Subtle mistakes in self-report surveys predict future transition to dementia

Early Identification of Cognitive Impairment in Community Environments Through Modeling Subtle Inconsistencies in Questionnaire Responses: Machine Learning Model Development and Validation

BACKGROUND

The underdiagnosis of cognitive impairment hinders timely intervention of dementia. Health professionals working in the community play a critical role in the early detection of cognitive impairment, yet still face several challenges such as a lack of suitable tools, necessary training, and potential stigmatization.

OBJECTIVE

This study explored a novel application integrating psychometric methods with data science techniques to model subtle inconsistencies in questionnaire response data for early identification of cognitive impairment in community environments.

METHODS

This study analyzed questionnaire response data from participants aged 50 years and older in the Health and Retirement Study (waves 8-9, n=12,942). Predictors included low-quality response indices generated using the graded response model from four brief questionnaires (optimism, hopelessness, purpose in life, and life satisfaction) assessing aspects of overall well-being, a focus of health professionals in communities. The primary and supplemental predicted outcomes were current cognitive impairment derived from a validated criterion and dementia or mortality in the next ten years. Seven predictive models were trained, and the performance of these models was evaluated and compared.

RESULTS

The multilayer perceptron exhibited the best performance in predicting current cognitive impairment. In the selected four questionnaires, the area under curve values for identifying current cognitive impairment ranged from 0.63 to 0.66 and was improved to 0.71 to 0.74 when combining the low-quality response indices with age and gender for prediction. We set the threshold for assessing cognitive impairment risk in the tool based on the ratio of underdiagnosis costs to overdiagnosis costs, and a ratio of 4 was used as the default choice. Furthermore, the tool outperformed the efficiency of age or health-based screening strategies for identifying individuals at high risk for cognitive impairment, particularly in the 50- to 59-year and 60- to 69-year age groups. The tool is available on a portal website for the public to access freely.

CONCLUSIONS

We developed a novel prediction tool that integrates psychometric methods with data science to facilitate "passive or backend" cognitive impairment assessments in community settings, aiming to promote early cognitive impairment detection. This tool simplifies the cognitive impairment assessment process, making it more adaptable and reducing burdens. Our approach also presents a new perspective for using questionnaire data: leveraging, rather than dismissing, low-quality data.

Can you tell people's cognitive ability level from their response patterns in questionnaires?

Questionnaires are ever present in survey research. In this study, we examined whether an indirect indicator of general cognitive ability could be developed based on response patterns in questionnaires. We drew on two established phenomena characterizing connections between cognitive ability and people's performance on basic cognitive tasks, and examined whether they apply to questionnaires responses. (1) The worst performance rule (WPR) states that people's worst performance on multiple sequential tasks is more indicative of their cognitive ability than their average or best performance. (2) The task complexity hypothesis (TCH) suggests that relationships between cognitive ability and performance increase with task complexity. We conceptualized items of a questionnaire as a series of cognitively demanding tasks. A graded response model was used to estimate respondents' performance for each item based on the difference between the observed and model-predicted response ("response error" scores). Analyzing data from 102 items (21 questionnaires) collected from a large-scale nationally representative sample of people aged 50+ years, we found robust associations of cognitive ability with a person's largest but not with their smallest response error scores (supporting the WPR), and stronger associations of cognitive ability with response errors for more complex than for less complex questions (supporting the TCH). Results replicated across two independent samples and six assessment waves. A latent variable of response errors estimated for the most complex items correlated .50 with a latent cognitive ability factor, suggesting that response patterns can be utilized to extract a rough indicator of general cognitive ability in survey research.

Cognitive Functioning and the Quality of Survey Responses: An Individual Participant Data Meta-Analysis of 10 Epidemiological Studies of Aging

OBJECTIVES

Self-reported survey data are essential for monitoring the health and well-being of the population as it ages. For studies of aging to provide precise and unbiased results, it is necessary that the self-reported information meets high psychometric standards. In this study, we examined whether the quality of survey responses in panel studies of aging depends on respondents' cognitive abilities.

METHODS

Over 17 million survey responses from 157,844 participants aged 50 years and older in 10 epidemiological studies of aging were analyzed. We derived 6 common statistical indicators of response quality from each participant's data and estimated the correlations with participants' cognitive test scores at each study wave. Effect sizes (correlations) were synthesized across studies, cognitive tests, and waves using individual participant data meta-analysis methods.

