Feedback and Financial Incentives for Reducing Cell Phone Use While Driving: A Randomized Clinical Trial.

Importance: Handheld phone use while driving is a major factor in vehicle crashes. Scalable interventions are needed to encourage drivers not to use their phones. Objective: To test whether interventions involving social comparison feedback and/or financial incentives can reduce drivers' handheld phone use. Design, Setting, and Participants: In a randomized clinical trial, interventions were administered nationwide in the US via a mobile application in the context of a usage-based insurance program (Snapshot Mobile application). Customers were eligible to be invited to participate in the study if enrolled in the usage-based insurance program for 30 to 70 days. The study was conducted from May 13 to June 30, 2019. Analysis was completed December 22, 2023. Interventions: Participants were randomly assigned to 1 of 6 trial arms for a 7-week intervention period: (1) control; (2) feedback, with weekly push notification about their handheld phone use compared with that of similar others; (3) standard incentive, with a maximum $50 award at the end of the intervention based on how their handheld phone use compared with similar others; (4) standard incentive plus feedback, combining interventions of arms 2 and 3; (5) reframed incentive plus feedback, with a maximum $7.15 award each week, framed as participant's to lose; and (6) doubled reframed incentive plus feedback, a maximum $14.29 weekly loss-framed award. Main Outcome and Measure: Proportion of drive time engaged in handheld phone use in seconds per hour (s/h) of driving. Analyses were conducted with the intention-to-treat approach. Results: Of 17 663 customers invited by email to participate, 2109 opted in and were randomized. A total of 2020 drivers finished the intervention period (68.0% female; median age, 30 [IQR, 25-39] years). Median baseline handheld phone use was 216 (IQR, 72-480) s/h. Relative to control, feedback and standard incentive participants did not reduce their handheld phone use. Standard incentive plus feedback participants reduced their use by -38 (95% CI, -69 to -8) s/h (P = .045); reframed incentive plus feedback participants reduced their use by -56 (95% CI, -87 to -26) s/h (P < .001); and doubled reframed incentive plus feedback participants reduced their use by -42 s/h (95% CI, -72 to -13 s/h; P = .007). The 5 active treatment arms did not differ significantly from each other. Conclusions and Relevance: In this randomized clinical trial, providing social comparison feedback plus incentives reduced handheld phone use while individuals were driving. Trial Registration: ClinicalTrials.gov Identifier: NCT03833219.

Using mobile health to expedite access to specialty care for youth presenting to the emergency department with concussion at highest risk of developing persisting symptoms: a protocol paper for a non-randomised hybrid implementation-effectiveness trial.

INTRODUCTION: Paediatric concussion is a common injury. Approximately 30% of youth with concussion will experience persisting postconcussion symptoms (PPCS) extending at least 1 month following injury. Recently, studies have shown the benefit of early, active, targeted therapeutic strategies. However, these are primarily prescribed from the specialty setting. Early access to concussion specialty care has been shown to improve recovery times for those at risk for persisting symptoms, but there are disparities in which youth are able to access such care. Mobile health (mHealth) technology has the potential to improve access to concussion specialists. This trial will evaluate the feasibility of a mHealth remote patient monitoring (RPM)-based care handoff model to facilitate access to specialty care, and the effectiveness of the handoff model in reducing the incidence of PPCS. METHODS AND ANALYSIS: This study is a non-randomised type I, hybrid implementation-effectiveness trial. Youth with concussion ages 13-18 will be enrolled from the emergency department of a large paediatric healthcare network. Patients deemed a moderate-to-high risk for PPCS using the predicting and preventing postconcussive problems in paediatrics (5P) stratification tool will be registered for a web-based chat platform that uses RPM to collect information on symptoms and activity. Those patients with escalating or plateauing symptoms will be contacted for a specialty visit using data collected from RPM to guide management. The primary effectiveness outcome will be the incidence of PPCS, defined as at least three concussion-related symptoms above baseline at 28 days following injury. Secondary effectiveness outcomes will include the number of days until return to preinjury symptom score, clearance for full activity and return to school without accommodations. The primary implementation outcome will be fidelity, defined as the per cent of patients meeting specialty care referral criteria who are ultimately seen in concussion specialty care. Secondary implementation outcomes will include patient-defined and clinician-defined appropriateness and acceptability. ETHICS AND DISSEMINATION: This study was approved by the Institutional Review Board of the Children's Hospital of Philadelphia (IRB 22-019755). Study findings will be published in peer-reviewed journals and disseminated at national and international meetings. TRIAL REGISTRATION NUMBER: NCT05741411.

