ABSTRACT
Background: Obesity is common in many industrialized nations and often accompanied by related health issues. Furthermore, individuals living with overweight or obesity are often confronted with stigmatization in their daily lives. These problems may be aggravated if the objectivity of health care professionals is compromised due to (unconscious) prejudices. If pharmaceutical companies, regulatory agencies, and health insurers are also susceptible to these biases, decisions related to the development, approval, and reimbursement of obesity-related therapies may be negatively impacted. Materials and Methods: The 'Implicit Association Test' (IAT) is a psychometric test allowing to measure these attitudes and could therefore assist to reveal unconscious preferences. A self-developed mobile version, in the form of a ResearchKit-based IAT app was employed in the presented study. The objective was to determine (potential) weight bias and its characteristics for professionals attending a national obesity-related conference in Germany (G1), compared to a control group (without stated interest in the topic, G2) - both using the mobile app - and a historical control (G3) based on data provided by Project Implicit acquired by a web app. Results: Explicit evaluations of G1 were neutral at a higher percentage compared with G2 and G3, while implicit preference toward lean individuals did not differ significantly between G2 and G3, and G1. Conclusion: The greater discrepancy between the (more neutral) explicit attitude and the unconscious preference pointing in the anti-obesity direction could indicate an underestimated bias for the professional participants in G1. Implicit preference is often ingrained from childhood on, and difficult to overcome. Thus, even for professionals, it may unconsciously influence decisions made in the care they provide. Professionals in any given health care sector directed at obesity care should thus be made aware of this inconsistency to enable them to consciously counteract this potential effect.
ABSTRACT
Objective: To determine whether a framework-based approach for mobile apps is appropriate for the implementation of psychological testing, and equivalent to established methods. Methods: Apple's ResearchKit was used for implementing native implicit association test methods (IAT), and an exemplary app was developed to examine users' implicit attitudes toward overweight or thin individuals. For comparison, a web-based IAT app, based on code provided by Project Implicit, was used. Adult volunteers were asked to test both versions on an iPad with touch as well as keyboard input (altogether four tests per participant, random order). Latency values were recorded and used to calculate parameters relevant to the implicit setting. Measurements were analyzed with respect to app type and input method, as well as test order (ANOVA and χ2 tests). Results: Fifty-one datasets were acquired (female, n = 21; male, n = 30, average age 35 ± 4.66 years). Test order and combination of app type and input method influenced the latency values significantly (both P<0.001). This was not mirrored for the D scores or average number of errors vs. app type combined with input method (D scores: P = 0.66; number of errors: P = 0.733) or test order (D scores: P = 0.096; number of errors: P = 0.85). Post-hoc power analysis of the linear ANOVA showed 0.8 by f 2=0.25, with α = 0.05 and 4 predictors. Conclusions: The results suggest that a native mobile implementation of the IAT may be comparable to established implementations. The validity of the acquired measurements seems to depend on the properties of the chosen test rather than the specifics of the chosen platform or input method.
ABSTRACT
The market for medical apps is rapidly expanding - both for professional use as well as for patient centered apps. There are numerous medical apps, but relevant information about their limitations and dangers is rarely available. In an exemplary evaluation of n=8 medical apps, deficits regarding data integrity, one half had security and privacy issues. Since standard users usually have neither the equipment, know-how nor time for such analyses before entrusting them with their data, ideally, manufacturers should provide information regarding the functionality and limits of their products. Mandatory information should cover data management, data protection and privacy issues. To increase transparency, a standardized reporting tool in the form of an app synopsis could be helpful for providing the necessary information.
