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Advancements in cancer-targeted immunotherapies have transformed care, yet these therapies present a high likelihood of cytokine release syndrome (CRS), a potentially severe immune-related adverse event. The ability to identify CRS earlier could improve care by mitigating risks, widening patient access, and reducing the burden on patients, caregivers, and health care providers. Digital health technologies (DHTs) are promising for early CRS detection by enabling continuous measurement of vital signs before symptoms are detected through standard intermittent clinical assessments. While the number of studies is increasing, inconsistencies in the symptoms and measures strongly associated with CRS highlight the need for a comprehensive review to identify the most reliable and commonly reported indicators. Despite this growing body of research, reliable predictive and diagnostic measures for early warning for CRS following the administration of immunotherapy have yet to be established. This scoping review aims to address this gap by developing an ontology of early warning signs for CRS-a structured model defining measurement concepts, properties, and interrelationships-for advancing early warning models for CRS. We conducted a mixed methods study including a scoping literature review, surveys, and interviews. The literature review searched PubMed and Embase (last searched March 19, 2024) for articles reporting measures collected between therapy administration and CRS onset and linked to CRS onset. Studies were limited to publications between January 2014 and March 2024, excluding those that did not assess an immunotherapy-based treatment, were not conducted in humans, did not compare collected measures to CRS diagnosed using standard of care, or were not available in English. Identified measures were further assessed through surveys and interviews with subject matter experts (SMEs; n=22) and key opinion leaders (KOLs; n=8) and analyzed using qualitative and quantitative methods. Thirty studies met eligibility criteria and used a variety of grading scales and thresholds for severe CRS. A comprehensive ontology of early warning signs for CRS that includes physiological signs, clinical symptoms, and laboratory markers was developed. Within the full ontology, a common set of early warning signs for CRS-temperature, heart rate, blood pressure, and oxygen saturation-was identified as the minimally necessary data to evaluate for their predictive value for CRS. Three of these 4 signs align with the American Society for Transplantation and Cellular Therapy (ASTCT) criteria for CRS grading and other clinical grading scales for CRS. Standardization and adoption of the ontology of early warning signs for CRS will streamline data collection to support the creation of robust, fit-for-purpose datasets. This approach ensures practical and informative data collection, ultimately enhancing the ability to predict and manage CRS effectively. Developing predictive models based on these early warning signs can enhance CRS risk assessment, support decentralized trials, and improve access to cancer-targeted immunotherapies.

The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient's condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.

Wearable devices are increasingly used for monitoring health and detecting digital biomarkers related to chronic diseases such as metabolic syndrome (MetS). Although circadian rhythm disturbances are known to contribute to MetS, few studies have explored wearable-derived circadian biomarkers for MetS identification. This study aimed to detect and analyze sleep and circadian rhythm biomarkers associated with MetS using step count and heart rate data from wearable devices and to identify the key biomarkers using explainable artificial intelligence (XAI). Data were analyzed from 272 participants in the Korean Medicine Daejeon Citizen Cohort, collected between 2020 and 2023, including 88 participants with MetS and 184 without any MetS diagnostic criteria. Participants wore Fitbit Versa or Inspire 2 devices for at least 5 weekdays, providing minute-level heart rate, step count, and sleep data. A total of 26 indicators were derived, including sleep markers (midsleep time and total sleep time) and circadian rhythm markers (midline estimating statistic of rhythm, amplitude, interdaily stability, and relative amplitude). In addition, a novel circadian rhythm marker, continuous wavelet circadian rhythm energy (CCE), was proposed using continuous wavelet transform of heart rate signals. Statistical tests (t test and the Wilcoxon rank sum test) and machine learning models-Shapley Additive Explanations, explainable boosting machine, and tabular neural network-were applied to evaluate marker significance and importance. Circadian rhythm markers, especially heart rate-based indicators, showed stronger associations with MetS than sleep markers. The newly proposed CCE demonstrated the highest importance for MetS identification across all XAI models, with significantly lower values observed in the MetS group (P<.001). Other heart rate-based markers, including relative amplitude and low activity period, were also identified as important contributors. Although sleep markers did not reach statistical significance, some were recognized as secondary predictors in XAI-based analyses. The CCE marker maintained a high predictive value even when adjusting for age, sex, and BMI. This study identified CCE and relative amplitude of heart rate as key circadian rhythm biomarkers for MetS monitoring, demonstrating their high importance across multiple XAI models. In contrast, traditional sleep markers showed limited significance, suggesting that circadian rhythm analysis may offer additional insights into MetS beyond sleep-related indicators. These findings highlight the potential of wearable-based circadian biomarkers for improving MetS assessment and management.

