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The goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships that exist amongst those obstacles.

Potential barriers and their interrelationships in their respective contexts have been uncovered. Using MICMAC analysis, the categorization of these barriers was done based on their degree of reliance and driving power (DP). Furthermore, an interpretive structural modeling (ISM) framework for the barriers to mHealth activities in India has been proposed.

The study explores a total of 15 factors that reduce the efficiency of mHealth adoption in India. The findings of the Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) investigation show that the economic situation of the government, concerns regarding the safety of intellectual technologies and privacy issues are the primary obstacles because of the significant driving power they have in mHealth applications.

Promoters of mHealth practices may be able to make better plans if they understand the social barriers and how they affect each other; this leads to easier adoption of these practices. The findings of this study might be helpful for governments of developing nations to produce standards relating to the deployment of mHealth; this will increase the efficiency with which it is adopted.

At this time, there is no comprehensive analysis of the factors that influence the adoption of mobile health care with social cognitive theory in developing nations like India. In addition, there is a lack of research in investigating how each of these elements affects the success of mHealth activities and how the others interact with them. Because developed nations learnt the value of mHealth practices during the recent pandemic, this study, by investigating the obstacles to the adoption of mHealth and their inter-relationships, makes an important addition to both theory and practice.

Today, the use of smart technologies in healthcare systems is experiencing exponential growth, and the future of healthcare is seemingly closely intertwined with such technologies. Thus, any exploration of the factors that influence human health and healthcare systems inevitably touches upon the subject of new technologies. This study aims to design a conceptual model to investigate the elements that affect individuals' openness to accepting and using mobile healthcare applications (mHealth apps) and their reciprocal effects.

After a brief review of the literature, the authors identify the influential factors in the acceptance of smart technologies in healthcare systems and present a conceptual model in this regard. Next, the authors analyze the factors and variables and the extent of their impact by a structural equation modeling (SEM) approach. The statistical population of this study consists of 421 individuals including the developers, consultants and users (i.e. patients) of mHealth apps. Data analysis was done on the statistical software SPSS v.26, while SEM was carried out using the partial least squares (PLS) method on the modeling software SmartPLS.

The results indicate that user, consultant and developer preferences have a positive and significant impact on time, quality of life, managing chronic conditions and cooperation, and these constructs (system performance) finally have a positive and significant impact on the acceptance of mobile healthcare technologies.

This paper shows that mHealth apps can have a remarkable role in the prevention and treatment of medical conditions, and it is strongly recommended that this technology be utilized in the studied region.

Using smart mobile devices, called mobile health (mHealth), facilitates providing health services, speeds up the process and reduces the costs and complications of direct services. Also, mHealth has many capabilities and applications in epidemic and pandemic outbreaks. This study aimed to systematically review the mHealth adoption researches in epidemic/pandemic outbreaks and provide some suggestions for future research for tackling for COVID-19.

The results produced in this study are based on the literature analysis of 36 articles on mHealth adoption. To find the relevant studies; searches were done in PubMed, Google, Web of Science and Scopus by related keywords during 2020–2022. After selecting the studies based on the inclusion and exclusion criteria, data were collected by using PRIZMA methods for systematically reviewing the literature.

Of the 727 retrieved studies, 36 studies related to mHealth services during the pandemic situation were included. This has been performed by collecting data including demographic details, methodological details, limitations and significance of relationships between the constructs from the available articles based on the mHealth services. All studies emphasized the positive effect of mHealth for usage in epidemic/pandemic outbreaks.

The main applications of mHealth for epidemic/pandemic outbreaks included public health aspects, data management, educational programs, diagnosis as well as treatment. mHealth is an appropriate method for encountering epidemic/pandemic outbreaks due to its extensive applications. In the pandemic outbreak of COVID-19, mHealth is one of the best choices to use in the patient-physician relationship as Tele-visits, using in fever coach, providing real-time information for healthcare providers, population monitoring and detecting the diseases based on data obtained from different locations. These findings will help the mHealth providers to design their services accordingly.

This study contributes to the researchers and academicians by providing relevant information regarding the mHealth during the COVID-19 pandemic. This is the first time initiative to explore the research questions and future research direction for the researchers during the COVID-19 outbreak. Based on this, we present a comprehensive and actionable research agenda and practical implications.

