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Depression is a prevalent mental disorder. Current clinical and self-reported assessment methods of depression are laborious and incur recall bias. Their sporadic nature often misses severity fluctuations. Previous research highlights the potential of in-situ quantification of human behaviour using mobile sensors to augment traditional methods of depression management. In this paper, we study whether self-reported mood scores and passive smartphone and wearable sensor data could be used to classify people as depressed or non-depressed. In a longitudinal study, our participants provided daily mood (valence and arousal) scores and collected data using their smartphones and Oura Rings. We computed daily aggregations of mood, sleep, physical activity, phone usage, and GPS mobility from raw data to study the differences between the depressed and non-depressed groups and created population-level Machine Learning classification models of depression. We found statistically significant differences in GPS mobility, phone usage, sleep, physical activity and mood between depressed and non-depressed groups. An XGBoost model with daily aggregations of mood and sensor data as predictors classified participants with an accuracy of 81.43% and an Area Under the Curve of 82.31%. A Support Vector Machine using only sensor-based predictors had an accuracy of 77.06% and an Area Under the Curve of 74.25%. Our results suggest that digital biomarkers are promising in differentiating people with and without depression symptoms. This study contributes to the body of evidence supporting the role of unobtrusive mobile sensor data in understanding depression and its potential to augment depression diagnosis and monitoring.

Background: Depression and anxiety are leading causes of disability worldwide but often remain undetected and untreated. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods. Objective: The current study aimed to explore the extent to which data from smartphone and wearable devices could predict symptoms of depression and anxiety. Methods: A total of N = 60 adults (ages 24–68) who owned an Apple iPhone and Oura Ring were recruited online over a 2-week period. At the beginning of the study, participants installed the Delphi data acquisition app on their smartphone. The app continuously monitored participants’ location (using GPS) and smartphone usage behavior (total usage time and frequency of use). The Oura Ring provided measures related to activity (step count and metabolic equivalent for task), sleep (total sleep time, sleep onset latency, wake after sleep onset and time in bed) and heart rate variability (HRV). In addition, participants were prompted to report their daily mood (valence and arousal). Participants completed self-reported assessments of depression, anxiety and stress (DASS-21) at baseline, midpoint and the end of the study. Results: Multilevel models demonstrated a significant negative association between the variability of locations visited and symptoms of depression (beta = −0.21, p = 0.037) and significant positive associations between total sleep time and depression (beta = 0.24, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression. Conclusion: The current findings demonstrate that wearable devices may provide valuable sources of data

Experimental studies of learning suggest that human temperament may depend on the molecular mechanisms for associative conditioning, which are highly conserved in animals. The main genetic pathways for associative conditioning are known in experimental animals, but have not been identified in prior genome-wide association studies (GWAS) of human temperament. We used a data-driven machine learning method for GWAS to uncover the complex genotypic–phenotypic networks and environmental interactions related to human temperament. In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified 3 clusters of people with distinct temperament profiles measured by the Temperament and Character Inventory regardless of genotype. Third, we found 51 SNP sets that identified 736 gene loci and were significantly associated with temperament. The identified genes were enriched in pathways activated by associative conditioning in animals, including the ERK, PI3K, and PKC pathways. 74% of the identified genes were unique to a specific temperament profile. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of the 51 Finnish SNP sets in healthy Korean (90%) and German samples (89%), as well as their associations with temperament. The identified SNPs explained nearly all the heritability expected in each sample (37–53%) despite variable cultures and environments. We conclude that human temperament is strongly influenced by more than 700 genes that modulate associative conditioning by molecular processes for synaptic plasticity and long-term memory.

