Digital phenotyping — the use of personal digital devices to capture real-world behavioral and physiological data — holds promise for measuring patient function over time. However, missing data remains a pervasive challenge in longitudinal studies where missingness may obscure clinically relevant signals. Existing statistical methods focus on group-level inference and offer limited guidance on how causes of missingness should inform clinical interpretation and decision-making. This study aimed to (1) characterize data missingness across multiple timescales in a longitudinal digital phenotyping study of patients with chronic pain, (2) evaluate the effect of imputation on clinical inference, and (3) propose a practical framework for categorizing and responding to missing data in clinical digital phenotyping research. We analyzed data from 85 patients with chronic musculoskeletal pain (mean age 55.2 years, SD 15.7; 51 female, 32 male, 1 transgender) recruited from the Pain Intervention and Digital Research Program. Active data (PROMIS-29 surveys, daily pain scores) and passive data (accelerometer, GPS) were collected via the Beiwe Research Platform over 180 days. Data completeness was computed at day, hour, and minute levels. Linear mixed-effects models assessed associations between daily missingness and Forest-derived summary measures (cadence, home time, significant locations). Cumulative Link Mixed Models and linear mixed models with autoregressive error structures evaluated associations between PROMIS domain scores and digital measures, adjusting for age, sex, race, and within-subject correlation. Complete-case analyses were compared against multiple imputation (predictive mean matching, proportional odds logistic regression, and MidasTouch) using 100 imputed datasets combined via Rubin's rules. Median accelerometer completeness was 60% at the day level, 37% at the hour level, and 26% at the minute level; GPS completeness followed a similar pattern (57%, 34%, and 5%, respectively). Cadence showed no significant association with missingness (false discovery rate [FDR]-adjusted P=.32). The number of significant locations declined modestly with higher missingness (beta=-0.073 per 10 percentage points; FDR-adjusted P<.001). In complete-case analysis, higher cadence was associated with lower depression scores (95% CI -2.09 to -0.28; P=.01); this association was attenuated after multiple imputation (P=.13). Older participants remained enrolled longer (hazard ratio 0.979 per year; P=.02) but were less engaged while enrolled (odds ratio 0.962; P=.004). Race and sex were not significantly associated with engagement or retention. Data missingness in digital phenotyping varies substantially by the timescale at which it is assessed, and imputation choices can alter clinical interpretations. We propose the Triage and Response for Interpreting Missingness (TRIM) framework, which categorizes causes of missing data into Technology Failure, Clinically Relevant events, and Extraneous life events, each requiring distinct operational and analytical responses depending on the clinical context. TRIM provides a shared vocabulary for clinicians, statisticians, and engineers to ensure that missing data is meaningfully interpreted rather than merely imputed.

Abstract Introduction Digital Phenotyping is the moment-by-moment quantification of the human phenotype in situ using personal digital devices. Patients who have experienced traumatic physical events often face long and complex recovery times. In non-trauma populations, digital phenotyping has already demonstrated utility in enhancing clinical outcomes, highlighting its positive potential. No prior review has comprehensively mapped the application of digital phenotyping in trauma populations. This review addresses this gap by synthesizing current evidence and exploring its role in the future of trauma rehabilitation. Methods A systematic search of six databases (2015-2025) was conducted to identify peer-reviewed articles investigating digital phenotyping in adult (≥18 years) trauma populations. Only articles that examined personal digital devices for collecting passive data were included. Articles that solely used self-reporting scales and invasive digital phenotyping devices were excluded. Data extraction includes: (1) study population, (2) digital phenotyping modality, (3) digital endpoints, and (4) feasibility indicators. The review follows PRISMA guidelines, with risk of bias assessed with the Mixed-Methods Appraisal Tool. Results A total of 4248 abstracts have been screened, 664 articles met inclusion criteria and are undergoing full-text review. Burn, spinal cord injury, and traumatic brain injury are the most frequently represented populations, underscoring both the relevance and need for digital phenotyping research in these populations. The most common phenotyping modality was wearable devices (accelerometers, actigraphy, and smartwatches), along with smartphone-based sensors. Digital endpoints included physical activity (duration, limb function, gait stability), sleep patterns (duration, efficiency, breathing disturbances), and physiologic parameters (resting heart rate, heart rate variability, blood pressure regulation). Feasibility outcomes were frequently reported, with most studies citing successful implementation of wearable monitoring. Conclusions Preliminary findings indicate that digital phenotyping in trauma populations is an emerging yet underexplored field, with the potential to enhance patient care by informing frameworks for its responsible and efficient integration into trauma rehabilitation. Applicability of Research to Practice Digital phenotyping has the power to inform the treatment of burn and trauma patient recovery by enhancing remote monitoring after injury. These tools can improve longitudinal follow-up, guide personalized treatment, and strengthen rehabilitation outcomes. Funding for the study This work is supported by the National Institute on Disability, Independent Living, and Rehabilitation Research (#90DPBU0008).

