RESUMEN
BACKGROUND: Despite the efficacy of treatment and severity of symptoms, medication adherence by many with bipolar disorder (BD) is variable at best. This poses a significant challenge for BD care management. OBJECTIVE: For this study, we set out to identify psychosocial and psychiatric predictors of medication adherence with BD. METHODS: Using microtargeted social media advertising, we recruited an international sample of young and older adults with BD living in North America (Canada and the United States), Western Europe (eg, United Kingdom and Ireland), Australia and New Zealand (N=92). On average, participants were 55.35 (SD 9.65; range 22-73) years of age, had been diagnosed with BD 14.25 (SD 11.14; range 1-46) years ago, and were currently prescribed 2.40 (SD 1.28; range 0-6) psychotropic medications. Participants completed questionnaires online including the Morisky Medication Adherence Scale. RESULTS: Medication adherence did not significantly differ across BD subtypes, country of residence, or prescription of lithium versus other mood stabilizers (eg, anticonvulsants). Path analyses indicate that alcohol misuse and subjective or perceived cognitive failures are direct predictors of medication adherence. BD symptoms, psychological well-being, and the number of comorbid psychiatric conditions emerged as indirect predictors of medication adherence via perceived cognitive failures. CONCLUSIONS: Alcohol misuse did not predict perceived cognitive failures. Nor did age predict medication adherence or cognitive failures. This is noteworthy given the 51-year age range of participants. That is, persons in their 20s with BD reported similar levels of medication adherence and perceived cognitive failures as those in their 60s. This suggests that perceived cognitive loss is a facet of adult life with BD, in contrast to the assumption that accelerated cognitive aging with BD begins in midlife.
RESUMEN
BACKGROUND: Reliable and consistent social support is associated with the mental health and well-being of persons with severe mental illness, including bipolar disorder (BD). Yet the COVID-19 pandemic and associated social distancing measures (eg, shelter in place) reduced access to regular social contacts, while social media use (SMU) increased concomitantly. Little is currently known about associations between the well-being of adults with BD and different types of SMU (eg, passive and active). OBJECTIVE: For this study, we had two goals. First, we report descriptive information regarding SMU by persons with BD during COVID-19 (all platforms). Specific to Facebook, we next developed and tested a hypothesized model to identify direct and indirect associations between BD symptoms, social support, loneliness, life satisfaction, and SMU. Responses were collected during the global spread of the Delta variant and prior/concurrent with the Omicron variant, 20 months after the World Health Organization declared COVID-19 a global pandemic. METHODS: Over 8 weeks, we obtained responses from an international sample of 102 adults with BD using the Qualtrics online platform. Most had previously participated in the BADAS (Bipolar Affective Disorders and older Adults) Study (n=89, 87.3%); the remainder were recruited specifically for this research (n=13, 2.7%). The subsamples did not differ in age (t100=1.64; P=.10), gender (χ22=0.2; P=.90), socioeconomic status (χ26=9.9; P=.13), or time since BD diagnosis (t97=1.27; P=.21). Both were recruited using social media advertising micro-targeted to adults with BD. On average, participants were 53.96 (SD 13.22, range 20-77) years of age, they had completed 15.4 (SD 4.28) years of education, and were diagnosed with BD 19.6 (SD 10.31) years ago. Path analyses were performed to develop and test our hypothesized model. RESULTS: Almost all participants (n=95, 93.1%) reported having both Facebook and LinkedIn accounts; 91.2% (n=93) reported regular use of either or both. During the pandemic, most (n=62, 60.8%) reported accessing social media several times a day; 36.3% (n=37) reported using social media more often since the emergence of COVID-19. Specific to Facebook, the model we hypothesized differed somewhat from what emerged. The resulting model suggests that symptoms of depression predict loneliness and, inversely, social support and life satisfaction. Social support predicts social Facebook use, whereas passive Facebook use predicts life satisfaction. Symptoms of depression emerged as indirect predictors of SMU via social support. CONCLUSIONS: Our findings suggest that the operational definition of passive-active SMU requires further analysis and refinement. In contrast to theory, passive Facebook use appears positively associated with well-being among certain populations. Longitudinal data collection over multiple points is required to identify associations between BD symptoms, SMU, and well-being over time.
