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

Nadal, Camille; Sas, Corina; Doherty, Gavin

doi:10.2196/17256

Cited by 320Mobile Health and mHealth ApplicationsHealth, Technology, Consumer BehaviorTechnology Adoption and User Behaviour

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

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Nadal, Camille, Sas, Corina, Doherty, Gavin·2020·Journal of Medical Internet Research

DOI 10.2196/17256PMC PMC7381045

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Abstract

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.

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The process of accepting a technology resembles a life cycle, with preconceptions of a technology being an essential part of this life cycle as they play a role in whether the participant will eventually adopt the technology.
“This is in line with existing research suggesting that the process of accepting a technology resembles a life cycle, with preconceptions of a technology being an essential part of this life cycle as they play a role in whether the participant will eventually adopt the technology [18].”
From Older Adults’ Experiences and Perceptions of Immersive Virtual Reality: Systematic Review and Thematic Synthesis (2022) · cites this paper as [18]
The Discussion’s “Technology Acceptance Lifecycle” section (page unavailable) proposes a progression from preuse acceptability through initial and sustained use to adoption. The Introduction’s “Background” section (page unavailable) describes preuse perceptions, but the paper does not establish that those preconceptions determine eventual adoption; it says further research is needed on factors influencing the stages. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Acceptability is a key determinant of whether a technology will be adopted in the future.
“Exploring further why exactly participants find IVR acceptable is important in future research as it is now considered a key determinant of whether a technology will be adopted in the future [18].”
From Older Adults’ Experiences and Perceptions of Immersive Virtual Reality: Systematic Review and Thematic Synthesis (2022) · cites this paper as [18]
The Introduction > Background describes acceptability as a prospective judgment and notes that some studies use it to mean willingness or intention to use a system. That supports a connection to future adoption, but the review does not establish acceptability as a key determinant of whether adoption will occur; it cautions that trying an application does not necessarily lead to long-term use. No page numbers are available in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Acceptability is now considered a key factor in determining whether a technology will be adopted and used by the intended user.
“It also offers the opportunity to explore more deeply whether the end user finds a technology acceptable—which is now considered a key factor in determining whether a technology will be adopted and used by the intended user [18].”
From Older Adults’ Experiences and Perceptions of Immersive Virtual Reality: Systematic Review and Thematic Synthesis (2022) · cites this paper as [18]

