Street-view greenspace exposure and objective sleep characteristics among children

2022  Journal Article

Street-view greenspace exposure and objective sleep characteristics among children

Pub TLDR

This study investigates the relationship between street-view greenspace exposure and sleep characteristics in children, using deep learning algorithms to analyze greenspace metrics from street view imagery. While unadjusted models indicated that greenspace and tree coverage were linked to longer sleep duration in early adolescence, only the percentage of grass was associated with wake time after sleep onset in fully adjusted models. The findings also revealed that the impact of greenspace on sleep varied across different racial and socioeconomic groups.

 

College of Health researcher(s)

Abstract

Greenspace may benefit sleep by enhancing physical activity, reducing stress or air pollution exposure. Studies on greenspace and children's sleep are limited, and most use satellite-derived measures that do not capture ground-level exposures that may be important for sleep. We examined associations of street view imagery (SVI)-based greenspace with sleep in Project Viva, a Massachusetts pre-birth cohort.

We used deep learning algorithms to derive novel metrics of greenspace (e.g., %trees, %grass) from SVI within 250m of participant residential addresses during 2007–2010 (mid-childhood, mean age 7.9 years) and 2012–2016 (early adolescence, 13.2y) (N = 533). In early adolescence, participants completed >5 days of wrist actigraphy. Sleep duration, efficiency, and time awake after sleep onset (WASO) were derived from actigraph data. We used linear regression to examine cross-sectional and prospective associations of mid-childhood and early adolescence greenspace exposure with early adolescence sleep, adjusting for confounders. We compared associations with satellite-based greenspace (Normalized Difference Vegetation Index, NDVI).

In unadjusted models, mid-childhood SVI-based total greenspace and %trees (per interquartile range) were associated with longer sleep duration at early adolescence (9.4 min/day; 95%CI:3.2,15.7; 8.1; 95%CI:1.7,14.6 respectively). However, in fully adjusted models, only the association between %grass at mid-childhood and WASO was observed (4.1; 95%CI:0.2,7.9). No associations were observed between greenspace and sleep efficiency, nor in cross-sectional early adolescence models. The association between greenspace and sleep differed by racial and socioeconomic subgroups. For example, among Black participants, higher NDVI was associated with better sleep, in neighborhoods with low socio-economic status (SES), higher %grass was associated with worse sleep, and in neighborhoods with high SES, higher total greenspace and %grass were associated with better sleep time.

SVI metrics may have the potential to identify specific features of greenspace that affect sleep.

Jimenez, M.P., Suel, E., Rifas-Shiman, S.L., Hystad, P., Larkin, A., Hankey, S., Just, A.C., Redline, S., Oken, E., James, P.(2022)Street-view greenspace exposure and objective sleep characteristics among childrenEnvironmental Research214
 
Publication FAQ

Street View Greenspace and Sleep: Frequently Asked Questions

What was the main goal of this study?

This study investigated the relationship between exposure to greenspace and sleep quality in children and adolescents. The researchers aimed to determine if there was a connection between the amount of greenspace visible in a child's environment and objective measures of their sleep, such as duration and efficiency.

How was greenspace exposure measured in this study?

The researchers used two methods to assess greenspace:

  • Street View Imagery (SVI): This innovative method involved using Google Street View images and deep learning algorithms to classify the types of vegetation (trees, grass, flowers, etc.) visible within 250 meters of the participants' homes. This provided a ground-level perspective on greenspace exposure.
  • Normalized Difference Vegetation Index (NDVI): This traditional satellite-based measure quantifies the amount of vegetation in an area, but it cannot distinguish between different types of greenery.

How was sleep assessed in this study?

Sleep was measured objectively using actigraphy, a method where participants wear a device on their wrist that tracks their movement. Actigraphy data were used to calculate sleep duration, sleep efficiency (the percentage of time spent asleep while in bed), and wake after sleep onset (WASO), a measure of sleep fragmentation.

What were the key findings regarding greenspace and sleep?

  • There was no consistent association between overall greenspace exposure and sleep duration or efficiency in adolescents.
  • An interesting finding was that higher exposure to grass in mid-childhood was associated with more sleep fragmentation (higher WASO) in early adolescence.
  • The study did not find support for differences in the relationship between greenspace and sleep based on sex or the level of urbanicity.

Did the study look at differences by race/ethnicity and socioeconomic status?

Yes, the researchers explored how the relationship between greenspace and sleep varied across different groups:

  • Race/Ethnicity: Higher NDVI (satellite-measured greenspace) was linked to better sleep efficiency and less sleep fragmentation in Black participants.
  • Socioeconomic Status (SES): In high-SES neighborhoods, more total greenspace and more grass were associated with longer sleep duration. However, in low-SES neighborhoods, more grass exposure was associated with worse sleep quality.

What are some of the strengths of this study?

  • Longitudinal data: The study followed participants over time, allowing for a prospective analysis of the relationship between greenspace in mid-childhood and sleep in adolescence.
  • Objective measures: Both greenspace exposure and sleep were measured objectively, reducing the risk of bias from self-reported data.
  • Detailed greenspace assessment: The use of SVI with deep learning provided a more precise and ground-level assessment of greenspace types than traditional methods.

What are some limitations to consider?

  • Sample size: The limited sample size may have affected the study's ability to detect small effects.
  • Confounding factors: Although the researchers controlled for many variables, the strong link between SES and greenspace exposure suggests that residual confounding (unmeasured factors) could still be present.
  • SVI limitations: SVI images are snapshots in time and may not capture seasonal changes or the actual time individuals spend in green spaces.

What are the implications of these findings?

This study highlights the complex relationship between greenspace and sleep. While the overall associations were weak, the findings suggest that the type of greenspace and socioeconomic context may play a role in how greenspace influences sleep. More research is needed to fully understand these relationships and to determine the most effective ways to design and promote greenspace to improve sleep health, especially for children and adolescents in diverse communities.