Dr Haomiao Jin

Rapid technological advancements and societal shifts have reshaped older adults’ daily lives. Emerging evidence suggests that Advanced Activities of Daily Living (AADL)—complex tasks beyond basic Activities of Daily Living (ADL) and Instrumental ADL (IADL)—may signal cognitive impairment earlier than traditional measures. This scoping review aims to identify AADL that could serve as early indicators of cognitive impairment. Scoping review. This review followed Arksey and O’Malley’s framework and adhered to the PRISMA-ScR guideline. We systematically searched Web of Science, PubMed, and PsycINFO for relevant studies published between 2013 and 2023. From 16,185 initially identified articles, 45 studies conducted across 11 countries were included after thorough screening and eligibility assessment. These studies examined eight domains of AADL (e.g., digital technology use, social engagement, cultural activities, naturalistic driving), across cognitive impairment stages from mild cognitive impairment (MCI) to dementia. Findings highlighted that frequent engagement in certain leisure, social, and intellectually stimulating activities (e.g., reading, playing board games, volunteering) was consistently associated with a reduced risk of cognitive decline. Digital behavioural indicators (e.g., computer usage patterns, naturalistic driving metrics, survey response times) showed promising predictive potential for early cognitive impairment identification, with several studies reporting good predictive accuracy (e.g., AUC > 0.80). AADL indicators hold promise for early detection and potential prevention of cognitive impairment among older adults. Future research should explore the feasibility, acceptability, and clinical integration of AADL assessments into routine geriatric care and public health practice, particularly in low- and middle-income countries.

Introduction Cognitive assessments are useful in ascertaining dementia but may be influenced by patient characteristics. India's distinct culture and demographics warrant investigation into population‐specific cutoffs. Methods Data were utilized from the Longitudinal Aging Study in India‐Diagnostic Assessment of Dementia (n = 2528). Dementia ascertainment was conducted by an online panel. A machine learning (ML) model was trained on these classifications, with explainable artificial intelligence to assess feature importance and inform cutoffs that were assessed across demographic groups. Results The Informant Questionnaire of Cognitive Decline in the Elderly (IQCODE) and Hindi Mini‐Mental State Examination (HMSE) were identified as the most impactful assessments with optimal cutoffs of 3.8 and 25, respectively. Discussion An ML assessment of clinician dementia ratings identified IQCODE and HMSE to be the most impactful assessments. Optimal cutoffs of 3.8 and 25 were identified and performed excellently in the overall sample, though did decrease in specific, more difficult‐to‐diagnose subgroups. Highlights Pioneers use of explainable artificial intelligence in the diagnosis of dementia. Creates assessment cutoffs specific to the nation of India. Highlights differences in cutoffs across nations.

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. 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. 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. 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.

Introduction. Accurate estimation of dementia prevalence is essential for making effective public and social care policy to support individuals and families suffering from the disease. The purpose of this paper is to estimate the prevalence of dementia in India using a semi-supervised machine learning approach based on a large nationally representative sample. Methods. The sample of this study is adults 60 years or older in the wave 1 (2017-2019) of the Longitudinal Aging Study in India (LASI). A subsample in LASI received extensive cognitive assessment and clinical consensus ratings and therefore have diagnoses of dementia. A semi-supervised machine learning model was developed to predict the status of dementia for LASI participants without diagnoses. After obtaining the predictions, sampling weights and age standardization to the World Health Organization (WHO) standard population were applied to generate the estimate for prevalence of dementia in India. Results. The prevalence of dementia for those aged 60 years and older in India was 8.44% (95% CI: 7.89%~9.01%). The age-standardized prevalence was estimated to be 8.94% (95% CI: 8.36%~9.55%). The prevalence of dementia was greater for those who were older, were females, received no education, and lived in rural areas. Discussion/Conclusion. The prevalence of dementia in India may be higher than prior estimates derived from local studies. These prevalence estimates provide the information necessary for making long-term planning of public and social care policy. The semi-supervised machine learning approach adopted in this paper may also be useful for other large population ageing studies that have a similar data structure.

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.

