mHealth Adoption Research Articles

Delivering a Drug Information App to Underserved Communities: A User-Centered Design Approach

ABSTRACT Two-thirds of the Sub-Saharan African population is rural and faces significant challenges accessing healthcare facilities and information. The mobile phone offers feasible opportunities to improve healthcare access to underserved communities. The study aims to develop an app for communities with limited access to health information. Design Science Research Methodology (DSRM) was adopted. A three-cycle DSRM guided the development of a mHealth app using a user-centered design process involving potential users, from conceptualizing the idea to prototype evaluation. The methodology allows the researcher to empathize with the participants, consider their contextual and environmental circumstances and understand their needs better. The prototype provides access to information about known drugs, their known uses, side effects, organs affected, or the human system they affect. A total of 206 participants evaluated the app for its usefulness, efficiency, error handling, memorability, learnability, and design. Participants concluded that the app was usable, easy to learn, error-free, and useful through a mean score of 3.95 and Cronbach’s alpha coefficient range of 0.75 to 0.85. Policymakers and researchers may find the results helpful in improving mHealth adoption. Integrating the app with the broader e-health systems will offer more convenience and efficiency, as healthcare systems in Africa are largely manual.

Four "E"s to speed adoption of mHealth and promote cardiovascular behavioral medicine.

Mobile health, also referred to as mHealth, is defined by the World Health Organization as the delivery of medical practice supported by mobile devices including smartphones, tablets, virtual assistants, and other wireless devices. Systematic reviews and meta-analyses confirm the effectiveness of mHealth tools at improving patient adherence to various self-care activities with downstream beneficial effects on blood pressure control and reduced health services utilization. Yet the clinical application of mHealth to cardiovascular behavioral medicine has been limited. Given high rates of smartphone ownership in the U.S., it is now possible, at least theoretically, to utilize mHealth on both a population-wide and highly personalized basis to prevent, diagnose, treat, and monitor response to therapy for a broad variety of cardiovascular behavioral medicine conditions. In keeping with the theme of this special issue of Health Psychology, we highlight the "4E" barriers of evidence, eminence, electronic health record, and economics that must be addressed to speed adoption of mHealth to promote cardiovascular behavioral medicine and improve health. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The Promise of mHealth for Chronic Disease Management Under Different Payment Systems

Problem definition: Rapid innovations in technology have created opportunities for different modes of healthcare delivery including digital services provided via mobile applications (mHealth). mHealth technology has the potential to provide efficient, effective, and patient-centered healthcare to manage chronic conditions. However, the economics associated with the adoption and integration of mHealth into the care delivery process is not well understood. In a chronic care clinical practice setting, we investigate fee-for-service (FFS) and capitation payment systems, and explore their performance in a traditional office-visit mode and in a mHealth-adopted mode. We identify conditions under which it is preferable to switch to an mHealth-based practice from an office visit-based practice. Methodology/results: We use an analytical model to track the progression of a chronic disease and formulate an optimization problem in which the clinic decides the time between scheduled visits and patient panel size. We consider many patient-doctor interaction factors including the risk-index of patients, the cost of being sick, and the effectiveness of treatment. We measure the performance based on four different criteria: physician net revenue, physician panel size, total patient utility, and payor net revenue. Although patients may find mHealth mode to be very beneficial, physicians under an FFS system may only adopt mHealth for moderately risky patients but for neither low-risk nor high-risk patients. Capitation clinics are likely to adopt mHealth (higher net revenue) even if the technology is moderately effective. Importantly, mHealth is preferred by patients (higher total utility) and policy makers (greater coverage) when the clinic serves moderate-risk or high-risk patients. Managerial implications: Chronic conditions need continuous care management and use of mHealth has been very promising. However, adoption of mHealth by healthcare providers has been very slow. Our research explores payment systems, physician incentives, and optimal conditions for mHealth to achieve its full potential. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1143 .

Adoption of mobile health services using the unified theory of acceptance and use of technology model: Self-efficacy and privacy concerns.

