Smartphone-based assessments are a promising tool for early detection of cognitive decline in midlife. Previous research has shown such cognitive markers can be sensitive to a range of potentially modifiable dementia risk factors even in healthy adults. However, their sensitivity to genetic risk factors like APOE-ε4 is likely to differ by cognitive domain, with evidence of strong negative effects on wayfinding tasks but mixed for other domains. We investigated the associations between a set of previously validated smartphone-based cognitive markers and APOE-ε4. N = 87 cognitively unimpaired, APOE-genotyped participants (aged 44-67, 57.5% female) of the PREVENT study completed a self-administered module in the smartphone application Neureka, including gamified cognitive tasks and memory self-evaluation (completion rates: 81%–99%). Controlling for age, gender, education, and parental history of dementia, we compared APOE-ε4 carriers ( n = 35) with non-carriers in their performance on five cognitive measures: model-based planning, visual working memory, processing speed (∼Trails A), cognitive flexibility (∼Trails B), and subjective memory problems. We conducted receiver operating characteristic (ROC) analysis to assess if APOE-ε4 status can be predicted from norm-adjusted cognitive scores, i.e., the difference between observed cognitive performance and what we would predict for given demographics based on an additional benchmark sample ( N = 3,376, aged 18-84, 65% female). In univariate analyses, APOE-ε4 carriers did not significantly differ from non-carriers in their demographic characteristics or cognitive scores except for visual working memory, which was better in carriers ( t (63.226) = 2.245; p = .028). After controlling for covariates, APOE-ε4 status was not significantly linked to, which was better in APOE-ε4 carriers ( β [bootstrapped 95% CI] = -0.29[-0.53,-0.06]; t = -2.30; p = .025). In ROC-analysis, norm-adjusted visual working memory scores modestly differentiated between carriers and non-carriers (AUC(SE) = .648(0.067); bootstrapped 95% CIs = 0.515–0.774). The non-significant associations between APOE-ε4 and most cognitive markers suggest limited sensitivity to genetic risk. Interestingly, APOE-ε4 carriers outperformed non-carriers in visual working memory, which could be due to antagonistic pleiotropy effects, but needs to be replicated in larger samples. The current study shows limited evidence for the clinical utility of our cognitive markers as indicators of genetic risk in middle-aged cognitively unimpaired adults.

Digital survey tools have all but replaced paper and pen in the psychological sciences, and consequently new forms of potentially useful research paradata are now routinely gathered. A particularly common byproduct of research is questionnaire timestamps, which some have suggested can be used as a measure of cognitive function. Here, we conducted a comprehensive validation of this measure, which we call the “digital questionnaire response time,” or “DQRT.” Using data from N = 2,977 users of a smartphone app, we first ran a data-driven bootstrapping approach to examine how best to quantify DQRT. DQRT was slower in older adults (r = 0.26) and in those with lower educational attainment and socioeconomic status. Testing the association between DQRT and working memory (range r = 0.11–0.14), model-based planning (range r = 0.03–0.06), and processing speed (range r = 0.29–0.39) across cross-sectional and longitudinal subsamples, we found support for a cognitive characterization of DQRT as a measure of cognitive processing speed. DQRT was more strongly correlated with nine out of 13 lifestyle and health factors, and four out of nine mental health factors than a task-based measure of processing speed. DQRT showed good test–retest reliability, and associations between DQRT and task-based processing speed were higher within individuals (r = 0.35) than between individuals (r = 0.25). Finally, we highlight substantial, but addressable, potential confounds inherent in the measure. We conclude that DQRT has important limitations, but overall can serve as a valid and reliable index of cognitive processing speed that can be gathered at unprecedented scale, unobtrusively, and repeatedly, during a variety of real-world digital behaviors.

