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. 2020 Jul 9;15(7):e0235833. doi: 10.1371/journal.pone.0235833

Main and interacting effects of physical activity and sedentary time on older adults’ BMI: The moderating roles of socio-demographic and environmental attributes

Delfien Van Dyck 1,2,*, Anthony Barnett 3, Jelle Van Cauwenberg 2,4, Casper J P Zhang 5, Cindy H P Sit 6, Ester Cerin 3,5,7
Editor: Martin Senechal8
PMCID: PMC7347204  PMID: 32645072

Abstract

Objectives

Our first aim was to examine the main and interacting effects of accelerometer-based sedentary time (ST) and moderate-to-vigorous physical activity (MVPA) with BMI and the likelihood of being overweight/obese in Hong Kong and Ghent (Belgium) older adults. Second, we examined whether these main associations and interactions between MVPA and ST were moderated by socio-demographics (gender, education) and objective neighbourhood attributes supposed to be associated with healthy food intake (food outlet density, neighbourhood-level SES). Finally, we determined whether the associations and interactions were generalisable across study sites.

Methods

Data from the ALECS (Hong Kong) and BEPAS Seniors studies (Ghent), two comparable observational studies, were used. Older adults (n = 829, 65+) provided self-reported socio-demographic information and objective MVPA and ST data using Actigraph accelerometers. Annual household income data and GIS software were used to assess neighbourhood-level SES and food outlet density. Generalised additive mixed models were conducted in R.

Results

ST was linearly and positively related to both weight outcomes in the overall sample, while MVPA was not. The overall-sample analyses including the two-way interaction between MVPA and ST showed no interactions between these behaviours on weight outcomes. Three site-specific findings were identified, showing distinct associations in Hong Kong compared to Ghent. Study site moderated the interaction between ST and MVPA on both weight outcomes, the interaction between education and ST on both weight outcomes and the interaction between the number of food outlets and ST on being overweight/obese.

Conclusions

The country-specific effects confirm the cultural dependency and complexity of the associations between PA, ST and weight outcomes. Future longitudinal international studies including older adults from multiple regions assessing PA, ST, weight outcomes and dietary intake should be encouraged. Such studies are needed to refine the recommendations regarding ST and PA in older adults in light of preventing overweight and obesity.

Introduction

In the last decades, the prevalence of overweight and obesity has increased dramatically in developed and most developing countries worldwide [1,2]. Also, evidence is clear that the global prevalence of overweight and obesity is alarmingly high in older adults [3,4]. This ‘obesity pandemic’ has severe consequences for the physical and mental health of older adults, including increased rates of chronic diseases such as type 2 diabetes and cardiovascular diseases, decreased cognitive functioning, decreased quality of life, higher rates of depression and increased health care costs [5–7]. Consequently, it is of utmost importance to identify the factors associated with overweight and obesity in older adults in order to prioritize prevention strategies.

Older adults are the population subgroup engaging in the highest levels of sedentary time (ST) [8] and the lowest levels of PA [9]. A large body of literature shows that achieving recommended levels of moderate-to-vigorous PA (MVPA) is negatively associated with markers of overweight and obesity (e.g. BMI, waist circumference) in older adults [10,11]. Furthermore, growing evidence documents a positive association between sedentary behaviours (mainly TV viewing) and the odds of overweight and obesity [12–14]. So, based on this evidence, it seems that interventions targeting PA and ST in older adults are key to prevent overweight, obesity and their related health consequences. Nonetheless, the currently available studies have some shortcomings and specific research questions remain to be answered.

First, most previous studies only included self-reported measures of ST, often limited to TV viewing [15]. It is known individuals tend to underreport socially undesirable behaviours [16] and more specifically it has been shown that older adults under-report their levels of sedentary behaviour [17]. Furthermore, one needs to be cautious when using TV viewing as a proxy for total ST, as the level of agreement between both seems to be limited [18]. Consequently, the use of objective measures of ST is strongly encouraged to examine the associations with health outcomes like overweight and obesity.

