Efficacy of a Digital Personalized Elimination Diet for the Self-Management of Irritable Bowel Syndrome and Comorbid Irritable Bowel Syndrome and Inflammatory Bowel Disease.

INTRODUCTION: Most patients with irritable bowel syndrome (IBS) and dual-diagnosis IBS and inflammatory bowel disease (IBD) report that symptoms originate from or are exacerbated by trigger foods. Despite patient interest and need, there is no consensus on what diet is optimal. Popular diets have notable limitations including cost, length, implementation complexity, and lack of personalization. METHODS: This pilot study evaluated the feasibility, desirability, and effect on gastrointestinal symptoms of a digitally delivered personalized elimination diet for patients with IBS and comorbid IBS/IBD, powered by machine learning. Participants were recruited online and were provided access to a digital personalized nutrition tool for 9 weeks (N = 37; IBS only = 16, Crohn's disease and IBS = 9, and ulcerative colitis and IBS = 12). RESULTS: Significant symptom improvement was seen for 81% of participants at study midpoint and persisted for 70% at end point, measured by the relevant symptom severity score (IBS symptom severity score, Patient Simple Clinical Colitis Activity Index, or Mobile Health Index for Crohn's disease). Clinically significant symptom improvement was observed in 78% of participants at midpoint and 62% at end point. Twenty-five participants (67.6%) achieved total symptomatic resolution by the end of study. Patient-reported quality of life improved for 89% of participants. Ninety-five percentage daily engagement, 95% retention, 89% adherence and 92% satisfaction with the program were reported. DISCUSSION: Dietary elimination can improve symptoms and quality of life in patients with IBS and comorbid IBS/IBD. Digital technology can personalize dietary interventions and improve adherence. Randomized controlled trials are warranted.

Belief or disbelief in feedback influences the detection efficiency of the feedback concealed information test.

The feedback concealed information test (fCIT) is a new variant of the CIT that added feedback about participants' concealing performances in the classical CIT. The advantage of the fCIT is that the resulting feedback related event-related potentials (ERPs) can be used to detect concealed information. However, the detection efficiency of feedback-based ERPs varies across studies. The present experiment examined whether the extent participants believed the feedback influenced their detection efficiency. Specifically, participants did a mock crime and were then tested in a fCIT. Following the fCIT, participants were asked to report how much they believed the feedback was accurate. Results showed that there were no significant correlations between the amplitude of the feedback related negativity (FRN), feedback P300, and participants' self-report at the group level. However, individual analyses showed that the detection efficiency of both the FRN and feedback P300 were influenced by participants' belief about the presented feedback. The detection efficiency of the FRN and the feedback P300 was higher among participants who believed the feedback. These findings suggest that the fCIT is dependent to some extent on the participants' level of belief in the feedback.

The effects of practicing a novel countermeasure on both the semantic and episodic memory-based complex trial protocols.

This study applied a countermeasure-resistant version of the Concealed Information Test - the Complex Trial Protocol (CTP) - in an information recognition scenario. We replicated and extended the effects of a novel countermeasure developed by Lukács et al., (2016) on both Semantic and Episodic CTPs. We measured participants' response time and P300 event-related potential to rare, crime-relevant probe stimuli, or frequent, non-crime-relevant irrelevant stimuli in two ways: 1) probe vs the average of all irrelevants (PvIall), and 2) probe vs the maximum irrelevant (PvImax). We hypothesized that countermeasure use would only impair information recognition (as indexed by P300) when participants had practiced the countermeasure beforehand. We further hypothesized that recognition of less salient, Episodic information (i.e., jewelry items from a mock crime) would be impaired by countermeasure use more than the recognition of highly salient, Semantic information (i.e., birthdates). Individual diagnostics based on the area under the receiver operating characteristic curve (Semantic CTP: practice n = 22, non-practice n = 23; Episodic CTP: practice n = 19, non-practice n = 18) revealed that the Semantic CTP was affected by the novel countermeasure, but both PvIall and PvImax analyses remained diagnostically useful. The Episodic CTP's performance, however, was reduced to chance, regardless of practice or analysis type. These results are important for both the field of deception detection and the CTP literature. Research on improvements to the Episodic CTP is required.

Bayesian Data Analysis: A Fresh Approach to Power Issues and Null Hypothesis Interpretation.

One of the first things one learns in a basic psychology or statistics course is that you cannot prove the null hypothesis that there is no difference between two conditions such as a patient group and a normal control group. This remains true. However now, thanks to ongoing progress by a special group of devoted methodologists, even when the result of an inferential test is p > .05, it is now possible to rigorously and quantitatively conclude that (a) the null hypothesis is actually unlikely, and (b) that the alternative hypothesis of an actual difference between treatment and control is more probable than the null. Alternatively, it is also possible to conclude quantitatively that the null hypothesis is much more likely than the alternative. Without Bayesian statistics, we couldn't say anything if a simple inferential analysis like a t-test yielded p > .05. The present, mostly non-quantitative article describes free resources and illustrative procedures for doing Bayesian analysis, with t-test and ANOVA examples.

Familiarity-related filler items enhance the RT CIT, but not the P300 CIT: Episodic vs. Semantic memory protocols.

The reaction time-based concealed information test (RT CIT) typically uses three types of stimuli: 1) probe (i.e., crime-related item), 2) irrelevant (i.e., crime-unrelated item) and 3) target (i.e., an irrelevant item assigned a unique response so as to ensure attention to the test). Lukács, et al., (2017) introduced familiarity-related filler items to the RT CIT, enhancing the efficacy of the test for both single and multiple probe (per block) protocols. Our study aims to A) replicate the effects of familiarity-related filler items on the RT CIT, B) use P300 to investigate the mechanisms of the fillers-related enhancement effect on the RT CIT, C) investigate whether these fillers can enhance the efficacy of the typical P300-based CIT, and D) explore the effects of fillers on the semantic and episodic versions of the P300 and RT CITs. We replicated a clear fillers-related enhancement of the RT CIT (N = 38 for Fillers, N = 37 for No Fillers). Semantic and Episodic experiments were enhanced equally-well by filler items. No effect of fillers was observed on P300 amplitude for CIT stimuli, however, an interaction involving P300 latency suggests that fillers facilitated the processing of Semantic information over Episodic information. Our study independently replicates a valuable improvement of the traditional RT CIT, investigates the potential mechanisms of this enhancement effect, and demonstrates important differences between the P300 and RT CITs.