Usability of SARS-CoV-2 Self-Testing in a Peer-Assisted Model among Factory Workers in Bengaluru, India: A Mixed- Methods Cross-Sectional Study

Usability of SARS-CoV-2 Self-Testing in a Peer-Assisted Model among Factory Workers in Bengaluru, India: A Mixed- Methods Cross-Sectional Study | 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 Research Article Usability of SARS-CoV-2 Self-Testing in a Peer-Assisted Model among Factory Workers in Bengaluru, India: A Mixed- Methods Cross-Sectional Study Meghana Ratna Pydi, Neha Parikh, Purnima Ranawat, Ravneet Kaur, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7186437/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: To mitigate inequities in healthcare access and outcomes among vulnerable populations during the COVID-19 pandemic, the Government of India introduced antigen-based SARS-CoV-2 self-testing kits for self-use. However, concerns remained around the correct and confident use of these kits by low-literacy and underserved groups. This study aimed to assess the usability of nasal-sampling SARS-CoV-2 self-tests in a peer-assisted model among factory workers in Bengaluru, India. Methods: A mixed-method cross-sectional study was conducted with 106 factory workers across two industrial sites in Bengaluru between February and March 2022. The Panbio™ COVID-19 Antigen Self-Test kit and the NAVICA mobile app for result reporting were used. Peer assistants distributed kits, demonstrated procedures using their own kits (without physical contact), and guided participants in using the kit and app. Observers recorded usability, result interpretation, and peer instruction effectiveness using standardized checklists and contrived result images. Post-test surveys and focus group discussions captured user perceptions of facilitators and barriers to usability. Results: Study findings show that the overall usability score of the test kit with peer assistance was 75.9%, rising to 80.7% for critical steps and 33.8% for all critical steps in uploading results through NAVICA. It was seen that peer assistants provided accurate instructions and support for 93.4% of the tests. Among the critical steps in test kit use, maximum errors were made in sample collection and using the correct amount of buffer solution. Concordance between the participant and observer/NAVICA was 97.9%. 62.0% and 56.6% of the participants reported confidence in a) performing and interpreting the test and b) capturing and uploading their results using the mobile application with the assistance of a peer, respectively. Less than half the participants reported confidence in performing these steps independently. Conclusions: The study demonstrates that SARS-CoV-2 self-testing kits have good usability among factory workers when delivered through a peer-assisted model. Peer support significantly improved test accuracy and participant confidence. Such workplace-based, peer-led models can help improve equitable access to early detection and self-care tools in low-resource and high-risk populations. Usability of self-testing Sars-CoV-2 testing Acceptability of self-testing peer-assisted self-testing Workplace COVID-19 testing Pandemic preparedness Community-based self-testing Self-testing for marginalized populations Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), took a severe health, social and economic toll worldwide. As of July 2023, India reported 44 million cases and 531,913 deaths ( 1 ). The COVID-19 pandemic in India initially concentrated in urban areas, with metropolitan cities and high income states contributing to 3/4ths of total cases ( 2 ). Urbanization, higher workforce participation, population density, and income were positively associated with the increase in COVID-19 prevalence and transmission ( 3 ). Globally, workplace transmission was a significant contributor to the overall spread of COVID-19, primarily due to the key risk factor of close contact—defined as being within 6 feet of an infected person for 15 minutes or more ( 4 , 5 ). In India, the manufacturing sector was particularly affected, as large numbers of workers operated in close proximity within labour-intensive environments. This risk was further compounded by the fact that a significant portion of the workforce comprised migrant and daily wage laborers who, fearing loss of income or employment, continued to attend work—even when unwell, potentially exposed, or asymptomatic—thereby contributing substantially to workplace transmission. Vaccination policies that initially restricted access to those above 45 years of age, coupled with vaccine hesitancy among factory workers, further exacerbated their vulnerability. Additionally, many factory workers, being migrants, faced challenges in accessing local health services due to language barriers. Low awareness of COVID-19 testing and vaccination services, poor understanding of transmission prevention, limited perception of risk, and fear of discrimination following the disclosure of a positive status all contributed to under-testing, delayed diagnosis and increased risk of infection, which then spread to their families and communities ( 6 ). The implementation of nationwide and state-level lockdowns led to factory closures, restrictions on mobility and disruptions to supply chains both domestic and international causing a halt in activity and negatively affecting production volumes and revenue ( 6 ). Between 2020-1, the Index of Industrial Production decreased by 9.6%, reflecting setbacks to core manufacturing during the pandemic's second wave ( 7 ). Karnataka saw a permanent shutdown of 754 factories, with nearly 46000 workers losing their jobs since the onset of the pandemic between 2020 and 2021( 8 ). As the second wave of infections eased after June 2021, businesses re-opened and implemented occupational safety programs to prevent outbreaks and support employees that tested positive. These were mandated for large workplaces by state governments ( 9 , 10 ). Although this increased economic activity, successful implementation of these programs required the availability of timely diagnostic testing to facilitate case identification and quarantine. However, the supply of Reverse Transcription - Polymerase Chain Reaction (RT-PCR) testing for SARS-CoV-2 in health facilities was constrained by the need for significant laboratory capacity and trained clinical staff, as well as a shortage of molecular reagents, supplies, and equipment. Fear of painful testing procedures and long waiting and travel times due to limited testing facilities further constrained demand ( 11 – 13 ). Rapid Antigen Testing (RAT) for COVID-19 has been recommended and successfully implemented globally in workplaces and clinical settings ( 14 , 15 ). It improves individuals' access to testing and, due to shorter turnaround times, allows for the early diagnosis of positive cases, preventing disease transmission ( 16 ). RATs may be administered by a healthcare worker or self-administered by the worker or patient. Self- administration, in which an individual collects a sample, runs the test, and interprets the results by themselves, is called “self-testing.” In recent years, self-testing has gained prominence as an essential self-care intervention. In 2016, the World Health Organization (WHO) recommended HIV self-testing. Since then, self-testing for Hepatitis C (HCVST) and COVID-19 has been recommended ( 17 – 20 ). Self-testing has proven to circumvent barriers to testing, such as stigma, privacy, time required for testing, and affordability ( 21 , 22 ). Recent studies have demonstrated the accuracy of the COVID-19 self-test results ( 21 , 22 ). COVID-19 self-test products are approved for use in the general population in India but have not been widely used in workplace settings. In manufacturing industries, which face a shortage of healthcare workers on-site, self-testing may enable large-scale screening programs within workplaces, particularly among vulnerable workers, Such programs have the potential to improve worker health and mitigate pandemic-related business disruptions which can impact workers' livelihood. WHO recommends including self-testing in national strategies, citing improved access and usability across diverse settings ( 20 ). While global studies show self-testing is feasible and scalable ( 23 , 24 ), evidence from Indian manufacturing settings remains limited but is crucial for expanding access and ensuring linkage to care. This study assessed the usability of a COVID-19 self-test in a peer-assisted self-testing model in two factories in Bengaluru, India. Peer assistance refers to a lay peer who was present during testing to guide workers through the testing procedure. This approach sought to balance the limitation of health worker availability with low literacy levels among the workforces. Additional acceptability assessments in the study determined how workers viewed the test and testing process. Materials and Methods Study Design Location This usability and acceptability study was a cross-sectional study conducted in Bengaluru, Karnataka, India, from February to March 2022 in two factories − 1) a mid-sized machine parts manufacturer with approximately 180 staff; and 2) a leading garment manufacturer and export company with approximately 650 workers. Organisation with existing workplace health programs in both factories conducted this study. Factories were selected based on size, diversity of workforce demographics, and management’s willingness to implement a COVID-19 self-testing program designed to mitigate business disruptions. Sampling Factory management opened the testing event to their entire workforce. From those that presented at the testing event, the project coordinator recruited participants into the usability study if they met the inclusion criteria - all adults (over the age of 18 years) formally employed by the factories who provided informed consent. Senior Management and Healthcare Workers were excluded from the study due to variance in knowledge, education, and previous experience administering SARS-CoV-2 rapid antigen tests (Fig. 1 ). Sampling was purposive to align with the underlying factory population. All participants were approached for a short quantitative survey after test completion to explore their individual preferences and experiences of peer-assisted self-testing. Four qualitative focus-group discussions were conducted with 17 factory workers who participated in the study. Participants were purposively sampled to ensure a diverse representation of age, sex, education, and roles (e.g., line workers, supervisors) in line with the sampling approach described for the study overall. Study Procedures Peers were selected based on specific criteria, including management recommendations, communication skills, learning ability or previous experience as peer educators, and literacy level to manage data entry support. Peers underwent training in infection control, proper use of personal protective equipment (PPE), correct administration of SARS-CoV-2 self-tests, and provision of support for test administration and result interpretation. They were also trained to provide a non-stigmatizing and supportive environment for self- testing. Additionally, peers learned how to use the manufacturer-provided app for reporting results and assisting participants in using the application. Organisation coordinated the procurement and inventory of rapid antigen test kits for the usability study, ensuring proper temperature conditions during transit. The study used the Panbio™ COVID-19 Ag Rapid self-test kit, a nasal AgRDT COVID-19 Self-Test approved by the Indian Council of Medical Research (ICMR), the apex medical research organization that led and managed the COVID-19 response in India. Peers checked and ensured that only approved and valid test kits were used according to the manufacturer’s instructions. A detailed diagram of study procedures is provided (Fig. 2 ). Trained peers and consented participants executed the following steps: The study commenced with a participant interview to capture demographic information. Participants were then given a self-test kit, personal protective equipment (a surgical mask, hand and surface sanitizer), and a data collection device with the NAVICA self-testing app for result reporting. The peer assistant provided an overview of the test components, orientation to the manufacturer- provided Instructions for Use (IFU), and guidance on result interpretation. They assisted the participants verbally as they performed each test step and offered demonstrations with their kit if the worker did not understand verbal instructions. The peers never touched the participant’s test during sample collection, test operation, or result interpretation During the 15-minute run time of the test, participants were asked to read a series of pictures representing strong positive, weak positive, negative, and invalid test results in a random order to assess result interpretation accuracy before they interpreted their test results. When the run time was complete, the observer, peer, and participant independently read and recorded the results. Participants were then asked to use the study devices to report their self-test results using Abbott's NAVICA India™ app. The app collects self-reported demographics, vaccination status, and COVID-19 exposure history and symptoms. Participants reported their test results using image capture. The image was auto analysed using artificial intelligence. The results were displayed and automatically shared with ICMR. No data was stored on the device, and neither the observer nor the peer assistant had access to the data reported via the app. Peer assistants helped participants report results via the NAVICA India™ app, where required. All results were managed based on the national testing guidelines at the time (Fig. 3 ). After completion of the test, the trained independent observers administered a quantitative survey to all the participants. A subset of the participants was invited to participate in FGDs to explore their reflections on self-testing. Data Collection Highly trained and qualified researchers, known as observers, were deployed to evaluate the test operation by study participants and the support provided by peer assistants in both factories. These observers had expertise in conducting qualitative interviews and focus group discussions in the local language. They received comprehensive training on the study protocol, data collection tools, and the manufacturer-provided application. Observers employed a usability checklist guided by the Panbio Self-test IFU and adapted from tools used in other self-test usability studies ( 25 ). The checklist noted 13 critical and five non-critical steps. Observers used a separate section of the checklist to assess the quality of peer assistance provided. To assess correct interpretation, (a) The actual test result was read and recorded by both the worker and the observer, in addition to automated results read by the NAVICA app. The observer’s interpretation was considered the reference (b) The observer used the Result Interpretation section in the usability checklist to record whether the employee accurately interpreted the pictures of self-test results. Data collected through the paper-based usability checklist was entered into Microsoft Excel files, and the data entry quality was checked for accuracy and completeness. Post-test Survey: After completing the test, all participants completed a short quantitative survey to explore their experiences and preferences for peer-assisted self-testing. They were also asked questions regarding post-test actions in case of both positive and negative results to determine whether they understood the instructions correctly. The observer also asked for comments about the study processes and the level of assistance required during and after the test. All data collection tools were tested internally for clarity and internal consistency. A subset of study participants and peer assistants were invited to participate in focus group discussions (FGDs) in the days following the test. The observers conducted these discussions at the factory in a quiet and private training room, using a semi-structured guide and probes developed by the study team to explore specific themes of interest. Key areas explored included past experience with COVID-19 testing and preference for COVID-19 self-testing. The duration of the FGDs ranged from 20–30 minutes. All the audio transcripts were translated from the local language and transcribed into English through a third-party translator service. Data Analysis Quantitative Analysis Data were analysed using IBM SPSS Statistics (v. 26.0). To analyze and describe participant and peer demographics, gender, age, and education were treated as categorical