Abstract Suicidal ideation affects 10% of primary care patients. Although guidelines recommend screening, few tools are suitable for use in this setting, and protective factors are often overlooked. This study aimed to develop a brief questionnaire to assess suicidality in primary care. The Suicide Prevention for Primary Care Questionnaire (SuPr-10) was validated in 521 participants with a PHQ-9 score ≥ 6 across six subsamples. Its structure was analyzed using exploratory and confirmatory factor analysis, item analysis, and psychometric evaluation. Diagnostic accuracy was assessed via logistic regression and ROC analysis, and patient acceptance was measured in a follow-up survey. A two-factor structure with ten items was identified: I. protective scale (ω = .817) and II. risk scale (ω = .928), showing strong correlations with suicidality (r = –.529, .854) and depression (r = –.736, .626) scales. SuPr-10 showed acceptable discriminatory ability for identifying individuals with a history of suicide attempts (AUC = .765; 83% sensitivity, 56% specificity). It provides a structured assessment of suicidal tendencies and protective factors in primary care patients with depressive symptoms, supporting clinical decision-making and resource-focused communication. These findings are based on a clinically enriched, German-speaking sample, do not imply prospective prediction of suicidal behavior, and require validation in broader primary care populations.

Scalable, objective judgments of individuals’ everyday cognitive functioning may facilitate decision making in key societal domains, including educational, occupational, and healthcare contexts. In a preregistered, theory-informed machine learning approach, we examine how digital traces of everyday smartphone usage allow for predictions of individuals’ general fluid intelligence (Gf), one of the most central human cognitive abilities. Leveraging a German quota sample (N = 367) providing smartphone sensing data of up to six months (1,269,491 phone usages), cross-validated results demonstrate that everyday smartphone usage consistently predicts individuals' Gf scores on a psychometric test (r_Md = .43, r_IQR = [.35, .51]) with comparable criterion validity. High-dimensional combinations of behaviors (particularly short-term activities related to basic cognitive tasks and dealing with complexity) were predictive for Gf, highlighting how cognitive abilities manifest pervasively yet highly uniquely across individuals. The findings suggest that smartphone-based cognitive judgment may complement traditional cognitive assessments, enhancing early identification of cognitive functioning, fit, and support.

In psychology, mobile sensing is increasingly used to record behavior in real-life situations. However, little is known about the selectivity of samples participating in these new data collection approaches and thus about potential risks to the validity of research findings. We therefore investigated coverage error and nonresponse error as two sources of selection bias in smartphone-based data collections. Specifically, we examined whether smartphone system ownership (Android versus iOS, i.e., coverage error) and willingness to participate (nonparticipation versus intention to participate versus actual participation, i.e., nonresponse error) are associated with socio-demographic, socio-economic, and personality characteristics. Using two large-scale panel studies, we found replicable patterns for coverage error (N = 1,218; N = 5,123) and nonresponse error (N = 1,673; N = 2,337): The ownership of Android devices (in comparison to iOS devices) was associated with lower levels of education, income, and extraversion. The willingness to participate in mobile sensing studies was found to be higher among younger age groups, males, those with higher levels of openness to experience, and those with lower levels of neuroticism. Furthermore, different person characteristics played different roles at different stages of the recruitment process. Taken together, the results show that some selection bias in mobile sensing studies exists and that the effects were small to moderate in magnitude as well as comparable to selection bias for other, more common data collection approaches, such as online surveys.

BACKGROUND:Self-explaining is an engagement activity that supports learners' active use of instructional scaffolds such as worked examples. Self-explanation quality is assumed to mediate the worked example effect on learning outcomes. However, prior investigations have relied on analytical approaches limited in scope and quality or have yielded inconclusive results AIMS: We replicate a previous investigation that examines whether self-explanation quality mediates the worked example effect in a worked examples - problem-solving paradigm, while simultaneously considering key cognitive aptitudes (prior knowledge, working memory capacity (WMC), fluid intelligence, shifting ability) as moderating variables METHOD: We analysed self-explanations from 115 university students solving six ill-defined statistics problems with or without worked examples. Self-explanation quality was defined as using specific case information from problem descriptions to justify statistical claims and coded by trained raters RESULTS: Results showed a moderated mediation of the worked example effect via self-explanation quality for prior knowledge and WMC, with stronger indirect effects at lower aptitude levels. The indirect effect was not moderated by fluid intelligence. No self-explanation mediation emerged for shifting ability; shifting ability was confirmed as a moderator of the worked example effect, with stronger scaffold benefits at lower ability levels CONCLUSIONS: Whereas the original study found no evidence for self-explanation quality as a mediator, the replication, drawing on stronger methodology, provides evidence for such mediation. However, this mechanism is not universal. Rather, worked examples exert their benefits either directly or via self-explanation, contingent on learners' cognitive resources.

