Protocol for a randomized controlled trial comparing phone-based prenatal mindfulness training to usual care for pregnant people at risk for hypertensive disorders of pregnancy.

Hypertensive disorders of pregnancy (HDP) are the most common medical conditions in pregnancy and a leading cause of maternal morbidity and mortality in the United States. There are few interventions available to prevent HDP, and those currently available do not target underlying mechanisms of disease. Mindfulness training (MT) is effective at reducing blood pressure in non-pregnant patients with pre-hypertension and hypertension and has proven more effective at blood pressure reduction than other stress management interventions. MT thus holds great promise as a mind-body intervention to prevent HDP. This randomized trial will harness subjective and objective ecological momentary assessment methodology combined with wearable biosensor technology to capture psychological, physiological, and interpersonal processes through which MT may lead to improved maternal cardiovascular parameters. Pregnant women at risk for HDP will be randomized to an 8-week phone-delivered MT intervention or usual care. Through these methods, we will evaluate psychological, physiological, and interpersonal responses to daily experiences linking MT to cardiovascular parameters among women at risk for HDP.

Predicting psychosocial intervention response from baseline gene expression.

To address the challenge of predicting psychological response to a psychosocial intervention we tested the possibility that baseline gene expression profiles might provide information above and beyond baseline psychometric measures. The genomics strategy utilized individual level inferences of transcription factor activity to predict changes in loneliness and affect in response to two well-established meditation interventions. Initial algorithm development analyses focused on three a-priori defined stress-related gene regulation pathways (CREB, GR, and NF-ĸB) as inferred from TELiS promoter-based bioinformatic analysis of basal (pre-intervention) blood samples from a randomized-controlled trial comparing a compassion-based meditation (CM, n = 45) with mindfulness meditation (MM, n = 44). Greater baseline CREB activity (but not GR or NF-ĸB) predicted greater reductions from pre- to post-intervention in loneliness (b = -0.24, p = 0.016) and negative emotions (b = -0.23, p = 0.017) for CM, but not for MM. A second algorithm validation analysis applied the same approach to another randomized controlled trial comparing CM (n = 42) with MM (n = 38) and a health education control condition (n = 41). Similarly, greater baseline CREB activity predicted greater pre- to post-intervention decreases in loneliness (b = -0.24, p = 0.029) and greater increases in satisfaction with life (b = 0.21, p = 0.046) for the CM condition only. Baseline CREB activity was not associated with baseline psychometric measures in either study. Results raise the possibility that pre-intervention gene expression profiles may reflect non-conscious psychobiological states that affect psychological responses to distinct psychosocial interventions, and thereby help personalize intervention selection.

How ready is speech-to-text for psychological language research? Evaluating the validity of AI-generated English transcripts for analyzing free-spoken responses in younger and older adults.

For the longest time, the gold standard in preparing spoken language corpora for text analysis in psychology was using human transcription. However, such standard comes at extensive cost, and creates barriers to quantitative spoken language analysis that recent advances in speech-to-text technology could address. The current study quantifies the accuracy of AI-generated transcripts compared to human-corrected transcripts across younger (n = 100) and older (n = 92) adults and two spoken language tasks. Further, it evaluates the validity of Linguistic Inquiry and Word Count (LIWC)-features extracted from these two kinds of transcripts, as well as transcripts specifically prepared for LIWC analyses via tagging. We find that overall, AI-generated transcripts are highly accurate with a word error rate of 2.50% to 3.36%, albeit being slightly less accurate for younger compared to older adults. LIWC features extracted from either transcripts are highly correlated, while the tagging procedure significantly alters filler word categories. Based on these results, automatic speech-to-text appears to be ready for psychological language research when using spoken language tasks in relatively quiet environments, unless filler words are of interest to researchers.

When I am sixty-four evaluating language markers of well-being in healthy aging narratives.

