Usability assessment of point-of-care diagnostics for infectious diseases in low-resource settings: a scoping review of current practices

Usability assessment of point-of-care diagnostics for infectious diseases in low-resource settings: a scoping review of current practices | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Usability assessment of point-of-care diagnostics for infectious diseases in low-resource settings: a scoping review of current practices Maria del Mar Castro, Horeya M. Ismail, Carlos Alberto Montenegro-Quiñonez, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8221943/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Usability is a critical determinant of successful implementation of diagnostic technologies, particularly for point-of-care (POC) and self-testing tools intended for resource-limited settings. Despite its importance, no prior review has systematically examined how usability is evaluated in diagnostic test development and implementation. This scoping review synthesizes current methods and practices used to assess usability of infectious disease diagnostics. Methods We conducted a scoping review following PRISMA-ScR and JBI guidance. Eligible studies reported usability evaluations of molecular or immunoassay-based diagnostics intended for decentralized or low-resource settings. Searches were performed in five databases and nine additional sources, including the WHO Prequalification of In Vitro Diagnostics registry. Data were extracted on study characteristics, user groups, settings, evaluation methods, sampling strategies, and reported usability outcomes. Results We identified 103 studies, most focused on HIV, COVID-19, malaria, or hepatitis C, and conducted in a limited number of countries. Self-testing evaluations generally used larger samples and assessed more outcome domains than those involving professional users; however, sample size justification was rare and participant selection methods were often unclear. Most studies relied on non-standardized questionnaires, with few using validated instruments or qualitative approaches. Usability outcomes most commonly addressed ease-of-use and effectiveness, while domains such as safety, memorability, and satisfaction were less consistently assessed. WHO prequalification dossiers provided minimal methodological detail. Synthesizing regulatory guidance with review findings, we developed a usability assessment framework comprising core domains (effectiveness, efficiency, errors and use safety), complementary domains (learnability, memorability, satisfaction), and contextual domains capturing environmental and system-level factors. Conclusions Substantial methodological heterogeneity exists in usability assessments of diagnostic tests. Standardized outcome definitions, broader methodological approaches, and improved reporting are needed to strengthen usability evidence for implementation. A Delphi consensus process is planned to define core usability outcomes and recommended methodologies for diagnostic evaluation. Diagnostics Point-Of-Care self-testing usability human factors LMICs Figures Figure 1 Figure 2 Figure 3 Contributions to the literature Usability is increasingly recognized as a determinant of successful diagnostic tests use and implementation, yet methods for its evaluation remain poorly defined. This is the first review to systematically assess how usability is evaluated during the development and implementation of infectious disease diagnostics for use in low-resource settings. We identified wide variation in current methods, including unclear sampling strategies, limited use of validated tools, and a narrow emphasis on task performance and ease-of-use, with infrequent reporting of outcomes like safety or memorability. We propose a framework and outcome domains to support consensus-building and improve the quality of diagnostic usability research. Introduction Advancements in decentralized diagnostics, particularly point-of-care (POC) tests, have transformed infectious disease management by enabling rapid detection, timely treatment, and reduced transmission ( 1 ). These tests are especially valuable in resource-limited settings, where access to centralized laboratories is often limited ( 2 ). POC diagnostics have significantly improved patient care for infections such as HIV, malaria, and syphilis, where rapid and accurate diagnosis is essential for effective clinical and public health responses ( 3 ). The COVID-19 pandemic further underscored the need for diagnostic tools that are rapid, scalable, and easily deployable across diverse settings and user groups ( 4 ). The ASSURED criteria (Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment-free, and Deliverable) offer a widely accepted benchmark for successful implementation ( 5 ). More recently complemented by the RE-ASSURED and REST-ASSURED frameworks, which also incorporate interoperability with Health Information Systems (HIS) ( 5 , 6 ). However, beyond user-friendliness ( 5 ), implementation success also depends on the interaction between users and diagnostic tools, often assessed through usability evaluations grounded in human factors research ( 7 ). Frameworks such as Nielsen’s usability emphasize dimensions including learnability, efficiency, error prevention, and user satisfaction ( 8 , 9 ). Usability evaluations differ substantially in scope, method, and rigor. Standardized instruments such as the System Usability Scale (SUS) are widely used in health technology assessment, including those of diagnostic tools ( 10 ). In addition to these structured approaches, usability studies encompass a wide range of methods, from simulated assessments of decision-making and workload ( 11 ) to longitudinal fieldwork, user interviews ( 12 ), and focused observations of device interaction ( 13 ). Recent reviews reflect this variability: Maqbool and Herold ( 14 ) noted a predominant reliance on inquiry and testing methods (e.g., questionnaires), with some usability characteristics such as memorability, accessibility, and operability being mostly overlooked. Similarly, a scoping review of sensor-based digital health technologies ( 15 ) identified that usability domains such as learnability and efficiency are often under-assessed, with limited inclusion of diverse end users in the evaluation process. These findings highlight the heterogeneity in usability evaluation practices. Regulatory and standards-based guidance exist for usability evaluation. The U.S. Food and Drug Administration (FDA) outlines structured approaches to usability and risk mitigation through its guidance on Applying Human Factors and Usability Engineering to Medical Devices ( 7 ). Similarly, ISO standards ( 16 , 17 ), the CLIA waiver process ( 18 ), and the European In Vitro Diagnostic Regulation (IVDR) ( 19 ) recommend the integration of usability engineering across the product lifecycle. However, the ways in which these guidance documents are applied, and the stages of the product life cycle at which they are implemented by academic and commercial diagnostic developers, remain underexplored, particularly for tests designed for resource-constrained settings. This scoping review aims to describe current practices in the usability evaluation of point-of-care diagnostics (as a broad term including near-POC, true-POC, and self-testing) for infectious diseases, with a focus on molecular and immunoassay-based tests for use in low- and middle-income countries (LMICs). This review draws upon regulatory and human factors guidance to assess how usability is currently conceptualized and evaluated, and to identify opportunities to support implementation through improved usability evaluation practices. Methods This scoping review follows the recommendations from the PRISMA extension for Scoping Reviews (PRISMA-SR) ( 20 , 21 ) and the JBI guidance for scoping reviews ( 22 ). A detailed protocol of this review has been published ( 23 ) We grounded our review in regulatory and standards-based guidance documents that currently shape usability evaluation practices for diagnostic technologies. These included the U.S. FDA’s Guidance on Human Factors and Usability Engineering ( 7 ), ISO 62366-1 (Application of Usability Engineering to Medical Devices) ( 16 ), and ISO 9241 − 210 (Human-Centered Design for Interactive Systems) ( 17 ). This approach was chosen to reflect the practical considerations developers and implementers must navigate in low-resource settings, where usability, safety, and regulatory compliance are closely tied to implementation feasibility. Eligibility criteria We included original research studies of all designs that reported usability evaluations of a diagnostic test for infectious diseases. This encompassed usability evaluations conducted as standalone studies, nested within larger clinical studies (e.g., diagnostic accuracy), and reported in product dossiers. Eligible technologies included prototype and design-locked tests intended for use within the care cascade of infectious diseases prior treatment. We focused on molecular tests (e.g., PCR, isothermal amplification) and antigen or antibody-detection rapid diagnostic tests (Ag-RDTs, Ab-RDTs) designed for use at the Point-of-Care (POC), near-POC, or self-testing. Tests were eligible if they required minimal laboratory infrastructure or incorporated automated features for result generation or interpretation. For the purpose of this review, we grouped near-POC and true-POC technologies into a single "POC" category. Intended users included healthcare providers with varying levels of training, as well as lay users such as community health workers, field agents, and individuals performing self-testing. The use settings included: a) Self-testing (e.g., at home); b) Level 0 (L0) – Community; c) Level 1 (L1) - Primary Care and d) Level 2 (L2) - District Hospital Laboratory. We excluded editorials, reviews, conference abstracts, opinion papers, letters, and preprints. Search strategy, databases and search terms The searches were performed from August to September 2023. Searches were conducted by an independent librarian in PubMed/MEDLINE, Embase, and Web of Science, and two independent investigators (CAMQ and HM) conducted searches in Google Scholar and grey literature databases: LILACS, WHOiris, PAHOiris, and EBSCO. Search terms were tailored to each database and are detailed in Supplementary Table S1 . No restrictions were applied regarding publication year, language, setting, or population. Searches were conducted in English, and translations were performed using Google Translate or ChatGPT when required. To capture relevant usability data outside the peer-reviewed literature, we screened nine websites belonging to regulatory agencies, product development partnerships, and diagnostic networks. Only the WHO list of prequalified in vitro diagnostic products contained product dossiers with