Mesial temporal lobe epilepsy (MTLE) seizures are known to alter neural architecture, yet imaging studies report conflicting findings of their effects on the brain. This study aimed to identify consistent regions exhibiting structural or functional changes in MTLE and compare the regional distributions of pathology detected by different neuroimaging modalities. To that end, thirty-six coordinate-based meta-analyses were performed by applying Alteration Likelihood Estimation to voxel-based morphometry (VBM) and voxel-based physiology (VBP) studies. The meta-analyses revealed convergent MTLE pathology in the epileptogenic hippocampus, bilateral thalamus (medial dorsal nucleus and pulvinar), and striatum (caudate and putamen); significant findings were partially colocalized between VBM-atrophy and VBP analyses, with VBP effects driven primarily by reports of cerebral hypometabolism. Subgroup meta-analyses of blood-oxygen-level-dependent (BOLD) signal-derived metrics revealed additional regions of functional disturbance but were underpowered, requiring further investigation to establish their potential for revealing novel aspects of MTLE pathophysiology via functional magnetic resonance imaging (fMRI). These findings support the current understanding of MTLE as a network-based pathology with progressive neurodegeneration in the hippocampus and connected regions. This study also highlights promising neuroimaging targets for investigating disease-related alterations and recommends incorporating these regions into functional network models of MTLE. Finally, the present work encourages further exploration of BOLD-derived metrics and specifically urges the epilepsy imaging research community to report amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) measures for resting-state fMRI studies in standard space coordinates, to advance neuroimaging approaches for improving diagnosis, prognosis, and treatment strategies in MTLE.

ObjectivesChronic pain is multifactorial and has large social and economic costs. Comprehensive pain management through an interdisciplinary approach addressing the biopsychosocial model of pain is beneficial. The purpose of this study was to assess the feasibility and functional outcomes following participation in the 8-week virtual interdisciplinary Functional Integrative Restoration (FINER) program.DesignCohort study.SettingVirtual platform (Zoom) utilized by participants and clinicians within a large academic institution.Subjects44 individuals with chronic pain meeting study criteria who participated in the virtual FINER program from September 2021 to April 2023 were included in final analysis.MethodsParticipants attended twice weekly seminars and group sessions focused on pain education, lifestyle medicine, integrative medicine, and psychological therapies virtually and completed pre- and post-program surveys. Outcomes included the Pain Catastrophizing Scale (PCS), Tampa Scale of Kinesiophobia (TSK), and Patient-Reported Outcomes Measurement Information System-29 (PROMIS-29). Qualitative feedback was also obtained.ResultsFrom September 2021 to April 2023, 44 adult FINER participants with chronic low back and/or neck pain completed pre- and post-intervention surveys. We observed significant improvements in PCS, TSK, and various domains of the PROMIS-29, including pain interference, participation, physical function, and sleep, with modest effect sizes.ConclusionsThe FINER program reduced self-reported functional outcomes related to the participants' chronic pain. Positive qualitative feedback from FINER participants suggested mental and physical health benefits. Future investigation will include a larger cohort and will deploy active (patient-reported outcomes) and passive (mobility and sociability) digital measures to further characterize functional changes.

