Psychiatry Apps

A key component of stress management and biofeedback training is the use of relaxation exercises, such as slow/deep breathing (6 breaths/minute) in heart coherence exercises (HCEs). Breathing exercises are also increasingly being integrated into smartphones as part of health apps, though their effectiveness in adolescents after acute stress has rarely been validated scientifically. The aim of the current study was to investigate the effectiveness of an app-guided HCE (n = 36) after an acute stress situation (Trier Social Stress Test) compared with natural relaxation (n = 37), among healthy adolescents (aged 11-17 years). Endocrine, autonomic, and psychological stress parameters (cortisol, alpha-amylase, heart rate, heart rate variability, mood) were examined in 73 adolescents (46 female, 27 male; M = 13.86, SD = 1.87). Significant group differences were found in heart rate variability, with higher values in the low frequency band and low-to-high frequency ratio for the HCE condition, possibly indicating improved physiological functions through the stimulation of vagal tone and baroreflex. The use of a general breathing technique (natural and app-guided) also resulted in stronger relaxation reactions in cortisol when controlling for the previous stronger stress reactivity. On the other hand, app-guided slow breathing without a long training may be experienced as more uncomfortable during relaxation. The integration of breathing exercises in health apps for adolescents appears to be useful, offering a helpful and low-threshold coping/relaxation strategy during acute stress situations. Further studies should examine the benefits of app-guided breathing exercises in both psychiatric samples and the general population across a wide age range.

Poor long-term recovery outcomes after treatment (e.g., readmission to inpatient treatment) are common among individuals with substance use disorders (SUDs). In-person mindfulness-based treatments (MBTs) are efficacious for SUDs and may improve recovery outcomes. However, existing MBTs for SUD have limited public health reach, and thus scalable delivery methods are needed. A digitally-delivered MBT for SUDs may hold promise.

People with dementia have complex palliative care needs that are often unmet, including physical and psycho-social needs. It is essential to empower people with dementia, family carers and professionals to better assess and manage care needs. We aimed to co-design a palliative dementia care Framework delivered through a digital app to support holistic assessment and decision making for care in the community and care homes-the EMBED-Care Framework.

Cognitive performance manifests regional differences, correlated with education. There is less information available about regional differences in performance-based measures of functional capacity. In multi-national trials focused on cognitive enhancement, it may be impossible to validate every measure in all locations. It is unknown if regional differences in healthy controls' (HC) performance affects relative levels of impairment in participants with schizophrenia (SCZ). A multi-site study comparing SCZ and HC administered the MATRICS Consensus Cognitive Battery (MCCB) and Virtual Reality Functional Capacity Assessment Tool (VRFCAT) to 164 HC participants and 164 with SCZ across three sites (Columbia, SC, Miami, and San Diego) and a representative sample of HC (n = 390) completed the VRFCAT in Durham, NC. Performance was compared between HC and SCZ participants at the validation sites. There were statistically significant cross-site differences in HC performance on both the MCCB and VRFCAT. Differences between HC and SCZ on MCCB and VRFCAT were substantial at all three sites, indicating that regional variations in HC performance did not induce reduced differences from SCZ participants. Regional differences were smaller than diagnostic group differences, suggesting that relative impairments of participants with SCZ is consistently preserved across performance differences in HC.

Emerging adult (EA) cannabis use is associated with increased risk for health consequences. Just-in-time adaptive interventions (JITAIs) provide potential for preventing the escalation and consequences of cannabis use. Powered by mobile devices, JITAIs use decision rules that take the person's state and context as input, and output a recommended intervention (e.g., alternative activities, coping strategies). The mHealth literature on JITAIs is nascent, with additional research needed to identify what intervention content to deliver when and to whom.

It has been reported that a scenario-based cognitive behavioral therapy mobile app including Todac Todac was effective in improving depression in the general public. However, no study has been conducted on whether Todac Todac is effective in dialysis patients. Therefore, this study was intended to determine whether the use of this app was effective in improving depression in dialysis patients. Sixty-five end-stage kidney disease patients receiving dialysis at Soonchunhyang University Cheonan Hospital were randomly assigned to the Todac Todac app program (experimental group) or an E-moods daily mood chart app program (control group) for 3 weeks. The degree of depression was measured before and after using the app.After the end of the 3-week program, a small but significant improvement was observed in the Trait anxiety (p < 0.05) and Beck depression index (p < 0.05) in E-moods group and DAS-K scores (p < 0.05) in Todac Todac group. However, no differences were seen in any parameters between the two groups. In addition, Todac Todac was not statistically more effective than the control intervention in the subgroup analysis. The Todac Todac, a scenario-based cognitive behavioral therapy mobile app, seemed to have a limited effect on improving depression in dialysis patients. Therefore, it is necessary to develop new tools to improve depression in dialysis patients.

Technology use may be one strategy to promote mental health and wellbeing among young adults in post-secondary education settings experiencing increasing distress and mental health difficulties. The JoyPop™ app is mobile mental health tool with a growing evidence base. The objectives of this research are to (1) evaluate the effectiveness of the JoyPop™ app in improving emotion regulation skills (primary outcome), as well as mental health, wellbeing, and resilience (secondary outcomes); (2) evaluate sustained app use once users are no longer reminded and determine whether sustained use is associated with maintained improvements in primary and secondary outcomes; (3) determine whether those in the intervention condition have lower mental health service usage and associated costs compared to those in the control condition; and (4) assess users' perspectives on the quality of the JoyPop™ app.

Wearable digital health technologies and mobile apps (personal digital health technologies [DHTs]) hold great promise for transforming health research and care. However, engagement in personal DHT research is poor.

Mindfulness-based interventions (MBI) are effective in relapse prevention in Major Depressive Disorder (MDD). Internet-based interventions have been demonstrated to be effective in the treatment of MDD. Consequently, the integration of MBI through mobile applications emerges as a promising supplementary intervention for MDD, contributing to the augmentation of mental health services, particularly within ambulatory care contexts. The current randomized controlled study is designed to evaluate the efficacy of adjunctive MBI delivered via a mobile app in mitigating symptom severity and stress levels. This assessment involves a comparison with standard treatment practices in an ambulatory setting among individuals diagnosed with MDD. A total of 83 patients diagnosed with MDD (depressive episode, recurrent depression or depressive phase of bipolar disorder) were randomly allocated to the intervention (41 patients) or control condition (42 patients). The intervention consisted of the daily use of the mindfulness mobile application "Headspace" for thirty days. The control condition was treatment as usual (TAU) only. The symptom severity has been assessed by the Beck Depression Inventory (BDI-II) as well as the Hamilton Depression Rating Scale (HDRS-17). Blood pressure and resting heart rate have been assessed as secondary outcome. Upon hospital discharge, the mean scores on the Beck Depression Inventory (BDI) and Hamilton Depression Rating Scale (HDRS) signaled partial remission of MDD in both treatment arms. In both groups, a subsequent decrease in both self-reported and expert-rated scores was evident after a 30-day period. However, the decrease in depression severity as measured by HDRS was significantly higher in the MBI group compared to the control group after 30 days. For secondary outcomes, systolic blood pressure was lower in the intervention group compared to control group. The total drop-out rate was 29%. Short term mindfulness intervention via mobile application (30 days) can be beneficial as adjunctive therapy to treatment as usual in patients with MDD.

Gastrointestinal (GI) symptoms such as bloating, constipation, and nausea are common in the days before menstruation, experienced by as many as 73 % of menstruating individuals. Mood may influence the link between menstrual cycle and GI symptoms, with prior studies indicating that even among healthy controls, GI symptoms worsen premenstrually and are associated with worsening mood. Associations between GI symptoms and mood are poorly understood among those with premenstrual syndrome (PMS), a cluster of mood and/or physical symptoms that occur in the week before menses affecting roughly 20 % of menstruators. Our primary aim was to examine associations between GI symptoms and mood symptoms across the menstrual cycle, in those who do and do not report PMS using a menstrual tracking app. We hypothesized that GI symptoms would be reported more frequently in the luteal phase than follicular phase, and that frequency of GI symptoms would be positively associated with mood symptoms in those with PMS. We analyzed data from 33,628 menstrual cycles across 32,241 participants, including n = 27,897 controls (29,137 menstrual cycles) and n = 4344 PMS participants (4491 menstrual cycles). GI symptoms were reported significantly more frequently in the luteal phase than the follicular phase in both control and PMS groups (p < 0.001). Mood symptoms were significantly positively associated with GI symptoms in both groups, in both follicular and luteal phases (p < 0.001). Results suggest that premenstrual GI symptoms are a common issue, and additional work is needed to explore associations between mood and GI symptoms in the context of the menstrual cycle.

Amyloid-β (Aβ) is thought to be neuronally derived in Alzheimer's disease (AD). However, transcripts of amyloid precursor protein (APP) and amyloidogenic enzymes are equally abundant in oligodendrocytes (OLs). By cell-type-specific deletion of Bace1 in a humanized knock-in AD model, APP, we demonstrate that OLs and neurons contribute to Aβ plaque burden. For rapid plaque seeding, excitatory projection neurons must provide a threshold level of Aβ. Ultimately, our findings are relevant for AD prevention and therapeutic strategies.