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Scoping studies: towards a methodological framework
Arksey, H., & O'Malley, L. (2005) · International Journal of Social Research Methodology · doi:10.1080/1364557032000119616 · Cited by 36,742 · see citing works
Scoping studies: advancing the methodology
Levac, D., Colquhoun, H., & O'Brien, K. K. (2010) · Implementation Science · doi:10.1186/1748-5908-5-69 · Cited by 15,268 · see citing works
The DeLone and McLean Model of Information Systems Success: A Ten-Year Update
(2003) · Journal of Management Information Systems · doi:10.1080/07421222.2003.11045748 · Cited by 11,791 · see citing works
Outcomes for Implementation Research: Conceptual Distinctions, Measurement Challenges, and Research Agenda
Proctor, E., Silmere, H., Raghavan, R., Hovmand, P., Aarons, G., Bunger, A., Griffey, R., & Hensley, M. (2011) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-010-0319-7 · Cited by 8,721 · see citing works
Untitled work
(1985) · Cited by 6,185 · see citing works
Acceptability of healthcare interventions: an overview of reviews and development of a theoretical framework
Sekhon, M., Cartwright, M., & Francis, J. J. (2017) · BMC Health Services Research · doi:10.1186/s12913-017-2031-8 · Cited by 3,938 · see citing works
Information Technology Adoption Across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs1
Karahanna, E., Straub, D. W., & Chervany, N. L. (1999) · MIS Quarterly · doi:10.2307/249751 · Cited by 3,709 · see citing works
The Health Belief Model and Preventive Health Behavior
Rosenstock, I. M. (1974) · Health Education Monographs · doi:10.1177/109019817400200405 · Cited by 3,643 · see citing works
Untitled work
(2006) · doi:10.1080/01449290500330331 · Cited by 3,026 · see citing works
Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies
Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., A'Court, C., Hinder, S., Fahy, N., Procter, R., & Shaw, S. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.8775 · Cited by 2,887 · cited 1× · see citing works
“Further exploring the temporal dimension of acceptance, Greenhalgh et al [38] proposed a framework for nonadoption, abandonment, scale-up, spread, and sustainability (NASSS).”
Background
The Technology Acceptance Model: Its past and its future in health care
Holden, R. J., & Karsh, B.-T. (2010) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2009.07.002 · Cited by 2,669 · see citing works
Diffusion of Innovations
Sullivan, L. E. (2009) · The SAGE Glossary of the Social and Behavioral Sciences · doi:10.4135/9781412972024.n747 · Cited by 2,442 · see citing works
The Role of Innovation Characteristics and Perceived Voluntariness in the Acceptance of Information Technologies
Agarwal, R., & Prasad, J. (1997) · Decision Sciences · doi:10.1111/j.1540-5915.1997.tb01322.x · Cited by 1,669 · see citing works
Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions
Yardley, L., Spring, B. J., Riper, H., Morrison, L. G., Crane, D. H., Curtis, K., Merchant, G. C., Naughton, F., & Blandford, A. (2016) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2016.06.015 · Cited by 1,118 · see citing works
Construct Validation of the Health Belief Model
Cummings, K. M., Jette, A. M., & Rosenstock, I. M. (1978) · Health Education Monographs · doi:10.1177/109019817800600406 · Cited by 1,000 · see citing works
Understanding experience in interactive systems
Forlizzi, J., & Battarbee, K. (2004) · Designing Interactive Systems Conference · doi:10.1145/1013115.1013152 · Cited by 930 · see citing works
Adherence to a Smartphone Application for Weight Loss Compared to Website and Paper Diary: Pilot Randomized Controlled Trial
Carter, M. C., Burley, V. J., Nykjaer, C., & Cade, J. E. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2283 · Cited by 666 · see citing works
User experience over time
Karapanos, E., Zimmerman, J., Forlizzi, J., & Martens, J.-B. (2009) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1518814 · Cited by 580 · see citing works
Acceptability of urban transport pricing strategies
Schade, J., & Schlag, B. (2003) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/s1369-8478(02)00046-3 · Cited by 498 · see citing works
Development of a Health Information Technology Acceptance Model Using Consumers’ Health Behavior Intention
Kim, J., & Park, H.-A. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2143 · Cited by 475 · see citing works
IT-adoption and the interaction of task, technology and individuals: a fit framework and a case study
Ammenwerth, E., Iller, C., & Mahler, C. (2006) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-6-3 · Cited by 459 · see citing works
Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support the Development of Effective and Implementable ML Systems
Thieme, A., Belgrave, D., & Doherty, G. (2020) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3398069 · Cited by 406 · see citing works
Self-citation
The Empirical Foundations of Telemedicine Interventions in Primary Care
Bashshur, R. L., Howell, J. D., Krupinski, E. A., Harms, K. M., Bashshur, N., & Doarn, C. R. (2016) · Telemedicine and e-Health · doi:10.1089/tmj.2016.0045 · Cited by 370 · see citing works
Engagement in HCI
Doherty, K., & Doherty, G. J. (2018) · ACM Computing Surveys · doi:10.1145/3234149 · Cited by 272 · see citing works
Self-citation
Annual review of information science and technology
Neal, J. G. (1995) · The Journal of Academic Librarianship · doi:10.1016/0099-1333(95)90072-1 · Cited by 233 · see citing works
Mobile Health Technology for Atrial Fibrillation Management Integrating Decision Support, Education, and Patient Involvement: mAF App Trial
Guo, Y., Chen, Y., Lane, D. A., Liu, L., Wang, Y., & Lip, G. Y. H. (2017) · The American Journal of Medicine · doi:10.1016/j.amjmed.2017.07.003 · Cited by 232 · see citing works
Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test
Dou, K., Yu, P., Deng, N., Liu, F., Guan, Y., Li, Z., Ji, Y., Du, N., Lü, X., & Duan, H. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.7886 · Cited by 232 · see citing works
HCI and Affective Health
Sanches, P., Janson, A., Karpashevich, P., Nadal, C., Qu, C., Roquet, C. D., Umair, M., Windlin, C., Doherty, G. J., Höök, K., et al. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300475 · Cited by 227 · see citing works
Self-citation
Actissist: Proof-of-Concept Trial of a Theory-Driven Digital Intervention for Psychosis
Bucci, S., Barrowclough, C., Ainsworth, J., Machin, M., Morris, R., Berry, K., Emsley, R., Lewis, S., Edge, D., Buchan, I., et al. (2018) · Schizophrenia Bulletin · doi:10.1093/schbul/sby032 · Cited by 196 · see citing works
Feasibility, acceptability, and potential efficacy of the PTSD Coach app: A pilot randomized controlled trial with community trauma survivors.
Miner, A., Kuhn, E., Hoffman, J. E., Owen, J. E., Ruzek, J. I., & Taylor, C. B. (2016) · Psychological Trauma: Theory, Research, Practice, and Policy · doi:10.1037/tra0000092 · Cited by 185 · see citing works
Designing and Evaluating mHealth Interventions for Vulnerable Populations: A Systematic Review
Stowell, E., Lyson, M. C., Saksono, H., Wurth, R. C., Jimison, H., Pavel, M., & Parker, A. G. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173589 · Cited by 169 · see citing works
Examining Consumers’ Adoption of Wearable Healthcare Technology: The Role of Health Attributes
Cheung, M. L., Chau, K. Y., Lam, M. H. S., Tse, G., Ho, K. Y., Flint, S. W., Broom, D. R., Tso, E. K. H., & Lee, K. Y. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16132257 · Cited by 167 · see citing works
Use of mHealth Systems and Tools for Non-Communicable Diseases in Low- and Middle-Income Countries: a Systematic Review
Peiris, D., Praveen, D., Johnson, C., & Mogulluru, K. (2014) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-014-9581-5 · Cited by 150 · see citing works
Measuring the implementation of behavioral intervention technologies: Recharacterization of established outcomes
Hermes, E. D., Lyon, A. R., Schueller, S. M., & Glass, J. E. (2019) · Journal of Medical Internet Research · doi:10.2196/11752 · Cited by 149 · see citing works
From Adoption to Use: the process of appropriating a mobile phone
Carroll, J. M., Howard, S., Peck, J., & Murphy, J. M. (2003) · AJIS. Australasian journal of information systems/AJIS. Australian journal of information systems/Australian journal of information systems · doi:10.3127/ajis.v10i2.151 · Cited by 133 · see citing works
L'acceptabilité sociale : la prise en compte des déterminants sociaux dans l'analyse de l'acceptabilité des systèmes technologiques
Terrade, F., Pasquier, H., Reerink-Boulanger, J., Guingouain, G., & Somat, A. (2010) · Le travail humain · doi:10.3917/th.724.0383 · Cited by 133 · see citing works
Mobile technology appropriation in a distant mirror: Baroquization, creolization, and cannibalism
Bar, F., Weber, M. S., & Pisani, F. (2016) · New Media & Society · doi:10.1177/1461444816629474 · Cited by 133 · see citing works
Effect of self-monitoring on long-term patient engagement with mobile health applications
Lee, K., Kwon, H., Lee, B., Lee, G., Lee, J. H., Park, Y. R., & Shin, S.-Y. (2018) · PLOS ONE · doi:10.1371/journal.pone.0201166 · Cited by 132 · see citing works
Determinants of readiness to adopt mHealth in a rural community of Bangladesh
