Summary
Background
The implementation of digital health technology (DHT) has not reached its full potential. Research on the factors influencing the acceptance and use of DHTs in low- and middle-income countries (LMICs) is fragmented and sparse. This study aimed to identify the facilitators and barriers to DHT acceptance and use in healthcare consumers.
Methods
The systematic review and meta-analysis were conducted using PubMed, Embase, and Web of Science from inception to July 1st, 2025. Facilitators and barriers to the acceptance and use of DHT were extracted, and a modified framework was developed based on the Unified Theory of Acceptance and Use of Technology. The strength of each pathway recorded in the framework was estimated through meta-analysis.
Findings
A total of 78 publications were included in the systematic review, comprising 64 quantitative studies and 14 qualitative studies. Twelve dimensions were summarized. Factors demonstrating the highest positive associations with intentions to use DHTs included attitude (r = 0.5644; 95% CI: 0.4498, 0.6607), trust (r = 0.5239; 95% CI: 0.4243, 0.6109), performance expectancy (r = 0.4851; 95% CI: 0.4335, 0.5335), technology self-efficacy (r = 0.4430; 95% CI: 0.2504, 0.6018), facilitating conditions (r = 0.4345; 95% CI: 0.3526, 0.5098), effort expectancy (r = 0.4171; 95% CI: 0.3508, 0.4793), and habit (r = 0.4213; 95% CI: 0.1684, 0.6222). Social influence was also positively associated (r = 0.3582; 95% CI: 0.2823, 0.4297), as well as hedonic motivation (r = 0.3963, 95% CI: 0.2280, 0.5416). Price value showed a near-null, imprecise association (r = 0.0304; 95% CI: −0.1483, 0.2072). Perceived risk to patient privacy, patient security and telemedicine systems security, showed a small negative but imprecise association with intentions (r = −0.0402; 95% CI: −0.2169, 0.1391). Anxiety showed a non-significant negative association (r = −0.2071; 95% CI: −0.5108, 0.1426).
Interpretation
The barriers identified in this analysis highlight the necessity of establishing and sustaining an integrated enabling environment as well as investing in digital health literacy to enhance healthcare consumers' self-efficacy. Findings of the study will facilitate the improvement of DHT implementation to address health concerns.
Funding
This study was funded by Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0524500, 2024ZD0524501), National Natural Science Foundation of China (72274005 and 72304013), Beijing Nova Program (20230484284).
Keywords: LMICs, UTAUT, Healthcare consumers, Self-efficacy, Facilitating conditions
Research in context.
Evidence before this study
We searched PubMed for studies that conceptualize the factors influencing the acceptance and utilization of digital health technologies (DHTs) in low- and middle-income countries (LMICs) from its inception to July 1st, 2025. The following search terms were used: (telehealth OR telemedicine OR digital health OR digital medicine OR mobile health)) AND (acceptance OR usage OR utilization) AND (low and middle income countr∗). Although we identified several studies investigating determinants of DHT acceptance and use, the evidence base in LMICs remains limited, and we found no prior evidence synthesis on this topic.
Added value of this study
Applying the systematic review and meta-analysis, this study is to the best of our knowledge the first one to offer a comprehensive examination of the facilitators and barriers to the acceptance and use of DHT from the perspective of healthcare consumers in LMICs. By synthesizing the evidence from 78 publications, a total of twelve dimensions were summarized as enablers or barriers. Effort expectancy, performance expectancy, facilitating conditions, social influence, trust, habit, hedonic motivation, technology self-efficacy and attitude may enhance healthcare consumers’ intention to utilize DHT in LMICs.
Implications of all the available evidence
The findings of this study provide LMICs with actionable evidence to address healthcare consumers' concerns about digital health technologies and to implement national digital health strategies and policies more effectively. By identifying priority areas for intervention, i.e., modifiable determinants such as facilitating conditions (infrastructure, technical support, affordability) and consumers’ technology self-efficacy, countries at different stages of digital health development can plan, build, finance, and govern their digital ecosystems to improve health outcomes, optimize service delivery, and advance equity.
Introduction
In light of the accelerated advancement of digital technologies and the growing interconnectedness of global populations, digital health technologies (DHTs) have emerged as indispensable tools for strengthening health systems and reducing health inequities.1 The accelerated development of innovative DHTs has given rise to a multitude of applications across a range of healthcare domains, including emergency response, health monitoring, diagnostic support, remote consultations, and beyond. Notwithstanding their potential, the implementation of DHTs remains suboptimal, particularly in low- and middle-income countries (LMICs), where barriers such as, low acceptance, limited usability, insufficient technical support, and unstable network connectivity2,3 significantly hinder widespread adoption.
Previous research into the factors that facilitate or impede the acceptance and use of DHT from the perspectives of healthcare consumers remains insufficient, particularly in resource-constrained settings. Prior investigations have predominantly focused on specific diseases or technologies, such as hypertension4 or neurological conditions,5 providing a fragmented understanding of the systemic factors influencing the acceptance and use of DHT in healthcare consumers. As a result, these studies provide only partial insights into the structural and contextual barriers that must be addressed to foster enabling environments for DHT integration. Moreover, many studies fail to account for the diverse socio-cultural, economic, and infrastructural factors that vary across healthcare settings, especially in low-resource environments. This gap in the literature highlights the need for more comprehensive, context-sensitive research that considers not only the technological and clinical aspects of DHT adoption but also the wider systemic and environmental factors that shape its uptake and impact.
A crucial but often overlooked factor is the role of consumer behavior in influencing DHT acceptance. Consumer behavior is a key determinant, as individual decisions and actions directly impact the effectiveness and adoption of technology. We frame this study with the Unified Theory of Acceptance and Use of Technology (UTAUT),6,7 which explains intention and use through four core constructs: performance expectancy (perceived benefits), effort expectancy (perceived ease of use), social influence (perceived normative pressure), and facilitating conditions (availability of support, infrastructure, and resources). In healthcare settings, these externally oriented determinants interact with internal psychological processes, particularly self-efficacy. We examined two domain-specific forms of self-efficacy: technology self-efficacy,8, 9, 10 defined as confidence in learning and using digital health tools (e.g., navigating apps, entering data, troubleshooting), and health self-efficacy,11,12 defined as confidence in managing one's health tasks (e.g., self-monitoring, adherence, decision-making, communicating with providers), including when supported by DHTs. Higher levels of these forms of self-efficacy are expected to increase both intention and actual use, especially in diverse socio-cultural and resource-constrained contexts.13 Positioning consumer behavior within this integrated framework clarifies the mechanisms through which interventions can improve uptake and sustained engagement with DHTs.
Methods
This systematic review and meta-analysis were conducted in line with the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) guidelines.14 We registered the review with PROSPERO (CRD42024559814). Patients were not involved in this review.
Eligibility criteria and outcomes
Both quantitative and qualitative studies were incorporated without imposition of language restrictions. Studies were deemed eligible for inclusion if they reported on the facilitators and barriers to the acceptance and utilization of DHT for healthcare consumers in LMICs. The income classification of included countries was determined using the World Bank's country income classifications at the time of the study, which categorizes economies into low-income, lower-middle-income, upper-middle-income, and high-income groups. For this review, we included only studies conducted in low-, lower-middle-, and upper-middle-income countries, excluding those from high-income countries.
The following criteria were used to exclude studies: 1) conducted in high-income countries; 2) solely reporting the effectiveness of DHTs without examining influencing factors; 3) comprising case studies, conference papers, systematic reviews, meta-analyses, or bibliometric analyses.
The primary outcome of this mixed-method systematic review was to identify the factors that facilitate or impede to the acceptance and use of DHT among healthcare consumers in LMICs. The secondary outcome was to estimate the path coefficient between the influencing factors and the intention to utilize DHTs. Both qualitative and quantitative studies contributed to the primary outcome, while only quantitative studies underwent meta-analysis for the secondary outcomes. All studies deemed eligible for inclusion will contribute to the synthesis of the factors influencing the primary outcome, with data from the quantitative analysis informing the synthesis of the secondary outcomes.
Literature search, data extraction and risk of bias assessment
To be included in the review, studies were required to be randomized controlled trials, observational studies, cross-sectional studies, or cohort studies published in peer-reviewed journals. We searched the study from its inception to July 1st 2025, in three electronic databases: PubMed, Embase, and Web of Science. A comprehensive literature search was conducted by using keywords and medical subject headings to identify studies related to digital health, technology acceptance, framework, and LMICs. We searched titles and abstracts for terms related to digital health and its synonyms, such as “telemedicine,” while using an all-fields search for LMIC-related terms. Our search was limited to database queries; we did not perform additional follow-up searches of reference lists or citation tracking of included studies. The detailed literature search strategy is presented in Supplemental Table S1. Three authors (K.H., X.L., and X.Z.) conducted independent screening of the titles and abstracts of retrieved citations to identify relevant studies. They then performed independent full-text evaluations of the selected articles, with disagreements resolved by consensus.
We extracted four parts of data from each study independently and in duplicate. These included: (1) general study information such as study design, sample size, and original country; (2) characteristics of the digital technology, i.e., targeted population, and classification of the DHT; (3) reported facilitators and barriers to the acceptance of DHT in the targeted population; and (4) strengths of the association, i.e., correlation coefficient (Pearson correlation coefficients) reported for the given influencing factor (for quantitative studies only). In this study, our focus was on we the healthcare consumers, which differs from the healthcare providers in the WHO's classification of the targeted primary user in digital health interventions. The term “healthcare consumer” encompasses individuals who are potential or current users of healthcare services, including those engaged in health prevention and wellness activities. In accordance with the recommendations set forth by the WHO in its classification of digital interventions, services and applications in health, the types of technology were classified.15
Two authors conducted an independent evaluation of the methodological quality and risk of bias of the included studies, in accordance with the recommendations set forth by the Agency for Healthcare Research and Quality (AHRQ).16 The risk of bias for each study was subsequently classified as low, moderate, or high. The detailed information is presented in Supplemental Tables S2 and S3.
Synthesis of influencing factors
The principal objective of this mixed-method systematic review was to synthesize the facilitators and barriers influencing the acceptance and utilization of DHT in LMICs among healthcare consumers. Accordingly, all identified influencing factors from the selected studies (both quantitative and qualitative) will be grouped according to the Unified Theory of Acceptance and Use of Technology (UTAUT) framework,17,18 which provides a comprehensive model for understanding users’ acceptance and use of digital technology. The UTAUT framework is derived from a number of foundational theories related to the adoption of innovations, including the Theory of Reasoned Action, the Technology Acceptance Model, the Motivational Model, the Theory of Planned Behavior, and the Innovation Diffusion Theory.17,18 The framework offers an integrated approach to analyzing the acceptance and use of DHT and summarizes the influencing factors into four main dimensions: performance expectancy, effort expectancy, social influence, and facilitating conditions. Factors that were not included in the original UTAUT framework but align with the aforementioned four dimensions would be incorporated into the UTAUT framework as extensions.