relevant information (listed in Supplementary Table S2 ). Study selection Search results were imported into Zotero ( www.zotero.org ) for de-duplication. Title and abstract screening were performed independently by two reviewers (HM and CAMQ) using Rayyan ( https://www.rayyan.ai ). Discrepancies were resolved through discussion or adjudicated by a third reviewer (MMC). Full-text articles meeting inclusion criteria were reviewed in detail. Reference lists of included articles were hand-searched to identify additional studies. Full text screening of the product dossiers followed the same approach as the article screening. Data charting A customized extraction form was used for data charting ( 23 ). The extraction form included a priori defined topics: citation details, geographic location, year of publication, study settings, participants, type of test, use settings, study design, type of data collected, type of evaluation, sample size, method for sample size estimation and sampling, description of critical tasks, use scenarios, test environment, data collection tools, methods for results documentation, reported usability outcomes and metrics. The type of evaluation was classified as formative, summative, or nested, with the latter referring to assessments embedded within larger studies, conducted at any stage of test development. Two reviewers (HM and CAMQ) extracted the data from the included studies, and any discrepancies were resolved through consensus, when consensus was not reached, a third researcher (MMC) had the final decision. For the WHO prequalified product dossiers, data extraction was supported by a custom GPT-based AI tool, trained using the extraction form and iteratively refined prompts to optimize accuracy and relevance ( https://chatgpt.com/g/g-m24WAgSc2-data-extraction?model=gpt-4o ). The AI tool was used to extract key variables from the full text of each dossier, including name and type of the test, developer, intended use setting, assay description intended participants, ease-of-use, and sample size. Extracted data were validated by a human reviewer (HM, CAMQ, or MMC) to ensure accuracy and completeness. Discrepancies were resolved by a second human reviewer. Risk of bias assessment Consistent with the aims and methodology of scoping reviews, risk of bias and quality assessments were not conducted, as the focus was on mapping usability evaluation practices rather than appraising study validity or effectiveness. Data synthesis We provide a narrative synthesis of the data. Categorical data were summarized using descriptive statistics, and findings are presented in tables, figures, and text. Tables and figures were generated using Stata Version 14 and R statistical software ( https://cran.r-project.org/mirrors.html ). To guide analysis and interpretation, we drew on regulatory and human factors frameworks, including the FDA’s Human Factors Engineering Guidance, ISO 62366-1, and ISO 9241 − 210 ( 7 , 16 , 17 ), as well as Nielsen’s usability attributes ( 8 ). These documents provided a practical lens to assess usability evaluation practices in contexts where formal implementation science frameworks may not be applied but where design, safety, and usability directly influence implementation feasibility. Based on these frameworks, we developed themes to categorize and compare evaluation practices across studies, including: User characteristics and training, Description of use scenarios and environments, Identification and assessment of critical tasks, Data collection methods and usability measures, and Reported outcomes (e.g., effectiveness, efficiency, satisfaction) This thematic approach enabled structured synthesis of heterogeneous data and highlighted areas of convergence and divergence relative to regulatory expectations and implementation-relevant considerations. Results The search strategy yielded 8060 references across all sources. After removing duplicates, 5747 articles were screened by title and abstract. Of these, 617 articles underwent full-text screening, resulting in the inclusion of 103 articles in the review. The main reasons for exclusion included the absence of usability-related outcomes and the focus on non-POC diagnostic tests (Fig. 1 ). Characteristics of included studies The 103 articles covered studies from 47 countries, with most publications concentrated in just a few (Fig. 2 ). The United States and Kenya each contributed more than 11 studies, followed by South Africa (n = 10), Germany (n = 8), Bangladesh (n = 7), and the United Kingdom (n = 6). An additional 10 countries, including Brazil, Cambodia, the Democratic Republic of Congo, Dominica, France, Malawi, Malaysia, Spain, Uganda, and Zambia, contributed 2 to 5 studies each. Other countries were represented by a single study or not at all. The majority of studies were conducted in a single-country setting; however, nine studies were multi-country, eight of which involved 2–3 countries, and one spanned nine countries ( 24 ). In terms of publication date, most studies were published within the last 15 years, with only one predating 2000 ( 25 ). This study assessed the ease-of-use of six rapid serological tests for HIV-1 antibody, nested in a diagnostic accuracy evaluation. The diversity of test types increased notably after 2015 ( Supplementary Fig S1 ). Characteristics of diagnostic tests A total of 162 tests were evaluated across the included studies. The majority (90.7%) were developed by industry, while 3.1% originated from academic institutions. In 6.2% of cases, the developer was not specified. The United States accounted for the largest share of developers (41.1%), followed by the United Kingdom (9.9%), India (9.3%), France (7.3%), and South Korea (6.6%). Additional developers were based in Canada, Australia, Germany, South Africa, or were multinational collaborations (n = 3). Regarding diagnostic technologies, 50.6% of tests were antibody-based, 35.2% antigen-based, and 11.7% molecular tests, while a small fraction (1.9%) combined antigen and antibody detection. One test (0.6%) employed a microfluidic assay (mChip), contributing to the overall technological diversity of the evaluated tests. The most frequently targeted disease was HIV/AIDS (30.3%), followed by COVID-19 (25.3%), malaria (14.2%), and hepatitis C (7.4%); 4.3% of studies assessed multi-disease diagnostic tests. Current methods and practices in usability evaluations Types of evaluations and data collected Most articles reported quantitative methods, either alone (n = 65 articles) or in combination with qualitative methods (n = 30); only eight used qualitative methods exclusively (Fig. 3 -A). The most common data collection tool was non-standardized questionnaires, used in nearly half of all studies (44.7%) and frequently applied across formative, summative, and nested study designs (Fig. 3 -B). Standardized or validated surveys were less common (12.6%) and primarily used for both summative evaluations and those nested within larger studies. Observation of users handling the tests were less frequently employed (5.8%) as single data collection method, although were often used in combination with other approaches. Among qualitative studies, interviews and focus group discussions (FGDs) were the primary method (Fig. 3 -B). Among the standardized questionnaires, six studies used the System Usability Scale ( 10 ), and other six reported using externally validated questionnaires. Two studies used the After-scenario questionnaire (ASQ), while one each reported using the Mobile Application Rating Scale (MARS), a 16-item WHO RDT training evaluation checklist, and a 15-item usability index build from WHO guidance. Notably, there was a predominance of Likert-scale type of questions, both for the standardized and the custom questionnaires (n = 47 described Likert-scale questions). Among studies that used observations either alone or in combination with other methods, checklists were the most employed tool (n = 32). Five studies relied on unstructured observations or did not specify the tool used. In terms of other approaches for data collection, one study reported a Failure Mode Effects Analysis approach ( 26 ), one used a rating system (similar to heuristic evaluation) ( 27 ). None reported using other approaches for usability data collection, such as think-aloud or cognitive walkthroughs. The majority of studies used descriptive statistics for data analysis (96%, n = 3 did not report an analysis approach), and, among the studies that reported analyzing qualitative data, thematic analysis was the most used approach (n = 12). One study reported using natural language processing for open-ended questions ( 28 ). Use scenarios and testing environments Regarding test environments, the majority utilized real-life scenarios (n = 96) while seven studies incorporated simulated scenarios ( 13 , 25 , 26 , 29 – 32 ). The simulated environments varied widely, ranging from simulated home and primary care environments to laboratory testing done by technicians and members of the research team. Eight different study settings were identified, including self-testing, community (Level 0), primary care (Level 1), district hospital laboratory (Level 2), regional or provincial laboratory (Level 3) ( 33 – 38 ), reference or national laboratory (Level 4). In addition, study settings included research laboratories, prisons, and homeless shelters ( 39 , 40 ), city-administrated laboratories ( 41 ), sex work establishments ( 42 ), communes ( 43 ) and clinical ambulatory testing facilities ( 36 , 44 – 47 ). User characteristics, training, sample size and sampling Usability assessments were conducted among a variety of participant groups. Self-testing users were the most common, reported in 50 studies (48.5%). Community health workers or field agents were involved in eight studies (7.8%), while laboratory technicians participated in 16 studies (15.5%). Medical technicians took part in three studies (2.9%), and nurses were included in four (3.9%). Additionally, 22 studies (21.4%) evaluated usability among multiple user groups. Of these, three studies (13.6%) involved only professional users, seven (31.8%) focused on non-professional users, and 12 studies (54.6%) assessed usability among both professional and non-professional user groups. Sample sizes varied across studies, with notable differences by both evaluation type and testing setting. Usability studies for tests intended for POC settings reported smaller sample sizes, with 5 and 6 participants (n = 2) for formative evaluations, a median of 6 participants (IQR: 19–40, n = 43) for studies nested within larger evaluations, and 10 participants (IQR: 10–68, n = 7) for summative evaluations. In contrast, studies conducted in self-testing contexts involved larger samples. Formative evaluations in self-testing settings had a median of 40 participants (IQR: 49–251; n = 6), while nested and summative