Bright light therapy versus physical exercise to prevent co-occurring depression in adolescents and young adults with attention-deficit/hyperactivity disorder: a multicentre, three-arm, randomised controlled, pilot phase-IIa trial.

Depression is common in attention-deficit/hyperactivity disorder (ADHD), but preventive behavioural interventions are lacking. This randomised controlled, pilot phase-IIa trial aimed to study a physical exercise intervention (EI) and bright light therapy (BLT)-both implemented and monitored in an individual, naturalistic setting via a mobile health (m-health) system-for feasibility of trial design and interventions, and to estimate their effects on depressive symptoms in young people with ADHD. Two hundred seven participants aged 14-45 years were randomised to 10-week add-on intervention of either BLT (10,000 lx; daily 30-min sessions) (n = 70), EI (aerobic and muscle-strengthening activities 3 days/ week) (n = 69), or treatment-as-usual (TAU) (n = 68), of whom 165 (80%) were retained (BLT: n = 54; EI: n = 52; TAU: n = 59). Intervention adherence (i.e. ≥ 80% completed sessions) was very low for both BLT (n = 13, 22%) and EI (n = 4, 7%). Usability of the m-health system to conduct interventions was limited as indicated by objective and subjective data. Safety was high and comparable between groups. Changes in depressive symptoms (assessed via observer-blind ratings, Inventory of Depressive Symptomatology) between baseline and end of intervention were small (BLT: -0.124 [95% CI: -2.219, 1.971], EI: -2.646 [95% CI: -4.777, -0.515], TAU: -1.428 [95% CI: -3.381, 0.526]) with no group differences [F(2,153) = 1.45, p = 0.2384]. These findings suggest that the m-health approach did not achieve feasibility of EI and BLT in young people with ADHD. Prior to designing efficacy studies, strategies how to achieve high intervention adherence should be specifically investigated in this patient group. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03371810, 13 December 2017.

Modeling Heterogeneity in Temporal Dynamics: Extending Latent State-Trait Autoregressive and Cross-lagged Panel Models to Mixture Distribution Models.

Longitudinal models suited for the analysis of panel data, such as cross-lagged panel or autoregressive latent-state trait models, assume population homogeneity with respect to the temporal dynamics of the variables under investigation. This assumption is likely to be too restrictive in a myriad of research areas. We propose an extension of autoregressive and cross-lagged latent state-trait models to mixture distribution models. The models allow researchers to model unobserved person heterogeneity and qualitative differences in longitudinal dynamics based on comparatively few observations per person, while taking into account temporal dependencies between observations as well as measurement error in the variables. The models are extended to include categorical covariates, to investigate the distribution of encountered latent classes across observed groups. The potential of the models is illustrated with an application to self-esteem and affect data in patients with borderline personality disorder, an anxiety disorder, and healthy control participants. Requirements for the models' applicability are investigated in an extensive simulation study and recommendations for model applications are derived.

Momentary within-subject associations of affective states and physical behavior are moderated by weather conditions in real life: an ambulatory assessment study.

BACKGROUND: Physical behavior (PB) is a key lifestyle factor in regulating and preventing diseases across the lifespan. Researchers identified affective, cognitive, and contextual factors like weather conditions, as significant contributors in determining if individuals are physically active. However, there is scarce empirical evidence about potential associations between PB and affective states influenced by weather conditions in daily life. Therefore, we explored if weather conditions moderated the within-subject association between momentary affective states and subsequent PB. METHODS: Utilizing ambulatory assessment, 79 participants completed electronic diaries about their affective states (i.e., valence, energetic arousal, and calmness) up to six times a day over five days, and their PB (i.e., physical activity and sedentariness) was simultaneously recorded via accelerometers. Weather conditions (i.e., temperature and precipitation) recorded near participants' locations served as moderators in the multilevel analyses. RESULTS: We confirmed earlier findings associating affective states with PB. Increased valence and energetic arousal were positively associated with physical activity (ß = 0.007; p < .001), whereas calmness predicted lower levels of physical activity (ß = -0.006; p < .001). Higher levels of calmness showed a positive association with sedentary behavior (ß = 0.054; p = .003). In addition, we revealed a significant positive association between temperature, as a momentary weather condition, and physical activity (ß = 0.025; p = .015). Furthermore, we showed that the association of affective states and physical activity was moderated by temperature. Higher temperatures enhanced the positive effects of valence on physical activity (ß = .001, p = .023) and attenuated the negative effects of calmness on physical activity (ß = .001, p = .021). Moreover, higher temperatures enhanced the positive effects of valence on reduced sedentary behavior (ß = -0.011, p = .043). CONCLUSIONS: Temperature alterations appeared to have an impact on subsequent physical activity. Furthermore, temperature alterations moderated the influence of affective states on conducted physical activity. This might offer the opportunity for just-in-time adaptive interventions to intervene in individually appropriate environmental conditions for promoting physical activity.

