An evaluation of mHealth adoption and health self-management in emerging adulthood (original) (raw)
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ADOPTION OF M-HEALTH APPLICATIONS FOR PROMOTING HEALTHIER LIFE
Deaths related with Non-Communicable Diseases (NCDs) have recently increases, similarly, development of mobile technologies have paved a way in which Smartphone users can use m-health applications to address high level of sedentary behaviours that relates to negative health consequences. Therefore, this study was conducted to examine factors which may influence generation Y who are Smartphone users to adopt m-health applications in controlling NCDs in Tanzania. TAM theory was extended with awareness, trust and perceived cost to develop a research conceptual model of this study. Google form was used to developed online questionnaire which was distributed through social media platforms to respondents. Furthermore, other questionnaires were physically administered using snowball sampling through Drop-off / Pickup method. A total of 396 valid and reliable questionnaires were received and used for data analysis. IBM-SPSS software was used to analyze the hypothesized relationships. Findings show that trust, perceived usefulness and perceived ease-of-use have significant influences on behaviour intention to adopt m-health applications in controlling NCDs. Nevertheless, awareness and perceived cost were found to have no significant effects on behaviour intention to adopt m-health applications. The study has provided theoretical and practical implications for researchers, m-health application owners and developers.
The Adoption of Mobile Health Applications Among University Students in Health Colleges
Journal of Multidisciplinary Healthcare
Background: Studies documented the important role of health-care professionals in encouraging their patients to adopt and use mobile health applications. Health-care professionals who use and believe in mobile health applications are more likely to encourage and empower their patients to use it. Likewise, students in health-profession related specialties who use mobile health applications are more likely to continue to do so when they join the workforce. Objective: The objective of this study was to examine the rate of adoption of mobile health applications in students in health colleges, identify the types of health applications used by health students, and identify the barriers that prevent the use of mobile health applications. Methods: A descriptive cross-sectional study was conducted among university students. Students were invited to participate in a self-administrated survey from five health colleges. The survey included questions about the pattern of utilization, the purpose of the healthrelated application being used, and the barriers to adoption. The analysis included descriptive statistics using SPSS. Results: A total of 383 students participated in this study. The within gender comparison showed that more female students were using health-related applications 59.8% compared to male students 49%. The most frequent purpose of using mobile health applications were tracking physical activities 72.5% followed by counting calories intake 44%. We also identified the frequency and duration of use as well as the factors which may impact students intention to use. Conclusion: Fitness and dietary related applications were more common which might be explained by the fact that we only included a younger age group. The reported levels of perceived usefulness, accessibility and ease of use suggest that students will continue to use it.
Current and Emerging mHealth Technologies : Adoption, Implementation, and Use
2018
Part I - Behavioral Change in mHealth -- Use of Persuasion Strategies in Mobile Health Applications -- Behavior Change Techniques Used in Mobile Applications Targeting Physical Activity: A Systematic Review -- Exploring Intention on Continuous Use of Mobile Health Applications Designed by Persuasive Technology: “Adimsayar” Case Study -- Part II - Monitoring and tracking with mHealth -- Mobile Health Integration in Pregnancy -- Utilizing M-Health Applications in Emergency Medical Services of Turkey -- User Adoption and Evaluation of Mobile Health Applications: The Case for Physical Activity Monitoring -- Unintended Users, Uses, and Consequences of Mobile Weight Loss Apps: Using Eating Disorders as a Case Study -- Part III - mHealth use and adoption in healthcare delivery -- Understanding Cross-Cultural Requirements in m-Health Design: Findings of a Usability Study of Indian Health Professionals -- Intention vs. Perception: Understanding the Differences in Physi-cians’ Attitudes towar...
Potential adoption of mHealth applications to induce healthy lifestyles among UKZN (PMB) students
2020
Over the years, the World Health Organization has reported an increasing number of young people affected by Non-Communicable Diseases (NCDs). There is also evidence that there is an increased prevalence of NCDs amongst the youth in South Africa. This surge in NCDs amongst the youth is often related to unhealthy lifestyles. It is also known that the rate of smartphone adoption among the youth in South Africa is high. This high rate of smartphone adoption presents an opportunity to devise mobile applications-driven interventions to induce healthy lifestyles amongst the youth. It is in this context that this study examined the potential adoption of mobile health applications to promote healthy lifestyles, that is, to monitor one's diet and physical exercise amongst the youth in South Africa. The study adopted the Unified Theory of Acceptance and Use of Technology (UTAUT) to explain the youth's intention to adopt mobile health applications that help induce healthy lifestyles. Data was collected from a convenient sample of 320 students registered at the University of KwaZulu-Natal, Pietermaritzburg campus using a survey questionnaire. The descriptive analysis was conducted to assess and understand the perceptions and awareness of students pertaining to the use of mobile health applications. The Partial Least Structured Equation Model was used to assess the proposed model for the adoption of mobile health applications that promote healthy lifestyles. The results revealed that effort expectancy (EE), performance expectancy (PE) and social influence (SI), and facilitating conditions are the factors that influence the adoption of mobile health applications that help to induce healthy lifestyles. In addition, the study found that there is limited awareness of mobile health applications among UKZN (PMB) students. The study recommended that there is a need for interventions to raise awareness on the use of mHealth applications that help induce healthy lifestyles amongst the youth. Furthermore, any interventions that seek to promote healthy lifestyles amongst the youth should consider the identified factors that significantly influence the adoption of mHealth applications that help induce healthy lifestyles.