Children with autism spectrum disorder (ASD) experience high rates of atypical eating behaviours, such as food neophobia. Mobile health (mHealth) interventions have been found to improve communication, behaviour and social skills for children with ASD. However, there is limited evidence examining mHealth nutrition interventions among children with ASD.

In this observational study, we determined the distribution of mHealth applications (apps) in Germany using data from the FeverApp registry.

Autophagy, the major lysosomal pathway for degrading damaged or obsolete constituents, protects neurons by eliminating toxic organelles and peptides, restoring nutrient and energy homeostasis, and inhibiting apoptosis. These functions are especially vital in neurons, which are postmitotic and must survive for many decades while confronting mounting challenges of cell aging. Autophagy failure, especially related to the declining lysosomal ("phagy") functions, heightens the neuron's vulnerability to genetic and environmental factors underlying Alzheimer's disease (AD) and other late-age onset neurodegenerative diseases. Components of the global autophagy-lysosomal pathway and the closely integrated endolysosomal system are increasingly implicated as primary targets of these disorders. In AD, an imbalance between heightened autophagy induction and diminished lysosomal function in highly vulnerable pyramidal neuron populations yields an intracellular lysosomal build-up of undegraded substrates, including APP-βCTF, an inhibitor of lysosomal acidification, and membrane-damaging Aβ peptide. In the most compromised of these neurons, β-amyloid accumulates intraneuronally in plaque-like aggregates that become extracellular senile plaques when these neurons die, reflecting an "inside-out" origin of amyloid plaques seen in human AD brain and in mouse models of AD pathology. In this review, the author describes the importance of lysosomal-dependent neuronal cell death in AD associated with uniquely extreme autophagy pathology (PANTHOS) which is described as triggered by lysosomal membrane permeability during the earliest "intraneuronal" stage of AD. Effectors of other cell death cascades, notably calcium-activated calpains and protein kinases, contribute to lysosomal injury that induces leakage of cathepsins and activation of additional death cascades. Subsequent events in AD, such as microglial invasion and neuroinflammation, induce further cytotoxicity. In major neurodegenerative disease models, neuronal death and ensuing neuropathologies are substantially remediable by reversing underlying primary lysosomal deficits, thus implicating lysosomal failure and autophagy dysfunction as primary triggers of lysosomal-dependent cell death and AD pathogenesis and as promising therapeutic targets.

Isolated REM Sleep Behavior Disorder (iRBD) is considered a prodrome of Parkinson's disease (PD). We investigate whether the potentially disease-modifying compound acetyl-DL-leucine (ADLL; 5 g/d) has an effect on prodromal PD progression in 2 iRBD-patients. Outcome parameters are RBD-severity sum-score (RBD-SS-3), dopamine-transporter single-photon emission computerized tomography (DAT-SPECT) and metabolic "Parkinson-Disease-related-Pattern (PDRP)"-z-score in F-fluorodeoxyglucose positron emission tomography (FDG-PET). After 3 weeks ADLL-treatment, the RBD-SS-3 drops markedly in both patients and remains reduced for >18 months of ADLL-treatment. In patient 1 (female), the DAT-SPECT putaminal binding ratio (PBR) decreases in the 5 years pretreatment from normal (1.88) to pathological (1.22) and the patient's FDG-PET-PDRP-z-score rises from 1.72 to 3.28 (pathological). After 22 months of ADLL-treatment, the DAT-SPECT-PBR increases to 1.67 and the FDG-PET-PDRP-z-score stabilizes at 3.18. Similar results are seen in patient 2 (male): his DAT-SPECT-PBR rises from a pretreatment value of 1.42 to 1.72 (close to normal) and the FDG-PET-PDRP-z-score decreases from 1.02 to 0.30 after 18 months of ADLL-treatment. These results support exploration of whether ADLL may have disease-modifying properties in prodromal PD.

CORESS is an independent charity, supported by AXA Health, the MDU and the Kirby Laing Foundation. We are grateful to those who have provided the material for these reports. The online reporting form is available via the CORESS app and on the website (coress.org.uk), which also includes previous Feedback reports. Published cases are acknowledged by a Certificate of Contribution, which may be included in the contributor's record of continuing professional development, or which may form part of appraisal or annual review of competence progression portfolio documentation. Contributions from surgeons in training are particularly welcome.

Currently available clinical treatments on alcohol use disorder (AUD) exhibit limited efficacy and new druggable targets are required. One promising approach to discover new molecular treatment targets involves the transcriptomic profiling of brain regions within the addiction neurocircuitry, utilizing animal models and postmortem brain tissue from deceased patients with AUD. Unfortunately, such studies suffer from large heterogeneity and small sample sizes. To address these limitations, we conducted a cross-species meta-analysis on transcriptome-wide data obtained from brain tissue of patients with AUD and animal models. We integrated 36 cross-species transcriptome-wide RNA-expression datasets with an alcohol-dependent phenotype vs. controls, following the PRISMA guidelines. In total, we meta-analyzed 964 samples - 502 samples from the prefrontal cortex (PFC), 282 nucleus accumbens (NAc) samples, and 180 from amygdala (AMY). The PFC had the highest number of differentially expressed genes (DEGs) across rodents, monkeys, and humans. Commonly dysregulated DEGs suggest conserved cross-species mechanisms for chronic alcohol consumption/AUD comprising MAPKs as well as STAT, IRF7, and TNF. Furthermore, we identified numerous unique gene sets that might contribute individually to these conserved mechanisms and also suggest novel molecular aspects of AUD. Validation of the transcriptomic alterations on the protein level revealed interesting targets for further investigation. Finally, we identified a combination of DEGs that are commonly regulated across different brain tissues as potential biomarkers for AUD. In summary, we provide a compendium of genes that are assessable via a shiny app, and describe signaling pathways, and physiological and cellular processes that are altered in AUD that require future studies for functional validation.

Esketamine, the s(+) enantiomer of ketamine, was approved in 2019 as the first rapid-acting intranasal spray medication for treatment-resistant depression; the drug is given in combination with an antidepressant. The treatment is self-administered in a clinical setting under the supervision of a healthcare provider and usually is well tolerated. Many of its adverse reactions are mild, temporary, and dose-dependent, and they improve with subsequent treatments. Although the prescribing information lists difficulty remembering or thinking as possible adverse reactions, a neurocognitive evaluation is not part of the initial patient evaluation. This case report focuses on a patient whose neurocognitive symptoms worsened with esketamine treatment, necessitating treatment discontinuation.

The emergence of long COVID as a COVID-19 sequela was largely syndromic in characterisation. Digital health technologies such as wearable devices open the possibility to study this condition with passive, objective data in addition to self-reported symptoms. We aimed to quantify the prevalence and severity of symptoms across collected mobile health metrics over 12 weeks following COVID-19 diagnosis and to identify risk factors for the development of post-COVID-19 condition (also known as long COVID).

Sleep problems are diverse and pervasive among the adolescent population. Current sleep health interventions are ill-equipped to address the sleep crisis. We developed DOZE (elivering nline zz's with mpirical Support), which is a self-management evidence-based app for sleep disturbances. In an initial study, we found that DOZE was perceived as an acceptable and effective support for teen sleep. In a qualitative follow-up study, we engaged with students and other stakeholders to understand their experiences with implementing, disseminating, and using DOZE. The study employed a combination of qualitative surveys and semi-structured interviews to students ( = 21) and stakeholders (teachers, social workers, and researchers; = 5), respectively. Reflexive thematic analysis was used to identify themes related to experiences implementing and engaging with the app. Students reported increased sleep regularity and sleep duration after using DOZE. Facilitators included greater integration of the app with school curriculum, timing of implementation, and researcher involvement in supporting knowledge dissemination and engagement. Barriers included worries about phone use at night and normalized poor sleep patterns among adolescents. There is need to identify ways to support implementation and engagement in different communities. Researchers continue to engage with stakeholders to support timely access to sleep health interventions for adolescents.

Rotating shiftwork schedules are known to disrupt sleep in a manner that can negatively impact safety. Consumer sleep technologies (CSTs) may be a useful tool for sleep tracking, but the standard feedback provided by CSTs may not be salient to shift-working populations. SleepTank is an app that uses the total sleep time data scored by a CST to compute a percentage that equates hours of sleep to the fuel in a car and warns the user to sleep when the "tank" is low. Royal Australian Navy aircraft maintenance workers operating on a novel rotational shift schedule were given Fitbit Versa 2s to assess sleep timing, duration, and efficiency across a 10-week period. Half of the participants had access to just the Fitbit app while the other half had access to Fitbit and the SleepTank app. The goal of this study was to evaluate differences in sleep behavior between shifts using an off-the-shelf CST and to investigate the potential of the SleepTank app to increase sleep duration during the 10-week rotational shift work schedule.

Major depressive disorder (MDD) is common worldwide and can be highly disabling. People with MDD face many barriers to treatment and may not experience full symptom relief even when treated. Therefore, new treatment modalities are needed for MDD. Digital therapeutics (DTx) may provide people with MDD an additional treatment option.