RESULTS

Respondents with lower cognitive scores showed significantly more missing item responses (overall effect size ρ^ = -0.144), random measurement error (ρ^ = -0.192), Guttman errors (ρ^ = -0.233), multivariate outliers (ρ^ = -0.254), and acquiescent responses (ρ^ = -0.078); the overall effect for extreme responses (ρ^ = -0.045) was not significant. Effect sizes were consistent across studies, modes of survey administsration, and different cognitive functioning domains, although some cognitive domain specificity was also observed.

DISCUSSION

Lower-quality responses among respondents with lower cognitive abilities add random and systematic errors to survey measures, reducing the reliability, validity, and reproducibility of survey study results in aging research.

Using Item Response Times in Online Questionnaires to Detect Mild Cognitive Impairment

OBJECTIVES

With the increase in web-based data collection, response times (RTs) for survey items have become a readily available byproduct in most online studies. We examined whether RTs in online questionnaires can prospectively discriminate between cognitively normal respondents and those with cognitive impairment, no dementia (CIND).

METHOD

Participants were 943 members of a nationally representative internet panel, aged 50 and older. We analyzed RTs that were passively recorded as paradata for 37 surveys (1,053 items) administered online over 6.5 years. A multilevel location-scale model derived 3 RT parameters for each survey: (1) a respondent's average RT and 2 components of intraindividual RT variability addressing (2) systematic RT adjustments and (3) unsystematic RT fluctuations. CIND status was determined at the end of the 6.5-year period.

RESULTS

All 3 RT parameters were significantly associated with CIND, with a combined predictive accuracy of area under the receiver-operating characteristic curve = 0.74. Slower average RTs, smaller systematic RT adjustments, and greater unsystematic RT fluctuations prospectively predicted a greater likelihood of CIND over periods of up to 6.5, 4.5, and 1.5 years, respectively.

DISCUSSION

RTs for survey items are a potential early indicator of CIND, which may enhance analyses of predictors, correlates, and consequences of cognitive impairment in online survey research.

Developing Early Markers of Cognitive Decline and Dementia Derived From Survey Response Behaviors: Protocol for Analyses of Preexisting Large-scale Longitudinal Data

BACKGROUND

Accumulating evidence shows that subtle alterations in daily functioning are among the earliest and strongest signals that predict cognitive decline and dementia. A survey is a small slice of everyday functioning; nevertheless, completing a survey is a complex and cognitively demanding task that requires attention, working memory, executive functioning, and short- and long-term memory. Examining older people's survey response behaviors, which focus on how respondents complete surveys irrespective of the content being sought by the questions, may represent a valuable but often neglected resource that can be leveraged to develop behavior-based early markers of cognitive decline and dementia that are cost-effective, unobtrusive, and scalable for use in large population samples.

OBJECTIVE

This paper describes the protocol of a multiyear research project funded by the US National Institute on Aging to develop early markers of cognitive decline and dementia derived from survey response behaviors at older ages.

METHODS

Two types of indices summarizing different aspects of older adults' survey response behaviors are created. Indices of subtle reporting mistakes are derived from questionnaire answer patterns in a number of population-based longitudinal aging studies. In parallel, para-data indices are generated from computer use behaviors recorded on the backend server of a large web-based panel study known as the Understanding America Study (UAS). In-depth examinations of the properties of the created questionnaire answer pattern and para-data indices will be conducted for the purpose of evaluating their concurrent validity, sensitivity to change, and predictive validity. We will synthesize the indices using individual participant data meta-analysis and conduct feature selection to identify the optimal combination of indices for predicting cognitive decline and dementia.

RESULTS

As of October 2022, we have identified 15 longitudinal ageing studies as eligible data sources for creating questionnaire answer pattern indices and obtained para-data from 15 UAS surveys that were fielded from mid-2014 to 2015. A total of 20 questionnaire answer pattern indices and 20 para-data indices have also been identified. We have conducted a preliminary investigation to test the utility of the questionnaire answer patterns and para-data indices for the prediction of cognitive decline and dementia. These early results are based on only a subset of indices but are suggestive of the findings that we anticipate will emerge from the planned analyses of multiple behavioral indices derived from many diverse studies.

CONCLUSIONS

Survey response behaviors are a relatively inexpensive data source, but they are seldom used directly for epidemiological research on cognitive impairment at older ages. This study is anticipated to develop an innovative yet unconventional approach that may complement existing approaches aimed at the early detection of cognitive decline and dementia.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/44627.