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A comprehensive study of mobile-health based assistive technology for the healthcare of dementia and Alzheimer’s disease (AD)

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Abstract

Assistive technology (AT) involvement in therapeutic treatment has provided simple and efficient healthcare solutions to people. Within a short span of time, mobile health (mHealth) has grown rapidly for assisting people living with a chronic disorder. This research paper presents the comprehensive study to identify and review existing mHealth dementia applications (apps), and also synthesize the evidence of using these applications in assisting people with dementia including Alzheimer’s disease (AD) and their caregivers. Six electronic databases searched with the purpose of finding literature-based evidence. The search yielded 2818 research articles, with 29 meeting quantified inclusion and exclusion criteria. Six groups and their associated sub-groups emerged from the literature. The main groups are (1) activities of daily living (ADL) based cognitive training, (2) monitoring, (3) dementia screening, (4) reminiscence and socialization, (5) tracking, and (6) caregiver support. Moreover, two commercial mobile application stores i.e., Apple App Store (iOS) and Google Play Store (Android) explored with the intention of identifying the advantages and disadvantages of existing commercially available dementia and AD healthcare apps. From 678 apps, a total of 38 mobile apps qualified as per defined exclusion and inclusion criteria. The shortlisted commercial apps generally targeted different aspects of dementia as identified in research articles. This comprehensive study determined the feasibility of using mobile Health based applications for dementia including AD individuals and their caregivers regardless of limited available research, and these apps have capability to incorporate a variety of strategies and resources to dementia community care.

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Acknowledgments

This work was supported by Artificial Intelligence and Data Analytics (AIDA) Lab, Prince Sultan University, Riyadh, Saudi Arabia, and Forensic Analysis, Machine Learning, and Information Retrieval (FAMLIR) research group of Department of Computer Engineering, University of Engineering and Technology, Taxila, Pakistan.

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Authors and Affiliations

  1. Department of Software Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan

    Kanwal Yousaf

  2. Department of Computer Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan

    Zahid Mehmood

  3. Department of Electrical and Telecommunication Engineering, National University of Computer and Emerging Sciences-FAST, Islamabad, 44000, Pakistan

    Israr Ahmad Awan

  4. College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Tanzila Saba

  5. College of Computer Science and Engineering, University of Jeddah, Jeddah, 21577, Saudi Arabia

    Riad Alharbey

  6. Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Talal Qadah

  7. Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Mayda Abdullateef Alrige

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  1. Kanwal Yousaf
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Correspondence to Zahid Mehmood.

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Yousaf, K., Mehmood, Z., Awan, I.A. et al. A comprehensive study of mobile-health based assistive technology for the healthcare of dementia and Alzheimer’s disease (AD). Health Care Manag Sci 23, 287–309 (2020). https://doi.org/10.1007/s10729-019-09486-0

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