Assessment of 24-hour physical behaviour in adults via wearables: a systematic review of validation studies under laboratory conditions.

BACKGROUND: Wearable technology is used by consumers and researchers worldwide for continuous activity monitoring in daily life. Results of high-quality laboratory-based validation studies enable us to make a guided decision on which study to rely on and which device to use. However, reviews in adults that focus on the quality of existing laboratory studies are missing. METHODS: We conducted a systematic review of wearable validation studies with adults. Eligibility criteria were: (i) study under laboratory conditions with humans (age ≥ 18 years); (ii) validated device outcome must belong to one dimension of the 24-hour physical behavior construct (i.e., intensity, posture/activity type, and biological state); (iii) study protocol must include a criterion measure; (iv) study had to be published in a peer-reviewed English language journal. Studies were identified via a systematic search in five electronic databases as well as back- and forward citation searches. The risk of bias was assessed based on the QUADAS-2 tool with eight signaling questions. RESULTS: Out of 13,285 unique search results, 545 published articles between 1994 and 2022 were included. Most studies (73.8% (N = 420)) validated an intensity measure outcome such as energy expenditure; only 14% (N = 80) and 12.2% (N = 70) of studies validated biological state or posture/activity type outcomes, respectively. Most protocols validated wearables in healthy adults between 18 and 65 years. Most wearables were only validated once. Further, we identified six wearables (i.e., ActiGraph GT3X+, ActiGraph GT9X, Apple Watch 2, Axivity AX3, Fitbit Charge 2, Fitbit, and GENEActiv) that had been used to validate outcomes from all three dimensions, but none of them were consistently ranked with moderate to high validity. Risk of bias assessment resulted in 4.4% (N = 24) of all studies being classified as "low risk", while 16.5% (N = 90) were classified as "some concerns" and 79.1% (N = 431) as "high risk". CONCLUSION: Laboratory validation studies of wearables assessing physical behaviour in adults are characterized by low methodological quality, large variability in design, and a focus on intensity. Future research should more strongly aim at all components of the 24-hour physical behaviour construct, and strive for standardized protocols embedded in a validation framework.

Longitudinal Digital Mood Charting in Bipolar Disorder: Experiences with ChronoRecord Over 20 Years.

INTRODUCTION: Longitudinal study is an essential methodology for understanding disease trajectories, treatment effects, symptom changes, and long-term outcomes of affective disorders. Daily self-charting of mood and other illness-related variables is a commonly recommended intervention. With the widespread acceptance of home computers in the early 2000s, automated tools were developed for patient mood charting, such as ChronoRecord, a software validated by patients with bipolar disorder. The purpose of this study was to summarize the daily mood, sleep, and medication data collected with ChronoRecord, and highlight some of the key research findings. Lessons learned from implementing a computerized tool for patient self-reporting are also discussed. METHODS: After a brief training session, ChronoRecord software for daily mood charting was installed on a home computer and used by 609 patients with affective disorders. RESULTS: The mean age of the patients was 40.3±11.8 years, a mean age of onset was 22±11.2 years, and 71.4% were female. Patients were euthymic for 70.8% of days, 15.1% had mild depression, 6.6% had severe depression, 6.6% had hypomania, and 0.8% had mania. Among all mood groups, 22.4% took 1-2 medications, 37.2% took 3-4 medications, 25.7 took 5-6 medications, 11.6% took 7-8 medications, and 3.1% took >8 medications. CONCLUSION: The daily mood charting tool is a useful tool for increasing patient involvement in their care, providing detailed patient data to the physician, and increasing understanding of the course of illness. Longitudinal data from patient mood charting was helpful in both clinical and research settings.

Mobile Data Collection of Cognitive-Behavioral Tasks in Substance Use Disorders: Where Are We Now?

