Correlates of depression in bipolar disorder.

We analyse time series from 100 patients with bipolar disorder for correlates of depression symptoms. As the sampling interval is non-uniform, we quantify the extent of missing and irregular data using new measures of compliance and continuity. We find that uniformity of response is negatively correlated with the standard deviation of sleep ratings (ρ = -0.26, p = 0.01). To investigate the correlation structure of the time series themselves, we apply the Edelson-Krolik method for correlation estimation. We examine the correlation between depression symptoms for a subset of patients and find that self-reported measures of sleep and appetite/weight show a lower average correlation than other symptoms. Using surrogate time series as a reference dataset, we find no evidence that depression is correlated between patients, though we note a possible loss of information from sparse sampling.

Mood dynamics in bipolar disorder.

The nature of mood variation in bipolar disorder has been the subject of relatively little research because detailed time series data has been difficult to obtain until recently. However some papers have addressed the subject and claimed the presence of deterministic chaos and of stochastic nonlinear dynamics. This study uses mood data collected from eight outpatients using a telemonitoring system. The nature of mood dynamics in bipolar disorder is investigated using surrogate data techniques and nonlinear forecasting. For the surrogate data analysis, forecast error and time reversal asymmetry statistics are used. The original time series cannot be distinguished from their linear surrogates when using nonlinear test statistics, nor is there an improvement in forecast error for nonlinear over linear forecasting methods. Nonlinear sample forecasting methods have no advantage over linear methods in out-of-sample forecasting for time series sampled on a weekly basis. These results can mean that either the original series have linear dynamics, the test statistics for distinguishing linear from nonlinear behaviour do not have the power to detect the kind of nonlinearity present, or the process is nonlinear but the sampling is inadequate to represent the dynamics. We suggest that further studies should apply similar techniques to more frequently sampled data.

Forecasting depression in bipolar disorder.

Bipolar disorder is characterized by recurrent episodes of mania and depression and affects about 1% of the adult population. The condition can have a major impact on an individual's ability to function and is associated with a long-term risk of suicide. In this paper, we report on the use of self-rated mood data to forecast the next week's depression ratings. The data used in the study have been collected using SMS text messaging and comprises one time series of approximately weekly mood ratings for each patient. We find a wide variation between series: some exhibit a large change in mean over the monitored period and there is a variation in correlation structure. Almost half of the time series are forecast better by unconditional mean than by persistence. Two methods are employed for forecasting: exponential smoothing and Gaussian process regression. Neither approach gives an improvement over a persistence baseline. We conclude that the depression time series from patients with bipolar disorder are very heterogeneous and that this constrains the accuracy of automated mood forecasting across the set of patients. However, the dataset is a valuable resource and work remains to be done that might result in clinically useful information and tools.

Evaluation of the psychometric properties of the BCPT Symptom Checklist with a sample of breast cancer patients before and after adjuvant therapy.

OBJECTIVE: The purpose of this analysis was to provide psychometric information related to the Breast Cancer Prevention Trial (BCPT) Symptom Checklist in women with breast cancer prior to the initiation of adjuvant therapy and 6 months post-initiation of therapy. METHODS: This investigation was a secondary analysis of baseline data from the Anastrozole Use in Menopausal Women (AIM) Study (R01 CA 107408). The data used in this study were obtained from women diagnosed with Stage I, II, and IIIa breast cancer and who received adjuvant therapy that included chemotherapy alone, anastrozole alone, and chemotherapy plus anastrozole. Data were examined before adjuvant therapy (n=278), and at 6 months post-adjuvant therapy (n=195). Construct validity was examined through exploratory and confirmatory factor analysis (CFA), and the internal consistency of each resulting subscale was computed. Discriminant validity evidence was obtained by correlating BCPT subscales with subscales from the MOS SF-36. RESULTS: A seven-factor structure was extracted from the 42 items at baseline; an eight-factor structure was found using 6-month data. CFA was performed to compare the baseline and 6-month models as well as an eight-factor model recommended by Cella et al. Findings revealed that the two eight-factor models best represented the data. Low negative correlations with the subscales of the MOS SF-36 provided discriminant validity evidence. CONCLUSION: This analysis provides evidence for the reliability, discriminant validity, and factor structure of the BCPT Symptom Checklist. Further testing of this instrument is needed to confirm the factor structure of this measure.