Natural language use is a promising candidate for the development of innovative measures of well-being to complement self-report measures. The type of words individuals use can reveal important psychological processes that underlie well-being across the lifespan. In this preregistered, cross-sectional study, we propose a conceptual model of language markers of well-being and use written narratives about healthy aging (N = 701) and computerized text analysis (LIWC) to empirically validate the model. As hypothesized, we identified a model with three groups of language markers (reflecting affective, evaluative, and social processes). Initial validation with established self-report scales (N = 30 subscales) showed that these language markers reliably predict core components of well-being and underlying processes. Our results support the concurrent validity of the conceptual language model and allude to the added benefits of language-based measures, which are thought to reflect less conscious processes of well-being. Future research is needed to continue validating language markers of well-being across the lifespan in a theoretically informed and contextualized way, which will lay the foundation for inferring people's well-being from their natural language use.

Harnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adults.

INTRODUCTION: Current cognitive assessments suffer from floor/ceiling and practice effects, poor psychometric performance in mild cases, and repeated assessment effects. This study explores the use of digital speech analysis as an alternative tool for determining cognitive impairment. The study specifically focuses on identifying the digital speech biomarkers associated with cognitive impairment and its severity. METHODS: We recruited older adults with varying cognitive health. Their speech data, recorded via a wearable microphone during the reading aloud of a standard passage, were processed to derive digital biomarkers such as timing, pitch, and loudness. Cohen's d effect size highlighted group differences, and correlations were drawn to the Montreal Cognitive Assessment (MoCA). A stepwise approach using a Random Forest model was implemented to distinguish cognitive states using speech data and predict MoCA scores based on highly correlated features. RESULTS: The study comprised 59 participants, with 36 demonstrating cognitive impairment and 23 serving as cognitively intact controls. Among all assessed parameters, similarity, as determined by Dynamic Time Warping (DTW), exhibited the most substantial positive correlation (rho = 0.529, p < 0.001), while timing parameters, specifically the ratio of extra words, revealed the strongest negative correlation (rho = -0.441, p < 0.001) with MoCA scores. Optimal discriminative performance was achieved with a combination of four speech parameters: total pause time, speech-to-pause ratio, similarity via DTW, and intelligibility via DTW. Precision and balanced accuracy scores were found to be 88.1 ± 1.2% and 76.3 ± 1.3%, respectively. DISCUSSION: Our research proposes that reading-derived speech data facilitates the differentiation between cognitively impaired individuals and cognitively intact, age-matched older adults. Specifically, parameters based on timing and similarity within speech data provide an effective gauge of cognitive impairment severity. These results suggest speech analysis as a viable digital biomarker for early detection and monitoring of cognitive impairment, offering novel approaches in dementia care.

Comparing the language style of heads of state in the US, UK, Germany and Switzerland during COVID-19.

The COVID-19 pandemic posed a global threat to nearly every society around the world. Individuals turned to their political leaders to safely guide them through this crisis. The most direct way political leaders communicated with their citizens was through official speeches and press conferences. In this report, we compare psychological language markers of four different heads of state during the early stage of the pandemic. Specifically, we collected all pandemic-related speeches and press conferences delivered by political leaders in the USA (Trump), UK (Johnson), Germany (Merkel), and Switzerland (Swiss Federal Council) between February 27th and August 31st, 2020. We used natural language analysis to examine language markers of expressed positive and negative emotions, references to the community (we-talk), analytical thinking, and authenticity and compare these language markers across the four nations. Level differences in the language markers between the leaders can be detected: Trump's language was characterized by a high expression of positive emotion, Merkel's by a strong communal focus, and Johnson's and the Swiss Federal Council by a high level of analytical thinking. Overall, these findings mirror different strategies used by political leaders to deal with the COVID-19 pandemic.

Diversity, equity, and inclusivity in observational ambulatory assessment: Recommendations from two decades of Electronically Activated Recorder (EAR) research.

