Promoting Relational Agent for Health Behavior Change in Low and Middle - Income Countries (LMICs): Issues and Approaches

  • Md Faisal Kabir
  • Daniel Schulman
  • Abu S. AbdullahEmail author
Mobile & Wireless Health
Part of the following topical collections:
  1. Mobile & Wireless Health


The use of contemporary technologies in healthcare systems to improve quality of care and to promote behavioral healthcare outcomes are prevalent in high-income countries. However, low and middle-income countries (LMICs) are not receiving the same advantages of technology, which may be due to inadequate technological infrastructure and financial resources, lack of interest among policy makers and healthcare service providers, lack of skills and capacity among healthcare professionals in using technology based interventions, and resistance of the public to the use of technologies for healthcare or health promotion activities. Technology-based interventions offer considerable promise to develop entirely new models of healthcare both within and outside of formal systems of care and offer the opportunity to have a large public health impact. Such technology-based interventions could be used to address targeted global health problems in LMICs, including the chronic non-communicable diseases (NCDs) - a growing health system burden in LMICs. Major preventable behavioral risk factors of chronic NCDs are increasing in LMICs, and innovative interventions are essential to address these risk factors. Computer-based or mobile-based virtual coaches or Relational Agents (RAs) are increasingly being explored for counseling patients to change their health behavior in high-income countries; however, the use of RAs in LMICs has not been studied. In this paper, we summarize the growing application of RA technology in behavior change interventions in high-income countries and describe the potential of its use in LMICs. Finally, we review the potential barriers and challenges in promoting RAs in LMICs.


Relational agent Mobile health (mHealth) Low and middle-income countries (LMICs) Information and communication technology (ICT) 



Low and middle-income countries


Relational Agents (RAs)


Information and Communication Technology


World Health Organization


Mobile health


Embodied conversational agents


Personal digital assistants


International Monetary Fund



The authors would like to thank the staff of the Department of General Internal Medicine at Boston University Medical Center for administrative supports. Md Faisal Kabir worked as a summer intern (doctoral trainee) in the project.

Availability of data and materials

Available upon request from the corresponding author.

Authors’ contributions

ASA planned the study and oversee the overall review process. FK led the review, identified relevant articles and summarized the findings. FK drafted the first draft of the manuscript and distributed to co-authors for comments. DS and ASA critically reviewed the draft manuscript and commented on the final draft. All authors approved the final draft of the paper.


This study was supported by the US National Institutes of Health (NIH) Fogarty International Centre [grant numbers R25TW009715; PI: Abu Abdullah]. The funders had no role in the design or conduct of the study; collection, management, analysis and interpretation of the data; or preparation, review, and approval of the manuscript.

Compliance with Ethical Standards

Ethics Approval and Consent to Participate

This is a review article and no ethical approval was required.

Consent for Publication

All authors provided consent for this publication.

Competing Interests

The authors declare that they have no competing interests.


