Search Results (153)

Awake Bruxism (AB) is defined as masticatory muscle activity during wakefulness, characterized by repetitive or sustained tooth contact and/or the bracing or thrusting of the mandible. AB remains less understood than Sleep Bruxism (SB), and its identification remains a methodological challenge. The aim of this study was to introduce the Awake Bruxism Identification Tool (ABIT), developed for children and adolescents aged 8 to 12 years, to facilitate the identification of AB. The tool integrates data from self-reports, clinical examinations, and the Ecological Momentary Assessment (EMA). It comprises questionnaires using a five-point Likert scale, an analog EMA component involving color-based responses, and a clinical inspection. The tool adopts the concept of an “AB Spectrum”, as it generates individualized scores based on the combined outcomes of these multiple assessment components. The ABIT was piloted with ten families to evaluate its comprehensibility, applicability, and reliability. The results demonstrated that the participants found the questions understandable, that the tool had a minimal impact on daily family routines, and that it required approximately 5–10 min to complete. Additionally, the test–retest reliability indicated temporal stability. In terms of identification, four children were classified within the “AB identified by report and self-report,” while three were identified through the “report, self-report, and EMA.” Based on participant feedback, adjustments were made to the instrument, including the addition of an item addressing Sleep Bruxism. Although the ABIT is being applied for the first time in a research setting, it presents a promising, clinically relevant approach grounded in the self-perception of children and their caregivers. Full article

Digital biomarkers for fatigue monitoring in neurological disorders represent an innovative approach to bridge the gap between mechanistic understanding and clinical application. This perspective paper examines how smartphone-derived measures, analyzed through artificial intelligence methods, can transform fatigue assessment from subjective, episodic reporting to continuous, objective monitoring. The proposed framework for smartphone-based digital phenotyping captures passive data (movement patterns, device interactions, and sleep metrics) and active assessments (ecological momentary assessments, cognitive tests, and voice analysis). These digital biomarkers can be validated through a multimodal approach connecting them to neuroimaging markers, clinical assessments, performance measures, and patient-reported experiences. Building on the previous research on frontal–striatal metabolism in multiple sclerosis and Long-COVID-19 patients, digital biomarkers could enable early warning systems for fatigue episodes, objective treatment response monitoring, and personalized fatigue management strategies. Implementation considerations include privacy protection, equity concerns, and regulatory pathways. By integrating smartphone-derived digital biomarkers with AI analysis approaches, the future envisions fatigue in neurological disorders no longer as an invisible, subjective experience but rather as a quantifiable, treatable phenomenon with established neural correlates and effective interventions. This transformative approach has significant potential to enhance both clinical care and the research for millions affected by disabling fatigue symptoms. Full article

Background: Borderline Personality Disorder (BPD) is a debilitating mental health condition characterized by emotional dysregulation and interpersonal dysfunction, with perceived social rejection exacerbating these issues. Emerging evidence suggests that a single session of transcranial direct current stimulation (tDCS) over the right ventrolateral prefrontal cortex (rVLPFC) may decrease the unique tendency of BPD patients to feel rejected even when socially included during a laboratory task. Objectives: This protocol outlines a double-blind, sham-controlled study evaluating the longitudinal effects of repeated anodal tDCS over the right ventrolateral prefrontal cortex (rVLPFC) on rejection-related emotions (RRE) during real-life social interactions in individuals with BPD. Methods: Sixty BPD patients will be randomized to receive real or sham tDCS across 10 daily sessions, coupled with an ecological momentary assessment (EMA) protocol capturing emotional and behavioral responses to real-life social interactions over four timepoints: baseline, during treatment, ten days post-treatment, and three months post-treatment. Primary outcomes include changes in RRE, with exploratory analyses examining feelings of social connection, aggressive tendencies, trust toward others, and interpersonal and affective dynamics. Multilevel modeling will assess temporal and group-level effects. Expected Results and Impact: This study aims to establish the efficacy of tDCS in reducing BPD patients’ negative emotional response in real-life social situations and to determine whether such effects are maintained in time. The findings could advance the clinical application of tDCS as an adjunctive intervention to alleviate social–emotional impairments in BPD, addressing gaps in current treatment approaches and guiding future research into the neural mechanisms of social emotion regulation. Full article

