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Separability of Diffusion of Innovations Attributes as Predictors of Mobile Health Adoption Among Health Professionals in Central and Southern Appalachia: Cross-Sectional Questionnaire Study
ABSTRACT
Background:
Surveys of clinicians' mobile health (mHealth) adoption usually rank Rogers' diffusion of innovations attributes by their association with current use. They rarely test whether the attributes can be told apart or predict adoption out of sample. This matters in Central and Southern Appalachia, where 377 of 380 nonmetropolitan counties in the 7 states studied are primary care shortage areas.
Objective:
To test whether the 5 attributes can be distinguished as correlates of current mHealth use among health professionals in 7 Appalachian states, and whether any predicts use out of sample.
Methods:
We re-analyzed, from raw responses, a cross-sectional web survey fielded through a compensated opt-in commercial panel from 11 to 14 December 2025 (N=100; 34 Likert items adapted from Moore and Benbasat). We used Horn's parallel analysis, Shapley decomposition of Nagelkerke R² over all 24 entry orders, 2000 bootstrap resamples for rank stability, and leave-one-out cross-validation against the no-information rate. Sensitivity analyses excluded 26 near-invariant responders. External benchmarks were the HRSA Area Health Resources Files and the 2018 and 2022 National Sample Survey of Registered Nurses (NSSRN).
Results:
Overall, 37% (37/100) reported current use; 53 held prescribing roles and 16 no clinical role. Compatibility, trialability and observability intercorrelated at 0.78 to 0.80, and parallel analysis retained 1 factor at the scale level (68% of variance). Shapley shares were 32.3% (compatibility), 27.7% (trialability), 26.7% (observability) and 13.3% (relative advantage); no attribute ranked first in more than 48.5% of resamples, and the ranking reported in the source dissertation depended on entry order. A single attribute over demographic controls classified adopters above the no-information rate (compatibility 74% vs 63%), and adding attributes did not improve on it. Among engaged respondents, this advantage disappeared (65% vs 68%). The strongest single item reported prior opportunity, not an attitude: 78% (28/36) of those who agreed they had had opportunity to try mHealth were current users, against 11% (3/28) of those who disagreed.
Conclusions:
In this sample the diffusion attributes behaved as one judgment, not as 5 separable levers, and no ranking among them is supported. What separated users from nonusers was whether respondents had been able to try mHealth at all. Where workplaces offered telehealth, 82% of nurse practitioners in these states used it (NSSRN 2022). Studies should measure access separately from attitudes and test attribute claims with designs that vary attributes independently. Clinical Trial: N/A
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