The Potential for Digital Phenotyping in Understanding Mindfulness App Engagement Patterns: A Pilot Study.

Background: Low app engagement is a central barrier to digital mental health efficacy. With mindfulness-based mental health apps growing in popularity, there is a need for new understanding of factors influencing engagement. This study utilized digital phenotyping to understand real-time patterns of engagement around app-based mindfulness. Different engagement metrics are presented that measure both the total number of app-based activities participants completed each week, as well as the proportion of days that participants engaged with the app each week. Method: Data were derived from two iterations of a four-week study exploring app engagement in college students (n = 169). This secondary analysis investigated the relationships between general and mindfulness-based app engagement with passive data metrics (sleep duration, home time, and screen duration) at a weekly level, as well as the relationship between demographics and engagement. Additional clinically focused analysis was performed on three case studies of participants with high mindfulness activity completion. Results: Demographic variables such as gender, race/ethnicity, and age lacked a significant association with mindfulness app-based engagement. Passive data variables such as sleep and screen duration were significant predictors for different metrics of general and mindfulness-based app engagement at a weekly level. There was a significant interaction effect for screen duration between the number of mindfulness activities completed and whether or not the participant received a mindfulness notification. K-means clusters analyses using passive data features to predict mindfulness activity completion had low performance. Conclusions: While there are no simple solutions to predicting engagement with mindfulness apps, utilizing digital phenotyping approaches at a population and personal level offers new potential. The signal from digital phenotyping warrants more investigation; even small increases in engagement with mindfulness apps may have a tremendous impact given their already high prevalence of engagement, availability, and potential to engage patients across demographics.

Association of Remote Patient-Reported Outcomes and Step Counts With Hospitalization or Death Among Patients With Advanced Cancer Undergoing Chemotherapy: Secondary Analysis of the PROStep Randomized Trial.

BACKGROUND: Patients with advanced cancer undergoing chemotherapy experience significant symptoms and declines in functional status, which are associated with poor outcomes. Remote monitoring of patient-reported outcomes (PROs; symptoms) and step counts (functional status) may proactively identify patients at risk of hospitalization or death. OBJECTIVE: The aim of this study is to evaluate the association of (1) longitudinal PROs with step counts and (2) PROs and step counts with hospitalization or death. METHODS: The PROStep randomized trial enrolled 108 patients with advanced gastrointestinal or lung cancers undergoing cytotoxic chemotherapy at a large academic cancer center. Patients were randomized to weekly text-based monitoring of 8 PROs plus continuous step count monitoring via Fitbit (Google) versus usual care. This preplanned secondary analysis included 57 of 75 patients randomized to the intervention who had PRO and step count data. We analyzed the associations between PROs and mean daily step counts and the associations of PROs and step counts with the composite outcome of hospitalization or death using bootstrapped generalized linear models to account for longitudinal data. RESULTS: Among 57 patients, the mean age was 57 (SD 10.9) years, 24 (42%) were female, 43 (75%) had advanced gastrointestinal cancer, 14 (25%) had advanced lung cancer, and 25 (44%) were hospitalized or died during follow-up. A 1-point weekly increase (on a 32-point scale) in aggregate PRO score was associated with 247 fewer mean daily steps (95% CI -277 to -213; P<.001). PROs most strongly associated with step count decline were patient-reported activity (daily step change -892), nausea score (-677), and constipation score (524). A 1-point weekly increase in aggregate PRO score was associated with 20% greater odds of hospitalization or death (adjusted odds ratio [aOR] 1.2, 95% CI 1.1-1.4; P=.01). PROs most strongly associated with hospitalization or death were pain (aOR 3.2, 95% CI 1.6-6.5; P<.001), decreased activity (aOR 3.2, 95% CI 1.4-7.1; P=.01), dyspnea (aOR 2.6, 95% CI 1.2-5.5; P=.02), and sadness (aOR 2.1, 95% CI 1.1-4.3; P=.03). A decrease in 1000 steps was associated with 16% greater odds of hospitalization or death (aOR 1.2, 95% CI 1.0-1.3; P=.03). Compared with baseline, mean daily step count decreased 7% (n=274 steps), 9% (n=351 steps), and 16% (n=667 steps) in the 3, 2, and 1 weeks before hospitalization or death, respectively. CONCLUSIONS: In this secondary analysis of a randomized trial among patients with advanced cancer, higher symptom burden and decreased step count were independently associated with and predictably worsened close to hospitalization or death. Future interventions should leverage longitudinal PRO and step count data to target interventions toward patients at risk for poor outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT04616768; https://clinicaltrials.gov/study/NCT04616768. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-054675.

