Neighborhood walkability, physical activity and changes in glycemic markers in people with type 2 diabetes: The Hoorn Diabetes Care System cohort

Braver, Nicolette R. den; Rutters, Femke; Wagtendonk, A.J.; Kok, Julia G.; Harms, Peter P.; Brug, Johannes; Beulens, Joline W.J.; Lakerveld, Jeroen

doi:10.1016/j.healthplace.2021.102560

Cited by 14 publications

(18 citation statements)

References 35 publications

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“…Second, Euclidian buffers do not require street network details and can thus be easily applied and adapted to other settings [ 51 ]. Third, we used circular buffers to be consistent with previous Dutch walkability studies [ 25 , 28 ]. While there currently is no general consensus on which buffer sizes are most relevant for walking [ 10 ], especially not for the Dutch context, we applied three different sizes to estimate walkability in the immediate, medium and larger environments - corresponding to 150 m, 500 m, and 1000 m radii respectively.…”

Section: Methodsmentioning

confidence: 99%

“…While there currently is no general consensus on which buffer sizes are most relevant for walking [ 10 ], especially not for the Dutch context, we applied three different sizes to estimate walkability in the immediate, medium and larger environments - corresponding to 150 m, 500 m, and 1000 m radii respectively. Walkability components were also calculated at administrative neighbourhood levels, as done in other walkability studies [ 28 , 41 ]. In order to do this, raw componential data were first rasterized into 25 m × 25 m cells.…”

Section: Methodsmentioning

confidence: 99%

“…Despite its extensive application in environmental epidemiological research in North America and Australia [ 20 – 22 ], walkability indices have had limited application in Europe and especially the Netherlands. Dutch studies on walkability so far have focused on individual components [ 23 , 24 ], specific sub-populations such as older adults [ 25 ], children [ 26 , 27 ], patients of type-2 diabetes [ 28 ] or only zoomed in on a local geographical region [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

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Development of an objectively measured walkability index for the Netherlands

Lam

Wang

Vaartjes

et al. 2022

Int J Behav Nutr Phys Act

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Background Walkability indices have been developed and linked to behavioural and health outcomes elsewhere in the world, but not comprehensively for Europe. We aimed to 1) develop a theory-based and evidence-informed Dutch walkability index, 2) examine its cross-sectional associations with total and purpose-specific walking behaviours of adults across socioeconomic (SES) and urbanisation strata, 3) explore which walkability components drive these associations. Methods Components of the index included: population density, retail and service density, land use mix, street connectivity, green space, sidewalk density and public transport density. Each of the seven components was calculated for three Euclidean buffers: 150 m, 500 m and 1000 m around every 6-digit postal code location and for every administrative neighbourhood in GIS. Componential z-scores were averaged, and final indices normalized between 0 and 100. Data on self-reported demographic characteristics and walking behaviours of 16,055 adult respondents (aged 18–65) were extracted from the Dutch National Travel Survey 2017. Using Tobit regression modelling adjusted for individual- and household-level confounders, we assessed the associations between walkability and minutes walking in total, for non-discretionary and discretionary purposes. By assessing the attenuation in associations between partial indices and walking outcomes, we identified which of the seven components drive these associations. We also tested for effect modification by urbanization degree, SES, age and sex. Results In fully adjusted models, a 10% increase in walkability was associated with a maximum increase of 8.5 min of total walking per day (95%CI: 7.1–9.9). This association was consistent across buffer sizes and purposes of walking. Public transport density was driving the index’s association with walking outcomes. Stratified results showed that associations with minutes of non-discretionary walking were stronger in rural compared to very urban areas, in neighbourhoods with low SES compared to high SES, and in middle-aged (36–49 years) compared to young (18–35 years old) and older adults (50–65 years old). Conclusions The walkability index was cross-sectionally associated with Dutch adult’s walking behaviours, indicating its validity for further use in research.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of an objectively measured walkability index for the Netherlands

Lam

Wang

Vaartjes

et al. 2022

Int J Behav Nutr Phys Act

View full text Add to dashboard Cite

show abstract

“…Finally, the sum score was rescaled such that the neighbourhood walkability index ranged between 0 and 100, with higher scores representing higher neighbourhood walkability levels [ 22 ]. No weights were applied to the components of the neighbourhood walkability index, since an equally weighted index seems to perform well in a Dutch context [ 46 , 47 ].…”