It is widely recognized that survey satisficing, inattentive, or careless responding in questionnaires reduce the quality of self-report data. In this study, we propose that such low-quality responding (LQR) can carry substantive meaning at older ages. Completing questionnaires is a cognitively demanding task and LQR among older adults may reflect early signals of cognitive deficits and pathological aging. We hypothesized that older people displaying greater LQR would show faster cognitive decline and greater mortality risk. We analyzed data from 9, 288 adults 65 years or older in the Health and Retirement Study. Indicators of LQR were derived from participants' response patterns in 102 psychosocial questionnaire items administered in 2006-2008. Latent growth models examined whether LQR predicted initial status and change in cognitive functioning, assessed with the modified Telephone Interview for Cognitive Status, over the subsequent 10 years. Discrete-time survival models examined whether LQR was associated with mortality risk over the 10 years. We also examined evidence for indirect (mediated) effects in which LQR predicts mortality via cognitive trajectories. After adjusting for age, gender, race, marital status, education, health conditions, smoking status, physical activity, and depressive symptoms, greater LQR was cross-sectionally associated with poorer cognitive functioning, and prospectively associated with faster cognitive decline over the follow-up period. Furthermore, greater LQR was associated with increased mortality risk during follow-up, and this effect was partially accounted for by the associations between LQR and cognitive functioning. Self-report questionnaires are not formally designed as cognitive tasks, but this study shows that LQR indicators derived from self-report measures provide objective, performance-based information about individuals' cognitive functioning and survival. Self-report surveys are ubiquitous in social science, and indicators of LQR may be of broad relevance as predictors of cognitive and health trajectories in older people.

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.

Body dissatisfaction (BD) and preoccupation with thoughts of food (PTF) are intertwined and are compo-nents of thought-shape fusion. Thought-shape fusion describes the process by which PTF lead to beliefs about weight and shape. To study thought-shape fusion in daily life and explore various transitions between BD and PTF, 30 women with binge eating completed ecological momentary assessment for 14 days. BD and PTF were assessed using continuous rating scales at each prompt. Multi-state modeling, which analyzes micro-temporal transitions between discrete states, was used to examine transitions among four states created with BD and PTF ratings. The four states included low BD/low PTF, low BD/high PTF, high BD/low PTF, and high BD/high PTF. Affect and disordered eating were examined as covariates of state transitions. Results showed high BD states were self-perpetrating, such that when in high BD states, transition to low BD states were less likely. Regarding covariates, positive affect buffered against mal-adaptive transitions whereas negative affect and disordered eating increased risk. Findings highlighted high BD states as influential, and negative affect and disordered eating as risk factors and positive affect as preventive. This study enhances theory of thought-shape fusion and implicates transitions from BD to PTF as possible underlying transitions. (c) 2021 Elsevier Ltd. All rights reserved.

Objective The objective of this study is to examine the within-person relationships between sleep duration and next-day stress and affect in the daily life of individuals with T1D. Methods Study participants were recruited in the Function and Emotion in Everyday Life with Type 1 Diabetes (FEEL-T1D) study. Sleep duration was derived by synthesizing objective (actigraphy) and self-report measures. General and diabetes-specific stress and positive and negative affect were measured using ecological momentary assessment. Multilevel regression was used to examine the within-person relationships between sleep duration and next-day stress and affect. Cross-level interactions were used to explore whether gender and baseline depression and anxiety moderated these within-person relationships. Results Adults with T1D (n = 166) completed measurements for 14 days. The average age was 41.0 years, and 91 participants (54.8%) were female. The average sleep duration was 7.3 h (SD = 1.2 h). Longer sleep was significantly associated with lower general stress (p < 0.001) but not diabetes-specific stress (p = 0.18) on the next day. There were significant within-person associations of longer sleep with lower levels on next-day negative affect (overall, p = 0.002, disappoint, p = 0.05; sad, p = 0.05; tense, p < 0.001; upset, p = 0.008; anxious, p = 0.04). There were no significant associations with positive affect. Examination of the interaction effects did not reveal significant differential relationships for men and women and for individuals with and without depression or anxiety at baseline. Conclusion Findings from this study suggest optimizing sleep duration as an important interventional target for better managing general stress and improving daily emotional wellbeing of individuals with T1D.

