Introduction To understand the potential influence of diversity on the measurement of functional impairment in dementia, we aimed to investigate possible bias caused by age, gender, education, and cultural differences. Methods A total of 3571 individuals (67.1 ± 9.5 years old, 44.7% female) from The Netherlands, Spain, France, United States, United Kingdom, Greece, Serbia, and Finland were included. Functional impairment was measured using the Amsterdam Instrumental Activities of Daily Living (IADL) Questionnaire. Item bias was assessed using differential item functioning (DIF) analysis. Results There were some differences in activity endorsement. A few items showed statistically significant DIF. However, there was no evidence of meaningful item bias: Effect sizes were low (ΔR2 range 0‐0.03). Impact on total scores was minimal. Discussion The results imply a limited bias for age, gender, education, and culture in the measurement of functional impairment. This study provides an important step in recognizing the potential influence of diversity on primary outcomes in dementia research.

ObjectiveTo determine whether multiple computer use behaviours can distinguish between cognitively healthy older adults and those in the early stages of cognitive decline, and to investigate whether these behaviours are associated with cognitive and functional ability.MethodsOlder adults with cognitive impairment (n = 20) and healthy controls (n = 24) completed assessments of cognitive and functional abilities and a series of semi‐directed computer tasks. Computer use behaviours were captured passively using bespoke software.ResultsThe profile of computer use behaviours was significantly different in cognitively impaired compared with cognitively healthy control participants including more frequent pauses, slower typing, and a higher proportion of mouse clicks. These behaviours were significantly associated with performance on cognitive and functional assessments, in particular, those related to memory.ConclusionUnobtrusively capturing computer use behaviours offers the potential for early detection of neurodegeneration in non‐clinical settings, which could enable timely interventions to ultimately improve long‐term outcomes.

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Background: Numbers of GP locums in the NHS have grown in recent years, yet evidence on the scale and scope of the locum workforce in general practice is sparse. Aim: To identify characteristics, geographical patterns and drivers of GP locum use. Design and setting: Observational study of routine data from general practices in England. Methods: Descriptive analyses of national GP workforce data betwen December 2017-September 2020, to determine the volume and geographical distribution of locum use and examine the characteristics of locums compared to other GP types. We modelled locum FTE using negative binomial regressions and estimated Incidence Rate Ratios (IRRs) for the association between the outcome and practice and population characteristics. Results: In December 2019, locums made up 1,217.9 (3.3%) of 33,996.6 total GP FTE which was fewer than other GP types. Median locum age was 42 years (IQR, 36–51), and the majority were UK qualified (660 of 1,034 total locum FTE), were male (642.6 of 1,178.9 locum FTE), and had long-term employment (834.1 of 1,127.9 total locum FTE). Rurality (IRR=1.250; 95%CI 1.095-1.428), inadequate CQC ratings (IRR=2.108; 95%CI 1.370-3.246) and single-handed practice (IRR=4.611; 95%CI 4.101-5.184), were strong predictors of locum use. There was substantial variation in locum use between regions. Conclusion: GP locum use remained stable over time. Compared to other GPs, locums are younger male GPs, a substantial percentage of whom did not qualify in the UK, who serve underperforming practices in rural areas. This is likely to reflect recruitment or high turnover challenges in these practices/areas.

We report experience in requirements elicitation of domain knowledge from experts in clinical and cognitive neurosciences. The elicitation target was a causal model for early signs of dementia indicated by changes in user behaviour and errors apparent in logs of computer activity. A Delphi-style process consisting of workshops with experts followed by a questionnaire was adopted. The paper describes how the elicitation process had to be adapted to deal with problems encountered in terminology and limited consensus among the experts. In spite of the difficulties encountered, a partial causal model of user behavioural pathologies and errors was elicited. This informed requirements for configuring data-and text-mining tools to search for the specific data patterns. Lessons learned for elicitation from experts are presented, and the implications for requirements are discussed as "unknown unknowns", as well as configuration requirements for directing data-/text-mining tools towards refining awareness requirements in healthcare applications.

Computer use is becoming ubiquitous among older adults. As computer use depends on complex cognitive functions, measuring individuals' computer-use behaviours over time may provide a way to detect changes in their cognitive functioning. However, it is uncertain which computer-use behaviour changes are most likely to be associated with declines of particular cognitive functions. To address this, we convened six experts from clinical and cognitive neurosciences to take part in two workshops and a follow-up survey to gain consensus on which computer-use behaviours would likely be the strongest indicators of cognitive decline. This resulted in a list of 21 computer-use behaviours that the majority of experts agreed would offer a 'strong indication' of decline in a specific cognitive function, across Memory, Executive function, Language and Perception and Action domains. This list enables a hypothesis-driven approach to analysing computer-use behaviours predicted to be markers of cognitive decline.

Abstract-We present a desktop monitoring application that combines keyboard, mouse, desktop and application-level activities. It has been developed to discover differences in cognitive functioning amongst older computer users indicative of mild cognitive impairment (MCI). Following requirements capture from clinical domain experts, the tool collects all Microsoft Windows events deemed potentially useful for detecting early clinical indicators of dementia, with a view to further analysis to determine the most pertinent. Further requirements capture from potential end-users has resulted in a system that has little impact on users' daily activities and ensures data security from initial recording of events through to data analysis. We describe two experiments: firstly, volunteers were asked to perform a short set of known tasks; the second (ongoing) experiment is a longitudinal study, with the software currently successfully running on participants' computers.

Objective: Commonly used measures of instrumental activities of daily living (IADL) do not capture activities for a technologically advancing society. This study aimed to adapt the proxy/informant-based Amsterdam IADL Questionnaire (A-IADL-Q) for use in the UK and develop a self-report version.Design: An iterative mixed method cross-cultural adaptation of the A-IADL-Q and the development of a self-report version involving a three-step design: (1) interviews and focus groups with lay and professional stakeholders to assess face and content validity; (2) a questionnaire to measure item relevance to older adults in the U.K.; (3) a pilot of the adapted questionnaire in people with cognitive impairment.Setting: Community settings in the UK.Participants: One hundred and forty-eight participants took part across the three steps: (1) 14 dementia professionals; 8 people with subjective cognitive decline (SCD), mild cognitive impairment (MCI), or dementia due to Alzheimer's disease; and 6 relatives of people with MCI or dementia; (2) 92 older adults without cognitive impairment; and (3) 28 people with SCD or MCI. Measurements:The cultural relevance and applicability of the A-IADL-Q scale items were assessed using a 6-point Likert scale. Cognitive and functional performance was measured using a battery of cognitive and functional measures.Results: Iterative modifications to the scale resulted in a 55-item adapted version appropriate for UK use (A-IADL-Q-UK). Pilot data revealed that the new and revised items performed well. Four new items correlated with the weighted average score ). An exploratory analysis of convergent validity found correlations in the expected direction with cognitive and functional measures. Conclusion:The A-IADL-Q-UK provides a measurement of functional decline for use in the UK that captures culturally relevant activities. A new self-report version has been developed and is ready for testing. Further evaluation of the A-IADL-Q-UK for construct validity is now needed.