Abstract

Mobile health (mHealth) apps increasingly provide monitoring of user biodata, but must still rely on self-reports for behaviors such as dietary intake that cannot be detected by wearable sensors. A persistent challenge with self-reports is that patients often report healthier behaviors than they actually follow, which can lead to poor treatment decisions, overtreatment, or misdiagnosis. Most existing approaches to address misreporting focus on detecting dishonesty, but in clinical contexts it is more valuable to deter dishonest behavior at its onset. This study examines two design strategies aimed at discouraging deliberate misreports. First, a traditional fear appeal (FA) message often found in information systems studies and grounded in Protection Motivation Theory warns users of health risks from misreporting and adds brief coping guidance to support honest reporting. Second, a novel Response Activation Model (RAM) slider bar artifact makes it more difficult to select the healthiest answers without reflection. We conducted a randomized online experiment (N=423) with four conditions (FA-only, RAM-only, FA+RAM, Control) where participants reported their recent dietary habits. The combination FA+RAM condition produced lower self-reported dietary scores than the control condition, consistent with reduced misreporting; FA-only and RAM-only conditions showed substantial but non-significant reduction of misreport alone. Moreover, we validated the dishonesty deterrence of both FA and RAM conditions by examining the speed with which users moved their slider bars. These findings suggest complementary roles for both designs: FA motivates accurate reporting, while RAM introduces friction to spur reflection during self-reports. Together, they offer complementary designs that deter mHealth misreporting.

Download

Abstract Only

Share

COinS