Although short-term blood glucose levels and variability are thought to underlie diminished function and emotional well-being in people with type 1 diabetes (T1D), these relationships are poorly understood. The Function and Emotion in Everyday Life with T1D (FEEL-T1D) study focuses on investigating these short-term dynamic relationships among blood glucose levels, functional ability, and emotional well-being in adults with T1D. The aim of this study is to present the FEEL-T1D study design, methods, and study progress to date, including adaptations necessitated by the COVID-19 pandemic to implement the study fully remotely. The FEEL-T1D study will recruit 200 adults with T1D in the age range of 18-75 years. Data collection includes a comprehensive survey battery, along with 14 days of intensive longitudinal data using blinded continuous glucose monitoring, ecological momentary assessments, ambulatory cognitive tasks, and accelerometers. All study procedures are conducted remotely by mailing the study equipment and by using videoconferencing for study visits. The study received institutional review board approval in January 2019 and was funded in April 2019. Data collection began in June 2020 and is projected to end in December 2021. As of June 2021, after 12 months of recruitment, 124 participants have enrolled in the FEEL-T1D study. Approximately 87.6% (7082/8087) of ecological momentary assessment surveys have been completed with minimal missing data, and 82.0% (82/100) of the participants provided concurrent continuous glucose monitoring data, ecological momentary assessment data, and accelerometer data for at least 10 of the 14 days of data collection. Thus far, our reconfiguration of the FEEL-T1D protocol to be implemented remotely during the COVID-19 pandemic has been a success. The FEEL-T1D study will elucidate the dynamic relationships among blood glucose levels, emotional well-being, cognitive function, and participation in daily activities. In doing so, it will pave the way for innovative just-in-time interventions and produce actionable insights to facilitate tailoring of diabetes treatments to optimize the function and well-being of individuals with T1D. DERR1-10.2196/30901.

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. 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. 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. 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. 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. DERR1-10.2196/44627.

While there is evidence that functioning, or ability to perform daily life activities, can be adversely influenced by type 1 diabetes, the impact of acute fluctuations in glucose levels on functioning is poorly understood. Using dynamic structural equation modeling, we examined whether overnight glucose (coefficient of variation[CV], percent time 250 mg/dL) predicted seven next-day functioning outcomes (mobile cognitive tasks, accelerometry-derived physical activity, self-reported activity participation) in adults with type 1 diabetes. We examined mediation, moderation, and whether short-term relationships were predictive of global patient-reported outcomes. Overall next-day functioning was significantly predicted from overnight CV (P = 0.017) and percent time >250 mg/dL (P = 0.037). Pairwise tests indicate that higher CV is associated with poorer sustained attention (P = 0.028) and lower engagement in demanding activities (P = 0.028), time 250 mg/dL is associated with more sedentary time (P = 0.024). The impact of CV on sustained attention is partially mediated by sleep fragmentation. Individual differences in the effect of overnight time

IntroductionCannabis use in the U.S. rose early in the COVID-19 pandemic, but it is unclear whether that rise was temporary or permanent. This study estimated the nature and sociodemographic correlates of U.S. adult subpopulations regularly using cannabis by examining weekly trajectories of use during the first year of the pandemic.MethodsData came from the Understanding America Study, a nationally representative panel of U.S. adults (N=8,397; March 10, 2020−March 29, 2021). A growth mixture model was deployed to identify subgroups with similar regular cannabis use. Sociodemographic correlates of subgroups were examined using multinomial logistic regression.ResultsFour cannabis-use groups were identified. Most participants did not regularly use cannabis (no regular use; 81.7%). The other groups increased regular use until April 2020 but then diverged. Some (7.1%) decreased thereafter, whereas others (3.4%) maintained their elevated use until October 26, 2020 before decreasing. The last group (7.7%) sustained their elevated use throughout. Individuals aged between 18 and 39 years, unmarried, living in poverty, without a college degree, and with longer unemployment or underemployment spells had higher odds of being in the other groups with more weekly use than in the no-regular-use group.ConclusionsThe analyses revealed population subgroups with prolonged regular cannabis use and a disproportionate concentration of socioeconomically vulnerable members of society in these subgroups. These findings elucidate important heterogeneity in the subpopulations using cannabis, highlighting the urgent need to tailor public health programs for subgroups that may have unique service needs.