Section: Methodsmentioning

confidence: 99%

Associations of changes in neighbourhood walkability with changes in walking activity in older adults: a fixed effects analysis

et al. 2021

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Background Supporting older adults to engage in physically active lifestyles requires supporting environments. Walkable environments may increase walking activity in older adults, but evidence for this subgroup is scarce, and longitudinal studies are lacking. This study therefore examined whether changes in neighbourhood walkability were associated with changes in walking activity in older adults, and whether this association differed by individual-level characteristics and by contextual conditions beyond the built environment. Methods Data from 668 participants (57.8–93.4 years at baseline) across three waves (2005/06, 2008/09 and 2011/12) of the Longitudinal Aging Study Amsterdam (LASA) were used. These individuals did not relocate during follow-up. Self-reported outdoor walking activity in minutes per week was assessed using the LASA Physical Activity Questionnaire. Composite exposure measures of neighbourhood walkability (range: 0 (low)-100 (high)) within 500-m Euclidean buffer zones around each participant’s residential address were constructed by combining objectively measured high-resolution Geographic Information System data on population density, retail and service destination density, land use mix, street connectivity, green space density, and sidewalk density. Fixed effects linear regression analyses were applied, adjusted for relevant time-varying confounders. Results Changes in neighbourhood walkability were not statistically significantly associated with changes in walking activity in older adults (β500m = − 0.99, 95% CI = -6.17–4.20). The association of changes in neighbourhood walkability with changes in walking activity did not differ by any of the individual-level characteristics (i.e., age, sex, educational level, cognitive impairment, mobility disability, and season) and area-level characteristics (i.e., road traffic noise, air pollution, and socioeconomic status). Conclusions This study did not show evidence for an association between changes in neighbourhood walkability and changes in walking activity in older adults. If neighbourhood walkability and walking activity are causally linked, then changes in neighbourhood walkability between 2005/06 and 2011/12 might have been not substantial enough to produce meaningful changes in walking activity in older adults.

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“…Adults living in high-walkability areas had lower 10-year incidences of diabetes 21 and cardiovascular disease 22 than those in low-walkability areas, although not glycaemic control. 23 While the literature suggests an inverse relationship between NRDs and body mass index (BMI) and a positive relationship between NRDs and physical function in high-density settings, there are few studies that include lower density settings. Data from the rural US found that a perceived lack of NRDs was associated with obesity.…”

Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

Exploring non-linear relationships between neighbourhood walkability and health: a cross-sectional study among US primary care patients with chronic conditions

2022

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BackgroundA recent study of licensed drivers found a non-linear relationship between density of non-residential destinations (NRDs), a proxy for walkability and body mass index (BMI) across a wide range of development patterns. It is unclear if this relationship can be replicated in a population with multiple chronic conditions or translated to health outcomes other than BMI.MethodsWe obtained health data and home addresses for 2405 adults with multiple chronic conditions from 44 primary care clinics across 13 states using the Integrating Behavioral health and Primary Care Trial. In this cross-sectional study, the relationships between density of NRDs (from a commercial database) within 1 km of the home address and self-reported BMI, and mental and physical health indices were assessed using several non-linear methods, including restricted cubic splines, LOWESS smoothing curves, non-parametric regression with a spline basis and piecewise linear regression.ResultsAll methods demonstrated similar non-linear relationships. Piecewise linear regression was selected for ease of interpretation. BMI had a positive marginal rate of change below the NRD density inflection point of 15 establishments/hectare (β=+0.09 kg/m2/non-residential buildings ha-1; 95% CI +0.01 to +0.14), and a negative marginal rate of change above the inflection point (β=−0.02; 95% CI −0.06 to 0.02). Mental health decreased with NRD density below the inflection point (β=−0.24; 95% CI −0.31 to −0.17) and increased above it (β=+0.03; 95% CI −0.00 to +0.07). Results were similar for physical health (β= −0.28; 95% CI −0.35 to −0.20) and (β=+0.06; 95% CI 0.01 to +0.10).ConclusionHealth indicators were the lowest in middle density (typically suburban) areas and got progressively better moving in either direction from the peak. NRDs may affect health differently depending on home-address NRD density.Trial registration numberNCT02868983.

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Neighborhood walkability, physical activity and changes in glycemic markers in people with type 2 diabetes: The Hoorn Diabetes Care System cohort

Cited by 14 publications

References 35 publications

Development of an objectively measured walkability index for the Netherlands

Development of an objectively measured walkability index for the Netherlands

Associations of changes in neighbourhood walkability with changes in walking activity in older adults: a fixed effects analysis

Exploring non-linear relationships between neighbourhood walkability and health: a cross-sectional study among US primary care patients with chronic conditions

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