In a recent paper by Melis and colleagues [1], exposure to certain built environment
characteristics—urban density and accessibility to public transit—is found to be related
to mental health, even more so among women, the elderly, and the residentially stable
(interactions between built environment and individual characteristics in relation
to mental health have unfortunately not been tested statistically, which could have
strengthened their demonstration). The authors argue that this may be because these
groups spend more time in their neighbourhoods [1]. Other studies have associated
the length of time spent in home neighborhoods with stronger links to health outcomes,
e.g., Chum, A., et al. [2], or mentioned this hypothesis [3,4]. One interpretation
of such findings may be, as the authors suggest, that some sort of dose-response relationship
is at play—the longer the exposure, the stronger the influence. However, this interpretation
also reveals limitations in how environmental exposure is being measured, and, more
globally, in how little actual processes linking exposure to health are being documented
and understood, i.e., “how environments actually get under the skin”.
Regarding measures of environmental exposure, a large body of research is still focusing
on residential environments only. However, following early calls to avoid local [5]
or residential traps [6], and acknowledging studies that showed differences in exposures
between home and non-residential destinations [7,8,9,10], activity space approaches
attempting to account for daily mobility and multiple exposures are increasingly being
used. Such approaches try to account for non-residential exposures, by using mobility
data from surveys of regular destinations [11,12] or from GPS tracking [13], by integrating
information on time spent within and outside the residential neighbourhood, or by
using measures of concentration of activities within/outside the residential neighbourhood
[3,4]. As people move around to conduct their daily activities, they may visit a number
of non-residential places. Consequently, they are exposed to sometimes contrasting
environments while spending time at these locations or during trips along routes.
Ignoring such non-residential exposures is problematic both because there are reasons
to believe that they may also play a role (unless there is a specific a priori hypothesis
that only residential exposure has an influence) and because doing so may translate
into systematic bias, or misclassification.
Our first argument is to reiterate previous statements that ignoring non-residential
environments provides a partial picture of actual exposure. This means that providing
comprehensive measures of exposure implies accounting for the various locations and
corresponding contrasting exposures that people experience. Doing so does not mean,
however, that all locations are of the same importance. Because precise processes
linking environments to health remain largely unknown, more comprehensive data collection
on daily mobilities, activities and conditions of environmental exposure is needed.
Beyond the multiple exposures themselves, other dimensions, yet undocumented, may
indeed play a role in the dose-response relationship linking exposure to health. Exposure
in some destinations may play a larger role than in others because of the nature of
activities that are conducted at these locations—e.g., workplace vs. leisure place—because
of the influence of time of day or day of the week, because social dimensions, such
as co-presence or absence of social network members modify how environments are perceived,
or because other psychosocial dimensions render ‘that place at that moment’ specifically
important. In other words, not only should multiple destinations be considered to
improve specificity in exposure measures, but there should also be a much richer documentation
of the conditions under which these exposures are being experienced.
Our second argument presents the possibility of systematic bias, as it appears that
misclassification in exposure, or the difference between actual exposures experienced
throughout daily mobilities and exposure measured in the residential environments
only, should be smaller among those who spend more time in their neighbourhood, and
consequently larger if they spend more time at other locations. The difference will
further increase along with increasingly contrasting characteristics of these other
locations compared to the home environment. It is important to state here that both
dimensions—i.e., the time spent within or outside the residential neighborhood, itself
linked to daily mobility patterns, and the range of difference between residential
and non-residential environments may vary according to individual profiles. This means
that magnitudes of misclassification in measures of exposure based on home environments
only may be related to other traits such as age, socio-economic status, occupation,
or gender [14,15], resulting in biased effect estimates.
These two arguments call for a change in how research on place effects is conducted.
We provide a number of recommendations to incorporate such change. First, spatial
data on daily mobility should be collected more often and more systematically to allow
specific estimates of exposures in multiple destinations and along routes. Solutions
to collect such data include map-based questionnaires, GPS tracking, or other surveys
about where people spend time. Second, complementary information about ‘what happens
where, when, with whom and why’ is needed to improve our understanding of the very
processes linking environments to health. This is especially true when conscious or
even unconscious cognitive processes such as chronic stress and allostatic load, feelings
of safety, or perceptions of environmental cues that shape behaviour are involved.
In short, exposure to environments needs to be further contextualised and individualized.
Accounting for complementary conditions such as activities being conducted, time of
day, presence and relation with peers, or variations in individuals’ feelings and
transient mind states will contribute to an improved understanding of how environments
get under the skin. Various tools, including ecological momentary assessment, socio-spatial
questionnaires, or individualised assessments through go-along methods or ethnographic
geographies, can be used to collect such information. Of course, with an increasing
number of variables to be considered, and possible explorations of various moderation
hypotheses, issues of power may arise.
Regarding this point, we further believe that a stronger consideration of not only
environmental exposure conditions, but rather of changes or variations thereof will
help increase power and identify meaningful associations. If environmental conditions
do in fact play a role, tracking their changes and linking those changes to variations
in behaviour or health will help reveal significant pathways. Relating changes in
exposure to changes in health not only increases the strength of causal proof, it
also increases the power for detection. Consequently, we further argue that more efforts
should be invested to assess change in environmental conditions in relation to change
in health states. Such changes may be observed within days, through micro-level and
continuous monitoring of variations in environmental exposure and health across space
and time within individuals, or within longer periods, with changes in environments
possibly documented through space-time geographic information systems, and changes
in health monitored through systematic longitudinal designs.
More should be done to document environmental change, including the development of
data platforms with systematic and continuous monitoring of environmental status,
and further integration of algorithms to produce indicators of environmental change.
Furthermore, because changes in the environment often result from planned decisions,
an improved monitoring of urban interventions and modifications of built environments
is warranted. With cities increasingly producing and relying on sensor networks, and
people increasingly generating individualised spatio-temporal information on mobilities
and health behaviour, new data and computing capabilities can help our field of research
understand how people and places interact. With new possibilities also come new challenges,
and increasingly multidisciplinary perspectives will be required to fully address
such complexities. The development of larger cohorts with detailed and continuous
monitoring of behaviour and health, including daily mobility and social contacts,
should be promoted. Novel tools, such as web-based questionnaires, or wearable sensors,
including mobile phone sensing, can help to achieve this goal. While we realise this
commentary’s recommendations go well beyond the paper by Melis et al., tackling built
environment effects on mental health, we acknowledge the authors’ important research
question and hope this commentary can help our field move forward.