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78 Remotely monitored in-home IADLs can discriminate between normal cognition and mild cognitive impairment
- Destiny J Weaver, Chao-Yi Wu, Zachary Beattie, Samuel Lee, Catherine H Ju, Kayla Chan, John Ferguson, Hiroko Dodge, Adriana Hughes
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 381-382
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Objective:
Approximately 6.5 million Americans ages 65 and older have Alzheimer’s disease and related dementias, a prevalence projected to triple by 2060. While subtle impairment in cognition and instrumental activities of daily living (IADLs) arises in the mild cognitive impairment (MCI) phase, early detection of these insidious changes is difficult to capture given limitations. Traditional IADL assessments administered infrequently are less sensitive to early MCI and not conducive to tracking subtle changes that precede significant declines. Continuous passive monitoring of IADLs using sensors and software in home environments is a promising alternative. The purpose of this study was to determine which remotely monitored IADLs best distinguish between MCI and normal cognition.
Participants and Methods:Participants were 65 years or older, independently community-dwelling, and had at least one daily medication and home internet access. Clinical assessments were performed at baseline. Electronic pillboxes (MedTracker) and computer software (Worktime) measured daily medication and computer habits using the Oregon Center for Aging and Technology (ORCATECH) platform. The Survey for Memory, Attention, and Reaction Time (SMART; Trail A, Trail B, and Stroop Tests) is a self-administered digital cognitive assessment that was deployed monthly. IADL data was aggregated for each participant at baseline (first 90 days) in each domain and various features developed for each. The receiver operating characteristic area under the curve (ROC-AUC) was calculated for each feature.
Results:Traditional IADL Questionnaires.
At baseline, 103 participants (normal n = 59, Mage = 73.6±5.5; MCI n = 44, Mage = 76.0±6.1) completed three functional questionnaires (Functional Activities Questionnaire; Measurement of Everyday Cognition (ECog), both self-report and informant). The Informant ECog demonstrated the highest AUC (72% AUC, p = <.001).
Remotely monitored in-home IADLs and self-administered brief online cognitive test performance.
Eighty-four had medication data (normal n = 48, Mage = 73.2±5.4; MCI n = 36, Mage = 75.6±6.9). Four features related to pillbox-use frequency (73% AUC) and four features related to pillbox-use time (62% AUC) were developed. The discrepancy between self-reported frequency of use versus actual use was the most discriminating (67% AUC, p = .03).
Sixty-six had computer data (normal n = 38, Mage = 73.6±6.1; MCI n = 28, Mage = 76.6±6.8). Average usage time showed 64% AUC (p = .048) and usage variability showed 60% AUC (p = .18).
One hundred and two completed the SMART (normal n = 59, Mage = 73.6±5.5; MCI n = 43, Mage = 75.9±6.2). Eleven features related to survey completion time demonstrated 80% AUC in discriminating cognition. Eleven features related to the number of clicks during the survey demonstrated 70% AUC. Lastly, seven mouse movement features demonstrated 71% AUC.
Conclusions:Pillbox use frequency combined features and self-administered brief online cognitive test combined features (e.g., completion times, mouse cursor movements) have acceptable to excellent ability to discriminate between normal cognition and MCI and are relatively comparable to informant rated IADL questionnaires. General computer usage habits demonstrated lower discriminatory ability. Our approach has applied implications for detecting and tracking older adults’ declining cognition and function in real world contexts.
34 Association Between Subjective Cognitive Decline and Mental Wellbeing in Normal Cognition and MCI Older Adults
- Kayla Y Chan, Samuel Lee, Catherine H Ju, Destiny J Weaver, John Ferguson, Adriana Hughes
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 344-345
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Objective:
Subjective cognitive decline (SCD, i.e., perceived cognitive decline without neuropsychological deficits) is associated with Alzheimer’s disease pathology and increased risk for cognitive impairment but is heterogenous in etiology and has been linked to other factors including personality and depression. Mental wellbeing (i.e., the perception and functioning of social, emotional, and health-related aspects of one’s life) has been associated with subjective memory complaints, but its relationship with other subjective cognitive domains is poorly understood. Further characterizing the relationship between mental wellbeing and SCD could refine understanding of SCD and inform development of interventions that prevent progression to objective cognitive decline. This study aimed to describe the relationship between mental wellbeing and subjective decline in multiple cognitive domains and examine whether this relationship differs between older adults with normal cognition and those with mild cognitive impairment (MCI).