Human personality is 30–60% heritable according to twin and adoption studies. Hundreds of genetic variants are expected to influence its complex development, but few have been identified. We used a machine learning method for genome-wide association studies (GWAS) to uncover complex genotypic–phenotypic networks and environmental interactions. The Temperament and Character Inventory (TCI) measured the self-regulatory components of personality critical for health (i.e., the character traits of self-directedness, cooperativeness, and self-transcendence). In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified five clusters of people with distinct profiles of character traits regardless of genotype. Third, we found 42 SNP sets that identified 727 gene loci and were significantly associated with one or more of the character profiles. Each character profile was related to different SNP sets with distinct molecular processes and neuronal functions. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of 95% of the 42 SNP sets in healthy Korean and German samples, as well as their associations with character. The identified SNPs explained nearly all the heritability expected for character in each sample (50 to 58%). We conclude that self-regulatory personality traits are strongly influenced by organized interactions among more than 700 genes despite variable cultures and environments. These gene sets modulate specific molecular processes in brain for intentional goal-setting, self-reflection, empathy, and episodic learning and memory.

Compassion is known to predict prosocial behavior and moral judgments related to harm. Despite the centrality of compassion to social life, factors predicting adulthood compassion are largely unknown. We examined whether qualities of parent–child-relationship, namely, emotional warmth and acceptance, predict offspring compassion decades later in adulthood. We used data from the prospective population-based Young Finns Study. Our sample included 2,761 participants (55.5% women). Parent–child-relationship qualities were reported by each participant’s parents at baseline in 1980 (T0) when participants were between 3 and 18 years old. Compassion was self-reported 3 times: in 1997 (T1), 2001 (T2), and 2012 (T3) with the Temperament and Character Inventory (Cloninger, Przybeck, Svrakic, & Wetzel, 1994). By using age at the assessment as a time-variant variable, we applied multilevel modeling for repeated measurements to examine developmental trajectories of compassion from the ages of 20 (the age of the youngest cohort at T1) to 50 (the age of the oldest cohort at T3). On average, compassion increased in a curvilinear pattern with age. Higher acceptance (p = .013) and higher emotional warmth (p < .001) were related to higher compassion in adulthood. After adjusting for childhood confounds (i.e., participant gender, birth cohort, externalizing behavior, parental socioeconomic status, and parental mental health problems), only emotional warmth (p < .001) remained a significant predictor of compassion. Quality of the parent–child-relationship has long-term effects on offspring compassion. An emotionally warm and close relationship, in particular, may contribute to higher offspring compassion in adulthood.

Purpose: Physical activity, sleep problems, and symptoms of depression contribute to overall well-being. The factors are reciprocally associated, but the nature of these associations remains unclear. The present study examined whether sleep problems mediated the association between physical activity and depressive symptoms. Methods: The eligible population (n = 3596) consisted of adults from the ongoing, population-based Cardiovascular Risk in Young Finns Study started in 1980. Participants’ leisure-time physical activity was assessed with physical activity index (2007) and sleep problems with Jenkins’ Sleep Questionnaire in 2007 and 2011. Depressive symptoms were measured using modified Beck Depression Inventory in 2007 and 2012, from which the items reflecting sleep problems were excluded. Mediation analyses, through which the associations between the variables were examined, were adjusted for sex and a set of health-related covariates assessed in 2007 and 2011. Results: Physical activity was associated with decreased levels of sleep problems and depressive symptoms (P < 0.05). The association between physical activity and depressive symptoms (b = −0.10, P < 0.01) was partly mediated by sleep problems (proportion mediated = 0.36, P < 0.01). The adjustment for depressive symptoms at baseline attenuated the mediation effect (proportion mediated = 0.30, P > 0.05). Conclusions: Physical activity’s favorable contribution to depressive symptoms was mediated partly by sleep, but the mediation effect disappeared after adjusting for the previous depressive symptoms in adulthood.

This study examined the association between five‐factor model personality traits and perceptions of organisational justice. The sample for the study comprised 903 participants (35–50 years old; 523 women) studied in 2007 and 2012. Measures used were the Neuroticism, Extraversion, Openness, Five‐Factor Inventory questionnaire and the short organisational justice measure. The results showed that high neuroticism was associated with low distributive, procedural and interactional justice. Furthermore, high agreeableness was associated with high procedural and interactional justice and high openness with high distributive justice. This study suggests that neuroticism, agreeableness and openness are involved in perceptions of organisational justice and that personality should be considered in research and in practices at the workplace.