Mobile health (mHealth) services have been widely used in medical services and health management through mobile devices and multiple channels, such as smartphones, wearable equipment, healthcare applications (Apps), and medical platforms. However, the number of the users who are currently receiving the mHealth services is small. In China, more than 70% of internet users have never used mHealth services. Such imbalanced situation could be attributed to users’ traditional concept of medical treatment, psychological factors (such as low self-efficacy) and privacy concerns. The purpose of this study is to explore the direct and indirect effects of mHealth users’ self-efficacy and privacy concerns on their intention to adopt mHealth services, providing guidelines for mHealth service providers to enhance users’ intention of adoption. A questionnaire was designed by the research team and 386 valid responses were collected from domestic participants in China. Based on the unified theory of acceptance and use of technology (UTAUT) model, a research model integrated self-efficacy and privacy concerns was constructed to investigate their effects on users’ intention to adopt mobile mHealth services. The results show that self-efficacy could facilitate users’ intention to adopt mHealth services, and had a significantly positive effect on perceived ubiquity, effort expectancy, performance expectancy and subjective norm. This study verifies the direct and indirect effects of self-efficacy and privacy concerns on users’ intention to adopt mHealth services, providing a different perspective for studying mHealth adoption behavior. The findings could provide guidelines for mHealth service providers to improve their service quality and enhance users’ intention of adoption.

Generation Z & Y preferences of user acceptance of mobile health recommender systems

Mobile health (mHealth) applications provide equal reach to health care services at an affordable cost. mHealth can play a major role in enhancing the quality and access of health services in developing countries, still the adoption of mHealth in these countries is very low. On the other hand, younger generations have great dependence on mobile devices and have high potential to adopt mobile applications. The aim of this study is to investigate factors that affect user acceptance of mobile health recommender system among Generations Z (between 10 and 25 years old) & Y (between 26 and 40 years old). The research adopts Technology Acceptance Model (TAM3) constructs and extends them to include constructs related to Trust and Reputation, as mHealth is prone to uncertainty and vulnerability. Based on 136 valid responses collected from a survey, structural equation modelling SEM was employed to examine the research questions. The data were analyzed using the Partial Least Squares (PLS) method. The results suggested that for generations Z & Y, Perceived Usefulness has a significant positive effect on behavioral intention to use mobile health recommender system. This direct relationship is affected by user characteristics, namely gender and experience using technology. Perceived Ease of Use and Trust in mHealth applications, significantly increase the Perceived Usefulness. The results reveal the importance of perceived reputation in building Trust in mHealth applications, and it is found that Output Quality has a positive influence on Reputation. Computer anxiety has a negative effect on Perceived Ease of Use. These findings provide a basis for improving the understanding toward factors affecting mHealth applications in developing countries. The study provides number of practical design recommendations for designers and service providers.

Challenges in the Adoption of eHealth and mHealth for Adult Mental Health Management-Evidence from Romania.

New methods of connecting physicians and patients have arisen. Technology is playing a crucial role and the concept of hybrid doctor–patient relationship is considered relevant for the competitive health management system. At the same time, the need for knowledge about implementing policies and best practices into the system is highly demanding. Digital tools, such as eHealth or mHealth can improve the traditional approach to consulting patients without requiring face-to-face interaction. However, due to the discussion surrounding the adoption of these technologies, the authors performed the study with two marketing research methods. The first is qualitative and is related to the opinions, attitudes, and beliefs of Romanian experts on the use of eHealth and mHealth for the prevention, detection, and treatment of mild mental disorders. The second method quantifies the opinions, attitudes, and behaviours of Romanian adults on their openness to adopt new technologies for mental health management. The main findings of the research highlight three factors that can increase the chances of adults using technology for health-related needs: (1) accessibility (2) data security, and (3) content. These are the main aspects that influence the well-being of both young and older adults, who both need support regarding mental health management.

Factors influencing the elderly’s adoption of mHealth: an empirical study using extended UTAUT2 model

BackgroundDespite the high usage of mobile phones in daily life in developing countries like Bangladesh, the adoption and usage of mHealth services have been significantly low among the elderly population. When searching previous studies, the researchers have found that no studies have empirically investigated whether the quality of life and service quality are significant for mHealth adoption by elderlies in Bangladesh. Hence, this study aimed to extend the Unified Theory of Acceptance and Use of Technology by adding service quality and the quality of life to empirically find the key factors that influence elderlies’ intention to adopt and use mHealth services in Bangladesh.MethodsA face-to-face structured questionnaire survey method was used to collect data from 493 elderlies (aged 60 years and above) in Bangladesh. The data were analyzed with the Structural Equations Modelling (SEM) and Fuzzy Set Qualitative Comparative Analysis (fsQCA).ResultsSEM results suggested that Social Influence, Hedonic Motivation, Price Value, Habit, and Service Quality had significant impact (p < 0.05) on the elderlies’ behavioral intention to adopt mHealth services. Service Quality, Quality of Life, and elderlies’ Habit were found significant in explaining the Use Behavior of mHealth services. Quality of Life did not show significant (p > 0.05) effect on Behavioral Intention, which is inconsistent with existing literature. In addition, fsQCA findings suggest how the intensity of the influencers may contribute to high versus low m-health behavioral outcomes.ConclusionsThe findings have significant implications for theory, practice and future research as explained in the paper. The originality of this study is the integration of quality of life and service quality into UTUAT2 to explain the users’ behavioural intention and use behaviour. Overall, the findings may contribute to shaping appropriate policies for designing and implementing mHealth services effectively for elderlies in developing countries.