Meta-analysis
The secondary objective of the study was to quantify the strength of the associations between each influencing factor and the acceptance and use of DHT by synthesizing the real-world evidence. The studies selected for meta-analysis were evaluated according to the following criteria: 1) They were empirical studies, as opposed to conceptual studies; 2) they reported Pearson correlation coefficients or other statistics that could be converted to correlation coefficients; 3) the method used to analyze the data was structural equation modelling, ensuring consistency of approach across all studies.
In the meta-analysis, the reported raw correlation coefficient (r) with a 95% confidence interval for the identified influencing factors were analyzed using a random-effects model. Fisher's Z transformation was first applied to the correlation coefficients to stabilize the variance. The pooled mean effect sizes and their 95% confidence intervals were subsequently calculated using the inverse-variance method weighted by sample size. The heterogeneity across studies was evaluated using Cochran's Q test and the I2 index.19 To further evaluate the potential for heterogeneity, the studies were categorized into subgroups based on the income level of the countries in question: upper-middle-income countries and lower-middle- and low-income countries. The strength of the association will be classified according to the estimated correlation: small (r = 0.10–0.30), medium (r = 0.31–0.50), and large (r > 0.50).20
Funnel plots were constructed to ascertain the presence of potential publication bias. Furthermore, the Begg rank correlation test and Egger linear regression test were conducted to ascertain the presence of publication bias, with a P-value of less than 0.05 indicating a statistically significant bias in the publication of results.21,22 A sensitivity analysis was subsequently performed, including only studies with a low or moderate risk of bias.
Ethics approval and consent to participate/for publication
This study utilized exclusively publicly available data from PubMed, Embase, and Web of Science, and did not involve any direct interaction with human subjects or animals. Therefore, ethical approval and informed consent were not applicable.
Role of the funding source
This study was funded by National Natural Science Foundation of China (72274005 and 72304013), Beijing Nova Program (20230484284), Beijing Natural Science Foundation (9232009). The study sponsor had no role in the study design, data analysis and interpretation of data, the writing of the manuscript, or the decision to submit the paper for publication.
Results
Study selection and characteristics
Across all databases, our search yielded a total of 9138 citations. After removing duplicates, we screened 6018 titles and abstracts, and obtained 899 publications for full-text review. Of these, 78 publications met our eligibility criteria, including 64 quantitative studies for meta-analysis and 14 qualitative studies for thematic analysis (Fig. 1). Some publications23, 24, 25 which appear to meet the inclusion criteria were excluded because even though they are exploring the influencing factors of DHT acceptance and use, they were not consistent with UTAUT's model path. Others26, 27, 28 because they used the regression equation or other methods instead of structural equation model.
Fig. 1.
Flow diagram of study disposition.
Detailed characteristics of the 64 selected quantitative studies are shown in Table 1. All studies were published after 2016, and 32 of them published after 2022, reflecting the emerging interest in DHT acceptance and use in LMICs. The included studies focused on five countries: China (37/64, 57.81%), Bangladesh (8/64, 12.5%), India (5/64, 7.81%), Malaysia (4/64, 6.25%), Pakistan (3/64, 4.68%). The sample size of the included studies also varied from 110 to 1453. According to the AHRQ recommendation, 14 studies were classified as high risk of bias, 45 studies were classified as moderate risk of bias, and 6 studies were classified as low risk of bias.
Table 1.
Characteristics and risk of bias of the studies included into current study and facilitators and barriers extracted from 78 quantitative and qualitative studies and clustered into UTAUT framework.
| No. | Author | Years | Nations | Sample size | Types of technology | Risk of bias | Facilitators | Barriers |
|---|---|---|---|---|---|---|---|---|
| Quantitative studies | ||||||||
| 1 | Hoque, M. R.29 | 2016 | Bangladesh | 227 | Personal health tracking | Medium | Intention to use mHealth adoption | |
| Perceived ease of use | ||||||||
| Personal innovativeness in IT | ||||||||
| Perceived usefulness | ||||||||
| Subjective norm | ||||||||
| 2 | Hoque, M. R.30 | 2017 | Bangladesh | 318 | Personal health tracking | Medium | Intention to use | |
| Perceived ease of use | ||||||||
| Privacy | ||||||||
| Perceived usefulness | ||||||||
| Trust | ||||||||
| 3 | Hoque, R.31 | 2017 | Bangladesh | 274 | Targeted communication to Persons | Medium | Performance expectancy | Technology anxiety |
| Effort expectancy | Resistance to change | |||||||
| Social influence | ||||||||
| Behavioral Intention | ||||||||
| Facilitating conditions | ||||||||
| Facilitating conditions | ||||||||
| 4 | Alam, M. Z.32 | 2019 | China | 250 | Untargeted communication to Persons | Medium | Performance expectancy | |
| Performance expectancy | ||||||||
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Perceived reliability | ||||||||
| Price value | ||||||||
| Behavioral Intention | ||||||||
| 5 | Pai, R. R.33 | 2019 | India | 409 | Telemedicine | Medium | Awareness | |
| Perceived usefulness | ||||||||
| Personal innovativeness | ||||||||
| 6 | Zobair, K. M.34 | 2019 | Bangladesh | 492 | Telemedicine | Low | Knowledge | Subjective norm |
| Self-efficacy | ||||||||
| Enjoy | ||||||||
| 7 | Alam, M. Z.35 | 2020 | Bangladesh | 296 | Untargeted communication to Persons | Low | Performance expectancy | Price value |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Perceived reliability | ||||||||
| Behavioral Intention | ||||||||
| 8 | Alam, M. Z.36 | 2020 | Bangladesh | 400 | Untargeted communication to Persons | Medium | Performance expectancy | Price value |
| Effort expectancy | Habit | |||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Hedonic motivation | ||||||||
| Privacy | ||||||||
| Self-efficacy | ||||||||
| Trust | ||||||||
| Behavioral Intention | ||||||||
| Facilitating conditions | ||||||||
| 9 | Kamal, S. A.37 | 2020 | Pakistan | 226 | Telemedicine | Medium | Trust | Technological anxiety |
| Perceived usefulness | Resistance to use | |||||||
| Perceived ease of use | Perceived risk | |||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Privacy | ||||||||
| 10 | Li, D.38 | 2020 | China | 638 | Telemedicine | Low | Attitude toward behavior | Perceived medical risk |
| Perceived behavioral control | ||||||||
| 11 | Zhang, X.39 | 2020 | Pakistan | 896 | Telemedicine | Medium | Traveling cost | |
| 12 | Ahadzadeh, A. S.40 | 2021 | Malaysia | 374 | Untargeted communication to Persons | Medium | Performance expectancy | |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Internal health locus of control | ||||||||
| 13 | Barua, Z.41 | 2021 | Bangladesh | 257 | Person to Person communication | Medium | Performance expectancy | Effort expectancy |
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Health consciousness | ||||||||
| Behavioral Intention | ||||||||
| 14 | Lei, P.42 | 2021 | China | 339 | Telemedicine | Medium | Perceived value | |
| Perceived trust | ||||||||
| 15 | Luo, Y.43 | 2021 | China | 368 | Telemedicine | High | Self-efficacy | |
| Response efficacy | ||||||||
| Direct network externalities | ||||||||
| Indirect network externalities | ||||||||
| Attitude | ||||||||
| 16 | Octavius, G. S.44 | 2021 | Indonesia | 787 | Untargeted communication to Persons | High | Innovativeness | Price value |
| Compatibility | Perceived privacy risk | |||||||
| Performance expectancy | ||||||||
| Effort expectancy | ||||||||
| Facilitating conditions | ||||||||
| Information seeking motive | ||||||||
| Perceived security | ||||||||
| Initial trust in doctor | ||||||||
| Initial trust in mhealth platform | ||||||||
| 17 | Qi, M.45 | 2021 | China | 934 | Telemedicine | Medium | Perceived usefulness | Perceived risk |
| Perceived ease of use | ||||||||
| Personal innovativeness | ||||||||
| 18 | Serrano, K. M.46 | 2021 | Brazil | 110 | Telemedicine | Medium | Performance expectancy | |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Perceived security and reliability | ||||||||
| 19 | Wang, H.47 | 2021 | China | 407 | Telemedicine | Medium | Performance expectancy | |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Perceived risk | ||||||||
| 20 | A. Dash48 | 2022 | India | 578 | Person to Person communication | Medium | Performance expectancy | Perceived risk |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Trust | ||||||||
| Behavioral Intention | ||||||||
| 21 | Amin, R.49 | 2022 | Bangladesh | 312 | Telemedicine | Medium | Performance expectancy | Effort expectancy |
| Information quality | Facilitating conditions | |||||||
| Functionality | Price value | |||||||
| 22 | C. Saxena50 | 2022 | India | 450 | Telemedicine | High | Affordability | |
| Hedonic motivation | ||||||||
| Offers/discounts | ||||||||
| Reliability | ||||||||
| Convenience | ||||||||
| Behavioral Intention | ||||||||
| Social influence | ||||||||
| 23 | F. Meng51 | 2022 | China | 270 | Personal health tracking | High | Trust | |
| Personal innovativeness in it | ||||||||
| Perceived health severity | ||||||||
| 24 | I. K. Mensah52 | 2022 | Ghana | 645 | Personal health tracking | High | Performance expectancy | |
| Effort expectancy | ||||||||
| Perceived risk | ||||||||
| Mobile self-efficacy | ||||||||
| Word-of-mouth | ||||||||
| 25 | I. K. Mensah53 | 2022 | China | 306 | Personal health tracking | High | Performance expectancy | |
| Effort expectancy | ||||||||
| Mobile self-efficacy | ||||||||
| 26 | T. Khan54 | 2022 | Pakistan | 286 | Person based reporting | Medium | Performance expectancy | |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Perceived ubiquity | ||||||||
| Perceived trust | ||||||||
| Technology anxiety | ||||||||
| 27 | V. E. Arkorful55 | 2022 | Ghana | 386 | Personal health tracking | Medium | Attitude towards Adoption | |
| Subjective Norm | ||||||||
