Lars Kuchinke

Autobiographical memory is affected by Alzheimer's dementia and depression in old age. We suggest that cognitive decline in Alzheimer's dementia affects both the life narrative construction of personal stability and change, whereas the decline of vitality in depression affects only the construction of change, but not stability of personal identity. Elderly adults with Alzheimer's dementia ( N = 21), major depression ( N = 20), and healthy controls ( N = 20) did not differ in age ( m _age = 76.9 years, range 65–94), gender (33 women), or education (mean years = 12.0) narrated their entire lives and provided turning point and self‐defining memory narratives. Individuals with Alzheimer's dementia were indeed affected both in indicators of stability and change. Individuals with depression produced more negative life narratives but unexpectedly did not differ from healthy controls in narrative indicators of personal change. Possibly, the expected reduction of personal change in life stories is limited to very severe depression in younger adults.

Background/Objectives: Infant regulatory disorders (RDs) describe the infants’ difficulties in regulating their physiological and emotional states, manifesting in problems with sleeping, feeding, or excessive crying. A post hoc analysis of data from the German SKKIPPI cohort study investigated the association of maternal smartphone use and the occurrence of RDs. Methods: In a randomly drawn sample from three local population registers, a self-administered online questionnaire was used to collect basic sociodemographic data, smartphone use behavior and the mother’s subjective perception of the infant’s RD. In case of psychosocial burden of the mother, a structured psychiatric-diagnostic interview (M.I.N.I.) was conducted and the Questionnaire for Crying, Feeding and Sleeping (SFS) was completed. Results: Of the 5954 parent–child dyads completing the online-questionnaire, 3.7% reported frequent smartphone use while interacting with the child, compared with 5.8% of the 972 mother–child dyads in the psychiatric interview. Among frequent smartphone users, 61.5% indicated RD compared to 41.6% of the infrequent users (p = 0.005). After adjustment for confounding by psychiatric disorder, educational level, single parent status, and age, perceived stress when interrupted during smartphone use was consistently associated with less favorable outcomes (OR of feeding easy 0.56, 95% CI 0.42; 0.75; OR of falling asleep easily 0.85, 95% CI 0.73; 0.99; OR of excessive crying 1.37, 95% CI 1.04; 1.81). RD based on the SFS was associated with elevated stress during smartphone use (OR 1.87, 95% CI 1.35; 2.58). Conclusions: Maternal stress when smartphone use is interrupted is more strongly associated with early regulatory difficulties than frequency of smartphone use alone, indicating the importance of also considering emotional and contextual factors in the analysis of technology use during parent–child interactions.

Emotion and motivation affect our perception, cognition, and action. Self-determined choice was previously applied in behavioral and neuroscientific studies to experimentally investigate effects of intrinsic motivation on task processing, but little is known about neural interactions between intrinsic motivation and affective processing. Here, we aim to investigate whether self-determined choice and affective picture content jointly or independently influence the late positive potential (LPP), associated with adjustments in attention allocation, during task processing. The presence of an interaction would indicate overlapping mechanisms. Additionally, effects of choice on task preparation were tested in the contingent negative variation (CNV). In two EEG experiments ( N = 32 each), intrinsic motivation and affective picture content were manipulated under different cognitive task demands. In Experiment 1, a time production task, participants estimated two-second intervals while viewing affective pictures (neutral, negative, positive), with picture categories (e.g., humans, vehicles, animals) either self-chosen or assigned pseudorandomly (‘self’ vs.‘no choice’). LPP amplitudes were enhanced for negative compared to neutral and positive pictures and also in ‘self’ compared to ‘no choice’ trials. However, no interaction of self-determined choice and affective processing was found. Task preparatory CNV was not reliably different between choice conditions. Experiment 2 involved a less demanding active viewing task. The data replicate the evidence for independent LPP modulation by choice and affective picture content from Experiment 1 as well as the absence of choice-related CNV effects. The findings align with models on resource competition in attention allocation but suggest distinct processes for intrinsic motivation induced by self-determined choice and affective processing.

Background: Postpartum maternal mental health problems affect the development of mother-child interaction and of children’s attachment security. Focus-based brief parent-infant psychotherapy (PIP-f) was developed to support parents ‘mentalize’ their child’s affective states and promote a healthy parent-child relationship and child development. Efficacy of PIP-f was evaluated against care-as-usual (CAU) in a randomized controlled trial within the SKKIPPI project. Method: N = 120 mothers diagnosed with an ICD-10 psychopathological disorder and their infants under one year were randomly assigned to PIP-f ( n = 57) or CAU ( n = 63). Primary outcome was maternal sensitivity in mother-child interaction after 6 weeks of intervention. Secondary outcomes include maternal and child psychopathological symptoms, child development, parenting stress, emotional availability, parental reflective functioning and child attachment after 6 weeks and 12 months. Results: PIP-f was not superior to CAU in maternal sensitivity ( b = 0.129, 95%CI [-0.161, 0.418], p = 0.378) at post-intervention, nor in maternal symptoms ( b =-0.702, [-2.603, 1.198], p = 0.463) or child symptoms of regulatory disorder ( b = 1.079, [-2.344, 4.502], p = 0.531). Attachment security assessed at 12 months did not differ between intervention groups (OR = 2.0, [0.64, 6.33], p = 0.226). Moderator analyses suggest that emotional availability and parental reflective functioning improve up to 12 months for more severe cases. Conclusion: Differences between PIP-f and CAU were smaller than expected, but PIP-f seemed to reduce symptoms of mental health problems. Some evidence exists that mothers with higher symptom burden benefit from PIP-f. High dropout rates and initially high emotional availability prevent generalizability. Trial registration : The trial was registered in the German Register for Clinical Studies (DRKS00016353) on January 16, 2019.