INTRODUCTION: Over the last decades, our understanding of the cognitive, motivational, and neural processes involved in addictive behavior has increased enormously. A plethora of laboratory-based and cross-sectional studies has linked cognitive-behavioral measures to between-subject differences in drinking behavior. However, such laboratory-based studies inevitably suffer from small sample sizes and the inability to link temporal fluctuations in task measures to fluctuations in real-life substance use. To overcome these problems, several existing behavioral tasks have been transferred to smartphones to allow studying cognition in the field. METHOD: In this narrative review, we first summarize studies that used existing behavioral tasks in the laboratory and self-reports of substance use with ecological momentary assessment (EMA) in the field. Next, we review studies on psychometric properties of smartphone-based behavioral tasks. Finally, we review studies that used both smartphone-based tasks and self-reports with EMA in the field. RESULTS: Overall, studies were scarce and heterogenous both in tasks and in study outcomes. Nevertheless, existing findings are promising and point toward several methodological recommendations: concerning psychometrics, studies show that - although more systematic studies are necessary - task validity and reliability can be improved, for example, by analyzing several measurement sessions at once rather than analyzing sessions separately. Studies that use tasks in the field, moreover, show that power can be improved by choosing sampling schemes that combine time-based with event-based sampling, rather than relying on time-based sampling alone. Increasing sampling frequency can further increase power. However, as this also increases the burden to participants, more research is necessary to determine the ideal sampling frequency for each task. CONCLUSION: Although more research is necessary to systematically study both the psychometrics of smartphone-based tasks and the frequency at which task measures fluctuate, existing studies are promising and reveal important methodological recommendations useful for researchers interested in implementing behavioral tasks in EMA studies.

Drivers of productivity: Being physically active increases yet sedentary bouts and lack of sleep decrease work ability.

Physical behavior (ie, physical activity, sedentary behavior, and sleep) is a crucial lifestyle factor for preventing and managing diseases across the lifespan. However, less is known about potential work-related psychological and cognitive outcomes such as productivity. The present study examined within-person associations between physical behavior and self-perceived work ability. To investigate the degree to which physical behavior parameters influence self-perceived work ability in everyday life, we conducted an Ambulatory Assessment study in 103 university students over 5 days. Physical behavior was assessed continuously via a multi-sensor system. Self-perceived work ability was assessed repeatedly up to six times per day on smartphones. We employed multilevel modeling to analyze the within-person effects of physical behavior on self-perceived work ability. Physical activity intensity (MET) (ß = 0.15 ± 0.06, t = 2.59, p = 0.012) and sit-to-stand transitions (ß = 0.07 ± 0.03, t = 2.44, p = 0.015) were positively associated with self-perceived work ability. Sedentary bouts (≥20 min) (ß = -0.21 ± 0.08, t = -2.74, p = 0.006) and deviation from a recommended sleep duration (ie, 8 h) (ß = -0.1 ± 0.04, t = -2.38, p = 0.018) were negatively associated with self-perceived work ability. Exploratory analyses supported the robustness of our findings by comparing various time frames. Total sedentary time and sleep quality were not associated with self-perceived work ability. Regular sleep durations, breaking up sedentary time through sit-to-stand transitions, and higher intensities of physical activity may be important for the regulation of self-perceived work ability in university students' daily lives.

Momentary mood predicts upcoming real-life sedentary behavior.

Humans in the industrialized world spend a large amount of daily time in sedentary behavior. Since sedentariness negatively impacts a variety of psychophysiological outcomes, the identification of antecedents that lead to sedentary behavior is an important public health issue. In this context, mood, a central indicator for both psychological well-being and mental health, is severely understudied. To investigate whether mood dimensions influence subsequent sedentary behavior, we assessed both constructs at baseline via questionnaires and via ambulatory assessment (AA) over 5 days in 92 university employees. We continuously measured sedentary behavior using accelerometers and assessed mood repeatedly 10 times each day on smartphone diaries. We employed multiple regression analyses to analyze between-subject effects and multilevel modeling to analyze within-subject effects. Higher momentary ratings of valence (P < .05) and energetic arousal (P < .01) predicted lower amounts of subsequent sedentary behavior, whereas higher ratings of calmness (P < .01) predicted higher amounts of subsequent sedentary behavior. The context moderated the effect of energetic arousal and calmness on sedentary behavior with increased effects in the home compared to the work context. Mood significantly predicted sedentary behavior on a within-subject level but not on a between-subject level. Preliminary evidence suggests that mood regulates sedentary behavior in everyday life. Time-sensitive analyses, such as from moment to moment revealed an association between mood and sedentary behavior (within-subject), whereas analyses between different individuals revealed no associations (between-subject). These preliminary findings may inform multicomponent intervention strategies that target mood, to reduce sedentary behavior in daily life.