Digital phenotyping data and anomaly detection methods to assess changes in mood and anxiety symptoms across a transdiagnostic clinical sample.

INTRODUCTION: Clinical assessment of mood and anxiety change often relies on clinical assessment or self-reported scales. Using smartphone digital phenotyping data and resulting markers of behavior (e.g., sleep) to augment clinical symptom scores offers a scalable and potentially more valid method to understand changes in patients' state. This paper explores the potential of using a combination of active and passive sensors in the context of smartphone-based digital phenotyping to assess mood and anxiety changes in two distinct cohorts of patients to assess the preliminary reliability and validity of this digital phenotyping method. METHODS: Participants from two different cohorts, each n = 76, one with diagnoses of depression/anxiety and the other schizophrenia, utilized mindLAMP to collect active data (e.g., surveys on mood/anxiety), along with passive data consisting of smartphone digital phenotyping data (geolocation, accelerometer, and screen state) for at least 1 month. Using anomaly detection algorithms, we assessed if statistical anomalies in the combination of active and passive data could predict changes in mood/anxiety scores as measured via smartphone surveys. RESULTS: The anomaly detection model was reliably able to predict symptom change of 4 points or greater for depression as measured by the PHQ-9 and anxiety as measured for the GAD-8 for both patient populations, with an area under the ROC curve of 0.65 and 0.80 for each respectively. For both PHQ-9 and GAD-7, these AUCs were maintained when predicting significant symptom change at least 7 days in advance. Active data alone predicted around 52% and 75% of the symptom variability for the depression/anxiety and schizophrenia populations respectively. CONCLUSION: These results indicate the feasibility of anomaly detection for predicting symptom change in transdiagnostic cohorts. These results across different patient groups, different countries, and different sites (India and the US) suggest anomaly detection of smartphone digital phenotyping data may offer a reliable and valid approach to predicting symptom change. Future work should emphasize prospective application of these statistical methods.

Optimizing the Combination of Common Clinical Concussion Batteries to Predict Persistent Postconcussion Symptoms in a Prospective Cohort of Concussed Youth.

BACKGROUND: Studies have evaluated individual factors associated with persistent postconcussion symptoms (PPCS) in youth concussion, but no study has combined individual elements of common concussion batteries with patient characteristics, comorbidities, and visio-vestibular deficits in assessing an optimal model to predict PPCS. PURPOSE: To determine the combination of elements from 4 commonly used clinical concussion batteries and known patient characteristics and comorbid risk factors that maximize the ability to predict PPCS. STUDY DESIGN: Cohort study; Level of evidence, 2. METHODS: We enrolled 198 concussed participants-87 developed PPCS and 111 did not-aged 8 to 19 years assessed within 14 days of injury from a suburban high school and the concussion program of a tertiary care academic medical center. We defined PPCS as a Post-Concussion Symptom Inventory (PCSI) score at 28 days from injury of ≥3 points compared with the preinjury PCSI score-scaled for younger children. Predictors included the individual elements of the visio-vestibular examination (VVE), Sport Concussion Assessment Tool, 5th Edition (SCAT-5), King-Devick test, and PCSI, in addition to age, sex, concussion history, and migraine headache history. The individual elements of these tests were grouped into interpretable factors using sparse principal component analysis. The 12 resultant factors were combined into a logistic regression and ranked by frequency of inclusion into the combined optimal model, whose predictive performance was compared with the VVE, initial PCSI, and the current existing predictive model (the Predicting and Prevention Postconcussive Problems in Pediatrics (5P) prediction rule) using the area under the receiver operating characteristic curve (AUC). RESULTS: A cluster of 2 factors (SCAT-5/PCSI symptoms and VVE near point of convergence/accommodation) emerged. A model fit with these factors had an AUC of 0.805 (95% CI, 0.661-0.929). This was a higher AUC point estimate, with overlapping 95% CIs, compared with the PCSI (AUC, 0.773 [95% CI, 0.617-0.912]), VVE (AUC, 0.736 [95% CI, 0.569-0.878]), and 5P Prediction Rule (AUC, 0.728 [95% CI, 0.554-0.870]). CONCLUSION: Among commonly used clinical assessments for youth concussion, a combination of symptom burden and the vision component of the VVE has the potential to augment predictive power for PPCS over either current risk models or individual batteries.

