A technology acceptance analysis for mhealth apps: the case of Turkey

The acceptance of mHealth (mobile health) apps has been on the increase throughout the world as well as in Turkey. There are two main indicators of mHealth success and acceptance, such as mHealth apps users’ satisfaction level and intention to use mHealth apps. In this context, the factors, including ease of use, trust, privacy, usefulness, and information quality are critical to analyze how they affect the acceptance of the mHealth apps by the Turkish users, and their satisfaction level with mHealth apps. Thus, the main objectives of this study are to (1) to explain how users perceive and use mHealth apps with technology acceptance analysis, (2) investigate whether the usefulness or uselessness of mHealth apps depends on user feelings about mHealth apps, (3) analyze the impacts of ease of use, trust, privacy, usefulness and information quality on mHealth users’ satisfaction and intention, and (4) identify users’ attitudes towards mHealth apps and their satisfaction level with mHealth apps in Turkey. A total of 282 participants from Turkey completed a survey analyzing the ease of use, trust, privacy, usefulness and information quality of mHealth apps to specify the reasons for mHealth acceptance. Statistical techniques were employed for data analysis. This study provides some managerial implications and scholarly recommendations to increase the acceptance of mHealth apps as well as helping mHealth apps designers to recognize the factors that influence the intention to adopt mHealth

Chapter 8 Factors influencing the adoption of mobile health apps in the UAE

Increasing technology use has led to an increase in mobile health (mHealth) applications (apps). Users are adopting these applications for many reasons related to acceptance and gamification; however, there still needs to be a consensus on which factors most affect user adoption. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this study examines the factors influencing the acceptance of mHealth technology. We collected data from 198 United Arab Emirate users who had previously used mHealth apps to improve their self-healthcare. The findings suggested that the intention of using this technology is positively influenced by: (1) the levels of performance expectancy and facilitating conditions concerning the use, (2) the level of gamification impact, and (3) the degree of personal innovativeness in the simple design of mHealth apps. These findings extend the theoretical concept of the UTAUT model in mobile healthcare technology. The value of the study lies in constructing an integrated digital transformation model that will assist healthcare companies in comprehending the influence of available resources and lead them to invest significantly in the incumbent digital infrastructure. The findings shall help healthcare practitioners identify critical drivers and key challenges faced by stakeholders concerning mobile health app use in an emerging Arab country that has the vision to improve its healthcare through digital transformation

Social, Organizational, and Technological Factors Impacting Clinicians’ Adoption of Mobile Health Tools: A Systematic Literature Review

Background: There is a growing body of evidence highlighting the potential of mobile health (mHealth) in reducing health care costs, enhancing access, and improving the quality of patient care. However, user acceptance and adoption are key prerequisites to harness this potential; hence, a deeper understanding of the factors impacting this adoption is crucial for its success. Objective: The aim of this review was to systematically explore relevant published literature to synthesize the current understanding of the factors impacting clinicians’ adoption of mHealth tools, not only from a technological perspective but also from social and organizational perspectives. Methods: A structured search was carried out of MEDLINE, PubMed, the Cochrane Library, and the SAGE database for studies published between January 2008 and July 2018 in the English language, yielding 4993 results, of which 171 met the inclusion criteria. The Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines and the Cochrane handbook were followed to ensure a systematic process. Results: The technological factors impacting clinicians’ adoption of mHealth tools were categorized into eight key themes: usefulness, ease of use, design, compatibility, technical issues, content, personalization, and convenience, which were in turn divided into 14 subthemes altogether. Social and organizational factors were much more prevalent and were categorized into eight key themes: workflow related, patient related, policy and regulations, culture or attitude or social influence, monetary factors, evidence base, awareness, and user engagement. These were divided into 41 subthemes, highlighting the importance of considering these factors when addressing potential barriers to mHealth adoption and how to overcome them. Conclusions: The study results can help inform mHealth providers and policymakers regarding the key factors impacting mHealth adoption, guiding them into making educated decisions to foster this adoption and harness the potential benefits

Culture in the design of mHealth UI:An effort to increase acceptance among culturally specific groups