Anxiety disorders are prevalent and undertreated among young adults. Digital mental health interventions for anxiety are promising but limited by a narrow range of therapeutic components and low user engagement.

Tinnitus, the perception of sound without an external source, affects 15% of the population, with 2.4% experiencing significant distress. In this review, we summarize the current state of knowledge about tinnitus management with a particular focus on the translation into clinical practice. In the first section, we analyze shortcomings, knowledge gaps, and challenges in the field of tinnitus research. Then, we highlight the relevance of the diagnostic process to account for tinnitus heterogeneity and to identify all relevant aspects of the tinnitus in an individual patient, such as etiological aspects, pathophysiological mechanisms, factors that contribute most to suffering, and comorbidities. In the next section, we review available treatment options, including counselling, cognitive-behavioral therapy (CBT), hearing aids and cochlear implants for patients with a relevant hearing loss, sound generators, novel auditory stimulation approaches, tinnitus retraining therapy (TRT), pharmacological treatment, neurofeedback, brain stimulation, bimodal stimulation, Internet- and app-based digital approaches, and alternative treatment approaches. The evidence for the effectiveness of the various treatment interventions varies considerably. We also discuss differences in current respective guideline recommendations and close with a discussion of how current pathophysiological knowledge, latest scientific evidence, and patient perspectives can be translated in patient-centered care.

Mental illnesses are a leading cause of disability globally, and functional disability is often in part caused by cognitive impairments across psychiatric disorders. However, studies have consistently reported seemingly opposite findings regarding the association between cognition and psychiatric symptoms.

Mental health in adolescence is critical in its own right and a predictor of later symptoms of anxiety and depression. To address these mental health challenges, it is crucial to understand the variables linked to anxiety and depression in adolescence.

Public safety personnel (PSP) such as firefighters, paramedics, and police are exposed to traumatic situations, which increase their risk for mental health issues. However, many PSP do not seek help in a timely manner. Peer support interventions have the potential to decrease stigma and increase treatment-seeking behaviours among PSP. However, little is known regarding how the organizational culture of public safety organizations (PSOs) affects the implementation of a peer-based intervention. This study aims to understand the extent to which organizational culture, including masculinity contest cultures (MCC), within Canadian PSOs could affect implementation of PeerOnCall, a new peer support app for PSP.

As digital phenotyping, the capture of active and passive data from consumer devices such as smartphones, becomes more common, the need to properly process the data and derive replicable features from it has become paramount. Cortex is an open-source data processing pipeline for digital phenotyping data, optimized for use with the mindLAMP apps, which is used by nearly 100 research teams across the world. Cortex is designed to help teams (1) assess digital phenotyping data quality in real time, (2) derive replicable clinical features from the data, and (3) enable easy-to-share data visualizations. Cortex offers many options to work with digital phenotyping data, although some common approaches are likely of value to all teams using it. This paper highlights the reasoning, code, and example steps necessary to fully work with digital phenotyping data in a streamlined manner. Covering how to work with the data, assess its quality, derive features, and visualize findings, this paper is designed to offer the reader the knowledge and skills to apply toward analyzing any digital phenotyping data set. More specifically, the paper will teach the reader the ins and outs of the Cortex Python package. This includes background information on its interaction with the mindLAMP platform, some basic commands to learn what data can be pulled and how, and more advanced use of the package mixed with basic Python with the goal of creating a correlation matrix. After the tutorial, different use cases of Cortex are discussed, along with limitations. Toward highlighting clinical applications, this paper also provides 3 easy ways to implement examples of Cortex use in real-world settings. By understanding how to work with digital phenotyping data and providing ready-to-deploy code with Cortex, the paper aims to show how the new field of digital phenotyping can be both accessible to all and rigorous in methodology.

A Disintegrin and Metalloproteinase 10 (ADAM10) is a crucial transmembrane protein involved in diverse cellular processes, including cell adhesion, migration, and proteolysis. ADAM10's ability to cleave over 100 substrates underscores its significance in physiological and pathological contexts, particularly in Alzheimer's disease (AD). This review comprehensively examines ADAM10's multifaceted roles, highlighting its critical function in the non-amyloidogenic processing of the amyloid precursor protein (APP), which mitigates amyloid beta (Aβ) production, a critical factor in AD development. We summarize the regulation of ADAM10 at multiple levels: transcriptional, translational, and post-translational, revealing the complexity and responsiveness of its expression to various cellular signals. A standardized nomenclature for ADAM10 isoforms is proposed to improve clarity and consistency in research, facilitating better comparison and replication of findings across studies. We address the challenges in detecting ADAM10 isoforms using antibodies, advocating for standardized detection protocols to resolve discrepancies in results from different biological matrices. By highlighting these issues, this review underscores the potential of ADAM10 as a biomarker for early diagnosis and a therapeutic target in AD. By consolidating current knowledge on ADAM10's regulation and function, we aim to provide insights that will guide future research and therapeutic strategies in the AD context.

The Hierarchical Taxonomy of Psychopathology (HiTOP) arranges phenotypes of mental disorders based on empirical covariation, ranging from narrowly defined symptoms to higher-order spectra of psychopathology. Since the introduction of personality functioning (PF) in DSM-5 and ICD-11, several studies have identified PF as a predictor of transdiagnostic aspects of psychopathology. However, the role of PF in the HiTOP classification system has not been systematically examined. This study investigates how PF can be integrated into HiTOP, whether PF accounts for transdiagnostic variance captured in higher-order spectra, and how its predictive value for future affective well-being (AWB) and psychosocial impairment (PSI) compares to the predictive value of specific psychopathology beyond PF. To this end, we examined two years of ambulatory assessed data on psychopathology, PF, PSI, and AWB of N = 27,173 users of a mental health app. Results of bass-ackwards analyses largely aligned with the current HiTOP working model. Using bifactor modeling, aspects of PF were identified to capture most of the internalizing, thought disorder, and externalizing higher-order factor variance. In longitudinal prediction analyses employing bifactor-(S-1) modeling, PF explained 58.6% and 30.6% of variance in PSI and AWB when assessed across one year, respectively, and 33.1% and 23.2% of variance when assessed across two years. Results indicate that personality functioning may largely account for transdiagnostic variance captured in the higher-order components in HiTOP as well as longitudinal outcomes of PSI and AWB. Clinicians and their patients may benefit from assessing PF aspects such as identity problems or internal relationship models in a broad range of mental disorders. Further, incorporating measures of PF may advance research in biological psychiatry by providing empirically sound phenotypes.

Specific moments of lapse among smokers attempting to quit often lead to full relapse, which highlights a need for interventions that target lapses before they might occur, such as just-in-time adaptive interventions (JITAIs). To inform the decision points and tailoring variables of a lapse prevention JITAI, we trained and tested supervised machine learning algorithms that use Ecological Momentary Assessments (EMAs) and wearable sensor data of potential lapse triggers and lapse incidence. We aimed to identify a best-performing and feasible algorithm to take forwards in a JITAI. For 10 days, adult smokers attempting to quit were asked to complete 16 hourly EMAs/day assessing cravings, mood, activity, social context, physical context, and lapse incidence, and to wear a Fitbit Charge 4 during waking hours to passively collect data on steps and heart rate. A series of group-level supervised machine learning algorithms (e.g., Random Forest, XGBoost) were trained and tested, without and with the sensor data. Their ability to predict lapses for out-of-sample (i) observations and (ii) individuals were evaluated. Next, a series of individual-level and hybrid (i.e., group- and individual-level) algorithms were trained and tested. Participants (N = 38) responded to 6,124 EMAs (with 6.9% of responses reporting a lapse). Without sensor data, the best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.899 (95% CI = 0.871-0.928). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUCper person = 0.524-0.994; median AUC = 0.639). 15/38 participants had adequate data for individual-level algorithms to be constructed, with a median AUC of 0.855 (range: 0.451-1.000). Hybrid algorithms could be constructed for 25/38 participants, with a median AUC of 0.692 (range: 0.523 to 0.998). With sensor data, the best-performing group-level algorithm had an AUC of 0.952 (95% CI = 0.933-0.970). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUCper person = 0.494-0.979; median AUC = 0.745). 11/30 participants had adequate data for individual-level algorithms to be constructed, with a median AUC of 0.983 (range: 0.549-1.000). Hybrid algorithms could be constructed for 20/30 participants, with a median AUC of 0.772 (range: 0.444 to 0.968). In conclusion, high-performing group-level lapse prediction algorithms without and with sensor data had variable performance when applied to out-of-sample individuals. Individual-level and hybrid algorithms could be constructed for a limited number of individuals but had improved performance, particularly when incorporating sensor data for participants with sufficient wear time. Feasibility constraints and the need to balance multiple success criteria in the JITAI development and implementation process are discussed.