Loneliness is associated with lower cognitive function and may increase dementia risk. However, it is unclear if this effect is mediated by depression. Resolving this issue is important to design effective interventions to promote healthy aging. We adopted a complementary between- and within-person approach, which allowed us to study cross-sectional relationships as well as the dynamic interactions between loneliness, mood, and cognition in natural environments over time. A total of 3,416 participants between 18 and 84 years (1,149 male; M age = 45.89±14.55) completed cross-sectional self-reported questionnaires of loneliness and depression alongside gamified assessments of memory, processing speed, cognitive flexibility, and planning through a smartphone app, Neureka. A subsample of 286 participants between 18 and 82 years (89 male; M age = 50.14±13.16) also underwent 8-week ecological momentary assessment (EMA) reporting every 12 hours how lonely and down they felt. We measured cognition at these same timepoints using a recently validated passive measure of cognitive processing speed (digital questionnaire response time, DQRT). Multiple regressions and network analysis were performed to analyze cross-sectional and EMA data, respectively. Loneliness and lower cognitive function were associated cross-sectionally (all ps<.001) except for planning (p = .08). Significant effects did not survive after controlling for depression (all ps>.06). Turning to EMA data, a contemporaneous network analysis showed that within-person 12-hour fluctuations in loneliness were related to fluctuations in mood (r = .35, p<.001). However, fluctuations in mood (but not loneliness) were linked to changes in DQRT (r = .13, p<.001). To understand the causal path, we conducted temporal network analysis, which revealed a bi-directional relationship between loneliness and low mood (β = .08 and β = .06, respectively, both p<.001). In contrast, lower mood predicted slower DQRT 12-hours later (β = .02, p = 0.03) and not the other way around (β = .005, p = 0.43). In older adults, loneliness and low mood were less related to one-another, but the relationship between low mood and slower DQRT was stronger. Depression symptoms mediate the effect of loneliness on cognition, both cross-sectionally and when assessed within-person. Older participants show less coupling between loneliness and low mood, but stronger coupling between low mood and slower cognition. Results have implications for differential interventions across the lifespan.

INTRODUCTION:Early detection of both objective and subjective cognitive impairment is important. Subjective complaints in healthy individuals can precede objective deficits. However, the differential associations of objective and subjective cognition with modifiable dementia risk factors are unclear. METHODS:We gathered a large cross-sectional sample (N = 3327, age 18 to 84) via a smartphone app and quantified the associations of 13 risk factors with subjective memory problems and three objective measures of executive function (visual working memory, cognitive flexibility, model-based planning). RESULTS:Depression, socioeconomic status, hearing handicap, loneliness, education, smoking, tinnitus, little exercise, small social network, stroke, diabetes, and hypertension were all associated with impairments in at least one cognitive measure. Subjective memory had the strongest link to most factors; these associations persisted after controlling for depression. Age mostly did not moderate these associations. DISCUSSION:Subjective cognition was more sensitive to self-report risk factors than objective cognition. Smartphones could facilitate detecting the earliest cognitive impairments. HIGHLIGHTS:Smartphone assessments of cognition were sensitive to dementia risk factors. Subjective cognition had stronger links to most factors than did objective cognition. These associations were not fully explained by depression. These associations were largely consistent across the lifespan.

Various health, social and lifestyle factors contribute to a healthy brain. However, it is unclear if different factors like socio-economic status or cardiovascular health have distinct cognitive footprints. Moreover, little is known about how this might shape our understanding of prior findings showing cognitive deficits in these domains in various psychiatric populations, where studies are typically small and do not control for these factors.

Effective strategies for early detection of cognitive decline, if deployed on a large scale, would have individual and societal benefits. However, current detection methods are invasive or time-consuming and therefore not suitable for longitudinal monitoring of asymptomatic individuals. For example, biological markers of neuropathology associated with cognitive decline are typically collected via cerebral spinal fluid, cognitive functioning is evaluated from face-to-face assessments by experts and brain measures are obtained using expensive, non-portable equipment. Here, we describe scalable, repeatable, relatively non-invasive and comparatively inexpensive strategies for detecting the earliest markers of cognitive decline. These approaches are characterized by simple data collection protocols conducted in locations outside the laboratory: measurements are collected passively, by the participants themselves or by non-experts. The analysis of these data is, in contrast, often performed in a centralized location using sophisticated techniques. Recent developments allow neuropathology associated with potential cognitive decline to be accurately detected from peripheral blood samples. Advances in smartphone technology facilitate unobtrusive passive measurements of speech, fine motor movement and gait, that can be used to predict cognitive decline. Specific cognitive processes can be assayed using 'gamified' versions of standard laboratory cognitive tasks, which keep users engaged across multiple test sessions. High quality brain data can be regularly obtained, collected at-home by users themselves, using portable electroencephalography. Although these methods have great potential for addressing an important health challenge, there are barriers to be overcome. Technical obstacles include the need for standardization and interoperability across hardware and software. Societal challenges involve ensuring equity in access to new technologies, the cost of implementation and of any follow-up care, plus ethical issues.