Khatun, F., Heywood, A. E., RAY, P. K., Hanifi, S., Bhuiya, A., & Liaw, S. (2015) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2015.06.008 · Cited by 130 · see citing works
User experience over time
Karapanos, E., Hassenzahl, M., & Martens, J.-B. (2008) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1358628.1358891 · Cited by 129 · see citing works
In the mood
Matthews, M. L., & Doherty, G. J. (2011) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979379 · Cited by 129 · see citing works
Self-citation
Mobilizing Your Medications: An Automated Medication Reminder Application for Mobile Phones and Hypertension Medication Adherence in a High-Risk Urban Population
Patel, S., Jacobus-Kantor, L., Marshall, L., Ritchie, C., Kaplinski, M., Khurana, P., & Katz, R. J. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700307 · Cited by 122 · see citing works
Acceptability of Mental Health Apps for Aboriginal and Torres Strait Islander Australians: A Qualitative Study
Povey, J., Mills, P. P. J. R., Dingwall, K. M., Lowell, A., Singer, J., Rotumah, D., Bennett-Levy, J., & Nagel, T. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5314 · Cited by 121 · see citing works
Acceptability and Acceptance of Autonomous Mobility on Demand
Distler, V., Lallemand, C., & Bellet, T. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174186 · Cited by 118 · see citing works
The Wild Wild West: A Framework to Integrate mHealth Software Applications and Wearables to Support Physical Activity Assessment, Counseling and Interventions for Cardiovascular Disease Risk Reduction
Lobelo, F., Kelli, H. M., Tejedor, S. C., Pratt, M., McConnell, M. V., Martin, S. S., & Welk, G. J. (2016) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2016.02.007 · Cited by 113 · see citing works
Development and evaluation of iManage: A self‐management app co‐designed by adolescents with sickle cell disease
Crosby, L. E., Ware, R. E., Goldstein‐Leever, A., Walton, A. E., Joffe, N. E., Vogel, C., & Britto, M. T. (2016) · Pediatric Blood & Cancer · doi:10.1002/pbc.26177 · Cited by 111 · see citing works
Acceptability and User Satisfaction of a Smartphone-Based, Interactive Blood Glucose Management System in Women With Gestational Diabetes Mellitus
Hirst, J. E., Mackillop, L. H., Loerup, L., Kevat, D., Bartlett, K., Gibson, O., Kenworthy, Y., Levy, J. C., Tarassenko, L., & Farmer, A. A. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296814556506 · Cited by 105 · see citing works
Enhancing early psychosis treatment using smartphone technology: A longitudinal feasibility and validity study
Niendam, T. A., Tully, L. M., Iosif, A., Kumar, D., Nye, K. E., Denton, J. C., Zakskorn, L. N., Fedechko, T. L., & Pierce, K. M. (2017) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2017.10.017 · Cited by 101 · see citing works
The DBT Coach mobile application as an adjunct to treatment for suicidal and self-injuring individuals with borderline personality disorder: A preliminary evaluation and challenges to client utilization.
Rizvi, S. L., Hughes, C. D., & Thomas, M. C. (2016) · Psychological Services · doi:10.1037/ser0000100 · Cited by 96 · see citing works
Randomized controlled feasibility trial of two telemedicine medication reminder systems for older adults with heart failure
Goldstein, C. M., Gathright, E. C., Dolansky, M. A., Gunstad, J., Sterns, A., Redle, J. D., Josephson, R., & Hughes, J. W. (2014) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x14541039 · Cited by 86 · see citing works
Utility and Efficacy of a Smartphone Application to Enhance the Learning and Behavior Goals of Traditional Cardiac Rehabilitation
Forman, D. E., LaFond, K., Panch, T., Allsup, K., Manning, K. M., & Sattelmair, J. R. (2014) · Journal of Cardiopulmonary Rehabilitation and Prevention · doi:10.1097/hcr.0000000000000058 · Cited by 82 · see citing works
Perceptions of the feasibility and acceptability of a smartphone application for the treatment of binge eating disorders: Qualitative feedback from a user population and clinicians
Juarascio, A. S., Goldstein, S. P., Manasse, S. M., Forman, E. M., & Butryn, M. L. (2015) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2015.06.004 · Cited by 81 · see citing works
A Pilot Study of the Acceptability and Usability of a Smartphone Application Intervention for Suicidal Adolescents and Their Parents
O’Brien, K. H. M., LeCloux, M., Ross, A. M., Gironda, C. M., & Wharff, E. A. (2016) · Archives of Suicide Research · doi:10.1080/13811118.2016.1182094 · Cited by 78 · see citing works
Utility of Consumer Physical Activity Trackers as an Intervention Tool in Cardiovascular Disease Prevention and Treatment
Hickey, A. M., & Freedson, P. S. (2016) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2016.02.006 · Cited by 77 · see citing works