The Utilization of Mobile Phones in Health Information Seeking in Rural China

Abstract This study aims to investigate the mobile phone utilization for health information seeking among adults who are 45 years or older in rural areas in China. Using Andersen’s Behavioral Model (BM), we examine the factors that influence the target group’s behaviors of online health information seeking and contacting healthcare providers via mobile phones. By using stratified sampling method, we conducted a survey among 607 rural adults who were 45 years or older from six villages in Jiangsu Province, China. The results reveal that participants can be excluded from such services for not using a smartphone. eHealth information literacy could serve as a strong influential factor for online health information seeking and contacting healthcare providers via mobile phones. Participants with lower emotional wellbeing are more inclined to seek health information online via mobile phones. The results indicate variables such as utilization of technological devices and eHealth literacy should be included in Andersen’s model when investigating the utilization of mHealth services. This study contributes to the literature on mHealth adoption in rural areas in China and has implications for practitioners to tailor the mHealth service for socio-economically disadvantaged groups.

Understanding the Drivers of Ghanaian Citizens' Adoption Intentions of Mobile Health Services.

Mobile health (m-health) application development and diffusion in developing countries have always been a challenge; therefore, research that seeks to provide an elucidation of the drivers of m-Health adoption is vital. Mobile health information systems and applications can contribute to the delivery of a good healthcare system. This study examined the factors influencing citizens' adoption of mobile health services. The Technology Acceptance Model (TAM) was used as the research underpinning for this study, while the data gathered were analyzed with SmartPLS through the use of the structural equation modeling technique. The results showed that perceived usefulness and ease of use were both significant predictors of the behavioral intention to use and recommend the adoption of mobile health services. Also, perceived risk was negative but significant in predicting the intention to use and recommend adoption. Mobile self-efficacy was found to significantly determine the behavioral intention to use, intention to recommend, perceived usefulness, and perceived ease of use of mobile health services. Besides, word-of-mouth showed a positive impact on both the intention to use and recommend. Contrary to expectations, the intention to use had no significant impact on the recommendation intention. The theoretical and practical implications of these findings are thoroughly examined.

MHealth Adoption Among Older Chinese Adults: A Conceptual Model With Design Suggestions

Despite the potential benefits of mobile health (mHealth) services and applications, older adults face challenges and barriers to adopt mHealth. Published work on opportunities and challenges of mHealth for aging China has focused on trends in mHealth and its clinical applications, but not older adults’ perceptions or their processes of adoption. In this study, questionnaires and interviews were conducted to understand older adults’ perceptions and adoption of mHealth services in China. Conceptual models were generated using existing theories and refined using the findings of the user research. The proposed “pine tree model” illustrates the factors influencing Chinese older adults’ adoption of mHealth services and describes the process for better adoption. Based on the research, suggestions were proposed to promote mHealth adoption among older adults, which will inform better design of mHealth for the aging population in China.

Intrinsic Antecedents to mHealth Adoption Intention

Healthcare is not left behind in the technological era, where almost every industry uses technological advances to serve customers' needs and wants. Increasingly, patients and doctors are using modern technological infrastructure to deliver care services. This study focuses on the intrinsic factors that lead to provider adoption of mHealth. The study uses PLS-SEM and neural networks to build on UTAUT theory. Study collects data from 316 care providers practicing in government and private health canters, hospitals and clinic found that intrinsic factors like self-efficacy, personal innovativeness, and performance expectancy positively related to mHealth adoption by physicians, whereas technology anxiety negatively related to adoption behaviour. Effort expectancy is not significant, indicating that m-Health adoption is driven by usefulness and result rather than convenience. If the expertise is not easily available, the physician's best interest for the patient may often drive them to adopt m-Health.

Sociotechnical Factors Affecting Patients' Adoption of Mobile Health Tools: Systematic Literature Review and Narrative Synthesis.