Effects of Aircraft Noise on Sleep: Federal Aviation Administration National Sleep Study Protocol.

Aircraft noise can disrupt sleep and impair recuperation. The last U.S. investigation into the effects of aircraft noise on sleep dates back more than 20 years. Since then, traffic patterns and the noise levels produced by single aircraft have changed substantially. It is therefore important to acquire current data on sleep disturbance relative to varying degrees of aircraft noise exposure in the U.S. that can be used to check and potentially update the existing noise policy. This manuscript describes the design, procedures, and analytical approaches of the FAA's National Sleep Study. Seventy-seven U.S. airports with relevant nighttime air traffic from 39 states are included in the sampling frame. Based on simulation-based power calculations, the field study aims to recruit 400 participants from four noise strata and record an electrocardiogram (ECG), body movement, and sound pressure levels in the bedroom for five consecutive nights. The primary outcome of the study is an exposure-response function between the instantaneous, maximum A-weighted sound pressure levels (dBA) of individual aircraft measured in the bedroom and awakening probability inferred from changes in heart rate and body movement. Self-reported sleep disturbance due to aircraft noise is the secondary outcome that will be associated with long-term average noise exposure metrics such as the Day-Night Average Sound Level (DNL) and the Nighttime Equivalent Sound Level (Lnight). The effect of aircraft noise on several other physiological and self-report outcomes will also be investigated. This study will provide key insights into the effects of aircraft noise on objectively and subjectively assessed sleep disturbance.

The impact of the COVID-19 pandemic on daily rhythms.

OBJECTIVE: The COVID-19 pandemic has significantly impacted daily activity rhythms and life routines with people adjusting to new work schedules, exercise routines, and other everyday life activities. This study examines temporal changes in daily activity rhythms and routines during the COVID-19 pandemic, emphasizing disproportionate changes among working adult subgroups. MATERIALS AND METHODS: In June 2021, we conducted a year-long study to collect high-resolution fitness tracker data and questionnaire responses from 128 working adults. Questionnaire data were analyzed to explore changes in exercise and work routines during the pandemic. We build temporal distributions of daily step counts to quantify their daily movement rhythms, then measure their consistency over time using the inverse of the Earth mover's distance. Linear mixed-effects models were employed to compare movement rhythm variability among subpopulations. RESULTS: During the pandemic, our cohort exhibited a shift in exercise routines, with a decrease in nonwalking physical exercises, while walking remained unchanged. Migrants and those living alone had less consistent daily movement rhythms compared to others. Those preferring on-site work maintained more consistent daily movement rhythms. Men and migrants returned to work more quickly after pandemic restriction measures were eased. DISCUSSION: Our findings quantitatively show the pandemic's unequal impact on different subpopulations. This study opens new research avenues to explore why certain groups return to on-site work, exercise levels, or daily movement rhythms more slowly compared to prepandemic times. CONCLUSIONS: Considering the pandemic's unequal impact on subpopulations, organizations and policymakers should address diverse needs and offer tailored support during future crises.

Effects of antimicrobial therapy duration and class on risk of antimicrobial-resistant Gram-negative bacillus bloodstream infection in patients with AML.