  1. 1.
    Bastawrous, A., and Armstrong, M. J., Mobile health use in low- and high-income countries: An overview of the peer-reviewed literature. J. Royal Soc. Med. 106(4):130–142, 2013. Scholar
  2. 2.
    mHealth, New horizons for health through mobile technologies. Global Observatory for eHealth series- volume 3. , accessed on March 10, 2017.
  3. 3.
    Krebs, P., and Duncan, D. T., Health app use among US Mobile phone owners: A National Survey. JMIR mHealth uHealth. 3(4):e101, 2015. Scholar
  4. 4.
    Nielsen, webcite Tech-styles: Are consumers really interested in wearing tech on their sleeves? New York, NY: The Nielsen Company, 2013, Scholar
  5. 5.
    Silva, B. M., Rodrigues, J. J., de la Torre Díez, I., López-Coronado, M., and Saleem, K., Mobile-health: A review of current state in 2015. J. Biomed. Inform. 56:265–272, 2015.PubMedCrossRefGoogle Scholar
  6. 6.
    Bickmore, T., Gruber, A., and Picard, R., Establishing the computer-patient working alliance in automated health behavior change interventions. Patient Educ. Counsel. 59(1):21–30, 2005.CrossRefGoogle Scholar
  7. 7.
    Bisio, I., Lavagetto, F., Marchese, M., and Sciarrone, A., A smartphone-centric platform for remote health monitoring of heart failure. Int. J. Commun. Syst., 2014. Scholar
  8. 8.
    Fayn, J., and Rubel, P., Toward a personal health society in cardiology. IEEE Trans. Inf. Technol Biomed 14(2):401–409, 2010.PubMedCrossRefGoogle Scholar
  9. 9.
    Lin, C.-T., Chang, K.-C., Lin, C.-L., Chiang, C.-C., Lu, S.-W., Chang, S.-S., Lin, B.-S., Liang, H.-Y., Chen, R.-J., Lee, Y.-T., and Ko, L.-W., An intelligent tele-cardiology system using a wearable and wireless ecg to detect atrial fibrillation. IEEE Trans. Inf. Technol. Biomed. 14(3):726–733, 2010.PubMedCrossRefGoogle Scholar
  10. 10.
    Sieverdes, J. C., Treiber, F., and Jenkins, C., Improving diabetes management with mobile health technology. Am. J. Med. Sci. 345(4):289–295, 2013.PubMedCrossRefGoogle Scholar
  11. 11.
    Kirwan, M., Vandelanotte, C., Fenning, A., and Duncan, J. M., Diabetes self-management smartphone application for adults with type 1 diabetes: Randomized controlled trial. J. Med. Internet Res. 15(11):e235, 2013. Scholar
  12. 12.
    Cafazzo, A. J., Casselman, M., Hamming, N., Katzman, K. D., and Palmert, R. M., Design of an mhealth app for the self-management of adolescent type 1 diabetes: A pilot study. J. Med. Internet Res. 14(3):e70, 2012.PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Maamar, H., Boukerche, A., and Petriu, E., 3-D streaming supplying partner protocols for mobile collaborative exergaming for health. IEEE Trans. Inf. Technol. Biomed. 16(6):1079–1095, 2012. Scholar
  14. 14.
    Lopes, I., Silva, B., Rodrigues, J., Lloret, J., and Proenca, M., A mobile health monitoring solution for weight control. 2011 International Conference on Wireless Communications and Signal Processing (WCSP): 1–5, 2011. doi:
  15. 15.
    Zhu, F., Bosch, M., Woo, I., Kim, S., Boushey, C. J., Ebert, D. S., and Delp, E. J., The use of mobile devices in aiding dietary assessment and evaluation. J. Sel. Top. Signal Process. 4(4):756–766, 2010.CrossRefGoogle Scholar
  16. 16.
    Whittaker, R., Dorey, E., Bramley, D., Bullen, C., Denny, S., Elley, R. C., Maddison, R., McRobbie, H., Parag, V., Rodgers, A., and Salmon, P., A theory-based video messaging mobile phone intervention for smoking cessation: Randomized controlled trial. J. Med. Internet Res. 13(1):e10, 2011.PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    Finkelstein, J., and Wood, J., Interactive mobile system for smoking cessation. 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC). 1169–1172, 2013. doi:
  18. 18.