Satisfaction with life (SWL) is central for mental well-being, making it a key focus for practitioners and researchers alike. Individual differences in SWL relate to self-reported patterns of everyday physical activity during daytime and overnight. However, these associations rarely replicate in objective physical activity measures, leaving real-world SWL manifestation unclear. Here, we use an interpretable machine learning approach to examine how physical activity patterns (across four weeks) captured via smartphones predict SWL in a diverse dataset (N = 2,272) spanning three countries, years, seasons, and sample demographics. The cross-validated results demonstrate that extremes (minima, maxima) and variation (standard deviation, skewness) in physical activity were particularly informative in robust out-of-sample predictions of SWL (r_Md = .18, r_IQR = [.14, .21]), and that personality traits were associated with the extent of activity predictiveness. Overall, these findings highlight the importance of more nuanced approaches to studying associations between SWL and everyday physical activity.

Music is more than just entertainment. It is a complex auditory stimulus that engages various cognitive processing systems. Accordingly, natural music-listening patterns may reveal insights into individual differences in general cognitive ability (GCA). In this study (N = 185), we used real-world smartphone-based music-listening records collected over five months to explore this question. We quantified participants’ listening habits (e.g., listening durations) and music preferences based on audio characteristics (e.g., tempo, mode) and lyrical characteristics (e.g., positive emotion words, affiliation words) of the songs they had listened to. These strictly behavioral features were used to predict GCA scores using linear LASSO regression and nonlinear random forest models. Out-of-sample cross-validation indicated modest predictive performance, with only the random forest model detecting small but reliable associations between music-listening behavior and GCA. Interpretable machine learning analyses showed that lyrics-based preferences were the most informative feature group, followed by listening habits, whereas audio characteristics contributed little predictive value. We discuss how these findings offer initial evidence that cognitive ability may be reflected, albeit subtly, in micro-patterns of everyday, non-achievement-related behavior, and outline conceptual and methodological challenges for future work using digital behavioral data to complement traditional cognitive assessment.

Digital phenotyping uses passively collected digital-sensing data to characterize real-world behavioral patterns. Such data may help identify everyday lifestyles that are relevant to mental well-being, but most prior approaches have used variable-centered methods that focus on single behaviors rather than person-centered combinations of behaviors across daily life. This study aimed to examine whether digitally captured behavioral and environmental data can be used to derive meaningful lifestyle profiles and whether these profiles are associated with mental well-being. The study used a two-week intensive longitudinal design with a German quota sample of 553 adults (Mage = 42.27, SD = 12.89; 44.4% female). Across the study period, participants contributed 7,635 person-days of smartphone-recorded data on social app use, mobility, physical activity, screen use, ambient loudness, and brightness, along with self-reported mental well-being and Big Five personality traits. We used an innovative two-level latent profile analysis to simultaneously identify day-level profiles at Level 1 and person-level profiles at Level 2. We then examined associations between person-level lifestyle profiles and mental well-being, including whether these associations were moderated by Big Five personality traits. The analysis identified eight day-level profiles and seven person-level profiles. One person-level profile characterized by lighter phone usage combined with heavier physical activity reported greater positive functioning, an important aspect of mental well-being, than another profile characterized by extensive mobility combined with intensive social app use. Personality traits did not significantly moderate the associations between lifestyle profiles and mental well-being. These insights advance digital phenotyping by showing that interpretable, person-centered lifestyle profiles could reflect aspects of mental well-being. Potential clinical implications, including transparent monitoring and multi-behavior interventions are discussed.

