Physical Activity Patterns Within Dementia Care Dyads.

Previous research has explored the physical activity habits of people with dementia and their family carers separately, with little consideration of how physical habits are associated within dyads. In this observational study, we sought to explore the relationship between people with dementia and their carers' physical activity, at a group level and at a dyadic level. Twenty-six participant dyads (persons with dementia and their carer spouses) were asked to wear an accelerometer for 30 days continuously. Comparisons were made at a group level and a dyadic level. People with dementia did not participate in significantly more moderate to vigorous physical activity (M = 15.44 min/day; SD = 14.40) compared with carers (M = 17.95 min/day; SD = 17.01). Within dyads, there were moderately strong associations between daily moderate to vigorous physical activity (r = .48-.54), but not with overall activity levels (r = .24). Despite physical activity habits remaining relatively low within people with dementia and carers, respectively, moderate to vigorous physical activity levels appear to be correlated within dyads. Understanding mutual influence on physical activity levels within dyads is an important pathway to promote an active lifestyle.

Artificial intelligence for dementia-Applied models and digital health.

INTRODUCTION: The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available. METHODS: 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. RESULTS: This review focuses 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 modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health. DISCUSSION: Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).

Emotion-focused dyadic coping styles used by family carers of people with dementia during the COVID-19 pandemic.

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.

The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control.

Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions-including Parkinson's disease, ataxia, and dementia-we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel 'big data' approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction.

Evaluation of daily walking activity and gait profiles: a novel application of a time series analysis framework.

Wearable technology allows an in-depth analysis of gait behaviour in free-living environments. This investigation aimed to use Alzheimer's disease as an example to apply the time series analysis technique of Statistical Parametric Mapping (SPM) to create daily gait profiles and test if they differed from cognitively intact controls. A framework of macro (habitual walking behaviours) and micro characteristics (spatiotemporal gait variables) characteristics were calculated on an hourly basis. SPM showed that select micro gait characteristics differed from controls at specific hours of the day. Therefore, the application of SPM may provide a more in-depth reflection of activity and gait time-dependent fluctuations than commonly used whole day values. Considering macro and micro gait hour-by- hour may have applications towards disease management, personalized care, monitoring medication and targeted interventions for people with a range of neurodegenerative diseases.