| Perceived behavioral control | ||||||||
| Adoption intention | ||||||||
| 28 | X. F. Tian56 | 2022 | China | 1453 | Personal health tracking | High | Performance expectancy | |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| 29 | Y. Liu57 | 2022 | China | 386 | Personal health tracking | Medium | Effort expectancy | Perceived ease of use |
| Performance expectancy | Subjective norm | |||||||
| Social influence | ||||||||
| Perceived ubiquity | ||||||||
| 30 | Y. Ma58 | 2022 | China | 1318 | Personal health tracking | Medium | Attitude | |
| Performance expectancy | ||||||||
| Facilitating conditions | ||||||||
| 31 | Y. Z. Liu59 | 2022 | China | 183 | Personal health tracking | High | Inconvenience | High switching cost |
| Peer influence | High risk | |||||||
| Attractiveness of mHealth APPs | Price value | |||||||
| Attitude | ||||||||
| Willingness to adopt | ||||||||
| 32 | Z. Xinyan60 | 2022 | China | 304 | Personal health tracking | High | Health improvement expectancy | |
| Effort expectancy | ||||||||
| Perceived vulnerability | ||||||||
| Health consciousness | ||||||||
| Perceived reliability | ||||||||
| Intention to use | ||||||||
| 33 | J. Liu61 | 2023 | China | 235 | Person based reporting | Medium | Performance expectancy | |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| 34 | Liu, D.62 | 2023 | China | 387 | Telemedicine | Low | Perceived value | |
| 35 | Low, M. P.63 | 2023 | Malaysia | 392 | Telemedicine | High | Perceived usefulness | |
| Perceived ease of use | ||||||||
| Perceived trust | ||||||||
| Perceived privacy | ||||||||
| Perceived innovativeness | ||||||||
| 36 | M. Y. Wang64 | 2023 | China | 180 | Telemedicine | Medium | Performance expectancy | |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Perceived risk | ||||||||
| Perceived trust | ||||||||
| Self-efficacy | ||||||||
| 37 | Misra, S.65 | 2023 | India | 139 | Telemedicine | High | Perceived ease of use | |
| Perceived usefulness | ||||||||
| Perceived privacy | ||||||||
| Fashionability | ||||||||
| 38 | Qu, S. J.66 | 2023 | China | 1364 | Personal health tracking | Medium | Perceived ease of use | Perceived risk |
| Perceived usefulness | ||||||||
| Attitude | ||||||||
| Health awareness | ||||||||
| 39 | Ren, Z.67 | 2023 | China | 200 | Person to Person communication | High | Performance expectancy | Facilitating conditions |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Attitude | ||||||||
| 40 | T. Li68 | 2023 | China | 793 | Telemedicine | Medium | Effort expectancy | |
| Performance expectancy | ||||||||
| Trust | ||||||||
| Social influence | ||||||||
| 41 | Y. Wang69 | 2023 | China | 265 | Telemedicine | High | Perceived ease of use | |
| Perceived usefulness | ||||||||
| Perceived reliability | ||||||||
| Self-perceived ageing | ||||||||
| Health promotion | ||||||||
| 42 | Zhang, M.70 | 2023 | China | 559 | Targeted communication to Persons | Low | Perceived usefulness | |
| Attitude | ||||||||
| 43 | Zhang, Q.71 | 2023 | China | 783 | Untargeted communication to Persons | Medium | Performance expectancy | |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Perceived risk | ||||||||
| Price value | ||||||||
| ehealth literacy | ||||||||
| Behavioral Intention | ||||||||
| 44 | Zhu, L.72 | 2023 | China | 465 | Telemedicine | Medium | Perceived usefulness | |
| Structural assurance-based trust | ||||||||
| Platform-based trust | ||||||||
| Physician-based trust | ||||||||
| 45 | Zhu, Y.73 | 2023 | China | 371 | Telemedicine | Medium | Performance expectancy | Perceived risk |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Price value | ||||||||
| Perceived trust | ||||||||
| Behavioral Intention | ||||||||
| 46 | Lian, R. Y.74 | 2024 | China | 272 | Telemedicine | Medium | Performance expectancy | Technology-related anxiety |
| Effort expectancy | Perceived risk | |||||||
| Social influence | Usage cost | |||||||
| Facilitating conditions | ||||||||
| Hedonic values | ||||||||
| Habit | ||||||||
| Perceived complexity | ||||||||
| 47 | Min, H.75 | 2024 | China | 853 | Untargeted communication to Persons | Medium | Performance expectancy | |
| Social influence | ||||||||
| Hedonic motivation | ||||||||
| Habit | ||||||||
| 48 | Chen, Y.76 | 2024 | China | 309 | Personal health tracking | Medium | Performance expectancy | Technology anxiety |
| Effort expectancy | Social influence | |||||||
| Facilitating conditions | ||||||||
| Behavioral Intention | ||||||||
| 49 | Song, L.77 | 2024 | China | 113 | Personal health tracking | Medium | Performance expectancy | |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Hedonic motivation | ||||||||
| Price value | ||||||||
| Habit | ||||||||
| 50 | Nayak, S.78 | 2024 | India | 280 | Telemedicine | Medium | Attitude | |
| Performance expectancy | ||||||||
| 51 | Malarvizhi, C. A. N79 | 2024 | Malaysia | 393 | Telemedicine | Medium | Performance expectancy | |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Behavioral Intention | ||||||||
| 52 | Liu, C.80 | 2024 | China | 590 | Telemedicine | Medium | Trust | Hedonic motivation |
| 53 | Wubete Abebe, A.81 | 2024 | Ethiopia | 1037 | Personal health tracking | Low | Performance expectancy | |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Price value | ||||||||
| Habit | ||||||||
| 54 | Timsina, S. M.82 | 2025 | Nepal | 280 | Personal health tracking | Medium | Performance expectancy | Effort expectancy |
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Hedonic motivation | ||||||||
| Price value | ||||||||
| Habit | ||||||||
| Behavioral Intention | ||||||||
| 55 | Akiogbe, O.83 | 2025 | China | 337 | Telemedicine | High | Trust | |
| Performance expectancy | ||||||||
| 56 | Tan, S. H.84 | 2025 | Malaysia | 119 | Telemedicine | Medium | Attitude | |
| Performance expectancy | ||||||||
| Effort expectancy | ||||||||
| Hedonic motivation | ||||||||
| 57 | Huang, H.85 | 2025 | China | 379 | On demand communication with persons | Medium | Performance expectancy | Technological anxiety |
| Effort expectancy | Perceived risk | |||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Trust | ||||||||
| Price value | ||||||||
| 58 | Soelasih, Y.86 | 2025 | Indonesia | 402 | Telemedicine | Medium | Behavioral Intention | |
| 59 | Su, J.87 | 2025 | China | 400 | Telemedicine | Medium | Performance expectancy | Perceived risk |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Perceived trust | ||||||||
| 60 | Yao, L.88 | 2025 | China | 880 | Telemedicine | Medium | Performance expectancy | |
| Social influence | ||||||||
| Attitude | ||||||||
| Behavioral Intention | ||||||||
| 61 | Wei, A.89 | 2025 | China | 389 | Person-centred financial transactions | Medium | Performance expectancy | |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| 61 | Wei, A.89 | 2025 | China | 323 | Person-centred financial transactions | Medium | Performance expectancy | |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| 62 | Xu, J.90 | 2025 | China | 313 | Personal health tracking | Medium | Perceived usefulness | |
| Perceived risk | ||||||||
| Perceived trust | ||||||||
| Perceived ease of use | ||||||||
| 63 | Tao, D.91 | 2024 | China | 614 | Telemedicine | Medium | Perceived usefulness | |
| Perceived ease of use | ||||||||
| social influence | ||||||||
| 64 | Schlottke, Jorge Anibal92 | 2024 | Argentina | 285 | Telemedicine | Medium | Perceived usefulness | |
| Perceived ease of use | ||||||||
| Social influence | ||||||||
| Qualitative studies | ||||||||
| 65 | Abdullah, A.93 | 2016 | Malaysia | 17 | Personal health tracking | High | Effort expectancy | Effort expectancy |
| Social influence | Trust | |||||||
| Effort expectancy | ||||||||
| 66 | Han, K. J.94 | 2019 | Sri Lanka | 29 | Telemedicine | High | Performance expectancy | Effort expectancy |
| Price value | ||||||||
| Privacy concern | ||||||||
| 67 | Namatovu, H. K.95 | 2021 | Uganda | 115 | Telemedicine | High | Performance expectancy | |
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Performance expectancy | ||||||||
| Social influence | ||||||||
| 68 | Sekandi, J. N.96 | 2021 | Uganda | 30 | Telemedicine | High | Facilitating conditions | Facilitating conditions |
| Performance expectancy | Social influence | |||||||
| Price value | ||||||||
| Privacy concern | ||||||||
| 69 | Du, Y.97 | 2022 | China | 27 | Telemedicine | Medium | Effort expectancy | Health self-efficacy |
| Technology self-efficacy | Attitude | |||||||
| Social influence | Facilitating conditions | |||||||
| 70 | Hasan, N.98 | 2022 | Bangladesh | 24 | Personal health tracking | Medium | Social influence | Social influence |
| 71 | Liverani, M.99 | 2022 | Cambodia | 18 | Personal health tracking | Medium | Facilitating conditions | Facilitating conditions |
| Risk | Health self-efficacy | |||||||
| Performance expectancy | Price value | |||||||
| 72 | Meyer, C. L.100 | 2022 | Turkey | 14 | Personal health tracking | Medium | Social influence | Risk |
| Facilitating conditions | Price value | |||||||
| 73 | Liu, J.61 | 2023 | China | 79 | Person based reporting | Medium | Technology self-efficacy | |
| Performance expectancy | ||||||||
| Effort expectancy | ||||||||
| Social influence | ||||||||
| Facilitating conditions | ||||||||
| Behavioral Intention | ||||||||
| 74 | Pan, J.101 | 2023 | China | 30 | Person to Person communication | Medium | Effort expectancy | Effort expectancy |
| Performance expectancy | Performance expectancy | |||||||
| Social influence | Price value | |||||||
| Effort expectancy | ||||||||
| Risk | ||||||||
| 75 | Zhou, L.102 | 2024 | China | 14 | Telemedicine | Medium | Performance expectancy | Risk |
| 76 | Sampene, A. K.103 | 2024 | South Africa | 28 | Telemedicine | Medium | Effort expectancy | Facilitating conditions |
| Performance expectancy | ||||||||
| Price value | ||||||||
| Trust | ||||||||
| 77 | Xu, X.104 | 2024 | China | 12 | Telemedicine | Medium | Performance expectancy | |
| 78 | Kizito, M.105 | 2025 | Uganda | 91 | Telemedicine | Low | Performance expectancy | Risk |
All 14 qualitative studies included in the review were published after 2016, with sample sizes ranging from 12 to 115 (Table 1). The leading countries were China (5/14, 35.71%) and Uganda (3/14, 21.43%). After quality assessment, 4 studies were considered to be at high risk of bias, 9 studies were considered to be at moderate risk of bias, and 1 study was classified as low risk of bias. Funnel plots of the review are listed in Supplemental Figure S16.