Wearable sensors can measure movement in daily life, an outcome that is salient to patients, and have been critical to accelerating progress in rehabilitation research and practice. However, collecting and processing sensor data is burdensome, leaving many scientists with limited access to such data. To address these challenges, we present a harmonized, wearable sensor dataset that combines 2,885 recording days of sensor data from the upper and lower limbs from eight studies. The dataset includes 790 individuals ages 0 - 90, nearly equal sex proportions (53% male, 47% female), and representation from a range of demographic backgrounds (69.4% White, 24.9% Black, 1.8% Asian) and clinical conditions (46% neurotypical, 31% stroke, 7% Parkinson's disease, 6% orthopedic conditions, and others). The dataset is publicly available and accompanied by open source code and an app that allows for interaction with the data. This dataset will facilitate the use of sensor data to advance rehabilitation research and practice, improve the reproducibility and replicability of wearable sensor studies, and minimize costs and duplicated scientific efforts.

To address the need for interactive visualization tools and databases in characterizing multimorbidity patterns across different populations, we developed the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME). This tool leverages three large-scale EHR systems to facilitate efficient analysis and visualization of disease multimorbidity, aiming to reveal both robust and novel disease associations that are consistent across different systems and to provide insight for enhancing personalized healthcare strategies.

The mental health burden in India is increasing at unprecedented rates. The increased demand for mental health care and the undersupply of services has widened the treatment gap. Due to several factors, such as increasing service costs and the circumstances surrounding the COVID-19 pandemic, India has witnessed an inclination toward using digital mental health solutions to overcome the treatment gap. Drawing from the collective evidence and experience in implementing mental health solutions using digital phenotyping and smartphone app-based care delivery in India, we define the scope, potential, and challenges of implementing synchronous and asynchronous digital mental health solutions that can serve as a template for improving global mental health.

Sexual and reproductive health is critical for the readiness of the warfighter, as costs of sexually transmitted infections and unintended pregnancy result in added health care costs, lost mission time, and impact on morale. The Multiphase Optimization Strategy (MOST) is an engineering-inspired framework used to optimize biobehavioral interventions. The Military Active-Duty Reproductive and Sexual Health (MARSH) research team applied the MOST framework to develop "Mission Wellness"-an electronic health intervention to promote sexual and reproductive health within the U.S. Military.

With a renewed focus on health equity in the United States driven by national crises and legislation to improve digital healthcare innovation, there is a need for the designers of digital health tools to take deliberate steps to design for equity in their work. A concrete toolkit of methods to design for health equity is needed to support digital health practitioners in this aim. This narrative review summarizes several health equity frameworks to help digital health practitioners conceptualize the equity dimensions of importance for their work, and then provides design approaches that accommodate an equity focus. Specifically, the Double Diamond Model, the IDEAS framework and toolkit, and community collaboration techniques such as participatory design are explored as mechanisms for practitioners to solicit input from members of underserved groups and better design digital health tools that serve their needs. Each of these design methods requires a deliberate effort by practitioners to infuse health equity into the approach. A series of case studies that use different methods to build in equity considerations are offered to provide examples of how this can be accomplished and demonstrate the range of applications available depending on resources, budget, product maturity, and other factors. We conclude with a call for shared rigor around designing digital health tools that deliver equitable outcomes for members of underserved populations.

Chronic pelvic pain (CPP) in women with no obvious pelvic pathology has few evidence-based treatment options. Our recent multicenter randomized controlled trial (GaPP2) in women with CPP and no obvious pelvic pathology showed that gabapentin did not relieve pain overall and was associated with more side effects than placebo. We conducted an exploratory genome-wide association study using eligible GaPP2 participants aiming to identify genetic variants associated with gabapentin response. One genome-wide significant association with gabapentin analgesic response was identified, rs4442490, an intron variant located in Neuregulin 3 () ( = 2·11×10; OR = 18·82 (95% CI 4·86-72·83). Analysis of a large sample of UK Biobank participants demonstrated phenome-wide significant brain imaging features of rs4442490, particularly implicating the orbitofrontal cortex. is expressed predominantly in central nervous system tissues and plays a critical role in nervous system development, maintenance, and repair, suggesting a neurobiologically plausible role in gabapentin efficacy and potential for personalized analgesic treatment.

Measurement-based care (MBC) comprises collecting patient-reported outcomes data using validated assessments and using that information to support treatment. The Veterans Health Administration (VHA) has developed technology platforms to support MBC, including the Mental Health Checkup (MHC) mobile health application (app). Our objective was to examine VHA mental health provider perspectives on the MHC app. We completed a mixed-methods, sequential explanatory evaluation of MHC. We surveyed 284 VHA mental health providers who used MHC, then conducted semistructured telephone interviews with a purposefully selected subset of survey respondents ( = 20). Approximately half of survey respondents agreed that MHC allowed them to collect assessment data from veterans more frequently than before (51%) and that they more frequently discussed assessment results with veterans because of MHC (50%) and used those results to inform goal-setting discussions (50%) and treatment decision making (51%). Bivariate analyses indicated a positive relationship between frequency of MHC use and the aforementioned impacts on care. Interview data conveyed both advantages (e.g., increased treatment efficiency, improved treatment decision making) and challenges (e.g., limited assessment availability, difficulties engaging veterans in completing assessments through the app) to using MHC. This evaluation demonstrated how MHC supported providers working to implement MBC. The app enhanced their ability to reach and engage veterans and incorporate assessment data into clinical encounters. Still, many did not perceive that MHC was impactful on mental health care delivery; given that providers who used MHC more frequently reported more positive impressions of MHC, this may be related to how frequently they used the app. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Our prototype system designed for clinical data acquisition and recording of studies is a novel electronic data capture (EDC) software for simple and lightweight data capture in clinical research. Existing software tools are either costly or suffer from very limited features. To overcome these shortcomings, we designed an EDC software together with a mobile client. We aimed at making it easy to set-up, modifiable, scalable and thereby facilitating research. We wrote the software in R using a modular approach and implemented existing data standards along with a meta data driven interface and database structure. The prototype is an adaptable open-source software, which can be installed locally or in the cloud without advanced IT-knowledge. A mobile web interface and progressive web app for mobile use and desktop computers is added. We show the software's capability, by demonstrating four clinical studies with over 1600 participants and 679 variables per participant. We delineate a simple deployment approach for a server-installation and indicate further use-cases. The software is available under the MIT open-source license. Conclusively the software is versatile, easily deployable, highly modifiable, and extremely scalable for clinical studies. As an open-source R-software it is accessible, open to community-driven development and improvement in the future.

A third of adults in Western countries have impaired sleep quality. A possible solution involves distributing sleep aids through smartphone apps, but most empirical studies are limited to small pilot trials in distinct populations (eg, soldiers) or individuals with clinical sleep disorders; therefore, general population data are required. Furthermore, recent research shows that sleep app users desire a personalized approach, offering an individually tailored choice of techniques. One such aid is Peak Sleep, a smartphone app based on scientifically validated principles for improving sleep quality, such as mindfulness meditation and cognitive behavioral therapy.

Antipsychotic polypharmacy (APP) is frequently prescribed for schizophrenia-spectrum disorders. Despite the inconsistent findings on efficacy, APP may be beneficial for subgroups of psychotic patients. This meta-analysis of individual patient data investigated moderators of efficacy and tolerability of APP in adult patients with schizophrenia-spectrum disorders.

(1) Background: Alzheimer's disease (AD) is a worldwide neurodegenerative disorder characterized by the buildup of abnormal proteins in the central nervous system and cognitive decline. Since no radical therapy exists, only symptomatic treatments alleviate symptoms temporarily. In this review, we will explore the latest advancements in precision medicine and biomarkers for AD, including their potential to revolutionize the way we diagnose and treat this devastating condition. (2) Methods: A literature search was performed combining the following Medical Subject Heading (MeSH) terms on PubMed: "Alzheimer's disease", "biomarkers", "APOE", "APP", "GWAS", "cerebrospinal fluid", "polygenic risk score", "Aβ42", "τP-181", " p-tau217", "ptau231", "proteomics", "total tau protein", and "precision medicine" using Boolean operators. (3) Results: Genome-wide association studies (GWAS) have identified numerous genetic variants associated with AD risk, while a transcriptomic analysis has revealed dysregulated gene expression patterns in the brains of individuals with AD. The proteomic and metabolomic profiling of biological fluids, such as blood, urine, and CSF, and neuroimaging biomarkers have also yielded potential biomarkers of AD that could be used for the early diagnosis and monitoring of disease progression. (4) Conclusion: By leveraging a combination of the above biomarkers, novel ultrasensitive immunoassays, mass spectrometry methods, and metabolomics, researchers are making significant strides towards personalized healthcare for individuals with AD.

There is high comorbidity of opioid use disorder (OUD) and chronic pain (CP), which is often addressed by prescribing buprenorphine (BUP). While BUP is effective in preventing overdose, it does not address the psychological aspects of OUD and CP comorbidity and treatment retention rates are as low as 50%. The Virtual Opioid use disorder Integrated Chronic Pain Treatment (VOICE) study (NCT05039554) is a novel effectiveness-implementation trial to test a 12-week virtual group Acceptance and Commitment Therapy (ACT) protocol and a care management smartphone application (app; Valera Health) on pain and opioid use in patients with OUD and CP receiving BUP. Using a 2 × 2 factorial design, participants (expected N = 280) are randomized into: ACT, Valera app, ACT + Valera, or Treatment as Usual arm. This study is taking place in the Bronx, NY, a racially/ethnically diverse community that faces numerous socioeconomic stressors and is one of the nation's epicenters of the opioid epidemic. We created a culturally responsive ACT group protocol, and Valera psychoeducational material. Outcome measures include NIH HEAL Common Data Elements and ACT and Valera-specific measures. We are conducting a novel 2 × 2 trial investigating augmenting BUP treatment with ACT and Valera, with the goal that improved mental health and access to care will result in decreased and opioid use and pain interference.