BackgroundMobile health (mHealth) tools have emerged as a promising health care technology that may contribute to cost savings, better access to care, and enhanced clinical outcomes; however, it is important to ensure their acceptance and adoption to harness this potential. Patient adoption has been recognized as a key challenge that requires further exploration.ObjectiveThe aim of this review was to systematically investigate the literature to understand the factors affecting patients’ adoption of mHealth tools by considering sociotechnical factors (from technical, social, and health perspectives).MethodsA structured search was completed following the participants, intervention, comparators, and outcomes framework. We searched the MEDLINE, PubMed, Cochrane Library, and SAGE databases for studies published between January 2011 and July 2021 in the English language, yielding 5873 results, of which 147 studies met the inclusion criteria. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the Cochrane Handbook were followed to ensure a systematic process. Extracted data were analyzed using NVivo (QSR International), with thematic analysis and narrative synthesis of emergent themes.ResultsThe technical factors affecting patients’ adoption of mHealth tools were categorized into six key themes, which in turn were divided into 20 subthemes: usefulness, ease of use, data-related, monetary factors, technical issues, and user experience. Health-related factors were categorized into six key themes: the disease or health condition, the care team’s role, health consciousness and literacy, health behavior, relation to other therapies, integration into patient journey, and the patients’ insurance status. Social and personal factors were divided into three key clusters: demographic factors, personal characteristics, and social and cultural aspects; these were divided into 19 subthemes, highlighting the importance of considering these factors when addressing potential barriers to mHealth adoption and how to overcome them.ConclusionsThis review builds on the growing body of research that investigates patients’ adoption of mHealth services and highlights the complexity of the factors affecting adoption, including personal, social, technical, organizational, and health care aspects. We recommend a more patient-centered approach by ensuring the tools’ fit into the overall patient journey and treatment plan, emphasizing inclusive design, and warranting comprehensive patient education and support. Moreover, empowering and mobilizing clinicians and care teams, addressing ethical data management issues, and focusing on health care policies may facilitate adoption.

Re-Conceptualizing the Drivers Toward mHealth Adoption in a Least Developing Country: A Qualitative Exploration

Despite the recent proliferation of mHealth, the present research has not yet re-conceptualized on how mHealth can be used to promote healthcare over time. Researches have indicated that mHealth adoption and acceptance problems must be re-addressed to provide improved healthcare delivery. It is essential to explore the end-user centric factors for the complex implications process of mHealth before implementing it in practice. This research aims to re-conceptualize the contextual predictors of mHealth adoption in a least developing country. In-depth interviews with purposive and convenient sampling techniques were conducted with end-users ( n = 24) and healthcare professionals ( n = 15) in Bangladesh because qualitative research provides opportunities to explore end-user experiences and get detailed information about how they perceive user engagement in technology adoption. This study used grounded theory and thematic analysis to explain the state of mHealth adoption and to establish a theoretical basis for further investigations. Our findings indicate that traditional predictors for mHealth intention to use might not be enough. This study suggested four new predictors: “patients as a decision support unit,” “personal awareness building,” “health information exchange,” and “reward” to re-conceptualize the mHealth adoption. The study tried to clarify mHealth drivers from both end-users’ and healthcare professionals’ perspectives, which offered an alternative avenue that could provide the foundation for accepting mHealth widely.

EHealth and mHealth in Chronic Diseases-Identification of Barriers, Existing Solutions, and Promoters Based on a Survey of EU Stakeholders Involved in Regions4PerMed (H2020).

Background: In recent years, rapid population ageing has become a worldwide phenomenon. Both electronic health services (eHealth) and mobile health services (mHealth) are becoming important components of healthcare delivery. The market for mHealth is growing extremely fast. However, despite the increasing investment and interest in eHealth, several challenges still need to be overcome to enable broader and more systematic implementation of ICT in healthcare. Methods: This study presents data from the survey “Barriers and facilitators of Personalised Medicine implementation- qualitative study under Regions4PerMed (H2020) project”. In addition, this paper discusses the results of the conference, Health Technology in Connected & Integrated Care, held under the Horizon 2020 project and interregional coordination for a fast and deep uptake of personalised health (Regions4Permed) (July 2020—online conference). The above sections were preceded by an analysis of existing articles. Results: The data obtained from the surveys show that the main barriers to the adoption of eHealth and mHealth are the lack of skills of seniors, but also the lack of user-friendly technology and a simple user interface. Access to individual data while ensuring its security and the lack of digitisation of medical data are also serious issues. In addition, medical digital solutions are overly fragmented due to national legislations that deviate from the General Data Protection Regulation. Conclusions: By using technological solutions, it is possible to improve diagnosis and treatment decisions, and better adapt treatment and reduce its duration and cost. However, there are still barriers to the development of eHealth. Clear recommendations for implementation are needed to enable further development of personalised eHealth and mHealth solutions