Functional MRI (fMRI) is crucial for studying brain function and diagnosing neurological disorders. However, existing analysis methods suffer from reproducibility and transferability challenges due to complex preprocessing pipelines and task-specific model designs. In this work, we introduce NeuroSTORM (Neuroimaging Foundation Model with Spatial-Temporal Optimized Representation Modeling) that learns generalizable representations directly from 4D fMRI volumes and enables efficient transfer to diverse downstream applications. Specifically, NeuroSTORM is pre-trained on 28.65 million fMRI frames from over 50,000 subjects, spanning multiple centers and ages 5 to 100. It combines an efficient spatiotemporal modeling design and lightweight task adaptation to enable scalable pre-training and fast transfer to downstream applications. Here we show that NeuroSTORM consistently outperforms existing methods across five downstream tasks, including demographic prediction, phenotype prediction, disease diagnosis, re-identification, and state classification. On two multi-hospital clinical cohorts with 17 diagnoses, NeuroSTORM achieves the best diagnosis performance while remaining predictive of psychological and cognitive phenotypes. These results suggest that NeuroSTORM could become a standardized foundation model for reproducible and transferable fMRI analysis.

Background: Impairment in social cognition, particularly eye gaze processing, is a shared feature common to autism spectrum disorder (ASD) and schizophrenia. However, it is unclear if a convergent neural mechanism also underlies gaze dysfunction in these conditions. The present study examined whether this shared eye gaze phenotype is reflected in a profile of convergent neurobiological dysfunction in ASD and schizophrenia. Methods: Activation likelihood estimation (ALE) meta -analyses were conducted on peak voxel coordinates across the whole brain to identify spatial convergence. Functional coactivation with regions emerging as significant was assessed using meta -analytic connectivity modeling. Functional decoding was also conducted. Results: Fifty-six experiments (n = 30 with schizophrenia and n = 26 with ASD) from 36 articles met inclusion criteria, which comprised 354 participants with ASD, 275 with schizophrenia and 613 healthy controls (1242 participants in total). In ASD, aberrant activation was found in the left amygdala relative to unaffected controls during gaze processing. In schizophrenia, aberrant activation was found in the right inferior frontal gyrus and supplementary motor area. Across ASD and schizophrenia, aberrant activation was found in the right inferior frontal gyrus and right fusiform gyrus during gaze processing. Functional decoding mapped the left amygdala to domains related to emotion processing and cognition, the right inferior frontal gyrus to cognition and perception, and the right fusiform gyrus to visual perception, spatial cognition, and emotion perception. These regions also showed meta -analytic connectivity to frontoparietal and frontotemporal circuitry. Conclusion: Alterations in frontoparietal and frontotemporal circuitry emerged as neural markers of gaze impairments in ASD and schizophrenia. These findings have implications for advancing transdiagnostic biomarkers to inform targeted treatments for ASD and schizophrenia.

