About

Gary Evans is associated with the Bronfenbrenner Center for Translational Research at Cornell University. The center assists faculty in developing translational research projects by providing proposal preparation assistance, training, technical support, and facilitating collaborative relationships. The center offers workshops, an intensive summer institute, and talks on current research to support researchers in the field of translational research. The BCTR also helps with gaining access to diverse research participants and unique data sets for secondary analysis, aiming to increase the likelihood of funding and successful dissemination of research results.

Research topics

• Computer Science
• Psychology
• Computer Security
• Developmental psychology
• Internal medicine
• Neuroscience
• Economics
• Geometry
• Mathematics
• Geology
• Communication
• Medicine
• Radiology
• Physics

Selected publications

• Heat Stress Metrics for US Census Tracts 1998–2020

  Scientific Data · 2026-02-25

  articleOpen access

  Extreme heat exposure is a growing public health threat. Heat-health research has commonly used dry-bulb temperature to characterize heat exposure, partly due to limited availability of spatially explicit, public-health-aligned datasets that integrate multiple meteorological factors to quantify heat stress. We address this gap by providing hourly Heat Index (HI), Wet-Bulb Globe Temperature (WBGT), and Universal Thermal Climate Index (UTCI) for U.S. census tract boundaries across the contiguous United States from 1998-2020. Heat-stress fields were generated by integrating PRISM, ERA5-Land, and National Solar Radiation Database (NSRDB) products, with near-surface temperature and moisture fields reconstructed and ancillary variables interpolated to a harmonized 800-m grid. Heat-stress indices were computed using validated physical models and aggregated to census tracts using area- and population-weighted methods. Validation against station networks shows stable performance for sample year 2010 May-September, with air-temperature root mean squared error (RMSE) of 1.70 °C, Heat Index RMSE of 3.20 °C, WBGT RMSE of 2.90 °C, and UTCI RMSE of 3.26 °C. These tract-level hourly heat-stress datasets enable direct linkage with public health data.

  PublisherOA PDFDOI

• Detecting Patient Position Using Bed-Reaction Forces for Pressure Injury Prevention and Management

  Sensors · 2024-10-09 · 5 citations

  articleOpen access

  A key best practice to prevent and treat pressure injuries (PIs) is to ensure at-risk individuals are repositioned regularly. Our team designed a non-contact position detection system that predicts an individual's position in bed using data from load cells under the bed legs. The system was originally designed to predict the individual's position as left-side lying, right-side lying, or supine. Our previous work suggested that a higher precision for detecting position (classifying more than three positions) may be needed to determine whether key bony prominences on the pelvis at high risk of PIs have been off-loaded. The objective of this study was to determine the impact of categorizing participant position with higher precision using the system prediction F1 score. Data from 18 participants was collected from four load cells placed under the bed legs and a pelvis-mounted inertial measurement unit while the participants assumed 21 positions. The data was used to train classifiers to predict the participants' transverse pelvic angle using three different position bin sizes (45°, ~30°, and 15°). A leave-one-participant-out cross validation approach was used to evaluate classifier performance for each bin size. Results indicated that our prediction F1 score dropped as the position category precision was increased.

  PublisherOA PDFDOI

• Neighborhood Greenspace, Extreme Heat Exposure, and Sleep Quality over Time among a Nationally Representative Sample of American Children

  International Journal of Environmental Research and Public Health · 2024-09-25 · 5 citations

  articleOpen accessSenior author

  Children's sleep is essential for healthy development, yet over a third of children in the United States experience inadequate sleep. Environmental factors can influence sleep: greenspace exposure can promote better sleep, while heat exposure can disrupt sleep. As global climate change raises nighttime and daytime temperatures, greenspace may mitigate the negative effects of heat stress on sleep. We examined the direct effects of neighborhood greenspace and extreme heat exposure on sleep and the statistical interaction between greenspace and heat exposure on sleep outcomes among a nationally representative, four-year longitudinal sample of 8580 U.S. children ages 9-10 years at baseline. Hierarchical linear models incorporated a neighborhood greenspace measure: percent open park space within individual child census tracts, a measure of extreme neighborhood heat exposure during the summer months, and extensive individual and neighborhood-level covariates to test main and interaction effects on child sleep quality. Neighborhood open park space was related to better sleep quality, after controlling for covariates. Additionally, neighborhood extreme heat exposure was associated with worse sleep quality. A two-way interaction was found between neighborhood open park space and neighborhood heat exposure on sleep quality, suggesting open park space mitigated the negative effects of heat on sleep. The results indicate the potential contribution of open greenspace to improve child sleep and enhance resilience to extreme heat, which is an adverse outcome of climate change.

  PublisherOA PDFDOI

• Pessimistic cognitive biases mediate socioeconomic status and children’s mental health problems

  Scientific Reports · 2023-03-30 · 7 citations

  articleOpen access

  Low socioeconomic status (SES) is associated with higher rates of emotional disorders in childhood and beyond. Here we assessed one possible contributor to this disparity, a cognitive bias in the interpretation of negative events, in a group of 341 9-year-olds (49% female, 94% White) ranging widely in SES. This cognitive bias, known as pessimism in the attributional style literature, is the tendency to interpret negative events as persistent (Stable) and pervasive (Global). It was found to be more common among lower SES children (effect sizes = 0.18-0.24 depending on SES measures: income to needs ratio, proportion of poverty from birth to age 9, and parental educational attainment). Moreover, persistent, pervasive adversity in children's lives predicted this bias and mediated the SES-pessimism link. Pessimistic attributional style, in turn, was related to childhood emotional problems and mediated the relation between SES and these problems. Finally, evidence for serial mediation of the SES-mental health problems relationship was found via persistent, pervasive adversity and pessimism, respectively.

