scholarly journals Technology Acceptance in Mobile Health: Scoping Review of Definitions, Models, and Measurement

10.2196/17256 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e17256 ◽  
Author(s):  
Camille Nadal ◽  
Corina Sas ◽  
Gavin Doherty
Keyword(s):  
Health Care ◽  
Technology Acceptance ◽  
Mobile Health ◽  
Patient Acceptance ◽  
Medical Subject Headings ◽  
Common Definition ◽  
Measurement Framework ◽  
Pubmed Database ◽  
Measurement Tools ◽  
Concept Usage

Background Designing technologies that users will be interested in, start using, and keep using has long been a challenge. In the health domain, the question of technology acceptance is even more important, as the possible intrusiveness of technologies could lead to patients refusing to even try them. Developers and researchers must address this question not only in the design and evaluation of new health care technologies but also across the different stages of the user’s journey. Although a range of definitions for these stages exists, many researchers conflate related terms, and the field would benefit from a coherent set of definitions and associated measurement approaches. Objective This review aims to explore how technology acceptance is interpreted and measured in mobile health (mHealth) literature. We seek to compare the treatment of acceptance in mHealth research with existing definitions and models, identify potential gaps, and contribute to the clarification of the process of technology acceptance. Methods We searched the PubMed database for publications indexed under the Medical Subject Headings terms “Patient Acceptance of Health Care” and “Mobile Applications.” We included publications that (1) contained at least one of the terms “acceptability,” “acceptance,” “adoption,” “accept,” or “adopt”; and (2) defined the term. The final corpus included 68 relevant studies. Results Several interpretations are associated with technology acceptance, few consistent with existing definitions. Although the literature has influenced the interpretation of the concept, usage is not homogeneous, and models are not adapted to populations with particular needs. The prevalence of measurement by custom surveys suggests a lack of standardized measurement tools. Conclusions Definitions from the literature were published separately, which may contribute to inconsistent usage. A definition framework would bring coherence to the reporting of results, facilitating the replication and comparison of studies. We propose the Technology Acceptance Lifecycle, consolidating existing definitions, articulating the different stages of technology acceptance, and providing an explicit terminology. Our findings illustrate the need for a common definition and measurement framework and the importance of viewing technology acceptance as a staged process, with adapted measurement methods for each stage.

scholarly journals Technology Acceptance in Mobile Health: Scoping Review of Definitions, Models, and Measurement (Preprint)

2019 ◽  
Author(s):  
Camille Nadal ◽  
Corina Sas ◽  
Gavin Doherty
Keyword(s):  
Health Care ◽  
Technology Acceptance ◽  
Mobile Health ◽  
Patient Acceptance ◽  
Medical Subject Headings ◽  
Common Definition ◽  
Measurement Framework ◽  
Pubmed Database ◽  
Measurement Tools ◽  
Concept Usage

BACKGROUND Designing technologies that users will be interested in, start using, and keep using has long been a challenge. In the health domain, the question of technology acceptance is even more important, as the possible intrusiveness of technologies could lead to patients refusing to even try them. Developers and researchers must address this question not only in the design and evaluation of new health care technologies but also across the different stages of the user’s journey. Although a range of definitions for these stages exists, many researchers conflate related terms, and the field would benefit from a coherent set of definitions and associated measurement approaches. OBJECTIVE This review aims to explore how technology acceptance is interpreted and measured in mobile health (mHealth) literature. We seek to compare the treatment of acceptance in mHealth research with existing definitions and models, identify potential gaps, and contribute to the clarification of the process of technology acceptance. METHODS We searched the PubMed database for publications indexed under the Medical Subject Headings terms “Patient Acceptance of Health Care” and “Mobile Applications.” We included publications that (1) contained at least one of the terms “acceptability,” “acceptance,” “adoption,” “accept,” or “adopt”; and (2) defined the term. The final corpus included 68 relevant studies. RESULTS Several interpretations are associated with technology acceptance, few consistent with existing definitions. Although the literature has influenced the interpretation of the concept, usage is not homogeneous, and models are not adapted to populations with particular needs. The prevalence of measurement by custom surveys suggests a lack of standardized measurement tools. CONCLUSIONS Definitions from the literature were published separately, which may contribute to inconsistent usage. A definition framework would bring coherence to the reporting of results, facilitating the replication and comparison of studies. We propose the Technology Acceptance Lifecycle, consolidating existing definitions, articulating the different stages of technology acceptance, and providing an explicit terminology. Our findings illustrate the need for a common definition and measurement framework and the importance of viewing technology acceptance as a staged process, with adapted measurement methods for each stage.