Background This study characterized the prevalence, drinking patterns, and sociodemographic characteristics of U.S. adult subpopulations with distinct drinking trajectories during the COVID‐19 pandemic's first 42 weeks. Methods Adult respondents (n = 8130) in a nationally representative prospective longitudinal study completed 21 biweekly web surveys (March 2020 to January 2021). Past‐week alcohol drinking frequency (drinking days [range: 0 to 7]) and intensity (binge drinking on usual past‐week drinking day [yes/no]) were assessed at each timepoint. Growth mixture models identified multiple subpopulations with homogenous drinking trajectories based on mean drinking days or binge drinking proportional probabilities across time. Results Four drinking frequency trajectories were identified: Minimal/stable (72.8% [95% CI = 71.8 to 73.8]) with

We conducted an idiographic analysis to examine the effect of survey burden, measured by the length of the most recent questionnaire, or number of survey invitations (survey frequency) in a one-year period preceding a new survey, on the response probability to a new survey in a probability-based Internet panel. The individual response process was modeled by a latent Markov chain with questionnaire length and survey frequency as explanatory variables. The individual estimates were obtained using a Monte Carlo based method and then pooled to derive estimates of the overall relationships and to identify specific subgroups whose responses were more likely to be impacted by questionnaire length or survey frequency. The results show an overall positive relationship between questionnaire length and response probability, and no significant relationship between survey frequency and response probability. Further analysis showed that longer questionnaires were more likely to be associated with decreased response rates among racial/ethnic minorities and introverted participants. Frequent surveys were more likely to be associated with decreased response rates among participants with a large household. We discuss the implications for panel management and advocate targeted interventions for the small subgroups whose response probability may be negatively impacted by longer questionnaires or frequent surveys.

Our objective was to investigate the validity of four-item and six-item versions of the National Aeronautics and Space Administration Task Load Index (NASA-TLX, or TLX for short) for measuring workload over a whole day in the repeated measures context. We analysed data on 51 people with type 1 diabetes from whom we collected ecological momentary assessment and daily diary data over 14 days. The TLX was administered at the last survey of every day. Confirmatory factor analysis fit statistics indicated that neither the TLX-6 nor TLX-4 were a unidimensional representation of whole day workload. In exploratory analyses, another set of TLX items we refer to as TLX-4v2 was sufficiently unidimensional. Raw sum scores from the TLX-6 and TLX-4v2 had plausible relationships with other measures, as evidenced by intra-person correlations and mixed-effects models. TLX-6 appears to capture multiple factors contributing to workload, while TLX-4v2 assesses the single factor of 'mental strain'. Practitioner Summary: Using within-person longitudinal data, we found evidence supporting the validity of a measure evaluating whole-day workload (i.e. workload derived from all sources, not only paid employment) derived from the NASA-TLX. This measure may be useful to assess how day-to-day variations in workload impact quality of life among adults. Abbreviations: NASA-TLX or TLX: National Aeronautics and Space Administration Task Load Index; TLX-6: six item version of the NASA-TLX; TLX-4: four item version of the NASA-TLX, TLX-4v2: four item NASA-TLX version two; NIOSH: National Institute for Occupational Safety and Health; CFA: confirmatory factor analysis; T1D: type 1 diabetes; EMA: ecological momentary assessment; BG: blood glucose; SD: standard deviation; CV: coefficient of variation; RMSEA: root mean square error of approximation; CFI: comparative fit index; TLI: Tucker-Lewis Index; SRMR: standardized root mean square residual; AIC: Akaike information criterion; BIC: Bayesian information criterion; χ2: Chi-square statistic

This mixed-methods study examined the health information-seeking behavior of Latino caregivers of people living with dementia. A structured survey and semi-structured interviews were conducted with 21 Latino caregivers in Los Angeles, California. For triangulation, semi-structured interviews were also conducted with six healthcare and social service providers. The interview transcripts were coded and analyzed via thematic analysis, while the survey data were summarized using descriptive statistics. The results show that caregivers sought information on what changes to expect as dementia progresses. Some desired detailed (limited) information to be better prepared (to worry less). The most common action to address their information needs was searching the Internet. However, those who did this tended to be concerned about the quality of information. Overall, this study sheds light on how much detail Latino caregivers desire in the information they need and the actions they take to obtain this information.