Ambient audio sampling methods such as the Electronically Activated Recorder (EAR) have become increasingly prominent in clinical and social sciences research. These methods record snippets of naturalistically assessed audio from participants' daily lives, enabling novel observational research about the daily social interactions, identities, environments, behaviors, and speech of populations of interest. In practice, these scientific opportunities are equaled by methodological challenges: researchers' own cultural backgrounds and identities can easily and unknowingly permeate the collection, coding, analysis, and interpretation of social data from daily life. Ambient audio sampling poses unique and significant challenges to cultural humility, diversity, equity, and inclusivity (DEI) in scientific research that require systematized attention. Motivated by this observation, an international consortium of 21 researchers who have used ambient audio sampling methodologies created a workgroup with the aim of improving upon existing published guidelines. We pooled formally and informally documented challenges pertaining to DEI in ambient audio sampling from our collective experience on 40+ studies (most of which used the EAR app) in clinical and healthy populations ranging from children to older adults. This article presents our resultant recommendations and argues for the incorporation of community-engaged research methods in observational ambulatory assessment designs looking forward. We provide concrete recommendations across each stage typical of an ambient audio sampling study (recruiting and enrolling participants, developing coding systems, training coders, handling multi-linguistic participants, data analysis and interpretation, and dissemination of results) as well as guiding questions that can be used to adapt these recommendations to project-specific constraints and needs.

Loneliness and social isolation are not associated with executive functioning in a cross-sectional study of cognitively healthy older adults.

The literature on the relationship between social interaction and executive functions (EF) in older age is mixed, perhaps stemming from differences in EF measures and the conceptualization/measurement of social interaction. We investigated the relationship between social interaction and EF in 102 cognitively unimpaired older adults (ages 65-90). Participants received an EF battery to measure working memory, inhibition, shifting, and global EF. We measured loneliness subjectively through survey and social isolation objectively through naturalistic observation. Loneliness was not significantly related to any EF measure (p-values = .13-.65), nor was social isolation (p-values = .11-.69). Bayes factors indicated moderate to extremely strong evidence (BF01 = 8.70 to BF01 = 119.49) in support of no relationship.. Overall, these findings suggest that, among cognitively healthy older adults, there may not be a robust cross-sectional relationship between EF and subjective loneliness or objective social isolation.

Emotional change in its "natural habitat": Measuring everyday emotion regulation with passive and active ambulatory assessment methods.

Ambulatory assessment methods have made it possible to study psychological phenomena in real-time, with translational potential for psychotherapy process research. This article uses case example data to demonstrate applications of ambulatory assessment to measuring emotion regulation, a process with relevance across diagnoses and treatment modalities that may be particularly important to measure in situ. Two methods are reviewed: Ecological Momentary Assessment (EMA), which enables self-reported momentary assessments as people go about their days, and the Electronically Activated Recorder (EAR), an unobtrusive naturalistic observation methodology that collects short audio recordings from participants' moment-to-moment environments, capturing an acoustic diary of their social interactions, daily behaviors, and natural daily language use. Using case example data from research applying EMA and EAR methods in the context of adolescent self-injurious thoughts and behaviors, we illustrate how EMA can be used to measure emotion regulation over time and across contexts, and how EAR can assess the behaviors and social-environmental factors that interact with emotion regulation in clinically important ways. We suggest applications of this measurement approach for investigations of clients' emotional change over the course of psychotherapy, as well as potential clinical applications of these methods.

Sleep Efficiency and Naturalistically-Observed Social Behavior Following Marital Separation: The Critical Role of Contact with an Ex-partner.

Marital disruption is associated with increased risk for a range of poor health outcomes, including disturbed sleep. This report examines trajectories of actigraphy-assessed sleep efficiency following marital separation as well as the extent to which daily social behaviors and individual differences in attachment explain variability in these trajectories over time. One hundred twenty-two recently-separated adults (N = 122) were followed longitudinally for three assessment periods over five months. To objectively assess daily social behaviors and sleep efficiency, participants wore the Electronically Activated Recorder (EAR) during the day (for one weekend at each assessment period) and an actiwatch at night (for seven days at each assessment period). Greater time spent with an ex-partner, as assessed by the EAR, was associated with decreased sleep efficiency between participants (p = .003). Higher attachment anxiety was also associated with decreased sleep efficiency (p = .03), as was the EAR-observed measure of "television on." The latter effect operated both between (p = .004) and within participants (p = .005). Finally, study timepoint moderated the association between EAR-observed measure of "television on" and sleep efficiency (p = .007). The current findings deepen our understanding of sleep disturbances following marital separation and point to contact with an ex-partner and time spent with the television on as behavioral markers of risk.

SocialBit: protocol for a prospective observational study to validate a wearable social sensor for stroke survivors with diverse neurological abilities.