mPneumonia, an Innovation for Diagnosing and Treating Childhood Pneumonia in Low-Resource Settings: A Feasibility, Usability and Acceptability Study in Ghana
Ginsburg, A. S., Agyemang, C. T., Ambler, G., Delarosa, J., Brunette, W., Levari, S., Larson, C., Sundt, M., Newton, S., Borriello, G., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0165201 · Cited by 74 · see citing works
Telemedicine and Mobile Health Technology Are Effective in the Management of Digestive Diseases: A Systematic Review
Helsel, B. C., Williams, J. E., Lawson, K. N., Liang, J., & Markowitz, J. E. (2018) · Digestive Diseases and Sciences · doi:10.1007/s10620-018-5054-z · Cited by 72 · see citing works
Feasibility of a mobile and web-based intervention to support self-management in outpatients with cancer pain
Hochstenbach, L. M. J., Zwakhalen, S. M. G., Courtens, A. M., van Kleef, M., & de Witte, L. (2016) · European Journal of Oncology Nursing · doi:10.1016/j.ejon.2016.03.009 · Cited by 70 · see citing works
Feasibility and Acceptability of Ecological Momentary Assessment of Daily Functioning Among Older Adults with HIV
Moore, R. C., Kaufmann, C. N., Rooney, A. S., Moore, D. J., Eyler, L. T., Granholm, E. L., Woods, S. P., Swendsen, J. D., Heaton, R. K., Scott, J. C., et al. (2016) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2016.11.019 · Cited by 70 · see citing works
A mobile application improves therapy-adherence rates in elderly patients undergoing rehabilitation
Mertens, A., Brandl, C., Miron‐Shatz, T., Schlick, C. M., Neumann, T., Kribben, A., Meister, S., Diamantidis, C. J., Albrecht, U., Horn, P. A., et al. (2016) · Medicine · doi:10.1097/md.0000000000004446 · Cited by 69 · see citing works
Mobile Health Technology for Improving Symptom Management in Low Income Persons Living with HIV
Schnall, R., Cho, H., Mangone, A., Pichon, A., & Jia, H. (2018) · AIDS and Behavior · doi:10.1007/s10461-017-2014-0 · Cited by 69 · see citing works
Feasibility and Efficacy of Patient-Initiated Mobile Teledermoscopy for Short-term Monitoring of Clinically Atypical Nevi
Wu, X., Oliveria, S. A., Yagerman, S., Chen, L., DeFazio, J., Braun, R., & Marghoob, A. A. (2015) · JAMA Dermatology · doi:10.1001/jamadermatol.2014.3837 · Cited by 67 · see citing works
Mobile health apps and recovery after surgery: what are patients willing to do?
Abelson, J. S., Symer, M., Peters, A., Charlson, M., & Yeo, H. (2017) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2017.06.009 · Cited by 64 · see citing works
Evaluation of the feasibility and acceptability of the ‘Care for Stroke’ intervention in India, a smartphone-enabled, carer-supported, educational intervention for management of disability following stroke
Kamalakannan, S., Murthy, G., Natarajan, S., Naveen, C., Goenka, S., & Kuper, H. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-009243 · Cited by 63 · see citing works
Acceptability of a mobile health intervention to enhance HIV care coordination for patients with substance use disorders
Westergaard, R. P., Genz, A., Panico, K., Surkan, P. J., Keruly, J. C., Hutton, H. E., Chang, L. W., & Kirk, G. D. (2017) · Addiction Science & Clinical Practice · doi:10.1186/s13722-017-0076-y · Cited by 63 · see citing works
MEDication reminder APPs to improve medication adherence in Coronary Heart Disease (MedApp-CHD) Study: a randomised controlled trial protocol
Santo, K., Chow, C. K., Thiagalingam, A., Rogers, K. D., Chalmers, J. P., & Redfern, J. (2017) · BMJ Open · doi:10.1136/bmjopen-2017-017540 · Cited by 63 · see citing works
Interactive Digital e-Health Game for Heart Failure Self-Management: A Feasibility Study
Radhakrishnan, K., Toprac, P., O'Hair, M., Bias, R. G., Kim, M. T., Bradley, P., & Mackert, M. (2016) · Games for Health Journal · doi:10.1089/g4h.2016.0038 · Cited by 62 · see citing works
Moderating factors influencing adoption of a mobile chronic disease management system in China
Zhu, Z., Liu, Y., Che, X., & Chen, X. (2017) · Informatics for Health and Social Care · doi:10.1080/17538157.2016.1255631 · Cited by 57 · see citing works
In Situ Design for Mental Illness
Matthews, M. L., Voida, S., Abdullah, S., Doherty, G. J., Choudhury, T., Im, S., & Gay, G. K. (2015) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/2785830.2785866 · Cited by 55 · see citing works
Self-citation
Students’ acceptance of E-books: An application of UTAUT
Lawson‐Body, A., Willoughby, L., Lawson-Body, L., & Tamandja, E. M. (2018) · Journal of Computer Information Systems · doi:10.1080/08874417.2018.1463577 · Cited by 55 · see citing works
Untitled work
Steinglass (2017) · doi:10.1002/da.22624 · Cited by 54 · see citing works