    Fontecha, J., Hervás, R., Bravo, J., and Navarro, J. F., A mobile and ubiquitous approach for supporting frailty assessment in elderly people. J. Med. Internet Res. 15(9):e197, 2013. Scholar
  19. 19.
    Chiarini, G., Ray, P., Akter, S., Masella, C., and Ganz, A., mHealth technologies for chronic diseases and elders: A systematic review. IEEE J. Sel. Areas Commun. 31(9):6–18, 2013. Scholar
  20. 20.
    Källander, K., Tibenderana, K. J., Akpogheneta, J. O., Strachan, L. D., Hill, Z., ten Asbroek, A. A. H., Conteh, L., Kirkwood, R. B., and Meek, R. S., Mobile health (mhealth) approaches and lessons for increased performance and retention of community health workers in low- and middle-income countries: A review. J. Med. Internet Res. 15(1):e17, 2013.PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Déglise, C., Suggs, S. L., and Odermatt, P., Short message service (SMS) applications for disease prevention in developing countries. J. Med. Internet Res. 14(1):e3, 2012. Scholar
  22. 22.
    Islam, S. M. S., Purnat, T. D., Phuong, N. T. A., Mwingira, U., Schacht, K., and Fröschl, G., Non-communicable diseases (NCDs) in developing countries: A symposium report. Global. Health. 10(81), 2014.
  23. 23.
    Patient Apps for Improved Healthcare: From Novelty to Mainstream. Parsippany, NJ: IMS Institute for Healthcare Informatics; 2013. URL: [accessed 2018-01-01].
  24. 24.
    Chib, A., van Velthoven, M. H., and Car, J., mHealth adoption in low-resource environments: A review of the use of mobile healthcare in developing countries. J. Health Commun. 20(1):4–34, 2015.PubMedCrossRefGoogle Scholar
  25. 25.
    Luxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C., and Reger, G. M., mHealth for mental health: Integrating smartphone technology in behavioral healthcare. Prof. Psychol.: Res. Pract. 42(6):505–512, Dec 2011.CrossRefGoogle Scholar
  26. 26.
    Peiris, D. et al., Use of mHealth systems and tools for non-communicable diseases in low-and middle-income countries: A systematic review. J. Cardiovasc. Translat. Res. 7(8):677–691, 2014.CrossRefGoogle Scholar
  27. 27.
    Modave, F., Bian, J., Leavitt, T., Bromwell, J., Harris III, C., and Vincent, H., Low quality of free coaching apps with respect to the American College of Sports Medicine guidelines: A review of current mobile apps. JMIR mHealth uHealth, 3(3), 2015.PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Eysenbach, G., The law of attrition. Journal of medical Internet research, 7(1), 2005.PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Tatara, N., Arsand, E., Skrøvseth, S. O., and Hartvigsen, G., Long-term engagement with a mobile self-management system for people with type 2 diabetes. JMIR Mhealth Uhealth 1(1):e1, 2013.PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Global Initiative for Asthma (GINA). Ginasthma. . Global strategy for asthma management and prevention (2016 update). 2016. URL:
  31. 31.
    Watson, A., Bickmore, T., Cange, A., Kulshreshtha, A., and Kvedar, J., An internet-based virtual coach to promote physical activity adherence in overweight adults: Randomized controlled trial. J. Med. Internet Res. 14(1):e1, 2012.PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Wolever, R. Q., Simmons, L. A., Sforzo, G. A., Dill, D., Kaye, M., Bechard, E. M., and Yang, N., A systematic review of the literature on health and wellness coaching: Defining a key behavioral intervention in healthcare. Global Adv. Health Med. 2(4):38–57, 2013.CrossRefGoogle Scholar
  33. 33.
    Campbell, R. H., Grimshaw, M. N., and Green, G. M., Relational agents: A critical review. Open Virt. Real. J. 1:1–7, 2009.CrossRefGoogle Scholar
  34. 34.
