The COVID-19 pandemic, and resultant “Stay-at-Home” orders, may have impacted adults’ positive health behaviors (sleep, physical activity) and negative health behaviors (alcohol consumption, drug use, and tobacco use). The purpose of this study was to investigate how these health behaviors changed (increased/improved or decreased/worsened) at the early stages of the pandemic, what participant characteristics were associated with health behavior changes, and why these behavioral changes may have occurred. A convenience sample of 1809 adults residing in the United States completed a 15-min self-report questionnaire in April and May 2020. Multinomial logistic regressions and descriptive statistics were used to evaluate how, for whom, and why these health behaviors changed. Participants were primarily female (67.4%), aged 35–49 years (39.8%), college graduates (83.3%), non-tobacco users (74.7%), and had previously used marijuana (48.6%). Overall, participants primarily reported a decrease in physical activity, while sleep and all of the negative health behaviors remained the same. Changes in negative health behaviors were related (p < 0.05) to sex, age, parental status, educational status, job status, BMI, and depression scores. Changes in positive health behaviors were related (p < 0.05) to sex, parental status, job status, and depression scores. Having more time available during the pandemic was the most commonly cited reason for changing health behaviors (negative and positive). Public health efforts should address the potential for long-term health consequences due to behavior change during COVID-19.

Importance: Large language models (LLMs) have potential to increase the efficiency of information extraction from unstructured clinical notes in electronic medical records.Objective: To assess the utility and reliability of an LLM, ChatGPT-4 (OpenAI), to analyze clinical narratives and identify helmet use status of patients injured in micromobility-related accidents.Design, Setting, and Participants: This cross-sectional study used publicly available, deidentified 2019 to 2022 data from the US Consumer Product Safety Commission’s National Electronic Injury Surveillance System, a nationally representative stratified probability sample of 96 hospitals in the US. Unweighted estimates of e-bike, bicycle, hoverboard, and powered scooter–related injuries that resulted in an emergency department visit were used. Statistical analysis was performed from November 2023 to April 2024.Main Outcomes and Measures: Patient helmet status (wearing vs not wearing vs unknown) was extracted from clinical narratives using (1) a text string search using researcher-generated text strings and (2) the LLM by prompting the system with low-, intermediate-, and high-detail prompts. The level of agreement between the 2 approaches across all 3 prompts was analyzed using Cohen κ test statistics. Fleiss κ was calculated to measure the test-retest reliability of the high-detail prompt across 5 new chat sessions and days. Performance statistics were calculated by comparing results from the high-detail prompt to classifications of helmet status generated by researchers reading the clinical notes (ie, a criterion standard review).Results: Among 54 569 clinical notes, moderate (Cohen κ = 0.74 [95% CI, 0.73-0.75) and weak (Cohen κ = 0.53 [95% CI, 0.52-0.54]) agreement were found between the text string–search approach and the LLM for the low- and intermediate-detail prompts, respectively. The high-detail prompt had almost perfect agreement (κ = 1.00 [95% CI, 1.00-1.00]) but required the greatest amount of time to complete. The LLM did not perfectly replicate its analyses across new sessions and days (Fleiss κ = 0.91 across 5 trials; P < .001). The LLM often hallucinated and was consistent in replicating its hallucinations. It also showed high validity compared with the criterion standard (n = 400; κ = 0.98 [95% CI, 0.96-1.00]).Conclusions and Relevance: This study’s findings suggest that although there are efficiency gains for using the LLM to extract information from clinical notes, the inadequate reliability compared with a text string–search approach, hallucinations, and inconsistent performance significantly hinder the potential of the currently available LLM.

Background: Most available evidence on the effects of the COVID-19 pandemic on child movement behaviors is from cross-sectional studies using self-report measures. This study aimed to identify change trajectories and their associated factors for objectively-assessed physical activity and sedentary time among an ethnically and socioeconomically diverse sample of school-age children from Central Texas, U.S.A., during COVID-19. Methods: Pre- (Sept. 2019 – Feb. 2020) and during- (Oct. 2020 – March 2021) COVID-19 physical activity and sedentary behavior data were collected for school-age children (8–11 years) enrolled in the Safe Travel Environment Evaluation in Texas Schools (STREETS) cohort study. Daily time spent in moderate- to vigorous-intensity physical activity (MVPA) and sedentary time were assessed using GT3X-wBT Actigraph accelerometers. Parent surveys were used to assess socio-ecological factors. Latent class linear mixed models were used to identify change trajectories of MVPA and sedentary time. Logistic regression models were used to assess the association between socio-ecological characteristics with physical activity and sedentary time change trajectory groups. Results: There was a significant decrease in mean daily MVPA (− 9.4 mins, SD = 18.54) and an increase in sedentary behavior (0.83 hrs, SD = 1.18). Two trajectory groups were identified for MVPA (‘decrease MPVA’ and ‘maintain high MVPA’), with the majority (82.1%) being in the ‘decrease MVPA’ group. Three trajectory groups were identified for sedentary behavior (‘moderate increase sedentary, ‘steep increase sedentary,’ and ‘decrease sedentary’), with most children (78.5%) being in the ‘moderate increase’ group. Girls had significantly lower odds of being in the ‘maintain high MVPA’ group than boys (OR = 0.27, 95% CI = 0.11, 0.61). Children living in neighborhoods with higher perceived social cohesion had significantly higher odds of being in the ‘maintain high MVPA’ group (OR = 1.22, 95% CI = 1.06, 1.41), while those in neighborhoods with higher social cohesion had lower odds of being in the ‘decrease sedentary’ group (OR = 0.86, 95% CI = 0.74, 0.99). Conclusions: Declines in physical activity and increases in sedentary time among most school-age children during COVID-19 in a socioeconomically and ethnically diverse U.S. sample, were observed in our study, especially among girls. These findings highlight the need to counteract the short-term negative changes in movement behaviors in response to COVID-19 among children.