INTRODUCTION: Social isolation has been found to be a significant risk factor for health outcomes, on par with traditional risk factors. This isolation is characterised by reduced social interactions, which can be detected acoustically. To accomplish this, we created a machine learning algorithm called SocialBit. SocialBit runs on a smartwatch and detects minutes of social interaction based on vocal features from ambient audio samples without natural language processing. METHODS AND ANALYSIS: In this study, we aim to validate the accuracy of SocialBit in stroke survivors with varying speech, cognitive and physical deficits. Training and testing on persons with diverse neurological abilities allows SocialBit to be a universally accessible social sensor. We are recruiting 200 patients and following them for up to 8 days during hospitalisation and rehabilitation, while they wear a SocialBit-equipped smartwatch and engage in naturalistic daily interactions. Human observers tally the interactions via a video livestream (ground truth) to analyse the performance of SocialBit against it. We also examine the association of social interaction time with stroke characteristics and outcomes. If successful, SocialBit would be the first social sensor available on commercial devices for persons with diverse abilities. ETHICS AND DISSEMINATION: This study has received ethical approval from the Institutional Review Board of Mass General Brigham (Protocol #2020P003739). The results of this study will be published in a peer-reviewed journal.

Association of plasma cytokines and antidepressant response following mild-intensity whole-body hyperthermia in major depressive disorder.

Whole-body hyperthermia (WBH) shows promise for the treatment of major depressive disorder (MDD). Because MDD is associated with increased inflammation, and anti-inflammatory agents show some promise as antidepressants, the current study sought to identify the acute and longer-term immune effects of WBH in participants with MDD and to explore whether these effects associate with the procedure's antidepressant properties. Thirty participants who met DSM-IV-TR criteria for MDD were randomized to receive a single session of WBH (n = 16) or sham treatment (n = 14). Hamilton Depression Rating Scale (HDRS) scores were assessed at baseline and 1, 2, 4, and 6 weeks post-treatment (WBH vs. sham), and plasma cytokine concentrations were assessed at baseline, immediately post-treatment, and 1 and 4 weeks post-treatment. As previously reported, WBH produced a rapid and sustained antidepressant effect. When compared to sham, WBH increased plasma interleukin (IL)-6 immediately post-treatment (time by treatment: χ2(3, N=108) = 47.33, p < 0.001), while having no effect on other cytokines acutely and no impact on IL-6, or any other cytokine, at 1 or 4 weeks post treatment. In the study sample as a whole, increased IL-6 post-treatment was associated with reduced HDRS depression scores over the 6 weeks of follow-up (F(1, 102.3) = 6.74, p = 0.01). These results suggest a hitherto unrecognized relationship between hyperthermia, the immune system, and depression, and may point to WBH as a novel modality for exploring behavioral effects of IL-6 when the cytokine is activated in isolation from the inflammatory mediators with which it frequently travels.

The paucity of morality in everyday talk.

Given its centrality in scholarly and popular discourse, morality should be expected to figure prominently in everyday talk. We test this expectation by examining the frequency of moral content in three contexts, using three methods: (a) Participants' subjective frequency estimates (N = 581); (b) Human content analysis of unobtrusively recorded in-person interactions (N = 542 participants; n = 50,961 observations); and (c) Computational content analysis of Facebook posts (N = 3822 participants; n = 111,886 observations). In their self-reports, participants estimated that 21.5% of their interactions touched on morality (Study 1), but objectively, only 4.7% of recorded conversational samples (Study 2) and 2.2% of Facebook posts (Study 3) contained moral content. Collectively, these findings suggest that morality may be far less prominent in everyday life than scholarly and popular discourse, and laypeople, presume.

Discovery of associative patterns between workplace sound level and physiological wellbeing using wearable devices and empirical Bayes modeling.