Does our speech-"what" we say and "how" we say it-reveal how we subjectively feel in daily life? Speech and emotion are often thought to be linked, but most supporting evidence comes from controlled laboratory settings and focuses on expressed, enacted, or externally labeled emotions, leaving open the question of whether naturalistic speech reflects subjective emotional experience in daily life. To address this question, we extracted both modern foundation-model embeddings and established speech-analysis variables from brief, prompted smartphone recordings, including Linguistic Inquiry and Word Count features and prosodic descriptors, and used them as inputs to supervised machine learning models predicting concurrent self-reported emotional states (934 participants; 12,285 observations). Cross-validated models evaluated on unseen participants captured information about self-reported emotional states (contentment: median Spearman ρ = 0.37; sadness: ρ = 0.24; arousal: ρ = 0.35), with spoken content represented by foundation-model text embeddings carrying the strongest emotional signal. Interpretability analyses provided further insights into the linguistic characteristics of everyday emotional language. These findings provide large-scale evidence that brief speech samples contain information about subjective emotional states in daily life.

In this paper, we examine artificial intelligence (AI) in the context of studying and measuring individual differences across three current topics: AI as a research tool, the design of human-AI interaction, and AI-assisted assessment. Drawing on selected examples, we illustrate the wide-ranging potentials of AI at the conceptual and methodological level, while also showing that its application raises new (research) questions - for instance regarding human competence in dealing with AI, the “personality” of AI systems, or psychometric standards in the use of AI-assisted assessment procedures. Against this backdrop, we discuss key prerequisites for a sustainable integration of AI into both research in personality psychology and individual differences, including critical validation, transparency, interdisciplinary collaboration, and the systematic consideration of individual differences in the responsible design of the human-AI interface.

Digital language is treated as a window into how people feel, but the conditions under which it reflects subjective affect remain unclear. We examined communication context and timescale as boundary conditions using smartphone keyboard logs, ecological momentary assessments, and surveys from 410 participants followed for up to six months. Across 3.45 million words, we tested whether word use, emoji use, and typing dynamics carried affective signal in private and public communication at trait, daily, and momentary timescales using predictive modeling. Prediction was modest (up to median r = .28), strongest for trait affect, and generally higher in private communication. This context difference was largely attenuated after matching trait-level text volume. Theory-guided associations were clearer for between-person differences in trait affect than for within-person daily or momentary fluctuations. These findings suggest that affective signal in digital language varies across communication contexts and timescales, with implications for using technology to study affect.

IntroductionIn elite sports, both individual and environmental factors play a central role in athletic success. Among the latter are organizational stressors, such as travel demands, team or coach conflicts, and issues with equipment. These have primarily been examined in qualitative studies. Although validated instruments for quantitative assessment exist internationally, a German version has not yet been developed.MethodsThis study translated and adapted the Organizational Stressor Indicator for Sport Performers into German and distributed it as an online questionnaire to national squad athletes from Olympic sports. A total of N = 347 athletes from 64 sports participated. Confirmatory and exploratory factor analyses were conducted to evaluate psychometric properties. Additionally, the association with psychological stress consequences was explored using a sport-adapted module from the Munich Employee Health Questionnaire.ResultsThe five-factor structure of the original questionnaire did not yield a satisfactory model fit. Exploratory analyses revealed a conceptually meaningful six-factor structure, including an additional factor ("long-term logistics"), with improved fit indices (comparative fit index (CFI) >= 0.91, root mean square error of approximation (RMSEA) = 0.05, standardized root mean square residual (SRMR) = 0.06). In exploratory analyses, the organizational stress correlated significantly with the module on psychological stress consequences (r >= 0.50, p < 0.001).ConclusionThe adapted German Organizational Stressor Indicator for Sport Performers shows structural differences from the original but remains conceptually sound. The findings highlight the relevance of organizational stressors for athletes' mental health. Future research could contribute to not only a more precise identification of these stressors, but also to their targeted mitigation, in order to sustainably improve the structural conditions within elite sport.