Barriers and facilitators of acceptance and use of digital health technology
The current study extracted barriers and facilitators of acceptance and use of DHT from all 78 studies included in the analysis (Table 1). The specific input data are presented in Supplemental Table S4 for quantitative studies and Supplemental Table S5 for qualitative studies and are summarized according to the UTAUT framework (Fig. 2). The template data forms, including data extracted from included studies, data used for analysis are presented in Supplemental Table S4. A correlation coefficient was applied through meta-analysis to quantify the strength of the relationship between the factor and DHT use.
Fig. 2.
UTAUT-guided model of facilitators and barriers of the acceptance and usage of digital health technologies for healthcare consumers.
Based on the UTAUT theory and the factors identified in the 78 studies as influencing the acceptance and use of DHT, a total of 12 dimensions were identified as potential barriers or facilitators for healthcare consumers in LMICs. These were performance expectancy, effort expectancy, social influence, facilitating conditions, anxiety, habit, hedonic motivation, price value, attitude, trust, risk, and self-efficacy (Fig. 2). In particular, the construct of “self-efficacy” was described in two sub-domains: technology self-efficacy and health self-efficacy. This approach allowed for a more precise delineation of the specific connotations associated with each sub-dimension. Technology self-efficacy was defined as perceived behavioral control and innovativeness in technology use, where health self-efficacy referred to a person's confidence in their ability to manage and engage with health-related decisions.106
The strength of the association in each pathway, as recorded in the 64 quantitative studies, was estimated through meta-analysis (Table 2). A positive association was observed between technology self-efficacy and intention to use (r = 0.4430, 95% CI: 0.2504–0.6018), and a similar positive association was evident between health self-efficacy and intention to use (r = 0.2538, 95% CI: 0.0514, 0.4361). Additionally, performance expectancy (r = 0.4851, 95% CI: 0.4335, 0.5335), trust (r = 0.5239, 95% CI: 0.4243, 0.6109) and attitude (r = 0.5644, 95% CI: 0.4498, 0.6607) were the factors that demonstrated the highest associations with intentions to utilize DHT. Other factors demonstrated consistent estimation with the initial UTAUT theory, including effort expectancy (r = 0.4171, 95% CI: 0.3508, 0.4793), facilitating conditions (r = 0.4345, 95% CI: 0.3526, 0.5098), social influence (r = 0.3582, 95% CI: 0.2823, 0.4297), habit (r = 0.4213, 95% CI: 0.1684, 0.6222), hedonic motivation (r = 0.3963, 95% CI: 0.2280, 0.5416) and price value (r = 0.0304, 95% CI: −0.1483, 0.2072). Conversely, risk (r = −0.0402, 95% CI: −0.2169, 0.1391) and anxiety (r = −0.2071; 95% CI: −0.51108, 0.1426) may have a detrimental impact on this intension. The intention to use DHT (r = 0.6523, 95% CI: 0.5494, 0.7358) and the facilitating conditions (r = 0.5327, 95% CI: 0.2335, 0.7398) were found to be statistically significant predictors of actual usage of DHT in LMICs. Forest plots for the key associations reported in Table 2 could be found in Supplemental Figures S1–S15.
Table 2.
Meta-analysis of the association in each pathway of the facilitators and barriers to the acceptance and usage of the healthcare consumers.
| Path | Study Num | Observation Num | Method | r (95% CI) | P value | Heterogeneity |
||
|---|---|---|---|---|---|---|---|---|
| Q | P-value | I2 | ||||||
| Effort expectancy → Behavioral Intention | 51 | 21,973 | Random effects | 0.4171 (0.3508, 0.4793) | <0.0001 | 1464.75 | <0.0001 | 96.60% |
| Performance expectancy → Behavioral Intention | 63 | 27,333 | Random effects | 0.4851 (0.4335, 0.5335) | <0.0001 | 1645.02 | <0.0001 | 96.20% |
| Social influence → Behavioral Intention | 40 | 18,004 | Random effects | 0.3582 (0.2823, 0.4297) | <0.0001 | 1160.27 | <0.0001 | 96.60% |
| Risk → Behavioral Intention | 24 | 11,136 | Random effects | −0.0402 (−0.2169, 0.1391) | 0.6618 | 1839.2 | 0 | 98.70% |
| Trust → Behavioral Intention | 21 | 9141 | Random effects | 0.5239 (0.4243, 0.6109) | <0.0001 | 689.54 | <0.0001 | 97.10% |
| Facilitating conditions → Behavioral Intention | 29 | 12,901 | Random effects | 0.4345 (0.3526, 0.5098) | <0.0001 | 765.34 | <0.0001 | 96.30% |
| Anxiety → Behavioral Intention | 6 | 1746 | Random effects | −0.2071 (−0.5108, 0.1426) | 0.4463 | 253.05 | <0.0001 | 98.00% |
| Attitude → Behavioral Intention | 12 | 6560 | Random effects | 0.5644 (0.4498, 0.6607) | <0.0001 | 444.66 | <0.0001 | 97.50% |
| Price value → Behavioral Intention | 17 | 7563 | Random effects | 0.0304 (−0.1483, 0.2072) | 0.7399 | 1187.3 | <0.0001 | 98.70% |
| Health self-efficacy → Behavioral Intention | 7 | 3470 | Random effects | 0.2538 (0.0514, 0.4361) | 0.0145 | 227.23 | <0.0001 | 97.40% |
| Hedonic motivation → Behavioral Intention | 10 | 4155 | Random effects | 0.3963 (0.2280, 0.5416) | <0.0001 | 441.85 | <0.0001 | 98.00% |
| Habit → Behavioral Intention | 7 | 3282 | Random effects | 0.4213 (0.1684, 0.6222) | <0.0001 | 351.79 | <0.0001 | 98.30% |
| Technology self-efficacy → Behavioral Intention | 19 | 8769 | Random effects | 0.4430 (0.2504, 0.6018) | <0.0001 | 1504.21 | <0.0001 | 98.80% |
| Facilitating conditions → Actual use | 5 | 2159 | Random effects | 0.5327 (0.2335, 0.7398) | 0.0011 | 238.07 | <0.0001 | 98.30% |
| Behavioral Intention → Actual use | 19 | 7341 | Random effects | 0.6523 (0.5494, 0.7358) | <0.0001 | 1020.24 | <0.0001 | 98.20% |
The strength of each pathway was further estimated in studies conducted in upper-middle income countries (45 studies) and low and lower-middle income countries (20 studies, Table 3). The results indicated a comparable positive trend in low- and lower-middle-income countries (health self-efficacy: 0.3849; technology self-efficacy: 0.3008) and upper-middle-income countries (health self-efficacy: 0.1462; technology self-efficacy: 0.5332) with regard to self-efficacy with respect to health and technology. The data suggested that risk was associated with diverse pathways in relation to behavioral intention. In upper-middle-income countries, the risk associated with technology was inversely correlated with the intention to use technology (r = −0.2061, 95% CI: −0.3418, −0.062). Conversely, in low- and lower-middle-income countries, a positive association was observed between risk and behavioral intention (r = 0.3579, 95% CI: 0.0163–0.6246). A higher correlation in low- and lower-middle-income countries was indicated (LICs and LMICs: 0.3849; UMICs: 0.1462),meanwhile, technology self-efficacy was more strongly correlated in upper-middle-income countries (LICs and LMICs: 0.3008; UMICs: 0.5332). The results of the analysis, which was based on studies of a medium to high quality, are presented in Supplemental Table S6. Sensitivity analyses demonstrated that the key associations remained consistent across different model specifications, supporting the robustness of our main findings.
Table 3.
Subgroup analysis of the association of facilitators and barriers with the behavioral intention and actual usage by countries’ income levels, from the perspective of healthcare consumers.
