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Distracted driving is responsible for nearly 1 million crashes each year in the United States alone, and a major source of driver distraction is handheld phone use. We conducted a randomized, controlled trial to compare the effectiveness of interventions designed to create sustained reductions in handheld use while driving (NCT04587609). Participants were 1,653 consenting Progressive® Snapshot® usage-based auto insurance customers ages 18 to 77 who averaged at least 2 min/h of handheld use while driving in the month prior to study invitation. They were randomly assigned to one of five arms for a 10-wk intervention period. Arm 1 (control) got education about the risks of handheld phone use, as did the other arms. Arm 2 got a free phone mount to facilitate hands-free use. Arm 3 got the mount plus a commitment exercise and tips for hands-free use. Arm 4 got the mount, commitment, and tips plus weekly goal gamification and social competition. Arm 5 was the same as Arm 4, plus offered behaviorally designed financial incentives. Postintervention, participants were monitored until the end of their insurance rating period, 25 to 65 d more. Outcome differences were measured using fractional logistic regression. Arm 4 participants, who received gamification and competition, reduced their handheld use by 20.5% relative to control (P < 0.001); Arm 5 participants, who additionally received financial incentives, reduced their use by 27.6% (P < 0.001). Both groups sustained these reductions through the end of their insurance rating period.
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Direção Distraída , Humanos , Feminino , Masculino , Adulto , Pessoa de Meia-Idade , Direção Distraída/prevenção & controle , Idoso , Adolescente , Condução de Veículo , Adulto JovemRESUMO
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.
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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.â¯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.
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Hospitalização , Medidas de Resultados Relatados pelo Paciente , Humanos , Pessoa de Meia-Idade , Masculino , Hospitalização/estatística & dados numéricos , Feminino , Idoso , Neoplasias/tratamento farmacológico , Neoplasias/mortalidade , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/mortalidade , Antineoplásicos/uso terapêutico , Antineoplásicos/efeitos adversos , Neoplasias Gastrointestinais/tratamento farmacológico , Neoplasias Gastrointestinais/mortalidadeRESUMO
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.
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Algoritmos , Modelos Estatísticos , Humanos , Adolescente , Simulação por Computador , Cadeias de Markov , Modelos Logísticos , TempoRESUMO
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.
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Redes Neurais de Computação , Humanos , Estudos Retrospectivos , Modelos LinearesRESUMO
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.
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Bacillus , Bacteriemia , Humanos , Cefepima/farmacologia , Estudos de Casos e Controles , Duração da Terapia , Antibacterianos/efeitos adversos , Bactérias Gram-Negativas , Carbapenêmicos/farmacologia , Bacteriemia/tratamento farmacológico , Bacteriemia/epidemiologia , Estudos Retrospectivos , Fatores de RiscoRESUMO
With increasing availability of smartphones with Global Positioning System (GPS) capabilities, large-scale studies relating individual-level mobility patterns to a wide variety of patient-centered outcomes, from mood disorders to surgical recovery, are becoming a reality. Similar past studies have been small in scale and have provided wearable GPS devices to subjects. These devices typically collect mobility traces continuously without significant gaps in the data, and consequently the problem of data missingness has been safely ignored. Leveraging subjects' own smartphones makes it possible to scale up and extend the duration of these types of studies, but at the same time introduces a substantial challenge: to preserve a smartphone's battery, GPS can be active only for a small portion of the time, frequently less than $10\%$, leading to a tremendous missing data problem. We introduce a principled statistical approach, based on weighted resampling of the observed data, to impute the missing mobility traces, which we then summarize using different mobility measures. We compare the strengths of our approach to linear interpolation (LI), a popular approach for dealing with missing data, both analytically and through simulation of missingness for empirical data. We conclude that our imputation approach better mirrors human mobility both theoretically and over a sample of GPS mobility traces from 182 individuals in the Geolife data set, where, relative to LI, imputation resulted in a 10-fold reduction in the error averaged across all mobility features.
