Helping motivation and well-being of chronic pain couples : a daily diary study

The authors propose new procedures for evaluating direct, indirect, and total effects in multilevel models when all relevant variables are measured at Level 1 and all effects are random. Formulas are provided for the mean and variance of the indirect and total effects and for the sampling variances of the average indirect and total effects. Simulations show that the estimates are unbiased under most conditions. Confidence intervals based on a normal approximation or a simulated sampling distribution perform well when the random effects are normally distributed but less so when they are nonnormally distributed. These methods are further developed to address hypotheses of moderated mediation in the multilevel context. An example demonstrates the feasibility and usefulness of the proposed methods.

This research develops and evaluates a simple method of grading the severity of chronic pain for use in general population surveys and studies of primary care pain patients. Measures of pain intensity, disability, persistence and recency of onset were tested for their ability to grade chronic pain severity in a longitudinal study of primary care back pain (n = 1213), headache (n = 779) and temporomandibular disorder pain (n = 397) patients. A Guttman scale analysis showed that pain intensity and disability measures formed a reliable hierarchical scale. Pain intensity measures appeared to scale the lower range of global severity while disability measures appeared to scale the upper range of global severity. Recency of onset and days in pain in the prior 6 months did not scale with pain intensity or disability. Using simple scoring rules, pain severity was graded into 4 hierarchical classes: Grade I, low disability--low intensity; Grade II, low disability--high intensity; Grade III, high disability--moderately limiting; and Grade IV, high disability--severely limiting. For each pain site, Chronic Pain Grade measured at baseline showed a highly statistically significant and monotonically increasing relationship with unemployment rate, pain-related functional limitations, depression, fair to poor self-rated health, frequent use of opioid analgesics, and frequent pain-related doctor visits both at baseline and at 1-year follow-up. Days in Pain was related to these variables, but not as strongly as Chronic Pain Grade. Recent onset cases (first onset within the prior 3 months) did not show differences in psychological and behavioral dysfunction when compared to persons with less recent onset. Using longitudinal data from a population-based study (n = 803), Chronic Pain Grade at baseline predicted the presence of pain in the prior 2 weeks. Chronic Pain Grade and pain-related functional limitations at 3-year follow-up. Grading chronic pain as a function of pain intensity and pain-related disability may be useful when a brief ordinal measure of global pain severity is required. Pain persistence, measured by days in pain in a fixed time period, provides useful additional information.

Scales with varying degrees of measurement reliability are often used in the context of multistage sampling, where variance exists at multiple levels of analysis (e.g., individual and group). Because methodological guidance on assessing and reporting reliability at multiple levels of analysis is currently lacking, we discuss the importance of examining level-specific reliability. We present a simulation study and an applied example showing different methods for estimating multilevel reliability using multilevel confirmatory factor analysis and provide supporting Mplus program code. We conclude that (a) single-level estimates will not reflect a scale's actual reliability unless reliability is identical at each level of analysis, (b) 2-level alpha and composite reliability (omega) perform relatively well in most settings, (c) estimates of maximal reliability (H) were more biased when estimated using multilevel data than either alpha or omega, and (d) small cluster size can lead to overestimates of reliability at the between level of analysis. We also show that Monte Carlo confidence intervals and Bayesian credible intervals closely reflect the sampling distribution of reliability estimates under most conditions. We discuss the estimation of credible intervals using Mplus and provide R code for computing Monte Carlo confidence intervals. (c) 2014 APA, all rights reserved.