Chronic pain affects up to 28% of U.S. adults, costing ∼$560 billion each year. Chronic pain is an instantiation of the perennial complexity of how to best assess and treat chronic diseases over time, especially in populations where age, medical comorbidities, and socioeconomic barriers may limit access to care. Chronic disease management poses a particular challenge for the healthcare system's transition from fee-for-service to value and risk-based reimbursement models. Remote, passive real-time data from smartphones could enable more timely interventions and simultaneously manage risk and promote better patient outcomes through predicting and preventing costly adverse outcomes; however, there is limited evidence whether remote monitoring is feasible, especially in the case of older patients with chronic pain. Here, we introduce the Pain Intervention and Digital Research (Pain-IDR) Program as a pilot initiative launched in 2022 that combines outpatient clinical care and digital health research. The Pain-IDR seeks to test whether functional status can be assessed passively, through a smartphone application, in older patients with chronic pain. We discuss two perspectives—a narrative approach that describes the clinical settings and rationale behind changes to the operational design, and a quantitative approach that measures patient recruitment, patient experience, and HERMES data characteristics. Since launch, we have had 77 participants with a mean age of 55.52, of which n = 38 have fully completed the 6 months of data collection necessitated to be considered in the study, with an active data collection rate of 51% and passive data rate of 78%. We further present preliminary operational strategies that we have adopted as we have learned to adapt the Pain-IDR to a productive clinical service. Overall, the Pain-IDR has successfully engaged older patients with chronic pain and presents useful insights for others seeking to implement digital phenotyping in other chronic disease settings.

ObjectiveTo examine the moderation effects of daily behavior on the associations between symptoms and social participation outcomes after burn injuryDesignA 6-month prospective cohort studySettingCommunityParticipants24 adult burn survivorsInterventionsNot applicableMain Outcome MeasuresSymptoms and social participation outcomes were assessed weekly using smartphone surveys, including symptoms of pain (Patient-Reported Outcomes Measurement Information System (PROMIS) Pain Intensity and Pain Interference), anxiety (PROMIS Anxiety), and depression (Patient Health Questionnaire (PHQ-8)), as well as outcomes of social interactions and social activities (Life Impact Burn Recovery Evaluation (LIBRE) Social Interactions and Social Activities). Daily behaviors were automatically recorded by a smartphone application and smartphone logs, including physical activity (steps, travel miles, and activity minutes), sleep (sleep hours), and social contact (number of phone call and message contacts).ResultsMultilevel models controlling for demographic and burn injury variables examined the associations between symptoms and social participation outcomes, and the moderation effects of daily behaviors. Lower (worse) LIBRE Social Interactions and LIBRE Social Activities scores were significantly associated with higher (worse) PROMIS Pain Intensity, PROMIS Pain Interference, PROMIS Anxiety, and PHQ-8 scores (p<0.05). Additionally, daily steps and activity minutes were associated with LIBRE Social Interactions and LIBRE Social Activities (p<0.05), and significantly moderated the association between PROMIS Anxiety and LIBRE Social Activities (p<0.001).ConclusionsSocial participation outcomes are associated with pain, anxiety, and depression symptoms after burn injury, and are buffered by daily physical activity. Future interventions studies should examine physical activity promotion on improving social recovery after burns.

AbstractBackgroundDigital phenotyping, the use of personal digital devices to capture and categorize real-world behavioral and physiological data, holds great potential for complementing traditional clinical assessments. However, missing data remains a critical challenge in this field, especially in longitudinal studies where missingness might obscure clinically relevant insights.ObjectiveThis paper examines the impact of data missingness on digital phenotyping clinical research, proposes a framework for reporting and accounting for data missingness, and explores its implications for clinical inference and decision-making.MethodsWe analyzed digital phenotyping data from a study involving 85 patients with chronic musculoskeletal pain, focusing on active (PROMIS-29 survey responses) and passive (accelerometer and GPS measures) data collected via the Beiwe Research Platform. We assessed data completeness and missingness at different timescales (day, hour, and minute levels), examined the relationship between data missingness and accelerometer measures and imputed GPS summary statistics, and studied the stability of regression models across varying levels of data missingness. We further investigated the association between functional status and day-level data missingness in PROMIS-29 subscores.ResultsData completeness showed substantial variability across timescales. Accelerometer-based cadence and imputed GPS-based home time and number of significant locations were generally robust to varying levels of data missingness. However, the stability of regression models was affected at higher thresholds (40% for cadence and 60% for home time). We also identified patterns wherein data missingness was associated with functional status.ConclusionData missingness in clinical digital phenotyping studies impacts individual- and group-level analyses. Given these results, we recommend that studies account for and report data at multiple timescales (we recommend day, hour, and minute-level where possible), depending on the clinical goals of data collection. We propose a modified framework for categorizing missingness mechanisms in digital phenotyping, emphasizing the need for clinically relevant reporting and interpretation of missing data. Our framework highlights the importance of integrating clinical with statistical expertise, specifically to ensure that imputing missing data does not obscure but helps capture clinically meaningful changes in functional status.