| Path | Group | Number of studies | Number of observations | r (95% CI) | Heterogeneity |
P value for interaction | |
|---|---|---|---|---|---|---|---|
| I2 | P value | ||||||
| Behavioral Intention → Actual use | Low-income and lower-middle income countries | 10 | 3466 | 0.6122 (0.4549, 0.7325) | 97.60% | <0.0001 | 0.3983 |
| Upper-middle income countries | 9 | 3875 | 0.6929 (0.5461, 0.7984) | 98.40% | <0.0001 | ||
| Effort expectancy → Behavioral Intention | Low-income and lower-middle income countries | 16 | 6011 | 0.4407 (0.2856, 0.5733) | 97.80% | <0.0001 | 0.6733 |
| Upper-middle income countries | 35 | 15,962 | 0.4060 (0.3370, 0.4707) | 95.70% | <0.0001 | ||
| Technology self-efficacy → Behavioral Intention | Low-income and lower-middle income countries | 8 | 3059 | 0.3008 (−0.1572, 0.6523) | 99.30% | <0.0001 | 0.2618 |
| Upper-middle income countries | 11 | 5710 | 0.5332 (0.4072, 0.6392) | 97.70% | <0.0001 | ||
| Performance expectancy → Behavioral Intention | Low-income and lower-middle income countries | 19 | 7022 | 0.4781 (0.3872, 0.5598) | 95.30% | <0.0001 | 0.8539 |
| Upper-middle income countries | 44 | 20,311 | 0.4881 (0.4240, 0.5473) | 96.50% | <0.0001 | ||
| Social influence → Behavioral Intention | Low-income and lower-middle income countries | 14 | 5751 | 0.3440 (0.1807, 0.4889) | 97.90% | <0.0001 | 0.8081 |
| Upper-middle income countries | 26 | 18,004 | 0.3655 (0.2836, 0.4422) | 95.20% | <0.0001 | ||
| Risk → Behavioral Intention | Low-income and lower-middle income countries | 7 | 2532 | 0.3579 (0.0163, 0.6246) | 98.90% | <0.0001 | 0.0031 |
| Upper-middle income countries | 17 | 8604 | −0.2061 (−0.3418, −0.0620) | 96.80% | <0.0001 | ||
| Trust → Behavioral Intention | Low-income and lower-middle income countries | 5 | 1808 | 0.4288 (0.0713, 0.6887) | 98.60% | <0.0001 | 0.4355 |
| Upper-middle income countries | 11 | 9141 | 0.5511 (0.4603, 0.6304) | 95.80% | <0.0001 | ||
| Facilitating conditions → Behavioral Intention | Low-income and lower-middle income countries | 11 | 4258 | 0.4327 (0.2643, 0.5755) | 97.40% | <0.0001 | 0.9713 |
| Upper-middle income countries | 18 | 8643 | 0.4360 (0.3450, 0.5188) | 95.30% | <0.0001 | ||
| Facilitating conditions → Actual use | Low-income and lower-middle income countries | 2 | 674 | 0.5839 (−0.2330, 0.9177) | 99.30% | <0.0001 | 0.8024 |
| Upper-middle income countries | 3 | 2159 | 0.4956 (0.1718, 0.7228) | 97.30% | <0.0001 | ||
| Attitude → Behavioral Intention | Low-income and lower-middle income countries | 2 | 666 | 0.5342 (0.2433, 0.7370) | 95.10% | <0.0001 | 0.7963 |
| Upper-middle income countries | 10 | 5894 | 0.5700 (0.4367, 0.6789) | 97.90% | <0.0001 | ||
| Anxiety → Behavioral Intention | Low-income and lower-middle income countries | 3 | 786 | −0.3312 (−0.5672, −0.0448) | 98.90% | <0.0001 | 0.4802 |
| Upper-middle income countries | 3 | 960 | −0.0752 (−0.6405, 0.5430) | 95.30% | <0.0001 | ||
| Price value → Behavioral Intention | Low-income and lower-middle income countries | 8 | 4121 | −0.1104 (−0.2915, 0.0783) | 97.20% | <0.0001 | 0.1205 |
| Upper-middle income countries | 9 | 3442 | 0.1551 (−0.1222, 0.4099) | 98.60% | <0.0001 | ||
| Health self-efficacy → Behavioral Intention | Low-income and lower-middle income countries | 3 | 1158 | 0.3849 (−0.0678, 0.7062) | 98.70% | <0.0001 | 0.2865 |
| Upper-middle income countries | 4 | 2312 | 0.1462 (0.1060, 0.1859) | 0.00% | <0.0001 | ||
| Hedonic motivation → Behavioral Intention | Low-income and lower-middle income countries | 6 | 2798 | 0.4223 (0.1477, 0.6364) | 98.80% | <0.0001 | 0.6133 |
| Upper-middle income countries | 4 | 1357 | 0.3471 (0.1973, 0.4810) | 88.10% | <0.0001 | ||
| Habit → Behavioral Intention | Low-income and lower-middle income countries | 3 | 1717 | 0.3884 (0.0001, 0.6749) | 98.50% | <0.0001 | 0.8209 |
| Upper-middle income countries | 4 | 1565 | 0.4452 (0.0466, 0.7215) | 98.60% | <0.0001 | ||
Discussion
This study is to the best of our knowledge the first one to offer a comprehensive examination of the facilitators and barriers to the acceptance and use of DHT from the perspective of healthcare consumers in LMICs. The results indicated that effort expectancy, facilitating conditions, social influence, habit, hedonic motivation and price value may enhance healthcare consumers’ intention to utilize DHT in LMICs.To enhance the explanatory power of the UTAUT framework, two additional domains were introduced: technology self-efficacy and health self-efficacy. The findings provide an evidence base to inform policy and practice with regard to the effective integration and equitable adoption of DHTs in LMICs.
A distinctive contribution of this study is the conceptual clarification of self-efficacy into two domain-specific forms relevant to healthcare consumers: technology self-efficacy and health self-efficacy. Technology self-efficacy denotes confidence in learning, navigating, and troubleshooting digital tools and platforms.107,108 Higher digital self-efficacy is associated with stronger intentions to use and sustained engagement with DHTs, whereas lower digital self-efficacy can impede uptake.109 The digital nature of these technologies, which often rely on online interfaces rather than face-to-face interactions,110 means that the adoption of DHT is profoundly influenced by the ideological and psychological traits of the recipients.111 Health self-efficacy refers to confidence in carrying out the tasks required to manage one's health (e.g., self-monitoring, adherence, decision-making, communicating with providers), including when supported by DHTs.24,112, 113, 114 Higher health management self-efficacy similarly predicts greater acceptance and use, underscoring its central role in the successful implementation of DHTs.115
It is equally important to consider health self-efficacy. The quantified association strength through meta-analysis demonstrated that individuals with higher levels of health literacy exhibited greater acceptance and utilization of DHT in the broader population. This finding suggests a promising strategy for integrating DHT implementation with health promotion programs. For example, the use of mobile health applications, in conjunction with community-based health literacy programs has been demonstrated to improve diabetes management among patients.116,117 The programs provided patients with essential knowledge about diabetes management and simultaneously familiarized them with digital tools for monitoring and controlling their condition. Similarly, the integration of telemedicine platforms with educational workshops on maternal health in rural Bangladesh and Laos resulted in an increased uptake of telemedicine and a significant improvement in maternal health outcomes.118 This was due to the fact that women were better equipped to utilize these digital services effectively.118 The incorporation of DHT implementation within broader health literacy promotion strategies enables healthcare systems to cultivate a more informed and empowered patient population, particularly in LMICs, where limited health literacy represents a significant obstacle to effective healthcare delivery.
Our findings suggest that health self-efficacy is more strongly associated with DHT use intention in LICs and LMICs, while technology self-efficacy plays a greater role in UMICs. This may reflect contextual differences: in lower-income settings, individuals confident in managing their own health may be more motivated to adopt DHTs as a means to compensate for limited healthcare access. In contrast, in UMICs where digital infrastructure is more developed, confidence in using technology becomes a more decisive factor. These insights underscore the need for context-specific strategies that build either health or technology self-efficacy depending on local needs.
Importantly, these two domains are not mutually exclusive. Rather, their intersection forms the basis of what is now widely recognized as digital health literacy—the ability to seek, understand, evaluate, and apply digital health information and tools.119 Enhancing this combined literacy is essential to overcome barriers to DHT adoption, a point emphasized by the WHO and increasingly supported by global evidence.120 Moreover, the Lancet and Financial Times Commission advocates for the development of not only digital and health literacy, but also civic literacy, to equip individuals with the skills to critically engage with digital health systems, understand their rights, and participate in governance.121 The review revealed that facilitating conditions were a frequently addressed domain, demonstrating a significant association with healthcare consumers' behavioral intention. This highlights the importance of the enabling ecosystem in the implementation of DHT and digital transformation. In the UTAUT theory, facilitating conditions are defined as the extent to which an individual perceives the existence of an organizational and technical infrastructure that supports the utilization of the system.106 The adoption of virtual technologies is contingent upon the existence of a robust physical and digital infrastructure. Facilitating conditions are defined as factors such as internet access, electricity supply, and IT infrastructure. For example, a study from China indicated that both direct and indirect network externalities had a positive effect on users’ continued intention to use mobile health applications.43 Similarly, studies from Bangladesh41 and Pakistan54 found that facilitating conditions, such as stable internet connectivity and adequate equipment, significantly impacted the use intention and actual behavior of specific populations, including older adults. In resource-constrained settings, the absence of stable networks or adequate equipment severely limits the impact of other factors aimed at promoting DHT adoption. This underscores the necessity for governments and healthcare institutions to invest in essential medical and technological infrastructure. By doing so, healthcare consumers are more likely to engage with and adapt to DHTs, fostering their innovation and development while realizing their potential to transform healthcare delivery and outcomes.
Among the factors examined, habit, attitude, trust, and performance expectancy showed the strongest positive associations with the intention to use DHTs. This finding suggests that individuals are more likely to adopt digital health technologies when they are already accustomed to using similar tools, perceive them positively, expect them to be beneficial, and trust the systems and institutions delivering them. These insights underscore the importance of not only ensuring functional effectiveness of DHTs but also fostering a conducive environment for their sustained use. From a policy and practice perspective, promoting digital literacy and integrating DHTs into routine health interactions can help cultivate habitual use. Clear communication of the benefits and reliability of DHTs can improve consumers’ attitudes and expectations. Furthermore, building trust through transparent data governance, user protections, and culturally sensitive implementation is crucial, particularly in settings where skepticism toward digital systems remains a barrier. These factors should be prioritized in the design and scaling of digital health interventions to improve uptake and impact.
Agencies engaged in digital health governance have long acknowledged the significance of acceptance and use in attaining the anticipated benefits of DHT. In 2005, the World Health Assembly, through its resolution WHA58.28 on eHealth, urged Member States the formulation of a long-term strategic plan for the development and implementation of eHealth services, the advancement of the infrastructure for information and communication technologies for health, and the promotion of equitable, affordable, and universal access to the benefits of eHealth.122 At the 73rd World Health Assembly, the WHO endorsed the establishment of the Global Strategy on Digital Health 2020–2025.123 The strategy is founded upon four core principles. A national system necessitates that countries determine and commit to it, recognize that successful digital technologies require an integrated strategy, promote the appropriate use of digital interventions for health, and address the significant impediments faced by least-developed countries implementing DHT. In this context, the findings of this study could contribute to the promotion of the implementation of WHO decisions on cybersecurity and investment in health facilities and training staff to enhance cyber-maturity. This would be achieved through with the development of a more specific implementation path for the Department of Digital Health and other relevant actors, with the aim of accelerating the achievement of the strategic objectives set out in the Global Strategy on Digital Health 2020–2025.123 Furthermore, this study presents specific targets for LMICs, with the aim of improving health outcomes, optimizing health services, and promoting health equity. These targets assist countries at all stages of development in effectively planning, building, financing, and governing their digital ecosystems.
This study has several strengths. First, it employs a systematic approach to identify and synthesize potential facilitators and barriers to the acceptance and use of DHTs in LMICs from the perspective of healthcare consumers. The study proposes a novel scale-up model to elucidate pathways of action and key influencing factors. Secondly, the study introduces an innovative approach to self-efficacy, which is divided into two distinct constructs: health self-efficacy and technology self-efficacy. This distinction provides a more detailed and complex understanding of the primary factors influencing healthcare consumers’ acceptance processes. Thirdly, the study emphasises the pivotal role of enabling conditions in the adoption of DHTs, particularly in LMICs, where infrastructural challenges are pronounced. It should be noted, however, that this study is not without limitations. The analysis was constrained by the meta-analytic approach and guided by the UTAUT framework, with factors mentioned in at least two studies included in the review. While this approach ensures the reliability of factor identification, it may have excluded less frequently reported but potentially important determinants. Second, although a comprehensive search and data organization were carried out, most of the included studies were from Asian countries with large populations, which means interpretation of the findings needs to be cautious. Further research in underrepresented regions was needed to strengthen the global evidence base. Thirdly, 99 out of 899 searching articles were unavailable, and this constraint may have limited access to some relevant studies and could influence the generalizability of our findings.