variables. To determine whether the samples from each factory resemble the overall demographics, statistical tests (t-test for age and chi- square test for gender and education levels) were performed. Sub-analyses by age, gender and factory were not performed due to the overall small sample size (N=106). Estimates of proportions of participants that completed the test steps correctly with peer assistance. (Usability index/ score) were calculated using the definitions and criteria described in the analysis plan. Usability scores for the test kit and the mobile application are reported separately. Estimates of the proportion of participants who correctly interpreted their actual COVID-19 self-testing results and pictures of contrived results are reported, as is the proportion of tests conducted with accurate peer support. The inter-reader agreement was also reported by the percentage of consistent results between each reader (participant, observer, and mobile application). Qualitative Analysis We explored the qualitative data using thematic and discourse analysis approaches. Two researchers first read the transcripts to understand the participants' views and preferences, considering the social context in which the discussions were conducted. In the second stage, the researchers identified concepts or codes inductively from the data. After one round of dialogue between both researchers, a set of codes were agreed upon and defined. In the next stage, relationships between the codes were described, and larger categories of codes were formed. The generated code categories and the attached definitions and text excerpts were reviewed with two other coders (Senior Researchers) to come to a consensus about the themes generated and modify any coding discrepancies through one more round of deductive coding. The latest version of NVivo 1.6.1 (1137) was used for analysis. Results Demographics A total of 106 individuals consented to participate in the study. The overall mean age of the sample was 35.7 yrs. More women than men participated in the study (Female: 84 (79.25%), Male: 22 (20.75%)). Just over half the sample had finished High school (12th grade) (52.9%) ( Table 1 ) . Demographics within each factory sample (Factory 1: N = 20 and Factory 2 N = 86) corresponded with the overall factory demographics ( Table 2 ) . All of the participants were yet to use a COVID-19 self-test before. Only 7(6.6%) participants noted that they had used a self-test for indications other than COVID-19 before (e.g. Pregnancy test). Table 1 Participant Demographics Demographics Categories Total sample N (%) Age 18–29 29(27.4) 30–41 46(43.4) 42–53 27(25.5) 54–65 4(3.7) Total 106(100) Education Did not attend 5 (4.7) Primary School & Middle school (1st-8th) 34( 32 ) High School(9-12th) 56(52.9) Undergraduate (including Vocational/Diploma) 11(10.4) Total 106 (100) Gender Female 84(79) Male 22( 21 ) Total 106 (100) Table 2 Population and Sample demographics Demographics Population Study Sample ACWA n(%) EB n(%) ACWA n(%) EB n(%) Age less than 18 7(3.6) 0(0.0) 0(0.0) 0(0.0) 18–29 94(48.5) 70(23.4) 9(45.0) 20(23.3) 30–41 60(30.9) 129(43.1) 8(40.0) 38(44.2) 42–53 27(13.9) 95(31.8) 2(10.0) 25(29.1) 54–65 6(3.1) 4(1.3) 1(5.0) 3(3.5) > 65 0(0.0) 1(0.3) 0(0.0) 0(0.0) Total 194(100.0) 299(100.0) 20(100.0) 86(100.0) Gender female 49(25.3) 261(87.3) 7(35.0) 77(89.5) male 145(74.7) 38(12.7) 13(65.0) 9(10.5) Total 194(100.0) 299(100.0) 20(100.0) 86(100.0) Education uneducated 2(1.0) 36(12.0) 0(0.0) 5(5.8) primary and middle school(1st to 8th) 5(2.6) 84(28.1) 1(5.0) 34(39.5) high school(9th to 12th) 93(47.9) 158(52.8) 14(70.0) 41(47.7) undergraduate 94(48.5) 21(7.0) 5(25.0) 6(7.0) Total 194(100.0) 299(100.0) 20(100.0) 86(100.0) Alt Text: Table showing a comparison of Population and Sample Demographics across the two factories where the Intervention was implemented. Age is broken down into less than 18, 18–29, 30–41, 42–53, 54–65, and over 65. Education levels include uneducated, primary and middle school (1st to 8th), high school (9th to 12th), and undergraduate. Gender includes female and male. For each subcategory, the table presents the number and percentage of participants across the population and study sample, disaggregated by intervention groups ACWA and EB. Analysis of Key Outcomes Usability of the Self-Test Usability of the SARS-CoV-2 Self-Test in a Peer-Assisted Model The usability of the self-test was 75.9% for the overall sample (N = 103) across all steps and 80.7% (N = 103) when restricted to critical steps ( Table 3 ) . The observer checklist data was not available for three individuals. The most commonly reported errors (< 75% participants performed accurately) were: Table 3 Usability Index - Test Kit Steps The proportion of participants that performed a step correctly (N = 103) n Yes n(%) No n(%) Total Total Was the employee able to squeeze the liquid from the Buffer Bottle into the Tube? 97 84(86.6) 13(13.4) Did the participant confirm that the buffer level was above the line provided in the tube? 97 70(72.16) 27(27.84) Was the employee able to remove the swab without touching the soft end? 98 89(90.82) 9 (9.18) Did the employee insert the soft end up to 2 cm into their nostrils? 98 57(58.16) 41(41.84) Did the employee rotate the swab five times in each nostril? 97 69(71.13) 28(28.87) Did the employee insert the swab into the fluid and swirl it five times while pushing against the tube wall? 98 74(75.51) 24(24.49) Was the employee able to place five drops (and not more or less) into the well? 96 79(82.29) 17(17.71) Did the employee place the solution into the well on the test device (and not any other location)? 96 89(92.71) 7 (7.29) Did the employee read the results within 15–20 minutes? 95 92(96.84) 3 (3.16) Usability Index total (%) 80.7 Using the incorrect amount of buffer Inaccurate swab insertion Failure to swab the nose five times, touching the walls. Usability of the App: The usability of the NAVICA mobile application to report the test results was 34.0% (N = 103) ( Table 4 ) . Table 4 Usability Index - Mobile Application Steps Proportion of participants that performed a step correctly (N = 103) n* (no of valid observations) Yes n(%) No n(%) Total Total Did the employee successfully sign up for the application? 95 30(31.6) 65(68.4) Could the employee enter all the details required to move to the next steps? 95 27(28.4) 68(71.6) Could the employee read the results analysed by the application? 92 47(51.1) 45(48.9) Did the employee finish uploading the results on the application? 92 22(23.9) 70(76.1) Usability Index (Weighted Average of the usability scores of steps) (%) 33.80 Less than 30% of participants scored accurately in the following steps: Enter all the mandatory data fields in the mobile application to proceed with result capture. Capture the results for automatic upload into the mobile app Reported ease of use: Fifty-three study participants (52.5%) (N = 101) in both factories reported that the test was easy to conduct. Thirty-seven (36.6%) participants said it was somewhat easy, and 11(10.9%) participants said it was not easy at all (Table 5 ). Usage of the mobile application was the step that most participants reported Table 5 Reported ease of use and assistance required Total n(%) How easy was the test to conduct? a. Not easy at all 11(10.9) b. Somewhat easy 37(36.6) c. Completely easy 53(52.5) Total 101(100.0) Which parts of the test did you find difficult and require assistance with? a. Test Preparation (preparing buffer and test kit elements) 20(20.8) b. Sample Collection (collecting sample through nasal swab) 17(17.7) c. Test Process (placing buffer with sample in well) 13(13.5) d. Use of Mobile Application 40(41.7) e. Result Interpretation 6(6.3) Total 96(100.0) Alt Text : Table showing participants’ self-reported ease of use and the areas in which they required assistance while conducting the test. For ease of use, responses are categorized as: not easy at all, somewhat easy, and completely easy. For difficulties and assistance, participants identified specific steps: test preparation (preparing buffer and kit components), sample collection (nasal swab), test process (adding sample to well), use of the mobile application, and result interpretation. For each response option, the table presents the number and percentage of participants (n%). needing assistance with (41.7%), followed by sample collection (17.7%) and preparing the buffer and test kit elements (20.8%) (Table 5 ). This corresponds with the steps where most participants performed errors. Table 5 HERE Interpretation of the Test Inter-reader concordance: Table 6 depicts the percentage of concordance in test result interpretation between the manufacturer’s Table 6 Inter-Reader Concordance for interpretation of results Inter-Reader Concordance for interpretation of results % of concordance n(%) % of discordance n(%) Participant interpretation vs observer interpretation 93(97.9) 2(2.1) Participant interpretation vs manufacturer app interpretation 93(97.9) 2(2.1) Observer interpretation and manufacturer app interpretation 91(95.8) 4(4.2) Alt Text : Table showing inter-reader concordance and discordance in the interpretation of test results. Concordance and discordance are reported as both percentages and number of cases n(%). Comparisons include: participant interpretation vs observer interpretation, participant interpretation vs manufacturer app interpretation, and observer interpretation vs manufacturer app interpretation. mobile application, participants, and observers (N = 95). Five participants deferred to the peer assistant/observer to interpret the results, and the app did not analyze six results due to a technical error and were not included in this sample. In all instances of discordance, the observer's interpretation was considered final. Concordance between participant and observer was 97.9% (93). The two instances of discordance were due to an incorrect interpretation of weak positive results by the participant. Concordance between the participant and the application was 97.9% (93). The two instances of discordance were due to inaccurate interpretation by the mobile application in which negative results were read as positive results. Concordance between the observer and the application was 95.8% (91). Out of the 4 discordant results, 50% (n = 2) of discordance was due to the app's interpretation of a negative test result as a positive one, and 50% of discordance was due to the app's interpretation of a positive test result as a negative one. Interpretation of Contrived Results: Most participants accurately interpreted the pictures of strong positive and negative results (~ 82.9% and ~ 80%, respectively). 69 (65.7%) participants interpreted weak positive results correctly, whereas 70 (66.7%) interpreted invalid results correctly. The proportions of participants who did and did not interpret the four types of potential results are detailed in Table 7 . Table 7 The proportion of participants who correctly interpreted SARS-CoV-2 self-testing results using high-resolution pictures of self-test results Self-test results Proportion of participants who interpreted the results correctly n(%) Proportion of participants who did not interpret the results correctly n(%) Negative 84(80.0) 21(20.0) Weak Positive 69(65.7) 36(34.3) Positive 87(82.9) 18(17.1) Invalid 70(66.7) 35(33.3) Peer Support Model The study involved ten peers who assisted participants during self-testing. Their mean age was 33.2 years. They were predominantly female (60%). More than half of the peers (70%) had completed high school (grades 9–12), peer assistant demographics are detailed in Table 8 below. Table 8 Peer assistant Demographics - N-10 Demographics Categories Total sample n(%) Gender Female 6(60) Male 4( 40 ) Total 10(100) Age 18–29 3( 30 ) 30–41 5(50) 42–53 2( 20 ) 54–65 0(0) Total 10(100) Education Did not attend 0(0) Primary School&Middle School (1st-8th) 0(0) High School(9-12th) 7(70) Undergraduate (including Vocational/Diploma) 3( 30 ) Total 10(100) Peer Assistance Efficiency Across all critical steps, peer assistants provided accurate instructions in 93.4% of tests performed. The number and proportion of tests for which the peer assistant performed an error are reported for each critical step in Table 9 . Among the steps critical to the test, peers missed or provided incomplete Table 9 Peer Assistant Efficiency Critical Steps Proportion of tests for which the peer provided accurate support (N = 103) n* (no of valid observations) Yes n(%) No n(%) Did the PA carefully explain different components of the test kit (swab, buffer bottle, tube, test device, tube rack, disposable bag, timing device). 99 91(91.9) 8(8.1) Did the PA instruct the employee to empty the buffer into the tube to avoid spillage? 98 98(100.0) 0(0.0) Did the PA check if the buffer is above the line marked in the tube? 98 80(81.6) 18(18.4) Did the PA provide instructions to unwrap the sample collection swab appropriately? 98 96(98.0) 2(2.0) Did the PA provide instructions to use the sample collection swab appropriately? 98 96(98.0) 2(2.0) Did the PA instruct you not to touch the swab at any point? 98 87(88.8) 11(11.2) Did the PA check with the employee to ensure they inserted the swab to the correct depth? 97 91(93.8) 6(6.2) Did the PA check with the employee to ensure they rotated the swab five times in each nostril? 97 95(97.9) 2(2.1) Did the PA check with the employee to ensure they inverted the swab into the buffer tube without putting it down anywhere? 97 95(97.9) 2(2.1) Did the PA instruct the employee to check the liquid for bubbles? 98 64(65.3) 34(34.7) Did the PA instruct the employee to place five drops (and not more or less) into the well and not any other location on the test? 94 93(98.9) 1(1.1) Did the PA alert the employee to read their results in the accurate read time window? 95 94(98.9) 1(1.1) Did the PA instruct the employee how to interpret their results? 94 94(100.0) 0(0.0) Did the PA give instructions about reporting results? 96 94(97.9) 2(2.1) Average score (%) 93.4 Alt Text: Table showing the proportion of tests for which peer assistants provided accurate support during critical steps of the SARS-CoV-2 self-testing process. Steps include explaining test kit components, instructing on buffer handling, swab use, checking swab depth and rotation, ensuring correct sample preparation, reading results timing, interpreting results, and reporting instructions. For each step, the table presents the number and percentage of accurate support (Yes) and inaccurate or no support (No), along with the number of valid observations. instructions for sample collection and verifying appropriate buffer levels. A list of the most common errors reported in peer assistance is in Table 10 . 55 (53.9%) participants from both factories found the verbal peer instructions “Completely easy” to understand, 36(35.3%) found the instructions “Somewhat easy”, and 11(10.8%) found them “Not easy at all” Table 10 Errors reported in Peer Assistance Common errors observed via the checklist: ● Peer did not provide instructions to wear a mask correctly at all times except sampling ● Peer did not provide instructions to check if an accurate amount of buffer is being used ● Peer did not provide instruction not to touch the swab at any point ● Peer did not provide instructions to check liquid for bubbles Qualitative data and informal observation suggest additional errors: ● Peer does not speak loudly ● Peer touches the participant's test kit to demonstrate a step ● Peer is giving instructions too fast ● Peer is giving incomplete instructions ● Peer does not explain the context or meaning of certain instructions Acceptability of the Test Confidence in using the test kit With peer assistance 62% (62) of participants reported they were completely confident in using the test and interpreting self-test results with the assistance of a peer. 28.0% ( 28 ) were somewhat confident and 10.0% ( 10 ) participants were not confident at all. Unassisted 43.9% ( 43 ) participants were completely confident in performing and interpreting the test on their own, 46.9% (46) participants were somewhat confident, and 9.2%( 9 ) participants were not confident at all. Confidence in using the mobile application With peer assistance Only 15.6% ( 15 ) participants were completely confident in capturing and uploading their results using the NAVICA mobile application with peer assistance. In comparison, 29.2% ( 28 ) and 55.2% (53) participants said they were somewhat confident and not confident at all, respectively. Unassisted Only 19.6% ( 19 ) participants were completely confident in capturing and reporting their results independently. 