To address enduring age-related tobacco disparities, it is critical to promote cessation treatment among older adults (aged 65+ years). Digital health platforms offer opportunities for wide dissemination of evidence-based behavioral cessation support. However, existing digital cessation treatments are not tailored to unique aging-related needs and preferences, resulting in low uptake. Detailed information is needed about how to best adapt these treatments for this age group.

 The overall goal of this work is to create a patient-reported outcome (PRO) and decision support system to help postpartum patients determine when to seek care for concerning symptoms. In this case study, we assessed differences in perspectives for application design needs based on race, ethnicity, and preferred language.

The COVID-19 pandemic gave rise to countless user-facing mobile apps to help fight the pandemic ("COVID-19 mitigation apps"). These apps have been at the center of data privacy discussions because they collect, use, and even retain sensitive personal data from their users (eg, medical records and location data). The US government ended its COVID-19 emergency declaration in May 2023, marking a unique time to comprehensively investigate how data privacy impacted people's acceptance of various COVID-19 mitigation apps deployed throughout the pandemic.

Perinatal depression affects a significant number of women during pregnancy and after birth, and early identification is imperative for timely interventions and improved prognosis. Mobile apps offer the potential to overcome barriers to health care provision and facilitate clinical research. However, little is known about users' perceptions and acceptability of these apps, particularly digital phenotyping and ecological momentary assessment apps, a relatively novel category of apps and approach to data collection. Understanding user's concerns and the challenges they experience using the app will facilitate adoption and continued engagement.

Youth screen media activity is a growing concern, though few studies include objective usage data. Through the longitudinal, U.S.-based Adolescent Brain Cognitive Development (ABCD) Study, youth (m = 14; n = 1415) self-reported their typical smartphone use and passively recorded three weeks of smartphone use via the ABCD-specific Effortless Assessment Research System (EARS) application. Here we describe and validate passively-sensed smartphone keyboard and app use measures, provide code to harmonize measures across operating systems, and describe trends in adolescent smartphone use. Keyboard and app-use measures were reliable and positively correlated with one another (r = 0.33) and with self-reported use (rs = 0.21-0.35). Participants recorded a mean of 5 h of daily smartphone use, which is two more hours than they self-reported. Further, females logged more smartphone use than males. Smartphone use was recorded at all hours, peaking on average from 8 to 10 PM and lowest from 3 to 5 AM. Social media and texting apps comprised nearly half of all use. Data are openly available to approved investigators ( https://nda.nih.gov/abcd/ ). Information herein can inform use of the ABCD dataset to longitudinally study health and neurodevelopmental correlates of adolescent smartphone use.

Voice features could be a sensitive marker of affective state in bipolar disorder (BD). Smartphone apps offer an excellent opportunity to collect voice data in the natural setting and become a useful tool in phase prediction in BD.

Individuals with intellectual disabilities often fail to learn complex tasks. Modified Goal Management Training (mGMT) or Errorless Learning combined with assistive technology (App + EL) can help. The goal is to demonstrate the effectiveness of mGMT and/or App + EL in learning complex tasks. We employed a randomized controlled crossover design. One group started with mGMT ( = 16), and the other with App + EL (N = 15). We compared their performance with that of a passive control group (N = 15). The training consisted of six sessions of 30 minutes each. Success was analyzed using the Goal Attainment Scale (GAS). Three different tasks were assessed before and after each intervention period: "Practiced", "Non-Practiced", or "Previously Practiced". Generalization was evaluated through neuropsychological tests. Results indicated that both interventions significantly improved "Practiced" tasks compared with "Non-Practiced" tasks and the control group. Crossing the intervention did not interfere with the stable performance on the "Previously Practiced" task. However, starting with mGMT reduced, but did not eliminate, the efficacy of App + EL after crossing, but this pattern was not observed for the reverse sequence. Only the Tower of London task documented improvements related to interventions. In conclusion, the mGMT and App + EL were effective in learning complex tasks and retaining performance after learning a second task. German Clinical Trials Register identifier: DRKS00021674.

We elucidated the molecular fingerprint of vulnerable excitatory neurons within select cortical lamina of individuals with Down syndrome (DS) for mechanistic understanding and therapeutic potential that also informs Alzheimer's disease (AD) pathophysiology. Frontal cortex (BA9) layer III (L3) and layer V (L5) pyramidal neurons were microisolated from postmortem human DS and age- and sex-matched controls (CTR) to interrogate differentially expressed genes (DEGs) and key biological pathways relevant to neurodegenerative programs. We identified > 2300 DEGs exhibiting convergent dysregulation of gene expression in both L3 and L5 pyramidal neurons in individuals with DS versus CTR subjects. DEGs included over 100 triplicated human chromosome 21 genes in L3 and L5 neurons, demonstrating a trisomic neuronal karyotype in both laminae. In addition, thousands of other DEGs were identified, indicating gene dysregulation is not limited to trisomic genes in the aged DS brain, which we postulate is relevant to AD pathobiology. Convergent L3 and L5 DEGs highlighted pertinent biological pathways and identified key pathway-associated targets likely underlying corticocortical neurodegeneration and related cognitive decline in individuals with DS. Select key DEGs were interrogated as potential hub genes driving dysregulation, namely the triplicated DEGs amyloid precursor protein (APP) and superoxide dismutase 1 (SOD1), along with key signaling DEGs including mitogen activated protein kinase 1 and 3 (MAPK1, MAPK3) and calcium calmodulin dependent protein kinase II alpha (CAMK2A), among others. Hub DEGs determined from multiple pathway analyses identified potential therapeutic candidates for amelioration of cortical neuron dysfunction and cognitive decline in DS with translational relevance to AD.

Impaired brain protein synthesis, synaptic plasticity, and memory are major hallmarks of Alzheimer's disease (AD). The ketamine metabolite (2R,6R)-hydroxynorketamine (HNK) has been shown to modulate protein synthesis, but its effects on memory in AD models remain elusive.

Smartphone apps may provide an opportunity to deliver mental health resources and interventions in a scalable and cost-effective manner. However, young people from marginalized and underserved groups face numerous and unique challenges to accessing, engaging with, and benefiting from these apps.

Adolescents and young adults (AYAs) with cancer are at risk of poor psychosocial outcomes. AYAs grew up with the internet and digital technology, and mobile Health (mHealth) psychosocial interventions have the potential to overcome care access barriers.

People with serious mental health problems (SMHP) are more likely to be admitted to psychiatric hospital following contact with crisis services. Admissions can have significant personal costs, be traumatic and are the most expensive form of mental health care. There is an urgent need for treatments to reduce suicidal thoughts and behaviours and reduce avoidable psychiatric admissions.

In China, standard smoking cessation practices are rarely used by healthcare service providers (HSPs). WeChat, a popular social media app, has been widely used in China.

Access to evidence-based interventions is urgently required, especially for individuals of minoritized identities who experience unique barriers to mental health care. Digital mental health interventions have the potential to increase accessibility. Previous pilot studies testing HabitWorks, a smartphone app providing an interpretation bias intervention, have found strong engagement and adherence for HabitWorks; however, previous trials' samples consisted of predominantly non-Hispanic, White individuals.

This study explores the experiences of young adults with psychosis using a smartphone application to promote patient activation and support shared decision making in their outpatient treatment.

Alzheimer's disease (AD) is a prevalent neurodegenerative disease characterized by progressive cognitive and functional decline. Nearly all patients with AD develop neuropsychiatric symptoms (NPSs). Agitation is one of the most distressing and challenging NPS. Brexpiprazole is an oral antipsychotic and is the first approved pharmacologic agent in the United States for the treatment of agitation associated with dementia due to AD. Its effect is thought to be from its partial serotonin 5-HT and dopamine D receptor agonist activity and serotonin 5-HT receptor antagonism. Brexpiprazole is a maintenance medication, and it should not be used "as needed" or as a "PRN" treatment for breakthrough agitation. Brexpiprazole is a major substrate of CYP2D6 and CYP3A4. Dose adjustments may be required for drug interactions or impaired renal or hepatic function. Clinical trials found brexpiprazole 2 to 3 mg/d demonstrated significant improvements in agitation, with brexpiprazole showing an approximate 5-point greater reduction on change in the Cohen-Mansfield Agitation Inventory total score at week 12 from baseline compared with placebo. Brexpiprazole is generally well tolerated and safe, and common adverse reactions when used for this indication include dizziness, headaches, insomnia, nasopharyngitis, somnolence, and urinary tract infections. Like other antipsychotics used for agitation in AD, brexpiprazole is associated with higher mortality rates compared with placebo. In a long-term care setting, there are several considerations for its use. Benefits include an oral agent that is well tolerated and clinical data showing statistically significant effects on agitation. However, brexpiprazole has not been studied in head-to-head clinical trials against other antipsychotics, and there are differing opinions if the agitation score reductions translate to a clinically meaningful difference. The approval of brexpiprazole signals favorably for upcoming agents for this indication, including escitalopram and dextromethorphan-bupropion. Both escitalopram and dextromethorphan-bupropion are currently undergoing clinical trials.

