Haomiao Jin

Importance: Occupational therapy practitioners often assist a range of clients (e.g., people with schizophrenia, older adults, students, etc.) by addressing their cognitive functioning so they can engage in daily tasks. Thus, a better research-based understanding of real-time biopsychosocial factors that can impact everyday cognitive performance in real life can be useful. Ambulatory cognitive assessments are administered in people's natural environments, making them better suited compared to traditional formal cognitive tests for understanding factors that can impact cognitive performance in real life. Objective: To examine how a range of biopsychosocial factors impact cognitive performance in real life as assessed by eight smartphone-based ambulatory cognitive tests and passively recorded survey response times. Design: Smartphone-based cognitive tests and surveys were completed daily by participants over 10 days. Associations between each cognitive test and biopsychosocial factor were examined in separate multilevel regression models. Participants: A total of 393 U.S. adults. Measures: The eight cognitive measures covered the domains of processing speed, working memory, recognition memory, and executive functioning. Biopsychosocial factors considered were distractors, prior activity, substances just taken, and psychological state. Results: Cognitive performance was better when participants reported no distractions, were alone, were more focused, had higher levels of energy or excitement, and had greater happiness. Conclusions and Relevance: Understanding how biopsychosocial factors affect cognitive performance may assist occupational therapy practitioners and other healthcare professionals in helping clients more deliberately adjust situations to facilitate cognitive performance when needed (e.g., for job tasks, exams, following instructions to take medications, etc.).

Objectives: Atypical survey response patterns, such as inconsistent or implausible responses, have been linked to lower cognitive functioning in later life. This study examined whether these behaviors predict future dementia onset across diverse longitudinal studies of aging. Methods: We conducted a coordinated analysis of data from eight longitudinal studies of aging (total N = 76,350). We derived 5 common types of atypical response pattern indicators from participants’ questionnaire data. Associations with subsequent dementia risk were examined within each cohort using Cox proportional hazards models adjusted for demographic covariates and accounting for death as a competing risk. Results were synthesized using random-effects meta-analysis methods. Results: Across cohorts, significant associations with incident dementia were observed for item-nonresponse (overall hazard ratio [HR] per SD increase = 1.06), random response errors (HR = 1.25), multivariate outlier responses (HR = 1.31), and incompatible response patterns indexed by Guttman errors and person-fit statistics (HRs = 1.22–1.25). Extreme responding was not significantly associated with dementia risk. Secondary analyses suggested stronger associations among individuals younger than 75 years and those with higher levels of education. Discussion: Routine survey responses contain behavioral signals associated with emerging dementia risk. Because these indicators can be derived from existing survey data without additional testing burden, they may provide a scalable complement to traditional approaches for studying cognitive aging in large population-based cohorts.

Background Research on unpaid carers often relies on survey and interview data that are cross-sectional or infrequently repeated, providing limited insight into how carers’ experiences and wellbeing evolve over time. Combining frequent longitudinal surveys with regular qualitative check-ins offers a promising mixed-method approach for capturing change, yet there is limited evidence on the feasibility of implementing such data collection strategies over extended periods. Methods As part of the NIHR-funded Care-Full study, we tested the feasibility of a mixed-method longitudinal data collection approach combining fortnightly digital self-report surveys with monthly qualitative check-ins facilitated by trained peer researchers with lived experience of caring. Fifteen unpaid carers were recruited across three sites in England and followed for up to ten months. Feasibility outcomes included recruitment and retention, completion rates, data missingness, engagement with an event-triggered “point of change” mini-survey component, and participant feedback on research participation, accessibility, and data collection approaches. Results Engagement with routine longitudinal data collection was high. No participants withdrew for reasons related to study burden, and completion of fortnightly surveys exceeded 93%, with minimal missing data across key measures of quality of life, health, and caregiving activities. The event-triggered “point of change” survey component proved difficult to implement in practice, with delayed reporting and lower completion rates, and was discontinued after four months. Monthly peer-researcher check-ins were consistently valued by participants, supporting sustained engagement and enabling contextualisation of survey responses over time. Conclusions This feasibility study demonstrates that a mixed-method longitudinal approach combining regular digital surveys with peer-researcher engagement can support sustained data collection with unpaid carers over extended periods. Predictable, low-burden survey schedules were feasible, while regular peer-facilitated qualitative engagement enhanced the interpretability of longitudinal data. The findings provide methodological insights to inform future research seeking to collect longitudinal data on carers’ experiences and wellbeing over time.

