Table 1.

Summary of data extracted.

Study	Location	Population	Exposure	Outcome & Sample Size	Covariates
The Effects of Naturalness, Gender, and Age on how Urban Green Space is Perceived and Used Sang, 2016, Urban Forestry & Urban Greening [45]	Gothenburg, Sweden	Households living close to six different urban green spaces in 2016	Perceived naturalness based on six areas of diverse character (urban park, woodland, nature area, residential, allotment) assessed by survey	Self-report wellbeing assessed by WHO (ten) well-being index n = 1347	age gender
Residential Green Space and Birth Outcomes in a Coastal Setting Glazer, 2018, Environmental Research [33]	Rhode Island, United States	Births occurring at Women & Infants Hospital of Rhode Island, to >17 years at delivery, singleton, living within RI, GA 22–44, birthweight 500–5000 g, with data on covariates from 2002–2004 & 2006–2012	Residential distance to and buffer density of green and blue spaces assessed by NDVI and linear distance	Preterm, birthweight, and small for gestational age assessed by birth record and standard cut points (<37 weeks, grams, birth weight < 10th percentile) n = 61,460	maternal age, race, number of prenatal visits, maternal education, marital status, insurance coverage, tobacco use, neighborhood SES, gestational age at birth, town of residence, distance to major roadways
The Association Between Natural Environments and Depressive Symptoms in Adolescents Living in the United States Bezold, 2018, Journal of Adolescent Health [32]	United States	GUTS (Growing Up Today) adolescents cohort 1999	Residential proximity and buffer density of green and blue space assessed by NDVI and linear distance	Depressive symptoms assessed by McKnight risk factor survey n = 9385	race, grade level, age, gender, household income, father’s education, maternal history of depression, median tract income, home value, percent tract white, tract college education, region of country, urban/rural, PM2.5 average for July 1999
Natural Environments and Suicide Mortality in the Netherlands: a Cross-sectional, Ecological Study Helbich, 2018, The Lancet Planetary Health [44]	Netherlands	National suicide register from 2005–2014	Proportion of greenspace/bluespace and coastal proximity per municipality assessed by Dutch land-use database	Registered suicide deaths assessed by death certificate n = 16,105	gender, divorce, unemployment, housing values, distance to nearest GP, voter alignment, urbancity
Are our Beaches Safe? Quantifying the Human Health Impact of Anthropogenic Beach Litter on People in New Zealand Campbell, 2019, Science of the Total Environment [48]	New Zealand	ACC insurance claims from 2007–2016	Reported insurance claims related to injury from beach litter per region	Injury type noted in insurance claim n = 161,261	age, gender, ethnicity, location
Effects of Freshwater Blue Spaces may be Beneficial for Mental Health: A First, Ecological Study in the North American Great Lakes Region Pearson, 2019, PLoS ONE [34]	Michigan, United States	Michigan residents in the MIDB during 2014	Proximity/coverage of bluespace assessed by linear distance and zip code overlap	MIDB reported anxiety/mood disorder n = 30,421	age, gender, median income, population density
Human Health Impacts from Litter on Beaches and Associated Perceptions: A Case Study of ‘clean’ Tasmanian Beaches Campbell, 2016, Ocean & Coastal Management [47]	Tasmania, Australia	Tasmania beach users from 2010–2011	Frequency of attendance to any of nine beaches across Tasmania assessed by survey	Survey self-reported injury occuring at beaches related to litter n = 173	NA
Using Deep Learning to Examine Street View Green and Blue Spaces and their Associations with Geriatric Depression in Beijing, China Helbich, 2019, Environment International [39]	Beijing, China	Elderly population residing in Haidian district during 2011	Neighborhood green/blue space measured by Landsat, NDVI,NDWI, and street view neighborhood green/blue space measured by Landsat, NDVI,NDWI, and street view	Depressive symptoms assessed by geriatric depression scale (GDS-15) n = 1190	gender, age, education, marital status, ADL score, multiple chronic diseases, air pollution
Designing Urban Green Spaces for Older Adults in Asian Cities Tan, 2019 [38]	Hong Kong and Tainan	Elderly population of Hong Kong and Tainan 2016–2018	Attendance to one of 31 small scale urban greenspaces	General health survey n = 326	NA
Neighbourhood Blue Space, Health and Wellbeing: The Mediating role of Different Types of Physical Activity Pasanen, 2019, International Journal of Environmental Research and Public Health [46]	England, United Kingdom	English households from 2008–2012	Coastal proximity to bluespace and present/absent freshwater bluespace assessed by land use database and linear distance	Self-reported general health assessed by standardized health survey n = 21,097	quantity/quality of blue and greenspace, urban/rural, deprivation index, age, gender, education, marital status, household income, employment, car availability, number of children, long-term illness, year
