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mHealth technology utilization in the Arab world : a systematic review of systems, usage, and challenges

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Abstract

The rapid growth in mobile technology has provided an opportunity for the design and development of mobile health technologies in the Arab region. Nonetheless, available literature has not been able to provide information on the types of systems, use patterns and challenges faced during the implementation of mobile Health (mHealth) systems in the Arab countries. This lack of evidence-based study to classify mHealth technologies, use and possible obstacles has an important role in the continuous development, implementation and future research trends of mHealth technologies in the Arab world. This study filled the gap by way of a systematic review of previous studies conducted within a decade from seven online databases to explore the current evidence on the use of mHealth in the Arab countries. The findings from a systematic review of 31 studies classified the main mHealth systems into four categories: self-healthcare management systems, assisted healthcare systems, supervised healthcare systems and continuous monitoring systems. Self-healthcare management systems were the dominant mHealth solutions while continuous monitoring systems were the least utilized. Generally, there was a low usage level of m-health systems in the Arab world underpinned by challenges such as User interface (UI), cloud storage, platforms, quality of service (QoS), security and data acquisition.

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Authors and Affiliations

  1. School of Architecture and Design, Department of Multimedia Technology, Al-Zaytoonah University of Jordan, Amman, Jordan

    Ahmed Alsswey

  2. School of Media and Performing Arts, Coventry University, Coventry, UK

    Hosam Al-Samarraie

  3. College of Distance Education(CoDE), University of Cape Coast, Cape Coast, Ghana

    Brandford Bervell

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  1. Ahmed Alsswey
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Correspondence to Ahmed Alsswey.

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Appendices

Appendix

Self-healthcare management

This incorporates self-management program referrals such as standards of care, care protocols and other provincial policies related to chronic disease care. It further integrates personal self-management goals into the care planning process. Self-healthcare management systems are emphasized on the independence, patient engagement/participation and self-confidence of people without the necessity for inclusion of external variables in the delivery of the mHealth service. These solutions usually involve a body sensor network (BSN) and a mobile-based unit (MBU). The BSN determines a network of reduced, low cost and wireless wearable or implantable biosensors and actuators that are interconnected to collect the patient’s physiological and contextual variables [22] for instance, blood pressure, body temperature and heart and respiration rates, etc. (see Table 4).

Table 4 Self-healthcare management systems
Full size table

Assisted healthcare

This category includes solutions which involve not only the self-component of obtaining the measurements of health variables but also engages the user in the process of sharing these data. These types of solutions usually add to the basic mobile architecture for self-measurements at a care centre [42]. Table 5 shows Assisted healthcare studies conducted on the Arab countries.

Table 5 Assisted healthcare systems
Full size table

Supervised healthcare

This type of mHealth system involves the information flow (i.e. patient’s physiological signals) stored remotely that are accessible by doctors which involves a further level of complexity in the system structure and can be categorized as supervised healthcare. These mHealth platforms comprise a monitoring system that mainly involves a remote database which gather physiological data periodically sent and stored, allowing family, doctors and friends (with different functionality privileges) to view and manage the current and the past conditions of the patient. The aims of these mHealth apps are usually associated with the monitoring of health parameters [6,49], the supervision of rehabilitation interventions [50,51], detection of falls [52,53] or medication adherence [54]. In addition, these systems can also combine a component to manage detected emergency situations (Assisted healthcare). Table [55, 56] shows the mHealth solution Supervised healthcare.

Table 6 Supervised mHealth systems
Full size table

Continuous monitoring

This group involves all the functionalities described in the previous categories along with a two way completely automated and continuous approach (Table 7). In specific, these systems present a completely automatic analysis of real-time important signs of the patient, and non-automatic clinical analysis by a specialist (supervised healthcare). This capability is implemented using a reasoning engine, which proactively uses data mining techniques (such as pattern detection) to link data from multiple sensors, evaluate risk levels and help to switch to any corresponding real-time assistance responses or preventive actions, suitable for the users. Moreover, it usually also delivers effective reporting mechanisms to both patients and caregivers.

Table 7 Continuous monitoring systems
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Alsswey, A., Al-Samarraie, H. & Bervell, B. mHealth technology utilization in the Arab world : a systematic review of systems, usage, and challenges. Health Technol. 11, 895–907 (2021). https://doi.org/10.1007/s12553-021-00549-3

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