Abstract Background Pain is a complex problem that is triaged, diagnosed, treated, and billed based on which body part is painful, almost without exception. While the “body part framework” guides the organization and treatment of individual patients’ pain conditions, it remains unclear how to best conceptualize, study, and treat pain conditions at the population level. Here, we investigate (1) how the body part framework agrees with population-level, biologically derived pain profiles; (2) how do data-derived pain profiles interface with other symptom domains from a whole-body perspective; and (3) whether biologically derived pain profiles capture clinically salient differences in medical history. Methods To understand how pain conditions might be best organized, we applied a carefully designed a multi-variate pattern-learning approach to a subset of the UK Biobank (n = 34,337), the largest publicly available set of real-world pain experience data to define common population-level profiles. We performed a series of post hoc analyses to validate that each pain profile reflects real-world, clinically relevant differences in patient function by probing associations of each profile across 137 medication categories, 1425 clinician-assigned ICD codes, and 757 expert-curated phenotypes. Results We report four unique, biologically based pain profiles that cut across medical specialties: pain interference, depression, medical pain, and anxiety, each representing different facets of functional impairment. Importantly, these profiles do not specifically align with variables believed to be important to the standard pain evaluation, namely painful body part, pain intensity, sex, or BMI. Correlations with individual-level clinical histories reveal that our pain profiles are largely associated with clinical variables and treatments of modifiable, chronic diseases, rather than with specific body parts. Across profiles, notable differences include opioids being associated only with the pain interference profile, while antidepressants linked to the three complimentary profiles. We further provide evidence that our pain profiles offer valuable, additional insights into patients’ wellbeing that are not captured by the body-part framework and make recommendations for how our pain profiles might sculpt the future design of healthcare delivery systems. Conclusion Overall, we provide evidence for a shift in pain medicine delivery systems from the conventional, body-part-based approach to one anchored in the pain experience and holistic profiles of patient function. This transition facilitates a more comprehensive management of chronic diseases, wherein pain treatment is integrated into broader health strategies. By focusing on holistic patient profiles, our approach not only addresses pain symptoms but also supports the management of underlying chronic conditions, thereby enhancing patient outcomes and improving quality of life. This model advocates for a seamless integration of pain management within the continuum of care for chronic diseases, emphasizing the importance of understanding and treating the interdependencies between chronic conditions and pain.

Chronic pain affects up to 28% of U.S. adults, costing ∼$560 billion each year with substantial comorbidity with depression and anxiety. Especially in populations where age, medical comorbidities, and socioeconomic barriers might limit access to care, remote, passive, real-time data from smartphones could enable more timely interventions, manage risk, and promote better patient outcomes. Here, we provide a preliminary report from the Pain Intervention and Digital Research (Pain-IDR) Program on how digital measures might trace functional status.

Working memory (WM) is a crucial resource for temporary memory storage and the guiding of ongoing behavior. N-methyl-D-aspartate glutamate receptors (NMDARs) are thought to support the neural underpinnings of WM. Ketamine is an NMDAR antagonist that has cognitive and behavioral effects at subanesthetic doses. To shed light on subanesthetic ketamine effects on brain function, we employed a multimodal imaging design, combining gas-free calibrated functional magnetic resonance imaging (fMRI) measurement of oxidative metabolism (CMRO 2 ), resting-state cortical functional connectivity assessed with fMRI, and WM-related fMRI. Healthy subjects participated in two scan sessions in a randomized, double-blind, placebo-controlled design. Ketamine increased CMRO 2 and cerebral blood flow (CBF) in prefrontal cortex (PFC) and other cortical regions. However, resting-state cortical functional connectivity was not affected. Ketamine did not alter CBF-CMRO 2 coupling brain-wide. Higher levels of basal CMRO 2 were associated with lower task-related PFC activation and WM accuracy impairment under both saline and ketamine conditions. These observations suggest that CMRO 2 and resting-state functional connectivity index distinct dimensions of neural activity. Ketamine’s impairment of WM-related neural activity and performance appears to be related to its ability to produce cortical metabolic activation. This work illustrates the utility of direct measurement of CMRO 2 via calibrated fMRI in studies of drugs that potentially affect neurovascular and neurometabolic coupling.

