Age and Ageing 2021; 50: 963–968 © The Author(s) 2020. Published by Oxford University Press on behalf of the British Geriatrics Society. doi: 10.1093/ageing/afaa246 This is an Open Access article distributed under the terms of the Creative Commons Attribution Published electronically 20 November 2020 Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com SHORT REPORT Neighbourhood walkability and physical activity: moderating role of a physical activity intervention in overweight and obese older adults with metabolic syndrome Antoni Colom1,2 , Suzanne Mavoa3,4 , Maurici Ruiz1,5 , Julia Wärnberg2,6 , Josep Muncunill7 , Jadwiga Konieczna1,2 , Guillem Vich8 , Francisco Javier Barón-López2,9 , Montserrat Fitó2,10 , Jordi Salas-Salvadó2,11,12 , Dora Romaguera1,2 1 Research Group on Nutritional Epidemiology & Cardiovascular Physiopathology, Health Research Institute of the Balearic Islands (IdISBa). University Hospital Son Espases, 07120 Palma, Spain 2 Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain 3 Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia 4 Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia 5 Servicio de SIG y Teledetección, Vicerectorat d’Innovació i Transferència, Universitat de les Illes Balears, Palma, Spain 6 Departamento de Enfermería, Facultad de Ciencias de la Salud, Universidad de Málaga – Instituto de Investigación en Biomedicina (IBIMA), Málaga, Spain 7 Genomics and Bioinformatics Platform, Balearic Islands Health Research Institute (IdISBa), University Hospital Son Espases, Palma, Spain 8 Geography Department, Autonomous University of Barcelona, Bellaterra, Spain 9 Departamento de Salud Pública, Facultad de Medicina, Universidad de Málaga – Instituto de Investigación en Biomedicina (IBIMA), Málaga, Spain 10 Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d’Investigació Mèdica (IMIM), Barcelona, Spain 11 Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain 12 Institut d’Investigació Pere Virgili (IISPV), Human Nutrition Unit: Prevention and Epigenetics, Reus, Spain Address correspondence to: Dora Romaguera. Tel: 871 205000; Fax: 871206868. Email: mariaadoracion.romaguera@ssib.es Abstract Background: While urban built environments might promote active ageing, an infrequently studied question is how the neighbourhood walkability modulates physical activity changes during a physical activity intervention programme in older adults. We assessed the influence of objectively assessed neighbourhood walkability on the change in physical activity during the intervention programme used in the ongoing PREvención con DIeta MEDiterránea (PREDIMED)-Plus trial. Method: The present study involved 228 PREDIMED-Plus senior participants aged between 55 and 75, recruited in Palma de Mallorca (Spain). Overweight/obese older adults with metabolic syndrome were randomised to an intensive weight- loss lifestyle intervention or a control group. A walkability index (residential density, land use mix, intersections density) was calculated using geographic information systems (1 km sausage-network buffer). Physical activity was assessed using accelerometer and a validated questionnaire, at baseline and two follow-up visits (6-months and 1-year later). Generalised additive mixed models were fitted to estimate the association between the neighbourhood walkability index and changes in physical activity during follow-up. 963 A. Colom et al. Results: Higher neighbourhood walkability (1 z-score increment) was associated with moderate-to-vigorous accelerometer assessed physical activity duration, (β = 3.44; 95% CI = 0.52; 6.36 min/day). When analyses were stratified by intervention arm, the association was only observed in the intervention group (β = 6.357; 95% CI = 2.07;10.64 min/day) (P for interaction = 0.055). Conclusions: The results indicate that the walkability of the neighbourhood could support a physical activity intervention, helping to maintain or increase older adults’ physical activity. Keywords: longitudinal study, physical activity intervention, walkability index, built environment, older people, PREDIMED-Plus trial Key Points • Living in a walkable neighbourhood plays a vital role in active ageing. • Interrelations between physical activity interventions and environmental factors are important determinants to engage older adults in regular physical activity. • It is crucial to consider the neighbourhood walkability when implementing