Relationships between incidental physical activity, exercise, and sports with subsequent mood in adolescents.

Physical activity is beneficial for human physical health and well-being. Accordingly, the association between physical activity and mood in everyday life has been a subject of several Ambulatory Assessment studies. This mechanism has been studied in children, adults, and the elderly, but neglected in adolescents. It is critical to examine this mechanism in adolescents because adolescence plays a key role in human development and adolescents' physical activity behavior translates into their behavior in adulthood. We investigated adolescents' mood in relation to distinct physical activities: incidental activity such as climbing stairs; exercise activity, such as skating; and sports, such as playing soccer. We equipped 134 adolescents aged 12-17 years with accelerometers and GPS-triggered electronic diaries to use in their everyday life. Adolescents reported on mood repeatedly in real time across 7 days, and these data were analyzed using multilevel-modeling. After incidental activity, adolescents felt better and more energized. After exercise, adolescents felt better but less calm. After sports, adolescents felt less energized. Analyses of the time course of the effects confirmed our findings. Physical activity influences mood in adolescents' everyday life, but has distinct effects depending on the kind of physical activity. Our results suggest incidental and exercise activities entail higher post-bout valence compared to sports in competitive settings. These findings may serve as an important empirical basis for the targeted application of distinct physical activities to foster well-being in adolescence.

Sedentary behavior in everyday life relates negatively to mood: An ambulatory assessment study.

Empirical evidence shows that physical behavior positively impacts human health. Recently, researchers have started to differentiate between physical activity and sedentary behavior showing independent effects on somatic health. However, whether this differentiation is also relevant for mood dimensions is largely unknown. For investigating the dynamic relationships between sedentary behavior and mood dimensions in daily life, ambulatory assessment (AA) has become the state-of-the-art methodology. To investigate whether sedentary behaviors influence mood dimensions, we conducted an AA study in the everyday life of 92 university employees over 5 days. We continuously measured sedentary behavior via accelerometers and assessed mood repeatedly 10 times each day on smartphone diaries. To optimize our sampling strategy, we used a sophisticated sedentary-triggered algorithm. We employed multilevel modeling to analyze the within-subject effects of sedentary behavior on mood. Sedentary time (15-minute intervals prior to each e-diary assessment) and sedentary bouts (30-minute intervals of uninterrupted sedentary behavior) negatively influenced valence and energetic arousal (all Ps < 0.015). In particular, the more participants were sedentary in their everyday life, the less they felt well and energized. Exploratory analyses of the temporal course of these effects supported our findings. Sedentary behavior can be seen as a general risk factor because it impacts both somatic and mental health. Most importantly, physical activity and sedentary behavior showed independent effects on mood dimensions. Accordingly, future studies should consider the two sides of the physical behavior coin: How should physical activity be promoted? and How can sedentary behavior be reduced?

Ambulatory Assessment in the Research on Aging: Contemporary and Future Applications.

Older adults have surprisingly high levels of well-being, which has been referred to as a paradox in the past. Improved emotion regulation has been suggested to underlie these high levels of well-being. Later life is also a period with enhanced exposure to critical life events, and this comes with risks. During such times, and towards the end of life, emotional well-being may and eventually does decline. We suggest that ambulatory assessment (AA) is ideally suited for the investigation of the above phenomena and for intervention purposes. More precisely, AA can be used to thoroughly examine within-person processes of emotion regulation, including the multiple levels on which emotions occur (physiology, experience, behavior, context, and nonverbal expressions). It thereby provides a basis for understanding competent emotion regulation, the well-being paradox, and emotionally critical periods. Such insights can be utilized to detect person-specific critical periods and for designing immediate person-specific interventions. Although this is still a vision, the benefits of such an approach seem invaluable. The major part of this paper is organized around three general principles that we suggest to further tap the potential of AA in aging research, namely (1) identify within-subject processes and their relations to important life outcomes; (2) capitalize on the full scope of AA technology via multivariate assessments, and (3) combine real-time monitoring with real-time interventions.