Background: As digital mental health delivery becomes increasingly prominent, a solid evidence base regarding its efficacy is needed. Objective: This study aims to synthesize evidence on the comparative efficacy of systemic psychotherapy interventions provided via digital versus face-to-face delivery modalities. Methods: We followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for searching PubMed, Embase, Cochrane CENTRAL, CINAHL, PsycINFO, and PSYNDEX and conducting a systematic review and meta-analysis. We included randomized controlled trials comparing mental, behavioral, and somatic outcomes of systemic psychotherapy interventions using self- and therapist-guided digital versus face-to-face delivery modalities. The risk of bias was assessed with the revised Cochrane Risk of Bias tool for randomized trials. Where appropriate, we calculated standardized mean differences and risk ratios. We calculated separate mean differences for nonaggregated analysis. Results: We screened 3633 references and included 12 articles reporting on 4 trials (N=754). Participants were youths with poor diabetic control, traumatic brain injuries, increased risk behavior likelihood, and parents of youths with anorexia nervosa. A total of 56 outcomes were identified. Two trials provided digital intervention delivery via videoconferencing: one via an interactive graphic interface and one via a web-based program. In total, 23% (14/60) of risk of bias judgments were high risk, 42% (25/60) were some concerns, and 35% (21/60) were low risk. Due to heterogeneity in the data, meta-analysis was deemed inappropriate for 96% (54/56) of outcomes, which were interpreted qualitatively instead. Nonaggregated analyses of mean differences and CIs between delivery modalities yielded mixed results, with superiority of the digital delivery modality for 18% (10/56) of outcomes, superiority of the face-to-face delivery modality for 5% (3/56) of outcomes, equivalence between delivery modalities for 2% (1/56) of outcomes, and neither superiority of one modality nor equivalence between modalities for 75% (42/56) of outcomes. Consequently, for most outcome measures, no indication of superiority or equivalence regarding the relative efficacy of either delivery modality can be made at this stage. We further meta-analytically compared digital versus face-to-face delivery modalities for attrition (risk ratio 1.03, 95% CI 0.52-2.03; P=.93) and number of sessions attended (standardized mean difference –0.11; 95% CI –1.13 to –0.91; P=.83), finding no significant differences between modalities, while CIs falling outside the range of the minimal important difference indicate that equivalence cannot be determined at this stage. Conclusions: Evidence on digital and face-to-face modalities for systemic psychotherapy interventions is largely heterogeneous, limiting conclusions regarding the differential efficacy of digital and face-to-face delivery. Nonaggregated and meta-analytic analyses did not indicate the superiority of either delivery condition. More research is needed to conclude if digital and face-to-face delivery modalities are generally equivalent or if—and in which contexts—one modality is superior to another. Trial Registration: PROSPERO CRD42022335013; https://tinyurl.com/nprder8h

Background: Suicide represents a critical public health concern, and machine learning (ML) models offer the potential for identifying at-risk individuals. Recent studies using benchmark datasets and real-world social media data have demonstrated the capability of pretrained large language models in predicting suicidal ideation and behaviors (SIB) in speech and text. Objective: This study aimed to (1) develop and implement ML methods for predicting SIBs in a real-world crisis helpline dataset, using transformer-based pretrained models as a foundation; (2) evaluate, cross-validate, and benchmark the model against traditional text classification approaches; and (3) train an explainable model to highlight relevant risk-associated features. Methods: We analyzed chat protocols from adolescents and young adults (aged 14-25 years) seeking assistance from a German crisis helpline. An ML model was developed using a transformer-based language model architecture with pretrained weights and long short-term memory layers. The model predicted suicidal ideation (SI) and advanced suicidal engagement (ASE), as indicated by composite Columbia-Suicide Severity Rating Scale scores. We compared model performance against a classical word-vector-based ML model. We subsequently computed discrimination, calibration, clinical utility, and explainability information using a Shapley Additive Explanations value-based post hoc estimation model. Results: The dataset comprised 1348 help-seeking encounters (1011 for training and 337 for testing). The transformer-based classifier achieved a macroaveraged area under the curve (AUC) receiver operating characteristic (ROC) of 0.89 (95% CI 0.81-0.91) and an overall accuracy of 0.79 (95% CI 0.73-0.99). This performance surpassed the word-vector-based baseline model (AUC-ROC=0.77, 95% CI 0.64-0.90; accuracy=0.61, 95% CI 0.61-0.80). The transformer model demonstrated excellent prediction for nonsuicidal sessions (AUC-ROC=0.96, 95% CI 0.96-0.99) and good prediction for SI and ASE, with AUC-ROCs of 0.85 (95% CI 0.97-0.86) and 0.87 (95% CI 0.81-0.88), respectively. The Brier Skill Score indicated a 44% improvement in classification performance over the baseline model. The Shapley Additive Explanations model identified language features predictive of SIBs, including self-reference, negation, expressions of low self-esteem, and absolutist language. Conclusions: Neural networks using large language model–based transfer learning can accurately identify SI and ASE. The post hoc explainer model revealed language features associated with SI and ASE. Such models may potentially support clinical decision-making in suicide prevention services. Future research should explore multimodal input features and temporal aspects of suicide risk.