physical activity programmes among older adults. Introduction 23 recruitment centres (University Hospital Son Espases (HUSE)) of the PREDIMED-Plus multicenter, parallel- Given the rapid increase of the older population [1,2] com- group, randomised trial. Details of the trial have been pub- bined with their rising trend of insufficient physical activ- lished elsewhere [14]. The Committee of Research Ethics of ity [3], active ageing has become a key issue for public the Balearic Islands approved this analysis, and all partici- health. Physical activity intervention programmes among pants provided written informed consent. older adults have been shown to increase physical activity PREDIMED-Plus participants were eligible if at enrol- significantly [4,5]. However, little is known about whether ment they were men aged 55–75 years and women aged the environments in which older adults are encouraged to be 60–75 years who were overweight or obese (body mass index active play a role. (BMI) ≥27 and < 40 kg/m2 ) and fulfilled at least three of the In environmental gerontology, the built environments are metabolic syndrome criteria [15]. well known to promote active ageing at the population level We included 228 participants in the analytic sample, [6,7]. Older adults spend more time within their immediate excluding participants who reported living outside the city neighbourhood environments than younger adults [8]. This limits of Palma de Mallorca or had no accelerometer data. situation makes them especially sensitive to physical barriers towards health promotion efforts [9]. Multiple attributes Physical activity intervention such as residential density, intersection density and land use mix are frequently conceptualised and assessed using a In the first year of the ongoing trial, participants in the walkability index, to account for built environment [10]. intervention group received a face-to-face educational pro- Additionally, socio-ecological models postulate that bio- gramme. Throughout the intervention, participants were logical, behavioural, psychosocial and also environments encouraged to gradually increase their physical activity levels factors can contribute to differential response to older adults to at least 150 min/week of moderate-to-vigorous physi- physical activity interventions [11,12]. cal activity (MVPA), with the ultimate goal of walking at In this context, the PREvención con DIeta MEDiter- least 45 min/day, 6 days per week, in line World Health ránea (PREDIMED)-Plus study, recently published the Organization recommendations for this age group [16]. individual-level effectiveness after 1-year of the physical activity intervention [13]. Using a sub-sample of participants Neighbourhood walkability index from the PREDIMED-Plus, the current study aimed to We objectively measured neighbourhood walkability, using (i) assess the association of neighbourhood walkability, on 1 km sausage buffer participants’ residential address [17]. 1-year change in physical activity assessed by accelerometer For each buffer, neighbourhood walkability features (res- and self-reported data and (ii) assess whether this association idential density, intersection density and land use mix) were is strengthened with a physical activity intervention. extracted and normalised following a z-score [10]. The walk- ability index was calculated by summing the z-scores of Methods residential density, intersection density and land use mix. Details on spatial data and methods can be found in the Study population online (Appendix 1: Methods Supporting Information) fol- The present analysis was designed to measure neighbour- lowing the recent spatial lifecourse epidemiology reporting hood walkability in a subset of participants from 1 of the standards statement [18]. 964 Neighbourhood walkability and physical activity intervention Table 1. Overall and specific study condition sample characteristics Study condition All Control group Intervention group P . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overall 228 122 106 Age (years) 65.0 (4.79) 65.3 (4.69) 64.8 (4.92) 0.463 Sex 0.576 Men 117 (51.3%) 60 (49.2%) 57 (53.8%) Women 111 (48.7%) 62 (50.8%) 49 (46.2%) BMI (kg/m2 ) 32.7 (3.31) 32.5 (3.55) 33.0 (3.01) 0.226 Educational level 0.639 Primary school or less 135 (59.2%) 70 (57.4%) 65 (61.3%) Secondary school or higher 93 (40.8%) 52 (42.6%) 41 (38.7%) Self-reported health 0.781 Excellent/very good/good 156 (68.4%) 82 (67.2%) 74 (69.8%) Fair/poor 72 (31.4%) 40 (32.5%) 32 (30.2%) Precipitation accelerometer wearing period 0.827 No rain 119 (52.2%) 65 (53.3%) 54 (50.9%) Rain 109 (47.8%) 57 (46.7%) 52 (49.1%) Baseline accelerometer-assessed MVPA (minutes/day) 34.1 (26.2) 32.2 (27.5) 36.3 (24.7) 0.239 Accelerometer wear time, valid days 7.82 (1.44) 7.80 (1.40) 7.85 (1.49) 0.779 Engaging in ≥150 min/week accelerometer-assessed MVPA 0.061 No 89 (39.0%) 55 (45.1%) 34 (32.1%) Yes 139 (61.0%) 67 (54.9%) 72 (67.9%) Baseline self-reported MVPA (minutes/day) 53.1 (59.5) 49.9 (60.7) 56.9 (58.1) 0.374 Engaging in ≥150 min/week self-reported leisure-time 0.026 MVPA No 83 (36.4%) 53 (43.4%) 30 (28.3%) Yes 145 (63.6%) 69 (56.6%) 76 (71.7%) Self-reported leisure-time brisk walking (minutes/day) 22.0 (29.6) 20.8 (33.2) 23.4 (24.9) 0.493 Engaging in ≥45 min/day 6 days/week self-reported 0.124 leisure-time brisk walking No 106 (46.5%) 63 (51.6%) 43 (40.6%) Yes 122 (53.5%) 59 (48.4%) 63 (59.4%) Walkability index −0.07 (1.09) −0.05 (1.03) −0.10 (1.16) 0.777 Z-score residential density −0.06 (1.02) −0.03 (1.01) −0.09 (1.03) 0.619 Z-score land use mix 0.05 (1.02) 0.02 (1.02) 0.09 (1.02) 0.638 Z-score intersection density −0.07 (1.02) −0.05 (0.97) −0.09 (1.08) 0.780 Deprivation index 1.92 (0.99) 1.95 (1.01) 1.90 (0.96) 0.726 n, sample size; SD, standard deviation. Values shown are n (%) for categorical variables and mean (SD) for continuous variables. The P-values are computed from t-test when row-variable is continuous normal-distributed, Kruskall–Wallis test when it is continuous non-normal. When row-variable is categorical, we used chi-squared or exact Fisher test when the expected frequencies are less than 5 in some cell. Outcome measure: physical activity accounted for two nested levels of variability in the out- Accelerometer and self-reported assessed physical activity, at come, variability at the person level (i.e. between-participant baseline, at 6 and 12 months. multiple observations differences) nested at the area-level Accelerometer assessed MVPA duration (minutes/day) (i.e. clustering effects). All coefficients were adjusted for (AA-MVPA) using GENEActiv monitors. The time spent in individual-level covariates: age, sex, education level, BMI MVPA was determined using older adults cutoff points [19]. and self-rated health. See Supplemental information for Self-reported leisure-time MVPA duration (minutes/day) additional information of neighbourhood deprivation calcu- (SRLT-MVPA) was assessed using the validated REGI- lations [21] and rain conditions. COR Short Physical Activity Questionnaire [20]. Also, For each model, we examined the effect modification a domain-specific physical activity self-reported leisure- analysis by study condition (intensive weight-loss lifestyle time brisk walking duration (minutes/day) (SRLT-BW) was intervention and unrestricted-energy Mediterranean diet assessed. control group), by adding cross-product terms between neighbourhood walkability and study condition. Addition- ally, stratified analyses were performed, by examining the Data analytic plan association in intervention and control group, separately. A generalised additive mixed models with Gaussian variance All analyses were conducted in R software version 3.3.3 was used to evaluate the effects of neighbourhood walkability (R Development Core Team, Vienna, Austria) using ‘stats’ on the duration of each physical activity variable All models and ‘mgcv’ packages and ArcGIS V10.5.1 software. 965 A. Colom et al. Table 2. Summary of associations between neighbourhood walkability and its components measured in the 1 Km buffer, and accelerometer-assessed moderate-to-vigorous physical activity (AA-MVPA), self-reported leisure-time moderate-to-vigorous physical activity (SRLT-MVPA) and self-reported leisure-time brisk walking (SRLT-BW) in the overall sample (n = 228) and after stratification according to the PREDIMED-Plus control (n = 122) and intervention (n = 106) groups Predictor Accelerometer-assessed MVPA Self-reported Leisure-Time Self-reported variable MVPA leisure-time brisk walking β 95%CI P β 95%CI P β 95%CI P . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Walkability index 3.44 0.52;6.36 0.021 −4.44 −10.00;1.13 0.119 −0.05 −2.97;2.87 0.975 P for Interaction 0.055 0.927 0.485 Walkability index control group 0.10 −3.94;4.15 0.960 −4.69 −12.80;3.42 0.258 −0.94 −6.08;4.20 0.721 Walkability index intervention group 6.36 2.07;10.64 0.004 −4.78 −12.48;2.91 0.224 1.05 −2.46;4.55 0.558 Z-score of residential density 2.92 −0.19;6.03 0.067 −0.16 −6.04;5.72 0.958 −0.41 −3.47;2.65 0.794 P for Interaction 0.398 0.44 0.311 Z-score of residential density control group 1.95 −2.01;5.91 0.335 −2.14 −10.23;5.93 0.603 −1.84 −6.87;3.19 0.474 Z-score of residential density intervention group 5.07 0.11;10.03 0.046 2.76 −5.98;11.49 0.537 1.66 −2.29;5.60 0.412 Z-score intersection density 4.32 1.19;7.45 0.007 −4.18 −10.18;1.82 0.173 −0.36 −3.50;2.79 0.824 P for Interaction 0.111 0.848 0.572 Z-score intersection density control group 1.38 −2.98;5.75 0.535 −4.61 −13.38;4.15 0.303 −1.10 −6.67;4.47 0.7 Z-score intersection density intervention group 6.86 2.25;11.48 0.004 −4.18 −12.47;4.12 0.325 0.60 −3.17;4.37 0.755 Z-score land use mix −3.20 −6.32;-0.09 0.045 −0.77 −6.65;5.11 0.798 0.70 −2.37;3.76 0.656 P for Interaction 0.683 0.365 0.421 Z-score land use mix control group −3.03 −7.00;0.94 0.136 1.42 −6.66;9.50 0.731 1.82 −3.20;6.84 0.477 Z-score land use mix intervention group −4.51 −9.49;0.47 0.077 −4.24 −12.94;4.46 0.340 −0.97 −4.93;2.98 0.629 n, sample size; β, non-standardised coefficient; 95%CI, confidence interval; P, P-value. β indicates change associated with physical activity duration according to minutes per day per increment in 1 z-score walkability index. All coefficients were adjusted for individual-level covariate (study condition, visit, sex, baseline age, baseline self-rated health, repeated measured BMI at each visit, baseline educational level and two-level random intercept participant nested in neighbourhood deprivation index; when the outcome was AA-MVPA, models were further adjusted for the repeated indicator of rainy conditions at each visit). Results Discussion Descriptive statistics This study provides new evidence on the association between The mean age of study participants was 65.0 years (range 55; neighbourhood walkability and physical activity in a tailored 75) and 48.7% were women. On average, at baseline, par- intervention to increase physical activity in older adults. ticipants did 34.1 min MVPA/day based on accelerometer Higher walkability combined with a physical activity inter- data, 53.1 min of SRLT-MVPA/day and 22.0 min of brisk vention could be the most effective strategy to increase walking/day (Table 1). physical activity among older adults. Associations of neighbourhood walkability on Neighbourhood walkability and physical activity physical activity duration Urbanisation and inactive ageing are transformative trends When considering the overall sample, AA-MVPA increased that have become a crucial global issue for public health per increment in 1 z-score neighbourhood walkability yet the influence of built environments is still unknown (β = 3.44; 95% CI = 0.52;6.36 min/day; P = 0.021). An [22]. The most important contribution of the presented interaction was detected between study condition (control study is the examination of the interacting effect of group and intervention group) and neighbourhood walka- neighbourhood walkability and individual-level interven- bility (P = 0.05). Stratified analyses showed that each unitary tion on physical activity changes among European older increment in 1 z-score neighbourhood walkability was adults using a prospective design within a randomised associated with an increase of 6.36 (95% CI = 2.07;10.64) trial. min/day among individuals assigned to the intervention Our findings indicate a higher walkability index was group. No association was observed for the control group. related to an increase in AA-MVPA during the PREDIMED- See supplemental information for additional information of Plus physical activity intervention suggesting that living in the associations of neighbourhood walkability components highly walkable areas supports this type of intervention. on physical activity. To date, only one previous study conducted in North Supplementary appendices 2–5 (online) shows additional America has explored the effects of objective neighbour- sensitivity analyses (different buffer sizes and different oper- hood walkability on self-reported physical activity among ationalists of outcome variables) confirming the primary older adults attempting to increase their physical activity analysis (Table 2). levels [23]. These findings are consistent with our results on 966 