We conducted a field study using multiple wearable devices on 231 federal office workers to assess the impact of the indoor environment on individual wellbeing. Past research has established that the workplace environment is closely tied to an individual's wellbeing. Since sound is the most-reported environmental factor causing stress and discomfort, we focus on quantifying its association with physiological wellbeing. Physiological wellbeing is represented as a latent variable in an empirical Bayes model with heart rate variability measures-SDNN and normalized-HF as the observed outcomes and with exogenous factors including sound level as inputs. We find that an individual's physiological wellbeing is optimal when sound level in the workplace is at 50 dBA. At lower (<50dBA) and higher (>50dBA) amplitude ranges, a 10 dBA increase in sound level is related to a 5.4% increase and 1.9% decrease in physiological wellbeing respectively. Age, body-mass-index, high blood pressure, anxiety, and computer use intensive work are person-level factors contributing to heterogeneity in the sound-wellbeing association.

Spinning Your Wheels: Psychological Overinvolvement and Actigraphy-Assessed Sleep Efficiency Following Marital Separation.

BACKGROUND: This study investigated the ways in which adults reflect on their psychological experiences amid a recent marital separation and how these patterns of thought, manifest in language, are associated with self-reported negative affect and actigraphy-assessed sleep disturbance. METHODS: In a sample of 138 recently separated adults assessed three times over five months, we examined within- and between-person associations among psychological overinvolvement (operationalized using verbal immediacy derived as a function of the language participants used to discuss their relationship history and divorce experience), continued attachment to an ex-partner, negative affect, and sleep efficiency. RESULTS: The association between psychological overinvolvement and negative affect operated at the within-person level, whereas the associations between psychological overinvolvement and sleep disturbance, as well as negative affect and sleep disturbance, operated at the between-person level. CONCLUSIONS: These findings shed light on the intraindividual processes that may explain why some people are more susceptible to poor outcomes after separation/divorce than others. Our findings suggest that individuals who express their divorce-related thoughts and feelings in a psychologically overinvolved manner may be at greatest risk for sleep disturbances after marital separation/divorce.

Adolescents hospitalised for suicidality: biomarkers, social and affective predictors: a cohort study.

OBJECTIVES: The present research examines genomics and in vivo dynamics of family context and experienced affect following discharge from psychiatric hospitalisation for suicidal thoughts and behaviours (STBs). The purpose of this paper is to provide an overview of a new model, description of model-guided integration of multiple methods, documentation of feasibility of recruitment and retention and a description of baseline sample characteristics. DESIGN: The research involved a longitudinal, multimethod observational investigation. SETTING: Participants were recruited from an inpatient child and adolescent psychiatric hospital. 194 participants ages 13-18 were recruited following hospitalisation for STB. PRIMARY AND SECONDARY OUTCOME MEASURES: Participants underwent a battery of clinical interviews, self-report assessments and venipuncture. On discharge, participants were provided with a phone with (1) the electronically activated recorder (EAR), permitting acoustic capture later coded for social context, and (2) ecological momentary assessment, permitting assessment of in vivo experienced affect and STB. Participants agreed to follow-ups at 3 weeks and 6 months. RESULTS: A total of 71.1% of approached patients consented to participation. Participants reported diversity in gender identity (11.6% reported transgender or other gender identity) and sexual orientation (47.6% reported heterosexual or straight sexual orientation). Clinical interviews supported a range of diagnoses with the largest proportion of participants meeting criteria for major depressive disorder (76.9%). History of trauma/maltreatment was prevalent. Enrolment rates and participant characteristics were similar to other observational studies. CONCLUSIONS: The research protocol characterises in vivo, real-world experienced affect and observed family context as associated with STB in adolescents during the high-risk weeks post discharge, merging multiple fields of study.

Leveraging Social Networks for the Assessment and Management of Neurological Patients.

Social networks are the persons surrounding a patient who provide support, circulate information, and influence health behaviors. For patients seen by neurologists, social networks are one of the most proximate social determinants of health that are actually accessible to clinicians, compared with wider social forces such as structural inequalities. We can measure social networks and related phenomena of social connection using a growing set of scalable and quantitative tools increasing familiarity with social network effects and mechanisms. This scientific approach is built on decades of neurobiological and psychological research highlighting the impact of the social environment on physical and mental well-being, nervous system structure, and neuro-recovery. Here, we review the biology and psychology of social networks, assessment methods including novel social sensors, and the design of network interventions and social therapeutics.

Beyond English: Considering Language and Culture in Psychological Text Analysis.