eMental Healthcare Technologies for Anxiety and Depression in Childhood and Adolescence: Systematic Review of Studies Reporting Implementation Outcomes
Wozney, L., McGrath, P. J., Gehring, N. D., Bennett, K., Huguet, A., Hartling, L., Dyson, M. P., Soleimani, A., & Newton, A. S. (2018) · JMIR Mental Health · doi:10.2196/mental.9655 · Cited by 50 · see citing works
AN INVESTIGATION OF THE ACCEPTABILITY OF VIDEOCONFERENCING WITHIN A SCHOOL‐BASED BEHAVIORAL CONSULTATION FRAMEWORK
Fischer, A. J., Dart, E. H., Leblanc, H., Hartman, K., Steeves, R. M. O., & Gresham, F. M. (2016) · Psychology in the Schools · doi:10.1002/pits.21900 · Cited by 49 · see citing works
Use of a Smartphone Recovery Tool for Latinos with Co-Occurring Alcohol and Other Drug Disorders and Mental Disorders
Muroff, J. R., Robinson, W., Chassler, D., López, L. M., Gaitan, E., Lundgren, L. M., Guauque, C., Dargon-Hart, S., Stewart, E. A., Dejesus, D., et al. (2017) · Journal of Dual Diagnosis · doi:10.1080/15504263.2017.1348649 · Cited by 48 · see citing works
A New mHealth application to support treatment of sleep apnoea patients
Isetta, V., Torres, M., González, K., Ruı́z, C., Dalmases, M., Embid, C., Navajas, D., Farré, R., & Montserrat, J. M. (2015) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x15621848 · Cited by 47 · see citing works
Evaluation of an Integrated Telemonitoring Surveillance System in Patients with Coronary Heart Disease
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Salovaara, A., Höök, K., Cheverst, K., Twidale, M. B., Chalmers, M., & Sas, C. (2011) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1979742.1979585 · Cited by 41 · see citing works
Self-citation
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Qu, C., Sas, C., & Doherty, G. J. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300740 · Cited by 41 · see citing works
Self-citation
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Gordon, J. S., Armin, J. S., Hingle, M. D., Giacobbi, P. R., Cunningham, J. K., Johnson, T. M., Abbate, K., Howe, C. L., & Roe, D. J. (2017) · Translational Behavioral Medicine · doi:10.1007/s13142-017-0463-7 · Cited by 35 · see citing works
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Liu, X., Wang, R., Zhou, D., & Hong, Z. (2015) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2015.11.024 · Cited by 34 · see citing works
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Chen, Y., Wong, J. E., Fatehah, A. A., Othman, N. A. S., & Poh, B. K. (2017) · Nutrients · doi:10.3390/nu9010062 · Cited by 34 · see citing works
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Localization of κ-casein on thin sections of casein micelles by the gold method
Horisberger, M., & Vauthey, M. (1984) · Histochemistry · doi:10.1007/bf00492764 · Cited by 11 · see citing works
Video chat technology to remotely quantify dietary, supplement and medication adherence in clinical trials
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Martin, N., Erhel, S., Jamet, É., & Rouxel, G. (2015) · Conference on l'Interaction Homme-Machine (IHM) · doi:10.1145/2820619.2825015 · Cited by 9 · see citing works
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Brittain, K., Kamp, K., Cassandras, C. G., Salaysay, Z., & Gómez-Márquez, J. (2018) · Gastroenterology Nursing · doi:10.1097/sga.0000000000000319 · Cited by 8 · see citing works
Services technologiques intégrés dans l’habitat des personnes âgées : examen des déterminants individuels, sociaux et organisationnels de leur acceptabilité
Reerink-Boulanger, J. (2012) · doi:10.70675/5341f827z77baz422czb3a8zf0ddc8c55151 · Cited by 7 · see citing works
Assessing the usability by clinicians of VISION: A hierarchical display of patient-collected physiological information to clinicians
Gardner, C. L., Liu, F., Fontelo, P. A., Flanagan, M. C., Hoang, A., & BURKE, H. B. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0435-3 · Cited by 6 · see citing works
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Yanling, W., Xiao, Q., Sun, L., & Wu, Y. (2016) · Studies in health technology and informatics · doi:10.3233/978-1-61499-658-3-889 · Cited by 5 · see citing works
The LEAD Project: Computer Support for Face-to-Face Collaborative Learning
van Diggelen, W., & Overdijk, M. (2006) · Lecture notes in computer science · doi:10.1007/11876663_56 · Cited by 3 · see citing works
How Acceptable Is A Wireless Pill Bottle That Monitors and Texts In Response To Missed Doses: Focus Groups With Young African American MSM Living With HIV
Dworkin, M. S., Panchal, P., Jimenez, A. D., Garofalo, R., Haberer, J. E., & Wiebel, W. W. (2017) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofx163.1060 · Cited by 3 · see citing works