Mobile health (mHealth) service is an Australian Government initiative aiming to improve the quality of health-care services. However, little is known about Australian health consumers’ willingness to adopt mHealth. The purpose of this paper is to study the usage behavior of mHealth service users in Australia. While various factors may impact users’ willingness to accept mHealth, this research investigates factors influencing the mHealth adoption decisions of the Australian health-care consumers, and the moderating impact of demographic factors on the usage behavior (UB) of mHealth services which has been rarely addressed in an Australian setting in the past.

The theoretical framework is based on the technology acceptance model (TAM). Data were collected from residents of the Australian Capital Territory using a survey questionnaire and examined using partial least squares structural equation modeling.

The proposed mHealth usage model demonstrated a good fit and indicated that perceived usefulness, perceived ease of use, social influence, service quality and government influence are critical issues shaping mHealth UB. The moderation analysis revealed that users’ demographics, namely, gender, age and education are instrumental in broadening the understanding of UB of mHealth service in Australia.

The findings will inform health-care service providers about the critical importance of the key factors driving the usage of mHealth services. Health-care providers and relevant authorities can develop targeted communication strategies that maximize the acceptance of mHealth services. Furthermore, deeper understanding of users’ demographic profiles would enable health-care service providers to promote their services to the right clients.

The above findings on the factors and user demographics informing the usage of mHealth services have unique practical, contextual and theoretical implications.

The purpose of this study was to explore the factors affecting adoption of mHealth services among the older women in Bangladesh. As this portion of the total population is rapidly increasing in Dhaka City.

Using the technology acceptance model as a theoretical framework, this study introduces perceived reliability, price value and technology anxiety as new factor reflecting the user’s reliability, beliefs and monetary concerns in the acceptance of mHealth services. A structured survey was conducted to collect the required data from convenience sampling of 271 mHealth end users from Dhaka city in Bangladesh.

The study confirmed that perceived usefulness, perceived reliability; price value and technology anxiety had a significant impact on mHealth adoption. Ease of use did not influence on the adoption of mHealth services.

To the best of the authors’ knowledge, this study is one of the first to investigate the determinant of mHealth adoption among older Women. Moreover, the insights from this study could benefit mHealth services providers and policymakers in implementing more effective marketing strategies to increase the acceptability of mHealth services.

This paper aims that mobile health (mHealth) applications have emerged as a key tool to support public health. However, there are only a few studies examining the influences of health-related ascribes on continuance intention to use mHealth apps and how these influences are contingent on gender in the mHealth app using context.

This study takes the protection motivation theory as a theoretical framework to examine the ordered relationship between threat and coping appraisals and their impacts on continuance intention to use mHealth apps. In addition, this study further extends the literature on gender differences into the mHealth app's context to investigate the moderating role of gender. The suggested hypotheses are confirmed by a structural equation modeling approach and multigroup investigation employing survey data of 345 users of Spring Rain Doctor in China, a typical mHealth app.

The findings suggest that the impact of perceived disease threat on user's continuance intention is mediated entirely by coping appraisals. Furthermore, the three coping appraisals' impacts are contingent upon gender. Specifically, response efficacy is more crucial for male users in forecasting continuance intention, whereas self-efficacy and response cost have a more salient influence on continuance intention for female users.

This study examines the ordered influences of threat and coping appraisal, moderated by gender, on continuance intention on use mHealth apps. These findings could contribute to relevant theoretical and practical implications.

The COVID-19 pandemic has created a devastating effect on public health. As “social distance” is a highly effective preventive measure of this pandemic, close contact between a patient and physician is strongly discouraged where possible. Consequently, mobile health (mHealth) technology is taking its momentum to fulfil this gap. The purpose of this study is, therefore, to empirically investigate the moderating as well as the direct role of situational constraint and health consciousness in the unified theory of acceptance and use of technology (UTAUT) constructs to understand the adoption and use behavior of mHealth care services amid pandemic.

Data were collected from existing mHealth users using an online survey questionnaire in Bangladesh. SmartPLS 3.0 and SPSS 23.0 were used for partial least squares-structural equation modeling.