    Bickmore, T., and Giorgino, T., Health dialog Systems for Patients and Consumers. J. Biomed. Inform. 39:556–571, 2006.PubMedCrossRefGoogle Scholar
  35. 35.
    Hudlicka, E., Virtual training and coaching of health behavior: Example from mindfulness meditation training. Patient Educ. Counsel. 92(2):160–166, 2013. Scholar
  36. 36.
    Bickmore, T., Caruso, L., Clough-Gorr, K., and Heeren, T., ‘It’s just like you talk to a friend’ – Relational agents for older adults. Interact. Comput. 17(6):711–735, 2005.CrossRefGoogle Scholar
  37. 37.
    Bickmore, T., Relational agents: Effecting change through human computer relationships. Ph.D. thesis, Media Arts & Sciences, MIT, Cambridge, MA, 2003.Google Scholar
  38. 38.
    Brickmore, T. W., and Picard, R. W., Establishing and maintaining long-term human-computer relationships. ACM Trans. Comput.-Human Interact. (TOCHI) 12(2):293–232, 2005.CrossRefGoogle Scholar
  39. 39.
    Glasgow, R. E., Vogt, T. M., and Boles, S. M., Evaluating the public health impact of health promotion interventions: The RE-AIM framework. Am. J. Publ. Health 89(9):1322–1327, 1999.CrossRefGoogle Scholar
  40. 40.
    Glasgow, R. E., Lichtenstein, E., and Marcus, A. C., Why don’t we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. Am. J. Publ. Health 93(8):1261–1267, 2003.CrossRefGoogle Scholar
  41. 41.
    Reeves, B., and Nass, C., The media equation: How people treat computers, television, and new media like real people and places. Cambridge University Press, 1996.Google Scholar
  42. 42.
    Nass, C., and Moon, Y., Machines and mindlessness: Social responses to computers. J. Social Issues 56(1):81–103, 2000.CrossRefGoogle Scholar
  43. 43.
    Cassell, J., Embodied conversational agents. MIT press, 2000.Google Scholar
  44. 44.
    Walther, J. B., Van Der, Heide, B., Ramirez, A., Burgoon, J. K., and Peña, J., Interpersonal and Hyperpersonal dimensions of computer-mediated communication. The handbook of the psychology of communication technology, 1–22, 2015.Google Scholar
  45. 45.
    Schulman, D., Embodied agents for long-term interaction (Doctoral dissertation, Northeastern University), 2013.Google Scholar
  46. 46.
    De Ruyter, B., Saini, P., Markopoulos, P., and van Breemen, A., Assessing the effects of building social intelligence in a robotic interface for the home. Interact. Comput. 17(5):522–541, 2005.CrossRefGoogle Scholar
  47. 47.
    Kidd, C.D., Designing long-term human-robot interaction and application to weight loss. Ph.D. dissertation, Media Arts & Sciences. Massachusetts Institute of Technology, Cambridge, MA, 2008.Google Scholar
  48. 48.
    Bickmore, T., and Schulman, D., Empirical validation of an accommodation theory-based model of user-agent relationship." International conference on intelligent virtual agents. Springer Berlin Heidelberg, 2012.CrossRefGoogle Scholar
  49. 49.
    Bickmore, T., Caruso, L., and Clough-Gorr, K., Acceptance and usability of a relational agent interface by urban older adults. Proceedings of CHI Conference, 2005.Google Scholar
  50. 50.
    T. Bickmore, and P. Picard, “Towards caring machines. Proceedings of CHI conference, 2004.Google Scholar
  51. 51.
    Bickmore, T., and Schulman, D., Practical approaches to comforting users with relational agents. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), CA: San Jose, ACM, 2007.Google Scholar
  52. 52.
    Bickmore, T., Pfeifer, L., and Paasche-Orlow, M., Using computer agents to explain medical documents to patients with low health literacy. Patient Educ. Counsel. 75(3):315–320, 2009.CrossRefGoogle Scholar
  53. 53.