Studying patients under drugs that produce rapid changes in mood and consciousness, such as ketamine and psychedelics, promises to yield critical findings about the neurobiology of consciousness and mood-transforming psychiatric therapeutics. However, such drugs can disrupt neurovascular-coupling, hampering correct interpretation of fMRI experimental results. Measuring the cerebral metabolic rate of oxygen (CMRO2) via calibrated fMRI is a possible solution. Here, we report initial results supporting this measure’s sensitivity to ketamine’s effects in healthy humans.

Abstract We consider a shift in pain medicine delivery systems from the conventional, body-part-based approach to one anchored in intricate, real-world pain experience and holistic profiles of patient function. Utilizing the largest biomedical dataset to date (n = 34,337), we unearth four unique, biologically-based pain profiles that cut across medical specialties: pain interference, depression, medical pain, and anxiety, each representing different facets of functional impairment. Importantly, these profiles do not specifically align with variables believed to be important to the standard pain evaluation, namely painful body part, pain intensity, sex, or BMI. Correlations with individual-level clinical histories (137 medication categories, 1,425 clinician-assigned diagnostic codes, and 757 lifestyle and behavioral phenotypes) reveal that our pain profiles are largely associated with clinical variables and treatments of modifiable, chronic diseases, rather than with specific body parts. Across profiles, notable differences include opioids being associated only with the pain interference profile, while antidepressants linked to the three complimentary profiles. We further provide evidence that our pain profiles offer valuable, additional insights into patients' wellbeing that are not captured by the body-part framework, and make recommendations for how our pain profiles might sculpt the future design of healthcare delivery systems.

We conducted a feasibility analysis to determine the quality of data that could be collected ambiently during routine clinical conversations. We used inexpensive, consumer-grade hardware to record unstructured dialogue and open-source software tools to quantify and model face, voice (acoustic and language) and movement features. We used an external validation set to perform proof-of-concept predictive analyses and show that clinically relevant measures can be produced without a restrictive protocol.

Psychedelics paired with new applications of computational tools might help bypass the imprecision of psychiatric diagnosis and connect measures of behavior to specific physiologic targets.

Individual differences in brain functional organization track a range of traits, symptoms and behaviours1-12. So far, work modelling linear brain-phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants13,14. A better understanding of in whom models fail and why is crucial to revealing robust, useful and unbiased brain-phenotype relationships. To this end, here we related brain activity to phenotype using predictive models-trained and tested on independent data to ensure generalizability15-and examined model failure. We applied this data-driven approach to a range of neurocognitive measures in a new, clinically and demographically heterogeneous dataset, with the results replicated in two independent, publicly available datasets16,17. Across all three datasets, we find that models reflect not unitary cognitive constructs, but rather neurocognitive scores intertwined with sociodemographic and clinical covariates; that is, models reflect stereotypical profiles, and fail when applied to individuals who defy them. Model failure is reliable, phenotype specific and generalizable across datasets. Together, these results highlight the pitfalls of a one-size-fits-all modelling approach and the effect of biased phenotypic measures18-20 on the interpretation and utility of resulting brain-phenotype models. We present a framework to address these issues so that such models may reveal the neural circuits that underlie specific phenotypes and ultimately identify individualized neural targets for clinical intervention.