scholarly journals Understanding Clinicians’ Adoption of Mobile Health Tools: A Qualitative Review of the Most Used Frameworks (Preprint)

2020 ◽  
Author(s):  
Christine Jacob ◽  
Antonio Sanchez-Vazquez ◽  
Chris Ivory
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Technology Acceptance ◽  
Mobile Health ◽  
Data Privacy ◽  
Health Care Organizations ◽  
Organizational Factors ◽  
Multidisciplinary Teams ◽  
Theoretical Frameworks ◽  
Qualitative Review

BACKGROUND Although there is a push toward encouraging mobile health (mHealth) adoption to harness its potential, there are many challenges that sometimes go beyond the technology to involve other elements such as social, cultural, and organizational factors. OBJECTIVE This review aimed to explore which frameworks are used the most, to understand clinicians’ adoption of mHealth as well as to identify potential shortcomings in these frameworks. Highlighting these gaps and the main factors that were not specifically covered in the most frequently used frameworks will assist future researchers to include all relevant key factors. METHODS This review was an in-depth subanalysis of a larger systematic review that included research papers published between 2008 and 2018 and focused on the social, organizational, and technical factors impacting clinicians’ adoption of mHealth. The initial systematic review included 171 studies, of which 50 studies used a theoretical framework. These 50 studies are the subject of this qualitative review, reflecting further on the frameworks used and how these can help future researchers design studies that investigate the topic of mHealth adoption more robustly. RESULTS The most commonly used frameworks were different forms of extensions of the Technology Acceptance Model (TAM; 17/50, 34%), the diffusion of innovation theory (DOI; 8/50, 16%), and different forms of extensions of the unified theory of acceptance and use of technology (6/50, 12%). Some studies used a combination of the TAM and DOI frameworks (3/50, 6%), whereas others used the consolidated framework for implementation research (3/50, 6%) and sociotechnical systems (STS) theory (2/50, 4%). The factors cited by more than 20% of the studies were usefulness, output quality, ease of use, technical support, data privacy, self-efficacy, attitude, organizational inner setting, training, leadership engagement, workload, and workflow fit. Most factors could be linked to one framework or another, but there was no single framework that could adequately cover all relevant and specific factors without some expansion. CONCLUSIONS Health care technologies are generally more complex than tools that address individual user needs as they usually support patients with comorbidities who are typically treated by multidisciplinary teams who might even work in different health care organizations. This special nature of how the health care sector operates and its highly regulated nature, the usual budget deficits, and the interdependence between health care organizations necessitate some crucial expansions to existing theoretical frameworks usually used when studying adoption. We propose a shift toward theoretical frameworks that take into account implementation challenges that factor in the complexity of the sociotechnical structure of health care organizations and the interplay between the technical, social, and organizational aspects. Our consolidated framework offers recommendations on which factors to include when investigating clinicians’ adoption of mHealth, taking into account all three aspects.

scholarly journals Understanding Clinicians’ Adoption of Mobile Health Tools: A Qualitative Review of the Most Used Frameworks

10.2196/18072 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e18072 ◽  
Author(s):  
Christine Jacob ◽  
Antonio Sanchez-Vazquez ◽  
Chris Ivory
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Technology Acceptance ◽  
Mobile Health ◽  
Data Privacy ◽  
Health Care Organizations ◽  
Organizational Factors ◽  
Multidisciplinary Teams ◽  
Theoretical Frameworks ◽  
Qualitative Review