Parkinson disease (PD) is a progressive neurodegenerative disorder characterized by motor and nonmotor symptoms that worsen over time, significantly impacting quality of life. While clinical evaluations such as the Unified Parkinson's Disease Rating Scale (UPDRS) are standard for assessing disease severity, they offer somewhat limited temporal resolution and are susceptible to observer variability. Smartphone apps present a viable method for capturing detailed fluctuations in motor and vocal functions in real-world settings. This study aimed to use a smartphone-based app to quantitatively evaluate the interaction effect between time and disease severity on motor and vocal symptoms in individuals with PD. This was an exploratory, cross-sectional pilot study. Disease severity in persons with PD was assessed using the modified Hoehn & Yahr Scale, Voice Handicap Index, and UPDRS. We used a custom smartphone app to administer finger-tapping tasks, sustained phonation (/a/ and /i/), and rapid syllable repetition (/dadada/ and /pa-ta-ka/). The total tap counts, tap-to-tap variability, and vocal parameters (loudness, jitter, shimmer, repeat counts, and their variability) were analyzed. Each task was divided into 5 equal time frames to analyze performance changes over a short duration. Time-severity interactions were examined using linear mixed models. In total, 20 persons with PD and 20 healthy adults were included in this study. Persons with PD showed worse motor and vocal performance compared to healthy adults, with higher dysrhythmia; worse jitter, shimmer, and jitter and shimmer variability; and fewer repeat counts. During finger-tapping tasks, individuals with PD showed an earlier onset of dysrhythmia than their healthy counterparts. While a higher UPDRS part III score was associated with greater finger-tapping variability, there was no significant time-severity interaction for this motor task. However, linear mixed model analysis revealed significant time-severity interaction effects for vocal tasks, including /a/ loudness (P=.001), /a/ jitter (P=.01), /a/ shimmer (P=.001), /i/ loudness (P=.001), /i/ jitter (P<.001), /i/ shimmer (P<.001), and /pa-ta-ka/-variability (P=.04). This indicates that individuals with higher UPDRS part III scores experienced a more rapid decline in vocal control during the assessment period. All measured smartphone-based characteristics showed a significant correlation with UPDRS part III scores, with finger-tapping variability having the strongest correlation. This study demonstrates that a smartphone-based assessment, conducted over just a few minutes, can detect subtle temporal changes in fine motor and vocal control. The app successfully captured the earlier onset of dysrhythmia in individuals with PD and, importantly, identified significant time-severity interaction effects in vocal performance. This suggests that such digital tools can provide sensitive, dynamic insights into symptom progression, potentially enabling more precise monitoring and timely clinical interventions for individuals with PD.