In conclusion, this study reviewed key facilitators and barriers to the adoption of digital health technologies in LMICs from the perspective of healthcare consumers. Our findings underscore the urgent need for an enabling environment supported by strong digital infrastructure and coordinated efforts across health systems. We call for increased investment in digital health literacy and skills training to improve uptake and effectiveness. Additionally, more research is needed in underrepresented regions—such as South America—to strengthen the global evidence base and guide future implementation efforts.
Contributors
All authors helped develop the study concept and design. MW and XZ contributed to the statistical analysis and manuscript writing. KH, XL, and XZ screened and extracted the data. LD, SH, XL, and MR provided technical guidance of the data interpretation. YJ provided overall guidance and critical revision. All authors revised the manuscript and approved the final version. The corresponding author YJ has full access to all the data in the study and takes responsibility for its integrity and the accuracy of the data analysis.
Data sharing statement
The template data forms, including data extracted from included studies, data used for analyse are presented in Supplemental Table S6. The code used to analyse are presented in Supplemental Data 1.
Declaration of interests
The authors declare no conflicts of interest.
And interpretation of data, the writing of the manuscript, or the decision to submit the paper for publication.
Acknowledgements
This study was supported by Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0524500, 2024ZD0524501), National Natural Science Foundation of China (72274005 and 72304013), Beijing Nova Program (20230484284).
Footnotes
Supplementary data related to this article can be found at https://doi.org/10.1016/j.lanwpc.2026.101875.
Appendix A. Supplementary data
References
- 1.World Health Organization . 2024. Digital health overview.https://www.who.int/health-topics/digital-health/#tab=tab_1 [Google Scholar]
- 2.Molinari-Ulate M., Mahmoudi A., Parra-Vidales E., Muñoz-Sánchez J.L., Franco-Martín M.A., van der Roest H.G. Digital health technologies supporting the application of comprehensive geriatric assessments in long-term care settings or community care: a systematic review. Digit Health. 2023;9 doi: 10.1177/20552076231191008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Whitelaw S., Pellegrini D.M., Mamas M.A., Cowie M., Van Spall H.G.C. Barriers and facilitators of the uptake of digital health technology in cardiovascular care: a systematic scoping review. Eur Heart J Digit Health. 2021;2(1):62–74. doi: 10.1093/ehjdh/ztab005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Palacholla R.S., Fischer N., Coleman A., et al. Provider- and patient-related barriers to and facilitators of digital health technology adoption for hypertension management: scoping review. JMIR Cardio. 2019;3(1) doi: 10.2196/11951. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Jarvis K., Thetford C., Turck E., Ogley K., Stockley R.C. Understanding the barriers and facilitators of digital health technology (DHT) implementation in neurological rehabilitation: an integrative systematic review. Health Serv Insights. 2024;17 doi: 10.1177/11786329241229917. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Dwivedi Y.K., Rana N.P., Jeyaraj A., Clement M., Williams M.D. Re-examining the unified theory of acceptance and use of technology (UTAUT): towards a revised theoretical model. Inf Syst Front. 2019;21(3):719–734. [Google Scholar]
- 7.Xue L., Rashid A.M., Ouyang S. The unified theory of acceptance and use of technology (UTAUT) in higher education: a systematic review. Sage Open. 2024;14(1) [Google Scholar]
- 8.Pan X. Technology acceptance, technological self-efficacy, and attitude toward technology-based self-directed learning: learning motivation as a mediator. Front Psychol. 2020;11 doi: 10.3389/fpsyg.2020.564294. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Ulfert-Blank A.-S., Schmidt I. Assessing digital self-efficacy: review and scale development. Comput Educ. 2022;191 [Google Scholar]
- 10.Ye L., Kuang M., Liu S. ICT self-efficacy, organizational support, attitudes, and the use of blended learning: an exploratory study based on English teachers in basic education. Front Psychol. 2022;13 doi: 10.3389/fpsyg.2022.941535. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Sheeran P., Maki A., Montanaro E., et al. The impact of changing attitudes, norms, and self-efficacy on health-related intentions and behavior: a meta-analysis. Health Psychol. 2016;35(11):1178–1188. doi: 10.1037/hea0000387. [DOI] [PubMed] [Google Scholar]
- 12.Kalantzi V., Tsiampalis T., Kouvari M., et al. Exploring the role of self-efficacy in maintaining healthy lifestyle habits among patients with cardiometabolic diseases; findings from the multi-center IACT cross-sectional study. Life. 2024;14(6) doi: 10.3390/life14060736. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Sun Y., Wang N., Guo X., Peng J. Understanding the acceptance of mobile health services: a comparison and integration of alternative models. J Electron Commer Res. 2013;14:183–200. [Google Scholar]
- 14.Page M.J., McKenzie J.E., Bossuyt P.M., et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ (Clinical research ed) 2021;372:n71. doi: 10.1136/bmj.n71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.World Health Organization Classification of digital interventions, services and applications in health: a shared language to describe the uses of digital technology for health, 2nd ed. https://www.who.int/publications/i/item/97892400819492023
- 16.Viswanathan M., Ansari M.T., Berkman N.D., et al. Agency for Healthcare Research and Quality (US); Rockville (MD): 2008. AHRQ Methods for Effective Health Care Assessing the Risk of Bias of Individual Studies in Systematic Reviews of Health Care Interventions. Methods Guide for Effectiveness and Comparative Effectiveness Reviews. [PubMed] [Google Scholar]
- 17.Chang A. UTAUT and UTAUT 2: a review and agenda for future research. The Winners. 2012;13(2):10–114. [Google Scholar]
- 18.Williams M.D., Rana N.P., Dwivedi Y.K. The unified theory of acceptance and use of technology (UTAUT): a literature review. J Enterprise Inf Manag. 2015;28(3):443–488. [Google Scholar]
- 19.Higgins J.P., Thompson S.G., Deeks J.J., Altman D.G. Measuring inconsistency in meta-analyses. BMJ (Clinical research ed) 2003;327(7414):557–560. doi: 10.1136/bmj.327.7414.557. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Cohen J. A power primer. Psychol Bull. 1992;112(1):155–159. doi: 10.1037//0033-2909.112.1.155. [DOI] [PubMed] [Google Scholar]
- 21.Egger M., Davey Smith G., Schneider M., Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ (Clinical research ed) 1997;315(7109):629–634. doi: 10.1136/bmj.315.7109.629. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Begg C.B., Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50(4):1088–1101. [PubMed] [Google Scholar]
- 23.Acharya A., Cunningham K., Manandhar S., Shrestha N., Chen M., Weissman A. Exploring the use of mobile health to improve community-based health and nutrition service utilization in the hills of Nepal: qualitative study. J Med Internet Res. 2020;22(9) doi: 10.2196/17659. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Jiang Y., Nuerdawulieti B., Chen Z., et al. Effectiveness of patient decision aid supported shared decision-making intervention in in-person and virtual hybrid pulmonary rehabilitation in older adults with chronic obstructive pulmonary disease: a pilot randomized controlled trial. J Telemed Telecare. 2024;30(10):1532–1542. doi: 10.1177/1357633X231156631. [DOI] [PubMed] [Google Scholar]
- 25.Wu Q., Ngien A., Jiang S. Descriptive norms and eHealth use among older adults: a cross-country comparative study. Health Commun. 2024;39(12):2971–2982. doi: 10.1080/10410236.2023.2297120. [DOI] [PubMed] [Google Scholar]
- 26.Almojaibel A.A., Munk N., Goodfellow L.T., et al. Health care practitioners' determinants of telerehabilitation acceptance. Int J Telerehabil. 2020;12(1):43–50. doi: 10.5195/ijt.2020.6308. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Meng D., Guo Z. Influence of doctor-patient trust on the adoption of mobile medical applications during the epidemic: a UTAUT-based analysis. Front Public Health. 2024;12 doi: 10.3389/fpubh.2024.1414125. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Yan C., Samphanwattanachai B., Ratarat C., Phinyo P. Factors influencing user decision of telemedicine applications in Thailand. PLoS One. 2025;20(6) doi: 10.1371/journal.pone.0325512. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Hoque M.R. An empirical study of mHealth adoption in a developing country: the moderating effect of gender concern. BMC Med Inform Decis Mak. 2016;16:51. doi: 10.1186/s12911-016-0289-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Hoque M.R., Bao Y., Sorwar G. Investigating factors influencing the adoption of e-Health in developing countries: a patient's perspective. Inform Health Soc Care. 2017;42(1):1–17. doi: 10.3109/17538157.2015.1075541. [DOI] [PubMed] [Google Scholar]
- 31.Hoque R., Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model. Int J Med Inform. 2017;101:75–84. doi: 10.1016/j.ijmedinf.2017.02.002. [DOI] [PubMed] [Google Scholar]
- 32.Alam M.Z., Hu W., Hoque M.R., Kaium M.A. Adoption intention and usage behavior of mHealth services in Bangladesh and China a cross-country analysis. Int J Pharm Healthc Mark. 2020;14(1):37–60. [Google Scholar]
- 33.Pai R.R., Alathur S. Determinants of individuals' intention to use mobile health: insights from India. Transforming Gov People Process Policy. 2019;13(3/4):306–326. [Google Scholar]
- 34.Zobair K.M., Sanzogni L., Sandhu K. Expectations of telemedicine health service adoption in rural Bangladesh. Soc Sci Med. 2019;238 doi: 10.1016/j.socscimed.2019.112485. [DOI] [PubMed] [Google Scholar]
- 35.Alam M.Z., Hoque M.R., Hu W., Barua Z. Factors influencing the adoption of mHealth services in a developing country: a patient-centric study. Int J Inf Manag. 2020;50:128–143. [Google Scholar]
- 36.Alam M.Z., Hu W., Kaium M.A., Hoque M.R., Alam M.M.D. Understanding the determinants of mHealth apps adoption in Bangladesh: a SEM-neural network approach. Technol Soc. 2020;61 [Google Scholar]
- 37.Kamal S.A., Shafiq M., Kakria P. Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM) Technol Soc. 2020;60 [Google Scholar]
- 38.Li D., Hu Y., Pfaff H., et al. Determinants of patients' intention to use the online inquiry services provided by internet hospitals: empirical evidence from China. J Med Internet Res. 2020;22(10) doi: 10.2196/22716. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Zhang X., Zaman B.U. Adoption mechanism of telemedicine in underdeveloped country. Health Informatics J. 2020;26(2):1088–1103. doi: 10.1177/1460458219868353. [DOI] [PubMed] [Google Scholar]