    Bickmore, T., and Schulman, D., Practical approaches to comforting users with relational agents, CHI '07 Extended Abstracts on Human Factors in Computing Systems, April 28-May 03, 2007, San Jose, CA, USA, 2007. doi:].
  54. 54.
    Ehrenfeld, J., Sandberg, W., Warren, L., Kwo, J., and Bickmore, T., Use of a computer agent to explain anesthesia concepts to patients. Annual meeting of the American Society of Anesthesiologists, 2010.Google Scholar
  55. 55.
    Ring, L., Barry, B., Totzke, K., and Bickmore, T., Addressing loneliness and isolation in older adults: Proactive affective agents provide better support. International Conference on Affective Computing and Intelligent Interaction (ACII), 2013.Google Scholar
  56. 56.
    Bickmore, T. Piloting a virtual health care helper: Partners Healthcare system to run trials of weight-loss coach 'Laura'. Mass High Tech, 2007.Google Scholar
  57. 57.
    Zhou, S., Bickmore, T., and Jack, B., Agent-user concordance and satisfaction with a virtual hospital discharge nurse. Intelligent Virtual Agents conference (IVA), 2014.Google Scholar
  58. 58.
    Bickmore, T. W., Pfeifer, L. M., and Jack, B. W., Taking the time to care: Empowering low health literacy hospital patients with virtual nurse agents. Proceedings of the SIGCHI conference on human factors in computing systems (CHI '09). ACM, New York, NY, USA, 1265–1274, 2009. doi:
  59. 59.
    Edwards, R., Bickmore, T., Jenkins, L., and Foley, M., Pilot study of the use of a computer agent to provide information and support to breastfeeding mothers. American Public Health Association annual meeting, 2012.Google Scholar
  60. 60.
    Gardiner, P., Hempstead, M., Ring, L., Bickmore, T., Yinusa-Nyahkoon, L., Tran, H., Paasche-Orlow, M., Damus, K., and Jack, B., Reaching women through health information technology: The gabby preconception care system. Am. J. Health Promot. 27(sp3):eS11–eS20, 2013.PubMedPubMedCentralCrossRefGoogle Scholar
  61. 61.
    Rahman, S., Islam, M. T., and Alam, D. S., Obesity and overweight in Bangladeshi children and adolescents: A scoping review. BMC Publ. Health. 14:70, 2014. Scholar
  62. 62.
    Thomas, D. P., and Hefler, M., How to reduce adolescent smoking in low-income and middle-income countries. Lancet Global Health 4(11):e762–e763, 2016.PubMedCrossRefGoogle Scholar
  63. 63.
    Weiss, B.D., Health literacy: A manual for clinicians. American Medical Association/American Medical Association Foundation. 7, 2003.Google Scholar
  64. 64.
    Williams, M. V., Baker, D. W., Honig, E. G., Lee, T. M., and Nowlan, A., Inadequate literacy is a barrier to asthma knowledge and self-care. Chest. 114:1008–1015, 1998.PubMedCrossRefGoogle Scholar
  65. 65.
    Baker, D. W., Parker, R. M., Williams, M. V., and Clark, W. S., Health literacy and the risk of hospital admission. J. Gen. Intern. Med. 13:791–798, 1998.PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    World Health Organization, Disease and injury regional estimates for 2004. Geneva, Switzerland: WHO, 2009a.Google Scholar
  67. 67.
    Ahern, L. R., and ERIC, Torment not treatment: Serbia’s segregation and abuse of children and adults with disabilities. Washington, DC: Mental Disability Rights International, 2007.Google Scholar
  68. 68.
    Global Health Workforce alliance. , Accessed on March 02, 2017.
  69. 69.
    GBD 2015 Risk Factors Collaborators, Global, regional, and national comparative risk assessment of 79 behavioral, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388(10053):1659–1724, 2016.CrossRefGoogle Scholar
  70. 70.
    Abdullah, A. S., and Husten, C. G., Promotion of smoking cessation in developing countries: A framework for urgent public health interventions. Thorax. 59(7):623–630, 2004.PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Bickmore, T. W., Schulman, D., and Sidner, C., Automated interventions for multiple health behaviors using conversational agents. Patient Educ. Couns. 92(2):142–148, 2013. Scholar
