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

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.

Willingness to Use Digital Health Screening and Tracking Tools for Public Health in Sexual Minority Populations in a National Probability Sample: Quantitative Intersectional Analysis

“…The present psychometric evaluation indicated that the COVID-19-related psychological distress measure was assessing the same construct in heterosexual participants and sexual minority participants. A previous study has already validated the psychometric properties of the present items (eg, construct validity and internal consistency) and demonstrated measurement invariance across age groups, genders, and races and ethnicities [28]. Other studies on willingness to use digital health tools have typically used a single item [63] or several items treated as separate measures [68,69] rather than a single, validated scale.…”

“…Based on the established definitions of a disparity [61,62] and the analytic framework of intersectionality theory [32,33,59], this study detected no referent disparity based on sexual orientation and no joint or excess joint disparity at the intersection of sexual orientation identity and other demographic characteristics. A previous study that used the same publicly available data without testing for sexual minority status as a predictor or moderator found no significant associations that would indicate an age-related, gender, or racial or ethnic disparity [28]. In contrast to other studies, which showed mixed findings for race or ethnicity and other demographics as a predictor without considering sexual minority status [26,27,[29][30][31], the significant main effects of race or ethnicity in this study occurred among heterosexual adults in the presence of interactions of race or ethnicity with sexual minority status.…”

“…Previous research is mixed on whether there are no demographic differences by age, gender, or race or ethnicity in willingness to use digital health tools for COVID-19 screening and tracking. Although some studies showed no differences [26][27][28], others found evidence of greater willingness or support among younger adults, women, and racial and ethnic minority people [29][30][31]. However, these studies did not examine sexual orientation diversity.…”