- 40.Ahadzadeh A.S., Wu S.L., Ong F.S., Deng R. 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. J Med Internet Res. 2021;23(12) doi: 10.2196/28086. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Barua Z., Barua A. Acceptance and usage of mHealth technologies amid COVID-19 pandemic in a developing country: the UTAUT combined with situational constraint and health consciousness. J Enabling Technol. 2021;15(1):1–22. [Google Scholar]
- 42.Lei P., Zheng J.J., Li Y., Li Z.J., Gao F., Li X.S. Factors influencing online orthopedic doctor-patient consultations. BMC Med Inform Decis Mak. 2021;21(1) doi: 10.1186/s12911-021-01709-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Luo Y., Wang G., Li Y., Ye Q. Examining protection motivation and network externality perspective regarding the continued intention to use M-Health apps. Int J Environ Res Public Health. 2021;18(11) doi: 10.3390/ijerph18115684. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Octavius G.S., Antonio F. Antecedents of intention to adopt mobile health (mHealth) application and its impact on intention to recommend: an evidence from Indonesian customers. Int J Telemed Appl. 2021;2021 doi: 10.1155/2021/6698627. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Qi M., Cui J., Li X., Han Y. Perceived factors influencing the public intention to use E-consultation: analysis of web-based survey data. J Med Internet Res. 2021;23(1) doi: 10.2196/21834. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Serrano K.M., Mendes G.H.S., Lizarelli F.L., Ganga G.M.D. Assessing the telemedicine acceptance for adults in Brazil. Int J Health Care Qual Assur. 2020;34(1):35–51. doi: 10.1108/IJHCQA-06-2020-0098. [DOI] [PubMed] [Google Scholar]
- 47.Wang H., Liang L., Du C., Wu Y. Implementation of online hospitals and factors influencing the adoption of mobile medical services in China: cross-sectional survey study. JMIR mHealth uHealth. 2021;9(2) doi: 10.2196/25960. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Dash A., Sahoo A.K. Exploring patient's intention towards e-health consultation using an extended UTAUT model. J Enabling Technol. 2022;16(4):266–279. [Google Scholar]
- 49.Amin R., Hossain M.A., Uddin M.M., Jony M.T.I., Kim M. Stimuli influencing engagement, satisfaction, and intention to use telemedicine services: an integrative model. Healthcare (Basel, Switzerland) 2022;10(7) doi: 10.3390/healthcare10071327. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Saxena C., Kumar P., Thakur P. Adoption and usage intention of consumers towards telemedicine among people during pandemic times. Asia Pac J Health Manag. 2022;17(1) [Google Scholar]
- 51.Meng F., Guo X., Peng Z., Zhang X., Lai K.H. Understanding the antecedents of the routine use of mobile health services: a person-technology-health framework. Front Psychol. 2022;13 doi: 10.3389/fpsyg.2022.879760. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Mensah I.K. Understanding the drivers of Ghanaian citizens' adoption intentions of mobile health services. Front Public Health. 2022;10 doi: 10.3389/fpubh.2022.906106. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Mensah I.K., Zeng G., Mwakapesa D.S. The behavioral intention to adopt mobile health services: the moderating impact of mobile self-efficacy. Front Public Health. 2022;10 doi: 10.3389/fpubh.2022.1020474. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Khan T., Khan K.D., Azhar M.S., Shah S.N.A., Uddin M.M., Khan T.H. Mobile health services and the elderly: assessing the determinants of technology adoption readiness in Pakistan. J Publ Aff. 2022;22(4) [Google Scholar]
- 55.Arkorful V.E., Zhao S.L., Lugu B.K., Chu J.X. Consumers' mobile health adoption intention prediction utilizing an extended version of the theory of planned behavior: the moderating role of internet bandwidth. Data Base Adv Inform Systems. 2022;53(2):96–114. [Google Scholar]
- 56.Tian X.F., Wu R.Z. Determinants of the mobile health continuance intention of elders with chronic diseases: an integrated framework of ECM-ISC and UTAUT. Int J Environ Res Public Health. 2022;19(16) doi: 10.3390/ijerph19169980. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Liu Y., Lu X., Zhao G., Li C., Shi J. Adoption of mobile health services using the unified theory of acceptance and use of technology model: self-efficacy and privacy concerns. Front Psychol. 2022;13 doi: 10.3389/fpsyg.2022.944976. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Ma Y., Luo M.Y. Older people's intention to use medical apps during the COVID-19 pandemic in China: an application of the Unified Theory of Acceptance and Use of Technology (UTAUT) model and the Technology of Acceptance Model (TAM) Ageing Soc. 2022 [Google Scholar]
- 59.Liu Y.Z., Liang Z.H., Li C.J., Guo J.Z., Zhao G. An investigation into the adoption behavior of mHealth users: from the perspective of the push-pull-mooring framework. Sustainability. 2022;14(21):14372. [Google Scholar]
- 60.Xinyan Z., Mamun A.A., Ali M.H., Siyu L., Yang Q., Hayat N. Modeling the adoption of medical wearable devices among the senior adults: using hybrid SEM-neural network approach. Front Public Health. 2022;10 doi: 10.3389/fpubh.2022.1016065. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Liu J., Gong X., Weal M., Dai W., Hou S., Ma J. Attitudes and associated factors of patients' adoption of patient accessible electronic health records in China - a mixed methods study. Digit Health. 2023;9 doi: 10.1177/20552076231174101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Liu D., Son S., Cao J. The determinants of public acceptance of telemedicine apps: an innovation diffusion perspective. Front Public Health. 2023;11 doi: 10.3389/fpubh.2023.1325031. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Low M.P., Ramayah T. It isn't enough to be easy and useful! combined use of SEM and necessary condition analysis for a better understanding of consumers' acceptance of medical wearable devices. Smart Health. 2023;27 [Google Scholar]
- 64.Wang M.Y., Chen H., Gong C., et al. Understanding the use intention and influencing factors of telerehabilitation in people with rehabilitation needs: a cross-sectional survey. Front Public Health. 2023;11 doi: 10.3389/fpubh.2023.1274080. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Misra S., Adtani R., Singh Y., Singh S., Thakkar D. Exploring the factors affecting behavioral intention to adopt wearable devices. Clin Epidemiol Global Health. 2023;24 [Google Scholar]
- 66.Qu S.J., Zhou M., Kong N., Campy K.S. Factors influencing user acceptance of weight management apps among Chinese obese individuals during the COVID-19 pandemic. Health Policy Technol. 2023;12(2):100758. doi: 10.1016/j.hlpt.2023.100758. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Ren Z., Zhou G. Analysis of driving factors in the intention to use the virtual nursing home for the elderly: a modified UTAUT model in the Chinese context. Healthcare (Basel, Switzerland) 2023;11(16) doi: 10.3390/healthcare11162329. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Li T., Zhang Y.C., Luo X.L., et al. Exploring patients' intentions for usage of video telemedicine Follow-Up services: cross-sectional study. Telemed J E Health. 2023 doi: 10.1089/tmj.2023.0253. [DOI] [PubMed] [Google Scholar]
- 69.Wang Y., Lu L., Zhang R., Ma Y., Zhao S., Liang C. The willingness to continue using wearable devices among the elderly: SEM and FsQCA analysis. BMC Med Inform Decis Mak. 2023;23(1):218. doi: 10.1186/s12911-023-02336-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Zhang M., Zhang H., Zhu R., et al. Factors affecting the willingness of patients with type 2 diabetes to use digital disease management applications: a cross-sectional study. Front Public Health. 2023;11 doi: 10.3389/fpubh.2023.1259158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 71.Zhang Q., Zhang R., Lu X., Zhang X. What drives the adoption of online health communities? An empirical study from patient-centric perspective. BMC Health Serv Res. 2023;23(1):524. doi: 10.1186/s12913-023-09469-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Zhu L., Jiang X., Cao J. Factors affecting continuance intention in non-face-to-face telemedicine services: trust typology and privacy concern perspectives. Healthcare (Basel, Switzerland) 2023;11(3) doi: 10.3390/healthcare11030374. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.Zhu Y., Zhao Z., Guo J., et al. Understanding use intention of mHealth applications based on the unified theory of acceptance and use of technology 2 (UTAUT-2) model in China. Int J Environ Res Public Health. 2023;20(4) doi: 10.3390/ijerph20043139. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Lian R.Y., Li Y., Wei W. Advancing elderly healthcare: adapting UTAUT2 with inhibitors for wearable device continuance. Libr Hi Tech. 2024 [Google Scholar]
- 75.Min H., Li J., Di M., et al. Factors influencing the continuance intention of the women's health WeChat public account: an integrated model of UTAUT2 and HBM. Front Public Health. 2024;12 doi: 10.3389/fpubh.2024.1348673. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Chen Y., Yuan J., Shi L., et al. Understanding the role of technology anxiety in the adoption of digital health technologies (DHTs) by older adults with chronic diseases in Shanghai: an extension of the unified theory of acceptance and use of technology (UTAUT) model. Healthcare (Basel) 2024;12(14) doi: 10.3390/healthcare12141421. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Song L., Li B., Wu H., Wu C., Zhang X. Understanding the factors of wearable devices among the patients with thyroid cancer: a modified UTAUT2 model. PLoS One. 2024;19(7) doi: 10.1371/journal.pone.0305944. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Nayak S., Nayak V.C., G S.S. Exploring the acceptance of robotic assisted surgery among the Indian population: an empirical investigation. F1000Res. 2024;13:544. doi: 10.12688/f1000research.145052.3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 79.Malarvizhi C.A.N., Al Mamun A., Reza M.N.H., Masud M.M. Ageing well with tech: exploring the determinants of e-healthcare services adoption in an emerging economy. Digit Health. 2024;10 doi: 10.1177/20552076241272577. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 80.Liu C., Wang J., Chen R., Zhou W. Exploring the influence of Chinese online patient trust on telemedicine behavior: insights into perceived risk and behavior intention. Front Public Health. 2024;12 doi: 10.3389/fpubh.2024.1415889. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 81.Wubete Abebe A., Tilahun B.C., Mekonnen Z.A., Tegegne M.D. Intention to use patient portal and its predictors among patient with diabetes in Amhara region referral hospitals, Northwest Ethiopia, 2023: UTAUT-2 model. Digit Health. 2024;10 doi: 10.1177/20552076241277177. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 82.Timsina S.M., Bhattarai U. Identifying factors shaping the behavioural intention of Nepalese youths to adopt digital health tools. Healthc Technol Lett. 2025;12(1) doi: 10.1049/htl2.70005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 83.Akiogbe O., Feng H., Kurata K., et al. Cross-analysis of mHealth social acceptance among youth: a comparative study between Japan and China. Behav Sci. 2025;15(2) doi: 10.3390/bs15020213. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 84.Tan S.H., Yap Y.Y., Tan S.K., Wong C.K. Determinants of telehealth adoption among older adults: cross-sectional survey study. JMIR Aging. 2025;8 doi: 10.2196/60936. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 85.Huang H., Zhu J., Chen J., Qin Y., Chen S. Exploring the factors influencing the adoption of online medical services by older adults: a modified UTAUT2 based study. Front Public Health. 2025;13 doi: 10.3389/fpubh.2025.1559701. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 86.Soelasih Y., Sumani, Efendi Consumer trust in telemedicine in Indonesia. Health Informatics J. 2025;31(2) doi: 10.1177/14604582251345328. [DOI] [PubMed] [Google Scholar]