The paper discusses the role of language and culture in the context of quantitative text analysis in psychological research. It reviews current automatic text analysis methods and approaches from the perspective of the unique challenges that can arise when going beyond the default English language. Special attention is paid to closed-vocabulary approaches and related methods (and Linguistic Inquiry and Word Count in particular), both from the perspective of cross-cultural research where the analytic process inherently consists of comparing phenomena across cultures and languages and the perspective of generalizability beyond the language and the cultural focus of the original investigation. We highlight the need for a more universal and flexible theoretical and methodological grounding of current research, which includes the linguistic, cultural, and situational specifics of communication, and we provide suggestions for procedures that can be implemented in future studies and facilitate psychological text analysis across languages and cultures.

Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach.

BACKGROUND: Language use and social interactions have demonstrated a close relationship with cognitive measures. It is important to improve the understanding of language use and behavioral indicators from social context to study the early prediction of cognitive decline among healthy populations of older adults. OBJECTIVE: This study aimed at predicting an important cognitive ability, working memory, of 98 healthy older adults participating in a 4-day-long naturalistic observation study. We used linguistic measures, part-of-speech (POS) tags, and social context information extracted from 7450 real-life audio recordings of their everyday conversations. METHODS: The methods in this study comprise (1) the generation of linguistic measures, representing idea density, vocabulary richness, and grammatical complexity, as well as POS tags with natural language processing (NLP) from the transcripts of real-life conversations and (2) the training of machine learning models to predict working memory using linguistic measures, POS tags, and social context information. We measured working memory using (1) the Keep Track test, (2) the Consonant Updating test, and (3) a composite score based on the Keep Track and Consonant Updating tests. We trained machine learning models using random forest, extreme gradient boosting, and light gradient boosting machine algorithms, implementing repeated cross-validation with different numbers of folds and repeats and recursive feature elimination to avoid overfitting. RESULTS: For all three prediction routines, models comprising linguistic measures, POS tags, and social context information improved the baseline performance on the validation folds. The best model for the Keep Track prediction routine comprised linguistic measures, POS tags, and social context variables. The best models for prediction of the Consonant Updating score and the composite working memory score comprised POS tags only. CONCLUSIONS: The results suggest that machine learning and NLP may support the prediction of working memory using, in particular, linguistic measures and social context information extracted from the everyday conversations of healthy older adults. Our findings may support the design of an early warning system to be used in longitudinal studies that collects cognitive ability scores and records real-life conversations unobtrusively. This system may support the timely detection of early cognitive decline. In particular, the use of a privacy-sensitive passive monitoring technology would allow for the design of a program of interventions to enable strategies and treatments to decrease or avoid early cognitive decline.

The Sounds of Memory: Extending the Age-Prospective Memory Paradox to Everyday Behavior and Conversations.

OBJECTIVES: Around the turn of the millennium, the "age-prospective memory (PM) paradox" challenged the classical assumption that older adults necessarily evidence a marked decline in PM functioning. As previous investigations highlighted ecological validity to be a potential explanation, the present study sought to extend established approaches by using novel real-world assessment technologies to examine PM unobtrusively in everyday-life conversations. METHOD: Next to laboratory PM tasks, real-life PM performance of 53 younger adults (19-32 years) and 38 older adults (60-81 years) was assessed from three sources: Over 9 days, participants completed an experimenter-given naturalistic task, a diary-based approach assessing self-assigned intentions, as well as an ambulatory assessment with the Electronically Activated Recorder (EAR), a device that unobtrusively samples ambient sounds to detect spontaneous speech production related to (lapses in) everyday PM. RESULTS: Older adults showed lower performance in laboratory PM only for the time-based task and performed either equally well as or even better than younger adults in everyday PM. With regard to PM performance as captured in real-life ambient audio data, younger adults talked more frequently about PM than older adults, but no significant difference between younger and older adults was found for speech related to PM errors. DISCUSSION: Findings confirmed older adults' preserved PM performance in everyday life across different indicators with increasing ecological validity. Furthermore, as a novel method to assess conversational PM in everyday life, the EAR opens new insights about the awareness of PM lapses and the communication of intentions in real life.