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Bioestatística/métodos , Métodos Epidemiológicos , Sistemas de Informação Geográfica , Análise Espacial , Mapeamento Geográfico , HumanosRESUMO
PURPOSE OF REVIEW: As rates of suicide continue to rise, there is urgent need for innovative approaches to better understand, predict, and care for those at high risk of suicide. Numerous mobile and sensor technology solutions have already been proposed, are in development, or are already available today. This review seeks to assess their clinical evidence and help the reader understand the current state of the field. RECENT FINDINGS: Advances in smartphone sensing, machine learning methods, and mobile apps directed towards reducing suicide offer promising evidence; however, most of these innovative approaches are still nascent. Further replication and validation of preliminary results is needed. Whereas numerous promising mobile and sensor technology based solutions for real time understanding, predicting, and caring for those at highest risk of suicide are being studied today, their clinical utility remains largely unproven. However, given both the rapid pace and vast scale of current research efforts, we expect clinicians will soon see useful and impactful digital tools for this space within the next 2 to 5 years.
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Aprendizado de Máquina/tendências , Aplicativos Móveis/tendências , Smartphone , Prevenção do Suicídio , Humanos , Smartphone/instrumentaçãoRESUMO
In the increasing number of sequencing studies aimed at identifying rare variants associated with complex traits, the power of the test can be improved by guided sampling procedures. We confirm both analytically and numerically that sampling individuals with extreme phenotypes can enrich the presence of causal rare variants and can therefore lead to an increase in power compared to random sampling. Although application of traditional rare variant association tests to these extreme phenotype samples requires dichotomizing the continuous phenotypes before analysis, the dichotomization procedure can decrease the power by reducing the information in the phenotypes. To avoid this, we propose a novel statistical method based on the optimal Sequence Kernel Association Test that allows us to test for rare variant effects using continuous phenotypes in the analysis of extreme phenotype samples. The increase in power of this method is demonstrated through simulation of a wide range of scenarios as well as in the triglyceride data of the Dallas Heart Study.
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Variação Genética , Modelos Genéticos , Proteína 3 Semelhante a Angiopoietina , Proteína 4 Semelhante a Angiopoietina , Proteínas Semelhantes a Angiopoietina , Angiopoietinas/genética , Estudos de Associação Genética , Humanos , Fenótipo , Análise de Sequência de DNA/métodos , Triglicerídeos/genéticaRESUMO
BACKGROUND: In occupational settings, boilermakers are exposed to high levels of metallic fine particulate matter (PM2.5) generated during the welding process. The effect of welding PM2.5 on heart rate variability (HRV) has been described, but the relationship between PM2.5, DNA methylation, and HRV is not known. METHODS: In this repeated-measures panel study, we recorded resting HRV and measured DNA methylation levels in transposable elements Alu and long interspersed nuclear element-1 (LINE-1) in peripheral blood leukocytes under ambient conditions (pre-shift) and right after a welding task (post-shift) among 66 welders. We also monitored personal PM2.5 level in the ambient environment and during the welding procedure. RESULTS: The concentration of welding PM2.5 was significantly higher than background levels in the union hall (0.43 mg/m3 vs. 0.11 mg/m3, p < 0.0001). The natural log of transformed power in the high frequency range (ln HF) had a significantly negative association with PM2.5 exposure (ß = -0.76, p = 0.035). pNN10 and pNN20 also had a negative association with PM2.5 exposure (ß = -0.16%, p = 0.006 and ß = -0.13%, p = 0.030, respectively). PM2.5 was positively associated with LINE-1 methylation [ß = 0.79%, 5-methylcytosince (%mC), p = 0.013]; adjusted for covariates. LINE-1 methylation did not show an independent association with HRV. CONCLUSIONS: Acute decline of HRV was observed following exposure to welding PM2.5 and evidence for an epigenetic response of transposable elements to short-term exposure to high-level metal-rich particulates was reported.
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Metilação de DNA/genética , Frequência Cardíaca , Leucócitos/metabolismo , Metais , Exposição Ocupacional/efeitos adversos , Material Particulado/efeitos adversos , Soldagem , Adulto , Estudos de Coortes , Elementos de DNA Transponíveis/genética , Humanos , Elementos Nucleotídeos Longos e Dispersos/genética , Estudos Longitudinais , Masculino , Pessoa de Meia-Idade , Exposição Ocupacional/análise , Material Particulado/análiseRESUMO
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.
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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.