The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is the first and only nationally representative study on late-life cognition and dementia in India (n=4096). LASI-DAD obtained clinical consensus diagnosis of dementia for a subsample of 2528 respondents. This study develops a machine learning model that uses data from the clinical consensus diagnosis in LASI-DAD to support the classification of dementia status. Clinicians were presented with the extensive data collected from LASI-DAD, including sociodemographic information and health history of respondents, results from the screening tests of cognitive status, and information obtained from informant interviews. Based on the Clinical Dementia Rating (CDR) and using an online platform, clinicians individually evaluated each case and then reached a consensus diagnosis. A 2-step procedure was implemented to train several candidate machine learning models, which were evaluated using a separate test set for predictive accuracy measurement, including the area under receiver operating curve (AUROC), accuracy, sensitivity, specificity, precision, F1 score, and kappa statistic. The ultimate model was selected based on overall agreement as measured by kappa. We further examined the overall accuracy and agreement with the final consensus diagnoses between the selected machine learning model and individual clinicians who participated in the clinical consensus diagnostic process. Finally, we applied the selected model to a subgroup of LASI-DAD participants for whom the clinical consensus diagnosis was not obtained to predict their dementia status. Among the 2528 individuals who received clinical consensus diagnosis, 192 (6.7% after adjusting for sampling weight) were diagnosed with dementia. All candidate machine learning models achieved outstanding discriminative ability, as indicated by AUROC >.90, and had similar accuracy and specificity (both around 0.95). The support vector machine model outperformed other models with the highest sensitivity (0.81), F1 score (0.72), and kappa (.70, indicating substantial agreement) and the second highest precision (0.65). As a result, the support vector machine was selected as the ultimate model. Further examination revealed that overall accuracy and agreement were similar between the selected model and individual clinicians. Application of the prediction model on 1568 individuals without clinical consensus diagnosis classified 127 individuals as living with dementia. After applying sampling weight, we can estimate the prevalence of dementia in the population as 7.4%. The selected machine learning model has outstanding discriminative ability and substantial agreement with a clinical consensus diagnosis of dementia. The model can serve as a computer model of the clinical knowledge and experience encoded in the clinical consensus diagnostic process and has many potential applications, including predicting missed dementia diagnoses and serving as a clinical decision support tool or virtual rater to assist diagnosis of dementia.

[Display omitted] •This article describes an informatics approach to study decision-making impairment.•Daily life data are collected from a natural sequential decision-making task.•The collected data are modeled by a reinforcement learning model.•Model parameters can indicate specific aspects of decision-making impairment.•The approach may generate useful information with high ecological validity. Mental health informatics studies methods that collect, model, and interpret a wide variety of data to generate useful information with theoretical or clinical relevance to improve mental health and mental health care. This article presents a mental health informatics approach that is based on the decision-making theory of depression, whereby daily life data from a natural sequential decision-making task are collected and modeled using a reinforcement learning method. The model parameters are then estimated to uncover specific aspects of decision-making impairment in individuals with depression. Empirical results from a pilot study conducted to examine decision-making impairments in the daily lives of university students with depression are presented to illustrate this approach. Future research can apply and expand on this approach to investigate a variety of daily life situations and psychiatric conditions and to facilitate new informatics applications. Using this approach in mental health research may generate useful information with both theoretical and clinical relevance and high ecological validity.

Objectives: The aim of the study was to evaluate whether unemployment and underemployment are associated with mental distress and whether employment insecurity and its mental health consequences are disproportionately concentrated among specific social groups in the United States during the COVID19 pandemic. Study design: This is a population-based longitudinal study. Methods: Data came from the Understanding America Study, a population-based panel in the United States. Between April and May 2020, 3548 adults who were not out of the labor force were surveyed. Analyses using targeted maximum likelihood estimation examined the association of employment insecurity with depression, assessed using the 2-item Patient Health Questionnaire, and anxiety, measured with the 2-item Generalized Anxiety Disorder scale. Stratified models were evaluated to examine whether employment insecurity and its mental health consequences are disproportionately concentrated among specific social groups. Results: Being unemployed or underemployed was associated with increased odds of having depression (adjusted odds ratio [AOR] = 1.66, 95% confidence interval [CI] = 1.36-2.02) and anxiety (AOR = 1.50, 95% CI = 1.26, 1.79), relative to having a full-time job. Employment insecurity was disproportionately concentrated among Hispanics (54.3%), Blacks (60.6%), women (55.9%), young adults (aged 18-29 years; 57.0%), and those without a college degree (62.7%). Furthermore, Hispanic workers, subsequent to employment insecurity, experienced worse effects on depression (AOR = 2.08, 95% CI = 1.28, 3.40) and anxiety (AOR = 1.95, 95% CI = 1.24, 3.09). Those who completed high school or less reported worse depression subsequent to employment insecurity (AOR = 2.44, 95% CI = 1.55, 3.85). Conclusions: Both unemployment and underemployment threaten mental health during the pandemic, and the mental health repercussions are not felt equally across the population. Employment insecurity during the pandemic should be considered an important public health concern that may exacerbate preexisting mental health disparities during and after the pandemic. (c) 2021 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Increased retention in residential treatment for substance use disorder (SUD) has been associated with more favorable clinical outcomes for residents. vet SUD treatment dropout remains high. It is essential to uncover factors contributing to these high rates. Lithe is known about whether features of an individual's social network prior to treatment entry are related to number of days in treatment or to clinical status at treatment termination. To examine these relationships, we analyzed data from 241 women (58.5% Hispanic) entering an SUD residential treatment facility, who agreed to participate in a parent randomized control trial. We assessed characteristics of these women's social networks prior to treatment entry at baseline. We extracted clinician-determined progress at treatment termination and days in treatment two months after treatment entry from clinical records. Data-driven analyses using purposeful selection of predictors showed that the overall size of the social network was associated with increased likelihood of being classified as having achieved good clinical progress in treatment at termination and that number of drug users in the pretreatment social network was related to staying fewer days in treatment. Contrary to our hypothesis, we found no significant associations between other pretreatment social support network characteristics (i.e., social support) and treatment retention or clinical discharge status. Future research should examine how features of social networks change through treatment and how these changes relate to treatment outcomes.

