Reactions to health-related social control in young adults with type 1 diabetes.
ABSTRACT Health-related social control refers to individuals' attempts to influence another's health behavior. We describe social control experienced by 109 adults aged 18-35 with Type 1 diabetes, and examine the influence of different types of social control on behavioral and psychological outcomes. Using a self-administered questionnaire, telephone interview, and chart review, we assessed individuals' social control experiences, behavioral and psychological reactions, psychological adjustment, metabolic control, socio-demographics, and clinical factors at baseline, and psychological adjustment and metabolic control at 6-months follow-up. Most participants (85%) reported experiencing social control. Regression analyses revealed that more frequent negative control predicted less behavior change and more negative cognitive reactions concurrently, and decreases in psychological adjustment over time. More frequent reinforcement/modeling and structural changes predicted more positive emotional reactions, but were not associated with behavior change, psychological adjustment, or metabolic control. Use of direct persuasion was associated with more pretending of behavior change. These results suggest that negative social control attempts by social network members may be counter-productive.
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ABSTRACT: The attempts of social network members to regulate individuals' health behaviors, or health-related social control, is one mechanism by which social relationships influence health. Little is known, however, about whether this process varies in married versus unmarried individuals managing a chronic illness in which health behaviors are a key component. Researchers have proposed that social control attempts may have dual effects on recipients' well-being, such that improved health behaviors may occur at the cost of increased emotional distress. The current study accordingly sought to examine marital status differences in the sources, frequency, and responses to health-related social control in an ethnically diverse sample of 1477 patients with type 2 diabetes from southern California, USA. Results from two-way ANCOVAs revealed that married individuals reported their spouses most frequently as sources of social control, with unmarried women naming children and unmarried men naming friends/neighbors most frequently as sources of social control. Married men reported receiving social control most often, whereas unmarried men reported receiving social control least often. Regression analyses that examined behavioral and emotional responses to social control revealed that social control using persuasion was associated with better dietary behavior among married patients. Results also revealed a complex pattern of emotional responses, such that social control was associated with both appreciation and hostility, with the effect for appreciation most pronounced among women. Findings from this study highlight the importance of marital status and gender differences in social network members' involvement in the management of a chronic illness.Social Science [?] Medicine 09/2010; 71(10):1831-8. DOI:10.1016/j.socscimed.2010.08.022 · 2.56 Impact Factor
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ABSTRACT: INTRODUCTION: The majority of smokers attempt to quit smoking on their own, but in any given year, only 5% or less are successful. To improve cessation rates, tapping social networks for social support during quitting has been recommended or tested in some interventions. Prior reviews of this research, however, have concluded that there is little to no evidence that partner support interventions are effective. DISCUSSION: Given the theoretical importance of the concept of social support, its demonstrated value in treatments that are implicitly supportive (e.g., telephone counseling), and the general lack of a guiding conceptual framework for research on the effects of peer or partner support for cessation, we describe theoretical models that explicitly incorporate social support constructs in predicting motivation for and success in quitting. Conclusion: Better differentiation of support concepts and elucidating causal pathways will lead to studies that demonstrate the value of social relationships in improving smokers' likelihood of cessation.Nicotine & Tobacco Research 07/2010; 12(7):695-707. DOI:10.1093/ntr/ntq077 · 2.81 Impact Factor
Article: Psychogenic nonepileptic seizures.[Show abstract] [Hide abstract]
ABSTRACT: This review summarizes the recent studies assessing patients with psychogenic nonepileptic seizures and developments in treatment. The misdiagnosis of nonepileptic seizure is costly to patients, the healthcare system, and to society. Patients with nonepileptic seizures are prescribed antiepileptic drugs that do not treat nonepileptic seizures, have multiple laboratory tests performed, and may not receive the necessary mental healthcare that could benefit them.The first step in nonepileptic seizure treatment is proper diagnosis. Video electroencephalography remains the gold standard for nonepileptic seizure diagnosis. Certain seizure types, such as frontal lobe seizures, may mimic nonepileptic seizure semiology. Bedside observations may augment video electroencephalography to establish nonepileptic seizure diagnosis. The methodology in nonepileptic seizure treatment trials is examined, describing the challenges in conducting clinical trials with patients with overlapping neurologic and psychiatric disorders. Finally, realizing that nonepileptic seizures are in a spectrum of somatoform disorders, diagnostic literature is reviewed in other conversion disorders. Nonepileptic seizure patients remain one of the most challenging populations to diagnose and treat in medical practice. Clinical findings and laboratory advances exist that more clearly establish the diagnosis of nonepileptic seizures. With the appropriate diagnosis, neurologists and mental health providers are better equipped to treat the underlying causes of nonepileptic seizures.Current Opinion in Neurology 05/2008; 21(2):195-201. DOI:10.1097/WCO.0b013e3282f7008f · 5.73 Impact Factor