Various digital biomarkers have been explored to detect cognitive impairment in community-dwelling older individuals, among which electricity consumption (EC) data obtained from smart meters are novel and promising because they pose no burden to the individuals. The study aimed to explore the potential of EC as a digital biomarker to screen older individuals with cognitive impairment living alone. We recruited 40 older individuals living alone and recorded their 1-year daily household EC data. We used the Japanese version of the Montreal Cognitive Assessment to categorize participants into 2 groups: those with and without cognitive impairment. As the pattern of daily household EC is different between lower and higher temperature ranges because of the use of heating and cooling equipment, we divided the daily household EC into 3 temperature ranges. Using a linear mixed model, we evaluated the association between daily household EC, daily outside temperature, and the groups. After excluding 12 participants, they were categorized into 2 groups: those with (10/28, 36%) and without cognitive impairment (18/28, 64%). The daily household EC data consisting of 9391 points showed two characteristics: (1) daily household EC was significantly lower in the group with cognitive impairment than in the group without cognitive impairment in the high temperature range (2.158 kWh at 25 °C, P=.02; 3.712 kWh at 30 °C, P<.001). The increase in EC with rising temperature from 25 °C to 30 °C was less in the group with cognitive impairment (2.387 kWh, P<.001) than in the group without cognitive impairment (3.940 kWh, P<.001); and (2) a tendency for lower daily household EC in the group with cognitive impairment was observed in the moderate temperature range (1.795 kWh at 15 °C, P=.06; 1.582 kWh at 20 °C, P=.08). The group with cognitive impairment may use less cooling equipment in the high temperature range and fewer home appliances in the moderate temperature range. Daily household EC might be useful in screening cognitive impairment in older individuals living alone.

Despite widespread use of symptom rating scales in psychiatry, these tools are limited by reliance on self-report, infrequent administration, and lack of predictive power. This constrains clinicians' ability to monitor illness trajectories or anticipate adverse outcomes like relapse. Actigraphy, a passive wearable-based method for measuring sleep and physical activity, offers objective, high-resolution behavioral data that may better reflect symptom fluctuations. Prior research has shown associations between actigraphy features and mood or psychosis symptoms, but most studies have focused on narrow diagnostic groups or fixed time windows, limiting clinical translation. This study aims to examine whether actigraphy-derived sleep and activity features correlate with psychiatric symptom severity in a transdiagnostic psychiatric sample, and to identify which features are most clinically relevant across multiple temporal resolutions. We present a feasibility case series study analyzing preliminary data from 8 outpatients (ages 18-52 years) enrolled in the Deep Phenotyping and Digitalization at Douglas (DeeP-DD) study, a prospective transdiagnostic study of digital phenotyping. Participants wore wrist-based actigraphy devices (GENEActiv) for up to 5 months. Symptom severity was measured using a variety of self- and clinician-rated scales. We performed intraindividual Spearman correlations and interindividual repeated measures correlations across daily, weekly, monthly, and full-duration averages. Intraindividual analyses revealed that later rise times were significantly associated with higher weekly 9-item Patient Health Questionnaire (PHQ-9) scores in participant 7 (ρ=0.74, P<.001) and participant 4 (ρ=0.78, P=.02), as well as higher weekly 7-item General Anxiety Disorder (GAD-7) scores in participant 7 (ρ=0.59, P=.03). While similar trends were observed at daily and monthly timescales, the weekly resolution yielded the most robust significance. Interindividual analyses showed that weeks with later average rise time correlated with higher PHQ-9 (r=0.48, P<.001) and GAD-7 scores (r=0.38, P=.03), with the PHQ-9 association remaining significant after Bonferroni correction (Bonferroni-corrected P=.02). Increased light physical activity was linked to lower PHQ-9 scores weekly (r=-0.44, P=.001) and monthly (r=-0.53, P=.01). Over the whole duration of the study, increased levels of sedentary activity were associated with lower GAD-7 scores (ρ=0.74; P<.001). Our findings highlight actigraphy-derived sleep and activity features, particularly rise time and physical activity, as promising transdiagnostic markers of psychiatric symptom burden. Their consistent associations across temporal scales and diagnostic groups underscore their potential utility for scalable, real-world clinical monitoring. Future work should validate these findings in larger cohorts and explore advanced analytical methods to capture circadian rhythmicity and symptom dynamics more precisely.