evaluations included median of 115 (IQR: 190–354; n = 30) and 101 participants (IQR: 305–767; n = 15) respectively. Only 16 of the included studies described a method or rationale for sample size determination; the remaining 87 did not report how sample size was estimated. Among formative studies, no formal sample size was calculated. For studies nested within larger evaluations, one cited the WHO technical specifications for prequalification ( 48 ), three used sample size calculations for a proportion ( 49 – 51 ), and five adopted the sample size originally estimated for the parent study ( 52 – 55 )( 56 ). Among these studies reporting sample size calculations, one assessed a POC test ( 55 ), and the rest were intended for self-testing. In summative evaluations, two studies referred to the WHO guidance as the basis for determining participant numbers ( 57 , 58 ). Across study types, when described, the most common sampling strategy was the inclusion of study team members or individuals already involved in the parent study. Reported outcomes Across the included studies, even broad outcome categories were identified, namely, ease-of-use, memorability, learnability, satisfaction, efficiency, effectiveness, and safety ( Supplementary Table S3, Supplementary Figure S2 ). The most commonly assessed outcomes across both self-testing and POC settings were ease-of-use and effectiveness. These were frequently operationalized through measures such as perceived ease-of-use, clarity of instructions, task completion, and for effectiveness, measures like agreement with reference standards or between user types. In self-testing contexts, usability evaluations tended to be broader in scope, often incorporating multiple domains. However, outcomes related to memorability (e.g., retention of instructions, confidence over time) and safety (e.g., sample handling risks, incorrect result interpretation) were still reported less frequently. Measures of learnability and satisfaction appeared with intermediate frequency, operationalized through assessments of comprehension of instructions, independent use, and willingness to reuse or recommend the test. In contrast, POC evaluations tended to focus more narrowly on task performance and perceived ease-of-use, with limited attention to the other outcomes. Only a small proportion of studies in POC or self-testing settings explicitly evaluated adverse events or user-related hazards. Product dossiers The product dossiers retrieved from the WHO Prequalification (PQ) registry provided limited information on usability evaluation. While most dossiers included brief descriptions related to ease-of-use (perceived ease-of-use and training requirements) none reported details on the methods used to assess usability, including sample size, data collection approaches, or user group characteristics. ( Supplementary Table S4 ) Discussion This scoping review described how usability of POC and self-testing diagnostics for infectious diseases intended for use in low-resource settings is evaluated in practice. Most studies employed quantitative methods, predominantly relying on non-standardized questionnaires, with limited use of standardized tools or qualitative approaches. Usability outcomes focused predominantly on ease of use and task performance (effectiveness), while domains such as memorability, safety, and learnability were assessed less consistently. Self-testing evaluations had broader scope in terms of outcomes and involved larger sample sizes compared to those targeting professional users. However, sample size estimation was infrequently reported for all types of evaluations, and participant selection strategies for usability evaluation were often poorly documented. While most reviews in the field of usability have focused on digital tools ( 14 , 59 ) and electronic health records ( 60 ), this is, to our knowledge, the first review dedicated to the usability evaluation of diagnostic tests. We identified a broad range of diagnostic technologies evaluated for usability and an increase in the number of publications in recent years; notably, studies were concentrated in a small number of countries and focused on four diseases (HIV/AIDS, COVID-19, malaria, and hepatitis C). In addition, most diagnostic tests were developed by industry actors based in high-income countries, and reported evaluations were largely done at the end stages of development. This distribution highlights the limited representation from low- and middle-income settings where many of these tests are intended for implementation. Costs and resource constraints may contribute to the limited scope and geographic distribution of these studies. In terms of use scenarios and type of users, the included studies represented a broad distribution of settings, as well as mostly non-professional user groups, which are in line with the intended use of POC and self-testing technologies. Larger sample sizes were reported for self-testing studies, which suggest that these evaluations are generally designed with broader inclusion criteria or greater regulatory expectations, likely reflecting the increased variability in lay-user populations and the need to demonstrate performance across diverse settings and levels of user training. However, the wide variability in sample sizes and the frequent absence of sample size justification limit the generalizability of findings. These limitations are further compounded by the common use of convenience sampling and poor description of sampling methods. This is a finding common to usability studies, as demonstrated by previous reviews in the field of digital health and wearable technologies ( 14 , 61 ). Evidence from a scoping review of sensor-based digital health technologies found that only 22% of studies included diverse user types (e.g., caregivers, clinicians), and few reported complete sociodemographic data, limiting insights into usability across varied real-world populations ( 61 ). While a commonly cited rule of thumb suggests that 5–10 participants can identify most usability issues ( 62 , 63 ), this approach does not account for variability in user characteristics or contextual factors affecting generalizability. Recent efforts have emphasized the need to improve sample size estimation and sampling strategies in usability studies ( 64 ), including the use of power calculations for quantitative data and the application of established qualitative sampling approaches where appropriate. Usability taxonomies ( 14 ) outline a broad set of evaluation methods across inquiry, inspection, and testing domains. In contrast, the studies included in this review relied heavily on questionnaires and basic observational tools, with minimal use of structured methods such as heuristic evaluation, scenario testing, or think-aloud protocols ( 65 ). This narrow methodological scope may limit the ability to detect usability issues that emerge under real-world or unsupervised conditions. Similar to previous reviews ( 61 ), the reported outcomes in the included studies focus on few usability attributes, predominantly ease-of-use. The findings of this review have implications for the implementation of POC and self-testing diagnostics in low-resource settings. Usability is a key determinant of successful adoption, particularly where end users may have limited training and where infrastructure for supervised testing is often unavailable. Incomplete assessment of usability, especially regarding safety, memorability, and satisfaction, may compromise implementation fidelity and acceptability. For self-testing, inadequate attention to user comprehension, confidence, or error risks could lead to inappropriate test use, misinterpretation, or loss of trust in results. For POC tests, usability challenges that arise from poorly defined critical tasks or insufficient training may reduce diagnostic accuracy or limit uptake. In both contexts, the lack of standardized evaluation frameworks constrains comparability across products and may hinder the generation of evidence needed for scale-up. Alignment with Regulatory Guidance Several aspects of current usability evaluation practices align with international regulatory guidance, particularly in the emphasis on task completion, perceived ease-of-use, and effectiveness. These domains are consistent with principles outlined in frameworks such as the FDA’s Guidance on Human Factors and Usability Engineering and ISO 62366-1 ( 7 , 16 ). However, other areas prioritized in regulatory standards, most notably user safety, error mitigation, and the identification of critical tasks were infrequently reported, especially in POC evaluations. The use of validated instruments, including the System Usability Scale (SUS) and WHO-aligned checklists, was limited. These gaps suggest that while some regulatory expectations are being met, usability evaluation practices in the diagnostic space require standardization. These findings also reflect partial alignment with the WHO Technical Specifications Series ( 66 ) for Prequalification of In Vitro Diagnostics, which outline specific usability-related requirements for diagnostic test dossiers. While a limited number of studies cited WHO guidance in relation to sample size justification or used adapted checklist tools, most did not explicitly describe elements emphasized in the guidance. These include the evaluation of critical user tasks, structured reporting of user errors and risk mitigations. This limited integration suggests that WHO recommendations are not consistently informing usability evaluation practices, particularly in studies conducted outside of formal prequalification processes. CUE-Dx: A proposed usability framework to guide future studies We developed CUE-Dx (Comprehensive Usability Evaluation for Diagnostics), a conceptual framework to define and evaluate usability of point-of-care (POC) diagnostic tests integrating regulatory guidance and the findings from this review. The framework organizes usability into three interrelated domains: core domains (effectiveness, efficiency, errors and use safety) representing the minimum constructs required for safe and accurate test use; complementary domains (learnability, memorability and satisfaction) capturing additional aspects of user experience; and contextual domains (infrastructure, workflow integration, organizational and policy context, and cultural and language considerations) reflecting environmental and system-level factors influencing usability in practice. A detailed description of the framework and operational definitions for each domain are provided in Supplementary file 1 . Strengths and limitations The strengths of this scoping review include a comprehensive and extensive search of the available evidence on usability evaluation or validation of diagnostic tests for infectious diseases, including information in the literature and information