Background: SMS text messaging is an inexpensive, private, and scalable technology-mediated assessment mode that can alleviate many barriers faced by the safety net population to receive depression screening. Some existing studies suggest that technology-mediated assessment encourages self-disclosure of sensitive health information such as depressive symptoms while other studies show the opposite effect. Objective: This study aimed to evaluate the validity of using SMS text messaging to screen depression and related conditions, including anxiety and functional disability, in a low-income, culturally diverse safety net primary care population. Methods: This study used a randomized design with 4 study groups that permuted the order of SMS text messaging and the gold standard interview (INTW) assessment. The participants for this study were recruited from the participants of the prior Diabetes-Depression Care-management Adoption Trial (DCAT). Depression was screened by using the 2-item and 8-item Patient Health Questionnaire (PHQ-2 and PHQ-8, respectively). Anxiety was screened by using the 2-item Generalized Anxiety Disorder scale (GAD-2), and functional disability was assessed by using the Sheehan Disability Scale (SDS). Participants chose to take up the assessment in English or Spanish. Internal consistency and test-retest reliability were evaluated by using Cronbach alpha and intraclass correlation coefficient (ICC), respectively. Concordance was evaluated by using an ICC, a kappa statistic, an area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. A regression analysis was conducted to examine the association between the participant characteristics and the differences in the scores between the SMS text messaging and INTW assessment modes. Results: Overall, 206 participants (average age 57.1 [SD 9.18] years; females: 119/206, 57.8%) were enrolled. All measurements except the SMS text messaging-assessed PHQ-2 showed Cronbach alpha values >=.70, indicating acceptable to good internal consistency. All measurements except the INTW-assessed SDS had ICC values >= 0.75, indicating good to excellent test-retest reliability. For concordance, the PHQ-8 had an ICC of 0.73 and AUROC of 0.93, indicating good concordance. The kappa statistic, sensitivity, and specificity for major depression (PHQ-8 >= 8) were 0.43, 0.60, and 0.86, respectively. The concordance of the shorter PHQ-2, GAD-2, and SDS scales was poor to fair. The regression analysis revealed that a higher level of personal depression stigma was associated with reporting higher SMS text messaging-assessed PHQ-8 and GAD-2 scores than the INTW-assessed scores. The analysis also determined that the differences in the scores were associated with marital status and personality traits. Conclusions: Depression screening conducted using the longer PHQ-8 scale via SMS text messaging demonstrated good internal consistency, test-retest reliability, and concordance with the gold standard INTW assessment mode. However, care must be taken when deploying shorter scales via SMS text messaging. Further regression analysis supported that a technology-mediated assessment, such as SMS text messaging, may create a private space with less pressure from the personal depression stigma and therefore encourage self-disclosure of depressive symptoms.

