Mobile Technology Applications in the Healthcare Industry for Disease Management and Wellness

Kalem, Güler; Turhan, Çiğdem

doi:10.1016/j.sbspro.2015.06.216

Cited by 28 publications

(12 citation statements)

References 14 publications

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“…Also, some studies conducted in HICs and other LMICs indicated a limited level of research on the use of mHealth interventions for diagnostic purposes [ 50 , 51 ] which are consistent with our study findings. Furthermore, there is a higher level of research from some studies conducted in HICs and other LMICs which showed that health workers used tablets, smartphones, personal digital assistants, handheld devices, and mobile phones to support disease diagnosis and treatment procedures [ 39 – 42 , 44 , 45 , 47 , 49 – 51 ] which are not consistent with this study findings.…”

Section: Discussioncontrasting

confidence: 93%

Mapping evidence of mobile health technologies for disease diagnosis and treatment support by health workers in sub-Saharan Africa: a scoping review

Osei

Kuupiel

Vezi

et al. 2021

BMC Med Inform Decis Mak

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Background The rapid growth of mobile technology has given rise to the development of mobile health (mHealth) applications aimed at treating and preventing a wide range of health conditions. However, evidence on the use of mHealth in high disease burdened settings such as sub-Sharan Africa is not clear. Given this, we systematically mapped evidence on mHealth for disease diagnosis and treatment support by health workers in sub-Saharan Africa. Methods We conducted a scoping review study guided by the Arksey and O’Malley’s framework, Levac et al. recommendations, and Joanna Briggs Institute guidelines. We thoroughly searched the following databases: MEDLINE and CINAHL with full text via EBSCOhost; PubMed; Science Direct and Google Scholar for relevant articles from the inception of mHealth technology to April 2020. Two reviewers independently screened abstracts and full-text articles using the eligibility criteria as reference. This study employed the mixed methods appraisal tool version 2018 to assess the methodological quality of the included studies. Results Out of the 798 articles identified, only 12 published articles presented evidence on the availability and use of mHealth for disease diagnosis and treatment support by health workers in SSA since 2010. Of the 12 studies, four studies were conducted in Kenya; two in Malawi; two in Nigeria; one in South Africa; one in Zimbabwe; one in Mozambique, and one in Lesotho. Out of the 12 studies, one reported the use of mHealth for diseases diagnosis; three reported the use of mHealth to manage HIV; two on the management of HIV/TB; two on the treatment of malaria; one each on the management of hypertension; cervical cancer; and three were not specific on any disease condition. All the 12 included studies underwent methodological quality appraisal with a scored between 70 and 100%. Conclusions The study shows that there is limited research on the availability and use of mHealth by health workers for disease diagnosis and treatment support in sub-Saharan Africa. We, therefore, recommend primary studies focusing on the use of mHealth by health workers for disease diagnosis and treatment support in sub-Saharan Africa.

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Section: Discussioncontrasting

confidence: 93%

Mapping evidence of mobile health technologies for disease diagnosis and treatment support by health workers in sub-Saharan Africa: a scoping review

Osei

Kuupiel

Vezi

et al. 2021

BMC Med Inform Decis Mak

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“…Many professions have evolved to incorporate the usage of mobile devices, including healthcare (Nasi, Cucciniello, & Guerrazzi, 2015;Kalem & Turhan, 2015), agriculture (Lightfoot, Gillman, Scheuermeier, & Nyimbo, 2008;Qiang, Kuek, Dymond, & Esselaar, 2012), and banking (Jenkins, 2008;Tiwari, Buse, & Herstatt, 2006). Unfortunately, teachers and students have not kept pace with the ongoing digital revolution (Buckingham, 2007;Norris & Soloway, 2015).…”

Section: Transition To Technology When Learningmentioning

confidence: 99%

Practicum teachers’ use of mobile technology as measured by the technology acceptance model

Walker

Kho

Tan

et al. 2019

Asia Pacific Journal of Education

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This study utilised the Technology Acceptance Model (TAM) to examine practicum teachers' current use of mobile technology and their intentions of use in the future. A survey was conducted to better understand the relationship between practicum teachers and their potential assimilation of technology into classroom practice. The survey questionnaire was administered to 100 undergraduate students at [institute withheld],[country withheld]'s teacher preparatory university. The following four key constructs of the TAM were assessed: perceived ease of use (PEU), perceived usefulness (PU), subjective norm (SN), and behavioural intention to use (BIU). The impact of practicum teachers' gender and academic subjects on their acceptance of technology were also investigated. The present study found strong links between PEU and PU. While practicum teachers perceived mobile technology as being useful and intended to use it in their classrooms, BIU was not significantly impacted by PEU, PU, or SN. The data also suggested that gender did not affect BIU while practicum teachers' academic subjects might be an indicator of their future intentions to use mobile technology. With these findings, recommendations are made to further improve the integration of mobile technology in classroom practice.

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“…It is seen as a simple and inexpensive solution for improving worldwide access to healthcare facilities, particularly in resource shortages (Källander et al, 2013). It can, however, encourage the goals of persuading individuals to embrace the technology because of the cost, accessibility and coverage benefits of technology (Srisawangwong & Kasemvilas, 2014), the integration with internet (Yusof & Iahad, 2012), the capacity to monitor illnesses and provide essential data to physicians who can then assist users more efficiently in recovery and well-being (Kalem & Turhan, 2015). Moreover, mHealth can improve the efficiency of healthcare workers for providers and distributors, i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

Understanding the insight of factors affecting mHealth adoption

Kaium

Bao²,

Alam³

et al. 2019

IJRBS

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Numerous studies have addressed the different context of mHealth services among diverse user groups. But due to a lack of understanding the insight of factors affecting the mHealth adoption, it’s crucial need to conduct a systematic review on this issue. The objective of this study was to synthesize the present understanding of the influential factors of mHealth adoption. We performed a systematic literature search on eight electronically reputed scientific databases from 2010 to March 2019, such as Science Direct, Springer, IEEE Xplore, JMIR, Taylor & Francis, Emerald, Mary Ann Liebert and Google Scholar. This was accomplished by gathering data including authors, countries, years, target population, sample size, models/theories, and key influential factors. Primarily, a total of 2969 potentially relatable papers were found, of which 50 met the inclusion criteria. It was found that cross-sectional approach, survey methods and structural equation modeling (SEM) were the most explored research methodologies whereas PLS-SEM was found to be the largest used analysis tools. From the analysis, a total of ninety-four influential factors were clearly recognized and the findings represent that the following 15 factors appeared most recurrently and significantly; perceived usefulness, perceived ease of use, social-influence, subjective norms, self-efficacy, trust, facilitating conditions, technology anxiety, performance expectancy, effort expectancy, cost, attitude, resistance to change, perceived privacy and security, and perceived behavioral control. The research results have significant theoretical and practical implications for mHealth services providers, researchers and policy makers with regards to the Sustainable Development Goals (SDGs) allied to healthcare.

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Mobile Technology Applications in the Healthcare Industry for Disease Management and Wellness

Cited by 28 publications

References 14 publications

Mapping evidence of mobile health technologies for disease diagnosis and treatment support by health workers in sub-Saharan Africa: a scoping review

Mapping evidence of mobile health technologies for disease diagnosis and treatment support by health workers in sub-Saharan Africa: a scoping review

Practicum teachers’ use of mobile technology as measured by the technology acceptance model

Understanding the insight of factors affecting mHealth adoption

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