Speech Features as Predictors of Momentary Depression Severity in Patients With Depressive Disorder Undergoing Sleep Deprivation Therapy: Ambulatory Assessment Pilot Study.

BACKGROUND: The use of mobile devices to continuously monitor objectively extracted parameters of depressive symptomatology is seen as an important step in the understanding and prevention of upcoming depressive episodes. Speech features such as pitch variability, speech pauses, and speech rate are promising indicators, but empirical evidence is limited, given the variability of study designs. OBJECTIVE: Previous research studies have found different speech patterns when comparing single speech recordings between patients and healthy controls, but only a few studies have used repeated assessments to compare depressive and nondepressive episodes within the same patient. To our knowledge, no study has used a series of measurements within patients with depression (eg, intensive longitudinal data) to model the dynamic ebb and flow of subjectively reported depression and concomitant speech samples. However, such data are indispensable for detecting and ultimately preventing upcoming episodes. METHODS: In this study, we captured voice samples and momentary affect ratings over the course of 3 weeks in a sample of patients (N=30) with an acute depressive episode receiving stationary care. Patients underwent sleep deprivation therapy, a chronotherapeutic intervention that can rapidly improve depression symptomatology. We hypothesized that within-person variability in depressive and affective momentary states would be reflected in the following 3 speech features: pitch variability, speech pauses, and speech rate. We parametrized them using the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) from open-source Speech and Music Interpretation by Large-Space Extraction (openSMILE; audEERING GmbH) and extracted them from a transcript. We analyzed the speech features along with self-reported momentary affect ratings, using multilevel linear regression analysis. We analyzed an average of 32 (SD 19.83) assessments per patient. RESULTS: Analyses revealed that pitch variability, speech pauses, and speech rate were associated with depression severity, positive affect, valence, and energetic arousal; furthermore, speech pauses and speech rate were associated with negative affect, and speech pauses were additionally associated with calmness. Specifically, pitch variability was negatively associated with improved momentary states (ie, lower pitch variability was linked to lower depression severity as well as higher positive affect, valence, and energetic arousal). Speech pauses were negatively associated with improved momentary states, whereas speech rate was positively associated with improved momentary states. CONCLUSIONS: Pitch variability, speech pauses, and speech rate are promising features for the development of clinical prediction technologies to improve patient care as well as timely diagnosis and monitoring of treatment response. Our research is a step forward on the path to developing an automated depression monitoring system, facilitating individually tailored treatments and increased patient empowerment.

Multimodal Associations of FKBP5 Methylation With Emotion-Regulatory Brain Circuits.

BACKGROUND: Understanding the biological processes that underlie individual differences in emotion regulation and stress responsivity is a key challenge for translational neuroscience. The gene FKBP5 is a core regulator in molecular stress signaling that is implicated in the development of psychiatric disorders. However, it remains unclear how FKBP5 DNA methylation in peripheral blood is related to individual differences in measures of neural structure and function and their relevance to daily-life stress responsivity. METHODS: Here, we characterized multimodal correlates of FKBP5 DNA methylation by combining epigenetic data with neuroimaging and ambulatory assessment in a sample of 395 healthy individuals. RESULTS: First, we showed that FKBP5 demethylation as a psychiatric risk factor was related to an anxiety-associated reduction of gray matter volume in the ventromedial prefrontal cortex, a brain area that is involved in emotion regulation and mental health risk and resilience. This effect of epigenetic upregulation of FKBP5 on neuronal structure is more pronounced where FKBP5 is epigenetically downregulated at baseline. Leveraging 208 functional magnetic resonance imaging scans during a well-established emotion-processing task, we found that FKBP5 DNA methylation in peripheral blood was associated with functional differences in prefrontal-limbic circuits that modulate affective responsivity to daily stressors, which we measured using ecological momentary assessment in daily life. CONCLUSIONS: Overall, we demonstrated how FKBP5 contributes to interindividual differences in neural and real-life affect regulation via structural and functional changes in prefrontal-limbic brain circuits.

Associations of Menstrual Cycle and Progesterone-to-Estradiol Ratio With Alcohol Consumption in Alcohol Use Disorder: A Sex-Separated Multicenter Longitudinal Study.