available in product dossiers. The limitations of this scoping review include potential publication and selection biases, as well as challenges in defining inclusion and exclusion criteria specifically related to the variable definitions of POC testing and usability. It is possible that the detailed evaluations of usability are included in regulatory submissions but not in the public domain. We did not apply restrictions of language, but the searches were carried out in English; therefore, evidence published in other languages could have been overlooked. Being a scoping review, quality or risk of bias from the individual studies was not assessed. Finally, definitions of POC testing have evolved over time, with recent classifications distinguishing between near-POC, true-POC, and self-testing. For the purposes of this review, near-POC and POC were grouped into a single category to facilitate synthesis. While this simplification allowed for clearer comparisons, it may have masked important differences in test delivery and user interaction across settings. Conclusions and future directions This scoping review highlights a growing body of literature on the usability of diagnostic tests for infectious diseases in decentralized, resource-constrained settings. The methodological heterogeneity across studies underscores the need for robust, standardized approaches to usability evaluation, particularly those that integrate regulatory guidance with real-world implementation considerations. Key areas for improvement include standardization of reporting, diversification of data collection and analysis methods, improved sample size estimation and sampling strategies to enhance generalizability, and greater harmonization in the definition and measurement of usability outcomes. To support this goal, we are planning a Delphi survey with international experts to build consensus on core usability outcomes and methodological approaches for diagnostics. Abbreviations POC: Point-of-Care LMICs : Low- and Middle-Income Countries PRISMA-P : Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols PRISMA-ScR: PRISMA extension Scoping Reviews PROSPERO : International Prospective Register of Systematic Reviews CLIA: Clinical Laboratory Improvement Amendments FDA : Food and Drug Administration ISO: International Organization for Standardization Ag-RDT: Antigens Rapid Diagnostic Test Ab-RDT: Antibody Rapid Diagnostic Test Declarations Ethics approval and consent to participate No ethical approval was required because individual patient data was not included. Consent for publication Not applicable Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. The list of included studies is available in Supplementary Table S5 . Competing interests The authors declare that they have no competing interests Funding This work was part of HEAD-Start which was funded from a grant from Unitaid and partly supported by the National Science Foundation award 1722665, both awarded to FIND. Authors' contributions MDMC led the conduct of the study. CMQ and HMI participated in study design, including the search strategies, and data collection screening and charting of manuscript data. SY, CMD and SS provided critical input to the study design, analysis and interpretation. SY provided resources for the study. MDMC, HMI and CMQ drafted the manuscript. All authors critically reviewed and approved the manuscript. MDMC is the guarantor. Acknowledgements We thank Professor Nira Pollock for her inputs and fruitful discussions in the development of the research idea and strategy. References Pai NP, Vadnais C, Denkinger C, Engel N, Pai M. 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Infection. 2022;50(2):395–406. Krüger LJ, Tanuri A, Lindner AK, Gaeddert M, Köppel L, Tobian F, et al. Accuracy and ease-of-use of seven point-of-care SARS-CoV-2 antigen-detecting tests: A multi-centre clinical evaluation. EBioMedicine. 2022;75:103774. Lindner AK, Nikolai O, Rohardt C, Kausch F, Wintel M, Gertler M, et al. Diagnostic accuracy and feasibility of patient self-testing with a SARS-CoV-2 antigen-detecting rapid test. J Clin Virol. 2021;141:104874. Majam M, Fischer AE, Rhagnath N, Msolomba V, Venter WD, Mazzola L, et al. Performance assessment of four HIV self-test devices in South Africa: A cross-sectional study. South Afr J Sci. 2021;117(1–2):1–6. Ivanova Reipold E, Fajardo E, Juma E, Bukusi D, Bermudez Aza E, Jamil MS, et al. Usability and acceptability of oral fluid hepatitis C self-testing among people who inject drugs in Coastal Kenya: a cross-sectional pilot study. BMC Infect Dis. 2022;22(1):738. Mukoka M, Sibanda E, Watadzaushe C, Kumwenda M, Abok F, Corbett EL, et al. COVID-19 self-testing using antigen rapid diagnostic tests: Feasibility evaluation among health-care workers and general population in Malawi. PLoS One. 2023;18(7):e0289291. Tonen-Wolyec S, Dupont R, Awaida N, Batina-Agasa S, Hayette MP, Bélec L. Evaluation of the Practicability of Biosynex Antigen Self-Test COVID-19 AG+ for the Detection of SARS-CoV-2 Nucleocapsid Protein from Self-Collected Nasal Mid-Turbinate Secretions in the General Public in France. Diagn Basel. 2021;11(12). Reipold EI, Farahat A, Elbeeh A, Soliman R, Aza EB, Jamil MS, et al. Usability and acceptability of self-testing for hepatitis C virus infection among the general population in the Nile Delta region of Egypt. BMC Public Health. 2021;21(1):1188. Tonen-Wolyec S, Muwonga Masidi J, Kamanga Lukusa LF, Nsiku Dikumbwa G, Sarassoro A, Bélec L. Analytical Performance of the Exacto Test HIV Self-Test: A Cross-Sectional Field Study in the Democratic Republic of the Congo. Open Forum Infect Dis. 2020;7(12):ofaa554. Tun W, Vu L, Dirisu O, Sekoni A, Shoyemi E, Njab J, et al. Uptake of HIV self-testing and linkage to treatment among men who have sex with men (MSM) in Nigeria: A pilot programme using key opinion leaders to reach MSM. J Int AIDS Soc. 2018;21:e25124. Xu W, Reipold EI, Zhao P, Tang W, Tucker JD, Ong JJ, et al. HCV Self-Testing to Expand Testing: A Pilot Among Men Who Have Sex With Men in China. Front Public Health. 2022;10:903747. Nguyen LT, Nguyen VTT, Le Ai KA, Truong MB, Tran TTM, Jamil MS, et al. Acceptability and Usability of HCV Self-Testing in High Risk Populations in Vietnam. Diagn Basel. 2021;11(2). Majam M, Mazzola L, Rhagnath N, Lalla-Edward ST, Mahomed R, Venter WDF, et al. Usability assessment of seven HIV self-test devices conducted with lay-users in Johannesburg, South Africa. PLoS One. 2020;15(1):e0227198. Majam M, Rhagnath N, Msolomba V, Singh L, Urdea MS, Lalla-Edward ST. Assessment of the Sedia HIV Self-Test Device: Usability and Performance in the Hands of Untrained Users in Johannesburg, South Africa. Diagn Basel. 2021;11(10). Wohlgemut JM, Pisirir E, Kyrimi E, Stoner RS, Marsh W, Perkins ZB, et al. Methods used to evaluate usability of mobile clinical decision support systems for healthcare emergencies: a systematic review and qualitative synthesis. JAMIA Open. 2023 July 4;6(3):ooad051. Wronikowska MW, Malycha J, Morgan LJ, Westgate V, Petrinic T, Young JD, et al. Systematic review of applied usability metrics within usability evaluation methods for hospital electronic healthcare record systems: Metrics and Evaluation Methods for eHealth Systems. J Eval Clin Pract. 2021 Dec;27(6):1403–16. Tandon A, Cobb B, Centra J, Izmailova E, Manyakov NV, McClenahan S, et al. Human Factors, Human-Centered Design, and Usability of Sensor-Based Digital Health Technologies: Scoping Review. J Med Internet Res. 2024 Nov 15;26:e57628. Virzi RA. Refining the Test Phase of Usability Evaluation: How Many Subjects Is Enough? Hum Factors. 1992 Aug 1;34(4):457–68. Lewis JR. Sample sizes for usability studies: additional considerations. Hum Factors. 1994 June;36(2):368–78. Bakker JP, Barge R, Centra J, Cobb B, Cota C, Guo CC, et al. V3+ extends the V3 framework to ensure user-centricity and scalability of sensor-based digital health technologies. Npj Digit Med. 2025 Jan 24;8(1):51. MHRA. Guidance on applying human factors and usability engineering to medical devices including drug-device combination products in Great Britain [Internet]. Medicines and Healthcare products Regulatory Agency; 2021. Available from: https://www.gov.uk/government/publications/guidance-on-applying-human-factors-to-medical-devices World Health Organization. Technical Specifications Series [Internet]. Technical Specifications Series. 2025. Available from: https://extranet.who.int/prequal/vitro-diagnostics/technical-specifications-series Additional Declarations No competing interests reported. Supplementary Files Supplementarytablesandfigures.docx Supplementaryfile1framework.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8221943","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":571463680,"identity":"984947ca-a346-4b66-ae47-1a7704779b9d","order_by":0,"name":"Maria del Mar Castro","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABOUlEQVRIie3Qv2qDQBzA8Z8IupxkvaKQJygYAqWFJr6KItjFpIVAccggBOxiO3cQfAXfoCeCXeyfMUMHJeDkYLcMJfQitB3UQLdS7jsdP34f7jgAFuuPRr5PNQACTCcITvVmgg4RAYC7/yH4IIEvwjcLuNnsJ8c3TwXZwnQ4DG+LzcR7U0BexaRy8HwgP75C5bTISXahxj6YoygVx+OZVyJQUj0OMrw4urOvuCBrE2LtX064SBAEeeYlSFvbaiJ52IgypPOS1yYvJcQfQLTQo+SMEsCXdSLtsPHQkF2brC2ga8RwU0q4htiQSC69BYmEl9wOUkKiqKYZpRY/8p/3xFLjIMULnCGgh46HWfx75Uwn4Srl8u11ogE2N3W1PJ8PfLHIq2X7l5vU7jFSSQ/oTcx/K1gsFutf9gleOWvhlITKHgAAAABJRU5ErkJggg==","orcid":"","institution":"University Hospital Heidelberg","correspondingAuthor":true,"prefix":"","firstName":"Maria","middleName":"del Mar","lastName":"Castro","suffix":""},{"id":571463681,"identity":"51fda399-3187-451f-bb4e-153782153bce","order_by":1,"name":"Horeya M. Ismail","email":"","orcid":"","institution":"University Hospital Heidelberg","correspondingAuthor":false,"prefix":"","firstName":"Horeya","middleName":"M.","lastName":"Ismail","suffix":""},{"id":571463682,"identity":"63a18cf9-55b5-49cd-a215-534149b2f36a","order_by":2,"name":"Carlos Alberto Montenegro-Quiñonez","email":"","orcid":"","institution":"University Hospital Heidelberg","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"Alberto","lastName":"Montenegro-Quiñonez","suffix":""},{"id":571463683,"identity":"9d04472f-5936-4438-92df-29ef8dcdb1d2","order_by":3,"name":"Sonjelle Shilton","email":"","orcid":"","institution":"FIND","correspondingAuthor":false,"prefix":"","firstName":"Sonjelle","middleName":"","lastName":"Shilton","suffix":""},{"id":571463684,"identity":"46e6cdae-653c-42c1-9ed4-249a30979212","order_by":4,"name":"Claudia M. 