Influence of Mobile ICT on the Adherence of Elderly People with Chronic Diseases

A great variety of applications for mobile devices are designed to support users during medical intake. One of these applications is ‘Medication Plan’ which aims at supporting regular and correct intake of medication and documentation of vital parameters. The purpose of this study is to examine the influence of demographic and health-related factors on user behavior and patterns of use. The application was available free of charge between 2010 and 2012 in the Apple™-App-Store™. The study is based on data collected via an online questionnaire. In total 1799 participants generated 1708 complete data sets. 69 % of the users (74 % male) with a median age of 45 applied ‘Medication Plan’ for more than one day. The mean duration of application increased substantially with age ( 60 years = 103.9 days). However, other demographic factors (sex, educational status etc.) had no effect on usage intensity. Users with complicated medical treatment or aged > 60 years applied the application for 3 month on average. This is a promising trend towards the support treatment of chronic conditions with mobile applications.

[1]  G. Elwyn,et al.  To serve and protect? Electronic health records pose challenges for privacy, autonomy and person-centered medicine , 2011 .

[2]  B. Chi,et al.  Mobile phones to improve HIV treatment adherence , 2010, The Lancet.

[3]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[4]  Carl K. Chang,et al.  Perceptions of technology among older adults. , 2013, Journal of gerontological nursing.

[5]  J. Hallas,et al.  Polypharmacy: correlations with sex, age and drug regimen A prescription database study , 1998, European Journal of Clinical Pharmacology.

[6]  A. Haines,et al.  The Effectiveness of Mobile-Health Technology-Based Health Behaviour Change or Disease Management Interventions for Health Care Consumers: A Systematic Review , 2013, PLoS medicine.

[7]  Steve Wheeler,et al.  How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX , 2011, Biomedical engineering online.

[8]  Shiming Yang,et al.  Usability of a CKD educational website targeted to patients and their family members. , 2012, Clinical journal of the American Society of Nephrology : CJASN.

[9]  Gunther Eysenbach,et al.  Promoting Business and Entrepreneurial Awareness in Health Care Professionals: Lessons From Venture Capital Panels at Medicine 2.0 Conferences , 2014, Journal of medical Internet research.

[10]  Joy Meier,et al.  Antihypertensive medication adherence in the Department of Veterans Affairs. , 2007, The American journal of medicine.

[11]  M M Hansen,et al.  Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives , 2014, Yearbook of Medical Informatics.

[12]  L. Osterberg,et al.  Adherence to medication. , 2005, The New England journal of medicine.

[13]  Przemyslaw Kardas,et al.  Non-compliance with antibiotic therapy for acute community infections: a global survey. , 2007, International journal of antimicrobial agents.

[14]  W. Jack,et al.  Effects of a mobile phone short message service on antiretroviral treatment adherence in Kenya (WelTel Kenya1): a randomised trial , 2010, The Lancet.

[15]  Fiorella Marcellini,et al.  Use and acceptance of new technology by older people. Findings of the international MOBILATE survey: ???Enhancing mobility in later life??? , 2005 .

[16]  W. J. Elliott,et al.  Adherence to prescribed antihypertensive drug treatments: longitudinal study of electronically compiled dosing histories , 2009 .

[17]  A. L. Dal-Fabbro,et al.  Adherence to long term therapies: evidence for action , 2005 .

[18]  S. Becker,et al.  User Profiles of a Smartphone Application to Support Drug Adherence — Experiences from the iNephro Project , 2013, PloS one.

[19]  K. Kroenke,et al.  Factors Associated with Drug Adherence and Blood Pressure Control in Patients with Hypertension , 2006, Pharmacotherapy.

[20]  Robert Nguyen,et al.  Social disparities in internet patient portal use in diabetes: evidence that the digital divide extends beyond access , 2011, J. Am. Medical Informatics Assoc..

[21]  Wendy Olphert,et al.  Older People and Digital Disengagement: A Fourth Digital Divide? , 2013, Gerontology.

[22]  L. Swartz,et al.  Scaling Up mHealth: Where Is the Evidence? , 2013, PLoS medicine.

[23]  Allison Gates,et al.  Evaluating User Perceptions of Mobile Medication Management Applications With Older Adults: A Usability Study , 2014, JMIR mHealth and uHealth.

[24]  E. Rogers Diffusion of Innovations , 1962 .

[25]  Urs-Vito Albrecht,et al.  mHealth 2.0: Experiences, Possibilities, and Perspectives , 2014, JMIR mHealth and uHealth.

[26]  C. Diamantidis,et al.  Usability testing and acceptance of an electronic medication inquiry system for CKD patients. , 2013, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[27]  Patricia Flatley Brennan,et al.  Factors affecting home care patients' acceptance of a web-based interactive self-management technology , 2011, J. Am. Medical Informatics Assoc..

[28]  C. McHorney,et al.  The Adherence Estimator: a brief, proximal screener for patient propensity to adhere to prescription medications for chronic disease. , 2009, Current medical research and opinion.

[29]  Daniel P. Lorence,et al.  Racial disparities in health information access: resilience of the digital divide , 2006, Journal of Medical Systems.

[30]  Andre Charland,et al.  Mobile application development , 2011, Commun. ACM.

[31]  Stefan Becker,et al.  Health information technology (IT) to improve the care of patients with chronic kidney disease (CKD) , 2014, BMC Nephrology.