Sociability is a widely studied trait that has been linked both with individual well- and ill-being. Although early childcare has been shown to affect social competence in children, its role in the development of different aspects of adulthood sociability is poorly understood. Using a longitudinal population-based sample (N = 464), this study investigated whether childcare arrangements at ages 3 or 6 are associated with self-reported adulthood sociability at ages 20 to 35 years. A total of five aspects of sociability were measured using three well-established personality inventories (EAS, NEO-FFI, and TCI). Multilevel modeling was applied to examine the association between early care and adulthood sociability, adjusting for several sources of random variation (between-individual variance, within-individual variance between measurement times, variance between used sociability indicators, and error variance that cannot be attributed to the previously mentioned) and potential confounders (disruptive behavior in childhood, parental socio-economic status, parent–child relationship quality, maternal age, and the number of children in the family). Based on our results, in comparison to home care, family daycare and center-based daycare at age 3 and center-based daycare at age 6 were associated with higher sociability later in life. The association was strongest for aspects of sociability that emphasize the willingness to be surrounded by other people and to be attached to them. In other words, characteristics of early care may contribute uniquely to the development of these aspects of sociability with effects that persist into adult life.

Sociability and social domain-related behaviors have been associated with better well-being and endogenous oxytocin levels. Inspection of the literature, however, reveals that the effects between sociability and health outcomes, or between sociability and genotype, are often weak or inconsistent. In the field of personality psychology, the social phenotype is often measured by error-prone assessments based on different theoretical frameworks, which can partly explain the inconsistency of the previous findings. In this study, we evaluated the generalizability of “sociability” measures by partitioning the population variance in adulthood sociability using five indicators from three personality inventories and assessed in two to four follow-ups over a 15-year period (n = 1,573 participants, 28,323 person-observations; age range 20–50 years). Furthermore, we tested whether this variance partition would shed more light to the inconsistencies surrounding the “social” genotype, by using four genetic variants (rs1042778, rs2254298, rs53576, rs3796863) previously associated with a wide range of human social functions. Based on our results, trait (between-individual) variance explained 23% of the variance in overall sociability, differences between sociability indicators explained 41%, state (within-individual) variance explained 5% and measurement errors explained 32%. The genotype was associated only with the sociability indicator variance, suggesting it has specific effects on sentimentality and emotional sharing instead of reflecting general sociability.

Background: Lack of social support is an established risk factor across health outcomes, making it important to examine its family environmental and genetic determinants. Methods: In a 27-year follow-up of the Young Finns Study (N = 2341), we examined with a latent growth curve model whether genes involved in the oxytocin signaling pathway—namely, oxytocin receptor gene (OXTR) variants rs1042778, rs2254298, and rs53576—moderate the effect of early-life social experiences on perceived social support across the life span. Mothers reported the emotional warmth and acceptance towards their children at baseline when the participants were from 3 to 18 years old (1980). Perceived family support and support from friends and peripheral sources were assessed in five follow-ups 18 years apart (1989–2007). Results: Maternal emotional warmth and acceptance predicted the initial level of perceived social support across subscales, while the rate of change in family support was affected by the family environment only if participants carried the T-allele of OXTR rs1042778. This gene-environment interaction was not found for the rate of change in support from friends and peripheral sources and we also did not find associations between latent growth in perceived social support and OXTR variants rs53576 and rs2254298. Limitations: Selective attrition in perceived social support, maternal emotional warmth and acceptance, gender, and SES. Family environment was assessed by a non-standardized measure. Conclusions: OXTR rs1042778 polymorphism seems to contribute to changes in perceived family support in that way that some individuals (T-allele carriers) ‘recover’, to some extent, from the effects of early-life social experiences, whereas others (G/G genotype carriers) do not.