Background: Musculoskeletal problems contribute to nurse attrition, which compromises patient safety and costs healthcare organizations millions of dollars. Recent research describes a physical activity paradox in which high amounts of work-related physical activity may be detrimental to health; however, there is a lack of evidence on the physical activity paradox with respect to musculoskeletal health in nurses. The purpose of this study was to examine the relationship between musculoskeletal symptoms (MSSs) and high amounts of physical activity at work in nurses. Methods: This was a 7-day observational design using direct measurement of physical activity and self-reported MSSs in nurses. Physical activity was measured in step counts using a wearable accelerometer and MSSs were reported using ecological momentary assessment. Step counts and MSSs were compared between work days and days off, and a regression model analyzed the combined effect of physical activity and work days on MSSs while controlling for age, exercise, and body mass index. Results: Musculoskeletal symptoms and step counts were significantly higher on work days compared to days off. Higher step counts on work days resulted in significantly higher expected MSS ratings than the same number of steps taken on a day off. Conclusions: This study supports the existence of a physical activity paradox in nurses with respect to MSSs. Understanding this paradox in the nursing workforce can translate to interventions that reduce the detrimental health effects of high levels of physical activity at work, which can minimize nurse attrition, improve patient outcomes, and reduce costs in healthcare organizations. Full article

The opioid epidemic in the United States has significantly impacted pregnant women with opioid use disorder (OUD), leading to increased health and social complications. This study explores the feasibility of using machine learning algorithms with consumer-grade smartwatches to identify medication-taking gestures. The research specifically focuses on treatments for OUD, investigating methadone and buprenorphine taking gestures. Participants (n = 16, all female university students) simulated medication-taking gestures in a controlled lab environment over two weeks, with data collected via Ticwatch E and E3 smartwatches running custom ASPIRE software. The study employed a RegNet-style 1D ResNet model to analyze gesture data, achieving high performance in three classification scenarios: distinguishing between medication types, separating medication gestures from daily activities, and detecting any medication-taking gesture. The model’s overall F1 scores were 0.89, 0.88, and 0.96 for each scenario, respectively. These findings suggest that smartwatch-based gesture recognition could enhance real-time monitoring and adherence to medication regimens for OUD treatment. Limitations include the use of simulated gestures and a small, homogeneous participant pool, warranting further real-world validation. This approach has the potential to improve patient outcomes and management strategies. Full article

Tinnitus is defined as the conscious awareness of a tonal or composite noise for which there is no identifiable corresponding external acoustic source, which becomes tinnitus disorder when the phantom sound is associated with suffering and/or disability. There is only limited knowledge about the time course of tinnitus disorder. Bereavement science has identified four different trajectories: resilience, recovery, chronic, and delayed. The question arises whether these four trajectories exist in tinnitus as well if one considers tinnitus as the loss of silence (at will). To verify whether these four trajectories exist, short-term tinnitus progression was analyzed retrospectively using an Ecological Momentary Assessment (EMA) approach, extracting the data from patients who started using the TrackYourTinnitus (TYT) app (version 1, Ulm University, 2013) from the start of their tinnitus perception. Four patients were identified retrospectively via the TYT app with acute tinnitus, and the bereavement trajectories were reconstructed based on EMA. In conclusion, this perspective suggests that the four known bereavement trajectories may exist in tinnitus, and prospective evaluations of larger samples are warranted to confirm or disprove this analogy between bereavement and tinnitus, in which tinnitus is conceived as the loss of (controllable) silence. Full article

Background/Objectives: Over 5 million youth under the age of 19 provide daily, hands-on care to an ill or injured family member across the United States. Yet how these young carers perceive the care they deliver in the moment, and how these perceptions relate to well-being, is unexplored, particularly in complex neurological conditions. This paper presents initial data on young carers for a family member with amyotrophic lateral sclerosis (ALS). Methods: Ecological momentary assessment (EMA) was used to measure perceptions of care in the moments of care and the cognitive and emotional states of the young carers during those moments. Young carers (n = 15) aged 10–19 were followed for seven days, completing assessments three times per day, which provided 260 total measurements. Young carers reported frequently engaging in caregiving (~39% of assessments). Results: The results indicated that it was not simply performing a caregiving task that related to outcomes, but rather how caregiving moments were perceived that mattered. Caregiving moments perceived as more fulfilling resulted in young carers feeling less discontent and more focused, whereas caregiving moments perceived as lacking resources predicted more discontent and distress. Exploratory analyses highlighted the potential for burden for young carers. They reported high levels of worry when they were not around the care recipient, with this worry predicting feeling more discontent and distressed. Conclusions: Young carers are deeply involved in care and perceive care differently across moments, both positive and negative. These initial data can be used to develop targeting support programs in the moment of care, potentially lessening the negative impacts of care. Full article