Second, although there is ample evidence for direct associations of PA and ST with weight outcomes, evidence regarding the interaction effects of both behaviours on weight-related outcomes is not straightforward. To our knowledge, very few studies have examined such interactions in older adults [15], and studies in adults show mixed results. On the one hand, some studies have found that high levels of PA may ameliorate the negative effects of ST on obesity [19,20] or that the association between ST and weight outcomes becomes non-significant once PA levels are included in the statistical models [21,22]. On the other hand, some studies found clear associations of ST with weight outcomes like body fat mass, irrespective of the levels of PA [23]. A more profound understanding of these interactions in older adults is needed to determine the focus of future behaviour change interventions. For example, reducing ST may be a more realistic long-term health behaviour goal for older adults facing physical limitations than increasing MVPA. If ST appears to be a determinant of overweight and obesity even at low levels of PA, targeting ST in interventions in older adults might be a promising strategy.

Third, little is known regarding the role of potential moderators in the associations of PA and ST with weight outcomes. Next to other factors (e.g. differences in measurement methods), the presence of moderators might explain some of the previous inconsistent interactions identified between PA and ST with regard to weight outcomes. Only some evidence regarding the moderating effect of gender is available from previous studies. For example, Suminski and colleagues [19] studied the moderating role of gender in the associations of PA and ST with percentage body fat, and found ST was positively associated with percentage body fat only in women who did not meet the PA guidelines. No such associations were identified in men who were insufficiently active. Similarly, in an international study including adults from 10 geographically and culturally diverse countries, ST was curvilinearly associated with BMI in women from the USA sites included in the study, but not in men or in adults from the other 9 countries [24]. Such differences in associations between men and women might be explained by the complex nature of ST and PA and the differences in prevalence of these behaviours according to gender [25].

Next to gender, a more thorough understanding is needed on the moderating effects of other socio-demographic variables (e.g. age, socio-economic status) that might impact the associations between PA, ST and weight outcomes. To our knowledge, the moderating role of these factors has not previously been examined in older adults. In addition, following the socio-ecological framework of health behaviours [26] that emphasizes the interplay between individual-level variables, physical environmental factors and health behaviours, physical environmental factors might also be interesting to examine as potential moderators of the association of PA and ST with overweight and obesity. More specifically, because BMI is largely dependent on energy intake [27], physical environmental moderators that are associated with food intake may attenuate or strengthen the relation of PA and ST with weight outcomes and should be studied. A review study of Rahmanian et al [28] concluded that, although most associations between the built environment and food intake were inconsistent because of methodological challenges, access to healthy food options in the neighbourhood (e.g. food stores, local farmer market) was associated with a higher consumption of fruit and vegetables. Furthermore, living in a low-income neighbourhood was associated with lower access to healthy food options, probably resulting in a less healthy food intake [29].

Considering the shortcomings of the current literature, we used data of two comparable cross-sectional studies conducted in older adults in Hong Kong and Ghent (Belgium). Including data from these geographically and culturally diverse study sites is a particular strength as it allows testing for the generalisability of findings across countries. The first aim of this study was to examine the main and interacting effects of accelerometer-assessed ST and MVPA with BMI and the likelihood of being overweight/obese in older adults. The second study aim was to examine whether these direct associations and interactions between MVPA and ST were moderated by socio-demographic characteristics (gender, education) and objective neighbourhood attributes that are supposed to be associated with healthy food intake (food outlet density, neighbourhood-level SES). Finally, we determined whether the associations and interactions studied above were generalisable across study sites (Hong Kong vs Ghent).

Materials and methods

Procedures and participants

Data from two observational cross-sectional studies with comparable protocols and measures were used in this paper: the Active Lifestyle and the Environment in Chinese Seniors (ALECS; first wave) [30] and Belgian Environmental Physical Activity Study in Seniors (BEPAS Seniors) [31]. Hong Kong data collection took place from October 2012 to December 2014 (for the data relevant to this study), Ghent data collection from October 2010 to September 2012. The ALECS project was approved by the ethics committee of the Department of Health (Hong Kong Special Administrative Region, HKSAR) and the University of Hong Kong Research Ethics Committee for Non-Clinical Faculties (EA270211). The BEPAS Seniors study protocol was approved by the Ethics Committee of the Ghent University Hospital (B670201423000).