BACKGROUND: Antimicrobial-resistant Gram-negative bacilli (ARGNB) bloodstream infection (BSI) has been associated with prior antibiotic exposure among hematologic malignancy patients. The relationships between days of therapy (DOT), antimicrobial class, and ARGNB BSI risk are poorly understood. METHODS: This is a single-center, case-control study of acute myeloid leukemia (AML) patients including 115 cases with ARGNB BSI and 230 matched controls with non-ARGNB BSI between January 1, 2007 and December 31, 2018. Fixed- and mixed-effects logistic regression was used to examine relationships between antibiotic DOT and risk of ARGNB BSI. Admission to an intensive care unit (ICU) within 7 days, 30-day mortality, and Pitt Bacteremia Score (PBS) were secondary outcomes. RESULTS: Prior isolation of a antimicrobial-resistant organism (ARO) (OR 4.45 95% CI 1.46, 13.54), surgery within 90 days (OR 3.71, 95% CI 1.57, 8.73), aminoglycoside DOT (OR 1.14, 95% CI 1.05, 1.23), cefepime DOT (OR 1.09, 95% CI 1.05, 1.13), and carbapenem DOT (OR 1.10, 95% CI 1.05, 1.16) were associated with increased odds of ARGNB BSI. Days since last antibiotic administration (OR 0.98, 95% CI 0.97, 0.99) and inpatient days within 90 days (OR 0.95, 95% CI 0.93, 0.98) showed reduced odds of ARGNB BSI. Total antimicrobial DOT regardless of class was not associated with ARGNB BSI. ARGNB BSI was associated with increased 30-day mortality (OR 2.86, 95% CI 1.57, 5.22) CONCLUSIONS: Among AML patients with GNB BSI, greater DOT of aminoglycosides, cefepime, and carbapenems in the 90 days prior to BSI were associated with increased odds of ARGNB BSI.

Combining mixed effects hidden Markov models with latent alternating recurrent event processes to model diurnal active-rest cycles.

Data collected from wearable devices can shed light on an individual's pattern of behavioral and circadian routine. Phone use can be modeled as alternating processes, between the state of active use and the state of being idle. Markov chains and alternating recurrent event models are commonly used to model state transitions in cases such as these, and the incorporation of random effects can be used to introduce diurnal effects. While state labels can be derived prior to modeling dynamics, this approach omits informative regression covariates that can influence state memberships. We instead propose an alternating recurrent event proportional hazards (PH) regression to model the transitions between latent states. We propose an expectation-maximization algorithm for imputing latent state labels and estimating parameters. We show that our E-step simplifies to the hidden Markov model (HMM) forward-backward algorithm, allowing us to recover an HMM with logistic regression transition probabilities. In addition, we show that PH modeling of discrete-time transitions implicitly penalizes the logistic regression likelihood and results in shrinkage estimators for the relative risk. This new estimator favors an extended stay in a state and is useful for modeling diurnal rhythms. We derive asymptotic distributions for our parameter estimates and compare our approach against competing methods through simulation as well as in a digital phenotyping study that followed smartphone use in a cohort of adolescents with mood disorders.

Maximizing the Accuracy of Adolescent Concussion Diagnosis Using Individual Elements of Common Standardized Clinical Assessment Tools.

CONTEXT: Multiple clinical evaluation tools exist for adolescent concussion with various degrees of correlation, presenting challenges for clinicians in identifying which elements of these tools provide the greatest diagnostic utility. OBJECTIVE: To determine the combination of elements from 4 commonly used clinical concussion batteries that maximize discrimination of adolescents with concussion from those without concussion. DESIGN: Cross-sectional study. SETTING: Suburban school and concussion program of a tertiary care academic center. PATIENTS OR OTHER PARTICIPANTS: A total of 231 participants with concussion (from a suburban school and a concussion program) and 166 participants without concussion (from a suburban school) between the ages of 13 and 19 years. MAIN OUTCOME MEASURE(S): Individual elements of the visio-vestibular examination (VVE), Sport Concussion Assessment Tool, fifth edition (SCAT5; including the modified Balance Error Scoring System), King-Devick test (K-D), and Postconcussion Symptom Inventory (PCSI) were evaluated. The 24 subcomponents of these tests were grouped into interpretable factors using sparse principal component analysis. The 13 resultant factors were combined with demographic and clinical covariates into a logistic regression model and ranked by frequency of inclusion into the ideal model, and the predictive performance of the ideal model was compared with each of the clinical batteries using the area under the receiver operating characteristic curve (AUC). RESULTS: A cluster of 4 factors (factor 1 [VVE saccades and vestibulo-ocular reflex], factor 2 [modified Balance Error Scoring System double-legged stance], factor 3 [SCAT5/PCSI symptom scores], and factor 4 [K-D completion time]) emerged. A model fit with the top factors performed as well as each battery in predicting concussion status (AUC = 0.816 [95% CI = 0.731, 0.889]) compared with the SCAT5 (AUC = 0.784 [95% CI = 0.692, 0.866]), PCSI (AUC = 0.776 [95% CI = 0.674, 0.863]), VVE (AUC = 0.711 [95% CI = 0.602, 0.814]), and K-D (AUC = 0.708 [95% CI = 0.590, 0.819]). CONCLUSIONS: A multifaceted assessment for adolescents with concussion, comprising symptoms, attention, balance, and the visio-vestibular system, is critical. Current diagnostic batteries likely measure overlapping domains, and the sparse principal component analysis demonstrated strategies for streamlining comprehensive concussion assessment across a variety of settings.

