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  1. Mental Imagery - an overview | ScienceDirect Topics
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Our knowledge regarding the magnitude of mental health benefits on their own may not be enough to justify the costs associated with increasing nature within cities, but together with benefits such as water quality, flood security, urban cooling, and recreation, we can obtain a more complete picture of the impact of these types of decisions. This step characterizes the elements of nature potentially influencing mental health and includes size total area , composition proportions of different types of natural elements , and spatial configuration e.

Other relevant natural attributes may include tree canopy density, vegetation structure, species composition, or biodiversity 68 , Data can be gathered from a variety of sources e. Determining which aspects of natural features are relevant to mental health—and should therefore be considered in this step—is a key research frontier and will be informed through an iterative process via an evolution of insights and evidence regarding effects see step 3. For example, are some tree species more beneficial than others 70?

Mental Imagery - an overview | ScienceDirect Topics

Is a diversity of tree species in a forest more beneficial than a monoculture stand 71? It is also important to note that little is known about relationships between ecological integrity or complexity and mental health benefits. It may be that places intermediate on the wild-anthropogenic spectrum, tuned to some common evolutionary-based human preferences, are associated with better mental health Our lack of certainty with respect to these and other questions regarding the relationship between nature and mental health underscores the need for future research.

It is also a reminder that the purpose of this endeavor is to create a conceptual model with which to integrate the best available evidence, wherever that may stand, as the field evolves. We must also consider how aspects of natural features result in various amounts of exposure, given the different opportunities for direct and indirect nature contact they afford. This is addressed in the next step. Exposure is a broad term, here referring to the amount of contact that an individual or population has with nature.

The proximity of people to nature is likely to be a large determinant of exposure. A watershed located 50 km outside a city might generate considerable ecosystem services in provision of clean water to the city but not much opportunity for everyday interaction with the landscape. Conversely, the presence of a small city park may result in extensive nature exposure for neighborhood residents and commuters. At present, there is a limited repertoire of methods for estimating nature exposure based on geography. Ekkel and de Vries 73 have identified two principal approaches: cumulative opportunity and proximity measures.

Using sources such as satellite images or land-use databases, this proportion is generally calculated as the percentage of an area of interest a zip code area, census block, etc. A cumulative exposure metric for a population can then be derived for a given spatial unit based on this composition score e. Proximity measures typically estimate use and exposure to nature as a function of direct physical distance to the nearest nature area of a certain size, usually from a place of residence.

Walking time from residence to nature has also been used as a proximity measure. These and other approaches, based primarily on estimations of average exposures, show mixed levels of reliability in their associations with health outcomes As metrics are further developed, frequency and duration of exposure should be considered, as well as aspects of the natural features themselves from step 1. For example, the composition, spatial configuration, and other features of nature will influence the amount of exposure that a population will experience intentionally or otherwise due to resultant differences in accessibility Fig.

Other characteristics of these natural spaces e. In future iterations of the model, we can look to methods used by recreation and cultural ecosystem service models for ways to isolate predictors of visitation to areas in a landscape e. Where available, primary data on actual nature exposure versus potential or opportunity for exposure can also be incorporated. Along with other factors, spatial configuration and composition should be considered when estimating nature exposure.

Different shades of green here represent different types of nature [e. Despite these differences, all panels have the same total amount of nature 34 street blocks. Vertical contrasts illustrate differences in configuration of this nature [e. With respect to characteristics of beneficiaries, the model should eventually account for the sociodemographic, cultural, perceptual, attitudinal, and behavioral differences that influence the tendency for seeking out nature exposure 76 , Measurement approaches based on location alone can fail to account for differences in exposure that are due to factors such as access to transportation corridors, time demands, income disparities, and perceived safety.

This consideration brings us to step 3. The third step in the model accounts for the experiential characteristics of nature exposure—what we term nature experience.

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In moving from nature exposure to mental health effects, we need to consider these specifics. Though attuned to pragmatic considerations regarding data availability, neither cumulative opportunity nor proximity measures account for some relevant aspects of nature experience, including, for example, the sensory qualities of the exposure. Although much of the research literature defaults to eyesight as the primary modality for nature contact 79 , the auditory, tactile, and olfactory modalities are also important to consider Effective park programming can also have a substantial impact on the ways in which users interact with natural spaces, and thereby help determine how these sensory pathways are engaged Two approaches suggest ways to classify nature exposure and characterize nature experience:.

The specific ways in which people interact with nature may account for differential impacts of nature exposure on mental health Looking at water is different from swimming in water, for example.

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For example, walking along the edge of water and land can occur at the ocean or alongside a lake or river. To date, around human-nature interaction patterns, with photos and descriptions for many of them, have been generated and catalogued 40 , Future studies can look at specific health outcomes of people who engage not only in specific forms of interaction but also in constellations of them.

Exposure and dose can vary considerably in toxicology, if, for example, two people exposed to the same concentration of an air pollutant breathe at very different rates. A similar phenomenon may operate with respect to nature contact 83 via different levels of attention, preference, and feelings of personal connection with nature People have different levels of awareness and perceptions of natural environments 85 in their attitudes and receptivity toward nature, childhood experiences, and sense of connectedness to nature—factors that probably affect the delivered dose that results from a given exposure.

The transition from dose to effects corresponds to what economists call a production function, and what toxicologists and epidemiologists quantify using a dose-response curve. We discuss more on the multiple potential causal mechanisms below in step 4. The fourth and final step of our conceptual model involves a characterization of the potential mental health impacts that follow from nature experience. The production of mental health benefits from nature experience may occur through multiple psychological causal mechanisms and pathways, including reduction of stress, increases in social cohesion or physical activity, or replenishment of cognitive capacities, to name just a few.