Neighbourhood walkability and physical activity intervention SRLT-MVPA, while the relationships between neighbour- measured in the 1 Km buffer. Appendix 4: Supplementary hood walkability and SRLT-BW, King and colleagues also table S2. Summary of associations measured in the 0.5 Km found no association [23]. buffer. Appendix 5: Supplementary Table S3. Summary of Among cross-sectional studies, there have been similar associations measured in the 0.5 Km buffer. Appendix 6: findings [24] and a recent study showed that older adults Availability of data and materials. Appendix 7: Abbrevia- residing in low walkable areas in a similar Mediterranean tions. environment were less likely to walk [25]. Even so, this Acknowledgements: The authors would like to acknowl- hypothesis was not strongly supported, as a recent interna- edge the investigators, personnel, and study participants of tional study found that neighbourhoods designed to support the PREDIMED-Plus study, as well as all affiliated primary transport walking also appeared to facilitate walking for care centres. Also, we would like to acknowledge the Institut leisure, as well as total MVPA [26]. In this context, our d’Investigació Sanitària Illes Balears to support the open results indicate that neighbourhood walkability combined access publication of this work. [A.C.] thank Dr. Alain F. with a physical activity intervention seems to be more related Zuur and Dra. Elena N. Ieno at Highland Statistics Ltd. for to transportation behaviours than leisure-time ones. teaching how to deal with the great amount of misery that comes together in the statistical analysis. Strengths and limitations Declaration of Conflicts of Interest: None. The present study has several strengths, including the use of a prospective design within a randomised trial with both Ethics Approval and Consent to Participate: The Com- self-reported and accelerometer measures of physical activity; mittee of Research Ethics of the Balearic Islands (CEI-IB) while other studies have mostly used cross-sectional data approved the study (approval number IB-2242/14-PI). All [6,7] or self-report questionnaires [23,27]. Physical activ- participants provided written informed consent to partici- ity intervention programmes focused on overweight/obese pate in the study. senior adults reflects an essential contribution to a critical Declaration of Sources of Funding: This work was sup- issue for public health [28]. Also, our study was conducted ported by Instituto de Investigación en Salud Carlos III in the context of a European region, adding to the body of [grant numbers PI14/00853, PI16/00662 and PI17/00525], evidence that was based on other non-European areas. and Consejería de Salud de la Junta de Andalucía [grant Even so, our results should be interpreted with caution number PS0358–2016]. Cofounded by FEDER. Institut since the follow-up period was only one-year, which might d’Investigació Sanitària Illes Balears fellowship TalentPlus be too short to detect major changes. Also, the smaller sample Tech Construyendo Valor Generando Salud [grant numbers sizes used in the stratified analyses should be acknowledged #ITS2018–002] to [A.C.]. AstraZeneca Foundation (Young as a limitation. In addition, we lacked a specific measure of investigator Award 2017 on obesity and type 2 diabetes to active transportation, which could have provided additional [D.R.]. Australian National Health and Medical Research insights related to this domain-specific physical activity [29]. Council fellowship [grant numbers #1121035] to [S.M.]. Future studies should explore combinations of environ- [J.S.S.], gratefully acknowledges the financial support by mental features, to explain more variation in physical activity ICREA under the ICREA Academia programme. than single variables. Conclusions References This study provides new evidence highlighting the impor- 1. World Health Organization. World Report on Ageing and tance of considering neighbourhood walkability and built Health. World Health Organization, Geneva, 2015. environment when designing and implementing physical 2. United Nations Department of Economic and Social Affairs activity programmes. Results indicate that among over- Population Division. World population ageing, 2017. weight and obese senior adults with metabolic syndrome 3. Guthold R, Stevens GA, Riley LM, Bull FC. 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