Flagged first, then by citations

Research works printed but not linked: 9

Coman, A, 2016, Bulletin of the Transilvania University of Braşov, Series VII: Social Sciences and Law, 9, 73
AdellEAcceptance of driver support systems2010Proceedings of the European conference on human centred design for intelligent transport systems2010Berlin, Germany475486
AcceptabilityCambridge Dictionary - Cambridge University Press20202020-05-21https://dictionary.cambridge.org/dictionary/english/acceptability
AcceptanceCambridge Dictionary - Cambridge University Press20202020-05-21https://dictionary.cambridge.org/dictionary/english/acceptance
ConnellyKOn Developing a Technology Acceptance Model for Pervasive Computing. 9th International Conference on Ubiquitous Computing -Workshop of Ubiquitous System Evaluation (USE) New York, NYIn 9th International Conference on Ubiquitous Computing - Workshop of Ubiquitous System Evaluation2007UBICOMPSeptember 2007Innsbruck, AustriaSpringer520
Ofili, EO, 2018, Trans Am Clin Climatol Assoc, 129, 215
Yasini, M, 2016, Stud Health Technol Inform, 221, 13
Wang, Y, 2016, Stud Health Technol Inform, 225, 889
Fallah, M, 2017, Stud Health Technol Inform, 235, 68

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Technology acceptance in mHealth:a scoping review of definitions, models and measurement
Nadal, C., Sas, C., & Doherty, G. (2020) · Lancaster EPrints (Lancaster University)
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Technology acceptability, acceptance and adoption - definitions and measurement
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Improving Acceptability of Mobile Health Applications (mHealth) - The Use of the Technology Acceptance Model to Assess the Acceptability of Mhealth: A Systematic ReviewPreprint
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Do Travellers Accept m-Health Technologies? A Literature Review
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Authors: Nadal, C., Sas, C., & Doherty, G.
Affiliations: Trinity College Dublin (IE)
Lancaster University (GB)
Corresponding author: Nadal, Camille
ORCID: Nadal, Camille
Sas, Corina
Doherty, Gavin
Venue: Journal of Medical Internet Research
Publisher: JMIR Publications Inc.
Published: 2020-07-06
Volume: 22
Issue: 7
Pages: e17256
ISSN: 1438-8871 (ISSN-L)
DOI: 10.2196/17256
PMC: PMC7381045
OpenAlex: W3024942079
Cited by: 320
References: 110
Citations by year: 2026: 53 · 2025: 65 · 2024: 67 · 2023: 63 · 2022: 43 · 2021: 26 · 2020: 3
FWCI: 6.41
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: Journal of Medical Internet Research (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Lancaster EPrints (Lancaster University) (submittedVersion)
Lenus, The Irish Health Repository (Dr Steevens Hospital Library) (submittedVersion)
Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
Funders: Science Foundation Ireland (13/RC/2106, 13/RC/2094)
Horizon 2020 Framework Programme (722022)
Topics: Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Keywords: technology acceptance, mobile health, technology acceptance model, technology adoption, measurement instruments, terminology, mobile health apps, user engagement
MeSH terms: Humans, Telemedicine, Biomedical Technology

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

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