Depression and anxiety are prevalent but commonly missed and misdiagnosed, an important concern because many patients do not experience spontaneous recovery, and the duration of untreated illness is associated with worse outcomes. This study aims to explore the potential of using smartphone-tracked behavioral markers to support diagnostics and improve recognition of these disorders. We used the dedicated Behapp digital phenotyping platform to passively track location and app usage in 217 individuals, comprising symptomatic (n=109; depression/anxiety diagnosis or symptoms) and asymptomatic individuals (n=108; no diagnosis/symptoms). After quantifying 46 behavioral markers (eg, % time at home), we applied a machine learning approach to (1) determine which markers are relevant for depression/anxiety recognition and (2) develop and evaluate diagnostic prediction models for doing so. Our analysis identifies the total number of GPS-based trajectories as a potential marker of depression/anxiety, where individuals with fewer trajectories are more likely to be symptomatic. Models using this feature in combination with demographics or in isolation outperformed demographics-only models (area under the receiver operating characteristic curveMdn=0.60 vs 0.60 vs 0.51). Collectively, these findings indicate that smartphone-tracked behavioral markers have limited discriminant ability in our study but potential to support future depression/anxiety diagnostics.

Accurate and accessible measurements of inflammatory biomarkers are crucial for the diagnosis and monitoring of inflammatory diseases. The gold-standard C-reactive protein (CRP) requires venipuncture, which, despite providing high-quality samples, can cause discomfort, anxiety, and pain, particularly in vulnerable populations such as older patients. It is also resource-intensive, is unsuitable for remote or at-home use, and lacks continuous monitoring capability. These limitations limit patient autonomy and self-management, potentially leading to poorer prognosis due to delays in assessment and medical treatments. As digital health technologies advance, there is increasing interest in leveraging digital biomarkers for remote and real-time monitoring of systemic inflammation. Digital biomarkers derived from noninvasive biofluids could provide a scalable solution for tracking inflammatory status, offering a patient-centered alternative to traditional blood-based assessments. To date, however, there is no consensus on the most suitable modality for assessment or its digitization potential. Therefore, a comprehensive evaluation of the feasibility, reliability, and patient acceptability toward noninvasive, digital inflammatory biomarkers is needed. Our aim is to evaluate the feasibility of various noninvasive methods to assess inflammatory markers and identify the optimal modality for predicting serum CRP levels. Inflammatory biomarkers were assessed in 20 participants (10 patients with systemic inflammation defined as a CRP level >5 mg/L and 10 controls) using 6 noninvasive samples (urine, sweat, saliva, exhaled breath, core body temperature, and stool samples) alongside serum samples. Patient preferences were retrieved via a questionnaire. Mann-Whitney U test, Spearman correlation, and all-subset regression were conducted to assess the relationships between serum and nonserum biomarkers and identify optimal predictive models for serum CRP levels. CRP levels were significantly elevated in the inflammation group compared to controls in urine (median 4.5, IQR 4.15-10.3 vs median 0.69, IQR 0.24-1.39 μg/mmol; P=.001) and saliva (median 4910, IQR 2735-13,275 vs median 473, IQR 309-700 pg/mL; P=.001). Urine and saliva CRP levels strongly correlated with serum CRP (rsp=0.886; P<.001; rsp=0.709; P<.001). The multimodal model using urine and saliva CRP predicted serum CRP levels with 76.1% outperforming single-modality models. Patients favored urine and saliva tests over blood tests. Urine and saliva represent promising noninvasive alternatives to traditional blood tests for assessing CRP, enabling more accessible and less invasive diagnostic and monitoring approaches.