- 87.Su J., Wang Y., Liu H., Zhang Z., Wang Z., Li Z. Investigating the factors influencing users' adoption of artificial intelligence health assistants based on an extended UTAUT model. Sci Rep. 2025;15(1) doi: 10.1038/s41598-025-01897-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 88.Yao L., Li Q., Li Q., et al. Factors influencing the adoption of telemedicine services among middle-aged and older patients with chronic conditions in rural China: a multicentre cross-sectional study. BMC Health Serv Res. 2025;25(1):775. doi: 10.1186/s12913-025-12931-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 89.Wei A., Abdul Talib Y.Y., Sharif Z. Comparative analysis of digital health insurance platform adoption among urban-rural users: a UTAUT and financial literacy integration perspective. Digit Health. 2025;11 doi: 10.1177/20552076251346656. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 90.Xu J., Guo S., Yu X., Ji X. Willingness and influencing factors of adults receiving hemodialysis to use mobile healthcare apps: a cross-sectional study. BMC Nephrol. 2025;26(1):314. doi: 10.1186/s12882-025-04228-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 91.Tao D., Liu K. How do self-motivation and social motivation contribute to consumers' acceptance of m-health services? Health Pol Technol. 2024;13(3) [Google Scholar]
- 92.Schlottke J.A., Alvarez-Risco A., Bertiche E.L., et al. Acceptance factors of telemedicine in times of COVID-19: case Argentina. Pharmacia. 2024;71:1–10. [Google Scholar]
- 93.Abdullah A., Liew S.M., Hanafi N.S., et al. What influences patients' acceptance of a blood pressure telemonitoring service in primary care? A qualitative study. Patient Prefer Adherence. 2016;10:99–106. doi: 10.2147/PPA.S94687. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 94.Han K.J., Subramanian R., Cameron G.T. Listen before you leap: Sri Lankan health professionals' perspectives on m-health. Health Informatics J. 2019;25(3):858–866. doi: 10.1177/1460458217725903. [DOI] [PubMed] [Google Scholar]
- 95.Namatovu H.K., Oyana T.J., Sol H.G. Barriers to eHealth adoption in routine antenatal care practices: perspectives of expectant mothers in Uganda - a qualitative study using the unified theory of acceptance and use of technology model. Digit Health. 2021;7 doi: 10.1177/20552076211064406. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 96.Sekandi J.N., Kasiita V., Onuoha N.A., et al. Stakeholders' perceptions of benefits of and barriers to using video-observed treatment for monitoring patients with tuberculosis in Uganda: exploratory qualitative study. JMIR mHealth uHealth. 2021;9(10) doi: 10.2196/27131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 97.Du Y., Zhou Q., Cheng W., et al. Factors influencing adoption and use of telemedicine services in rural areas of China: mixed methods study. JMIR Public Health Surveill. 2022;8(12) doi: 10.2196/40771. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 98.Hasan N., Sultana R., Bao Y.K. Re-Conceptualizing the drivers toward mHealth adoption in a least developing country: a qualitative exploration. Sage Open. 2022;12(2) [Google Scholar]
- 99.Liverani M., Ir P., Perel P., Khan M., Balabanova D., Wiseman V. Assessing the potential of wearable health monitors for health system strengthening in low- and middle-income countries: a prospective study of technology adoption in Cambodia. Health Policy Plan. 2022;37(8):943–951. doi: 10.1093/heapol/czac019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 100.Meyer C.L., Surmeli A., Hoeflin Hana C., Narla N.P. Perceptions on a mobile health intervention to improve maternal child health for Syrian refugees in Turkey: opportunities and challenges for end-user acceptability. Front Public Health. 2022;10 doi: 10.3389/fpubh.2022.1025675. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 101.Pan J., Dong H. mHealth adoption among older Chinese adults: a conceptual model with design suggestions. Int J Hum Comput Interact. 2023;39(5):1072–1083. [Google Scholar]
- 102.Zhou L., Li Y., Zhang Y., Chen X., Zhang S., Hu X. Perceptions of telehealth services among rural lung cancer patients in China: a qualitative study using the technology acceptance model. Semin Oncol Nurs. 2024;40(5) doi: 10.1016/j.soncn.2024.151710. [DOI] [PubMed] [Google Scholar]
- 103.Sampene A.K., Li C., Wiredu J. Unravelling the shift: exploring consumers' adoption or resistance of E-Pharmacy through behavioural reasoning theory. BMC Public Health. 2024;24(1):2789. doi: 10.1186/s12889-024-20265-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 104.Xu X., Zhang G., Xia Y., et al. Influencing factors and implementation pathways of adherence behavior in intelligent personalized exercise prescription: qualitative study. JMIR mHealth uHealth. 2024;12 doi: 10.2196/59610. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 105.Kizito M., Mugabi E.N., Ford S., Holtz B., Hirko K. Characterizing telehealth barriers and preferences to promote acceptable implementation strategies in central Uganda: multilevel formative evaluation. JMIR Form Res. 2025;9 doi: 10.2196/60843. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 106.Venkatesh V., Morris M.G., Davis G.B., Davis F.D. User acceptance of information technology: toward A unified View 1. Manage Inf Syst Q. 2003;27(3):425–478. [Google Scholar]
- 107.Mekheimer M. Technological self-efficacy, motivation, and contextual factors in advanced EFL e-learning: a mixed-methods study of strategy use and satisfaction. Hum Soc Sci Commun. 2025;12(1):677. [Google Scholar]
- 108.Hampel N., Sassenberg K., Scholl A., Ditrich L. Enactive mastery experience improves attitudes towards digital technology via self-efficacy – a pre-registered quasi-experiment. Behav Inf Technol. 2024;43(2):298–311. [Google Scholar]
- 109.Warner L.M., Schüz B., Knittle K., Ziegelmann J.P., Wurm S. Sources of perceived self-efficacy as predictors of physical activity in older adults. Applied Psych Health Well. 2011;3(2):172–192. [Google Scholar]
- 110.Yan K., Balijepalli C., Druyts E. The impact of digital therapeutics on current health technology assessment frameworks. Front Digit Health. 2021;3 doi: 10.3389/fdgth.2021.667016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 111.John D., Dutta Majumdar A., Pillai R.N., et al. Health technology assessment for digital health technologies in India: a framework for action. Int J Technol Assess Health Care. 2024;40(1):e70. doi: 10.1017/S0266462324000345. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 112.Wong A.K.C., Bayuo J., Su J.J., et al. Effectiveness of the support from community health workers and health care professionals on the sustained use of wearable monitoring devices among community-dwelling older adults: feasibility randomized controlled trial. J Med Internet Res. 2024;26 doi: 10.2196/52435. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 113.Chong C.J., Makmor-Bakry M., Hatah E., et al. Factors infuencing type 2 diabetes mellitus patients’ readiness, acceptance and barriers towards mobile apps adoption for medication adherence. Int J Diabetes Dev Ctries. 2025;45(3):635–645. [Google Scholar]
- 114.McHugh M.C., Fowler C.A., Philbin S., et al. Qualitative evaluation informs the implementation of a telehealth program to manage chronic pain. J Pain. 2024;25(7) doi: 10.1016/j.jpain.2024.02.007. [DOI] [PubMed] [Google Scholar]
- 115.Shao C., Nah S., Makady H., McNealy J. Understanding user attitudes towards AI-Enabled technologies: an integrated model of self-efficacy, TAM, and AI ethics. Int J Hum Comput Interact. 2025;41(5):3053–3065. [Google Scholar]
- 116.Jeffrey B., Bagala M., Creighton A., et al. Mobile phone applications and their use in the self-management of type 2 diabetes mellitus: a qualitative study among app users and non-app users. Diabetol Metab Syndr. 2019;11:84. doi: 10.1186/s13098-019-0480-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 117.Guo S.H., Hsing H.C., Lin J.L., Lee C.C. Relationships between mobile eHealth literacy, diabetes self-care, and glycemic outcomes in Taiwanese patients with type 2 diabetes: cross-sectional study. JMIR mHealth uHealth. 2021;9(2) doi: 10.2196/18404. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 118.Turkmani S., Smith R.M., Tan A., et al. An evaluation of the introduction of telehealth for remote antenatal and postnatal contacts in Bangladesh and Lao People's Democratic Republic during the COVID-19 pandemic. PLOS Glob Public Health. 2023;3(5) doi: 10.1371/journal.pgph.0000786. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 119.van Kessel R., Wong B.L.H., Clemens T., Brand H. Digital health literacy as a super determinant of health: more than simply the sum of its parts. Internet Interv. 2022;27 doi: 10.1016/j.invent.2022.100500. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 120.WHO . 2023. Digital Health Literacy Key to Overcoming Barriers for Health Workers, WHO Study Says. [Google Scholar]
- 121.Kickbusch I., Piselli D., Agrawal A., et al. The Lancet and Financial Times Commission on governing health futures 2030: growing up in a digital world. Lancet. 2021;398(10312):1727–1776. doi: 10.1016/S0140-6736(21)01824-9. [DOI] [PubMed] [Google Scholar]
- 122.World Health Organization . Fifty-Eighth World Health Assembly; 2005. WHA58. 28 eHealth; p. 108. [Google Scholar]
- 123.Global Strategy on Digital Health 2020-2025. World Health Organization; Geneva: 2021. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.