Cognitive behavioral therapy for insomnia (CBTi) is the first-line therapy for chronic insomnia. Mobile app-based CBTi (MCBTi) can enhance the accessibility of CBTi treatment; however, few studies have evaluated the effectiveness of MCBTi using a multicenter, randomized controlled trial design.

To pilot test QuitGuide for Natives, a culturally aligned version of the National Cancer Institute's QuitGuide smartphone app for smoking cessation.

This article examines the implementation, participation rates, and potential determinants of participation in the digital addiction prevention program "ready4life." A two-arm cluster-randomized trial recruited German vocational students via class-based strategies. Intervention group received 16 weeks of in-app coaching; the control group received health behavior information, with coaching offered after 12 months. Potential determinants of participation were analyzed based on class and individual characteristics. Out of 525 contacted schools, 35 participated, enrolling 376 classes. Implementation during the pandemic required flexible adjustments, with 49.7% of introductions conducted in person, 43.1% digitally via online streaming, and 7.2% received a video link via email. Despite challenges, 72.3% of the vocational students downloaded the app, and 46.7% gave informed consent. Participation rates were highest among (associate) professionals, vocational grammar school classes, classes introduced by females, younger individuals, members of the project team, and classes introduced face-to-face. Female gender, lower social competencies, lifetime cannabis use, higher problematic internet use, and higher perceived stress were associated with higher individual participation. The study highlights the importance of proactive outreach and personalized interventions for addiction prevention programs in vocational schools. While reached students aligned with the aims of the app, tailored recruitment strategies could enhance engagement among under-represented groups. The trial was registered in the German Clinical Trials Register (DRKS): DRKS00022328; registration date 09.10.2020.

Standardized definitions of suicidality phenotypes, including suicidal ideation (SI), attempt (SA), and death (SD) are a critical step towards improving understanding and comparison of results in suicide research. The complexity of suicidality contributes to heterogeneity in phenotype definitions, impeding evaluation of clinical and genetic risk factors across studies and efforts to combine samples within consortia. Here, we present expert and data-supported recommendations for defining suicidality and control phenotypes to facilitate merging current/legacy samples with definition variability and aid future sample creation.

Structural and functional changes of the choroid plexus (ChP) have been reported in Alzheimer's disease (AD). Nonetheless, the role of the ChP in the pathogenesis of AD remains largely unknown. We aim to unravel the relation between ChP functioning and core AD pathogenesis using a unique proteomic approach in mice and humans.

Time-to-event prediction is a key task for biological discovery, experimental medicine, and clinical care. This is particularly true for neurological diseases where development of reliable biomarkers is often limited by difficulty visualising and sampling relevant cell and molecular pathobiology. To date, much work has relied on Cox regression because of ease-of-use, despite evidence that this model includes incorrect assumptions. We have implemented a set of deep learning and spline models for time-to-event modelling within a fully customizable 'app' and accompanying online portal, both of which can be used for any time-to-event analysis in any disease by a non-expert user. Our online portal includes capacity for end-users including patients, Neurology clinicians, and researchers, to access and perform predictions using a trained model, and to contribute new data for model improvement, all within a data-secure environment. We demonstrate a pipeline for use of our app with three use-cases including imputation of missing data, hyperparameter tuning, model training and independent validation. We show that predictions are optimal for use in downstream applications such as genetic discovery, biomarker interpretation, and personalised choice of medication. We demonstrate the efficiency of an ensemble configuration, including focused training of a deep learning model. We have optimised a pipeline for imputation of missing data in combination with time-to-event prediction models. Overall, we provide a powerful and accessible tool to develop, access and share time-to-event prediction models; all software and tutorials are available at www.predictte.org.

Behavioral interventions require considerable practice of treatment skills in between therapy sessions. The effects of these treatments may vary with the degree to which patients are able to implement these practices. In offspring of parents with bipolar and major depressive disorders, we examined whether youth who frequently practiced communication and problem-solving skills between family-focused therapy (FFT) sessions had less severe mood symptoms and better psychosocial functioning over 6 months than youth who practiced less frequently.

• ESMO Clinical Practice Guideline with key recommendations for communication and support of cancer patients and caregivers. • The guideline discusses training in communication of oncology clinicians and research on communication in cancer care. • Practical recommendations aim to support oncology clinicians in their communication with patients and caregivers. • Figures summarising the responsibilities of the clinician, the oncology team and the health care institution are provided.

Transmembrane and coiled-coil 2 (TMCC2) is a human orthologue of the Drosophila gene dementin, mutant alleles of which cause neurodegeneration with features of Alzheimer's disease (AD). TMCC2 and Dementin further have an evolutionarily conserved interaction with the amyloid protein precursor (APP), a protein central to AD pathogenesis. To investigate if human TMCC2 might also participate in mechanisms of neurodegeneration, we examined TMCC2 expression in late onset AD human brain and age-matched controls, familial AD cases bearing a mutation in APP Val717, and Down syndrome AD. Consistent with previous observations of complex formation between TMCC2 and APP in the rat brain, the dual immunocytochemistry of control human temporal cortex showed highly similar distributions of TMCC2 and APP. In late onset AD cases stratified by APOE genotype, TMCC2 immunoreactivity was associated with dense core senile plaques and adjacent neuronal dystrophies, but not with Aβ surrounding the core, diffuse Aβ plaques or tauopathy. In Down syndrome AD, we observed in addition TMCC2-immunoreactive and methoxy-X04-positive pathological features that were morphologically distinct from those seen in the late onset and familial AD cases, suggesting enhanced pathological alteration of TMCC2 in Down syndrome AD. At the protein level, western blots of human brain extracts revealed that human brain-derived TMCC2 exists as at least three isoforms, the relative abundance of which varied between the temporal gyrus and cerebellum and was influenced by APOE and/or dementia status. Our findings thus implicate human TMCC2 in AD via its interactions with APP, its association with dense core plaques, as well as its alteration in Down syndrome AD.

Fathers can be a critical source of breastfeeding support for their partner, but little is known about what fathers would like to learn about breastfeeding. Partner's support and encouragement enhances mother's breastfeeding confidence and boost the capacity to address breastfeeding difficulties effectively. The aims of this study were to explore what fathers regard as important to learn around breastfeeding, and their current and preferred sources of information.

The management of stress has evolved in recent years due to widespread availability of mobile-device applications (apps) and their capacity to deliver psychological interventions. We evaluated the efficacy of mental health apps on stress and sought to identify characteristics associated with effect size estimates. Sixty-nine randomised controlled trials (RCTs) were included. Random effects meta-analyses were performed and putative moderators were examined at univariate and multivariate (combinations and interactions) levels. From 78 comparisons, we observed a small but significant pooled effect of apps over control conditions on perceived stress levels ( = 0.27; 95% CI = 0.20, 0.34; = 68%). This effect weakened after taking into account small-study bias according to the trim-and-fill procedure ( = 0.10; 95% CI = 0.02, 0.19; = 78%). Delivery of apps with stress monitoring features produced smaller efficacy estimates, although this association interacted with other trial features (small sample size and inactive control group) in multivariate analyses, suggesting that this effect may have been explained by features characteristic of low-quality trials. Mental health apps appear to have small, acute effects on reducing perceived stress. Future research should shift focus towards identifying change mechanisms, longitudinal outcomes, features that facilitate sustained app usage, and tangible pathways to integrating apps into real-world clinical settings.

The extent to which interventions are perceived as acceptable to users impacts engagement and efficacy.

Intensive longitudinal research-including experience sampling and smartphone sensor monitoring-has potential for identifying proximal risk factors for psychopathology, including suicidal thoughts and behaviors (STB). Yet, missing data can complicate analysis and interpretation. This study aimed to address whether clinical and study design factors are associated with missing data and whether missingness predicts changes in symptom severity or STB. Adolescents ages 13- to 18 years old ( = 179) reporting depressive, anxiety, and/or substance use disorders were enrolled; 65% reported current suicidal ideation and 29% indicated a past-year attempt. Passively acquired smartphone sensor data (e.g., global positioning system, accelerometer, and keyboard inputs), daily mood surveys, and weekly suicidal ideation surveys were collected during the 6-month study period using the effortless assessment research system smartphone app. First, acquisition of passive smartphone sensor data (with data on ∼80% of days across the whole sample) was strongly associated with survey data acquisition on the same day (∼44% of days). Second, STB and psychiatric symptoms were largely not associated with missing data. Rather, temporal features (e.g., length of time in study, weekends, and summer) explained more missingness of survey and passive smartphone sensor data. Last, within-participant changes in missing data over time neither followed nor predicted subsequent change in suicidal ideation and psychiatric symptoms. Findings indicate that considering technical and study design factors impacting missingness is critical and highlight several factors that should be addressed to maximize the validity of clinical interpretations in intensive longitudinal research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Cannabigerol (CBG) is a phytocannabinoid increasing in popularity, with preclinical research indicating it has anxiolytic and antidepressant effects. However, there are no published clinical trials to corroborate these findings in humans. The primary objective of this study was to examine acute effects of CBG on anxiety, stress, and mood. Secondary objectives were to examine whether CBG produces subjective drug effects or motor and cognitive impairments. A double-blind, placebo-controlled cross-over field trial was conducted with 34 healthy adult participants. Participants completed two sessions (with a one-week washout period) via Zoom. In each, they provided ratings of anxiety, stress, mood, and subjective drug effects prior to double-blind administration of 20 mg hemp-derived CBG or placebo tincture (T0). These ratings were collected again after participants ingested the product and completed an online survey (T1), the Trier Social Stress Test (T2), a verbal memory test and the DRUID impairment app (T3). Relative to placebo, there was a significant main effect of CBG on overall reductions in anxiety as well as reductions in stress at T1. CBG also enhanced verbal memory relative to placebo. There was no evidence of subjective drug effects or impairment. CBG may represent a novel option to reduce stress and anxiety in healthy adults.

