Patient Acceptance and Barriers to IoT Utilization in Healthcare: A Systematic Literature Review (Preprint)
Bibliographic record
Abstract
Background: The Internet of Things (IoT) represents a transformative paradigm in health care service delivery, offering unprecedented potential to enhance quality of care, operational efficiency, and patient outcomes through interconnected devices and real-time data analytics. Despite rapid adoption, implementation depends on patient acceptance as end users. While literature extensively documents technical and institutional perspectives, a comprehensive understanding of patient acceptance factors remains fragmented, with high technology abandonment rates. Systematic synthesis of patient perspectives is critically needed to inform user-centered design, effective implementation strategies, and supportive policy frameworks. Objective: This systematic literature review aims to identify and synthesize factors influencing patient acceptance of IoT technology in health care services, barriers hindering adoption, and effective strategies for enhancing acceptance. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, we systematically searched eight electronic databases (PubMed/MEDLINE, Scopus, IEEE Xplore, Web of Science, ScienceDirect, ACM Digital Library, ProQuest, and Google Scholar) for empirical studies published between January 2016 and December 2024. Inclusion criteria encompassed peer-reviewed empirical research examining patient perspectives on IoT technology in health care services, published in English or Indonesian. From 2537 initially identified papers, 62 studies met inclusion criteria after systematic screening and full-text evaluation. Quality assessment was conducted using the Mixed Methods Appraisal Tool. Results: The 62 included studies represented diverse geographic contexts (Asia, Europe, North America, and the Middle East) and methodological approaches. Quality assessment revealed 45 (73%) studies of good-to-excellent quality. Perceived usefulness emerged as the strongest acceptance facilitator, identified in 55 of 62 (89%) studies, followed by perceived ease of use in 47 (76%) studies, and trust and security in 42 (68%) studies. Cost-effectiveness was identified in 32 (52%) studies as an important consideration. Primary barriers included data security concerns in 26 (42%) studies, privacy issues in 24 (39%) studies, lack of digital literacy in 22 (36%) studies, and resistance to change in 20 (32%) studies. Interoperability issues and high costs were identified in 19 (31%) studies and 18 (29%) studies, respectively. User-centered design was the most frequently recommended enhancement strategy in 20 (32%) studies, followed by user-friendly interface development in 19 (31%) studies, digital literacy programs in 18 (29%) studies, and health care professional involvement in 15 (24%) studies. Digital literacy functioned as a significant moderator, and trust served as both direct predictor and mediator. Conclusions: Patient acceptance of IoT in health care represents a complex, multidimensional phenomenon requiring holistic approaches that integrate user-centered technology design, comprehensive digital literacy programs, trust-building mechanisms, and supportive policy frameworks. Successful implementation necessitates multilevel strategies addressing individual, organizational, and system factors simultaneously. With evidence-based, patient-centered approaches, IoT technology holds substantial potential to transform health care delivery into more proactive, personalized, and accessible services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".