Purpose: Designers of mobile applications have long understood the importance of users’ preferences in making the user experience easier, convenient and therefore valuable. The cultural aspects of groups of users are among the key features of users’ design preferences, because each group’s preferences depend on various features that are culturally compatible. The process of integrating culture into the design of a system has always been an important ingredient for effective and interactive human computer interface. This study aims to investigate the design of a mobile health (mHealth) application user interface (UI) based on Arabic culture. It was argued that integrating certain cultural values of specific groups of users into the design of UI would increase their acceptance of the technology. Design/methodology/approach: A total of 135 users responded to an online survey about their acceptance of a culturally designed mHealth. Findings: The findings showed that culturally based language, colours, layout and images had a significant relationship with users’ behavioural intention to use the culturally based mHealth UI. Research limitations/implications: First, the sample and the data collected of this study were restricted to Arab users and Arab culture; therefore, the results cannot be generalized to other cultures and users. Second, the adapted unified theory of acceptance and use of technology model was used in this study instead of the new version, which may expose new perceptions. Third, the cultural aspects of UI design in this study were limited to the images, colours, language and layout. Practical implications: It encourages UI designers to implement the relevant cultural aspects while developing mobile applications. Originality/value: Embedding Arab cultural aspects in designing UI for mobile applications to satisfy Arab users and enhance their acceptance toward using mobile applications, which will reflect positively on their lives.</p

mHealth Acceptance and Usage among South Asian Adults in the U.S.

Background: Modifiable lifestyle factors such as physical inactivity and unhealthy diet contribute to the increased risk of cardiovascular diseases (CVD) and diabetes (DM) in South Asians (SAs) (Volgman et al., 2018). Interventions using mobile health (mHealth) have demonstrated feasibility and potential efficacy for ethnic minorities (Bender et al., 2018), and have the potential to be of preventive and therapeutic value in reducing the burden of CVD and DM in SAs living in the US. However, there is a gap in knowledge regarding the usage and acceptance of mHealth among SAs. Purpose: The objectives were to examine the overall usage of mHealth and examine factors associated with the acceptance, usage, non-usage, and discontinuation of mHealth technology among SA adults living in the US. Methods: The study utilized a cross-sectional research design. A total of 134 South Asian adults were recruited to the study. Self-reported measures included demographics, health status, motivations for using mHealth, factors associated with technology acceptance and usage, reasons for non-usage and discontinuation of mHealth applications (apps) and smart and connected devices using the survey developed by Paré, Leaver, & Bourget (2018). Correlation analyses were conducted using Pearson’s and Spearman’s correlation tests. Chi-square and Kruskal-Wallis analyses were conducted to compare group differences among current users, past users, and non-users of mHealth technology. Results: About 62.4% of the participants were current users of mobile health apps, and 43.1% were current users of smart and connected devices. Users were on an average between the ages 35-54 years, female, healthy, employed, university educated, with an annual family income of over $80,000. There was a statistically significant difference in age (χ2 (2) = 9.638, p = .007) and employment (χ2 (4, N = 105) = 12.262, p = 0.019) between the current users, past users, and non-users of smart devices. Non-users of smart devices were more likely to be students, and between 18-34 years of age. The mean scores for the scales of perceived ease of use, perceived usefulness, confirmation of expectations, user satisfaction, and intent to continue using mHealth technology ranged from 3.5 – 4.2 (somewhat agree to strongly agree) for mobile health apps and from 4.1 to 4.4 (somewhat agree to strongly agree) for smart and connected devices. Conclusions: mHealth technology was used, accepted, and appreciated by more than half of the South Asian adults that we surveyed. The results from this study may help in selecting and utilizing the most accepted mHealth technology for designing interventions for South Asian adults living in the US to lower the risk of CVD and DM

Empowering Diabetes Patient with Mobile Health Technologies

Chronic diseases, especially diabetes mellitus, are huge public health burden. Therefore, new health care models for sharing the responsibility for care among health care providers and patients themselves are needed. The concept of empowerment promotes patient’s active involvement and control over their own health. It can be achieved through education, self-management, and shared decision making. All these aspects can be covered by mobile health technologies, the so-called mHealth. This term comprises mobile phones, patient monitoring devices, tablets, personal digital assistants, other wireless devices, and numerous apps. Many challenges of diabetics can be addressed by mHealth, including glycemic control, nutrition control, physical activity, high blood pressure, medication adherence, obesity, education, diabetic retinopathy screening, diabetic foot screening, and psychosocial care. However, mHealth plays only minor role in diabetes management, despite numerous apps on the market. Namely, these apps have many shortcomings and the majority of them does not include important functions. Moreover, these apps lack the perceived additional benefit by the user and the ease of use, important factors for acceptance of mHealth. Studies of diabetes apps regarding usability and accessibility have shown moderate results. Beside improvements of apps usability, the future of diabetes mHealth lies probably in personalized education and self-management with the help of decision support systems. At the same time, work on artificial pancreas is in progress and smartphone could be used as user interface

Contextual barriers to mobile health technology in African countries: a perspective piece

On a global scale, healthcare practitioners are now beginning to move from traditional desktop-based computer technologies towards mobile computing environments[1]. Consequently, such environments have received immense attention from both academia and industry, in order to explore these promising opportunities, apparent limitations, and implications for both theory and practice[2]. The application of mobile IT within a medical context, referred to as mobile health or mHealth, has revolutionised the delivery of healthcare services as mobile technologies offer the potential of retrieving, modifying and entering patient-related data/information at the point-of-care. As a component of the larger health informatics domain mHealth may be referred as all portable computing devices (e.g. mobile phones, mobile clinical assistants and medical sensors) used in a healthcare context to support the delivery of healthcare services

"Call a Teenager… That's What I Do!" - Grandchildren Help Older Adults Use New Technologies: Qualitative Study.