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These tests are especially valuable in resource-limited settings, where access to centralized laboratories is often limited (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). POC diagnostics have significantly improved patient care for infections such as HIV, malaria, and syphilis, where rapid and accurate diagnosis is essential for effective clinical and public health responses (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The COVID-19 pandemic further underscored the need for diagnostic tools that are rapid, scalable, and easily deployable across diverse settings and user groups (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ASSURED criteria (Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment-free, and Deliverable) offer a widely accepted benchmark for successful implementation (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). More recently complemented by the RE-ASSURED and REST-ASSURED frameworks, which also incorporate interoperability with Health Information Systems (HIS) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, beyond user-friendliness (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), implementation success also depends on the interaction between users and diagnostic tools, often assessed through usability evaluations grounded in human factors research (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Frameworks such as Nielsen\u0026rsquo;s usability emphasize dimensions including learnability, efficiency, error prevention, and user satisfaction (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsability evaluations differ substantially in scope, method, and rigor. Standardized instruments such as the System Usability Scale (SUS) are widely used in health technology assessment, including those of diagnostic tools (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In addition to these structured approaches, usability studies encompass a wide range of methods, from simulated assessments of decision-making and workload (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) to longitudinal fieldwork, user interviews (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), and focused observations of device interaction (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Recent reviews reflect this variability: Maqbool and Herold (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) noted a predominant reliance on inquiry and testing methods (e.g., questionnaires), with some usability characteristics such as memorability, accessibility, and operability being mostly overlooked. Similarly, a scoping review of sensor-based digital health technologies (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) identified that usability domains such as learnability and efficiency are often under-assessed, with limited inclusion of diverse end users in the evaluation process. These findings highlight the heterogeneity in usability evaluation practices.\u003c/p\u003e \u003cp\u003eRegulatory and standards-based guidance exist for usability evaluation. The U.S. Food and Drug Administration (FDA) outlines structured approaches to usability and risk mitigation through its guidance on Applying Human Factors and Usability Engineering to Medical Devices (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Similarly, ISO standards (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), the CLIA waiver process (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), and the European In Vitro Diagnostic Regulation (IVDR) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) recommend the integration of usability engineering across the product lifecycle. However, the ways in which these guidance documents are applied, and the stages of the product life cycle at which they are implemented by academic and commercial diagnostic developers, remain underexplored, particularly for tests designed for resource-constrained settings.\u003c/p\u003e \u003cp\u003eThis scoping review aims to describe current practices in the usability evaluation of point-of-care diagnostics (as a broad term including near-POC, true-POC, and self-testing) for infectious diseases, with a focus on molecular and immunoassay-based tests for use in low- and middle-income countries (LMICs). This review draws upon regulatory and human factors guidance to assess how usability is currently conceptualized and evaluated, and to identify opportunities to support implementation through improved usability evaluation practices.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis scoping review follows the recommendations from the PRISMA extension for Scoping Reviews (PRISMA-SR) (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e) and the JBI guidance for scoping reviews (\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e). A detailed protocol of this review has been published (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eWe grounded our review in regulatory and standards-based guidance documents that currently shape usability evaluation practices for diagnostic technologies. These included the U.S. FDA\u0026rsquo;s Guidance on Human Factors and Usability Engineering (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e), ISO 62366-1 (Application of Usability Engineering to Medical Devices) (\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e), and ISO 9241\u0026thinsp;\u0026minus;\u0026thinsp;210 (Human-Centered Design for Interactive Systems) (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e). This approach was chosen to reflect the practical considerations developers and implementers must navigate in low-resource settings, where usability, safety, and regulatory compliance are closely tied to implementation feasibility.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eEligibility criteria\u003c/h2\u003e\n \u003cp\u003eWe included original research studies of all designs that reported usability evaluations of a diagnostic test for infectious diseases. This encompassed usability evaluations conducted as standalone studies, nested within larger clinical studies (e.g., diagnostic accuracy), and reported in product dossiers. Eligible technologies included prototype and design-locked tests intended for use within the care cascade of infectious diseases prior treatment. We focused on molecular tests (e.g., PCR, isothermal amplification) and antigen or antibody-detection rapid diagnostic tests (Ag-RDTs, Ab-RDTs) designed for use at the Point-of-Care (POC), near-POC, or self-testing. Tests were eligible if they required minimal laboratory infrastructure or incorporated automated features for result generation or interpretation. For the purpose of this review, we grouped near-POC and true-POC technologies into a single \u0026quot;POC\u0026quot; category.\u003c/p\u003e\n \u003cp\u003eIntended users included healthcare providers with varying levels of training, as well as lay users such as community health workers, field agents, and individuals performing self-testing. The use settings included: a) Self-testing (e.g., at home); b) Level 0 (L0) \u0026ndash; Community; c) Level 1 (L1) - Primary Care and d) Level 2 (L2) - District Hospital Laboratory.\u003c/p\u003e\n \u003cp\u003eWe excluded editorials, reviews, conference abstracts, opinion papers, letters, and preprints.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSearch strategy, databases and search terms\u003c/h3\u003e\n\u003cp\u003eThe searches were performed from August to September 2023. Searches were conducted by an independent librarian in PubMed/MEDLINE, Embase, and Web of Science, and two independent investigators (CAMQ and HM) conducted searches in Google Scholar and grey literature databases: LILACS, WHOiris, PAHOiris, and EBSCO. Search terms were tailored to each database and are detailed in \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e. No restrictions were applied regarding publication year, language, setting, or population. Searches were conducted in English, and translations were performed using Google Translate or ChatGPT when required.\u003c/p\u003e\n\u003cp\u003eTo capture relevant usability data outside the peer-reviewed literature, we screened nine websites belonging to regulatory agencies, product development partnerships, and diagnostic networks. Only the WHO list of prequalified in vitro diagnostic products contained product dossiers with relevant information (listed in \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003ch3\u003eStudy selection\u003c/h3\u003e\n\u003cp\u003eSearch results were imported into Zotero (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.zotero.org\u003c/span\u003e\u003c/span\u003e) for de-duplication. Title and abstract screening were performed independently by two reviewers (HM and CAMQ) using Rayyan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rayyan.ai\u003c/span\u003e\u003c/span\u003e). Discrepancies were resolved through discussion or adjudicated by a third reviewer (MMC). Full-text articles meeting inclusion criteria were reviewed in detail. Reference lists of included articles were hand-searched to identify additional studies. Full text screening of the product dossiers followed the same approach as the article screening.\u003c/p\u003e\n\u003ch3\u003eData charting\u003c/h3\u003e\n\u003cp\u003eA customized extraction form was used for data charting (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e). The extraction form included a priori defined topics: citation details, geographic location, year of publication, study settings, participants, type of test, use settings, study design, type of data collected, type of evaluation, sample size, method for sample size estimation and sampling, description of critical tasks, use scenarios, test environment, data collection tools, methods for results documentation, reported usability outcomes and metrics. The type of evaluation was classified as formative, summative, or nested, with the latter referring to assessments embedded within larger studies, conducted at any stage of test development.\u003c/p\u003e\n\u003cp\u003eTwo reviewers (HM and CAMQ) extracted the data from the included studies, and any discrepancies were resolved through consensus, when consensus was not reached, a third researcher (MMC) had the final decision.\u003c/p\u003e\n\u003cp\u003eFor the WHO prequalified product dossiers, data extraction was supported by a custom GPT-based AI tool, trained using the extraction form and iteratively refined prompts to optimize accuracy and relevance (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://chatgpt.com/g/g-m24WAgSc2-data-extraction?model=gpt-4o\u003c/span\u003e\u003c/span\u003e). The AI tool was used to extract key variables from the full text of each dossier, including name and type of the test, developer, intended use setting, assay description intended participants, ease-of-use, and sample size. Extracted data were validated by a human reviewer (HM, CAMQ, or MMC) to ensure accuracy and completeness. Discrepancies were resolved by a second human reviewer.\u003c/p\u003e\n\u003ch3\u003eRisk of bias assessment\u003c/h3\u003e\n\u003cp\u003eConsistent with the aims and methodology of scoping reviews, risk of bias and quality assessments were not conducted, as the focus was on mapping usability evaluation practices rather than appraising study validity or effectiveness.