Mental health problems among workers cause enormous losses to companies in Japan. However, workers have been considered to have limited access to psychological support because of time constraints, which makes it difficult for them to engage in face-to-face psychological support interventions.

We present results from a 2-site, randomized clinical trial to assess the efficacy of a brief intervention (As Safe As Possible [ASAP]), a safety plan phone application (BRITE), and their combination on suicide attempts, suicidal ideation, non-suicidal self-injury, re-hospitalizations. and suicidal events among adolescents.

Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used approach for the generation of gene co-expression networks. However, networks generated with this tool usually create large modules with a large set of functional annotations hard to decipher. We have developed TGCN, a new method to create Targeted Gene Co-expression Networks. This method identifies the transcripts that best predict the trait of interest based on gene expression using a refinement of the LASSO regression. Then, it builds the co-expression modules around those transcripts. Algorithm properties were characterized using the expression of 13 brain regions from the Genotype-Tissue Expression project. When comparing our method with WGCNA, TGCN networks lead to more precise modules that have more specific and yet rich biological meaning. Then, we illustrate its applicability by creating an APP-TGCN on The Religious Orders Study and Memory and Aging Project dataset, aiming to identify the molecular pathways specifically associated with APP role in Alzheimer's disease. Main biological findings were further validated in two independent cohorts. In conclusion, we provide a new framework that serves to create targeted networks that are smaller, biologically relevant and useful in high throughput hypothesis driven research. The TGCN R package is available on Github: https://github.com/aliciagp/TGCN .

People with epilepsy frequently under- or inaccurately report their seizures, which poses a challenge for evaluating their treatment. The introduction of epilepsy health apps provides a novel approach that could improve seizure documentation. This study assessed the documentation performance of an app-based seizure diary and a conventional paper seizure diary. At two tertiary epilepsy centers patients were asked to use one of two offered methods to report their seizures (paper or app diary) during their stay in the epilepsy monitoring unit. The performances of both methods were assessed based on the gold standard of video-EEG annotations. In total 89 adults (54 paper and 35 app users) with focal epilepsy were included in the analysis, of which 58 (33 paper and 25 app users) experienced at least one seizure and made at least one seizure diary entry. We observed a high precision of 85.7% for the app group, whereas the paper group's precision was lower due to overreporting (66.9%). Sensitivity was similar for both methods. Our findings imply that performance of seizure self-reporting is patient-dependent but is more precise for patients who are willing to use digital apps. This may be relevant for treatment decisions and future clinical trial design.

Mobile mental health apps are a cost-effective option for managing mental health problems, such as posttraumatic stress disorder (PTSD). The efficacy of mobile health (mHealth) apps depends on engagement with the app, but few studies have examined how users engage with different features of mHealth apps for PTSD.

Neurovascular defects are one of the most common alterations in Alzheimer's disease (AD) pathogenesis, but whether these deficits develop before the onset of amyloid beta (Aβ) accumulation remains to be determined. Using in vivo optical imaging in freely moving mice, we explored activity-induced hippocampal microvascular blood flow dynamics in App knock-in and J20 mouse models of AD at early stages of disease progression. We found that prior to the onset of Aβ accumulation, there was a pathologically elevated blood flow response to context exploration, termed functional hyperemia. After the onset of Aβ accumulation, this context exploration-induced hyperemia declined rapidly relative to that in control mice. Using in vivo electrophysiology recordings to explore the neural circuit mechanism underlying this blood flow alteration, we found that hippocampal interneurons before the onset of Aβ accumulation were hyperactive during context exploration. Chemogenetic tests suggest that hyperactive activation of inhibitory neurons accounted for the elevated functional hyperemia. The suppression of nitric oxide (NO) produced from hippocampal interneurons in young AD mice decreased the accumulation of Aβ. Together, these findings reveal that neurovascular coupling is aberrantly elevated before Aβ deposition, and this hyperactive functional hyperemia declines rapidly upon Aβ accumulation.

Specific phobias impact over 400 million people worldwide. Digitalizing mental health could alleviate the burden. Still, although the corporate-driven Metaverse is expanding rapidly, there needs to be more momentum in harnessing virtual reality exposure therapy uptake. This study aims to conceptualize, develop, and deploy a free Virtual Reality Exposure Therapy (VRET) application specifically designed for treating acrophobia and claustrophobia. This pilot study, which holds the promise of a future where mental health is more accessible and effective, explores the feasibility of leveraging transdisciplinary collaboration among specialists to create a safe, accessible, and effective VRET solution. We conducted a Delphi heuristic approach involving bioethicists, neuroscientists, and tech developers. Second, we reviewed the existing psychological theories and therapeutic strategies for addressing phobias in VR. Third, we conceptualized a thematic analysis-derived framework for a safe, adaptive-gamified free exposure to virtual reality acrophobia and claustrophobia (SAFEvR ACT). Finally, we provide an overview of the iterative improvements made during 12 workshops and 76 weekly briefings on developmental implementations. We developed the SAFEvR ACT into a proof-of-concept application freely deployed on the MentalVerse app platform. Our safety-focused approach can benefit from prevalidation perspectives within future randomized control trials. The resulting application derived from the SAFEvR ACT framework represents a blueprint to counter the current lack of iVR mental health uptake by offering a free VRET alternative. Future research should aim towards developing similar free platforms to lessen mental health burdens and gather quantitative data. We conclude with a call to action to researchers to fine-tune our current approach and take a stand for free digital mental health within MentalVeRse.app.

The purpose of this pilot study was to conduct a process evaluation of a mental health and wellness mobile health (mHealth) application for medical students designed to increase resilience and decrease mental health stigma.

Despite a marked increase in the prevalence of pediatric IgE-mediated food allergy (FA) in recent decades, there is a dearth of age-appropriate management education and support tools for youth with FA. The purpose of this report is to detail our methods for intervention design and refinement of an interactive educational software program for school-aged children with FA.

Pediatric oncology patients use totally implantable venous access devices (TIVADs) to enable central venous access. Anxiety, pain and/or discomfort are common despite anesthesia. Virtual reality (VR) is a non-pharmacological approach that may reduce pain and anxiety in these patients. We aimed to assess the use of VR for reducing anxiety/pain in patients with TIVADs while facilitating the task of healthcare providers when accessing a TIVAD.

This is a process evaluation of a large UK-based randomised controlled trial (RCT) (n = 5602) evaluating the effectiveness of recommending an alcohol reduction app, Drink Less, compared with usual digital care in reducing alcohol consumption in increasing and higher risk drinkers. The aim was to understand whether participants' engagement ('self-reported adherence') and behavioural characteristics were mechanisms of action underpinning the effectiveness of Drink Less. Self-reported adherence with both digital tools was over 70% (Drink Less: 78.0%, 95% CI = 77.6-78.4; usual digital care: 71.5%, 95% CI = 71.0-71.9). Self-reported adherence to the intervention (average causal mediation effect [ACME] = -0.250, 95% CI = -0.42, -0.11) and self-monitoring behaviour (ACME = -0.235, 95% CI = -0.44, -0.03) both partially mediated the effect of the intervention (versus comparator) on alcohol reduction. Following the recommendation (self-reported adherence) and the tracking (self-monitoring behaviour) feature of the Drink Less app appear to be important mechanisms of action for alcohol reduction among increasing and higher risk drinkers.

Traditional cognitive assessments in schizophrenia are time-consuming and necessitate specialized training, making routine evaluation challenging. To overcome these limitations, this study investigates the feasibility and advantages of utilizing smartphone-based assessments to capture both cognitive functioning and digital phenotyping data and compare these results to gold standard measures. We conducted a secondary analysis of data from 76 individuals with schizophrenia, who were recruited across three sites (one in Boston, two in India) was conducted. The open-source mindLAMP smartphone app captured digital phenotyping data and Trails A/B assessments of attention / memory for up to 12 months. The smartphone-cognitive tasks exhibited potential for normal distribution and these scores showed small but significant correlations with the results from the Brief Assessment of Cognition in Schizophrenia, especially the digital span and symbol coding tasks (r2 = 0.21). A small but significant correlation (r2 = 0.29) between smartphone-derived cognitive scores and health-related behaviors such as sleep duration patterns was observed. Smartphone-based cognitive assessments show promise as cross-cultural tools that can capture relevant data on momentary states among individuals with schizophrenia. Cognitive results related to sleep suggest functional applications to digital phenotyping data, and the potential of this multimodal data approach in research.