Background/Objectives: Early childhood can be a stressful period for families with a significant impact on parents’ mental health, the child’s healthy development, and the development of a secure mother–child attachment. The goal of the cross-sectional study part of SKKIPPI was to assess the occurrence of psychosocial stress and mental health disorders in parents as well as in their offspring in early childhood in three German regions. Methods: Based on random samples from three residents’ registration offices, parents with infants aged up to 12 months were invited to participate. An online screening questionnaire was developed in four languages to assess common psychosocial stressors and mental health problems of parents with small children. Results: The study enrolled 4984 mothers and 962 fathers. The most common potential psychosocial stressors were professional problems (mothers 22%, fathers 33%), lack of social support (20%, 14%), and severe, negative experiences in childhood (22%, 16%). Obsessive–compulsive thoughts (21%, 16%) and depressive (9%, 9%) and anxiety symptoms (11%, 7%) were the most frequently reported mental health problems by both parents. Regulatory problems of the child were reported by between 1.5% and 5.1% of parents. Conclusions: The study showed that a substantial proportion of parents are burdened by psychosocial problems and suffer from mental health problems in the first years after the birth of their children. Early preventive and low-threshold support measures should be available in the health and social care system. Low-threshold questionnaires, which cover a wide range of possible stress factors, should be further developed for the practical healthcare of this group of people.

BACKGROUND Suicide represents a critical public health concern, and machine learning (ML) models offer the potential for identifying at-risk individuals. Recent studies using benchmark datasets and real-world social media data have demonstrated the capability of pretrained large language models in predicting suicidal ideation and behaviors (SIB) in speech and text. OBJECTIVE This study aimed to (1) develop and implement ML methods for predicting SIBs in a real-world crisis helpline dataset, using transformer-based pretrained models as a foundation; (2) evaluate, cross-validate, and benchmark the model against traditional text classification approaches; and (3) train an explainable model to highlight relevant risk-associated features. METHODS We analyzed chat protocols from adolescents and young adults (aged 14-25 years) seeking assistance from a German crisis helpline. An ML model was developed using a transformer-based language model architecture with pretrained weights and long short-term memory layers. The model predicted suicidal ideation (SI) and advanced suicidal engagement (ASE), as indicated by composite Columbia-Suicide Severity Rating Scale scores. We compared model performance against a classical word-vector-based ML model. We subsequently computed discrimination, calibration, clinical utility, and explainability information using a Shapley Additive Explanations value-based post hoc estimation model. RESULTS The dataset comprised 1348 help-seeking encounters (1011 for training and 337 for testing). The transformer-based classifier achieved a macroaveraged area under the curve (AUC) receiver operating characteristic (ROC) of 0.89 (95% CI 0.81-0.91) and an overall accuracy of 0.79 (95% CI 0.73-0.99). This performance surpassed the word-vector-based baseline model (AUC-ROC=0.77, 95% CI 0.64-0.90; accuracy=0.61, 95% CI 0.61-0.80). The transformer model demonstrated excellent prediction for nonsuicidal sessions (AUC-ROC=0.96, 95% CI 0.96-0.99) and good prediction for SI and ASE, with AUC-ROCs of 0.85 (95% CI 0.97-0.86) and 0.87 (95% CI 0.81-0.88), respectively. The Brier Skill Score indicated a 44% improvement in classification performance over the baseline model. The Shapley Additive Explanations model identified language features predictive of SIBs, including self-reference, negation, expressions of low self-esteem, and absolutist language. CONCLUSIONS Neural networks using large language model–based transfer learning can accurately identify SI and ASE. The post hoc explainer model revealed language features associated with SI and ASE. Such models may potentially support clinical decision-making in suicide prevention services. Future research should explore multimodal input features and temporal aspects of suicide risk.