Dataset Information

Can a one-item mood scale do the trick? Predicting relapse over 5.5-years in recurrent depression.

ABSTRACT:

Background

To examine whether a simple Visual Analogue Mood Scale (VAMS) is able to predict time to relapse over 5.5-years.

Methodology/principal findings

187 remitted recurrently depressed out-patients were interviewed using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) and the 17-item Hamilton Depression rating scale (HAM-D) to verify remission status (HAM-D <10). All patients rated their current mood with the help of a Visual Analogue Mood Scale (VAMS) at baseline and at a follow-up assessment three months later. Relapse over 5.5-years was assessed by the SCID-I. Cox regression revealed that both the VAMS at baseline and three months later significantly predicted time to relapse over 5.5-years. Baseline VAMS even predicted time to relapse when the number of previous depressive episodes and HAM-D scores were controlled for. The baseline VAMS explained 6.3% of variance in time to relapse, comparable to the HAM-D interview.

Conclusions/significance

Sad mood after remission appears to play a pivotal role in the course of depression. Since a simple VAMS predicted time to relapse, the VAMS might be an easy and time-effective way to monitor mood and risk of early relapse, and offers possibilities for daily monitoring using e-mail and SMS.

Trial registration

International Standard Randomized Controlled Trial Register Identifier: ISRCTN68246470.

SUBMITTER: van Rijsbergen GD

PROVIDER: S-EPMC3463530 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Can a one-item mood scale do the trick? Predicting relapse over 5.5-years in recurrent depression.

van Rijsbergen Gerard D GD Bockting Claudi L H CL Berking Matthias M Koeter Maarten W J MW Schene Aart H AH

PloS one 20121003 10

<h4>Background</h4>To examine whether a simple Visual Analogue Mood Scale (VAMS) is able to predict time to relapse over 5.5-years.<h4>Methodology/principal findings</h4>187 remitted recurrently depressed out-patients were interviewed using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) and the 17-item Hamilton Depression rating scale (HAM-D) to verify remission status (HAM-D <10). All patients rated their current mood with the help of a Visual Analogue Mood Scale (VAMS) ...[more]

PMID: 23056456

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Project description:Chronic insomnia tends to "wax and wane" over lifetime. The presence of residual insomnia symptoms is common, especially among naturally remitted individuals. This study aims to examine the features of these residual symptoms and their potential association with future relapse. A population-based data set on the natural history of insomnia was used for this secondary analysis. Residual insomnia symptoms were investigated in those who had insomnia symptoms/syndrome at baseline and achieved full remission (according to predetermined diagnostic algorithm) within the following 1 year. Cox regressions were used to determine the hazard ratio (HR) of each residual symptom for predicting relapse in the next 4 years. The nature and severity of residual symptoms were examined with an extended version of the Insomnia Severity Index (ISI), which incorporates additional items on sleep quality and specific sleep-related daytime impairments (on daytime fatigue, cognitive functioning, mood, interpersonal relationship, and daily activities). In addition, the presence of depressive symptoms and medical conditions were controlled for in investigating risks of insomnia relapse. A total of 434 participants were included in this study (age ranges from 18 to 94; 65.9% female); 248 of them had relapsed within 4 years. The response rate ranged from 78% to 83%. The most frequently reported residual symptoms with at least moderate severity (ISI items ≥2 on 0-4 ISI item scale) were poor "Quality of sleep" (39.2 %), followed by "difficulty maintaining sleep" (DMS; 27%). The most common residual daytime impairments related to insomnia were fatigue (24.7 %), mood disturbances (23%) and cognitive disturbances (22.6%). After controlling for baseline insomnia and depression severity and concurrent physical diseases, impairments of cognition (HR = 1.46), poor quality of sleep (HR = 1.43), disturbed mood (HR = 1.39), being female (HR = 1.36), DMS (HR = 1.35), and fatigue (HR = 1.24) were significantly associated with insomnia relapse in the next 4 years. Moreover, residual poor sleep quality and daytime insomnia symptoms were independent of DMS in predicting relapse. Subgroup regressions according to sex showed that for male participants, residual cognition impairments (HR = 1.98) was the most significant predictors of future relapse, whereas residual DMS (HR = 1.46) significantly predicted relapse for women only. A wide range of residual symptoms exists in individuals with naturally remitted insomnia. Notably, residual DMS is the most common residual nighttime symptom and the only nighttime symptom associated with insomnia relapse. Additionally, perceived poor sleep quality and cognitive, mood, and somatic impairments attributed to sleep disturbances are also related to future relapse. Attention to these residual symptoms when initiating insomnia treatment is warranted to minimize future relapse.

Project description:BackgroundItem response theory (IRT; Lord & Novick, 1968) is a psychometric framework that can be used to model the likelihood that an individual will respond correctly to an item. Using archival data (Mirman et al., 2010), Fergadiotis, Kellough, and Hula (2015) estimated difficulty parameters for the Philadelphia Naming Test (PNT; Roach, Schwartz, Martin, Grewal, & Brecher, 1996) using the 1-parameter logistic IRT model. Although the use of IRT in test development is advantageous, its reliance on sample sizes exceeding 200 participants make it difficult to implement in aphasiology. Therefore, alternate means of estimating the item difficulty of confrontation naming test items warrant investigation. In a preliminary study aimed at automatic item calibration, Swiderski, Fergadiotis, and Hula (2016) regressed the difficulty parameters from the PNT on word length, age of acquisition (Kuperman et al., 2012), lexical frequency as quantified by the Log10CD index (Brysbaert & New, 2009), and naming latency (Székely et al., 2003). Although this model successfully explained a substantial proportion of variance in the PNT difficulty parameters, a substantial proportion (20%) of the response time data were missing. Further, only 39% of the picture stimuli from Székely and colleagues (2003) were identical to those on the PNT. Given that the IRT sample size requirements limit traditional calibration approaches in aphasiology and that the initial attempts in predicting IRT difficulty parameters in our pilot study were based on incomplete response time data this study has two specific aims.AimsTo estimate naming latencies for the 175 items on the PNT, and assess the utility of psycholinguistic variables and naming latencies for predicting item difficulty.Methods and proceduresUsing a speeded picture naming task we estimated mean naming latencies for the 175 items of the Philadelphia Naming test in 44 cognitively healthy adults. We then re-estimated the model reported by Swiderski et al (2016) with the new naming latency data.ResultsThe predictor variables described above accounted for a substantial proportion of the variance in the item difficulty parameters (Adj. R 2 = .692).ConclusionsIn this study we demonstrated that word length, age of acquisition, lexical frequency, and naming latency from neurotypical young adults usefully predict picture naming item difficulty in people with aphasia. These variables are readily available or easily obtained and the regression model reported may be useful for estimating confrontation naming item difficulty without the need for collection of response data from large samples of people with aphasia.

Dataset Information

Can a one-item mood scale do the trick? Predicting relapse over 5.5-years in recurrent depression.

Background

Methodology/principal findings

Conclusions/significance

Trial registration

Publications

Can a one-item mood scale do the trick? Predicting relapse over 5.5-years in recurrent depression.

Similar Datasets

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