  72. 72.
    Fiore MC, Jaen CR, Baker TB. Treating tobacco use and dependence:2008 UPDATE. Rockville, MD: U.S. Department of Health and Human Services. Public Health ServiceGoogle Scholar
  73. 73.
    Lancaster, T., and Stead, L. F., Individual behavioural counselling for smoking cessation. Cochrane Database Syst. Rev. 3:CD001292, 2017. Scholar
  74. 74.
    Whittaker, R., McRobbie, H., Bullen, C., Rodgers, A., and Gu, Y., Mobile phone-based interventions for smoking cessation. Cochrane Database Syst. Rev. 4:CD006611, 2016. Scholar
  75. 75.
    Abdullah, A. S., Gaehde, S., and Bickmore, T., A tablet based embodied conversational agent to promote smoking cessation among veterans: A feasibility study. J. Epidemiol. Glob. Health. 8(3–4):225–230, 2018. Scholar
  76. 76.
    Thompson, D., Cullen, K. W., Redondo, M. J., and Anderson, B., Use of relational agents to improve family communication in type 1 diabetes: Methods. JMIR Res. Protoc. 5(3):e151, 2016. Scholar
  77. 77.
    Martínez-Miranda, J., Embodied conversational agents for the detection and prevention of suicidal behaviour: Current applications and open challenges. J. Med. Syst. 41(9):135, 2017. Scholar
  78. 78.
    Social Protection of Older People,, accessed on 18, February, 2018.
  79. 79.
    Coyle, C. E., and Dugan, E., Social isolation, loneliness and health among older adults. J. Aging Health 24(8):1346–1363, 2012.PubMedCrossRefGoogle Scholar
  80. 80.
    Gardiner, P. M., McCue, K. D., Negash, L. M., Cheng, T., White, L. F., Yinusa-Nyahkoon, L., Jack, B. W., and Bickmore, T. W., Engaging women with an embodied conversational agent to deliver mindfulness and lifestyle recommendations: A feasibility randomized control trial. Patient Educ. Couns. 100(9):1720–1729, 2017. Scholar
  81. 81.
    World Health Organization. The world health report 2006: Working together for health. World Health Organization, 2006.Google Scholar
  82. 82.
    Firoz, T., Sanghvi, H., Merialdi, M., and von Dadelszen, P., Pre-eclampsia in low and middle income countries. Best Pract. Res. Clin. Obstet. Gynaecol. 25:537–548, 2011.PubMedCrossRefGoogle Scholar
  83. 83.
    Anne, L., Lawn, J., Cousens, S. et al., Linking families and facilities for care at birth: What works to avert intrapartum-related deaths? Int. J. Gynecol. Obstetr. 107:S65–S66, 2009.Google Scholar
  84. 84.
    MCDAID, D., Countering the stigmatisation and discrimination of people with mental health problems in Europe. Luxembourg: European Commission, 2008.Google Scholar
  85. 85.
    Thompson, R. J., Mata, J., Jaeggi, S. M., Buschkuehl, M., Jonides, J., and Gotlib, I. H., Maladaptive coping, adaptive coping, and depressive symptoms: Variations across age and depressive state. Behav. Res. Ther. 48(6):459–466, 2010. Scholar
  86. 86.
    Bickmore, T. W., Pfeifer, L. M., and Paasche-Orlow, M. K., Health document explanation by virtual agents. International workshop on intelligent virtual agents. Springer, Berlin, Heidelberg, 2007.Google Scholar
  87. 87.
    Howard, M. O., McMillen, C. J., and Pollio, D. E., Teaching evidence-based practice: Toward a new paradigm for social work education. Res. Social Work Pract. 13:234–259, 2003.CrossRefGoogle Scholar
  88. 88.
    Regehr, C., Stern, S., and Shlonsky, A., Operationalizing evidence-based practice the development of an institute for evidence-based social work. Res. Soc. Work Pract. 17:408–416, 2007.CrossRefGoogle Scholar
  89. 89.