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eData synthesis\u003c/h2\u003e\n \u003cp\u003eWe provide a narrative synthesis of the data. Categorical data were summarized using descriptive statistics, and findings are presented in tables, figures, and text. Tables and figures were generated using Stata Version 14 and R statistical software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/mirrors.html\u003c/span\u003e\u003c/span\u003e). To guide analysis and interpretation, we drew on regulatory and human factors frameworks, including the FDA\u0026rsquo;s Human Factors Engineering Guidance, ISO 62366-1, and ISO 9241\u0026thinsp;\u0026minus;\u0026thinsp;210 (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e), as well as Nielsen\u0026rsquo;s usability attributes (\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e). These documents provided a practical lens to assess usability evaluation practices in contexts where formal implementation science frameworks may not be applied but where design, safety, and usability directly influence implementation feasibility.\u003c/p\u003e\n \u003cp\u003eBased on these frameworks, we developed themes to categorize and compare evaluation practices across studies, including:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eUser characteristics and training,\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eDescription of use scenarios and environments,\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIdentification and assessment of critical tasks,\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eData collection methods and usability measures, and\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eReported outcomes (e.g., effectiveness, efficiency, satisfaction)\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThis thematic approach enabled structured synthesis of heterogeneous data and highlighted areas of convergence and divergence relative to regulatory expectations and implementation-relevant considerations.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe search strategy yielded 8060 references across all sources. After removing duplicates, 5747 articles were screened by title and abstract. Of these, 617 articles underwent full-text screening, resulting in the inclusion of 103 articles in the review. The main reasons for exclusion included the absence of usability-related outcomes and the focus on non-POC diagnostic tests (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCharacteristics of included studies\u003c/h3\u003e\n\u003cp\u003eThe 103 articles covered studies from 47 countries, with most publications concentrated in just a few (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The United States and Kenya each contributed more than 11 studies, followed by South Africa (n\u0026thinsp;=\u0026thinsp;10), Germany (n\u0026thinsp;=\u0026thinsp;8), Bangladesh (n\u0026thinsp;=\u0026thinsp;7), and the United Kingdom (n\u0026thinsp;=\u0026thinsp;6). An additional 10 countries, including Brazil, Cambodia, the Democratic Republic of Congo, Dominica, France, Malawi, Malaysia, Spain, Uganda, and Zambia, contributed 2 to 5 studies each. Other countries were represented by a single study or not at all. The majority of studies were conducted in a single-country setting; however, nine studies were multi-country, eight of which involved 2\u0026ndash;3 countries, and one spanned nine countries (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In terms of publication date, most studies were published within the last 15 years, with only one predating 2000 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This study assessed the ease-of-use of six rapid serological tests for HIV-1 antibody, nested in a diagnostic accuracy evaluation. The diversity of test types increased notably after 2015 (\u003cb\u003eSupplementary Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of diagnostic tests\u003c/h2\u003e \u003cp\u003eA total of 162 tests were evaluated across the included studies. The majority (90.7%) were developed by industry, while 3.1% originated from academic institutions. In 6.2% of cases, the developer was not specified. The United States accounted for the largest share of developers (41.1%), followed by the United Kingdom (9.9%), India (9.3%), France (7.3%), and South Korea (6.6%). Additional developers were based in Canada, Australia, Germany, South Africa, or were multinational collaborations (n\u0026thinsp;=\u0026thinsp;3). Regarding diagnostic technologies, 50.6% of tests were antibody-based, 35.2% antigen-based, and 11.7% molecular tests, while a small fraction (1.9%) combined antigen and antibody detection. One test (0.6%) employed a microfluidic assay (mChip), contributing to the overall technological diversity of the evaluated tests. The most frequently targeted disease was HIV/AIDS (30.3%), followed by COVID-19 (25.3%), malaria (14.2%), and hepatitis C (7.4%); 4.3% of studies assessed multi-disease diagnostic tests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCurrent methods and practices in usability evaluations\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eTypes of evaluations and data collected\u003c/h2\u003e \u003cp\u003eMost articles reported quantitative methods, either alone (n\u0026thinsp;=\u0026thinsp;65 articles) or in combination with qualitative methods (n\u0026thinsp;=\u0026thinsp;30); only eight used qualitative methods exclusively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-A). The most common data collection tool was non-standardized questionnaires, used in nearly half of all studies (44.7%) and frequently applied across formative, summative, and nested study designs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-B). Standardized or validated surveys were less common (12.6%) and primarily used for both summative evaluations and those nested within larger studies. Observation of users handling the tests were less frequently employed (5.8%) as single data collection method, although were often used in combination with other approaches. Among qualitative studies, interviews and focus group discussions (FGDs) were the primary method (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-B).\u003c/p\u003e \u003cp\u003eAmong the standardized questionnaires, six studies used the System Usability Scale (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), and other six reported using externally validated questionnaires. Two studies used the After-scenario questionnaire (ASQ), while one each reported using the Mobile Application Rating Scale (MARS), a 16-item WHO RDT training evaluation checklist, and a 15-item usability index build from WHO guidance. Notably, there was a predominance of Likert-scale type of questions, both for the standardized and the custom questionnaires (n\u0026thinsp;=\u0026thinsp;47 described Likert-scale questions).\u003c/p\u003e \u003cp\u003eAmong studies that used observations either alone or in combination with other methods, checklists were the most employed tool (n\u0026thinsp;=\u0026thinsp;32). Five studies relied on unstructured observations or did not specify the tool used. In terms of other approaches for data collection, one study reported a Failure Mode Effects Analysis approach (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), one used a rating system (similar to heuristic evaluation) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). None reported using other approaches for usability data collection, such as think-aloud or cognitive walkthroughs. The majority of studies used descriptive statistics for data analysis (96%, n\u0026thinsp;=\u0026thinsp;3 did not report an analysis approach), and, among the studies that reported analyzing qualitative data, thematic analysis was the most used approach (n\u0026thinsp;=\u0026thinsp;12). One study reported using natural language processing for open-ended questions (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eUse scenarios and testing environments\u003c/h2\u003e \u003cp\u003eRegarding test environments, the majority utilized real-life scenarios (n\u0026thinsp;=\u0026thinsp;96) while seven studies incorporated simulated scenarios (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The simulated environments varied widely, ranging from simulated home and primary care environments to laboratory testing done by technicians and members of the research team. Eight different study settings were identified, including self-testing, community (Level 0), primary care (Level 1), district hospital laboratory (Level 2), regional or provincial laboratory (Level 3) (\u003cspan additionalcitationids=\"CR34 CR35 CR36 CR37\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), reference or national laboratory (Level 4). In addition, study settings included research laboratories, prisons, and homeless shelters (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), city-administrated laboratories (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), sex work establishments (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), communes (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) and clinical ambulatory testing facilities (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eUser characteristics, training, sample size and sampling\u003c/h2\u003e \u003cp\u003eUsability assessments were conducted among a variety of participant groups. Self-testing users were the most common, reported in 50 studies (48.5%). Community health workers or field agents were involved in eight studies (7.8%), while laboratory technicians participated in 16 studies (15.5%). Medical technicians took part in three studies (2.9%), and nurses were included in four (3.9%). Additionally, 22 studies (21.4%) evaluated usability among multiple user groups. Of these, three studies (13.6%) involved only professional users, seven (31.8%) focused on non-professional users, and 12 studies (54.6%) assessed usability among both professional and non-professional user groups.\u003c/p\u003e \u003cp\u003eSample sizes varied across studies, with notable differences by both evaluation type and testing setting. Usability studies for tests intended for POC settings reported smaller sample sizes, with 5 and 6 participants (n\u0026thinsp;=\u0026thinsp;2) for formative evaluations, a median of 6 participants (IQR: 19\u0026ndash;40, n\u0026thinsp;=\u0026thinsp;43) for studies nested within larger evaluations, and 10 participants (IQR: 10\u0026ndash;68, n\u0026thinsp;=\u0026thinsp;7) for summative evaluations. In contrast, studies conducted in self-testing contexts involved larger samples. Formative evaluations in self-testing settings had a median of 40 participants (IQR: 49\u0026ndash;251; n\u0026thinsp;=\u0026thinsp;6), while nested and summative evaluations included median of 115 (IQR: 190\u0026ndash;354; n\u0026thinsp;=\u0026thinsp;30) and 101 participants (IQR: 305\u0026ndash;767; n\u0026thinsp;=\u0026thinsp;15) respectively.