 61. Hebly P. Willingness to pay for mobile apps [Dissertation]. Rotterdam (Holland): Erasmus University Rotterdam; 2012.
  62. 62. Lister C, West JH, Cannon B, Sax T, Brodegard D. Just a fad? Gamification in health and fitness apps. JMIR Serious Games. 2014;2(2):e9. pmid:25654660.
  63. 63. Dolan B. The rise of the seemingly serious but “just for entertainment purposes” medical app [Internet]. 2014. Available: http://mobihealthnews.com/35444/the-rise-of-the-seemingly-serious-but-just-for-entertainment-purposes-medical-app. Accessed 20 January 2016.
  64. 64. Phillips DC, Burbules NC. Postpositivism and educational research. Lanham: Rowman & Littlefield Publishers; 2000.
  65. 65. Apple. App Store review guidelines [Internet]. 2015. Available: https://developer.apple.com/app-store/review/guidelines/. Accessed 12 April 2016.
  66. 66. Android. Launch checklist [Internet]. 2016. Available: http://developer.android.com/distribute/tools/launch-checklist.html. Accessed 12 April 2016.
  67. 67. OptumHealth. OptumHealth debuts OptumizeMe fitness app to help Microsoft(R) Windows Phone 7 users connect and compete for better health [Internet]. c2010. Available: http://www.businesswire.com/news/home/20101115005281/en/OptumHealth-Debuts-OptumizeMe-Fitness-App-Microsoft%C2%AE-Windows. Accessed 17 January 2015.
  68. 68. Anderson K, Emmerton LM. The contribution of mobile health applications to self-management by consumers: review of published evidence. Aust Health Rev. 2015;In Press. pmid:26681206.
  69. 69. Korhonen I, Parkka J, Van Gils M. Health monitoring in the home of the future. IEEE Eng Med Biol Mag. 2003;22(3):66–73. pmid:12845821