This paper examined the magnitude of differences in performance across domains of cognitive functioning between participants who attrited from studies and those who did not, using data from longitudinal ageing studies where multiple cognitive tests were administered. Individual participant data meta-analysis. Data are from 10 epidemiological longitudinal studies on ageing (total n=209 518) from several Western countries (UK, USA, Mexico, etc). Each study had multiple waves of data (range of 2-17 waves), with multiple cognitive tests administered at each wave (range of 4-17 tests). Only waves with cognitive tests and information on participant dropout at the immediate next wave for adults aged 50 years or older were used in the meta-analysis. For each pair of consecutive study waves, we compared the difference in cognitive scores (Cohen's d) between participants who dropped out at the next study wave and those who remained. Note that our operationalisation of dropout was inclusive of all causes (eg, mortality). The proportion of participant dropout at each wave was also computed. The average proportion of dropouts between consecutive study waves was 0.26 (0.18 to 0.34). People who attrited were found to have significantly lower levels of cognitive functioning in all domains (at the wave 2-3 years before attrition) compared with those who did not attrit, with small-to-medium effect sizes (overall d=0.37 (0.30 to 0.43)). Older adults who attrited from longitudinal ageing studies had lower cognitive functioning (assessed at the timepoint before attrition) across all domains as compared with individuals who remained. Cognitive functioning differences may contribute to selection bias in longitudinal ageing studies, impeding accurate conclusions in developmental research. In addition, examining the functional capabilities of attriters may be valuable for determining whether attriters experience functional limitations requiring healthcare attention.

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). 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 (1053 items) administered online over 6.5 years. A multilevel location-scale model derived three RT parameters for each survey: (1) a respondent's average RT and two 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. All three RT parameters were significantly associated with CIND, with a combined predictive accuracy of AUC=.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 years, 4.5 years, and 1.5 years, respectively. 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.

Researchers have become increasingly interested in response times to survey items as a measure of cognitive effort. We used machine learning to develop a prediction model of response times based on 41 attributes of survey items (e.g., question length, response format, linguistic features) collected in a large, general population sample. The developed algorithm can be used to derive reference values for expected response times for most commonly used survey items.

India, with its rapidly aging population, faces an alarming burden of dementia. We implemented DSM-5 criteria in large-scale, nationally representative survey data in India to characterize the prevalence of mild and major Neurocognitive disorder. The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) (N = 4,096) is a nationally representative cohort study in India using multistage area probability sampling methods. Using neuropsychological testing and informant reports, we defined DSM-5 mild and major neurocognitive disorder, reported its prevalence, and evaluated criterion and construct validity of the algorithm using clinician-adjudicated Clinical Dementia Ratings (CDR)®. The prevalence of mild and major neurocognitive disorder, weighted to the population, is 17.6% and 7.2%. Demographic gradients with respect to age and education conform to hypothesized patterns. Among N = 2,390 participants with a clinician-adjudicated CDR, CDR ratings and DSM-5 classification agreed for N = 2,139 (89.5%) participants. The prevalence of dementia in India is higher than previously recognized. These findings, coupled with a growing number of older adults in the coming decades in India, have important implications for society, public health, and families. We are aware of no previous Indian population-representative estimates of mild cognitive impairment, a group which will be increasingly important in coming years to identify for potential therapeutic treatment.

Associations between various forms of activity engagement (e.g. work, leisure) and the experience of stress in workers have been widely documented. The mechanisms underlying these effects, however, are not fully understood. Our goal was to investigate if perceived whole day workload accounted for the relationships between daily frequencies of activities (i.e. work hours and leisure/rest) and daily stress. We analysed data from 56 workers with type 1 diabetes (T1D) who completed approximately two weeks of intensive longitudinal assessments. Daily whole day workload was measured with an adapted version of the National Aeronautics and Space Administration Task Load Index (NASATLX). A variety of occupations were reported, including lawyer, housekeeper and teacher. In multilevel path analyses, day-to-day changes in whole day workload mediated 67% (p < .001), 61% (p < .001), 38% (p < .001), and 55% (p < .001) of the within-person relationships between stress and work hours, rest frequency, active leisure frequency, and day of week, respectively. Our results provided evidence that whole day workload perception may contribute to the processes linking daily activities with daily stress in workers with T1D. Perceived whole day workload may deserve greater attention as a possible stress intervention target, ones that perhaps ergonomists would be especially suited to address.

Occupational health and safety is experiencing a paradigm shift from focusing only on health at the workplace toward a holistic approach and worker well-being framework that considers both work and non-work factors. Aligned with this shift, the purpose of this pilot study was to examine how, within a person, frequencies of high-workload and recovery activities from both work and non-work periods were associated with same day well-being measures. We analyzed data on 45 workers with type 1 diabetes from whom we collected activity data 5-6 times daily over 14 days. More frequent engagement in high-workload activities was associated with lower well-being on multiple measures including higher stress. Conversely, greater recovery activity frequency was mostly associated with higher well-being indicated by lower stress and higher positive affect. Overall, our results provide preliminary validity evidence for measures of high-workload and recovery activity exposure covering both work and non-work periods that can inform and support evaluations of worker well-being.