Neural networks for clustered and longitudinal data using mixed effects models.

Although most statistical methods for the analysis of longitudinal data have focused on retrospective models of association, new advances in mobile health data have presented opportunities for predicting future health status by leveraging an individual's behavioral history alongside data from similar patients. Methods that incorporate both individual-level and sample-level effects are critical to using these data to its full predictive capacity. Neural networks are powerful tools for prediction, but many assume input observations are independent even when they are clustered or correlated in some way, such as in longitudinal data. Generalized linear mixed models (GLMM) provide a flexible framework for modeling longitudinal data but have poor predictive power particularly when the data are highly nonlinear. We propose a generalized neural network mixed model that replaces the linear fixed effect in a GLMM with the output of a feed-forward neural network. The model simultaneously accounts for the correlation structure and complex nonlinear relationship between input variables and outcomes, and it utilizes the predictive power of neural networks. We apply this approach to predict depression and anxiety levels of schizophrenic patients using longitudinal data collected from passive smartphone sensor data.

Measuring Daily Activity Rhythms in Young Adults at Risk of Affective Instability Using Passively Collected Smartphone Data: Observational Study.

BACKGROUND: Irregularities in circadian rhythms have been associated with adverse health outcomes. The regularity of rhythms can be quantified using passively collected smartphone data to provide clinically relevant biomarkers of routine. OBJECTIVE: This study aims to develop a metric to quantify the regularity of activity rhythms and explore the relationship between routine and mood, as well as demographic covariates, in an outpatient psychiatric cohort. METHODS: Passively sensed smartphone data from a cohort of 38 young adults from the Penn or Children's Hospital of Philadelphia Lifespan Brain Institute and Outpatient Psychiatry Clinic at the University of Pennsylvania were fitted with 2-state continuous-time hidden Markov models representing active and resting states. The regularity of routine was modeled as the hour-of-the-day random effects on the probability of state transition (ie, the association between the hour-of-the-day and state membership). A regularity score, Activity Rhythm Metric, was calculated from the continuous-time hidden Markov models and regressed on clinical and demographic covariates. RESULTS: Regular activity rhythms were associated with longer sleep durations (P=.009), older age (P=.001), and mood (P=.049). CONCLUSIONS: Passively sensed Activity Rhythm Metrics are an alternative to existing metrics but do not require burdensome survey-based assessments. Low-burden, passively sensed metrics based on smartphone data are promising and scalable alternatives to traditional measurements.

Quality of Life and Physical Activity in 629 Individuals With Sarcoidosis: Prospective, Cross-sectional Study Using Smartphones (Sarcoidosis App).