Physical activity (PA) is essential for healthy aging, but its accurate assessment in older adults remains challenging due to the limitations and biases of traditional clinical assessment. Mobile technologies and wearable sensors offer a more ecological, less biased alternative for evaluating PA in this population. This study aimed to optimize digital phenotyping strategies for assessing PA patterns in older adults, by integrating ecological momentary assessment (EMA) and continuous wearable sensor data collection. Over two weeks, 108 community-dwelling older adults provided real-time EMA responses while their PA was continuously monitored using Garmin Vivo 5 sensors. The combined approach proved feasible, with 67.2% adherence to EMA prompts, consistent across time points (morning: 68.1%; evening: 65.4%). PA predominantly occurred at low (51.4%) and moderate (46.2%) intensities, with midday activity peaks. Motivation and self-efficacy were significantly associated with low-intensity PA (R = 0.20 and 0.14 respectively), particularly in the morning. However, discrepancies between objective step counts and self-reported PA measures, which showed no correlation (R = −0.026, p = 0.65), highlight the complementary value of subjective and objective data sources. These findings support integrating EMA, wearable sensors, and temporal frameworks to enhance PA assessment, offering precise insights for personalized, time-sensitive interventions to promote PA. Full article

Background/Objectives: Excess adiposity, affecting 43% of the global adult population, is a major contributor to cardiometabolic diseases. Lifestyle behaviours, specifically dietary habits, play a key role in weight management. Real-time assessment methods such as Ecological Momentary Assessment (EMA) provide context-rich data that reduce recall bias and offer insights into dietary triggers and lapses. This study examines dietary triggers among adults with excess adiposity in Singapore using EMA, focusing on factors influencing dietary adherence and lapses. Methods: A total of 250 participants with a BMI ≥ 23 kg/m² were recruited to track dietary habits for one week, at least three times a day, using the Eating Behaviour Lapse Inventory Survey Singapore (eBLISS) embedded within the Eating Trigger Response Inhibition Program (eTRIP© V.1) smartphone app. Logistic regression analysis was used to identify predictors of dietary adherence. Results: Of the 4708 responses, 76.4% of the responses were indicative of adherence to dietary plans. Non-adherence was primarily associated with food accessibility and negative emotions (stress, nervousness, and sadness). Factors such as meals prepared by domestic helpers and self-preparation were significantly associated with adherence. Negative emotions and premenstrual syndrome were identified as significant predictors of dietary lapses. Conclusions: EMA offers valuable insights into dietary behaviours by identifying real-time triggers for dietary lapses. Future interventions can utilise technology-driven approaches to predict and prevent lapses, potentially improving adherence and weight management outcomes. Full article

Substance use disorders affect 17.3% of Americans. Digital health solutions that use machine learning to detect substance use from wearable biosignal data can eventually pave the way for real-time digital interventions. However, difficulties in addressing severe between-subject data heterogeneity have hampered the adaptation of machine learning approaches for substance use detection, necessitating more robust technological solutions. We tested the utility of personalized machine learning using participant-specific convolutional neural networks (CNNs) enhanced with self-supervised learning (SSL) to detect drug use. In a pilot feasibility study, we collected data from 9 participants using Fitbit Charge 5 devices, supplemented by ecological momentary assessments to collect real-time labels of substance use. We implemented a baseline 1D-CNN model with traditional supervised learning and an experimental SSL-enhanced model to improve individualized feature extraction under limited label conditions. Results: Among the 9 participants, we achieved an average area under the receiver operating characteristic curve score across participants of 0.695 for the supervised CNNs and 0.729 for the SSL models. Strategic selection of an optimal threshold enabled us to optimize either sensitivity or specificity while maintaining reasonable performance for the other metric. Conclusion: These findings suggest that Fitbit data have the potential to enhance substance use monitoring systems. However, the small sample size in this study limits its generalizability to diverse populations, so we call for future research that explores SSL-powered personalization at a larger scale. Full article

Our study presents an innovative variational Bayesian parameter estimation method for the Quantile Nonlinear Dynamic Latent Variable Model (QNDLVM), particularly when dealing with missing data and nonparametric priors. This method addresses the computational inefficiencies associated with the traditional Markov chain Monte Carlo (MCMC) approach, which struggles with large datasets and high-dimensional parameters due to its prolonged computation times, slow convergence, and substantial memory consumption. By harnessing the deterministic variational Bayesian framework, we convert the complex parameter estimation into a more manageable deterministic optimization problem. This is achieved by leveraging the hierarchical structure of the QNDLVM and the principle of efficiently optimizing approximate posterior distributions within the variational Bayesian framework. We further optimize the evidence lower bound using the coordinate ascent algorithm. To specify propensity scores for missing data manifestations and covariates, we adopt logistic and probit models, respectively, with conditionally conjugate mean field variational Bayes for logistic models. Additionally, we utilize Bayesian local influence to analyze the Ecological Momentary Assessment (EMA) dataset. Our results highlight the variational Bayesian approach’s notable accuracy and its ability to significantly alleviate computational demands, as demonstrated through simulation studies and practical applications. Full article