Acceptability of mHealth Technology for Self-Monitoring Eating and Activity among Rural Men
Public Health Nursing, 2016
Objective: To examine rural mens use and perceptions of mobile and wireless devices to self-monitor eating and physical activity (mHealth). Design and Sample: Men in this 3-week pilot study used FitBit One ® to log daily food intake and monitor activity. A companion application (app) allowed activity monitoring of fellow participants. Health-related text messages were received 1-3 times daily. A purposive sample of 12 rural men (ages 40-67) was recruited by community leaders. Measures: (1) baseline heart rate, blood pressure, and BMI, (2) FitBit One ® usage, (3) investigator-generated surveys on acceptability of mHealth, and (4) focus group on experience with mHealth. Results: Men were overweight (n = 3) or obese (n = 9) and 9 of 12 were hypertensive. Nine of twelve wore FitBit One ® all 21 days. Eleven of 12 men logged food, with 9 of 12 doing this at least 15 of 21 days. Self-monitoring and daily text messaging increased awareness of energy intake and output. Companion apps food log needed targeting for rural foods. Rotating seasons (occupational, religious, recreational) and weak cellular signals created contextual barriers to self-monitoring eating and activity. Conclusions: FitBit One ® and text messaging were perceived as useful among the rural men, while the companion apps require adaptation to reflect dietary norms.
E-Health Telecommunication Systems and Networks, 2019
This study aims to explore the Sultan Qaboos University undergraduate stu-dents' perceptions and attitudes towards using m-Health app and its impact on improving their sports and health habits. Triangulation was used by applying a questionnaire and focus groups interviews to answer the research questions. The survey sample was (20) male and female students from four science and humanities colleges. Both quantitative and qualitative findings unequivocally reveal the students' positive perceptions and attitudes toward the use of the mobile health app. No statistically significant differences were found between the responses of either males or females, or the science and humanitarian colleges of the sample students. In light of these findings, the study set forth its conclusions and recommendations.
mHealth adoption for the self-management of type 2 diabetes
2019
This research examines the adoption of mobile health (mHealth) to facilitate the self- management of type 2 diabetes. mHealth encompasses the practice of supporting healthcare through the utilisation of mobile devices, such as smartphones and tablets. Previous research has shown that mHealth in various forms can facilitate self- management behaviour and improve health outcomes for individuals diagnosed with type 2 diabetes. However, less is understood about why some people are more predisposed to utilising mHealth for self-management than others. Successful and sustained mHealth engagement can be very difficult for some individuals to achieve, even though they may be motivated to utilise the functionality of mobile devices for other activities in their everyday lives. Despite the recent and rapid evolution of mHealth, academic research into a theoretical understanding of the discipline has been limited, with most studies based on clinical trials and pilot studies of the technology itself. This study contributes to that research, by identifying factors that augment the adoption of mHealth by individuals diagnosed with type 2 diabetes. Because of the paucity of research in this specific field, an inductive approach begins the investigation. This is augmented by a quantitative examination that triangulates and expands upon the qualitative findings. The mixed methodology examines whether individuals that have been diagnosed with type 2 diabetes, and who use mHealth to facilitate their self-management, share common characteristics in terms of behaviours or self-discipline that may predict engagement. Seventeen purposively sampled semi- structured interviews initiated the investigation, and this inductive approach identified theories of self-efficacy and multidimensional health locus of control (MHLOC) that were important components of effective self-management and successful mHealth adoption. The quantitative phase of the mixed methods design comprised a convenience sample of 382 self-administered online surveys, which were employed to triangulate the qualitative findings and provide a deeper understanding of how individuals adjust to their health condition by utilising mHealth technology. Statistical analysis revealed a significant positive correlation between self-efficacy and mHealth adoption (p<.01), however, a relationship between MHLOC and mHealth utilisation was not supported by the quantitative results. Online health information seeking behaviour (OHISB) was widespread among the qualitative participants that adopted mHealth to facilitate their self-management. This finding was also confirmed by the quantitative analysis, which recorded significant correlations between OHISB and successful mHealth engagement (p<.01). While digital literacy and access to the internet are clearly essential elements of mHealth adoption, they did not prove sufficient in isolation to trigger engagement with mobile devices to enhance health outcomes. However, frequency of mobile device use was highly correlated to mHealth adoption (p<.01). This research project found three significant predictors of mHealth adoption for the self-management of type 2 diabetes. Individuals with good diabetes management self- efficacy, those who frequently use mobile devices in their everyday lives, and those who search the internet for information that is relevant to their own health status, are more likely to adopt mHealth technology to facilitate their diabetes self-management behaviours. Potential limitations to this research include the fact that all 17 of the qualitative participants were recruited from the same GP superclinic in Canberra. It also became clear during the research process that comorbidities were a major issue for some of the participants. As type 2 diabetes is a condition that is usually diagnosed later in life, comorbidities are a recognised complication of the condition. Because of this, and the fact that diabetes varies in severity for individuals diagnosed with the disease, each individual’s self-management priorities are varied, and people with type 2 diabetes should not be considered a homogenous group who collectively face identical challenges with the same solutions. This study is based on individuals’ own accounts of their illness, and no clinical or biometric data was used to establish outcomes of self- management behaviours.