\u003c/p\u003e \u003cp\u003eOnly 16 of the included studies described a method or rationale for sample size determination; the remaining 87 did not report how sample size was estimated. Among formative studies, no formal sample size was calculated. For studies nested within larger evaluations, one cited the WHO technical specifications for prequalification (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), three used sample size calculations for a proportion (\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), and five adopted the sample size originally estimated for the parent study (\u003cspan additionalcitationids=\"CR53 CR54\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e)(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Among these studies reporting sample size calculations, one assessed a POC test (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e), and the rest were intended for self-testing. In summative evaluations, two studies referred to the WHO guidance as the basis for determining participant numbers (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Across study types, when described, the most common sampling strategy was the inclusion of study team members or individuals already involved in the parent study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eReported outcomes\u003c/h2\u003e \u003cp\u003eAcross the included studies, even broad outcome categories were identified, namely, ease-of-use, memorability, learnability, satisfaction, efficiency, effectiveness, and safety (\u003cb\u003eSupplementary Table S3, Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). The most commonly assessed outcomes across both self-testing and POC settings were ease-of-use and effectiveness. These were frequently operationalized through measures such as perceived ease-of-use, clarity of instructions, task completion, and for effectiveness, measures like agreement with reference standards or between user types. In self-testing contexts, usability evaluations tended to be broader in scope, often incorporating multiple domains. However, outcomes related to memorability (e.g., retention of instructions, confidence over time) and safety (e.g., sample handling risks, incorrect result interpretation) were still reported less frequently. Measures of learnability and satisfaction appeared with intermediate frequency, operationalized through assessments of comprehension of instructions, independent use, and willingness to reuse or recommend the test.\u003c/p\u003e \u003cp\u003eIn contrast, POC evaluations tended to focus more narrowly on task performance and perceived ease-of-use, with limited attention to the other outcomes. Only a small proportion of studies in POC or self-testing settings explicitly evaluated adverse events or user-related hazards.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eProduct dossiers\u003c/h2\u003e \u003cp\u003eThe product dossiers retrieved from the WHO Prequalification (PQ) registry provided limited information on usability evaluation. While most dossiers included brief descriptions related to ease-of-use (perceived ease-of-use and training requirements) none reported details on the methods used to assess usability, including sample size, data collection approaches, or user group characteristics. (\u003cb\u003eSupplementary Table S4\u003c/b\u003e)\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis scoping review described how usability of POC and self-testing diagnostics for infectious diseases intended for use in low-resource settings is evaluated in practice. Most studies employed quantitative methods, predominantly relying on non-standardized questionnaires, with limited use of standardized tools or qualitative approaches. Usability outcomes focused predominantly on ease of use and task performance (effectiveness), while domains such as memorability, safety, and learnability were assessed less consistently. Self-testing evaluations had broader scope in terms of outcomes and involved larger sample sizes compared to those targeting professional users. However, sample size estimation was infrequently reported for all types of evaluations, and participant selection strategies for usability evaluation were often poorly documented.\u003c/p\u003e \u003cp\u003eWhile most reviews in the field of usability have focused on digital tools (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e) and electronic health records (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), this is, to our knowledge, the first review dedicated to the usability evaluation of diagnostic tests. We identified a broad range of diagnostic technologies evaluated for usability and an increase in the number of publications in recent years; notably, studies were concentrated in a small number of countries and focused on four diseases (HIV/AIDS, COVID-19, malaria, and hepatitis C). In addition, most diagnostic tests were developed by industry actors based in high-income countries, and reported evaluations were largely done at the end stages of development. This distribution highlights the limited representation from low- and middle-income settings where many of these tests are intended for implementation. Costs and resource constraints may contribute to the limited scope and geographic distribution of these studies.\u003c/p\u003e \u003cp\u003eIn terms of use scenarios and type of users, the included studies represented a broad distribution of settings, as well as mostly non-professional user groups, which are in line with the intended use of POC and self-testing technologies. Larger sample sizes were reported for self-testing studies, which suggest that these evaluations are generally designed with broader inclusion criteria or greater regulatory expectations, likely reflecting the increased variability in lay-user populations and the need to demonstrate performance across diverse settings and levels of user training. However, the wide variability in sample sizes and the frequent absence of sample size justification limit the generalizability of findings. These limitations are further compounded by the common use of convenience sampling and poor description of sampling methods. This is a finding common to usability studies, as demonstrated by previous reviews in the field of digital health and wearable technologies (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Evidence from a scoping review of sensor-based digital health technologies found that only 22% of studies included diverse user types (e.g., caregivers, clinicians), and few reported complete sociodemographic data, limiting insights into usability across varied real-world populations (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). While a commonly cited rule of thumb suggests that 5–10 participants can identify most usability issues (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e), this approach does not account for variability in user characteristics or contextual factors affecting generalizability. Recent efforts have emphasized the need to improve sample size estimation and sampling strategies in usability studies (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e), including the use of power calculations for quantitative data and the application of established qualitative sampling approaches where appropriate.\u003c/p\u003e \u003cp\u003eUsability taxonomies (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) outline a broad set of evaluation methods across inquiry, inspection, and testing domains. In contrast, the studies included in this review relied heavily on questionnaires and basic observational tools, with minimal use of structured methods such as heuristic evaluation, scenario testing, or think-aloud protocols (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). This narrow methodological scope may limit the ability to detect usability issues that emerge under real-world or unsupervised conditions. Similar to previous reviews (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e), the reported outcomes in the included studies focus on few usability attributes, predominantly ease-of-use.\u003c/p\u003e \u003cp\u003eThe findings of this review have implications for the implementation of POC and self-testing diagnostics in low-resource settings. Usability is a key determinant of successful adoption, particularly where end users may have limited training and where infrastructure for supervised testing is often unavailable. Incomplete assessment of usability, especially regarding safety, memorability, and satisfaction, may compromise implementation fidelity and acceptability. For self-testing, inadequate attention to user comprehension, confidence, or error risks could lead to inappropriate test use, misinterpretation, or loss of trust in results. For POC tests, usability challenges that arise from poorly defined critical tasks or insufficient training may reduce diagnostic accuracy or limit uptake. In both contexts, the lack of standardized evaluation frameworks constrains comparability across products and may hinder the generation of evidence needed for scale-up.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAlignment with Regulatory Guidance\u003c/h2\u003e \u003cp\u003eSeveral aspects of current usability evaluation practices align with international regulatory guidance, particularly in the emphasis on task completion, perceived ease-of-use, and effectiveness. These domains are consistent with principles outlined in frameworks such as the FDA’s Guidance on Human Factors and Usability Engineering and ISO 62366-1 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, other areas prioritized in regulatory standards, most notably user safety, error mitigation, and the identification of critical tasks were infrequently reported, especially in POC evaluations. The use of validated instruments, including the System Usability Scale (SUS) and WHO-aligned checklists, was limited. These gaps suggest that while some regulatory expectations are being met, usability evaluation practices in the diagnostic space require standardization.