35.1% ( 34 ) participants and 45.4%( 44 ) participants stated they were not confident in capturing and uploading their results using the mobile application without assistance. Accessing the Test 57.4% (58) participants reported that they would prefer to use the self-test at home, followed by 36.6% ( 37 ) who preferred to test at the workplace. Finally, 5.0% ( 5 ) of participants preferred to test at a hospital. When asked where participants wished to purchase a test, 36.1% ( 35 ) of participants preferred their nearest pharmacy. 29.9%( 29 ) participants preferred to access the test at the workplace, and 14.4%( 14 ) participants would like to access the test at the PHC/local clinic. Qualitative Results: Motivations and barriers to self-testing, perceived benefits of self-testing, and reflections on the peer- assisted self-testing model were the key themes that emerged from the qualitative thematic analysis of focus group discussions with the participants. 1. Motivators and barriers for Self-testing All participants were first-time users of a COVID-19 self-test. Participants reported being fearful of testing but were motivated to accept and perform self-testing due to concerns about getting infected and spreading the infection to others. In both factories, participants’ hesitancy to test was precipitated by the fear of a positive result resulting in isolation from their peers and the stigma associated with working with an infected person. "When we are in close contact with infected individuals, there is a risk of transmission to us. This is why we should undergo testing." "It is important to prioritize testing to prevent harm or danger to others." (Male, 25 years, Factory 1). “If any of us test positive, it may instill fear among others, leading to hesitation in working together.” (Female, 21 years, Factory 2) 2. Perceived Benefits of Self-testing Participants in both factories showed mixed sentiments about the COVID-19 self-testing experience, with generally positive attitudes toward the test. Several participants spoke about their experiences with other testing methods (RT-PCR and professionally administered RAT) and compared them with their self- testing experience. Specifically, participants preferred collecting samples from the nose (as opposed to nasopharyngeal collection), describing it as a comfortable and painless procedure. Sometimes the testing will be painful. But, if we do it on our own, it is not painful. So we feel this is better. (Female, 32 years, Factory 1) Most respondents mentioned that self-testing saved time and reduced risks posed by the long wait for testing in government facilities. Participants thought that self-testing at the workplace was quick and easy in terms of time taken to set up, test and interpret results. If we go to a Bruhat Bengaluru Mahanagara Palike (BBMP - Local administrative body in the Greater Bengaluru metropolitan area) test center, we are unsure of the safety. There were long queues, and we doubted whether someone among these people was infected. (Male, 25 years, Factory 1). One point is that we can do it easily and leisurely at work. When we go somewhere else, it takes too much time. (Female, 32 years, Factory 1) Participants highlighted that the ability to understand the process and to do the test at home with family members is one of the critical benefits of the self-testing kit. “We can understand this (self-testing process). We can do this at our houses when our husbands or children or relatives are infected. Or we can guide them on how to do the testing.” (Female, 35 years, Factory 2) 3. Reflections on Peer-Assisted Self-Testing Model Participants also shared their experiences with the specific peer-assistance models used during self- testing in factories. Participants from both factories claimed that peer assistance was a critical component of self-testing, finding comfort in performing the test after receiving in-person instructions from their peers. Having colleagues as peer instructors was preferred over healthcare workers, as instructions provided by their co-workers were straightforward to understand and follow. “When we went to a government hospital, they simply took the swab and sent us back. They did not explain anything. Here (at the workplace), they (peers) have explained it to us.” (Female, 35 years, Factory 2) Some participants mentioned that peer assistants should have more experience and knowledge about the tests, most likely someone with healthcare experience. Others highlighted that their co-workers would need more training and handholding support from the study staff to perfect their support. “We prefer your team (Organization’s team) as you have more knowledge than our co-workers. They (peer assistants) have been trained very recently, and their knowledge is quite different. It is better if we learn something from you.” (Male, 21 years, Factory 1) We were comfortable as our co-workers in the company were providing us with instructors. (Female, 24 years, Factory 1) In addition to peer support, participants reported that more time to perform and interpret the results would also strengthen testing quality. “If we are taking time from work, our attention will be split between work and testing, and we might make mistakes. But if we have good enough time, we will do it better and with concentration.” (Female, 24 years, Factory 1) Discussion This study assessed the usability of antigen-based SARS-CoV-2 self-testing in a peer-assisted model among factory workers - a key vulnerable group facing inequitable access to early diagnosis and testing. Prior research on self-testing for SARS-CoV-2 evaluated unassisted and healthcare worker-assisted models in various settings, including educational institutions ( 26 , 27 ) hospitals ( 28 ), and the general population ( 16 ). The majority of these studies were limited to high-income settings. While workplace models have been described, these models have relied on healthcare worker administration, home-based self-testing, or secondary distribution to household members ( 14 , 29 , 30 ). This is the first study to describe peer-assisted self-testing in a workplace setting where there is a need for increased user support due to lower literacy levels. Our study found that most factory workers could accurately conduct the critical steps for a nasal sampling-based test kit (80.7%) and interpret their results accurately with high levels of concordance (97.9%) with trained observer-interpreted results. This is comparable to the work by Sibanda, Choko ( 31 ), who found the usability of the Panbio self-test on 12 critical steps to be 90.4% and 70.6% in Malawi and Zimbabwe, respectively. In our study, most participants reported the test was easy to conduct but required assistance in results interpretation, followed by sample collection. This is in line with the usability study findings from Lesotho and Zambia, where the common errors included failure to insert the swab to the correct depth, the inadequate swirling of the swab to touch the nasal passage walls, and buffer preparation ( 32 ). Low usability scores on some of the critical test steps in our study could have been driven by multiple factors such as demographics, first-time use of a self-test, clarity of peer instruction, nature of sample collection, and limited time for testing due to the workplace setting ( 33 , 34 ). Simple changes to test design could address some usability challenges. The design for SARS- CoV-2 self-test kits did not include populations with limited education and English-speaking abilities highlighting the need for instructions to be printed in local languages. Using a quick reference guide could target key usability challenges and improve the accuracy of SARS-CoV-2 test interpretation ( 35 ). Instructional videos and pictures have also been suggested to improve the usability of other self-tests ( 33 , 36 ). Providing a pre-filled extraction tube or designing an easy-to-open cap for the buffer bottle and enhancing the clarity of the buffer line could also address some user errors. Such work will be critical to bring the reported usability of SARS-CoV-2 self-tests in-line with other self-test products, such as oral and blood-based HIV self-tests ( 37 ). Reported challenges with the instructions for use underscore the importance of peer assistance for users who reported reduced confidence in performing the test without peer assistance. Data on the accuracy of peer instructions suggests that workplace testing programs must closely and continuously monitor the quality of peer instruction and focus on ensuring strong self-efficacy among peers via regular refresher training with online/self-paced modules, toolkits, job aids, and supervision by management. These interventions can strengthen accurate use and decrease the time required for testing services, a critical priority reported by management. Such investments will strengthen the role of peers, who are essential in driving the uptake of self- testing services in the workplace ( 36 , 38 ). As these tests are used more frequently, individuals are expected to gain more confidence in using them independently without relying on peer support, especially in places they prefer, such as their homes. Similar results have been documented in workplace settings in Kedah, Malaysia ( 30 ). This study demonstrated that time constraints can influence the peer’s quality of instruction and the participant’s focus on the test, resulting in instructional and testing errors. Workplace programs, therefore, need to ensure that workers have sufficient testing time and privacy to interpret results in tandem with withdrawing workers from an active production line. We found strong concordance in test interpretation between users and trained observers (97.9%). Discordance between users and observers was driven by incorrect user interpretation of weak positive results. Low accuracy in the interpretation of contrived results underscores poor understanding of weak positive and invalid results. Result interpretation errors, especially in contrived image review, were most likely due to an insufficient wait time for interpretation, not having the IFU in a local language for reference, and lack of attention during peer support. These findings align with the broader self-test literature, including tests for SARS-CoV-2, underscoring the potential for misinterpretation of these test outcomes ( 37 , 39 ). Concordance between the mobile application and trained observers was lower (95.8%) and driven by false interpretations by the app. In the case of false negatives, participants were often hesitant to accept the interpretation of the trained observer and required additional counselling or re-testing. We hypothesize that false results may have been driven by improper lighting or poor focus for image capture. This aligns with our findings that the usability of the manufacturer-provided (NAVICA™) app for results reporting in a factory setting is low. Participants and the peer assistants faced multiple challenges with an app-based model for reporting, including unavailability of content in the local language, multiple mandatory fields, usage of technical and hard-to-understand words, and frequent technical errors. Findings suggest that a mobile-based reporting model has low usability and is moderately accepted by factory workers and similar populations with restricted smartphone access and digital literacy. Industrial workers cannot carry their mobile phones to their workspace, limiting their ability to report and view results on their devices. Our findings are similar to recent research ( 17 , 19 ) that suggests mobile application-supported HIVST is feasible only for youth and tech-savvy populations ( 40 , 41 ). Point-of-care diagnostic manufacturers must be cognizant of these challenges if product penetration reaches the last mile and is acceptable to vulnerable populations. Self-test program implementers must design programs to circumvent these limitations, reducing dependencies on worker devices by providing program devices, stable internet, and peer support to mitigate technical errors and minimize false results. Our findings also demonstrate that SARS-CoV-2 self testing continues to offer end users many of the same benefits of self-testing for other infectious diseases, including knowledge of health status, increased comfort, and reduced time requirements ( 30 , 42 ). These data add to a growing body of evidence demonstrating user preference for self-care technologies ( 43 ). With health worker shortages and a lack of access to affordable healthcare services, more significant investments are needed in making community-centric self-care tools, including self-tests, more user- friendly, affordable, and acceptable. These findings indicate that self-testing is a feasible tool to facilitate manufacturer implementation of worker screening programs. It also allows vulnerable groups like factory workers to take charge of their health by monitoring their well-being, making informed healthcare choices, and improving communication and advocacy with employers and healthcare providers. Self-test technology should be preparative for future pandemics and support health system resilience. By reaching hard-to-reach groups, the peer-assisted self-testing model may provide an approach to expanding access to address the country's unique needs. Limitations While this study makes a nuanced contribution to the literature on the adoption and use of self- testing amongst vulnerable groups, the study has a few limitations. First, while purposive sampling of the participants allowed for targeted representation from the factories, it also introduced selection bias. Second, the purposive selection of factories based on their willingness to implement the program could also indicate that the participating factories are already open to health innovations. Third, the small sample size and the limited geographic scope, confined to 2 factories in Bangalore, restricted the ability to conduct sub-population-level analyses and limited the generalizability of the findings. Finally, the presence of observers and peer-assistants might have contributed to observer and social desirability bias, influencing the performance and responses of the participants to appear in favor of the intervention.To mitigate this, observers were instructed to be discreet and not interfere in the procedure. Standardized training followed by assessing their proficiency was provided to all peers to minimize the impact of varying quality of peer assistance on usability. To minimize the interpretation bias that occurs due to having multiple readers (observer, participant, peer assistant, and the mobile application), the participants' interpretation was recorded before having the app or observer read and record the result. The observer did not disclose the results that s/he interpreted to the participant to prevent interpretation bias further and avoid any impact on acceptability responses. Conclusion The findings add to a growing body of research demonstrating the usability, feasibility, acceptability, and cost-effectiveness of workplace testing, including self-testing, for SARS-CoV-2 ( 14 , 29 , 30 , 44 ). They are the first to demonstrate the usability of SARS-CoV-2 self-tests in a peer-assisted model in factory settings with disadvantaged populations. Our findings suggest that peer-based models ensure that SARS-CoV-2 self-tests are usable with strong indications of acceptability. Determining the feasibility of implementing this model at scale is the next step to inform and advocate for other workplace self-testing programs for vulnerable populations. Abbreviations Abbreviation Definition COVID-19 Coronavirus disease 2019 SARS-CoV-2 Severe Acute Respiratory Syndrome CoronaVirus-2 GDP Gross Domestic Product RT-PCR Reverse transcription polymerase chain reaction RAT Rapid Antigen Test WHO World Health Organization ICMR Indian Council of Medical Research GOI Government of India PPE Personal Protective Equipment AgRDT Antigen based Rapid Diagnostic Test PHC Primary Health Center BBMP Bruhat Bengaluru Mahanagara Palike IFU Instructions for Use CHW Community Health Worker Declarations Ethics approval and consent to participate: The Catalyst Foundation Institutional Ethics Committee approved all human participant-related documents on January 19, 2022, before the commencement of the study. All procedures in this study adhered to the tenets of the Declaration of Helsinki. Before data collection, participants received briefings on the study's purpose and potential risks, with the freedom to pause interviews at any time, ask clarifying questions, or skip questions that caused discomfort. Participants were apprised of the stringent measures taken to protect their identity and confidentiality. Consent for publication: Participants were apprised of and consented to the possibility of using information gathered from the study in an aggregated form or as individual perspectives in manuscripts or reports after de-identifying any personal information. Availability of data and materials: The data underlying this article will be shared on reasonable request to the corresponding author as it contains sensitive information. However, our tools, code book and data analysis plan has been shared as supplementary data. Competing interests: there are no competing interests Funding: This work was supported by a sub-grant from FIND, from the more significant grant FIND received from BMZ (Federal Ministry for Economic Cooperation and Development, Germany). The funders had no role in data collection and analysis, the decision to publish, or the preparation of the manuscript. FIND supported the study design and feedback on the protocols and the manuscript. Authors' contributions: MRP: Data Curation, Formal Analysis, Investigation, Project Administration, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing. PS: Conceptualization, Writing – Original Draft Preparation, Writing –Review & Editing, Methodology, Validation, Visualization. NP: Project Administration, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing. PR:-Conceptualization, Project Administration, Supervision, Writing – Review & Editing. RK: Formal Analysis, Writing – Review & Editing. SAG: Project Administration, Investigation, Writing – Review & Editing. AC: Supervision, Funding Acquisition, Writing – Review & Editing. SS: Conceptualization, Writing – Review & Editing, Methodology, Validation. AS: Project Administration, Funding Acquisition, Writing – Review & Editing. JC: Project Administration, Writing – Review & Editing. EIR: Conceptualization, Writing – Review &cEditing, Methodology, Validation. SBS: critical review and editing the final version of the manuscript. Acknowledgements: Not applicable Clinical Trial Number: Not applicable References World Health Organization (WHO). Health Emergency Dashboard [Internet]. World Health Organization (WHO) 2020. Available from: https://extranet.who.int/publicemergency/ Gupta D, Biswas D, Kabiraj P. COVID-19 outbreak and Urban dynamics: regional variations in India. GeoJournal. 