Participants and Methods:Community-dwelling older adults (normal: n = 58, Mage = 73.7±5.6; MCI: n = 43, Mage = 75.9±6.1) completed the Everyday Cognition scale, a validated self-report measure of SCD, and the RAND-36 Health Survey, a validated self-report measure of health-related quality of life which includes a mental wellbeing subscale. Spearman’s rank correlations were conducted between self-reported mental wellbeing and each self-reported cognitive domain (i.e., memory, language, visuospatial, and executive function) for the Normal Cognition and MCI groups.
Results:Worse mental wellbeing was associated with worse subjective language and executive function in the normal group, rs(56) = -.42, p =.001; rs(56) = -.37, p =.005, but not for the MCI group, rs(41) = -.23, p =.15; rs(41) = -.12, p =.46. Worse mental wellbeing was associated with worse subjective visuospatial function in the MCI group, rs(41) = -.39, p =.009, but not in the normal group, rs(56) = -.11, p =.39. For both groups, worse mental wellbeing was associated with worse subjective memory, rs(56) = -.45, p < .001; rs(41) = -.37, p =.02. While this correlation was greater in the normal group, the difference was not significant (z = 0.38, p =.71).
Conclusions:These results suggest that perceptions of mental wellbeing are related to perceptions of cognitive decline in multiple domains, and that the specific domains involved differ between normal and MCI groups. The differential associations may mean perception of specific cognitive domains more strongly affect mental wellbeing, or mental wellbeing more acutely influences perception of those domains. The overall observed relationship between SCD and mental wellbeing may have several explanations: the impact of broader health perceptions may extend to cognitive perception, behavioral changes associated with poor wellbeing may reduce subjective cognitive function, or worse subjective cognitive function may lead to negative experiences of wellbeing. Future longitudinal investigation could inform causal inferences. The more limited associations between mental wellbeing and SCD among MCI individuals may point to the role of decreased self-awareness (due to cognitive impairment) precluding detection of subtle changes in cognition or wellbeing. This study highlights the importance of better understanding mental wellbeing in experiences of SCD in both normal and MCI older adults to improve cognitive and mental health outcomes.
A New Approach to Measuring Health System Output and Productivity
- Adriana Castelli, Diane Dawson, Hugh Gravelle, Rowena Jacobs, Paul Kind, Pete Loveridge, Stephen Martin, Mary O'Mahony, Philip Andrew Stevens, Lucy Stokes, Andrew Street, Martin Weale
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- Journal:
- National Institute Economic Review / Volume 200 / 01 April 2007
- Published online by Cambridge University Press:
- 26 March 2020, pp. 105-117
- Print publication:
- 01 April 2007
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This paper considers methods to measure output and productivity in the delivery of health services, with an application to NHS hospital sector. It first develops a theoretical framework for measuring quality adjusted outputs and then considers how this might be implemented given available data. Measures of input use are discussed and productivity growth estimates are presented for the period 1998/9-2003/4. The paper concludes that available data are unlikely fully to capture quality improvements.
ten - The challenges of measuring government output in the healthcare sector
- Edited by Martin Powell, Linda Bauld, Karen Clarke
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- Book:
- Social Policy Review 17
- Published by:
- Bristol University Press
- Published online:
- 05 February 2022
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- 22 June 2005, pp 183-202
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Summary
Introduction
The government is a major actor in every economy. It sets policy, helps shape and maintain social structures, collects taxation and redistributes resources. Most governments also have an extensive role in either the provision or financing of a wide range of goods and services. Often political debate over the adequacy and quality of these public services draws on ideology rather than evidence. These debates include defining the boundaries between the public and private sector, and how the public sector might be reformed or re-organised. But such debates are usually restricted by the limited amount of information about what the public sector does and what it achieves.
There are a few areas of public policy where (relatively) independent data exist that can be used to inform debates about the impact of government policies. If policies are implemented to stimulate economic growth or redistribute income, the effect might be captured within a year or two in the National Accounts or in data compiled routinely by the Inland Revenue. But, in most areas of public policy, only limited data are collected on a routine basis. In recent years, there has been considerable international interest in finding ways of routinely collecting and reporting data on the goods and services being produced by the public sector. This is partly due to the importance of public production in overall economic activity, but another important driver has been the realisation that examination of the efficiency and effectiveness of public services requires data on what is actually being delivered.
Political concern with the output and efficiency of public services is not new. In the UK, Klein points to the early 1980s as the time when the debate started to shift from measuring inputs (the number of doctors and nurses employed in the health sector; the number of teachers in schools) to outputs (the number of operations performed; the number of children taught) (Klein, 2000). At a time when a political priority was to contain the growth of public expenditure, seeking ways to improve the efficiency with which resources were used became increasingly important. Performance indicators flourished, often a motley collection of whatever administrative data happened to be available (Pollitt, 1985; Hood, 1991; Smith, 1995).