\u003c/p\u003e \u003cp\u003eThese findings also reflect partial alignment with the WHO Technical Specifications Series (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e) for Prequalification of In Vitro Diagnostics, which outline specific usability-related requirements for diagnostic test dossiers. While a limited number of studies cited WHO guidance in relation to sample size justification or used adapted checklist tools, most did not explicitly describe elements emphasized in the guidance. These include the evaluation of critical user tasks, structured reporting of user errors and risk mitigations. This limited integration suggests that WHO recommendations are not consistently informing usability evaluation practices, particularly in studies conducted outside of formal prequalification processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCUE-Dx: A proposed usability framework to guide future studies\u003c/h2\u003e \u003cp\u003eWe developed CUE-Dx (Comprehensive Usability Evaluation for Diagnostics), a conceptual framework to define and evaluate usability of point-of-care (POC) diagnostic tests integrating regulatory guidance and the findings from this review. The framework organizes usability into three interrelated domains: core domains (effectiveness, efficiency, errors and use safety) representing the minimum constructs required for safe and accurate test use; complementary domains (learnability, memorability and satisfaction) capturing additional aspects of user experience; and contextual domains (infrastructure, workflow integration, organizational and policy context, and cultural and language considerations) reflecting environmental and system-level factors influencing usability in practice. A detailed description of the framework and operational definitions for each domain are provided in \u003cb\u003eSupplementary file 1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThe strengths of this scoping review include a comprehensive and extensive search of the available evidence on usability evaluation or validation of diagnostic tests for infectious diseases, including information in the literature and information available in product dossiers. The limitations of this scoping review include potential publication and selection biases, as well as challenges in defining inclusion and exclusion criteria specifically related to the variable definitions of POC testing and usability. It is possible that the detailed evaluations of usability are included in regulatory submissions but not in the public domain. We did not apply restrictions of language, but the searches were carried out in English; therefore, evidence published in other languages could have been overlooked. Being a scoping review, quality or risk of bias from the individual studies was not assessed. Finally, definitions of POC testing have evolved over time, with recent classifications distinguishing between near-POC, true-POC, and self-testing. For the purposes of this review, near-POC and POC were grouped into a single category to facilitate synthesis. While this simplification allowed for clearer comparisons, it may have masked important differences in test delivery and user interaction across settings.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusions and future directions","content":"\u003cp\u003eThis scoping review highlights a growing body of literature on the usability of diagnostic tests for infectious diseases in decentralized, resource-constrained settings. The methodological heterogeneity across studies underscores the need for robust, standardized approaches to usability evaluation, particularly those that integrate regulatory guidance with real-world implementation considerations. Key areas for improvement include standardization of reporting, diversification of data collection and analysis methods, improved sample size estimation and sampling strategies to enhance generalizability, and greater harmonization in the definition and measurement of usability outcomes. To support this goal, we are planning a Delphi survey with international experts to build consensus on core usability outcomes and methodological approaches for diagnostics.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003ePOC:\u003c/strong\u003e Point-of-Care\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLMICs\u003c/strong\u003e: Low- and Middle-Income Countries\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePRISMA-P\u003c/strong\u003e: Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePRISMA-ScR:\u003c/strong\u003e PRISMA extension Scoping Reviews\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePROSPERO\u003c/strong\u003e: International Prospective Register of Systematic Reviews\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCLIA:\u003c/strong\u003e Clinical Laboratory Improvement Amendments\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFDA\u003c/strong\u003e: Food and Drug Administration\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eISO:\u003c/strong\u003e International Organization for Standardization\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAg-RDT:\u003c/strong\u003e Antigens Rapid Diagnostic Test\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAb-RDT:\u003c/strong\u003e Antibody Rapid Diagnostic Test\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo ethical approval was required because individual patient data was not included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files. The list of included studies is available in\u0026nbsp;\u003cstrong\u003eSupplementary Table S5\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was part of HEAD-Start which was funded from a grant from Unitaid and partly supported by the National Science Foundation award 1722665, both awarded to FIND.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMDMC led the conduct of the study. CMQ and HMI participated in study design, including the search strategies, and data collection screening and charting of manuscript data. SY, CMD and SS provided critical input to the study design, analysis and interpretation. SY provided resources for the study. \u0026nbsp;MDMC, HMI and CMQ drafted the manuscript. All authors critically reviewed and approved the manuscript. MDMC is the guarantor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Professor Nira Pollock for her inputs and fruitful discussions in the development of the research idea and strategy.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePai NP, Vadnais C, Denkinger C, Engel N, Pai M. Point-of-Care Testing for Infectious Diseases: Diversity, Complexity, and Barriers in Low- And Middle-Income Countries. PLoS Med. 2012 Sept 4;9(9):e1001306. \u003c/li\u003e\n\u003cli\u003ePeeling RW, Mabey D. Point-of-care tests for diagnosing infections in the developing world. Clin Microbiol Infect. 2010 Aug;16(8):1062\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eDrain PK, Hyle EP, Noubary F, Freedberg KA, Wilson D, Bishai WR, et al. 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J Eval Clin Pract. 2021 Dec;27(6):1403\u0026ndash;16. \u003c/li\u003e\n\u003cli\u003eTandon A, Cobb B, Centra J, Izmailova E, Manyakov NV, McClenahan S, et al. Human Factors, Human-Centered Design, and Usability of Sensor-Based Digital Health Technologies: Scoping Review. J Med Internet Res. 2024 Nov 15;26:e57628. \u003c/li\u003e\n\u003cli\u003eVirzi RA. Refining the Test Phase of Usability Evaluation: How Many Subjects Is Enough? Hum Factors. 1992 Aug 1;34(4):457\u0026ndash;68. \u003c/li\u003e\n\u003cli\u003eLewis JR. Sample sizes for usability studies: additional considerations. Hum Factors. 1994 June;36(2):368\u0026ndash;78. \u003c/li\u003e\n\u003cli\u003eBakker JP, Barge R, Centra J, Cobb B, Cota C, Guo CC, et al. V3+ extends the V3 framework to ensure user-centricity and scalability of sensor-based digital health technologies. Npj Digit Med. 2025 Jan 24;8(1):51. \u003c/li\u003e\n\u003cli\u003eMHRA. 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Available from: https://extranet.who.int/prequal/vitro-diagnostics/technical-specifications-series\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diagnostics, Point-Of-Care, self-testing, usability, human factors, LMICs","lastPublishedDoi":"10.21203/rs.3.rs-8221943/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8221943/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUsability is a critical determinant of successful implementation of diagnostic technologies, particularly for point-of-care (POC) and self-testing tools intended for resource-limited settings. Despite its importance, no prior review has systematically examined how usability is evaluated in diagnostic test development and implementation. This scoping review synthesizes current methods and practices used to assess usability of infectious disease diagnostics.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e We conducted a scoping review following PRISMA-ScR and JBI guidance. Eligible studies reported usability evaluations of molecular or immunoassay-based diagnostics intended for decentralized or low-resource settings. Searches were performed in five databases and nine additional sources, including the WHO Prequalification of In Vitro Diagnostics registry. Data were extracted on study characteristics, user groups, settings, evaluation methods, sampling strategies, and reported usability outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified 103 studies, most focused on HIV, COVID-19, malaria, or hepatitis C, and conducted in a limited number of countries. Self-testing evaluations generally used larger samples and assessed more outcome domains than those involving professional users; however, sample size justification was rare and participant selection methods were often unclear. Most studies relied on non-standardized questionnaires, with few using validated instruments or qualitative approaches. Usability outcomes most commonly addressed ease-of-use and effectiveness, while domains such as safety, memorability, and satisfaction were less consistently assessed. WHO prequalification dossiers provided minimal methodological detail. Synthesizing regulatory guidance with review findings, we developed a usability assessment framework comprising core domains (effectiveness, efficiency, errors and use safety), complementary domains (learnability, memorability, satisfaction), and contextual domains capturing environmental and system-level factors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSubstantial methodological heterogeneity exists in usability assessments of diagnostic tests. Standardized outcome definitions, broader methodological approaches, and improved reporting are needed to strengthen usability evidence for implementation. A Delphi consensus process is planned to define core usability outcomes and recommended methodologies for diagnostic evaluation.\u003c/p\u003e","manuscriptTitle":"Usability assessment of point-of-care diagnostics for infectious diseases in low-resource settings: a scoping review of current practices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 07:12:58","doi":"10.21203/rs.3.rs-8221943/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1a058c78-5d01-4c4b-9b32-1abff43b0d10","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T07:12:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 07:12:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8221943","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8221943","identity":"rs-8221943","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}