OBJECTIVE: Alcohol use disorder (AUD) constitutes a critical public health issue and has sex-specific characteristics. Initial evidence suggests that progesterone and estradiol might reduce or increase alcohol intake, respectively. However, there is a need for a better understanding of how the menstrual cycle in females and the ratio of progesterone to estradiol in females and males influence alcohol use patterns in individuals with AUD. METHODS: In this sex-separated multicenter longitudinal study, the authors analyzed 12-month data on real-life alcohol use (from 21,460 smartphone entries), menstrual cycle, and serum progesterone-to-estradiol ratios (from 667 blood samples at four individual study visits) in 74 naturally cycling females and 278 males with AUD between 2020 and 2022, using generalized and general linear mixed modeling. RESULTS: Menstrual cycle phases were significantly associated with binge drinking and progesterone-to-estradiol ratio. During the late luteal phase, females showed a lower predicted binge drinking probability of 13% and a higher predicted marginal mean of progesterone-to-estradiol ratio of 95 compared with during the menstrual, follicular, and ovulatory phases (binge drinking probability and odds ratios vs. late luteal phase, respectively: 17%, odds ratio=1.340, 95% CI=1.031, 1.742; 19%, odds ratio=1.523, 95% CI=1.190, 1.949; and 20%, odds ratio=1.683, 95% CI=1.285, 2.206; difference in progesterone-to-estradiol ratios, respectively: -61, 95% CI=-105.492, -16.095; -78, 95% CI=-119.322, -37.039; and -71, 95% CI=-114.568, -27.534). In males, a higher progesterone-to-estradiol ratio was related to lower probabilities of binge drinking and of any alcohol use, with a 10-unit increase in the hormone ratio resulting in odds ratios of 0.918 (95% CI=0.843, 0.999) and 0.914 (95% CI=0.845, 0.988), respectively. CONCLUSIONS: These ecologically valid findings suggest that high progesterone-to-estradiol ratios can have a protective effect against problematic alcohol use in females and males with AUD, highlighting the progesterone-to-estradiol ratio as a promising treatment target. Moreover, the results indicate that females with AUD may benefit from menstrual cycle phase-tailored treatments.

Assessing affect in adolescents with e-diaries: multilevel confirmatory factor analyses of different factor models.

In the last two decades, e-diary studies have gained increasing interest, with a dominant focus on mood and affect. Although requested in current guidelines, psychometric properties are rarely reported, and methodological investigations of factor structure, model fit, and the reliability of mood and affect assessment are limited. We used a seven-day e-diary dataset of 189 adolescent participants (12-17 years). The e-diary affect assessments revealed a considerable portion of within-person variance. The six-factor model showed the best model fit compared to the less complex models. Factor loadings also improved with the complexity of the models. Accordingly, we recommend that future e-diary studies of adolescents use the six-factor model of affect as well as reporting psychometric properties and model fit. For future e-diary scale development, we recommend using a minimum of three items per scale to enable the use of confirmatory multilevel factor analyses.

The importance of high quality real-life social interactions during the COVID-19 pandemic.

The coronavirus pandemic has brought about dramatic restrictions to real-life social interactions and a shift towards more online social encounters. Positive social interactions have been highlighted as an important protective factor, with previous studies suggesting an involvement of the amygdala in the relationship between social embeddedness and well-being. The present study investigated the effect of the quality of real-life and online social interactions on mood, and explored whether this association is affected by an individual's amygdala activity. Sixty-two participants of a longitudinal study took part in a one-week ecological momentary assessment (EMA) during the first lockdown, reporting their momentary well-being and their engagement in real-life and online social interactions eight times per day (N ~ 3000 observations). Amygdala activity was assessed before the pandemic during an emotion-processing task. Mixed models were calculated to estimate the association between social interactions and well-being, including two-way interactions to test for the moderating effect of amygdala activity. We found a positive relationship between real-life interactions and momentary well-being. In contrast, online interactions had no effect on well-being. Moreover, positive real-life social interactions augmented this social affective benefit, especially in individuals with higher amygdala being more sensitive to the interaction quality. Our findings demonstrate a mood-lifting effect of positive real-life social interactions during the pandemic, which was dependent on amygdala activity before the pandemic. As no corresponding effect was found between online social interactions and well-being, it can be concluded that increased online social interactions may not compensate for the absence of real-life social interactions.

Digital mental health: challenges and next steps.