The global burden of non-communicable diseases requires multi-facetted, preventive and curative interventions. The diseases are no longer attributed to " old age " but are increasingly becoming prevalent amongst young people. Hence, there is a need for preventive and curative interventions that are particularly appealing to young people. The wide adoption of mobile technologies amongst the youth, especially in developing countries, makes mobile health interventions appealing to the youth. Thus, self-healthcare monitoring applications could be widely adopted by the youth, if designed in a way that is appealing to them. It is in this context that this paper investigates factors that could influence the use of self-healthcare monitoring mobile applications by the South African youth. From a sample of 280 students conveniently sampled from an institution of higher learning in South Africa, findings reveal that factors that motivate the use of mobile self-healthcare applications include results demonstrability, performance expectancy factors, savings, social aspects, awareness, connectivity, accessibility, ease of use, privacy, user satisfaction and affordability. Hence, this paper advocates for a multi-stakeholder approach to ensure that these factors are taken into consideration during the design and implementation of such mHealth applications.
The Influence of the Smartphone User’s Characteristics on the Intention to Use of M-Health
IJASOS- International E-journal of Advances in Social Sciences, 2016
M-Health is a new technology which has evolved from the unprecedented spread of mobile technologies as well as advancements in their innovative application to address health priorities. Implementing mobile technology will promote healthcare services with regards to improvement in accessibility, effectiveness, and affordability. This technology is growing rapidly since it was introduced 20 years ago. There are over 7 billion mobile cellular subscribers worldwide and 1.48 million of these are Malaysian subscribers. The penetration of mobile phone networks in Malaysia has transformed the way health services and information are accessed, delivered, and managed. While this technology is still new, there is a scarcity of research material on the acceptance of M-Health in Malaysia.This study proposes and empirically tests the smartphone users' characteristics such as users' sex, age, level of education and health condition as important factors that affected the adoption of new M-Health systems. To fill in this gap, this study is to explore the relationship between the smartphone users' characteristics, with the intention of using M-Health. This study has recruited 80 smartphone users in Sarawak through random sampling using a survey method. Data were analysed using Statistical Package of Social Sciences (SPSS) Version 17, and various statistical techniques were used such as descriptive analysis, T-Test and One-way ANOVA. Findings indicate that there are no significant differences between sex, age, level of education and having health problems were there was an intention to use M-Health. In conclusion, the acceptance of M-Health is not dependent on the users' characteristics, hence it is important for health technologies and mobile companies to handle different aspects in the planning and development of healthcare applications for a market strategy.
A Review of Factors that Increases or Reduces the Adoption of mHealth
International Journal of Research and Scientific Innovation, 2019
Introduction: In the last decade, the world has witnessed the proliferation of mobile telephony across various countries be it developed or developing. This increase is in part due to expansion and the availability of connectivity. Further, there has been a significant increase in the number of mobile phone manufacturers. This has reduced the cost of mobile devices. We can take advantage of technology so as to be able to improve access to healthcare and health information, and to also improve the management of medical and health information as well as access to the latest medical knowledge for healthcare workers. Methods: Three (3) electronic databases and journals (BMC Medical Informatics and Decision Making, Elsevier and Google Scholar) were searched with search items including mHealth, Telemedicine, Mobile Health, e-Health, Sub-Saharan Africa, developing countries, low resource countries, behaviour change, adoption of health messages and mobile health solutions. The design focused specifically on empirical research evaluating the effectiveness and the factors influencing the acceptance of mHealth interventions in low income countries not older than 5 years and published in English. Results: From the search, it was noticed that the qualitative design method was mostly used by the researchers. They found that the extended Unified Theory of Acceptance and Use of Technology (UTAUT) is a good predictive model of general people's intention to use mHealth. Trust, perceived usefulness as well as perceived ease of use have been identified to positively correlated with adoption intention while privacy and performance risks negatively correlated with trust and adoption intention toward mHealth services. The researchers also found that personal innovativeness in Information technology has less significant effect on mHealth adoption. cost is also identified as an important determining factor for mHealth app adoption. The also found that perceived reliability has a significant impact on the adoption of mHealth. Conclusion: This synthesis paper gives an indication that there still exists some gaps in knowledge in the area of mHealth adoption. None of the papers have specifically focused on the factors that increases or reduces adoption. The papers however provide a set of key guiding elements that paves way for further research on factors that increases or reduces the adoption of mHealth in specific contexts.