2022;87(4):2719–37. Sridhar KS, Urbanization. COVID-19 Prevalence in India. 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Supplementary Data Supplementary Data files 1 and 3-4 are available in the supplementary file section. Supplementary Data file 2 is not available with this version. Additional Declarations No competing interests reported. Supplementary Files InformedConsentForm.docx Informed Consent Form Codebook1.docx Code Book for qualitative interpretation DataAnalysisPlan.xlsx Data analysis Plan 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. 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1","display":"","copyAsset":false,"role":"figure","size":91515,"visible":true,"origin":"","legend":"\u003cp\u003eSample selection\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7186437/v1/93d32205f6340272498da621.png"},{"id":92800575,"identity":"03d01fd0-14f9-4098-8bda-dd7f655f2c98","added_by":"auto","created_at":"2025-10-05 11:32:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":169828,"visible":true,"origin":"","legend":"\u003cp\u003eStudy procedures\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7186437/v1/0ef80d306154edfd564fc6d9.png"},{"id":92800577,"identity":"3eee4c06-6218-43de-ba65-ef64b645e9f9","added_by":"auto","created_at":"2025-10-05 11:32:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64130,"visible":true,"origin":"","legend":"\u003cp\u003eTesting algorithms\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7186437/v1/982e7fbe5b97f5da5772f7d9.png"},{"id":92800587,"identity":"36d29d39-60a3-4f1b-b581-c05a92ba1144","added_by":"auto","created_at":"2025-10-05 11:32:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":177569,"visible":true,"origin":"","legend":"\u003cp\u003ePictures of contrived results\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7186437/v1/1b06f2293457bce1583a24a9.png"},{"id":96276156,"identity":"9b0ba3c0-4eb0-4221-8fe3-c75d111e2c2b","added_by":"auto","created_at":"2025-11-19 10:08:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2811405,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7186437/v1/7f8fcc06-2cef-4bb6-b5fa-85e943707ae1.pdf"},{"id":92800570,"identity":"5d9d99de-42c4-423a-9c8f-83bfa38dfef9","added_by":"auto","created_at":"2025-10-05 11:32:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12139,"visible":true,"origin":"","legend":"\u003cp\u003eInformed Consent Form\u003c/p\u003e","description":"","filename":"InformedConsentForm.docx","url":"https://assets-eu.researchsquare.com/files/rs-7186437/v1/55e117c1b18c2d278e69790f.docx"},{"id":92803381,"identity":"fea22b6d-e2f9-4ec8-9c46-b628397d21ab","added_by":"auto","created_at":"2025-10-05 11:56:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7991,"visible":true,"origin":"","legend":"\u003cp\u003eCode Book for qualitative interpretation\u003c/p\u003e","description":"","filename":"Codebook1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7186437/v1/ad00bf20964577cd1c0915be.docx"},{"id":92802987,"identity":"d5f4a19f-0e52-4dc5-95ae-5449510a044b","added_by":"auto","created_at":"2025-10-05 11:48:56","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18652,"visible":true,"origin":"","legend":"\u003cp\u003eData analysis Plan\u003c/p\u003e","description":"","filename":"DataAnalysisPlan.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7186437/v1/a56fb86c974c8c7630258acd.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Usability of SARS-CoV-2 Self-Testing in a Peer-Assisted Model among Factory Workers in Bengaluru, India: A Mixed- Methods Cross-Sectional Study","fulltext":[{"header":"Background","content":"\u003cp\u003eCoronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), took a severe health, social and economic toll worldwide. As of July 2023, India reported 44\u0026nbsp;million cases and 531,913 deaths (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The COVID-19 pandemic in India initially concentrated in urban areas, with metropolitan cities and high income states contributing to 3/4ths of total cases (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Urbanization, higher workforce participation, population density, and income were positively associated with the increase in COVID-19 prevalence and transmission (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Globally, workplace transmission was a significant contributor to the overall spread of COVID-19, primarily due to the key risk factor of close contact\u0026mdash;defined as being within 6 feet of an infected person for 15 minutes or more (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In India, the manufacturing sector was particularly affected, as large numbers of workers operated in close proximity within labour-intensive environments. This risk was further compounded by the fact that a significant portion of the workforce comprised migrant and daily wage laborers who, fearing loss of income or employment, continued to attend work\u0026mdash;even when unwell, potentially exposed, or asymptomatic\u0026mdash;thereby contributing substantially to workplace transmission.\u003c/p\u003e\u003cp\u003eVaccination policies that initially restricted access to those above 45 years of age, coupled with vaccine hesitancy among factory workers, further exacerbated their vulnerability. Additionally, many factory workers, being migrants, faced challenges in accessing local health services due to language barriers. Low awareness of COVID-19 testing and vaccination services, poor understanding of transmission prevention, limited perception of risk, and fear of discrimination following the disclosure of a positive status all contributed to under-testing, delayed diagnosis and increased risk of infection, which then spread to their families and communities (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The implementation of nationwide and state-level lockdowns led to factory closures, restrictions on mobility and disruptions to supply chains both domestic and international causing a halt in activity and negatively affecting production volumes and revenue (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Between 2020-1, the Index of Industrial Production decreased by 9.6%, reflecting setbacks to core manufacturing during the pandemic's second wave (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Karnataka saw a permanent shutdown of 754 factories, with nearly 46000 workers losing their jobs since the onset of the pandemic between 2020 and 2021(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). As the second wave of infections eased after June 2021, businesses re-opened and implemented occupational safety programs to prevent outbreaks and support employees that tested positive. These were mandated for large workplaces by state governments (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Although this increased economic activity, successful implementation of these programs required the availability of timely diagnostic testing to facilitate case identification and quarantine. However, the supply of Reverse Transcription - Polymerase Chain Reaction (RT-PCR) testing for SARS-CoV-2 in health facilities was constrained by the need for significant laboratory capacity and trained clinical staff, as well as a shortage of molecular reagents, supplies, and equipment. Fear of painful testing procedures and long waiting and travel times due to limited testing facilities further constrained demand (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRapid Antigen Testing (RAT) for COVID-19 has been recommended and successfully implemented globally in workplaces and clinical settings (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). It improves individuals' access to testing and, due to shorter turnaround times, allows for the early diagnosis of positive cases, preventing disease transmission (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). RATs may be administered by a healthcare worker or self-administered by the worker or patient. Self- administration, in which an individual collects a sample, runs the test, and interprets the results by themselves, is called \u0026ldquo;self-testing.\u0026rdquo;\u003c/p\u003e\u003cp\u003eIn recent years, self-testing has gained prominence as an essential self-care intervention. In 2016, the World Health Organization (WHO) recommended HIV self-testing. Since then, self-testing for Hepatitis C (HCVST) and COVID-19 has been recommended (\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Self-testing has proven to circumvent barriers to testing, such as stigma, privacy, time required for testing, and affordability (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Recent studies have demonstrated the accuracy of the COVID-19 self-test results (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCOVID-19 self-test products are approved for use in the general population in India but have not been widely used in workplace settings. In manufacturing industries, which face a shortage of healthcare workers on-site, self-testing may enable large-scale screening programs within workplaces, particularly among vulnerable workers, Such programs have the potential to improve worker health and mitigate pandemic-related business disruptions which can impact workers' livelihood. WHO recommends including self-testing in national strategies, citing improved access and usability across diverse settings (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). While global studies show self-testing is feasible and scalable (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), evidence from Indian manufacturing settings remains limited but is crucial for expanding access and ensuring linkage to care.\u003c/p\u003e\u003cp\u003eThis study assessed the usability of a COVID-19 self-test in a peer-assisted self-testing model in two factories in Bengaluru, India. Peer assistance refers to a lay peer who was present during testing to guide workers through the testing procedure. This approach sought to balance the limitation of health worker availability with low literacy levels among the workforces. Additional acceptability assessments in the study determined how workers viewed the test and testing process.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Design\u003c/h2\u003e\n \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\n \u003ch2\u003eLocation\u003c/h2\u003e\n \u003cp\u003eThis usability and acceptability study was a cross-sectional study conducted in Bengaluru, Karnataka, India, from February to March 2022 in two factories \u0026minus;\u0026thinsp;1) a mid-sized machine parts manufacturer with approximately 180 staff; and 2) a leading garment manufacturer and export company with approximately 650 workers. Organisation with existing workplace health programs in both factories conducted this study. Factories were selected based on size, diversity of workforce demographics, and management\u0026rsquo;s willingness to implement a COVID-19 self-testing program designed to mitigate business disruptions.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eSampling\u003c/h3\u003e\n\u003cp\u003eFactory management opened the testing event to their entire workforce. From those that presented at the testing event, the project coordinator recruited participants into the usability study if they met the inclusion criteria - all adults (over the age of 18 years) formally employed by the factories who provided informed consent. Senior Management and Healthcare Workers were excluded from the study due to variance in knowledge, education, and previous experience administering SARS-CoV-2 rapid antigen tests (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Sampling was purposive to align with the underlying factory population. All participants were approached for a short quantitative survey after test completion to explore their individual preferences and experiences of peer-assisted self-testing. Four qualitative focus-group discussions were conducted with 17 factory workers who participated in the study. Participants were purposively sampled to ensure a diverse representation of age, sex, education, and roles (e.g., line workers, supervisors) in line with the sampling approach described for the study overall.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003eStudy Procedures\u003c/h3\u003e\n\u003cp\u003ePeers were selected based on specific criteria, including management recommendations, communication skills, learning ability or previous experience as peer educators, and literacy level to manage data entry support. Peers underwent training in infection control, proper use of personal protective equipment (PPE), correct administration of SARS-CoV-2 self-tests, and provision of support for test administration and result interpretation. They were also trained to provide a non-stigmatizing and supportive environment for self- testing. Additionally, peers learned how to use the manufacturer-provided app for reporting results and assisting participants in using the application.\u003c/p\u003e\n\u003cp\u003eOrganisation coordinated the procurement and inventory of rapid antigen test kits for the usability study, ensuring proper temperature conditions during transit. The study used the Panbio\u0026trade; COVID-19 Ag Rapid self-test kit, a nasal AgRDT COVID-19 Self-Test approved by the Indian Council of Medical Research (ICMR), the apex medical research organization that led and managed the COVID-19 response in India. Peers checked and ensured that only approved and valid test kits were used according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003cp\u003eA detailed diagram of study procedures is provided (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTrained peers and consented participants executed the following steps:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe study commenced with a participant interview to capture demographic information.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eParticipants were then given a self-test kit, personal protective equipment (a surgical mask, hand and surface sanitizer), and a data collection device with the NAVICA self-testing app for result reporting.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe peer assistant provided an overview of the test components, orientation to the manufacturer- provided Instructions for Use (IFU), and guidance on result interpretation. They assisted the participants verbally as they performed each test step and offered demonstrations with their kit if the worker did not understand verbal instructions. The peers never touched the participant\u0026rsquo;s test during sample collection, test operation, or result interpretation\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eDuring the 15-minute run time of the test, participants were asked to read a series of pictures representing strong positive, weak positive, negative, and invalid test results in a random order to assess result interpretation accuracy before they interpreted their test results.