Objectives. To describe the national burden of injuries associated with e-bikes, bicycles, hoverboards, and powered scooters (micromobility devices) in the United States. Methods. We compared patterns and trends for 1 933 296 estimated injuries associated with micromobility devices from 2019 to 2022 using National Electronic Injury Surveillance System data. Results. The population-based rates of e-bike and powered scooter injuries increased by 293.0% and 88.0%, respectively. When reported, powered scooter injuries had the highest proportion for alcohol use (9.0%) compared with other modes, whereas e-bike injuries had the highest proportion for motor vehicle involvement (35.4%). Internal injuries were more likely among e-bike diagnoses than hoverboard and bicycle ( P < .05), but fractures and concussions were more likely among hoverboard diagnoses compared with all other devices ( P < .05). When helmet use was identified in clinical notes (20.3%), helmet usage was higher among e-bike injuries (43.8%) compared with powered scooter (34.8%) and hoverboard (30.3%) injuries but lower compared with bicycle injuries (48.7%). Conclusions. The incidence of severe e-bike and powered scooter injuries increased over the 4-year period. Public health stakeholders should focus on improved surveillance and prevention of injuries associated with electric micromobility devices. ( Am J Public Health. 2024;114(12):1365–1374. https://doi.org/10.2105/AJPH.2024.307820 )

Wearable activity trackers hold potential as a research tool to increase physical activity. However, long-term wearable adherence is low among users, which may be due to the limited understanding of the factors related to use and relation to health behavior theory. The purpose of this study was to qualitatively explore the perceptions of wearables among active adult users and non-users. Findings will inform potential barriers and facilitators for the adherence and adoption of wearables through the application of the Self-Determination Theory. Six focus groups were conducted and equally stratified to wearable users (n = 10) and non-users (n = 10). Data were audio recorded, transcribed, and analyzed using an iterative approach creating first-level codes. This was followed by developing second-level codes that allowed for generating themes. For users, the wearables’ feedback provided them with validation, a sense of achievement, and other-determined motivation. Users appreciated the functionality of wearables, particularly with simpler and newer models. They also reported improvements in health behaviors. While both users and non-users had a general positive feeling towards wearables, they held similar concerns about cost, guilt, dependency, and accuracy. Non-users were unique in their concerns for materialism and functionality (i.e. ease of use and charge) associated with wearables. They also seemed to be more intrinsically motivated to be physically active by relying less on external sources of motivation as potentially provided by wearables. Findings show that while both adult users and non-users held positive perceptions of wearables and concerns for feelings of guilt and dependency, widespread adoption and adherence may be prevented by differences in motivation for physical activity and concerns for cost, materialism, and functionality.

Purpose: Little is known about children’s experience with high-intensity interval training (HIIT) during elementary school. Perceptions of enjoyment, perceived competence, and motivation are likely to drive long-term adherence and are, therefore, critical to understanding how HIIT might be implemented in this setting. The purpose of this study was to describe children’s vigorous physical activity (VPA), enjoyment, and perceived competency of HIIT activities within a Self-Determination Theory framework (autonomy) in elementary school physical education (PE) classes. Methods: Participants were 402 children from a single, diverse elementary school (49.4% female, 59.6% Non-Hispanic white). Student enjoyment and perceived competency were collected following teacher- (non-autonomous) and student-led (autonomous) conditions. A sub-set of 201 children wore accelerometers to measure percent of time in VPA during HIIT conditions. This mixed factorial design was assessed through a series of repeated measures ANOVAs. Results: While children reported generally positive enjoyment and perceived competence in both conditions (rating over 4.0 on a 5-point scale), children significantly enjoyed (d = 0.44) and felt more competent (d = 0.11) during the autonomous HIIT condition compared to the nonautonomous HIIT condition. In contrast, the percent of time in VPA (d = 0.94) was significantly lower in the autonomous condition. Conclusions: These data illustrate a challenging trade-off with the design of HIIT trials. Increasing autonomy through the inclusion of student choice increased enjoyment and perceived competence of HIIT, which are likely to improve adherence, but lowered VPA, which is likely to reduce cardiometabolic impact.Abbreviations: U.S.: United States; PA: Physical activity; MVPA; Moderate-to-vigorous physical activity; PE: Physical education; CDC: Centers for Disease Control and Prevention; Randomized control trial, RCT; SDT: Self-Determination Theory; HIIT: High-intensity interval training; RCT: Randomized control trial; VPA: Vigorous physical activity; IRB; Institutional Review Board; HFZ: Healthy Fitness Zone; PACES: Physical Activity Enjoyment Scale; M: Mean; SD: Standard deviation