Background Although there is a push toward encouraging mobile health (mHealth) adoption to harness its potential, there are many challenges that sometimes go beyond the technology to involve other elements such as social, cultural, and organizational factors. Objective This review aimed to explore which frameworks are used the most, to understand clinicians’ adoption of mHealth as well as to identify potential shortcomings in these frameworks. Highlighting these gaps and the main factors that were not specifically covered in the most frequently used frameworks will assist future researchers to include all relevant key factors. Methods This review was an in-depth subanalysis of a larger systematic review that included research papers published between 2008 and 2018 and focused on the social, organizational, and technical factors impacting clinicians’ adoption of mHealth. The initial systematic review included 171 studies, of which 50 studies used a theoretical framework. These 50 studies are the subject of this qualitative review, reflecting further on the frameworks used and how these can help future researchers design studies that investigate the topic of mHealth adoption more robustly. Results The most commonly used frameworks were different forms of extensions of the Technology Acceptance Model (TAM; 17/50, 34%), the diffusion of innovation theory (DOI; 8/50, 16%), and different forms of extensions of the unified theory of acceptance and use of technology (6/50, 12%). Some studies used a combination of the TAM and DOI frameworks (3/50, 6%), whereas others used the consolidated framework for implementation research (3/50, 6%) and sociotechnical systems (STS) theory (2/50, 4%). The factors cited by more than 20% of the studies were usefulness, output quality, ease of use, technical support, data privacy, self-efficacy, attitude, organizational inner setting, training, leadership engagement, workload, and workflow fit. Most factors could be linked to one framework or another, but there was no single framework that could adequately cover all relevant and specific factors without some expansion. Conclusions Health care technologies are generally more complex than tools that address individual user needs as they usually support patients with comorbidities who are typically treated by multidisciplinary teams who might even work in different health care organizations. This special nature of how the health care sector operates and its highly regulated nature, the usual budget deficits, and the interdependence between health care organizations necessitate some crucial expansions to existing theoretical frameworks usually used when studying adoption. We propose a shift toward theoretical frameworks that take into account implementation challenges that factor in the complexity of the sociotechnical structure of health care organizations and the interplay between the technical, social, and organizational aspects. Our consolidated framework offers recommendations on which factors to include when investigating clinicians’ adoption of mHealth, taking into account all three aspects.

scholarly journals Effects of a novel mobile health intervention compared to a multi-component behaviour changing program on body mass index, physical capacities and stress parameters in adolescents with obesity: a randomized controlled trial

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
A. Stasinaki ◽  
D. Büchter ◽  
C.-H. I. Shih ◽  
K. Heldt ◽  
S. Güsewell ◽  
...  
Keyword(s):  
Health Care ◽  
Body Mass Index ◽  
Muscle Mass ◽  
Body Mass ◽  
Body Fat ◽  
Mobile Health ◽  
Health Intervention ◽  
Randomized Controlled ◽  
Physical Capacities ◽  
Stress Parameters

Abstract Background Less than 2% of overweight children and adolescents in Switzerland can participate in multi-component behaviour changing interventions (BCI), due to costs and lack of time. Stress often hinders positive health outcomes in youth with obesity. Digital health interventions, with fewer on-site visits, promise health care access in remote regions; however, evidence for their effectiveness is scarce. Methods This randomized controlled not blinded trial (1:1) was conducted in a childhood obesity center in Switzerland. Forty-one youth aged 10–18 years with body mass index (BMI) > P.90 with risk factors or co-morbidities or BMI > P.97 were recruited. During 5.5 months, the PathMate2 group (PM) received daily conversational agent counselling via mobile app, combined with standardized counselling (4 on-site visits). Controls (CON) participated in a BCI (7 on-site visits). We compared the outcomes of both groups after 5.5 (T1) and 12 (T2) months. Primary outcome was reduction in BMI-SDS (BMI standard deviation score: BMI adjusted for age and sex). Secondary outcomes were changes in body fat and muscle mass (bioelectrical impedance analysis), waist-to-height ratio, physical capacities (modified Dordel-Koch-Test), blood pressure and pulse. Additionally, we hypothesized that less stressed children would lose more weight. Thus, children performed biofeedback relaxation exercises while stress parameters (plasma cortisol, stress questionnaires) were evaluated. Results At intervention start median BMI-SDS of all patients (18 PM, 13 CON) was 2.61 (obesity > + 2SD). BMI-SDS decreased significantly in CON at T1, but not at T2, and did not decrease in PM during the study. Muscle mass, strength and agility improved significantly in both groups at T2; only PM reduced significantly their body fat at T1 and T2. Average daily PM app usage rate was 71.5%. Cortisol serum levels decreased significantly after biofeedback but with no association between stress parameters and BMI-SDS. No side effects were observed. Conclusions Equally to BCI, PathMate2 intervention resulted in significant and lasting improvements of physical capacities and body composition, but not in sustained BMI-SDS decrease. This youth-appealing mobile health intervention provides an interesting approach for youth with obesity who have limited access to health care. Biofeedback reduces acute stress and could be an innovative adjunct to usual care.

scholarly journals The most used questionnaires for evaluating telemedicine services