BackgroundAlthough family technical support seems intuitive, there is very little research exploring this topic.ObjectiveThe objective of this study was to conduct a subanalysis of data collected from a large-scale qualitative project regarding older adults' experiences in using health information technology. Specifically, the subanalysis explored older adults' experiences with technology support from family members to inform strategies for promoting older adults' engagement with new health technologies. Although the primary analysis of the original study was theoretically driven, this paper reports results from an inductive, open-coding analysis.MethodsThis is a subanalysis of a major code identified unexpectedly from a qualitative study investigating older adults' use experience of a widespread health technology, the patient portal. A total of 24 older patients (≥65 years) with multiple chronic conditions (Charlson Comorbidity Index &gt;2) participated in focus groups conducted at the patients' primary clinic. While conducting the primary theoretically driven analysis, coders utilized an open-coding approach to ensure important ideas not reflected in the theoretical code book were captured. Open coding resulted in 1 code: family support. This subanalysis further categorized family support by who provided tech support, how tech support was offered, and the opinions of older participants about receiving family tech support.ResultsThe participants were not specifically asked about family support, yet themes around family assistance and encouragement for technology emerged from every focus group. Participants repeatedly mentioned that they called their grandchildren and adult children if they needed help with technology. Participants also reported that family members experienced difficulty when teaching technology use. Family members struggled to explain simple technology tasks and were frustrated by the slow teaching process.ConclusionsThe results suggest that older adults ask their family members, particularly grandchildren, to support them in the use of new technologies. However, family may experience difficulties in providing this support. Older adults will be increasingly expected to use health technologies, and family members may help with tech support. Providers and health systems should consider potential family support and engagement strategies to foster adoption and use among older patients

A smartphone-based health care chatbot to promote self-management of chronic pain (SELMA) : pilot randomized controlled trial

Background: Ongoing pain is one of the most common diseases and has major physical, psychological, social, and economic impacts. A mobile health intervention utilizing a fully automated text-based health care chatbot (TBHC) may offer an innovative way not only to deliver coping strategies and psychoeducation for pain management but also to build a working alliance between a participant and the TBHC. Objective: The objectives of this study are twofold: (1) to describe the design and implementation to promote the chatbot painSELfMAnagement (SELMA), a 2-month smartphone-based cognitive behavior therapy (CBT) TBHC intervention for pain self-management in patients with ongoing or cyclic pain, and (2) to present findings from a pilot randomized controlled trial, in which effectiveness, influence of intention to change behavior, pain duration, working alliance, acceptance, and adherence were evaluated. Methods: Participants were recruited online and in collaboration with pain experts, and were randomized to interact with SELMA for 8 weeks either every day or every other day concerning CBT-based pain management (n=59), or weekly concerning content not related to pain management (n=43). Pain-related impairment (primary outcome), general well-being, pain intensity, and the bond scale of working alliance were measured at baseline and postintervention. Intention to change behavior and pain duration were measured at baseline only, and acceptance postintervention was assessed via self-reporting instruments. Adherence was assessed via usage data. Results: From May 2018 to August 2018, 311 adults downloaded the SELMA app, 102 of whom consented to participate and met the inclusion criteria. The average age of the women (88/102, 86.4%) and men (14/102, 13.6%) participating was 43.7 (SD 12.7) years. Baseline group comparison did not differ with respect to any demographic or clinical variable. The intervention group reported no significant change in pain-related impairment (P=.68) compared to the control group postintervention. The intention to change behavior was positively related to pain-related impairment (P=.01) and pain intensity (P=.01). Working alliance with the TBHC SELMA was comparable to that obtained in guided internet therapies with human coaches. Participants enjoyed using the app, perceiving it as useful and easy to use. Participants of the intervention group replied with an average answer ratio of 0.71 (SD 0.20) to 200 (SD 58.45) conversations initiated by SELMA. Participants’ comments revealed an appreciation of the empathic and responsible interaction with the TBHC SELMA. A main criticism was that there was no option to enter free text for the patients’ own comments. Conclusions: SELMA is feasible, as revealed mainly by positive feedback and valuable suggestions for future revisions. For example, the participants’ intention to change behavior or a more homogenous sample (eg, with a specific type of chronic pain) should be considered in further tailoring of SELMA