Awareness and Use of Home-Based Respiratory Pathogen Testing Services in the Internet Era: Postpandemic Questionnaire Study
Bibliographic record
Abstract
Background: Home-based respiratory pathogen testing services (HRPTS), an emerging internet-based health care model, enable rapid pathogen identification within hours through digital platforms and eCommerce logistics. This decentralized approach overcomes conventional testing delays to accelerate diagnosis. However, public awareness, adoption, and influencing factors remain largely unknown. Objective: In this study, we aimed to investigate digitally connected metropolitan residents' awareness and intention to adopt HRPTS and analyze factors influencing adoption intention. Methods: This study used a structured questionnaire grounded in the technology acceptance model, which measured perceived usefulness, ease of use, risk, and behavioral intention. Questionnaire development involved focus group discussions to ensure content validity. Statistical analysis included descriptive statistics and multivariate linear regression, with scale reliability and validity confirmed by exploratory factor analysis. Using a convenience sampling strategy, 1850 volunteers completed questionnaires via Wenjuanxing. After data validation, 1756 surveys met the inclusion criteria (effective response rate: 94.92%) and were analyzed. Results: Among 1756 respondents, 54.7% (n=961) knew about HRPTS for respiratory diseases, and 15.3% (n=269) had previously used them. Perceived usefulness was high among respondents: fast pathogen identification (n=1092, 62.2%), early treatment (n=1136, 64.7%), time or cost savings (n=1119, 63.7%), and anxiety alleviation (n=1110, 63.2%). Regarding perceived ease of use, 55.9% (n=982) of the respondents cited robust logistics, 53.8% (n=945) cited online appointment convenience, and 54.2% (n=952) cited simple self-sampling. However, respondents expressed concerns regarding privacy (n=925, 52.7%), test accuracy questions (n=871, 49.6%), and insufficient regulations (n=948, 54.0%). Nevertheless, >70% of the respondents were willing to adopt HRPTS, if available. Multivariate regression showed that higher education (β=.598; P<.001), living with family (β=.271; P=.04), and absence of underlying chronic diseases (β=.321; P=.03) were significant predictors of adoption intention. Additionally, not having used HRPTS before (β=-1.203; P<.001) and less frequent health care-seeking behaviors were negatively associated with adoption intention. Conclusions: HRPTS as an internet-based health care service holds value for early diagnosis, treatment, and health care optimization in urban China. However, significant concerns regarding test accuracy, data privacy, and regulatory accountability within this evolving digital health sector should be addressed to strengthen respiratory disease prevention in the postpandemic era.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".