The Positive and Negative Syndrome Scale (PANSS-30) is the standard instrument for assessing symptoms of schizophrenia and related psychotic disorders. However, its long administration time and structural issues have prompted the development of shorter versions. The PANSS-6, derived through Item Response Theory and Rasch analysis of the PANSS-8, emerged as a potential alternative. Comprising three positive and three negative symptom items, the PANSS-6 offers a more feasible assessment tool. However, its measurement properties have never been systematically reviewed. We applied the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guideline for systematic reviews and meta-analytical procedures to assess the psychometric properties of the PANSS-6. COSMIN comprises several steps: literature search, risk-of-bias assessments, assessing the updated criteria for good measurement properties, grading the quality of the evidence and feasibility aspects. We included 13 publications. The PANSS-6 showed sufficient content validity, structural validity, measurement invariance, reliability, criterion validity, construct validity and responsiveness according to COSMIN. On some of them only a small body of evidence is currently available. For internal consistency, cross-cultural validity and measurement error there was not enough evidence for a definite rating. Its short administration time of only 15-20 mins renders feasibility good. The PANSS-6 does not cover all schizophrenia symptoms but focuses on the core symptoms in favor of a feasible administration time. The evidence available on its measurement properties yields sufficient results for its purpose - the assessment of symptom severity and its change. According to COSMIN it can potentially be recommended for use.

In studies using the increasingly popular Experience Sampling Method (ESM), design decisions are often guided by theoretical or practical considerations. Yet limited empirical evidence exists on how these choices impact data quantity (e.g., response probabilities), data quality (e.g., response latency), and potential biases in study outcomes (e.g., characteristics of study variables). In a preregistered, four-week study (N = 395), we experimentally manipulated two key ESM protocol characteristics for sending ESM surveys: timing (fixed versus varying times) and contingency (directly versus indirectly after unlocking the smartphone). We evaluated the ESM protocols resulting from the combination of these two characteristics with regard to different criteria: As hypothesized for contingency, indirect protocols resulted in higher response probabilities (increased data quantity). But they also led to higher response latencies (reduced data quality). Contrary to our expectations, the combined effect of contingency and timing did not significantly influence response probability. We did also not observe other effects of timing or contingency on data quality. In exploratory follow-up analyses, we discovered that timing significantly affected response probability and smartphone usage behaviors, as measured by screen logs; however, these effects were likely attributable to time of day effects. Notably, self-reported states showed no differences based on the chosen ESM protocol, and similar trends were found when correlating primary outcomes with external criteria such as trait affect and well-being. Based on the study’s findings, we discuss the trade-offs that researchers should consider when choosing their ESM protocols to optimize data quantity, data quality, and biases in study outcomes.

Post-traumatic stress disorder (PTSD) is a common mental disorder. This systematic review and meta-analysis examined the association between mobile sensing features and PTSD symptoms. Studies were sourced from the Database for Mobile Sensing Studies in Mental Healthcare (DAMOS), with inclusion criteria requiring correlations between mobile sensing data and PTSD symptoms assessed by validated tools. Seventeen studies encompassing 1847 participants (mean age = 38.68, 63.18% female) remained after study selection. Of 18 features across sleep, mobility, activity, and social activity, only wake after sleep onset (r = 0.14, 95% CI = [0.03, 0.25]) and relative amplitude of physical activity (r = −0.10, 95% CI = [−0.17, −0.03]) were significantly associated with PTSD symptoms. Findings were consistent across PTSD measurements, populations, demographics, and sensing durations. Although mobile sensing offers unobtrusive, objective, and ecologically valid insights into PTSD, confirmatory studies and research to optimize sensor assessment are needed before clinical practice.