Value of normative belief in intention to use workplace health promotion apps

BackgroundMobile applications (apps) have started to be used for workplace health promotion (WHP). However, the factors that lead to the usage of apps in the workplace from the end-user perspective remain unclear.MethodsTo investigate the research gap, the study evaluates a model for the adoption of WHP apps by combining the theory of planned behavior, the health belief model, and the technology acceptance model. A self-administered questionnaire with validated scales among 354 participants was used to evaluate the proposed model for WHP.ResultsAlthough the study indicated a limited overall model fit, interesting aspects were derived. In particular, the study demonstrated that normative belief (especially), perceived usefulness, and attitudinal belief play important roles in the intention to use WHP apps.ConclusionThe study is among the first to validate the theoretical models of mHealth adoption for WHP. Moreover, it shows that not only normative belief but also adjustment to several target groups is a necessary factor to be considered in the development and implementation of an app for WHP.

The development of an instrument to predict patients’ adoption of mHealth in the developing world

IntroductionThere are many tools for measuring patient's potential adoption of mHealth (i.e. mobile health) in the developed world, but none of these instruments provides a comprehensive means for measuring critical issues affecting the adoption of mHealth by patients in the developing world. The aim of this paper was to develop a valid and reliable assessment instrument for predicting mHealth adoption by patients in the developing world. MethodA Patients mHealth Technology Adoption Questionnaire (PmTAQ) was developed based on themes identified through a prior published structured literature review of factors affecting patients’ mHealth adoption in the developing world, from which eight constructs evolved. Face and content validity was confirmed by 15 mothers who had used mHealth (the Mobile Technology for Community Health (MoTeCH) service) for maternal care, and the findings were used to improve the instrument. To assess the validity and reliability of the instrument at least 64 mothers who used MoTeCH were randomly selected from each of nine clusters of health posts in one district in Ghana. The assessment instrument consisted of 39 items, categorised under eight components: Cost and ownership, user characteristics, language and literacy, infrastructure, collaboration and funding, governance, system utility, and intention to adopt. Exploratory and confirmatory factor analysis were performed. ResultsThe data from 585 mothers were analysed. Exploratory factor analysis showed the eigenvalue of all eight components to be significant (cumulative total greater than 1.0). Bartlett's test of sphericity was significant, the Kaiser-Meyer-Olkin value was 0.84 and the mean Cronbach's α value was 0.82 (range 0.81–0.83). The components were found to be valid. Confirmatory factor analysis showed that all indices for the measurement model were within acceptable limit leading to the use of structural equation modelling to show the causal relationship between components, resulting in the development of the mHealth Adoption Impact Model (mAIM). The mAIM shows a strong relationship between latent constructs for patients' mHealth adoption. ConclusionThe study presents an evidence-based, reliable and valid instrument and model for application in future research, policy development, and implementations related to patient mHealth adoption in the developing world.

Achieving the potential of mHealth in medicine requires challenging the ethos of care delivery.

Mobile Health (mHealth) interventions have received a mix of praise and excitement, as well as caution and even opposition over recent decades. While the rapid adoption of mHealth solutions due to the COVID-19 pandemic has weakened resistance to integrating these digital approaches into practice and generated renewed interest, the increased reliance on mHealth signals a need for optimizing development and implementation. Despite an historically innovation-resistant medical ethos, mHealth is becoming a normalized supplement to clinical practice, highlighting increased demand. Reaching the full potential of mHealth requires new thinking and investment. The current challenge to broaden mHealth adoption and to ensure equity in access may be overcoming a "design purgatory," where innovation fails to connect to practice. We recommend leveraging the opportunity presented by the COVID-19 pandemic to disrupt routine practice and with a new focus on theory-driven replicability of mHealth tools and strategies aimed at medical education and professional organizations.

The Mediating Influence of the Unified Theory of Acceptance and Use of Technology on the Relationship Between Internal Health Locus of Control and Mobile Health Adoption: Cross-sectional Study.