    Kumpfer, K. L., Alvarado, R., Smith, P., and Bellamy, N., Cultural sensitivity and adaptation in family-based prevention interventions. Prev. Sci. 3(3):241–246, 2002.PubMedCrossRefGoogle Scholar
  90. 90.
    Sue, D. W., Arredondo, P., and McDavis, R. J., Multicultural counseling competencies and standards: A call to the profession. J. Multicult. Counsel. Dev. 20:64–68, 1992.CrossRefGoogle Scholar
  91. 91.
    Sanders, M. R., Community-based parenting and family support interventions and the prevention of drug abuse. Addict. Behav. 25(6):929–942, 2000.PubMedCrossRefGoogle Scholar
  92. 92.
    Yin, L., Bickmore, T., and Cortés, D. E., The impact of linguistic and cultural congruity on persuasion by conversational agents. International conference on intelligent virtual agents. Springer, Berlin, Heidelberg, 2010.CrossRefGoogle Scholar
  93. 93.
    Rehm, M., et al., Culture-specific first meeting encounters between virtual agents. International Workshop on Intelligent Virtual Agents. Springer, Berlin, Heidelberg, 2008.Google Scholar
  94. 94.
    Travis, P., Bennett, S., Haines, A. et al., Overcoming health-systems constraints to achieve the millennium development goals. Lancet. 364(9437):900–906, 2004.PubMedCrossRefGoogle Scholar
  95. 95.
    Institute of Medicine, PEPFAR implementation: Progress and promise. Washington, DC: National Academies Press, 2007.Google Scholar
  96. 96.
    Ooms, G., Van Damme, W., and Temmerman, M., Medicines without doctors: Why the Global Fund must fund salaries of health workers to expand AIDS treatment. PLoS Med. 4(4):e128, 2007.PubMedPubMedCentralCrossRefGoogle Scholar
  97. 97.
    Pfeiffer, J., Johnson, W., Fort, M. et al., Strengthening health systems in poor countries: A code of conduct for nongovernmental organizations. Strengthening health systems in poor countries: do we need an NGO code of conduct? AJPH. 98(12):2134–2140, 2008.CrossRefGoogle Scholar
  98. 98.
    Bickmore, T, Gruber, A (2010). “Relational agents in clinical psychiatry. Harvard Review of Psychiatry, special issue on Psychiatry and Cyberspace, 18(2).PubMedCrossRefGoogle Scholar
  99. 99.
    World Health Organization, A strategic framework for health workforce development in the eastern Mediterranean region. East Mediterr Health J. 23(5):388–389, 2017.CrossRefGoogle Scholar
  100. 100.
    Heller, R. F., Chongsuvivatwong, V., Hailegeorgios, S., Dada, J., Torun, P., Madhok, R., and Sandars, J., Capacity-building for public health: Bull. World Health Organ. 85(12):930–934, 2007.PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    Kim, J. Y., Farmer, P., and Porter, M. E., Redefining global health-care delivery. Lancet. 382(9897):1060–1069, 2013.PubMedCrossRefGoogle Scholar
  102. 102.
    Bollinger, R., Chang, L., Jafari, R. et al., Leveraging information technology to bridge the health workforce gap. Bulletin of the World Health Organization 91:890–891, 2013. Scholar
  103. 103.
    Likofata Esanga, J. R., Viadro, C., McManus, L. et al., How the introduction of a human resources information system helped the Democratic Republic of Congo to mobilise domestic resources for an improved health workforce. Health Policy Plan 32(suppl 3):iii25–iii31, 2017. Scholar
  104. 104.
    World Health Organisation (WHO), Classification of digital health interventions. Geneva: WHO, 2018, Accessed September 12, 2018.Google Scholar
  105. 105.
    Aranda-Jan, C. B., Mohutsiwa-Dibe, N., and Loukanova, S., Systematic review on what works, what does not work and why of implementation of mobile health (mHealth) projects in Africa. BMC Publ. Health. 14(188), 2014.
  106. 106.
    Medhanyie, A. A., Little, A., Yebyo, H. et al., Health workers’ experiences, barriers, preferences and motivating factors in using mHealth forms in Ethiopia. Hum. Resour. Health. 13(2), 2015.Google Scholar