Workload experienced over the whole day, not just work periods, may impact worker cognitive performance. We hypothesized that experiencing greater than typical whole day workload would be associated with lower visual processing speed and lower sustained attention ability, on the next day. To test this, we used dynamic structural equation modeling to analyze data from 56 workers with type 1 diabetes. For a two-week period, on smartphones they answered questions about whole day workload at the end of each day, and completed cognitive tests 5 or 6 times throughout each day. Repeated smartphone cognitive tests were used, instead of traditional one- time cognitive assessment in the laboratory, to increase the ecological validity of the cognitive tests. Examples of reported occupations in our sample included housekeeper, teacher, physician, and cashier. On workdays, the mean number of work hours reported was 6.58 (SD 3.5). At the within-person level, greater whole day workload predicted decreased mean processing speed the next day (standardized estimate=-0.10, 95% CI -0.18 to -0.01) using a random intercept model; the relationship was not significant and only demonstrated a tendency toward the expected effect (standardized estimate= -0.07, 95% CI -0.15 to 0.01) in a model with a random intercept and a random regression slope. Whole day workload was not found to be associated with next-day mean sustained attention ability. Study results suggested that just one day of greater than average workload could impact next day processing speed, but future studies with larger sample sizes are needed to corroborate this finding.

Background: Various populations with chronic conditions are at risk for decreased cognitive performance, making assessment of their cognition important. Formal mobile cognitive assessments measure cognitive performance with greater ecological validity than traditional laboratory-based testing but add to participant task demands. Given that responding to a survey is considered a cognitively demanding task itself, information that is passively collected as a by-product of ecological momentary assessment (EMA) may be a means through which people’s cognitive performance in their natural environment can be estimated when formal ambulatory cognitive assessment is not feasible. We specifically examined whether the item response times (RTs) to EMA questions (eg, mood) can serve as approximations of cognitive processing speed. Objective: This study aims to investigate whether the RTs from noncognitive EMA surveys can serve as approximate indicators of between-person (BP) differences and momentary within-person (WP) variability in cognitive processing speed. Methods: Data from a 2-week EMA study investigating the relationships among glucose, emotion, and functioning in adults with type 1 diabetes were analyzed. Validated mobile cognitive tests assessing processing speed (Symbol Search task) and sustained attention (Go-No Go task) were administered together with noncognitive EMA surveys 5 to 6 times per day via smartphones. Multilevel modeling was used to examine the reliability of EMA RTs, their convergent validity with the Symbol Search task, and their divergent validity with the Go-No Go task. Other tests of the validity of EMA RTs included the examination of their associations with age, depression, fatigue, and the time of day. Results: Overall, in BP analyses, evidence was found supporting the reliability and convergent validity of EMA question RTs from even a single repeatedly administered EMA item as a measure of average processing speed. BP correlations between the Symbol Search task and EMA RTs ranged from 0.43 to 0.58 (P

Monitoring of cognitive abilities in large‐scale survey research is receiving increasing attention. Conventional cognitive testing, however, is often impractical on a population level highlighting the need for alternative means of cognitive assessment. We evaluated whether response times (RTs) to online survey items could be useful to infer cognitive abilities. We analyzed >5 million survey item RTs from >6000 individuals administered over 6.5 years in an internet panel together with cognitive tests (numerical reasoning, verbal reasoning, task switching/inhibitory control). We derived measures of mean RT and intraindividual RT variability from a multilevel location‐scale model as well as an expanded version that separated intraindividual RT variability into systematic RT adjustments (variation of RTs with item time intensities) and residual intraindividual RT variability (residual error in RTs). RT measures from the location‐scale model showed weak associations with cognitive test scores. However, RT measures from the expanded model explained 22–26% of the variance in cognitive scores and had prospective associations with cognitive assessments over lag‐periods of at least 6.5 years (mean RTs), 4.5 years (systematic RT adjustments) and 1 year (residual RT variability). Our findings suggest that RTs in online surveys may be useful for gaining information about cognitive abilities in large‐scale survey research.