Home-based psychosocial care has the potential to improving outcomes in patients with schizophrenia and related disorders (SCZ). There is lack of India data for such care in early psychosis. We developed the "Saksham" programme, a bespoke self-managed home-based psychosocial care model, available in two formats: manual-based and mobile-application based. With the anticipated success of recruitment of early psychosis cases in our setting, we plan to test the such intervention in this population in future trials.

Non-compliance to medications remains a challenging problem in schizophrenia. Newer strategies with high feasibility and acceptability are always being researched. This study aimed to assess the effectiveness of technology-based intervention in improving medication compliance in individuals with schizophrenia.

Mobile apps provide a unique platform for mental health assessment and monitoring. They can provide real time, accessible data on symptoms of mental disorders that may yield rich data for detailed clinical assessment and help individuals gain insight into their current mental state. We developed one of the first apps for tracking symptoms of prolonged grief disorder.

Model-based planning is thought to protect against over-reliance on habits. It is reduced in individuals high in compulsivity, but effect sizes are small and may depend on subtle features of the tasks used to assess it. We developed a diamond-shooting smartphone game that measures model-based planning in an at-home setting, and varied the game's structure within and across participants to assess how it affects measurement reliability and validity with respect to previously established correlates of model-based planning, with a focus on compulsivity. Increasing the number of trials used to estimate model-based planning did remarkably little to affect the association with compulsivity, because the greatest signal was in earlier trials. Associations with compulsivity were higher when transition ratios were less deterministic and depending on the reward drift utilised. These findings suggest that model-based planning can be measured at home via an app, can be estimated in relatively few trials using certain design features, and can be optimised for sensitivity to compulsive symptoms in the general population.

The mobile health application "Stress Autism Mate" (SAM) was designed to support adults with autism in identifying and managing daily stress. SAM measures stress four times daily, provides a daily and weekly stress overview, and provides personalised stress reduction advice. This study aimed to assess the effectiveness of SAM over four weeks in reducing perceived stress and internalised stigma, and enhancing coping self-efficacy, quality of life, and resilience among adults with autism.

There is scant empirical work on associations between current and past cannabis use and parenting skills in parents of young children. As recreational cannabis use is now legal in nearly half of states in the U.S., cannabis use is becoming more ubiquitous.

Paranoia is relatively common but can lead to significant distress, impairment and need for care. Digital technologies offer a valuable extension to service provision and are increasingly being integrated into healthcare. This systematic review evaluated feasibility, acceptability, and effectiveness of digitally enhanced psychological assessments and treatments for paranoia across the paranoia continuum (PROSPERO: CRD42023393257).

Adverse effects of chronically high levels of stress on physical and mental health are well established. In physicians, the effects of elevated stress levels exceed the individual level and include treatment errors and reduced quality of patient-doctor relationships. Breathing and mindfulness-based exercises have been shown to reduce stress and could serve as an immediate and easy-to-implement anti-stress intervention among physicians. Due to the heterogeneity of their effect on stress, we aim to evaluate the intervention effect of performing a short daily breathwork-based or mindfulness-based intervention on the everyday level of perceived stress in physicians in residence in Germany in a series of N-of-1 trials.

The chapter explores the role of neuropsychology in understanding brain fog as a subjective complaint in the context of COVID-19. It discusses the historical and medical significance of the term "brain fog" and its psychological and neurological aspects. The chapter identifies the cognitive domains commonly affected by brain fog, such as attention, executive function, memory, and language. Additionally, it emphasizes the impact of societal changes during the COVID-19 pandemic on the general population as a crucial backdrop for understanding the issue. The chapter also highlights the important role of clinical and research neuropsychologists in gaining clarity on grouped data and individual patients' cognitive and emotional difficulties after COVID-19 infection. It discusses indications for neuropsychological rehabilitation and therapy and describes typical therapy phases and methods, including new approaches like telemedicine, virtual reality, and mobile app-based rehabilitation and self-tracking. The chapter underscores that experiences of brain fog can vary among COVID-19 patients and may change over time. It provides clinicians and interested parties with an in-depth understanding of brain fog and its manifestations, concomitant subtypes, and concrete strategies for addressing it. The chapter emphasizes the critical role of neuropsychology in scientifically examining brain fog and advocating for personalized approaches to cognitive rehabilitation.

Modulation of physical activity represents an important intervention that may delay, slow, or prevent mild cognitive impairment (MCI) or dementia due to Alzheimer's disease (AD). One mechanism proposed to underlie the beneficial effect of physical exercise (PE) involves the apparent stimulation of adult hippocampal neurogenesis (AHN). BCI-838 is a pro-drug whose active metabolite BCI-632 is a negative allosteric modulator at group II metabotropic glutamate receptors (mGluR2/3). We previously demonstrated that administration of BCI-838 to a mouse model of brain accumulation of oligomeric Aβ ( = "") reduced learning behavior impairment and anxiety, both of which are associated with the phenotype of mice.

Neuroinflammation includes the activation of immune glial cells in the central nervous system, release pro-inflammatory cytokines, which disrupt normal neural function and contribute to various neurological disorders, including Alzheimer's disease (AD), Parkinson's disease, multiple sclerosis, and stroke. AD is characterized by various factors including amyloidogenesis, synaptic dysfunction, memory impairment and neuroinflammation. Lipopolysaccharide (LPS) constitutes a vital element of membrane of the gram-negative bacterial cell, triggering vigorous neuroinflammation and facilitating neurodegeneration. Lupeol, a naturally occurring pentacyclic triterpene, has demonstrated several pharmacological properties, notably its anti-inflammatory activity. In this study, we evaluated the anti-inflammatory and anti-Alzheimer activity of lupeol in lipopolysaccharide (LPS)-injected mice model. LPS (250ug/kg) was administered intraperitoneally to C57BL/6 N male mice for 1 week to induce neuroinflammation and cognitive impairment. For biochemical analysis, acetylcholinesterase (AChE) assay, western blotting and confocal microscopy were performed. AChE, western blot and immunofluorescence results showed that lupeol treatment (50 mg/kg) along with LPS administration significantly inhibited the LPS-induced activation of neuroinflammatory mediators and cytokines like nuclear factor (NF-κB), tumor necrosis factor (TNF-α), cyclooxygenase (COX-2) and interleukin (IL-1β). Furthermore, we found that LPS-induced systemic inflammation lead to Alzheimer's symptoms as LPS treatment enhances level of amyloid beta (Aβ), amyloid precursor protein (APP), Beta-site APP cleaving enzyme (BACE-1) and hyperphosphorylated Tau (p-Tau). Lupeol treatment reversed the LPS-induced elevated level of Aβ, APP, BACE-1 and p-Tau in the hippocampus, showing anti-Alzheimer's properties. It is also determined that lupeol prevented LPS-induced synaptic dysfunction via enhanced expression of pre-and post-synaptic markers like SNAP-23, synaptophysin and PSD-95. Overall, our study shows that lupeol prevents memory impairment and synaptic dysfunction via inhibition of neuroinflammatory processes. Hence, we suggest that lupeol might be a useful therapeutic agent in prevention of neuroinflammation-induced neurological disorders like AD.

Trace amine-associated receptor 1 (TAAR1) agonism shows promise for treating psychosis, prompting us to synthesise data from human and non-human studies.

Anhedonia is a key symptom of depression, and it has been suggested as a potential target for future individualised treatments. However, much is unknown about how interventions enhancing dopaminergic pathways may affect anhedonia symptoms in the context of depression. We will perform independent searches in multiple electronic databases to identify clinical and animal experimental studies on pro-dopaminergic interventions in individuals with depression or animal models for depression. The primary outcomes will be overall anhedonia symptoms and their behavioural proxies in animals. Secondary outcomes will include side effects and neurobiological measures. At least two independent reviewers will conduct the study selection, data extraction, and risk of bias assessments using pre-defined tools according to each record's study design. We will develop ontologies to facilitate study identification and data extraction. We will synthesise data from clinical and animal studies separately. If appropriate, we will use random-effects meta-analyses, or synthesis without meta-analyses. We will investigate study characteristics as potential sources of heterogeneity. We will evaluate the confidence in the evidence for each outcome and source of evidence, considering the summary of the association, potential concerns regarding internal and external validity, and reporting biases. When multiple sources of evidence are available for an outcome, we will draw an overall conclusion in a triangulation meeting involving a multidisciplinary team of experts. We plan updates of the review every 6 months, and any future modifications to the protocol will be documented. We will co-produce this review with multiple stakeholders. CRD42023451821.

Addressing the limited access to treatments for alcohol dependence, we developed ALM-002, a therapeutic application to be "prescribed" for non-abstinence-oriented treatment in internal medicine settings. Our objective was to preliminarily assess the efficacy and safety of ALM-002.