BACKGROUND: Large gaps exist in understanding the symptomatic and functional impact of sarcoidosis, a rare multisystem granulomatous disease affecting fewer than 200,000 individuals in the United States. Smartphones could be used for prospective research, especially for rare diseases where organizing large cohorts can be challenging, given their near ubiquitous ownership and ability to track objective and subjective data with increasingly sophisticated technology. OBJECTIVE: We aimed to investigate whether smartphones could assess the quality of life (QoL) and physical activity of a large cohort of individuals with sarcoidosis. METHODS: We developed a mobile app (Sarcoidosis App) for a prospective, cross-sectional study on individuals with sarcoidosis. The Sarcoidosis App was made available on both Apple and Android smartphones. Individuals with sarcoidosis were recruited, consented, and enrolled entirely within the app. Surveys on sarcoidosis history, medical history, and medications were administered. Patients completed modules from the Sarcoidosis Assessment Tool, a validated patient-reported outcomes assessment of physical activity, fatigue, pain, skin symptoms, sleep, and lungs symptoms. Physical activity measured by smartphones was tracked as available. RESULTS: From April 2018 to May 2020, the App was downloaded 2558 times, and 629 individuals enrolled (404, 64.2% female; mean age 51 years; 513, 81.6% White; 86, 13.7% Black). Two-thirds of participants had a college or graduate degree, and more than half of them reported an income greater than US $60,000. Both QoL related to physical activity (P<.001, ρ=0.250) and fatigue (P<.01, ρ=-0.203) correlated with actual smartphone-tracked physical activity. Overall, 19.0% (98/517) of participants missed at least 1 week of school or work in an observed month owing to sarcoidosis, and 44.4% (279/629) reported that finances "greatly" or "severely" affected by sarcoidosis. Furthermore, 71.2% (437/614) of participants reported taking medications for sarcoidosis, with the most common being prednisone, methotrexate, hydroxychloroquine, and infliximab. Moreover, 46.4% (244/526) reported medication side effects, most commonly due to prednisone. CONCLUSIONS: We demonstrate that smartphones can prospectively recruit, consent, and study physical activity, QoL, and medication usage in a large sarcoidosis cohort, using both passively collected objective data and qualitative surveys that did not require any in-person encounters. Our study's limitations include the study population being weighted toward more educated and wealthier individuals, suggesting that recruitment was not representative of the full spectrum of patients with sarcoidosis in the United States. Our study provides a model for future smartphone-enabled clinical research for rare diseases and highlights key technical challenges that future research teams interested in smartphone-based research for rare diseases should anticipate.

Trajectories of Visual and Vestibular Markers of Youth Concussion.

Visual and vestibular deficits, as measured by a visio-vestibular examination (VVE), are markers of concussion in youth. Little is known about VVE evolution post-injury, nor influence of age or sex on trajectory. The objective was to describe the time trend of abnormal VVE elements after concussion. Two cohorts, 11-18 years, were enrolled: healthy adolescents (n = 171) from a high school with VVE assessment before or immediately after their sport seasons and concussed participants (n = 255) from a specialty care concussion program, with initial assessment ≤28 days from injury and VVE repeated throughout recovery during clinical visits. The primary outcome, compared between groups, is the time course of recovery of the VVE examination, defined as the probability of an abnormal VVE (≥2/9 abnormal elements) and modeled as a cubic polynomial of days after injury. We explored whether probability trajectories differed by: age (<14 years vs. 14+ years), sex, concussion history (0 versus 1+), and days from injury to last assessment (≤28 days vs. 29+ days). Overall, abnormal VVE probability peaked at 0.57 at day 8 post-injury, compared with an underlying prevalence of 0.083 for uninjured adolescents. Abnormal VVE probability peaked higher for those 14+ years, female, with a concussion history and whose recovery course was longer than 28 days post-injury, compared with their appropriate strata subgroups. Females and those <14 years demonstrated slower resolution of VVE abnormalities. VVE deficits are common in adolescents after concussion, and the trajectory of resolution varies by age, sex, and concussion history. These data provide insight to clinicians managing concussions on the timing of deficit resolution after injury.

Mobile footprinting: linking individual distinctiveness in mobility patterns to mood, sleep, and brain functional connectivity.

