Developing and Evaluating a Measure of the Willingness to Use Pandemic-Related mHealth Tools Using National Probability Samples in the United States: Quantitative Psychometric Analyses and Tests of Sociodemographic Group Differences
Bibliographic record
Abstract
BACKGROUND: There are no psychometrically validated measures of the willingness to engage in public health screening and prevention efforts, particularly mobile health (mHealth)-based tracking, that can be adapted to future crises post-COVID-19. OBJECTIVE: The psychometric properties of a novel measure of the willingness to participate in pandemic-related screening and tracking, including the willingness to use pandemic-related mHealth tools, were tested. METHODS: Data were from a cross-sectional, national probability survey deployed in 3 cross-sectional stages several weeks apart to adult residents of the United States (N=6475; stage 1 n=2190, 33.82%; stage 2 n=2238, 34.56%; and stage 3 n=2047, 31.62%) from the AmeriSpeak probability-based research panel covering approximately 97% of the US household population. Five items asked about the willingness to use mHealth tools for COVID-19-related screening and tracking and provide biological specimens for COVID-19 testing. RESULTS: In the first, exploratory sample, 3 of 5 items loaded onto 1 underlying factor, the willingness to use pandemic-related mHealth tools, based on exploratory factor analysis (EFA). A 2-factor solution, including the 3-item factor, fit the data (root mean square error of approximation [RMSEA]=0.038, comparative fit index [CFI]=1.000, standardized root mean square residual [SRMR]=0.005), and the factor loadings for the 3 items ranged from 0.849 to 0.893. In the second, validation sample, the reliability of the 3-item measure was high (Cronbach α=.90), and 1 underlying factor for the 3 items was confirmed using confirmatory factor analysis (CFA): RMSEA=0, CFI=1.000, SRMR=0 (a saturated model); factor loadings ranged from 1.000 to 0.962. The factor was independently associated with COVID-19-preventive behaviors (eg, "worn a face mask": r=0.313, SE=0.041, P<.001; "kept a 6-foot distance from those outside my household": r=0.282, SE=0.050, P<.001) and the willingness to provide biological specimens for COVID-19 testing (ie, swab to cheek or nose: r=0.709, SE=0.017, P<.001; small blood draw: r=0.684, SE=0.019, P<.001). In the third, multiple-group sample, the measure was invariant, or measured the same thing in the same way (ie, difference in CFI [ΔCFI]<0.010 across all grouping categories), across age groups, gender, racial/ethnic groups, education levels, US geographic region, and population density (ie, rural, suburban, urban). When repeated across different samples, factor-analytic findings were essentially the same. Additionally, there were mean differences (ΔM) in the willingness to use mHealth tools across samples, mainly based on race or ethnicity and population density. For example, in SD units, suburban (ΔM=-0.30, SE=0.13, P=.001) and urban (ΔM=-0.42, SE=0.12, P<.001) adults showed less willingness to use mHealth tools than rural adults in the third sample collected on May 30-June 8, 2020, but no differences were detected in the first sample collected on April 20-26, 2020. CONCLUSIONS: Findings showed that the screener is psychometrically valid. It can also be adapted to future public health crises. Racial and ethnic minority adults showed a greater willingness to use mHealth tools than White adults. Rural adults showed more mHealth willingness than suburban and urban adults. Findings have implications for public health screening and tracking and understanding digital health inequities, including lack of uptake.
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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 | 0.028 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".