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eWhen the run time was complete, the observer, peer, and participant independently read and recorded the results.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eParticipants were then asked to use the study devices to report their self-test results using Abbott\u0026apos;s NAVICA India\u0026trade; app. The app collects self-reported demographics, vaccination status, and COVID-19 exposure history and symptoms. Participants reported their test results using image capture. The image was auto analysed using artificial intelligence. The results were displayed and automatically shared with ICMR.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eNo data was stored on the device, and neither the observer nor the peer assistant had access to the data reported via the app. Peer assistants helped participants report results via the NAVICA India\u0026trade; app, where required. All results were managed based on the national testing guidelines at the time (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). After completion of the test, the trained independent observers administered a quantitative survey to all the participants. A subset of the participants was invited to participate in FGDs to explore their reflections on self-testing.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eHighly trained and qualified researchers, known as observers, were deployed to evaluate the test operation by study participants and the support provided by peer assistants in both factories. These observers had expertise in conducting qualitative interviews and focus group discussions in the local language. They received comprehensive training on the study protocol, data collection tools, and the manufacturer-provided application. Observers employed a usability checklist guided by the Panbio Self-test IFU and adapted from tools used in other self-test usability studies (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e). The checklist noted 13 critical and five non-critical steps. Observers used a separate section of the checklist to assess the quality of peer assistance provided. To assess correct interpretation, (a) The actual test result was read and recorded by both the worker and the observer, in addition to automated results read by the NAVICA app. The observer\u0026rsquo;s interpretation was considered the reference (b) The observer used the Result Interpretation section in the usability checklist to record whether the employee accurately interpreted the pictures of self-test results. Data collected through the paper-based usability checklist was entered into Microsoft Excel files, and the data entry quality was checked for accuracy and completeness.\u003c/p\u003e\n\u003cp\u003ePost-test Survey: After completing the test, all participants completed a short quantitative survey to explore their experiences and preferences for peer-assisted self-testing. They were also asked questions regarding post-test actions in case of both positive and negative results to determine whether they understood the instructions correctly. The observer also asked for comments about the study processes and the level of assistance required during and after the test. All data collection tools were tested internally for clarity and internal consistency.\u003c/p\u003e\n\u003cp\u003eA subset of study participants and peer assistants were invited to participate in focus group discussions (FGDs) in the days following the test. The observers conducted these discussions at the factory in a quiet and private training room, using a semi-structured guide and probes developed by the study team to explore specific themes of interest. Key areas explored included past experience with COVID-19 testing and preference for COVID-19 self-testing. The duration of the FGDs ranged from 20\u0026ndash;30 minutes. All the audio transcripts were translated from the local language and transcribed into English through a third-party translator service.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eData Analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003eQuantitative Analysis\u003c/h2\u003e\n \u003cp\u003eData were analysed using IBM SPSS Statistics (v. 26.0). To analyze and describe participant and peer demographics, gender, age, and education were treated as categorical variables. To determine whether the samples from each factory resemble the overall demographics, statistical tests (t-test for age and chi- square test for gender and education levels) were performed. Sub-analyses by age, gender and factory were not performed due to the overall small sample size (N=106). Estimates of proportions of participants that completed the test steps correctly with peer assistance. (Usability index/ score) were calculated using the definitions and criteria described in the analysis plan. Usability scores for the test kit and the mobile application are reported separately. Estimates of the proportion of participants who correctly interpreted their actual COVID-19 self-testing results and pictures of contrived results are reported, as is the proportion of tests conducted with accurate peer support. The inter-reader agreement was also reported by the percentage of consistent results between each reader (participant, observer, and mobile application).\u0026nbsp;\u003c/p\u003e\n \u003ch3\u003eQualitative Analysis\u003c/h3\u003e\n \u003cp\u003eWe explored the qualitative data using thematic and discourse analysis approaches. Two researchers first read the transcripts to understand the participants\u0026apos; views and preferences, considering the social context in which the discussions were conducted. In the second stage, the researchers identified concepts or codes inductively from the data. After one round of dialogue between both researchers, a set of codes were agreed upon and defined. In the next stage, relationships between the codes were described, and larger categories of codes were formed. The generated code categories and the attached definitions and text excerpts were reviewed with two other coders (Senior Researchers) to come to a consensus about the themes generated and modify any coding discrepancies through one more round of deductive coding. The latest version of NVivo 1.6.1 (1137) was used for analysis.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eDemographics\u003c/h2\u003e\n \u003cp\u003eA total of 106 individuals consented to participate in the study. The overall mean age of the sample was 35.7 yrs. More women than men participated in the study (Female: 84 (79.25%), Male: 22 (20.75%)). Just over half the sample had finished High school (12th grade) (52.9%) \u003cspan class=\"Underline\"\u003e(\u003c/span\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cspan class=\"Underline\"\u003e)\u003c/span\u003e. Demographics within each factory sample (Factory 1: N\u0026thinsp;=\u0026thinsp;20 and Factory 2 N\u0026thinsp;=\u0026thinsp;86) corresponded with the overall factory demographics \u003cspan class=\"Underline\"\u003e(\u003c/span\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cspan class=\"Underline\"\u003e)\u003c/span\u003e. All of the participants were yet to use a COVID-19 self-test before. Only 7(6.6%) participants noted that they had used a self-test for indications other than COVID-19 before (e.g. Pregnancy test).\u003c/p\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" style=\"width: 464.431px;\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eParticipant Demographics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth style=\"width: 88px;\" align=\"left\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 227px;\" align=\"left\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 121px;\" align=\"left\"\u003e\n \u003cp\u003eTotal sample N (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\" rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\" align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\" align=\"left\"\u003e\n \u003cp\u003e29(27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\" align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\" align=\"left\"\u003e\n \u003cp\u003e46(43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\" align=\"left\"\u003e\n \u003cp\u003e42\u0026ndash;53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\" align=\"left\"\u003e\n \u003cp\u003e27(25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\" align=\"left\"\u003e\n \u003cp\u003e54\u0026ndash;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\" align=\"left\"\u003e\n \u003cp\u003e4(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\" align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 121px;\" align=\"left\"\u003e\n \u003cp\u003e106(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\" rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\" align=\"left\"\u003e\n \u003cp\u003eDid not attend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\" align=\"left\"\u003e\n \u003cp\u003e5 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\" align=\"left\"\u003e\n \u003cp\u003ePrimary School \u0026amp; Middle school (1st-8th)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\" align=\"left\"\u003e\n \u003cp\u003e34(\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\" align=\"left\"\u003e\n \u003cp\u003eHigh School(9-12th)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\" align=\"left\"\u003e\n \u003cp\u003e56(52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\" align=\"left\"\u003e\n \u003cp\u003eUndergraduate\u003c/p\u003e\n \u003cp\u003e(including\u003c/p\u003e\n \u003cp\u003eVocational/Diploma)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\" align=\"left\"\u003e\n \u003cp\u003e11(10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\" align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 121px;\" align=\"left\"\u003e\n \u003cp\u003e106 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\" align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\" align=\"left\"\u003e\n \u003cp\u003e84(79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\" align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\" align=\"left\"\u003e\n \u003cp\u003e22(\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\" align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 121px;\" align=\"left\"\u003e\n \u003cp\u003e106 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePopulation and Sample demographics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eStudy Sample\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACWA n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEB n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACWA n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEB n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eless than 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94(48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70(23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60(30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129(43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u0026ndash;53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95(31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u0026ndash;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49(25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261(87.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77(89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145(74.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003euneducated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eprimary and middle school(1st to 8th)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84(28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehigh school(9th to 12th)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93(47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158(52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41(47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eundergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94(48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003eAlt Text: Table showing a comparison of Population and Sample Demographics across the two factories where the Intervention was implemented. Age is broken down into less than 18, 18\u0026ndash;29, 30\u0026ndash;41, 42\u0026ndash;53, 54\u0026ndash;65, and over 65. Education levels include uneducated, primary and middle school (1st to 8th), high school (9th to 12th), and undergraduate. Gender includes female and male. For each subcategory, the table presents the number and percentage of participants across the population and study sample, disaggregated by intervention groups ACWA and EB.\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalysis of Key Outcomes Usability of the Self-Test\u003c/h2\u003e\n \u003cp\u003eUsability of the SARS-CoV-2 Self-Test in a Peer-Assisted Model\u003c/p\u003e\n \u003cp\u003eThe usability of the self-test was 75.9% for the overall sample (N\u0026thinsp;=\u0026thinsp;103) across all steps and 80.7% (N\u0026thinsp;=\u0026thinsp;103) when restricted to critical steps \u003cspan class=\"Underline\"\u003e(\u003c/span\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cspan class=\"Underline\"\u003e)\u003c/span\u003e. The observer checklist data was not available for three individuals. The most commonly reported errors (\u0026lt;\u0026thinsp;75% participants performed accurately) were:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUsability Index - Test Kit\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eSteps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eThe proportion of participants that performed a step correctly (N\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWas the employee able to squeeze the liquid from the Buffer Bottle into the Tube?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84(86.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDid the participant confirm that the buffer level was above the line provided in the tube?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70(72.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(27.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWas the employee able to remove the swab without touching the soft end?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89(90.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (9.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDid the employee insert the soft end up to 2 cm into their nostrils?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57(58.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41(41.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDid the employee rotate the swab five times in each nostril?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69(71.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(28.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDid the employee insert the swab into the fluid and swirl it five times while pushing against the tube wall?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74(75.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(24.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWas the employee able to place five drops (and not more or less) into the well?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79(82.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(17.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDid the employee place the solution into the well on the test device (and not any other location)?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89(92.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (7.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDid the employee read the results within 15\u0026ndash;20 minutes?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92(96.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (3.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsability Index total (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e80.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003col\u003e\n \u003cli\u003e\n \u003cp\u003eUsing the incorrect amount of buffer\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eInaccurate swab insertion\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eFailure to swab the nose five times, touching the walls.