Disentangling the Roles of Parental Monitoring and Family Conflict in Adolescents' Management of Type 1 Diabetes

Health Psychology (Impact Factor: 3.59). 04/2012; 32(4). DOI: 10.1037/a0027811
Source: PubMed


Objective: Less parental monitoring of adolescents' diabetes self-care and more family conflict are each associated with poorer diabetes outcomes. However, little is known about how these two family factors relate with one another in the context of self-care and glycemic control. Diabetes self-care was evaluated as a mediator of the associations among parental monitoring, family conflict, and glycemic control in early adolescents with type 1 diabetes. Methods: Adolescent-parent dyads (n = 257) reported on the frequency of parental monitoring, family conflict, and diabetes self-care. Hemoglobin A1c was abstracted from medical charts. Structural equation modeling was used for mediation analysis. Results: A mediation model linking parental involvement and family conflict with A1c through diabetes self-care fit the data well. Monitoring and conflict were inversely correlated (β = -0.23, p < .05) and each demonstrated indirect associations with A1c (standardized indirect effects -0.13 and 0.07, respectively) through their direct associations with self-care (β = 0.39, p < .001 and β = -0.19, p < .05, respectively). Conflict also was positively associated with higher A1c (β = 0.31, p < .01). Conclusions: Elevated family conflict and less parental monitoring are risk factors for poorer glycemic control, and diabetes self-care is one mediator linking these variables. Interventions to promote parental monitoring of diabetes management during early adolescence may benefit from emphasizing strategies to prevent or reduce family conflict. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

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    • "Within the structural equation modeling framework, the magnitude of an indirect effect was estimated with an iterative algorithm (e.g., maximum likelihood ), and the significance was tested by dividing the magnitude of the indirect effect by its standard error, producing a standard normally distributed variable Z (e.g., Iacobucci, Saldanha, & Deng, 2007; Kline, 2011; MacKinnon, 2008). Testing indirect effects with structural equation models has been applied in different contexts (e.g., Ahearne, Mathieu, & Rapp, 2005; Hilliard et al., 2013; King, King, Fairbank, Keane, & Adams, 1998; Quilty, Godfrey, Kennedy, & Bagby, 2010). "
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