Design and evaluation of a mobile application for monitoring patients with Alzheimer's disease: A day center case study

BACKGROUND AND OBJECTIVE This paper presents Alzheed, a mobile application for monitoring patients with Alzheimer's disease at day centers as well as a set of design recommendations for the development of healthcare mobile applications. The Alzheed project was conducted at Day Center "Dorita de Ojeda" that is focused on the care of patients with Alzheimer's disease. MATERIALS AND METHODS A software design methodology based on participatory design was employed for the design of Alzheed. This methodology is both iterative and incremental and consists of two main iterative stages: evaluation of low-fidelity prototypes and evaluation of high-fidelity prototypes. Low-fidelity prototypes were evaluated by 11 day center's healthcare professionals (involved in the design of Alzheed), whereas high-fidelity prototypes were evaluated using a questionnaire based on the technology acceptance model (TAM) by the same healthcare professionals plus 30 senior psychology undergraduate students uninvolved in the design of Alzheed. RESULTS Healthcare professional participants perceived Alzheed as extremely likely to be useful and extremely likely to be usable, whereas senior psychology undergraduate students perceived Alzheed as quite likely to be useful and quite likely to be usable. Particularly, the median and mode of the TAM questionnaire were 7 (extremely likely) for healthcare professionals and 6 (quite likely) for psychology students (for both constructs: perceived usefulness and perceived ease of use). One-sample Wilcoxon signed-rank tests were performed to confirm the significance of the median for each construct. CONCLUSIONS From the experience of designing Alzheed, it can be concluded that co-designing with healthcare professionals leads to (i) fostering group endorsement, which prevents resistance to change and (ii) helps to meet the needs of both healthcare professionals and patients, guaranteeing the usefulness of the application. In addition, evaluation of mobile healthcare applications by users involved and uninvolved in the application's design process helps to improve the ease of use of the application.

[1]  R. Mitchell Self-harm in older adults: room to improve clinical care. , 2018, The lancet. Psychiatry.

[2]  Mirza Mansoor Baig,et al.  Smart Health Monitoring Systems: An Overview of Design and Modeling , 2013, Journal of Medical Systems.

[3]  Paula Escalada-Hernández,et al.  Design and evaluation of a prototype of augmented reality applied to medical devices , 2019, Int. J. Medical Informatics.

[4]  Káthia Marçal de Oliveira,et al.  Standardized Usability Questionnaires: Features and Quality Focus , 2016 .

[5]  Younghwa Lee,et al.  The Technology Acceptance Model: Past, Present, and Future , 2003, Commun. Assoc. Inf. Syst..

[6]  M. Ienca,et al.  Intelligent Assistive Technology for Alzheimer's Disease and Other Dementias: A Systematic Review. , 2017, Journal of Alzheimer's disease : JAD.

[7]  Susanne Bødker,et al.  Participatory Design that Matters—Facing the Big Issues , 2018, ACM Trans. Comput. Hum. Interact..

[8]  Habib M Fardoun,et al.  Recognition of familiar people with a mobile cloud architecture for Alzheimer patients , 2017, Disability and rehabilitation.

[9]  Sara Jones,et al.  Computing technologies for reflective, creative care of people with dementia , 2013, CACM.

[10]  Ara Darzi,et al.  Exploring mobile working in healthcare: Clinical perspectives on transitioning to a mobile first culture of work , 2019, Int. J. Medical Informatics.

[11]  Ruqayya Abdulrahman,et al.  iCare: Applying IoT Technology for Monitoring Alzheimer's Patients , 2018, 2018 1st International Conference on Computer Applications & Information Security (ICCAIS).

[12]  Miguel A. Estudillo-Valderrama,et al.  User-centred design for developing e-Health system for renal patients at home (AppNephro) , 2019, Int. J. Medical Informatics.

[13]  Karin Slegers,et al.  Active collaboration in healthcare design: participatory design to develop a dementia care app , 2013, CHI Extended Abstracts.

[14]  M. Sambrook,et al.  Motor and cognitive function in Lewy body dementia: comparison with Alzheimer's and Parkinson's diseases. , 1997, Journal of neurology, neurosurgery, and psychiatry.

[15]  Heiko Horst Hornung,et al.  Towards Participatory Prototyping with Older Adults with and Without Cognitive Impairment: Challenges and Lessons Learned , 2017, INTERACT.

[16]  Ahmed Helmy,et al.  The Alzimio App for Dementia, Autism & Alzheimer's: Using Novel Activity Recognition Algorithms and Geofencing , 2016, 2016 IEEE International Conference on Smart Computing (SMARTCOMP).

[17]  Christine Van Broeckhoven,et al.  The genetic landscape of Alzheimer disease: clinical implications and perspectives , 2015, Genetics in Medicine.

[18]  Kathryn Ziegler-Graham,et al.  Forecasting the global burden of Alzheimer’s disease , 2007, Alzheimer's & Dementia.

[19]  J. Galvin,et al.  Distinguishing Alzheimer’s disease from other major forms of dementia , 2011, Expert review of neurotherapeutics.

[20]  Sara Jones,et al.  A software app to support creativity in dementia care , 2013, Creativity & Cognition.

[21]  Oussama Metatla,et al.  Designing for Reminiscence with People with Dementia , 2019, CHI Extended Abstracts.

[22]  L. Schneider,et al.  Defeating Alzheimer's disease and other dementias: a priority for European science and society , 2016, The Lancet Neurology.

[23]  C. Nucci,et al.  Glaucoma and Alzheimer Disease: One Age-Related Neurodegenerative Disease of the Brain , 2017, Current neuropharmacology.

[24]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..