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n \u003cp\u003eUsability of the App:\u003c/p\u003e\n \u003cp\u003eThe usability of the NAVICA mobile application to report the test results was 34.0% (N\u0026thinsp;=\u0026thinsp;103) \u003cspan class=\"Underline\"\u003e(\u003c/span\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cspan class=\"Underline\"\u003e)\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUsability Index - Mobile Application\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eSteps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eProportion of participants that performed a step correctly (N\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en* (no of valid observations)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the employee successfully sign up for the application?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65(68.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCould the employee enter all the details required to move to the next steps?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68(71.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCould the employee read the results analysed by the application?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47(51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45(48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the employee finish uploading the results on the application?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70(76.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsability Index (Weighted Average of the usability scores of steps) (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e33.80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eLess than 30% of participants scored accurately in the following steps:\u003c/p\u003e\n \u003col\u003e\n \u003cli\u003e\n \u003cp\u003eEnter all the mandatory data fields in the mobile application to proceed with result capture.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eCapture the results for automatic upload into the mobile app\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n \u003cp\u003eReported ease of use:\u003c/p\u003e\n \u003cp\u003eFifty-three study participants (52.5%) (N\u0026thinsp;=\u0026thinsp;101) in both factories reported that the test was easy to conduct. Thirty-seven (36.6%) participants said it was somewhat easy, and 11(10.9%) participants said it was not easy at all (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Usage of the mobile application was the step that most participants reported\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eReported ease of use and assistance required\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eHow easy was the test to conduct?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea. Not easy at all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eb. Somewhat easy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec. Completely easy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53(52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e101(100.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eWhich parts of the test did you find difficult and require assistance with?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea. Test Preparation (preparing buffer and test kit elements)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eb. Sample Collection (collecting sample through nasal swab)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec. Test Process (placing buffer with sample in well)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ed. Use of Mobile Application\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ee. Result Interpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e96(100.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cstrong\u003eAlt Text\u003c/strong\u003e: Table showing participants\u0026rsquo; self-reported ease of use and the areas in which they required assistance while conducting the test. For ease of use, responses are categorized as: not easy at all, somewhat easy, and completely easy. For difficulties and assistance, participants identified specific steps: test preparation (preparing buffer and kit components), sample collection (nasal swab), test process (adding sample to well), use of the mobile application, and result interpretation. For each response option, the table presents the number and percentage of participants (n%).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eneeding assistance with (41.7%), followed by sample collection (17.7%) and preparing the buffer and test kit elements (20.8%) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). This corresponds with the steps where most participants performed\u003c/p\u003e\n \u003cp\u003eerrors.\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e HERE\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eInterpretation of the Test\u003c/h2\u003e\n \u003cp\u003eInter-reader concordance:\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e depicts the percentage of concordance in test result interpretation between the manufacturer\u0026rsquo;s\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInter-Reader Concordance for interpretation of results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInter-Reader Concordance for interpretation of results\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% of concordance n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% of discordance n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticipant interpretation vs observer interpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93(97.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticipant interpretation vs manufacturer app interpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93(97.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObserver interpretation and manufacturer app interpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91(95.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cstrong\u003eAlt Text\u003c/strong\u003e: Table showing inter-reader concordance and discordance in the interpretation of test results. Concordance and discordance are reported as both percentages and number of cases n(%). Comparisons include: participant interpretation vs observer interpretation, participant interpretation vs manufacturer app interpretation, and observer interpretation vs manufacturer app interpretation.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003emobile application, participants, and observers (N\u0026thinsp;=\u0026thinsp;95). Five participants deferred to the peer assistant/observer to interpret the results, and the app did not analyze six results due to a technical error and were not included in this sample. In all instances of discordance, the observer\u0026apos;s interpretation was considered final.\u003c/p\u003e\n \u003col\u003e\n \u003cli\u003e\n \u003cp\u003eConcordance between participant and observer was 97.9% (93). The two instances of discordance were due to an incorrect interpretation of weak positive results by the participant.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eConcordance between the participant and the application was 97.9% (93). The two instances of discordance were due to inaccurate interpretation by the mobile application in which negative results were read as positive results.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eConcordance between the observer and the application was 95.8% (91). Out of the 4 discordant results, 50% (n\u0026thinsp;=\u0026thinsp;2) of discordance was due to the app\u0026apos;s interpretation of a negative test result as a positive one, and 50% of discordance was due to the app\u0026apos;s interpretation of a positive test result as a negative one.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n \u003cp\u003eInterpretation of Contrived Results:\u003c/p\u003e\n \u003cp\u003eMost participants accurately interpreted the pictures of strong positive and negative results (~\u0026thinsp;82.9% and\u003c/p\u003e\n \u003cp\u003e~\u0026thinsp;80%, respectively). 69 (65.7%) participants interpreted weak positive results correctly, whereas 70 (66.7%) interpreted invalid results correctly. The proportions of participants who did and did not interpret the four types of potential results are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe proportion of participants who correctly interpreted SARS-CoV-2 self-testing results using high-resolution pictures of self-test results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSelf-test results\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion of participants who interpreted the results correctly n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion of participants who did not interpret the results correctly n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84(80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeak Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69(65.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87(82.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInvalid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003ePeer Support Model\u003c/h2\u003e\n \u003cp\u003eThe study involved ten peers who assisted participants during self-testing. Their mean age was 33.2 years. They were predominantly female (60%). More than half of the peers (70%) had completed high school (grades 9\u0026ndash;12), peer assistant demographics are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e below.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab8\" style=\"width: 452.273px;\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePeer assistant Demographics - N-10\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth style=\"width: 88px;\" align=\"left\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 221px;\" align=\"left\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 116px;\" align=\"left\"\u003e\n \u003cp\u003eTotal sample n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 221px;\" align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\" align=\"left\"\u003e\n \u003cp\u003e6(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\" align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\" align=\"left\"\u003e\n \u003cp\u003e4(\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\" align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 221px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 116px;\" align=\"left\"\u003e\n \u003cp\u003e10(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\" rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 221px;\" align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\" align=\"left\"\u003e\n \u003cp\u003e3(\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\" align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\" align=\"left\"\u003e\n \u003cp\u003e5(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\" align=\"left\"\u003e\n \u003cp\u003e42\u0026ndash;53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\" align=\"left\"\u003e\n \u003cp\u003e2(\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\" align=\"left\"\u003e\n \u003cp\u003e54\u0026ndash;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\" align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\" align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 221px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 116px;\" align=\"left\"\u003e\n \u003cp\u003e10(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\" rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 221px;\" align=\"left\"\u003e\n \u003cp\u003eDid not attend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\" align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\" align=\"left\"\u003e\n \u003cp\u003ePrimary School\u0026amp;Middle School (1st-8th)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\" align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\" align=\"left\"\u003e\n \u003cp\u003eHigh School(9-12th)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\" align=\"left\"\u003e\n \u003cp\u003e7(70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\" align=\"left\"\u003e\n \u003cp\u003eUndergraduate\u003c/p\u003e\n \u003cp\u003e(including\u003c/p\u003e\n \u003cp\u003eVocational/Diploma)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\" align=\"left\"\u003e\n \u003cp\u003e3(\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\" align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 221px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 116px;\" align=\"left\"\u003e\n \u003cp\u003e10(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePeer Assistance Efficiency\u003c/p\u003e\n \u003cp\u003eAcross all critical steps, peer assistants provided accurate instructions in 93.4% of tests performed. The number and proportion of tests for which the peer assistant performed an error are reported for each critical step in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e. Among the steps critical to the test, peers missed or provided incomplete\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePeer Assistant Efficiency\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCritical Steps\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eProportion of tests for which the peer provided accurate support (N\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en* (no of valid observations)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the PA carefully explain different components of the test kit (swab, buffer bottle, tube, test device, tube rack, disposable bag, timing device).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91(91.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the PA instruct the employee to empty the buffer into the tube to avoid spillage?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the PA check if the buffer is above the line marked in the tube?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80(81.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the PA provide instructions to unwrap the sample collection swab appropriately?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96(98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the PA provide instructions to use the sample collection swab appropriately?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96(98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the PA instruct you not to touch the swab at any point?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87(88.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the PA check with the employee to ensure they inserted the swab to the correct depth?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91(93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the PA check with the employee to ensure they rotated the swab five times in each nostril?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95(97.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the PA check with the employee to ensure they inverted the swab into the buffer tube without putting it down anywhere?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95(97.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the PA instruct the employee to check the liquid for bubbles?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64(65.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the PA instruct the employee to place five drops (and not more or less) into the well and not any other location on the test?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93(98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the PA alert the employee to read their results in the accurate read time window?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94(98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the PA instruct the employee how to interpret their results?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDid the PA give instructions about reporting results?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94(97.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage score (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e93.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eAlt Text: Table showing the proportion of tests for which peer assistants provided accurate support during critical steps of the SARS-CoV-2 self-testing process. Steps include explaining test kit components, instructing on buffer handling, swab use, checking swab depth and rotation, ensuring correct sample preparation, reading results timing, interpreting results, and reporting instructions. For each step, the table presents the number and percentage of accurate support (Yes) and inaccurate or no support (No), along with the number of valid observations.