The neighborhood effect of exposure to blue space on elderly individual’s mental health: A case study in Guangzhou, China Chen & Yuan, 2020, Health and Place [40]	Guangzhou, China	Elderly adults sampled from 18 neighborhoods in 2018	Remote sensed neighborhood blue space (characteristics, nearness, visitation)	Self-reported mental health assessed by 36-item Short Form Health Survey n = 966	age, gender, education, marital status, hukou status, monthly household income, employment information
Green and Blue Space Availability and Self-Rated health among Seniors in China: Evidence from a National Survey Lin & Wu, 2021, International journal of environmental research and public health [41]	China	Chinese Social Survey respondents aged 60 years or more from 2011	Neighborhood green and blue space assessed by linear distance and buffer area coverage via NDVI/Lansat, Inland Surface Water Dataset	Self-reported overall health assessed via Chinese Social Survey n = 1773	age, marital status, ethnicity, insurance, lifestyle education, household registration location, occupation, income, assets, distance to major roadway, population density, GDP production per km2
The effect of urban nature exposure on mental health—a case study of Guangzhou Liu, 2021, Journal of Cleaner Production [42]	Guangzhou, China	Survey respondents from 23 residential communities across Guangzhou from 2020	Nearest park and network distance to park and buffer area coverage of blue space using Open Street Map	Self-reported mental health assessed by the Mental Health Inventory n = 933	age, gender, education, income, education, income, occupation, marital status, and residence location, urban, life events
General health and residential proximity to the coast in Belgium: Results from a cross-sectional health survey Hooyberg, 2020, Environmental research [43]	Belgium	Respondents of the Belgian Health Interview Survey as of 2013	Network distance to the coast assessed via Open Street Map	Self-reported general health via Belgian Health Interview Survey n = 60,939	age, sex, chronic disease, body mass index, employment, income, smoking, urbanization, year, season, green space, blue space
Different types of urban natural environments influence various dimensions of self-reported health Jarvis, 2020, Environmental research [37]	Vancouver, Canada	Respondents of the Canadian Community Health Surveys from 2013–2014	Buffer landcover type via 2008–2015 LiDAR and aerial photography plus access to public greenspace via presence of greenspace within 300 m	Self-reported general health and mental health assessed via the Canadian Community Health Survey n = 2,183,170	age, gender, race/cultural background, education, household income, urbancity
Cross-sectional association between the neighborhood built environment and physical activity in a rural setting: the Bogalusa Heart Study Gustat, 2020, BMC public health [35]	Bogalusa, United States	Questionnaire respondents of the Bogalusa Heart Study from 2012–2013	Built environment scores for buffer area surrounding residence assessed via the Rural Active Living Assessment and Google Street View	Physical Activity Questionnaire data weekly metabolic equivalent minuets for leisure, transport, and total physical data. n = 1245	age, race, body mass index, education, income, smoking, alcohol consumption, percent census block below poverty, population density
Perceived biodiversity, sound, naturalness, and safety enhance the strotive quality and wellbeing benefits of green and blue space in a neotropical city Fisher, 2021, Science of the Total Environment [49]	Georgetown, Guyana	Survey respondents from 15 natural sites across Georgetown in 2019	Live birdsong and species diversity assessed via recordings and photography	Self-reported wellbeing assessed via the Positive and Negative Affect Schedule n = 409	age, ethnicity, religion, education, household income, location of residence
Greenspace Inversely Associated with Risk of Alzheimer’s Disease in the Mid-Atlantic United States Wu & Jackson, 2021, Earth [36]	United States	Centers for Medicaid and Medicare recipients 65 years and older residing in Mid-Atlantic Region from 1999–2013	Landcover type assessed via aerial photography and classified at the zipcode level	Diagnosis of Alzheimer’s Disease via ICD-9 code in patient record. n = 109,405	monthly average PM2.5, percent greenspace, percent water area, houshold income, zip code area, population density, road density
The Restorative Health Benefits of a Tactical Urban Intervention: An Urban Waterfront Study Roe, 2019, Frontiers in Built Environment [31]	West Palm Beach, United States	Pedestrians along West Palm Beach Promenade Spring 2017	Crossover trial comparing normal promenade conditions (i.e., no changes) to one with minor aesthetic changes	Real-time heart rate variability, subjective mood, and perceived restorativeness assessed via wearable device and surveys n = 23	NA

Abbreviations (in order of appearance): WHO, World Health Organization; RI, Rhode Island; GA, Gestational Age; g, Grams; NDVI, Normalized Difference Vegetative Index; SES, Socioeconomic Status; PM_2.5, Particular Matter (≤2.5 μm in diameter); GP, General Practitioner; ACC, Accident Compensation Corporation; MIDB, Michigan Inpatient Database; NDWI, Normalized Difference Water Index; ADL, Activities of Daily Life; GDP, Gross Domestic Product.