Digital innovations in mental health offer great potential, but present unique challenges. Using a consensus development panel approach, an expert, international, cross-disciplinary panel met to provide a framework to conceptualise digital mental health innovations, research into mechanisms and effectiveness and approaches for clinical implementation. Key questions and outputs from the group were agreed by consensus, and are presented and discussed in the text and supported by case examples in an accompanying appendix. A number of key themes emerged. (1) Digital approaches may work best across traditional diagnostic systems: we do not have effective ontologies of mental illness and transdiagnostic/symptom-based approaches may be more fruitful. (2) Approaches in clinical implementation of digital tools/interventions need to be creative and require organisational change: not only do clinicians and patients need training and education to be more confident and skilled in using digital technologies to support shared care decision-making, but traditional roles need to be extended, with clinicians working alongside digital navigators and non-clinicians who are delivering protocolised treatments. (3) Designing appropriate studies to measure the effectiveness of implementation is also key: including digital data raises unique ethical issues, and measurement of potential harms is only just beginning. (4) Accessibility and codesign are needed to ensure innovations are long lasting. (5) Standardised guidelines for reporting would ensure effective synthesis of the evidence to inform clinical implementation. COVID-19 and the transition to virtual consultations have shown us the potential for digital innovations to improve access and quality of care in mental health: now is the ideal time to act.

Daily patterns of fatigue after subarachnoid haemorrhage: an ecological momentary assessment study.

OBJECTIVE: To examine the daily course of, and factors associated with, momentary fatigue after subarachnoid haemorrhage, and to explore subgroups of patients with distinct diurnal patterns of fatigue. DESIGN: Observational study using ecological momentary assessment. SUBJECTS: A total of 41 participants with subarachnoid haemorrhage. METHODS: Patients with fatigue were included within one year post-onset. Momentary fatigue (scale 1-7) was assessed with repeated measurements (10-11 times/day) during 7 consecutive days. Multilevel-mixed-model analyses and latent-class trajectory modelling were conducted. RESULTS: Mean (standard deviation; SD) age of the group was 53.9 (13.0) years, 56% female, and mean (SD) time post-subarachnoid haemorrhage onset was 9.3 (3.2) months. Mean (SD) momentary fatigue over all days was 3.22 (1.47). Fatigue increased significantly (p < 0.001) over the day, and experiencing more burden of fatigue and day type (working day vs weekend day) were significantly (p < 0.05) associated with higher momentary fatigue. Three subgroups could be distinguished based on diurnal patterns of fatigue. The largest group (n = 17, 41.5%) showed an increasing daily pattern of fatigue. CONCLUSION: Momentary fatigue in patients with subarachnoid haemorrhage increases over the day, and diurnal patterns of fatigue differ between  participants. In addition to conventional measures, momentary measures of fatigue might provide valuable information for physicians to optimize personalized management of fatigue after subarachnoid haemorrhage.

Self-esteem instability might be more characteristic of borderline personality disorder than affective instability: Findings from an e-diary study with clinical and healthy controls.

Borderline personality disorder (BPD) is commonly characterized by pervasive instability. Affective instability, despite being a diagnostic criterion in the DSM-5, is commonly seen as a transdiagnostic feature, but recent studies have brought new attention to the importance of self-esteem instability as a potential defining feature of BPD. However, evidence is lacking regarding whether heightened self-esteem instability is a specific feature of BPD when patients with BPD are compared to clinical controls. Using ambulatory assessment, we examined self-esteem instability and affective instability in participants' daily lives. We assessed momentary self-esteem and affective state 12 times daily for 4 consecutive days in 71 patients with BPD, 121 patients with anxiety disorders (ADs), and 74 healthy controls (HCs). To determine group differences, we used established instability indices and analyzed multilevel models. Compared to HCs, patients with BPD and those with ADs exhibited heightened self-esteem instability and affective instability. Importantly, the clinical groups did not differ in affective instability, whereas self-esteem instability was significantly higher in patients with BPD than in those with ADs across all instability indices. Beyond the influence of mean self-esteem, patients with BPD had the highest general instability, the most frequent extreme changes, and the largest decreases in self-esteem, especially from high levels of self-esteem. Our results support previous findings on affective instability, which may constitute a transdiagnostic feature, and they provide the first evidence that heightened self-esteem instability is particularly prominent in BPD, underscoring the importance of self-esteem for the understanding of dysregulation in BPD. (PsycInfo Database Record (c) 2022 APA, all rights reserved).