Background: Both hostile parenting and parental psychopathology have been shown to predict depression in the offspring. However, whether and how they interact in predicting the longitudinal course of depression from adolescence to adulthood remains unclear. Methods: Participants were from the prospective Cardiovascular Risk in Young Finns study, aged 3–18 years at baseline in 1980. We used multilevel modeling for repeated measurements to examine the associations of hostile parenting (i.e., parental intolerance and emotional distance) and parental history of psychopathology with trajectories of depressive symptoms across five study phases from 1992 to 2012. Results: On average, depressive symptoms decreased in a curvilinear pattern with age. A relatively steep decreasing trend was also observed among offspring of parents with a history of psychopathology but low intolerance. By contrast, among the offspring of parents with a history of psychopathology and high intolerance there was a rising trend in depressive symptoms starting from young adulthood. There was no similar interaction between parental history of psychopathology, emotional distance, and age. Limitations: Non-standardized, parental self-report scales were used to measure hostile parenting. The observed effects were small, and the depressive symptoms scale applied in the study may not be used for measuring clinical depression. Conclusions: Parental psychopathology might render individuals sensitive to the unfavorable characteristics of the caregiving environment. Intolerance towards the child can exacerbate the effects of parental psychopathology and have a long-term significance on the developmental trajectory of depressive symptoms over the life-course.

We examined the association of perfectionism with depressive symptoms and tested whether psychological detachment from work would both mediate and moderate the association. The participants were 76 primary school teachers (87% female) who responded to measures of perfectionism (Multidimensional Inventory on Perfectionism in Sports adapted for teachers), psychological detachment from work (The Recovery Experience Questionnaire), and depressive symptoms (Beck Depression Inventory-II). Perfectionism comprised both adaptive and maladaptive dimensions. Adaptive perfectionism referred to striving for perfection, whereas maladaptive perfectionism involved negative reactions to imperfection and perceived pressure to be perfect. According to our results, negative reactions to imperfection were associated with higher depressive symptoms, and lower level of psychological detachment from work played a minor mediating role in the association. There was, however, no association between negative reactions to imperfection and higher depressive symptoms when detachment from work was high. Our findings suggest that striving for perfection and perceived pressure to be perfect might not contribute to depressive symptoms in teaching. Instead, teachers experiencing negative reactions to imperfection and low psychological detachment from work could be at risk for developing depressive symptoms. Finding ways to psychologically detach from work may benefit teachers characterized by negative reactions to imperfection.

Background: The association between depressive symptoms and subclinical atherosclerosis has been inconsistent. Purpose: We sought to replicate our previous study, which demonstrated a positive relation between depressive symptoms and subclinical atherosclerosis assessed with carotid intima-media thickness (IMT) in men, using a newer measurement of carotid IMT and a cumulative loading of depressive symptoms over three follow-ups. Methods: The sample comprised 996 adults (352 men) aged 30 to 45 years in 2007 from a prospective population-based Finnish sample. The participants completed a modified version of Beck Depression Inventory in 1992, 1997, and 2001. Carotid IMT was assessed with ultrasound in 2001 and 2007. Cardiovascular risk factors (i.e., body mass index, systolic blood pressure, low-density lipoprotein cholesterol, and smoking) were measured in childhood (1980) and in adulthood (2007). Results: We found no association between the accumulative depression index and carotid IMT before or after controlling for the traditional risk factors (all p values ≥0.67). Depressive symptoms did not predict IMT progression over two time points and the highest level of carotid wall thickening. Imputed and non-imputed data sets provided similar results. Results remained the same when men and women were analyzed separately. Additional analyses revealed no significant interactions between depressive symptoms and cardiovascular risk factors (i.e., body mass index and systolic blood pressure) on carotid IMT (all p values >0.15). Conclusions: The findings of this population-based study did not indicate any direct association between depressive symptoms and carotid IMT in asymptomatic, young adults.