Mapping individual differences in behavior is fundamental to personalized neuroscience, but quantifying complex behavior in real world settings remains a challenge. While mobility patterns captured by smartphones have increasingly been linked to a range of psychiatric symptoms, existing research has not specifically examined whether individuals have person-specific mobility patterns. We collected over 3000 days of mobility data from a sample of 41 adolescents and young adults (age 17-30 years, 28 female) with affective instability. We extracted summary mobility metrics from GPS and accelerometer data and used their covariance structures to identify individuals and calculated the individual identification accuracy-i.e., their "footprint distinctiveness". We found that statistical patterns of smartphone-based mobility features represented unique "footprints" that allow individual identification (p < 0.001). Critically, mobility footprints exhibited varying levels of person-specific distinctiveness (4-99%), which was associated with age and sex. Furthermore, reduced individual footprint distinctiveness was associated with instability in affect (p < 0.05) and circadian patterns (p < 0.05) as measured by environmental momentary assessment. Finally, brain functional connectivity, especially those in the somatomotor network, was linked to individual differences in mobility patterns (p < 0.05). Together, these results suggest that real-world mobility patterns may provide individual-specific signatures relevant for studies of development, sleep, and psychopathology.

Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates.

In this paper, we present and evaluate a novel Bayesian regime-switching zero-inflated multilevel Poisson (RS-ZIMLP) regression model for forecasting alcohol use dynamics. The model partitions individuals' data into two phases, known as regimes, with: (1) a zero-inflation regime that is used to accommodate high instances of zeros (non-drinking) and (2) a multilevel Poisson regression regime in which variations in individuals' log-transformed average rates of alcohol use are captured by means of an autoregressive process with exogenous predictors and a person-specific intercept. The times at which individuals are in each regime are unknown, but may be estimated from the data. We assume that the regime indicator follows a first-order Markov process as related to exogenous predictors of interest. The forecast performance of the proposed model was evaluated using a Monte Carlo simulation study and further demonstrated using substance use and spatial covariate data from the Colorado Online Twin Study (CoTwins). Results showed that the proposed model yielded better forecast performance compared to a baseline model which predicted all cases as non-drinking and a reduced ZIMLP model without the RS structure, as indicated by higher AUC (the area under the receiver operating characteristic (ROC) curve) scores, and lower mean absolute errors (MAEs) and root-mean-square errors (RMSEs). The improvements in forecast performance were even more pronounced when we limited the comparisons to participants who showed at least one instance of transition to drinking.

The asymptotic distribution of modularity in weighted signed networks.

Modularity is a popular metric for quantifying the degree of community structure within a network. The distribution of the largest eigenvalue of a network's edge weight or adjacency matrix is well studied and is frequently used as a substitute for modularity when performing statistical inference. However, we show that the largest eigenvalue and modularity are asymptotically uncorrelated, which suggests the need for inference directly on modularity itself when the network size is large. To this end, we derive the asymptotic distributions of modularity in the case where the network's edge weight matrix belongs to the Gaussian orthogonal ensemble, and study the statistical power of the corresponding test for community structure under some alternative models. We empirically explore universality extensions of the limiting distribution and demonstrate the accuracy of these asymptotic distributions through Type I error simulations. We also compare the empirical powers of the modularity based tests with some existing methods. Our method is then used to test for the presence of community structure in two real data applications.

Determining sample size and length of follow-up for smartphone-based digital phenotyping studies.

OBJECTIVE: Studies that use patient smartphones to collect ecological momentary assessment and sensor data, an approach frequently referred to as digital phenotyping, have increased in popularity in recent years. There is a lack of formal guidelines for the design of new digital phenotyping studies so that they are powered to detect both population-level longitudinal associations as well as individual-level change points in multivariate time series. In particular, determining the appropriate balance of sample size relative to the targeted duration of follow-up is a challenge. MATERIALS AND METHODS: We used data from 2 prior smartphone-based digital phenotyping studies to provide reasonable ranges of effect size and parameters. We considered likelihood ratio tests for generalized linear mixed models as well as for change point detection of individual-level multivariate time series. RESULTS: We propose a joint procedure for sequentially calculating first an appropriate length of follow-up and then a necessary minimum sample size required to provide adequate power. In addition, we developed an accompanying accessible sample size and power calculator. DISCUSSION: The 2-parameter problem of identifying both an appropriate sample size and duration of follow-up for a longitudinal study requires the simultaneous consideration of 2 analysis methods during study design. CONCLUSION: The temporally dense longitudinal data collected by digital phenotyping studies may warrant a variety of applicable analysis choices. Our use of generalized linear mixed models as well as change point detection to guide sample size and study duration calculations provide a tool to effectively power new digital phenotyping studies.