  107. 107.
    de Grood, C., Eso, K., and Santana, M. J., Physicians’ experience adopting the electronic transfer of care communication tool: Barriers and opportunities. J. Multidiscip. Healthc. 8:21–31, 2015.PubMedPubMedCentralGoogle Scholar
  108. 108.
    Lewis, T., Synowiec, C., Lagomarsino, G., and Schweitzer, J., E-health in low- and middle-income countries: Findings from the center for health market innovations. Bull. World Health Org. 90:332–340, 2012. Scholar
  109. 109.
    Ahmed, T., Lucas, H., Khan, A. S., Islam, R., Bhuiya, A., and Iqbal, M., eHealth and mHealth initiatives in Bangladesh: A scoping study. BMC Health Serv. Res. 14:260, 2014. Scholar
  110. 110.
    Bloom, G., Sarwar, R., Standing, H., Begum, T. and Rahman, S., Innovations for health in Bangladesh. Report to DFID-funded Srijon Project, 2013.Google Scholar
  111. 111.
    Curioso, W. H., and Kurth, A. E., Access, use and perceptions regarding internet, cell phones and PDAs as a means for health promotion for people living with HIV in Peru. BMC Med. Inform. Decis. Mak. 7:24, 2007.PubMedPubMedCentralCrossRefGoogle Scholar
  112. 112.
    Cho, J. H., Lee, H. C., Lim, D. J., Kwon, H. S., and Yoon, K. H., Mobile communication using a mobile phone with a glucometer for glucose control in type 2 patients with diabetes: As effective as an internet-based glucose monitoring system. J. Telemed. Telecare 15(2):77–82, 2009.PubMedCrossRefGoogle Scholar
  113. 113.
    Nadzam, D. M., and Mackles, R. M., Promoting patient safety: Is technology the solution? Jt Comm. J. Qual. Improv. 27:430–436, 2001.PubMedGoogle Scholar
  114. 114.
    Han, Y. Y., Carcillo, J. A., Venkataraman, S. T. et al., Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics. 116:1506–1512, 2005.PubMedCrossRefGoogle Scholar
  115. 115.
    Patterson, E. S., Rogers, M. L., and Render, M. L., Simulation-based embedded probe technique for human-computer interaction evaluation. Cognit. Technol. Work. 6(3):197–205, 2004.CrossRefGoogle Scholar
  116. 116.
    Squires, M., Bieslada, D., Fanizza, R. et al., New approaches to improving patient safety: Strategy, technology and funding. Healthc Q. 8(3):120–2. 124, 2005.PubMedCrossRefGoogle Scholar
  117. 117.
    Eastwood, B., Premera says data breach may affect 11 million consumers. FierceHealthIT. 2015; Scholar
  118. 118.
    Sriram, J, et al., Challenges in data quality Assurance in Pervasive Health Monitoring Systems. Gawrock D, et al. Future of Trust in Computing. Vieweg+Teubner: 129–142, 2009.Google Scholar
  119. 119.
    He, D. et al., AMIA; 2014. Security concerns in android mHealth apps. Proc AMIA Ann Symp. 14:645–654.Google Scholar
  120. 120.
    The Office of the National Coordinator for Health Information Technology (ONC). Health Information Privacy Law and Policy. Available at: Accessed on May 15, 2019.
  121. 121.
    U.S. Department of Health & Human Service. Health Information Technology., Available at: Accessed on May 15, 2019.
  122. 122.
    American Psychological Association (APA), Ethical principles of psychologists and code of conduct. Am. Psychol. 57:1060–1073, 2002.CrossRefGoogle Scholar
  123. 123.
    Krieger, W. H., Medical apps: Public and academic perspectives. Perspect. Biol. Med. 56(2):259–273, 2013. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceNorth Dakota State UniversityFargoUSA
  2. 2.Philips Research North AmericaCambridgeUSA
  3. 3.Boston University School of Medicine, Boston Medical CenterBostonUSA
  4. 4.Duke Global Health InstituteDuke UniversityDurhamUSA
  5. 5.Global Health ProgramDuke Kunshan UniversityKunshanChina

Personalised recommendations