Extraversion is a relatively stable and heritable personality trait associated with numerous psychosocial, lifestyle and health outcomes. Despite its substantial heritability, no genetic variants have been detected in previous genome-wide association (GWA) studies, which may be due to relatively small sample sizes of those studies. Here, we report on a large meta-analysis of GWA studies for extraversion in 63,030 subjects in 29 cohorts. Extraversion item data from multiple personality inventories were harmonized across inventories and cohorts. No genome-wide significant associations were found at the single nucleotide polymorphism (SNP) level but there was one significant hit at the gene level for a long non-coding RNA site (LOC101928162). Genome-wide complex trait analysis in two large cohorts showed that the additive variance explained by common SNPs was not significantly different from zero, but polygenic risk scores, weighted using linkage information, significantly predicted extraversion scores in an independent cohort. These results show that extraversion is a highly polygenic personality trait, with an architecture possibly different from other complex human traits, including other personality traits. Future studies are required to further determine which genetic variants, by what modes of gene action, constitute the heritable nature of extraversion.

IMPORTANCE Neuroticism is a pervasive risk factor for psychiatric conditions. It genetically overlaps with major depressive disorder (MDD) and is therefore an important phenotype for psychiatric genetics. The Genetics of Personality Consortium has created a resource for genome-wide association analyses of personality traits in more than 63 000 participants (including MDD cases). OBJECTIVES To identify genetic variants associated with neuroticism by performing a meta-analysis of genome-wide association results based on 1000 Genomes imputation; to evaluate whether common genetic variants as assessed by single-nucleotide polymorphisms (SNPs) explain variation in neuroticism by estimating SNP-based heritability; and to examine whether SNPs that predict neuroticism also predict MDD. DESIGN, SETTING, AND PARTICIPANTS Genome-wide association meta-analysis of 30 cohorts with genome-wide genotype, personality, and MDD data from the Genetics of Personality Consortium. The study included 63 661 participants from 29 discovery cohorts and 9786 participants from a replication cohort. Participants came from Europe, the United States, or Australia. Analyses were conducted between 2012 and 2014. MAIN OUTCOMES AND MEASURES Neuroticism scores harmonized across all 29 discovery cohorts by item response theory analysis, and clinical MDD case-control status in 2 of the cohorts. RESULTS A genome-wide significant SNP was found on 3p14 in MAGI1 (rs35855737; P = 9.26 × 10-9 in the discovery meta-analysis). This association was not replicated (P = .32), but the SNP was still genome-wide significant in the meta-analysis of all 30 cohorts (P = 2.38 × 10-8). Common genetic variants explain 15%of the variance in neuroticism. Polygenic scores based on the meta-analysis of neuroticism in 27 cohorts significantly predicted neuroticism (1.09 × 10-12 < P <.05) and MDD (4.02 × 10-9 < P < .05) in the 2 other cohorts. CONCLUSIONS AND RELEVANCE This study identi

Mega- or meta-analytic studies (e.g. genome-wide association studies) are increasingly used in behavior genetics. An issue in such studies is that phenotypes are often measured by different instruments across study cohorts, requiring harmonization of measures so that more powerful fixed effect meta-analyses can be employed. Within the Genetics of Personality Consortium, we demonstrate for two clinically relevant personality traits, Neuroticism and Extraversion, how Item-Response Theory (IRT) can be applied to map item data from different inventories to the same underlying constructs. Personality item data were analyzed in >160,000 individuals from 23 cohorts across Europe, USA and Australia in which Neuroticism and Extraversion were assessed by nine different personality inventories. Results showed that harmonization was very successful for most personality inventories and moderately successful for some. Neuroticism and Extraversion inventories were largely measurement invariant across cohorts, in particular when comparing cohorts from countries where the same language is spoken. The IRT-based scores for Neuroticism and Extraversion were heritable (48 and 49 %, respectively, based on a meta-analysis of six twin cohorts, total N = 29,496 and 29,501 twin pairs, respectively) with a significant part of the heritability due to non-additive genetic factors. For Extraversion, these genetic factors qualitatively differ across sexes. We showed that our IRT method can lead to a large increase in sample size and therefore statistical power. The IRT approach may be applied to any mega- or meta-analytic study in which item-based behavioral measures need to be harmonized.