  PublisherOA PDFDOI

• Cumulative Neurotoxicological Air Pollution Exposure Is Associated with Lower Reading Improvement and Diminished Benefits of Literacy Interventions for Urban Elementary Students of Color

  Journal of Urban Health · 2023-06-01 · 1 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Psychologie environnementale : 100 notions clés

  Dunod eBooks · 2022-04-27

  book-chapter1st authorCorresponding
  PublisherDOI

• The Physical Environment and Social Development

  2022-03-18 · 3 citations

  otherSenior author
  PublisherDOI

• Crowding

  Elsevier eBooks · 2022-06-28 · 1 citations

  book-chapterSenior authorCorresponding
  PublisherDOI

• Measuring Repositioning in Home Care for Pressure Injury Prevention and Management

  medRxiv · 2022-03-21 · 3 citations

  preprintOpen access

  Abstract A critical best practice for prevention and management of pressure injuries is regularly repositioning individuals who are at risk of these injuries are when they are in bed. However, despite the widespread agreement of the need for regular repositioning (typically every two hours), adherence to repositioning schedules remains poor in the clinical environment and there are some indications that the situation in home environment is even worse. Our team has recently developed a non-contact system that can continuously determine an individual’s position in bed (left-side lying, right-side lying or supine) using data from a set of inexpensive load cells placed under the bed frame. A proof of principle study showed that our system was able to detect whether healthy participants were supine, left-side lying or right-side lying with 94.2% accuracy in the lab environment. The objective of the present work was to deploy our system into the home environment to evaluate how well the system was able to detect the position of individuals sleeping in their own beds overnight by comparing to ground truth time-lapse camera images using eight machine learning classifiers. Nine participants were recruited for this study and we found our system was able to detect an individual’s position in bed with 98.1% accuracy and an F1 score of 0.982 using the XGBoost classifier. Future work will include using this system to evaluate interventions focused on improving adherence to 2-hour repositioning schedules for pressure injury prevention or management as well as incorporating this technology in a repositioning prompting system to alert caregiver when a patient has remained in the same position for too long.

  PublisherOA PDFDOI

• Detecting Patient Position Using Bed-Reaction Forces and Monitoring Skin-Bed Interface Forces for Pressure Injury Prevention and Management

  medRxiv · 2022-03-17 · 4 citations

  preprintOpen access

  Abstract Pressure injuries are largely preventable, yet they affect one in four Canadians across all healthcare settings. A key best practice to prevent and treat pressure injuries is to minimize prolonged tissue deformation by ensuring at-risk individuals are repositioned regularly (typically every 2 hours). However, adherence to repositioning is poor in clinical settings and expected to be even worse in homecare settings. Our team has designed a position detection system for home use that uses machine learning approaches to predict a patient’s position in bed using data from load cells under the bed legs. The system predicts the patient’s position as one of three position categories: left-side lying, right-side lying, or supine. The objectives of this project were to: i) determine if measuring ground truth patient position with an inertial measurement unit can improve our system accuracy (predicting left-side lying, right-side lying, or supine) ii) to determine the range of transverse pelvis angles (TPA) that fully offloaded each of the great trochanters and sacrum and iii) evaluate the potential benefit of being able to predict the individual’s position with higher precision (classifying position into more than three categories) by taking into account a potential drop in prediction accuracy as well as the range of TPA for which the greater trochanters and sacrum were fully offloaded. Data from 18 participants was combined with previous data sets to train and evaluate classifiers to predict the participants’ TPA using four different position bin sizes (∼70°, 45°, ∼30°, and 15°) and the effects of increasing precision on performance, where patients are left side-lying at -90°, right side-lying at 90° and supine at 0°). A leave-one-participant-out cross validation approach was used to select the best performing classifier, which was found to have an accuracy of 84.03% with an F1 score of 0.8399. Skin-bed interface forces were measured using force sensitive resistors placed on the greater trochanters and sacrum. Complete offloading for the sacrum was only achieved for the positions with TPA angles <-90° or >90°, indicating there was no benefit to predicting with greater precision than with three categories: left, right, and supine.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01HL047325

  NIH · $209k · 1996

• NIH Grant RC2MD004767

  NIH · $1.4M · 2012

• NIH Grant F33HD008473

  NIH · $37k

Frequent coauthors

• Tilak Dutta

  Toronto Rehabilitation Institute

  26 shared

• Geoff Fernie

  Toronto Rehabilitation Institute

  25 shared

• Sharon Gabison

  24 shared

• Nikola Pupic

  Toronto Rehabilitation Institute

  24 shared

• Richard Wener

  New York University

  17 shared

• James E. Swain

  Stony Brook University

  14 shared

• Gunn Johansson

  12 shared

• Elham Dolatabadi

  12 shared

Awards & honors

• Honorary Doctorate from Stockholm University in Sweden (2006…
• Guggenheim Fellow
• EDRA Career Award
• Senior National Research Service Award from the National Ins…