Construct validity and temporal stability of the abridged 31-item Illness Behaviour Questionnaire

Does Illness Behavior Contribute to the Understanding of Self-Efficacy and Quality of Life Among People With Hearing Loss? A Test of Concept

PURPOSE

The study sought to first confirm the mediating role of self-efficacy (SE) in the link between hearing loss and reduced quality of life (QOL) and introduce the construct of illness behavior (IB) as a further correlate of self-reported QOL that may itself be mediated by SE.

METHOD

Cross-sectional data were attained using a questionnaire that was completed by 61 adults with self-reported acquired hearing loss.

RESULTS

Support was provided for low SE being a barrier to QOL, with hearing loss only predictive of emotional QOL when SE was an intermediary (mediating) variable. Cognitive and affective indices of IB also predicted QOL. Those participants with elevated emotional distress and, to a lesser extent, exaggerated concerns for their health, were found to have both lower SE and poorer QOL.

CONCLUSIONS

Attempts to replicate these findings longitudinally and with larger and more diverse samples (e.g., congenital or illness-derived hearing loss) are encouraged. A more objective assessment of hearing loss may also reduce the potential for spurious associations that may arise from the use of self-reported data. Nevertheless, the analytical results provide encouragement for the continued consideration of IB in the evaluation of the well-being of individuals with hearing loss. It is proposed that IB could be a useful supplement to the study of motivation among this cohort, such as an addition to the commonly used Health Belief Model, to improve the predictive validity of appropriate health behaviors.

Personality traits, illness behaviors, and psychiatric comorbidity in individuals with psychogenic nonepileptic seizures (PNES), epilepsy, and other nonepileptic seizures (oNES): Differentiating between the conditions

OBJECTIVES

The study aimed to investigate if South African individuals with psychogenic nonepileptic seizures (PNES) differ from individuals with epileptic seizures (ES) and other nonepileptic seizures (oNES) in terms of demographic and seizure characteristics, personality traits, illness behaviors, and depression, anxiety, and posttraumatic stress disorder (PTSD) comorbidity in statistically significant ways; and if so, to test if these differences can be utilized in raising suspicion of PNES as the differential diagnosis to epilepsy and oNES in practice.

METHODS

Data were analyzed from 29 adults with seizure complaints recruited using convenience sampling from a private and a government hospital with video-electroencephalography (vEEG) technology. A quantitative double-blind convenient sampling comparative design was used. A demographic and seizure questionnaire, the NEO Five Factor Inventory-3 (NEO-FFI-3), an abbreviated version of Illness Behavior Questionnaire (IBQ), and the Beck Anxiety Inventory - Primary Care (BAI-PC) were administered. Cronbach's alphas, analysis of variance (ANOVA), cross-tabulation, Fisher exact test, and receiver operating characteristic (ROC) analyses were performed on the dataset.

RESULTS

The total sample consisted of 29 participants, of which 5 had PNES (17%), 21 ES (73%), and 3 oNES (10%). The final sample was comprised of 24 participants from the private hospital and 5 from the government hospital. The group with PNES was found to be significantly more male, to experience significantly more monthly seizures, and chronic pain when comparing the PNES with the ES group, and the PNES with the combined ES and oNES group in both private only sample, as well as the private and government hospital combined sample. Patients with PNES also had a higher level of education compared with the group with ES in the combined private and government hospital sample, something that was not evident in the private hospital only sample. No significant differences between groups were found in either sample in terms of age, population group, language, age at first seizure, and the NEO-FFI-3 subscales. All three groups scored above the cutoff point of 5 exhibiting depression, anxiety, and PTSD symptoms on the BAI-PC in both samples. However, the group with PNES tended to score significantly higher than the group with ES and the combined ES and oNES group in the private hospital sample. A cutoff point of 12 on the BAI-PC was found to predict PNES in this seizure population with 80% sensitivity and 89% specificity. However, once the analysis was repeated on the combined private and government hospital sample, significance in BAI-PC scores between groups was lost. All scales showed good reliability in our study, with the exception of the "Openness to Experience" subscale of the NEO-FFI-3 once reliability analysis was carried out on the combined private and government hospital group.

CONCLUSIONS

This study provides an important stepping stone in the understanding of demographic and seizure factors, personality domains, abnormal illness behaviors, and psychiatric comorbidity in the South African population with PNES. The study also reported on a cutoff score of 12 on the BAI-PC predicting PNES with 80% sensitivity and 89% specificity in a private hospital sample.

Predicting wellbeing among people with epilepsy using illness cognitions

OBJECTIVE

This study sought to examine the synergistic contribution of illness-related perceptions (stigma, severity, and threat) and illness behavior to wellbeing among people with epilepsy. Poorer wellbeing was expected among those who perceived greater stigma, illness severity, and threat and had more extreme illness behavior.

METHODS

Individuals with a diagnosis of epilepsy (N=210), recruited through local and online support groups, completed a questionnaire comprising demographic and epilepsy-specific information, and validated measures of illness perceptions and behavior, epilepsy-related quality of life, and general psychological health.

RESULTS

Bivariate associations among illness cognition, illness behavior, and wellbeing were all as expected. Structural equation modeling highlighted the strong, direct effect of illness threat on quality of life, with other contributions from perceived stigma and an abnormal illness behavior syndrome (i.e., maladaptive illness responses). Significant variance was accounted for in both quality of life (64%) and psychological health (34%).

CONCLUSION

Preliminary evidence of the contributions of illness threat and maladaptive illness responses to wellbeing highlights the need for longitudinal research to examine the dynamic nature of such findings. Clinicians are encouraged to consider the potential value of screening for both illness threat and abnormal illness behavior to facilitate interventions.