"Life-altering": A qualitative analysis of social media birth stories from mothers of children with Down syndrome.

Perinatal depression/anxiety is a significant concern for mothers of children with Down syndrome. This is influenced by the way the Down syndrome diagnosis is shared with parents. This study examined social media posts from mothers regarding experiences of the birth of their child with Down syndrome. Forty-three total stories were coded using thematic content analysis. Results highlighted the experiences of mother's who received a diagnosis of Down syndrome for their newborn. Two themes were found with relevant subthemes (i.e., receiving the diagnosis-mother's intuition, confirmation of the diagnosis, influence of potential health concerns, initial emotions, impact of the medical professional; Processing the diagnosis-life-altering, child not meeting expectations, reframing the diagnosis, impact of partner and others). Findings highlight the need for medical personnel to understand this identity-changing process, as perinatal depression/anxiety looks different for these mothers. There is also a need to help them engage in services accordingly.

Establishing Safety in Emotionally Focused Couple Therapy: A Single-Case Process Study.

Emotionally Focused Couple Therapy (EFT) is an effective approach to working with distressed couples. It is not only effective in reducing relationship distress, but also in successfully maintaining gains over time. In the current study, we sought to understand the therapy processes that result in the creation of safety during stage one of EFT. Safety is a key ingredient in stage one, setting the stage for deeper interpersonal and intrapsychic work in later stages of treatment. In order to achieve the study aim, we analyzed video recordings of the first six sessions of a single couple case from an EFT randomized controlled trial. Using a theory building case study approach, results reveal that all expected components related to the creation of safety were achieved. Additional findings from our analyses indicate that the therapist was highly active, especially in seizing key moments in the therapy provided by this particular case, which shifted the couple in the direction of safety and deescalated their interactional cycle. Implications of key findings are discussed.

Randomized controlled trial of emotionally focused couple therapy compared to treatment as usual for depression: Outcomes and mechanisms of change.

This randomized controlled trial examined the effectiveness of Emotionally Focused Therapy (EFT) for depression and relationship satisfaction versus usual care (i.e., couple therapy other than EFT), and explored mechanisms of change. Mixed model trajectory analyses of 16 couples indicated EFT was associated with greater improvement in relationship satisfaction among men and women. Men receiving EFT reported greater improvements in depressive symptoms compared to usual care. Unified structural equation modeling revealed changes in relationship satisfaction preceded changes in depressive symptoms in one cluster of partners, while changes in depression preceded changes in relationship satisfaction in a second cluster. Two other clusters reported simultaneous changes in satisfaction and depression. This study provides encouraging results on the effectiveness of EFT for depression, and insight into mechanisms of change.

Identifying and Understanding Communities Using Twitter to Connect About Depression: Cross-Sectional Study.

BACKGROUND: Depression is the leading cause of diseases globally and is often characterized by a lack of social connection. With the rise of social media, it is seen that Twitter users are seeking Web-based connections for depression. OBJECTIVE: This study aimed to identify communities where Twitter users tweeted using the hashtag #MyDepressionLooksLike to connect about depression. Once identified, we wanted to understand which community characteristics correlated to Twitter users turning to a Web-based community to connect about depression. METHODS: Tweets were collected using NCapture software from May 25 to June 1, 2016 during the Mental Health Month (n=104) in the northeastern United States and Washington DC. After mapping tweets, we used a Poisson multilevel regression model to predict tweets per community (county) offset by the population and adjusted for percent female, percent population aged 15-44 years, percent white, percent below poverty, and percent single-person households. We then compared predicted versus observed counts and calculated tweeting index values (TIVs) to represent undertweeting and overtweeting. Last, we examined trends in community characteristics by TIV using Pearson correlation. RESULTS: We found significant associations between tweet counts and area-level proportions of females, single-person households, and population aged 15-44 years. TIVs were lower than expected (TIV 1) in eastern, seaboard areas of the study region. There were communities tweeting as expected in the western, inland areas (TIV 2). Counties tweeting more than expected were generally scattered throughout the study region with a small cluster at the base of Maine. When examining community characteristics and overtweeting and undertweeting by county, we observed a clear upward gradient in several types of nonprofits and TIV values. However, we also observed U-shaped relationships for many community factors, suggesting that the same characteristics were correlated with both overtweeting and undertweeting. CONCLUSIONS: Our findings suggest that Web-based communities, rather than replacing physical connection, may complement or serve as proxies for offline social communities, as seen through the consistent correlations between higher levels of tweeting and abundant nonprofits. Future research could expand the spatiotemporal scope to confirm these findings.

#MyDepressionLooksLike: Examining Public Discourse About Depression on Twitter.

BACKGROUND: Social media provides a context for billions of users to connect, express sentiments, and provide in-the-moment status updates. Because Twitter users tend to tweet emotional updates from daily life, the platform provides unique insights into experiences of mental health problems. Depression is not only one of the most prevalent health conditions but also carries a social stigma. Yet, opening up about one's depression and seeking social support may provide relief from symptoms. OBJECTIVE: The aim of this study was to examine the public discourse of the trending hashtag #MyDepressionLooksLike to look more closely at how users talk about their depressive symptoms on Twitter. METHODS: We captured 3225 original content tweets for the hashtag #MyDepressionLooksLike that circulated in May of 2016. Eliminating public service announcements, spam, and tweets with links to pictures or videos resulted in a total of 1978 tweets. Using qualitative content analysis, we coded the tweets to detect themes. RESULTS: The content analysis revealed seven themes: dysfunctional thoughts, lifestyle challenges, social struggles, hiding behind a mask, apathy and sadness, suicidal thoughts and behaviors, and seeking relief. CONCLUSIONS: The themes revealed important information about the content of the public messages that people share about depression on Twitter. More research is needed to understand the effects of the hashtag on increasing social support for users and reducing social stigma related to depression.