Situational constraint and health consciousness both have strong direct positive effects on both behavioral intention (in all models) and use behavior (in Models 2 and 3). Further, this study revealed that effort expectancy remains insignificant in both direct and interaction effects whereas social influence becomes insignificant in interaction effects from direct significant effect (Models 1 and 2). Besides, the study reported that the relationship between behavioral intention and use behavior is moderated by situational constraint.

To the best of the authors’ knowledge, this study is the first in terms of mHealth empirical investigation considering the current pandemic situation. The incorporation of the situational constraint and health consciousness into the UTAUT model provides a holistic framework to understand the influence of the adoption and use behavior of mHealth amid pandemic.

The purpose of this paper is threefold: first, to draw health managers’, clinicians’, entrepreneurs’ and mobile apps designers’ attention toward new mobile health applications (mHealth apps); second, to define mHealth apps design characteristics intended for doctors; and third, to highlight how mHealth apps can be designed using quality function deployment/house of quality (QFD/HOQ) techniques from doctors’ perspectives.

Data were collected through a survey and in-depth interviews with doctors to understand their needs and attitudes toward mHealth apps. Analytic hierarchy process, QFD and HOQ methods were used to analyze data.

Doctors agreed that mHealth apps provide them with the tools to improve their service and to become more efficient. Once the 12 doctors’ wants were collected, they were prioritized according to their significance and used for mHealth apps development. Eight technical characteristics that cater to doctors’ expectations were sorted. The authors suggest that mHealth app designers need to provide design requirements recommended by health personnel for a higher satisfaction level.

Healthcare managers are focusing on increasing their efficiency, patient satisfaction and care quality, and decreasing costs. For these purposes, mHealth revolution and mHealth apps have high potential for improving doctor effectiveness and healthcare quality. This study is among the first to: define Turkish doctors’ wants from mHealth apps; elaborate the app’s technical characteristics; and increase design quality, which is implied in improving app design. This research makes a significant contribution to define doctors’ wants from mHealth apps, to elaborate their technical characteristics and to increase mHealth apps design quality using QFD.

mHealth services are effective and cost efficient, yet wide-scale adoption of these services by consumers has yet to be achieved, constraining their public health benefit. Further investigation of non-technological determinants of mHealth adoption is needed; specifically, the role of consumers' goals has received scant attention and forms the research focus.

Study 1 comprised 20 interviews with participants who possess a health goal, with the data analysed using an abductive reasoning approach. Study 2 was a 15-min online survey (n = 653), with the data analysed using multi-group structural equation modelling.

Study 1 identified several antecedents to the desirability and feasibility of consumers' health goals, which influence their desire to use mHealth services. Study 2 shows significant differences in the determinants of mHealth service acceptance depending on whether consumers set concrete as opposed to abstract goals, but social acceptance of mHealth services of these services is important for both groups.

The findings suggest emphasising the importance of health goals to achieving other consumer goals (e.g. work or travel goals), the efficacy of mHealth services relative to other service alternatives for achieving those health goals, and the social acceptance of mHealth services to increase their uptake.

This study is the first to use construal-level theory to improve understanding of the role of consumers' goals in the adoption of mHealth services. By identifying the antecedents to goal desirability and feasibility, it also broadens the model of goal-directed behaviour.

The emergence of mobile health (mHealth) products has created a capability of monitoring and managing the health of patients with chronic diseases. These mHealth technologies would not be beneficial unless they are adopted and used by their target users. This study identifies key factors affecting the usage of mHealth apps based on user usage data collected from an mHealth app.

Using a dataset collected from an mHealth app named mPower, developed for patients with Parkinson's disease (PD), this paper investigated the effects of disease diagnosis, disease progression and mHealth app difficulty level on app usage, while controlling for user information. App usage is measured by five different activity counts of the app.

The results across five measures of mHealth app usage vary slightly. On average, previous professional diagnosis and high user performance scores encourage user participation and engagement, while disease progression hinders app usage.

The findings potentially provide insights into better design and promotion of mHealth products and improve the capability of health management of patients with chronic diseases.

Studies on the mHealth app usage are critical but sparse because large-scale and reliable mHealth app usage data are limited. Unlike earlier works based solely on survey data, this research used a large user usage data collected from an mHealth app to study key factors affecting app usage. The methods presented in this study can serve as a pioneering work for the design and promotion of mHealth technologies.