Psoriasis is a chronic inflammatory skin disorder that has been increasingly linked to metabolic imbalances, particularly obesity. Conventional anthropometric indicators such as BMI and waist circumference (WC) may not sufficiently capture body fat distribution or reflect metabolic risk. The body roundness index (BRI), which integrates both height and waist measurements, has emerged as a potentially superior metric, though its relevance to psoriasis risk remains underexplored. This study aimed to investigate the use of BRI as a digital biomarker for assessing psoriasis risk and to compare its predictive strength against BMI and WC across various demographic and metabolic subgroups using data from a nationally representative sample. A cross-sectional analysis was conducted using data from 13,798 adults aged 20 to 59 years who participated in the National Health and Nutrition Examination Survey between 2003 and 2006 as well as between 2009 and 2014. Psoriasis status was self-reported. Anthropometric measures (BRI, BMI, and WC) were calculated from standardized physical assessments. Weighted multivariable logistic regression models and restricted cubic spline analyses were used to examine associations while adjusting for demographic, metabolic, and lifestyle variables. A nomogram was constructed to quantify the relative predictive contributions of each metric. BRI exhibited a strong linear association with psoriasis risk (odds ratio [OR] 1.11 per unit increase, 95% CI 1.05-1.17; P<.001), outperforming BMI (OR 1.03) and WC (OR 1.01). Tertile analysis revealed a 1.73-fold increased risk of psoriasis in the highest BRI group (P=.003). Subgroup analyses confirmed consistent associations across age, sex, race or ethnicity, and metabolic status (P for interaction >.05). The nomogram highlighted BRI as the most influential predictor, indicated by its broad scoring range. BRI shows stronger and more consistent associations with psoriasis risk than BMI or WC, supporting its potential role as a digital biomarker for early risk stratification. Incorporating BRI into clinical decision-making tools may enhance personalized approaches to psoriasis prevention and management.

Research typically shows a higher preference for professionally led face-to-face mental health interventions over digital ones. It remains unclear in which circumstances digital self-help tools are preferred. To address this gap, it is important to examine user characteristics that may help predict when digital interventions are more desirable, ultimately guiding their design to enhance engagement and appeal. This study aims to examine how distress severity and capacity to invest effort relate to intervention preferences, using an ecological assessment of individuals seeking to receive feedback on their mental health. A comprehensive digital mental health screening tool with automated feedback was developed and advertised on social media. The sample comprised 684 adult participants aged 18 to 82 years who opted to complete the screening to receive feedback on their mental health state. Participants completed questionnaires measuring general psychological distress, depression, generalized anxiety, and demographics. The Kessler Psychological Distress Scale-6 was used as the primary measure for distress. Participants were also presented with questions measuring capacity to invest effort and preferences for a professional therapist versus digital self-help tools and for psychotherapy versus a mobile app. The effectiveness of distress, capacity to invest effort, and background characteristics in predicting preferences (a professional vs digital self-help tools; psychotherapy vs a mobile app) was examined using hierarchical linear regressions. The distributions of dichotomized preferences were plotted against distress and capacity to invest effort for transparent visualization. A hierarchical linear regression found that distress, capacity to invest, and currently being in psychotherapy significantly predicted preference for a professional versus digital self-help tools. Distress (β=.25, 95% CI .18-.32; P<.001) and capacity to invest effort (β=.23, 95% CI .16-.30; P<.001) were the strongest predictors, with similar effect size. The model explained 20% of the variance in preference, with the capacity to invest effort uniquely contributing 5%. Most participants experiencing distress with low capacity (158/239, 66.1%) preferred digital self-help tools, whereas most participants experiencing distress with high capacity (147/243, 60.5%) favored a professional. Similar results were obtained when using the Patient Health Questionnaire-4 as an alternative distress measure. Capacity to invest effort remained significant (β=.18, 95% CI .10-.26; P<.001) when predicting a preference for psychotherapy versus a mobile app, while distress was not significant (β=-.03, 95% CI -.10 to .05; P=.51). This study highlights that the preference for digital interventions is driven by a reduced capacity to invest effort in an intervention. Attempts to reduce the mental health treatment gap through digital interventions should focus on optimizing the effort elicited by users to improve desirability and engagement.