In many cases, the same natural area will engage multiple mechanisms during each single experience 24 , 86 , 87 so that the cumulative effects attributed to any single pathway may be misestimated. Current insight into each of these mechanisms is incomplete.


However, the evidence regarding the effects themselves is sufficient to support some decision-making contexts discussed below that encourage nature contact to promote health 24 , 33 , As with the characteristics of exposure, the effects of nature experience will also depend on age, gender, current affective state, and other personal characteristics e.

The types of mental health benefits will also vary e. Some will relate to psychological well-being, and others will relate to relevant factors for the onset of disease. The range of these outcomes includes population-level indicators and clinical-level measures, assessed through self-report, physiological measures, and other approaches.

Given the degree of complexity and the need for future iteration, it is helpful to consider an example application of this conceptual model to illustrate its potential for informing decisions regarding land use, urban planning, or environmental management Fig. We briefly describe a hypothetical decision-making context below and define the steps that one might take in applying the conceptual model. As stated above, while many factors affect health through complex pathways, this model relates only to a subset of environmental factors.

In addition, we do not account here for the other potential impacts or benefits of scenarios involving planned environmental change e. Information is gathered for each of the three steps. As illustrated in Fig. The model allows us to compare net benefits total benefits less costs of different viable plans. Benefits will also likely vary according to the moderating influences of individual differences and sociocultural context, here represented conceptually by groups A to D, as people receive different benefits from nature experience given these moderators.

Consider a decision-making context in which practitioners would like to estimate the impacts of planting residential street trees on the prevalence of mental illness. Using our model, they would initially gather information regarding the natural features of the relevant region step 1. In this particular case, available data might consist of information on existing and proposed tree distribution, the species of trees, and perhaps some information on tree structure, likely gathered from city databases and natural history accounts.

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Practitioners could also deduce planned composition and configuration of the trees from consulting the planning proposals from the city. However, other aspects of the natural features e. As the body of empirical research grows, practitioners could consult a central data repository containing multiple studies or meta-analyses with effect sizes documented at the relevant scale for given outcomes of interest.

This would be necessary to make a prediction with any degree of certainty or scientific rigor. Continuing with the conceptual exercise, in our present example, decision makers might consider the outcome of antidepressant prescription rates as a crude proxy for depression prevalence. To do this, they could consult Taylor et al. In this case, they would need to temper the confidence in their prediction with the knowledge that they were extrapolating from a single correlational study.

Given the approach used in this particular study, the calculation of exposure step 2 would be based on the change in street tree density in a residential neighborhood. From this exposure metric, practitioners could then apply the association found between the density of street trees and the rate of antidepressant prescribing, in which a regression analysis indicated that each additional tree per kilometer of street was associated with 1. Critically, we note that this rough, potential exposure metric i. Factors from other studies that do speak to mechanisms thought to mediate the relationship between the presence of street trees and the distribution and characteristics of mental health outcomes could be integrated in future iterations of the model and will almost certainly increase the accuracy and validity of the effects on a causal level.

We have also not accounted for effect modification by individual- and population-level differences in potential beneficiaries. Basing predictions on simple regression analyses lacks precision. This rough calculation thus likely comes with a large degree of error, but can provide insight nonetheless, and inform decisions better than not taking mental health services into account at all.

Controlling for other independent predictors of antidepressant prescription derived from Taylor et al. It is possible then to calculate a value of this output using global estimates of the cost of depression. Despite the limitations of this approach, it is possible that this prediction will be a lower bound of the total mental health benefits provided, as it is context dependent i.

We emphasize again the intention behind this exercise, especially given the cross-sectional results upon which it is based: to give a hypothetical workthrough of a conceptual model, the accuracy of which will be refined through successive iteration and incorporation of empirical data as they are generated by the research community. Diverse stakeholders, including city planners, landscape architects, engineers, parks departments, developers, infrastructure providers, health professionals, community-based organizations, and environmental advocates, could use a tool that helps them anticipate the mental health impacts of decisions they make relating to the environment.

Although the magnitude and distributions of these impacts are still questions requiring further research, practitioners are nonetheless in need of the best available evidence to inform decisions that may have repercussions for mental health. With respect to general health, models are already starting to be applied within these contexts. Examples include urban tree canopy restoration to improve air quality 91 , the siting of new park locations to improve physical activity 92 , and efforts to use environmental investments to advance health equity This last point is critical.

Given the emerging evidence base for the benefits of nature contact, greater effort should be made to increase access to nature to help address the significant health inequities that people from low-opportunity neighborhoods experience, in contrast to their privileged counterparts. A greater recognition of the relationship between nature exposure and mental health is also likely to highlight income-related inequalities and provide one of many possible pathways to reduce them.

Removing social and physical barriers to nature contact is an issue of environmental justice 94 — 98 see the Supplementary Materials for additional references. Throughout this paper, we have been careful to note the limitations of the evidence base today, as well as the capacity and opportunity to integrate existing evidence into predictions using a conceptual model. These limitations point to important research frontiers in i moving beyond correlation to causal understanding of relationships and ii filling priority gaps in predictive capacity through consideration of often-confounded predictors of health.

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A great challenge is to distinguish the nature experience signal from other in many cases stronger social and environmental predictors of health lack of opportunity, insufficient amenities, racial prejudice, etc.