Collaborative care is a multicomponent intervention for patients with chronic disease in primary care. Previous meta-analyses have proven the effectiveness of collaborative care for depression; however, individual participant data (IPD) are needed to identify which components of the intervention are the principal drivers of this effect. To assess which components of collaborative care are the biggest drivers of its effectiveness in reducing symptoms of depression in primary care. Data were obtained from MEDLINE, Embase, Cochrane Library, PubMed, and PsycInfo as well as references of relevant systematic reviews. Searches were conducted in December 2023, and eligible data were collected until March 14, 2024. Two reviewers assessed for eligibility. Randomized clinical trials comparing the effect of collaborative care and usual care among adult patients with depression in primary care were included. The study was conducted according to the IPD guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline. IPD were collected for demographic characteristics and depression outcomes measured at baseline and follow-ups from the authors of all eligible trials. Using IPD, linear mixed models with random nested effects were calculated. Continuous measure of depression severity was assessed via validated self-report instruments at 4 to 6 months and was standardized using the instrument’s cutoff value for mild depression. A total of 35 datasets with 38 comparisons were analyzed (N = 20 046 participants [57.3% of all eligible, with minimal differences in baseline characteristics compared with nonretrieved data]; 13 709 [68.4%] female; mean [SD] age, 50.8 [16.5] years). A significant interaction effect with the largest effect size was found between the depression outcome and the collaborative care component therapeutic treatment strategy (−0.07; P < .001). This indicates that this component, including its key elements manual-based psychotherapy and family involvement, was the most effective component of the intervention. Significant interactions were found for all other components, but with smaller effect sizes. Components of collaborative care most associated with improved effectiveness in reducing depressive symptoms were identified. To optimize treatment effectiveness and resource allocation, a therapeutic treatment strategy, such as manual-based psychotherapy or family integration, may be prioritized when implementing a collaborative care intervention.

INTRODUCTION:The general practitioners' (GP) approach to diagnosing depression has not yet been included in depression questionnaires. Therefore, the 'Questionnaire for the assessment of DEpression SYmptoms in Primary Care' (DESY-PC) has been developed. The DESY-PC consists of two parts, comprising the patient's perspective and psychiatric diagnostic criteria (DESY-PAT), and additionally the GP's heuristics and knowledge of patients (DESY-GP). The aim was to investigate the diagnostic accuracy and factor structure of the DESY-PC. METHODS:A multicentre diagnostic accuracy study was conducted in ten practices. Patients completed the DESY-PAT and PHQ-9 (Patient Health Questionnaire-9), while their GPs completed the DESY-GP. The Structured Clinical Interview for DSM-V disorders (SCID-V-CV) was used as reference standard. Sensitivity, specificity, receiver operating characteristic curves (ROC) and area under the curve (AUC) values were calculated to determine the diagnostic accuracy of the DESY-PC and PHQ-9. Factorial validity was assessed. RESULTS:435 patients (mean age 47.6 years, 60.1% female, prevalence of depression 15.9%) were analysed. The diagnostic accuracy of the DESY-PAT (AUC=0.862, 95% Confidence Interval 0.815-0.908) was significantly higher (p<0.001) than that of PHQ-9 (AUC=0.821, 0.764-0.878). The diagnostic accuracy increased further when DESY-PAT was combined with DESY-GP for the overall questionnaire DESY-PC (AUC=0.874, 0.834-0.914). Goodness of fit indices indicated a plausible fit for the DESY-PC. CONCLUSIONS:Incorporating the GP's heuristics, judgement and knowledge of the patient contributes to a more accurate diagnosis. The DESY-PC integrates the GP's perspective, patient-specific factors, and psychiatric criteria into the diagnostic assessment, which might contribute to improved diagnostic decision-making in primary care.

Zur Lage der PsychologieMarkus BühnerMarkus BühnerProf. Dr. Markus Bühner, Lehrstuhl für Psychologische Methodenlehre und Diagnostik, Ludwig-Maximilians-Universität München, Leopoldstraße 13, 80802 München, Deutschland, [email protected]Lehrstuhl für Psychologische Methodenlehre und Diagnostik, Ludwig-Maximilians-Universität München, DeutschlandPublished Online:December 19, 2022https://doi.org/10.1026/0033-3042/a000616PDFView Full Text ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInReddit SectionsMoreLiteraturAbele-Brehm, A., Bühner, M., Deutsch, R., Erdfelder, E., Fydrich, T., Gollwitzer, M. et al. (2014). Bericht der Kommission "Studium und Lehre" der Deutschen Gesellschaft für Psychologie. Psychologische Rundschau, 65, 230 – 235. https://doi.org/10.1026/0033-3042/a000226 First citation in articleLink, Google ScholarAbele-Brehm, A. (2017). Zur Lage der Psychologie. Psychologische Rundschau, 68, 1 – 19. 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