Using Ecological Momentary Assessment (EMA), this study examined associations between momentary availability of physical activity (PA) space and accessibility of PA opportunities among 608 elementary and middle school students who were participating in an obesity prevention trial in one mid-Atlantic state in the U.S. Smartphones prompted EMA surveys at random times to assess children’s perceived availability of PA space and accessibility of PA opportunities during out-of-school time, three to seven times each day over seven days. Multilevel logistic regression, which accounted for multiple responses per student, examined within- and between-person relations as well as the moderating effects of locale. The participants (M age = 10.88 years) lived in suburban (64%), rural (23%), and urban locales (13%). PA space availability was associated with greater PA opportunity accessibility (within-person OR = 9.82, p < 0.001; between-person OR = 22.61, p < 0.001). Locale moderated within-person relationships (p < 0.001), indicating that urban students with space were unable to use it or could be active but were without space. These findings advance our knowledge of temporal and environmental aspects related to childhood PA across diverse locales and can be used by policymakers to make informed decisions to ensure the use of age-appropriate, high quality, and safe spaces, particularly for children in urban areas. Full article

In this perspective paper, we propose a novel tech-driven method to evaluate body representations (BRs) in autistic individuals. Our goal is to deepen understanding of this complex condition by gaining continuous and real-time insights through digital phenotyping into the behavior of autistic adults. Our innovative method combines cross-sectional and longitudinal data gathering techniques to investigate and identify digital phenotypes related to BRs in autistic adults, diverging from traditional approaches. We incorporate ecological momentary assessment and time series data to capture the dynamic nature of real-life events for these individuals. Statistical techniques, including multivariate regression, time series analysis, and machine learning algorithms, offer a detailed comprehension of the complex elements that influence BRs. Ethical considerations and participant involvement in the development of this method are emphasized, while challenges, such as varying technological adoption rates and usability concerns, are acknowledged. This innovative method not only introduces a novel vision for evaluating BRs but also shows promise in integrating traditional and dynamic assessment approaches, fostering a more supportive atmosphere for autistic individuals during assessments compared to conventional methods. Full article

Suicidal behavior and Non-Suicidal Self-Injuries (NSSIs) are a major health problem in the adolescent population. New technologies can contribute to the development of innovative interventions in suicide prevention. Here, we present the SmartCrisis-Teen study protocol. The study consists of a randomized clinical trial which aims to evaluate the effectiveness of a digital safety plan to prevent suicidal behavior and NSSIs in adolescents. This is a multicentric study which will be conducted among the adolescent population, both in clinical and student settings, with a target sample of 1080 participants. The intervention group will receive an Ecological Momentary Intervention (EMI) consisting of a digital safety plan on their mobile phone. All participants will receive their Treatment As Usual (TAU). Participants will be followed for six months, with weekly and monthly telephone visits and face-to-face visits at three and six months. Participants will be assessed using traditional questionnaires as well as Ecological Momentary Assessment (EMA) and Implicit Association Tests (IATs). With this intervention, we expect a reduction in NSSIs through the acquisition of coping strategies and a decrease in suicidal behavior over the course of follow-up. This study provides a novel, scalable digital intervention for preventing suicidal behavior and NSSIs in adolescents, which could contribute to improving adolescent mental health outcomes globally. Full article

The 2020–2025 Dietary Guidelines for Americans recommend limiting intakes of saturated fat and added sugars (SF/AS) to <10% total energy. Data-driven approaches to identify sources of SF/AS are needed to meet these goals. We propose using a population-based approach to identify the leading food and beverage sources of SF/AS consumed by US adults. Foods and beverages reported as consumed were assessed from two, 24 h dietary recalls (24HRDR) from 36,378 adults aged 19 years and older from the 2005–2018 National Health and Nutrition Examination Survey. Intakes of SF/AS were aggregated across both 24HRDR to identify What We Eat in America food categories accounting for ≥90% of SF/AS, respectively, by the total population and within population subgroups. Data were weighted to estimate a nationally representative sample. Ninety-five discrete food categories accounted for ≥90% of the total SF/AS intakes for >88% of the representative sample of U.S. adults. The top sources of SF were cheese, pizza, ice cream, and eggs. The leading sources of AS were soft drinks, tea, fruit drinks, and cakes and pies. This analysis reflects a parsimonious approach to reliably identify foods and beverages that contribute to SF/AS intakes in U.S. adults. Full article