ABSTRACT Objective The Boston Naming Test (BNT) is the most widely used test to assess visual confrontation naming in both research and clinical settings. Recently, an abbreviated Czech version of the BNT was described. The purpose of this study is to assess the validity of this new test at the item level with advanced psychometric methods to assess its equivalence with the original test. The rationale was to help busy clinicians in the differential diagnosis of language disorders. Method We administered the BNT-30 (odd item form of BNT-60) (N = 535; 75.61 ± 9.11; 60–96 years) and shortened the BNT-15 (N = 754; 71.94 ± 7.88; 60–96 years) to a large sample of healthy older adults. Results Significant but low associations between BNT performance and age, education, and sex were found. We found strong evidence for the unidimensionality of both BNT-15/BNT-30 versions in healthy adults (p’s < .001). Conclusion In-depth psychometric analysis of the BNT-15 and BNT-30 Czech versions show that test stimuli function in a similar fashion as the original BNT. Normative values adjusting for the influence of age, education, and sex are provided for use in clinical settings and future cross-cultural comparisons.

The SARS-CoV-2 pandemic is not only a threat to physical health but is also having severe impacts on mental health. Although increases in stress-related symptomatology and other adverse psycho-social outcomes, as well as their most important risk factors have been described, hardly anything is known about potential protective factors. Resilience refers to the maintenance of mental health despite adversity. To gain mechanistic insights about the relationship between described psycho-social resilience factors and resilience specifically in the current crisis, we assessed resilience factors, exposure to Corona crisis-specific and general stressors, as well as internalizing symptoms in a cross-sectional online survey conducted in 24 languages during the most intense phase of the lockdown in Europe (22 March to 19 April) in a convenience sample of N = 15,970 adults. Resilience, as an outcome, was conceptualized as good mental health despite stressor exposure and measured as the inverse residual between actual and predicted symptom total score. Preregistered hypotheses (osf.io/r6btn) were tested with multiple regression models and mediation analyses. Results confirmed our primary hypothesis that positive appraisal style (PAS) is positively associated with resilience ( p < 0.0001). The resilience factor PAS also partly mediated the positive association between perceived social support and resilience, and its association with resilience was in turn partly mediated by the ability to easily recover from stress (both p < 0.0001). In comparison with other resilience factors, good stress response recovery and positive appraisal specifically of the consequences of the Corona crisis were the strongest factors. Preregistered exploratory subgroup analyses (osf.io/thka9) showed that all tested resilience factors generalize across major socio-demographic categories. This research identifies modifiable protective factors that can be targeted by public mental health efforts in this and in future pandemics.

Česká adaptace Wechslerovy zkrácené paměťové škály (dále WMS-IIIa) je zkrácenou verzí třetí revize Wechslerovy paměťové škály (dále WMS-III). Zaměřuje se na orientační měření aktuálního fungování deklarativní epizodické paměti, konkrétně na paměť sluchovou a zrakovou z hlediska bezprostředního a oddáleného vybavení (Wechsler, 2011). Je určena pro populaci ve věku 20–89 let. Dle autorů je metoda využitelná především v klinické praxi, a to pro screeningovou diagnostiku narušení paměti či jako součást komplexního psychologického nebo neuropsychologického vyšetření.Českou adaptaci této rozšířené paměťové škály lze těžko ohodnotit, a to primárně z důvodu nedostatku informací. Zejména chybějící informace o shodě posuzovatelů se mohou ukázat jako problematické, a to kvůli změnám v překladu oproti původní anglické verzi. Doporučovaly bychom proto její další testování a vývoj. Je možné tuto metodu použít expertním uživatelem za pečlivě kontrolovaných podmínek nebo ve velmi omezených aplikačních oblastech za předpokladu, že uživatel testu bude obezřetný v interpretaci výsledků.