Supporting Accurate Interpretation of Self-Administered Medical Test Results for Mobile Health: Assessment of Design, Demographics, and Health Condition.

Jess C Hohenstein,Seoho Lee,Erin Rieger,Lindsay Reynolds,Elizabeth L Murnane,Shion Guha,Dakota O'Dell,Yu Qi,Eric Ps Baumer,Geri Gay

doi:10.2196/humanfactors.8620

Jess C Hohenstein, Seoho Lee + Show 8 more

Open Access

https://doi.org/10.2196/humanfactors.8620

Copy DOI

Abstract

BackgroundTechnological advances in personal informatics allow people to track their own health in a variety of ways, representing a dramatic change in individuals’ control of their own wellness. However, research regarding patient interpretation of traditional medical tests highlights the risks in making complex medical data available to a general audience.ObjectiveThis study aimed to explore how people interpret medical test results, examined in the context of a mobile blood testing system developed to enable self-care and health management.MethodsIn a preliminary investigation and main study, we presented 27 and 303 adults, respectively, with hypothetical results from several blood tests via one of the several mobile interface designs: a number representing the raw measurement of the tested biomarker, natural language text indicating whether the biomarker’s level was low or high, or a one-dimensional chart illustrating this level along a low-healthy axis. We measured respondents’ correctness in evaluating these results and their confidence in their interpretations. Participants also told us about any follow-up actions they would take based on the result and how they envisioned, generally, using our proposed personal health system.ResultsWe find that a majority of participants (242/328, 73.8%) were accurate in their interpretations of their diagnostic results. However, 135 of 328 participants (41.1%) expressed uncertainty and confusion about their ability to correctly interpret these results. We also find that demographics and interface design can impact interpretation accuracy, including false confidence, which we define as a respondent having above average confidence despite interpreting a result inaccurately. Specifically, participants who saw a natural language design were the least likely (421.47 times, P=.02) to exhibit false confidence, and women who saw a graph design were less likely (8.67 times, P=.04) to have false confidence. On the other hand, false confidence was more likely among participants who self-identified as Asian (25.30 times, P=.02), white (13.99 times, P=.01), and Hispanic (6.19 times, P=.04). Finally, with the natural language design, participants who were more educated were, for each one-unit increase in education level, more likely (3.06 times, P=.02) to have false confidence.ConclusionsOur findings illustrate both promises and challenges of interpreting medical data outside of a clinical setting and suggest instances where personal informatics may be inappropriate. In surfacing these tensions, we outline concrete interface design strategies that are more sensitive to users’ capabilities and conditions.

Highlights

BackgroundWith the increasing pervasiveness of self-monitoring technology, much of the health data that had previously been gathered and analyzed by experienced practitioners are being collected and interpreted by individuals outside of traditional health care settings [1]
Potentially less health hazardous than false confidence, such situations emerged as a consistent theme in our preliminary investigation and could still lead to hesitation or failure to take an appropriate course of action to address an unhealthy result
We created a binary variable capturing both whether a respondent supplied an incorrect interpretation of the result and whether his or her confidence was above the mean confidence of all respondents who supplied incorrect interpretations

Summary

Introduction

With the increasing pervasiveness of self-monitoring technology, much of the health data that had previously been gathered and analyzed by experienced practitioners are being collected and interpreted by individuals outside of traditional health care settings [1]. This paper explores these questions through both small-scale interviews (N=27) and a large-scale survey (N=303) that examine how various interface designs impact diverse users’ accuracy and confidence in interpreting the results of medical tests. A characterization of the advantages and challenges of using personal informatics technology to self-gather and interpret various types of medical data, including insights into situations where hesitation is warranted before deploying mobile health–based interventions. 3. A set of concrete design recommendations to support users’ accuracy and confidence in interpreting feedback from mobile health tests. Research regarding patient interpretation of traditional medical tests highlights the risks in making complex medical data available to a general audience

Methods

Results

Conclusion