Heart rate variability (HRV) is widely recognized as an indicator of general health, particularly time domain measures like the root mean square of successive differences (RMSSD) between consecutive heartbeats. Consumer wearables measuring HRV have potential for wide accessibility meaning that their broad use to capture HRV as a health biomarker is possible. Our objective was to investigate the validity of HRV measured by wearables as a general health indicator. We examined whether resting HRV assessed by wearables across five studies—two using smartwatches, two using heart rate chest straps, and one using a smartring—exhibited expected associations with diverse health domains, including mental, physical, behavioral, functional, and physiological. We focused on resting HRV measures recorded while in primarily stationary conditions, either upon waking or while sleeping, because such measures would theoretically reduce the effects of potential confounders such as movement artifacts, daytime caffeine intake, and postural changes. Wearables measured resting HRV had small-to-moderate associations with more clinically oriented and trait-like (or slow-changing) health measures like Hba1c (average blood glucose, r = −0.21, p = 0.014), depressive symptoms (r = −0.22, p = 0.024), and sleep difficulty (r = −0.11, p = 0.003). Wearable-measured resting HRV can potentially serve as a health biomarker, but further research is needed.

Background Underemployment, working fewer hours than desired, has a negative impact on mental health. Research suggests that unemployment benefits can support the mental health of unemployed individuals, but it is unknown if a similar benefit occurs for underemployed workers. This study aims to assess the relationship between unemployment benefits and mental health for underemployed workers. Methods Data was analysed from 18 waves (June 2020-March 2021) of the COVID-19 survey, a sub-study of the Understanding America Study. The dataset included responses from individuals (n=1,241) who self-identified as employed before COVID-19. A meta-regression created pooled effect sizes measuring the impact of unemployment benefits on the change in mental health of underemployed workers with two-, four-, six-, or eight-week effect lags. Results Receiving unemployment benefits is associated with a trend towards reduced depression at four weeks (−0.13, 95%CI −0.25; 0.00) and reduced depression six weeks later (−0.14, 95%CI −0.23; −0.05). A significant reduction in anxiety scores was seen six weeks after receiving unemployment benefits (−0.18; 95%CI −0.34; −0.01). Results from gender were mixed with female workers noting a positive effect from unemployment benefits, and male workers showing no or even negative effects, particularly on anxiety. Conclusion These findings underscore the impact of unemployment benefits on underemployed workers’ mental health. Unemployment insurance, not a typically utilized policy strategy to improve mental health, may mitigate mental health repercussions of underemployment. Expanding benefits to include underemployed workers should be considered particularly during societal crises such as COVID-19. What is already known on this topic It is known that underemployment and employment insecurity can have negative impacts on mental and physical health, however it is not known whether providing benefits to underemployed individuals. What this study adds We now know that adding benefits to underemployed works can improve depression and anxiety, particularly in female workers. How this study might affect research, practice or policy This provides avenues for future research to address the impacts of benefits for underemployed workers, particularly for assessing causality. Further, it provides initial evidence that can be used to expand policy by including underemployed workers within unemployment benefits.

Survey response times (RTs) have hitherto untapped potential to allow researchers to gain more detailed insights into the cognitive performance of participants in online panel studies. We examined if RTs recorded from a brief online survey could serve as a digital biomarker for processing speed. Data from 9893 adults enrolled in the nationally representative Understanding America Study were used in the analyses. Hypotheses included that people’s average survey RTs would have a large correlation with an established processing speed test, small to moderate correlations with other cognitive tests, and associations with functional impairment. We also hypothesized that survey RTs would have sensitivity to various participant characteristics comparable to the established processing speed test’s sensitivity (eg, similar standardized means by sex). Overall, results support the validity and reliability of people’s average RTs to survey items as a digital biomarker for processing speed. The correlation between survey RTs (reverse scored) and the formal processing speed test was 0.61 (P < .001), and small to moderate associations with most other cognitive and functional status measures were observed. Sensitivity of survey RTs to various participant characteristics was nearly identical to the formal processing speed test. Survey RTs may be useful as proxies for processing speed.

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.

Background Employment insecurity is a socioeconomic factor influencing mental health, yet the empirical evidence supporting this claim has important limitations. The fluctuations in employment insecurity throughout the COVID-19 pandemic offered a distinctive opportunity to delve deeper into this issue. By viewing employment as a dynamic process, this study explores the within-person relationship between shifts in employment status and corresponding changes in mental health. Methods 24 waves of data between April 2020 and March 2021 from the Understanding America Study (N=3824) were analysed using a within-person multilevel model. Employment security was modelled as a dynamic process involving transitions between secure employment, underemployment and unemployment with or without benefits. Mental health was measured by questionnaires on core symptoms of depression and anxiety. Results Downward transitions in employment security, from secure employment to underemployment or unemployment without benefits, were associated with worse mental health. Persisting in unemployment without benefits was also associated with poorer mental health, and regaining job security did not immediately improve it. Timely provision of unemployment benefits mitigated the adverse mental health impacts. Significant cross-level moderation effects were observed for prepandemic mental health status, Hispanic ethnicity and education level. Conclusion Downward transitions in employment security compromise mental health, and certain segments of the population experience worse consequences. Regaining job security is not associated with immediate improvement in mental health. Timely provision of unemployment benefits, providing support for both unemployment and underemployment and targeting vulnerable groups are vital for alleviating adverse mental health impacts from losing job security.