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Concussão Encefálica , Síndrome Pós-Concussão , Humanos , Criança , Adolescente , Estudos de Coortes , Estudos Prospectivos , Concussão Encefálica/etiologia , Síndrome Pós-Concussão/diagnóstico , Síndrome Pós-Concussão/etiologia , Fatores de RiscoRESUMO
BACKGROUND: Growing interest has motivated recent studies to examine differences in recovery after sport-related concussion (SRC) by sex. However, heterogeneity in study design, participants, and recovery outcomes has led to mixed findings. Further work is needed to evaluate potential differences by sex and to investigate the role of related characteristics, such as sport contact-level, in recovery timelines. This study aimed to investigate whether concussion recovery trajectories differ by sex, considering a priori clinical and demographic covariates, and accounting for the sequence of recovery outcomes. Our secondary question was whether sport contact-level modifies the relationship between sex and time to outcomes. Using data from the Ivy League-Big Ten Epidemiology of Concussion Study, we included SRCs reported across five academic years; 2015-2020 (February 2020). We used Cox proportional hazards regressions to estimate associations between sex and time from injury to three outcomes: (1) symptom resolution, (2) return to academics, (3) return to full play, accounting for measured confounders. RESULTS: Among 1160 SRCs (male, n = 667; female, n = 493) with complete data, median age overall was 20 years (25th-75th percentiles:19-21), and most occurred among athletes playing high-contact sports (78.0%). Males were slightly more likely to complete symptom resolution over time compared to females (HR = 1.18, 95%CI = 1.05-1.33), but results were attenuated in fully adjusted models (HR 1.13, 95%CI = 0.99-1.29). Similarly, the HR of full academic return for males compared to females was 1.22 (95%CI = 1.07-1.38), but was attenuated in fully adjusted models (HR = 1.11, 95%CI = 0.97-1.28). The HR of full return to play for males compared to females was 1.14 (95%CI = 1.02-1.28), and was attenuated after adjustment (HR = 1.06, 95%CI = 0.93-1.20) as well. The interaction between sex and playing a high/low-contact sport was not statistically significant across models, though differences were apparent. CONCLUSIONS: Among a cohort of collegiate athletes with SRC, recovery timelines appeared similar between male and female athletes, adjusting for measured confounders. Differences by sex, considering sport contact-level, were evident and may be important clinically and in future studies. This study used robust methods, accounting for nesting in the sequence of RTP outcomes. Results inform concussion management protocols and planned qualitative work to further elucidate how collegiate athletes experience concussion recovery. KEY POINTS: Heterogeneity in study design, participants, and recovery outcomes has led to mixed findings in determining differences in recovery trajectories after concussion by sex. We found that having longer time to symptom resolution, and also the sequence of having academic return before symptoms resolve and longer time to academic return were confounders in the relationship between sex and RTP timelines. Time to sequential recovery outcomes appeared similar between male and female athletes, adjusting for observable confounders. Further differences by sex were evident when considering contact-level, and may be important to consider clinically and in future research. Results indicate that differences in concussion recovery trajectories by sex may be largely attributed to and driven by differences in sports with a men's or women's team only, such as football, and this should be explored further.
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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.
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Concussão Encefálica , Serviço Hospitalar de Emergência , Síndrome Pós-Concussão , Telemedicina , Humanos , Adolescente , Concussão Encefálica/terapia , Síndrome Pós-Concussão/terapia , Acessibilidade aos Serviços de Saúde , Masculino , FemininoRESUMO
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 17â¯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.
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Condução de Veículo , Motivação , Humanos , Feminino , Masculino , Adulto , Condução de Veículo/psicologia , Condução de Veículo/estatística & dados numéricos , Pessoa de Meia-Idade , Uso do Telefone Celular/estatística & dados numéricos , Aplicativos Móveis , Retroalimentação , Estados UnidosRESUMO
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.
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COVID-19 , Exercício Físico , Adulto , Humanos , Masculino , COVID-19/psicologia , Monitores de Aptidão Física , Modelos Lineares , Pandemias , Atividades CotidianasRESUMO
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.
Assuntos
Traumatismos em Atletas , Concussão Encefálica , Esportes , Humanos , Adolescente , Adulto Jovem , Adulto , Estudos Transversais , Testes Neuropsicológicos , Concussão Encefálica/diagnóstico , Instituições Acadêmicas , Traumatismos em Atletas/diagnósticoRESUMO
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.