\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003einstructions for sample collection and verifying appropriate buffer levels. A list of the most common errors reported in peer assistance is in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e. 55 (53.9%) participants from both factories found the verbal peer instructions \u003cem\u003e\u0026ldquo;Completely easy\u0026rdquo;\u003c/em\u003e to understand, 36(35.3%) found the instructions \u003cem\u003e\u0026ldquo;Somewhat easy\u0026rdquo;, and\u003c/em\u003e 11(10.8%) found them \u003cem\u003e\u0026ldquo;Not easy at all\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eErrors reported in Peer Assistance\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommon errors observed via the checklist:\u003c/p\u003e\n \u003cp\u003e● Peer did not provide instructions to wear a mask correctly at all times except sampling\u003c/p\u003e\n \u003cp\u003e● Peer did not provide instructions to check if an accurate amount of buffer is being used\u003c/p\u003e\n \u003cp\u003e● Peer did not provide instruction not to touch the swab at any point\u003c/p\u003e\n \u003cp\u003e● Peer did not provide instructions to check liquid for bubbles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQualitative data and informal observation suggest additional errors:\u003c/p\u003e\n \u003cp\u003e● Peer does not speak loudly\u003c/p\u003e\n \u003cp\u003e● Peer touches the participant\u0026apos;s test kit to demonstrate a step\u003c/p\u003e\n \u003cp\u003e● Peer is giving instructions too fast\u003c/p\u003e\n \u003cp\u003e● Peer is giving incomplete instructions\u003c/p\u003e\n \u003cp\u003e● Peer does not explain the context or meaning of certain instructions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eAcceptability of the Test\u003c/h2\u003e\n \u003cp\u003eConfidence in using the test kit\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWith peer assistance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e62% (62) of participants reported they were completely confident in using the test and interpreting self-test results with the assistance of a peer. 28.0% (\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e) were somewhat confident and 10.0% (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e) participants were not confident at all.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eUnassisted\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e43.9% (\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e) participants were completely confident in performing and interpreting the test on their own, 46.9% (46) participants were somewhat confident, and 9.2%(\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e) participants were not confident at all.\u003c/p\u003e\n \u003cp\u003eConfidence in using the mobile application\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWith peer assistance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOnly 15.6% (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e) participants were completely confident in capturing and uploading their results using the NAVICA mobile application with peer assistance. In comparison, 29.2% (\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e) and 55.2% (53) participants said they were somewhat confident and not confident at all, respectively.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eUnassisted\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOnly 19.6% (\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e) participants were completely confident in capturing and reporting their results independently. 35.1% (\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e) participants and 45.4%(\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e) participants stated they were not confident in capturing and uploading their results using the mobile application without assistance.\u003c/p\u003e\n \u003cp\u003eAccessing the Test\u003c/p\u003e\n \u003cp\u003e57.4% (58) participants reported that they would prefer to use the self-test at home, followed by 36.6% (\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e) who preferred to test at the workplace. Finally, 5.0% (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e) of participants preferred to test at a hospital. When asked where participants wished to purchase a test, 36.1% (\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e) of participants preferred their nearest pharmacy. 29.9%(\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e) participants preferred to access the test at the workplace, and 14.4%(\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e) participants would like to access the test at the PHC/local clinic.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eQualitative Results:\u003c/h2\u003e\n \u003cp\u003eMotivations and barriers to self-testing, perceived benefits of self-testing, and reflections on the peer- assisted self-testing model were the key themes that emerged from the qualitative thematic analysis of focus group discussions with the participants.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1. Motivators and barriers for Self-testing\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAll participants were first-time users of a COVID-19 self-test. Participants reported being fearful of testing but were motivated to accept and perform self-testing due to concerns about getting infected and spreading the infection to others. In both factories, participants\u0026rsquo; hesitancy to test was precipitated by the fear of a positive result resulting in isolation from their peers and the stigma associated with working with an infected person.\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026quot;When we are in close contact with infected individuals, there is a risk of transmission to us. This is why we should undergo testing.\u0026quot; \u0026quot;It is important to prioritize testing to prevent harm or danger to others.\u0026quot; (Male, 25 years, Factory 1).\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;If any of us test positive, it may instill fear among others, leading to hesitation in working together.\u0026rdquo; (Female, 21 years, Factory 2)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2. Perceived Benefits of Self-testing\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eParticipants in both factories showed mixed sentiments about the COVID-19 self-testing experience, with generally positive attitudes toward the test. Several participants spoke about their experiences with other testing methods (RT-PCR and professionally administered RAT) and compared them with their self- testing experience. Specifically, participants preferred collecting samples from the nose (as opposed to nasopharyngeal collection), describing it as a comfortable and painless procedure.\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cem\u003eSometimes the testing will be painful. But, if we do it on our own, it is not painful. So we feel this is better. (Female, 32 years, Factory 1)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eMost respondents mentioned that self-testing saved time and reduced risks posed by the long wait for testing in government facilities. Participants thought that self-testing at the workplace was quick and easy in terms of time taken to set up, test and interpret results.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eIf we go to a Bruhat Bengaluru Mahanagara Palike (BBMP - Local administrative body in the Greater Bengaluru metropolitan area) test center, we are unsure of the safety. There were long queues, and we doubted whether someone among these people was infected. (Male, 25 years, Factory 1).\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eOne point is that we can do it easily and leisurely at work. When we go somewhere else, it takes too much time. (Female, 32 years, Factory 1)\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eParticipants highlighted that the ability to understand the process and to do the test at home with family members is one of the critical benefits of the self-testing kit.\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;We can understand this (self-testing process). We can do this at our houses when our husbands or children or relatives are infected. Or we can guide them on how to do the testing.\u0026rdquo; (Female, 35 years, Factory 2)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3. Reflections on Peer-Assisted Self-Testing Model\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eParticipants also shared their experiences with the specific peer-assistance models used during self- testing in factories. Participants from both factories claimed that peer assistance was a critical component of self-testing, finding comfort in performing the test after receiving in-person instructions from their peers. Having colleagues as peer instructors was preferred over healthcare workers, as instructions provided by their co-workers were straightforward to understand and follow.\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;When we went to a government hospital, they simply took the swab and sent us back. They did not explain anything. Here (at the workplace), they (peers) have explained it to us.\u0026rdquo; (Female, 35 years, Factory 2)\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eSome participants mentioned that peer assistants should have more experience and knowledge about the tests, most likely someone with healthcare experience. Others highlighted that their co-workers would need more training and handholding support from the study staff to perfect their support.\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;We prefer your team (Organization\u0026rsquo;s team) as you have more knowledge than our co-workers. They (peer assistants) have been trained very recently, and their knowledge is quite different. It is better if we learn something from you.\u0026rdquo; (Male, 21 years, Factory 1)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eWe were comfortable as our co-workers in the company were providing us with instructors. (Female, 24 years, Factory 1)\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eIn addition to peer support, participants reported that more time to perform and interpret the results would also strengthen testing quality.\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;If we are taking time from work, our attention will be split between work and testing, and we might make mistakes. But if we have good enough time, we will do it better and with concentration.\u0026rdquo; (Female, 24 years, Factory 1)\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study assessed the usability of antigen-based SARS-CoV-2 self-testing in a peer-assisted model among factory workers - a key vulnerable group facing inequitable access to early diagnosis and testing. Prior research on self-testing for SARS-CoV-2 evaluated unassisted and healthcare worker-assisted models in various settings, including educational institutions (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) hospitals (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), and the general population (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The majority of these studies were limited to high-income settings. While workplace models have been described, these models have relied on healthcare worker administration, home-based self-testing, or secondary distribution to household members (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This is the first study to describe peer-assisted self-testing in a workplace setting where there is a need for increased user support due to lower literacy levels. Our study found that most factory workers could accurately conduct the critical steps for a nasal sampling-based test kit (80.7%) and interpret their results accurately with high levels of concordance (97.9%) with trained observer-interpreted results. This is comparable to the work by Sibanda, Choko (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), who found the usability of the Panbio self-test on 12 critical steps to be 90.4% and 70.6% in Malawi and Zimbabwe, respectively. In our study, most participants reported the test was easy to conduct but required assistance in results interpretation, followed by sample collection. This is in line with the usability study findings from Lesotho and Zambia, where the common errors included failure to insert the swab to the correct depth, the inadequate swirling of the swab to touch the nasal passage walls, and buffer preparation (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Low usability scores on some of the critical test steps in our study could have been driven by multiple factors such as demographics, first-time use of a self-test, clarity of peer instruction, nature of sample collection, and limited time for testing due to the workplace setting (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSimple changes to test design could address some usability challenges. The design for SARS- CoV-2 self-test kits did not include populations with limited education and English-speaking abilities highlighting the need for instructions to be printed in local languages. Using a quick reference guide could target key usability challenges and improve the accuracy of SARS-CoV-2 test interpretation (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Instructional videos and pictures have also been suggested to improve the usability of other self-tests (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Providing a pre-filled extraction tube or designing an easy-to-open cap for the buffer bottle and enhancing the clarity of the buffer line could also address some user errors. Such work will be critical to bring the reported usability of SARS-CoV-2 self-tests in-line with other self-test products, such as oral and blood-based HIV self-tests (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eReported challenges with the instructions for use underscore the importance of peer assistance for users who reported reduced confidence in performing the test without peer assistance. Data on the accuracy of peer instructions suggests that workplace testing programs must closely and continuously monitor the quality of peer instruction and focus on ensuring strong self-efficacy among peers via regular refresher training with online/self-paced modules, toolkits, job aids, and supervision by management. These interventions can strengthen accurate use and decrease the time required for testing services, a critical priority reported by management. Such investments will strengthen the role of peers, who are essential in driving the uptake of self- testing services in the workplace (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). As these tests are used more frequently, individuals are expected to gain more confidence in using them independently without relying on peer support, especially in places they prefer, such as their homes. Similar results have been documented in workplace settings in Kedah, Malaysia (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This study demonstrated that time constraints can influence the peer’s quality of instruction and the participant’s focus on the test, resulting in instructional and testing errors. Workplace programs, therefore, need to ensure that workers have sufficient testing time and privacy to interpret results in tandem with withdrawing workers from an active production line.\u003c/p\u003e\u003cp\u003eWe found strong concordance in test interpretation between users and trained observers (97.9%). Discordance between users and observers was driven by incorrect user interpretation of weak positive results. Low accuracy in the interpretation of contrived results underscores poor understanding of weak positive and invalid results. Result interpretation errors, especially in contrived image review, were most likely due to an insufficient wait time for interpretation, not having the IFU in a local language for reference, and lack of attention during peer support. These findings align with the broader self-test literature, including tests for SARS-CoV-2, underscoring the potential for misinterpretation of these test outcomes (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConcordance between the mobile application and trained observers was lower (95.8%) and driven by false interpretations by the app. In the case of false negatives, participants were often hesitant to accept the interpretation of the trained observer and required additional counselling or re-testing. We hypothesize that false results may have been driven by improper lighting or poor focus for image capture. This aligns with our findings that the usability of the manufacturer-provided (NAVICA™) app for results reporting in a factory setting is low. Participants and the peer assistants faced multiple challenges with an app-based model for reporting, including unavailability of content in the local language, multiple mandatory fields, usage of technical and hard-to-understand words, and frequent technical errors.