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sadrieh Hajesmaeel-Gohari ◽  
Kambiz Bahaadinbeigy
Keyword(s):  
Technology Acceptance ◽  
User Interaction ◽  
End User ◽  
Publication Type ◽  
Patient Satisfaction Questionnaire ◽  
Satisfaction Questionnaire ◽  
Implementation Processes ◽  
Pubmed Database ◽  
System Usability Scale ◽  
Acceptance Model

Abstract Background Questionnaires are commonly used tools in telemedicine services that can help to evaluate different aspects. Selecting the ideal questionnaire for this purpose may be challenging for researchers. This study aims to review which questionnaires are used to evaluate telemedicine services in the studies, which are most common, and what aspects of telemedicine evaluation do they capture. Methods The PubMed database was searched in August 2020 to retrieve articles. Data extracted from the final list of articles included author/year of publication, journal of publication, type of evaluation, and evaluation questionnaire. Data were analyzed using descriptive statistics. Results Fifty-three articles were included in this study. The questionnaire was used for evaluating the satisfaction (49%), usability (34%), acceptance (11.5%), and implementation (2%) of telemedicine services. Among telemedicine specific questionnaires, Telehealth Usability Questionnaire (TUQ) (19%), Telemedicine Satisfaction Questionnaire (TSQ) (13%), and Service User Technology Acceptability Questionnaire (SUTAQ) (5.5%), were respectively most frequently used in the collected articles. Other most used questionnaires generally used for evaluating the users’ satisfaction, usability, and acceptance of technology were Client Satisfaction Questionnaire (CSQ) (5.5%), Questionnaire for User Interaction Satisfaction (QUIS) (5.5%), System Usability Scale (SUS) (5.5%), Patient Satisfaction Questionnaire (PSQ) (5.5%), and Technology Acceptance Model (TAM) (3.5%) respectively. Conclusion Employing specifically designed questionnaires or designing a new questionnaire with fewer questions and more comprehensiveness in terms of the issues studied provides a better evaluation. Attention to user needs, end-user acceptance, and implementation processes, along with users' satisfaction and usability evaluation, may optimize telemedicine efforts in the future.

scholarly journals Electronic Health Record Acceptance by Physicians: A Single Hospital Experience in Daily Practice

2021 ◽  
Vol 1 (1) ◽  
pp. 6-17
Author(s):  
Andrija Pavlovic ◽  
Nina Rajovic ◽  
Jasmina Pavlovic Stojanovic ◽  
Debora Akinyombo ◽  
Milica Ugljesic ◽  
...  
Keyword(s):  
Health Care ◽  
Logistic Regression ◽  
Electronic Health Record ◽  
Technology Acceptance ◽  
Daily Practice ◽  
Ease Of Use ◽  
Future Research ◽  
Health Record ◽  
Cross Sectional ◽  
Electronic Health

Introduction: Potential benefits of implementing an electronic health record (EHR) to increase the efficiency of health services and improve the quality of health care are often obstructed by the unwillingness of the users themselves to accept and use the available systems. Aim: The aim of this study was to identify factors that influence the acceptance of the use of an EHR by physicians in the daily practice of hospital health care. Material and Methods: The cross-sectional study was conducted among physicians in the General Hospital Pancevo, Serbia. An anonymous questionnaire, developed according to the technology acceptance model (TAM), was used for the assessment of EHR acceptance. The response rate was 91%. Internal consistency was assessed by Cronbach’s alpha coefficient. A logistic regression analysis was used to identify the factors influencing the acceptance of the use of EHR. Results: The study population included 156 physicians. The mean age was 46.4 ± 10.4 years, 58.8% participants were female. Half of the respondents (50.1%) supported the use of EHR in comparison to paper patient records. In multivariate logistic regression modeling of social and technical factors, ease of use, usefulness, and attitudes towards use of EHR as determinants of the EHR acceptance, the following predictors were identified: use of a computer outside of the office for reading daily newspapers (p = 0.005), EHR providing a greater amount of valuable information (p = 0.007), improvement in the productivity by EHR use (p < 0.001), and a statement that using EHR is a good idea (p = 0.014). Overall the percentage of correct classifications in the model was 83.9%. Conclusion: In this research, determinants of the EHR acceptance were assessed in accordance with the TAM, providing an overall good model fit. Future research should attempt to add other constructs to the TAM in order to fully identify all determinants of physician acceptance of EHR in the complex environment of different health systems.

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