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Emotional wellbeing of family carers and people with dementia is associated with not only how each individual copes with stress and conflict, but also by how they cope together. Finding ways to positively cope together was particularly important during COVID-19 lockdown restrictions, when other avenues of emotional support were less available. We explored how carers experienced and used emotion-focused dyadic coping styles during the COVID-19 pandemic. In-depth qualitative interviews were conducted during the pandemic with 42 family carers, supplemented by quality of life scores collected both pre- and during the pandemic and household status. Abductive thematic analysis identified five styles of emotion-focused dyadic coping: common, supportive, hostile, disengaged avoidance and protective. The COVID-19 pandemic left many dyads unsupported. While many carers adapted, reporting increases in quality of life and enjoying the extra time with the person with dementia, others experienced dyadic conflict and reductions in quality of life. This variation was associated with dyadic coping styles, including challenges in using ‘positive’ styles and the protective use of ‘negative’ disengaged avoidance in the right situations. Dyadic coping styles also differed as a function of whether the dyad lived together. As many people with dementia are supported by an informal carer, considering how they cope together could help us to better support them. We make suggestions for dyadic interventions tailored by co-residency status that could help dyads identify and communicate coping needs, reconnect following avoidance coping, and replenish their coping resources through social support.

Supplemental Material for Emotion-focused dyadic coping styles used by family carers of people with dementia during the COVID-19 pandemic by Carmen Colclough, Eleanor Miles, Rotem Perach, Ben Hicks, Josie Dixon, Jennifer Rusted, Kate Gridley, Yvonne Birks, Margaret Dangoor, Paul Donaghy, Riona Mcardle, Elen Moseley, Harsharon K. Sondh, Sube Banerjee in Dementia

The use of applied modelling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefit, particularly as ‘deep phenotyping’ cohorts with multi-omics health data become available. This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk-scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors. This review focusses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access, integration of high-throughput biomarker and electronic health record data into modelling, and progressing beyond primary prediction of dementia to secondary outcomes e.g., treatment response and physical health. Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g. online), or naturalistic (e.g. watch-based accelerometry).