\u003c/p\u003e\u003cp\u003eFindings suggest that a mobile-based reporting model has low usability and is moderately accepted by factory workers and similar populations with restricted smartphone access and digital literacy. Industrial workers cannot carry their mobile phones to their workspace, limiting their ability to report and view results on their devices. Our findings are similar to recent research (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) that suggests mobile application-supported HIVST is feasible only for youth and tech-savvy populations (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Point-of-care diagnostic manufacturers must be cognizant of these challenges if product penetration reaches the last mile and is acceptable to vulnerable populations. Self-test program implementers must design programs to circumvent these limitations, reducing dependencies on worker devices by providing program devices, stable internet, and peer support to mitigate technical errors and minimize false results. Our findings also demonstrate that SARS-CoV-2 self testing continues to offer end users many of the same benefits of self-testing for other infectious diseases, including knowledge of health status, increased comfort, and reduced time requirements (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). These data add to a growing body of evidence demonstrating user preference for self-care technologies (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWith health worker shortages and a lack of access to affordable healthcare services, more significant investments are needed in making community-centric self-care tools, including self-tests, more user- friendly, affordable, and acceptable. These findings indicate that self-testing is a feasible tool to facilitate manufacturer implementation of worker screening programs. It also allows vulnerable groups like factory workers to take charge of their health by monitoring their well-being, making informed healthcare choices, and improving communication and advocacy with employers and healthcare providers. Self-test technology should be preparative for future pandemics and support health system resilience. By reaching hard-to-reach groups, the peer-assisted self-testing model may provide an approach to expanding access to address the country's unique needs.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eWhile this study makes a nuanced contribution to the literature on the adoption and use of self- testing amongst vulnerable groups, the study has a few limitations. First, while purposive sampling of the participants allowed for targeted representation from the factories, it also introduced selection bias. Second, the purposive selection of factories based on their willingness to implement the program could also indicate that the participating factories are already open to health innovations. Third, the small sample size and the limited geographic scope, confined to 2 factories in Bangalore, restricted the ability to conduct sub-population-level analyses and limited the generalizability of the findings. Finally, the presence of observers and peer-assistants might have contributed to observer and social desirability bias, influencing the performance and responses of the participants to appear in favor of the intervention.To mitigate this, observers were instructed to be discreet and not interfere in the procedure. Standardized training followed by assessing their proficiency was provided to all peers to minimize the impact of varying quality of peer assistance on usability. To minimize the interpretation bias that occurs due to having multiple readers (observer, participant, peer assistant, and the mobile application), the participants' interpretation was recorded before having the app or observer read and record the result. The observer did not disclose the results that s/he interpreted to the participant to prevent interpretation bias further and avoid any impact on acceptability responses.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings add to a growing body of research demonstrating the usability, feasibility, acceptability, and cost-effectiveness of workplace testing, including self-testing, for SARS-CoV-2 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). They are the first to demonstrate the usability of SARS-CoV-2 self-tests in a peer-assisted model in factory settings with disadvantaged populations. Our findings suggest that peer-based models ensure that SARS-CoV-2 self-tests are usable with strong indications of acceptability. Determining the feasibility of implementing this model at scale is the next step to inform and advocate for other workplace self-testing programs for vulnerable populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCOVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoronavirus disease 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSARS-CoV-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSevere Acute Respiratory Syndrome CoronaVirus-2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGross Domestic Product\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRT-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReverse transcription polymerase chain reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRapid Antigen Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eICMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndian Council of Medical Research\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGOI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGovernment of India\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePersonal Protective Equipment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAgRDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAntigen based Rapid Diagnostic Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary Health Center\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBBMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBruhat Bengaluru Mahanagara Palike\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIFU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInstructions for Use\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCHW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCommunity Health Worker\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003eEthics approval and consent to participate: The Catalyst Foundation Institutional Ethics Committee approved all human participant-related documents on January 19, 2022, before the commencement of the study. All procedures in this study adhered to the tenets of the Declaration of Helsinki. Before data collection, participants received briefings on the study's purpose and potential risks, with the freedom to pause interviews at any time, ask clarifying questions, or skip questions that caused discomfort. Participants were apprised of the stringent measures taken to protect their identity and confidentiality.\u003c/li\u003e\n \u003cli\u003eConsent for publication: Participants were apprised of and consented to the possibility of using information gathered from the study in an aggregated form or as individual perspectives in manuscripts or reports after de-identifying any personal information.\u003c/li\u003e\n \u003cli\u003eAvailability of data and materials: The data underlying this article will be shared on reasonable request to the corresponding author as it contains sensitive information. However, our tools, code book and data analysis plan has been shared as supplementary data.\u003c/li\u003e\n \u003cli\u003eCompeting interests: there are no competing interests\u003c/li\u003e\n \u003cli\u003eFunding: This work was supported by a sub-grant from FIND, from the more significant grant FIND received from BMZ (Federal Ministry for Economic Cooperation and Development, Germany). The funders had no role in data collection and analysis, the decision to publish, or the preparation of the manuscript. FIND supported the study design and feedback on the protocols and the manuscript.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAuthors' contributions: MRP: Data Curation, Formal Analysis, Investigation, Project Administration, Visualization, Writing – Original Draft Preparation, Writing – Review \u0026amp; Editing. PS: Conceptualization, Writing – Original Draft Preparation, Writing –Review \u0026amp; Editing, Methodology, Validation, Visualization. NP: Project Administration, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review \u0026amp; Editing. PR:-Conceptualization, Project Administration, Supervision, Writing – Review \u0026amp; Editing. RK: Formal Analysis, Writing – Review \u0026amp; Editing. SAG: Project Administration, Investigation, Writing – Review \u0026amp; Editing. AC: Supervision, Funding Acquisition, Writing – Review \u0026amp; Editing. SS: Conceptualization, Writing – Review \u0026amp; Editing, Methodology, Validation. AS: Project Administration, Funding Acquisition, Writing – Review \u0026amp; Editing. JC: Project Administration, Writing – Review \u0026amp; Editing. EIR: Conceptualization, Writing – Review \u0026amp;cEditing, Methodology, Validation. SBS: critical review and editing the final version of the manuscript.\u003c/li\u003e\n \u003cli\u003eAcknowledgements: Not applicable\u003c/li\u003e\n \u003cli\u003eClinical Trial Number: Not applicable\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization (WHO). Health Emergency Dashboard [Internet]. World Health Organization (WHO) 2020. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://extranet.who.int/publicemergency/\u003c/span\u003e\u003cspan address=\"https://extranet.who.int/publicemergency/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGupta D, Biswas D, Kabiraj P. COVID-19 outbreak and Urban dynamics: regional variations in India. 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J Int AIDS Soc. 2019;22(1):e25253.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStohr JJJM, Zwart VF, Goderski G, Meijer A, Nagel-Imming CRS, Kluytmans-van den Bergh MFQ, et al. Self-testing for the detection of SARS-CoV-2 infection with rapid antigen tests for people with suspected COVID-19 in the community. Clin Microbiol Infect. 2022;28(5):695\u0026ndash;700.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePapenburg J, Campbell JR, Caya C, Dion C, Corsini R, Cheng MP, et al. Adequacy of Serial Self-performed SARS-CoV-2 Rapid Antigen Detection Testing for Longitudinal Mass Screening in the Workplace. JAMA Netw Open. 2022;5(5):e2210559.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeck RB, Lim JM, van Rooyen H, Mukoma W, Chepuka L, Bansil P, et al. What Should the Ideal HIV Self-Test Look Like? A Usability Study of Test Prototypes in Unsupervised HIV Self-Testing in Kenya, Malawi, and South Africa. AIDS Behav. 2014;18(4):422\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMajam 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.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuwanguzi PA, Bollinger RC, Ray SC, Nelson LE, Kiwanuka N, Bauermeister JA, et al. Drivers and barriers to workplace-based HIV self-testing among high-risk men in Uganda: a qualitative study. BMC Public Health. 2021;21(1):1002.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAtchison C, Prister\u0026agrave; P, Cooper E, Papageorgiou V, Redd R, Piggin M, et al. Usability and Acceptability of Home-based Self-testing for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibodies for Population Surveillance. Clin Infect Dis. 2021;72(9):e384\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePai N, Esmail A, Saha Chaudhuri P, Oelofse S, Pretorius M, Marathe G et al. Impact of a personalised, digital, HIV self-testing app-based program on linkages and new infections in the township populations of South Africa. BMJ Glob Health. 2021;6(9).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcGuire M, de Waal A, Karellis A, Janssen R, Engel N, Sampath R, et al. HIV self-testing with digital supports as the new paradigm: A systematic review of global evidence (2010\u0026ndash;2021). EClinicalMedicine. 2021;39:101059.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRao A, Patil S, Kulkarni PP, Devi AS, Borade SS, Ujagare DD, et al. Acceptability of HIV oral self-test among truck drivers and youths: a qualitative investigation from Pune, Maharashtra. BMC Public Health. 2021;21(1):1931.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO. WHO guideline on self-care interventions for health and well-being, 2022 revision. Geneva: World Health Organization (WHO). 2022 27/06/2022. Contract No.: ISBN: 978 92 4 005219 2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHansen MA, Han AX, Chevalier JM, Klock E, Pandithakoralage H, Nooy A, et al. Cost-effectiveness of SARS-CoV-2 self-testing at routine gatherings to minimize community-level infections in lower-middle income countries: A mathematical modeling study. PLoS ONE. 2024;19(10):e0311198.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Supplementary Data","content":"\u003cp\u003eSupplementary Data files 1 and 3-4 are available in the supplementary file section. Supplementary Data file 2 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Usability of self-testing, Sars-CoV-2 testing, Acceptability of self-testing, peer-assisted self-testing, Workplace COVID-19 testing, Pandemic preparedness, Community-based self-testing, Self-testing for marginalized populations","lastPublishedDoi":"10.21203/rs.3.rs-7186437/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7186437/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003cbr\u003e\n\u003c/strong\u003eTo mitigate inequities in healthcare access and outcomes among vulnerable populations during the COVID-19 pandemic, the Government of India introduced antigen-based SARS-CoV-2 self-testing kits for self-use. However, concerns remained around the correct and confident use of these kits by low-literacy and underserved groups. This study aimed to assess the usability of nasal-sampling SARS-CoV-2 self-tests in a peer-assisted model among factory workers in Bengaluru, India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003cbr\u003e\n\u003c/strong\u003eA mixed-method cross-sectional study was conducted with 106 factory workers across two industrial sites in Bengaluru between February and March 2022. The Panbio™ COVID-19 Antigen Self-Test kit and the NAVICA mobile app for result reporting were used. Peer assistants distributed kits, demonstrated procedures using their own kits (without physical contact), and guided participants in using the kit and app. Observers recorded usability, result interpretation, and peer instruction effectiveness using standardized checklists and contrived result images. Post-test surveys and focus group discussions captured user perceptions of facilitators and barriers to usability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003cbr\u003e\n\u003c/strong\u003eStudy findings show that the overall usability score of the test kit with peer assistance was 75.9%, rising to 80.7% for critical steps and 33.8% for all critical steps in uploading results through NAVICA. It was seen that peer assistants provided accurate instructions and support for 93.4% of the tests. Among the critical steps in test kit use, maximum errors were made in sample collection and using the correct amount of buffer solution. Concordance between the participant and observer/NAVICA was 97.9%. 62.0% and 56.6% of the participants reported confidence in a) performing and interpreting the test and b) capturing and uploading their results using the mobile application with the assistance of a peer, respectively. Less than half the participants reported confidence in performing these steps independently.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003cbr\u003e\n\u003c/strong\u003eThe study demonstrates that SARS-CoV-2 self-testing kits have good usability among factory workers when delivered through a peer-assisted model. Peer support significantly improved test accuracy and participant confidence. Such workplace-based, peer-led models can help improve equitable access to early detection and self-care tools in low-resource and high-risk populations.\u003c/p\u003e","manuscriptTitle":"Usability of SARS-CoV-2 Self-Testing in a Peer-Assisted Model among Factory Workers in Bengaluru, India: A Mixed- Methods Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-05 11:32:51","doi":"10.21203/rs.3.rs-7186437/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":"d7e21633-a70a-48a0-9b98-523264ec9480","owner":[],"postedDate":"October 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-19T10:08:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-05 11:32:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7186437","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7186437","identity":"rs-7186437","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}