BackgroundMobile health (mHealth) as an innovative form of information and communications technology can efficiently deliver high-quality health care by enhancing communication and health management, reducing costs, and increasing access to health services. An individual’s internal health locus of control (HLOC) is found to be associated with the behavioral intent to adopt mHealth. However, little is known about the underlying mechanism of this association.ObjectiveThe primary objective of this study was to test the mediation influence of the Unified Theory of Acceptance and Use of Technology (UTAUT) on the relationship between internal HLOC and the behavioral intention to use mHealth.MethodsA total of 374 responses were collected from Malaysian adult users of mHealth, using convenience and snowball sampling methods. Partial least squares structural equation modeling was used to analyze the data. Data were collected for variables, including demographics, internal HLOC, and modified UTAUT constructs (ie, performance expectancy, effort expectancy, and social influence).ResultsThe results showed that there was no direct relationship between internal HLOC and the behavioral intention to use mHealth (β=−0.039, P=.32). The indirect relationship between internal HLOC and the intent to adopt mHealth was supported, indicating that the UTAUT constructs performance expectancy (β=0.104, P<.001), effort expectancy (β=0.056, P=.02), and social influence (β=0.057, P=.002) mediated this relationship. The results showed full mediation, with total variance explained at 47.2%.ConclusionsThis study developed an integrative model, where a health-related disposition (internal HLOC), mHealth-related beliefs (performance expectancy and effort expectancy), and normative pressure (social influence) were combined to explain the underlying mechanism of the behavioral intent to adopt mHealth. The results showed that the intention to adopt mHealth is mediated by the influence of UTAUT factors, while HLOC has no direct effect on adoption intention. The findings provide insights into augmenting mHealth adoption among the public by enhancing the perceived benefits of mHealth, helping design more effective and user-friendly mHealth tools, and capitalizing on social normative influence to adopt mHealth. This study utilized the constructs of the UTAUT model to determine the intention to use mHealth. Future research should focus on other health- and technology-related theories to ascertain other possible factors influencing the behavioral intent of mHealth adoption.

Adoption and Appropriateness of mHealth for Weight Management in the Real World: A Qualitative Investigation of Patient Perspectives.

BackgroundMobile health (mHealth) interventions for weight management can result in weight loss outcomes comparable to in-person treatments. However, there is little information on implementing these treatments in real-world settings.ObjectiveThis work aimed to answer two implementation research questions related to mHealth for weight management: (1) what are barriers and facilitators to mHealth adoption (initial use) and engagement (continued use)? and (2) what are patient beliefs about the appropriateness (ie, perceived fit, relevance, or compatibility) of mHealth for weight management?MethodsWe conducted semistructured interviews with patients with obesity at a single facility in an integrated health care system (the Veterans Health Administration). All participants had been referred to a new mHealth program, which included access to a live coach. We performed a rapid qualitative analysis of interviews to identify themes related to the adoption of, engagement with, and appropriateness of mHealth for weight management.ResultsWe interviewed 24 veterans, seven of whom used the mHealth program. Almost all participants were ≥45 years of age and two-thirds were White. Rapid analysis identified three themes: (1) coaching both facilitates and prevents mHealth adoption and engagement by promoting accountability but leading to guilt among those not meeting goals; (2) preferences regarding the mode of treatment delivery, usability, and treatment content were barriers to mHealth appropriateness and adoption, including preferences for in-person care and a dislike of self-monitoring; and (3) a single invitation was not sufficient to facilitate adoption of a new mHealth program. Themes were unrelated to participants’ age, race, or ethnicity.ConclusionsIn a study assessing real-world use of mHealth in a group of middle-aged and older adults, we found that—despite free access to mHealth with a live coach—most did not complete the registration process. Our findings suggest that implementing mHealth for weight management requires more than one information session. Findings also suggest that focusing on the coaching relationship and how users’ lives and goals change over time may be an important way to facilitate engagement and improved health. Most participants thought mHealth was appropriate for weight management, with some nevertheless preferring in-person care. Therefore, the best way to guarantee equitable care will be to ensure multiple routes to achieving the same behavioral health goals. Veterans Health Administration patients have the option of using mHealth for weight management, but can also attend group weight management programs or single-session nutrition classes or access fitness facilities. Health care policy does not allow such access for most people in the United States; however, expanded access to behavioral weight management is an important long-term goal to ensure health for all.

Personal goals in consumers' adoption of mHealth services

PurposemHealth 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.Design/methodology/approachStudy 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.FindingsStudy 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.Practical implicationsThe 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.Originality/valueThis 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.