Table 5.

Research hypotheses.

Hypothesis number	Hypothesis description	Model 1a	Model 2b	Model 3c	Table or figure
H1a	Instrumentality judgments of mHealthd apps should be higher when data are analyzed by a human physician versus an AIe algorithm.	√	X	X	Figure 1
H1b	Instrumentality judgments of mHealth apps should be higher when there are few versus many controls, regardless of mHealth data analysis mode.	√	√	√	Figure 1
H2a	Aesthetic judgments of mHealth apps should be similar when data are analyzed either by a human physician or an AI algorithm.	√	√	X	Figure 2
H2b	Symbolism judgments of mHealth apps should be similar when data are analyzed either by a human physician or an AI algorithm.	√	√	√	Figure 3
H3a	Instrumentality should be a salient predictor of preference variance for apps that engage a human physician versus an AI algorithm.	√	X	X	Table 2
H3b	Aesthetics should be a salient predictor of preference variance for apps that engage a human physician and those that engage an AI algorithm.	√	X	X	Table 2
H3c	Symbolism should be a salient predictor of preference variance for apps that engage a human physician and those that engage an AI algorithm.	X	√	X	Table 2
H4	Preference is higher for apps that engage a human physician versus an AI algorithm.	X	X	X	Figure 4

^a4 controls in the design.

^b8 controls in the design.

^c12 controls in the design.

^dmHealth: mobile health.

^eAI: artificial intelligence.