A model clarifying the role of mediators in the variability of mood states over time in people who stutter.

PURPOSE Elevated negative mood states such as social anxiety and depressive mood have been found in adults who stutter. Research is needed to assist in the development of a model that clarifies how factors like self-efficacy and social support contribute to the variability of negative mood states over time. METHOD Participants included 200 adults who stutter. A longitudinal design was employed to assess change in mood states over a period of five months. Hierarchical directed regression (path analysis) was used to determine contributory relationships between change in mood states and self-efficacy, social support, socio-demographic and stuttering disorder variables. Participants completed a comprehensive assessment regimen, including validated measures of mood states, perceived control (self-efficacy) and social support. RESULTS Results confirmed that self-efficacy performs a protective role in the change in mood states like anxiety and depressive mood. That is, self-efficacy cushioned the impact of negative mood states. Social support was only found to contribute a limited protective influence. Socio-demographic variables had little direct impact on mood states, while perceived severity of stuttering also failed to contribute directly to mood at any time point. CONCLUSIONS Mood was found to be influenced by factors that are arguably important for a person to cope and adjust adaptively to the adversity associated with fluency disorder. A model that explains how mood states are influenced over time is described. Implications of these results for managing adults who stutter with elevated negative mood states like social anxiety are discussed. EDUCATIONAL OBJECTIVES The reader will be able to describe: (a) the method involved in hierarchical (directed) regression used in path analysis; (b) the variability of mood states over a period of five months; (c) the nature of the mediator relationship between factors like self-efficacy and social support and mood states like anxiety, and (d) the contribution to mood states of socio-demographic factors like age and education and stuttering disorder variables like stuttering frequency and perceived severity.