Details regarding neighbourhood selection and participant recruitment of ALECS and BEPAS Seniors have been described elsewhere [30,31]. In both Hong Kong and Ghent, participants were equally recruited across four types of neighbourhoods, based on neighbourhood-level socioeconomic status (SES; high versus low) and objective neighbourhood-level walkability (Geographic Information System (GIS)-based index of dwelling density, intersection density and land use mix; high versus low). Ghent participants were selected from 20 neighbourhoods, each consisting of 1 to 5 adjacent statistical sectors, the smallest administrative units for which socio-demographic information is available [31]. Hong Kong participants were selected from 124 Tertiary Planning Units, the smallest administrative units with publicly-available census-level data [30].

In Ghent, 1750 community-dwelling older adults (≥65 years) were randomly selected from the statistical sectors and received an information letter through mail with a follow-up home visit approximately one week after receiving the letter. Inclusion criteria were being able to understand and speak Dutch, living independently and able to walk 200 meters without severe physical restrictions. In total 1260 potential participants were found at home at the follow-up home visit. Of those, 125 were not eligible to participate and 508 participated in the study. In total, 427 participants provided all data necessary for the current analyses. In Hong Kong, residential addresses or other contact data could not be directly obtained because of the Personal Data (Privacy) Ordinance [32]. Consequently, participants (≥65 years) were recruited in person in Elderly Health Centres of the Health Department (two thirds) and from elderly community centres close to the selected neighbourhoods (one third). Inclusion criteria were being able to understand and speak Cantonese, living in one of the selected neighbourhoods for at least six months, being able to walk unassisted for at least 10 meters and being cognitively intact (Mini-Mental State Examination Score >22). Of the 1602 potential participants initially contacted, 322 were ineligible and 909 participated in the study. Approximately 45% of the participants were randomly selected to wear an accelerometer to objectively assess their PA and ST. Of these 416 participants, 402 provided valid accelerometer data and could be included in the current analyses. So, in total 829 older adults (402 Hong Kong and 427 Ghent) were included in the analyses. All participants in Ghent and Hong Kong provided written informed consent, and a trained interviewer collected data in their homes and health/community centres, respectively.

Measures

Dependent variable: BMI

BMI (kg/m2) was calculated using objectively-assessed weight and height. In Ghent, a SECA 214 stadiometer (0.1 cm) and a SECA 813 Robusta weight scale (0.1 kg) were used. In Hong Kong, the height and weight of each participant were measured using a portable stadiometer Invicta model 2007246 (0.1 cm) and Tanita BC-541 scales (0.1 kg). Before these measurements, participants were asked to remove their shoes and only wear light clothing. BMI was treated as both a continuous and categorical variable. Due to the relatively small number of sample participants in the obese and underweight categories, BMI values were categorised as normal/underweight or overweight/obese. For Ghent, BMI categorisation was ≤ 24.9 kg/m2 for normal/underweight and ≥ 25 kg/m2 for overweight/obese). For Hong Kong, the categorisation was ≤ 22.9 kg/m2 and ≥ 23 kg/m2 respectively [33].

Independent variables: Accelerometer-assessed MVPA and ST

In both study sites, MVPA and ST were objectively assessed using Actigraph accelerometers (Fort Walton Beach, FL, USA; GT3X in Hong Kong, GT3X and GT3X+ in Ghent). Accelerometers are valid and reliable tools to assess PA and ST in older adults [34,35]. Participants were asked to wear the accelerometer above the right hip bone for seven consecutive days during waking hours, and to remove it only for water-based activities and to sleep. Data were collected using 60-second epochs, as in previous studies in older adults [36,37]. Only data capturing the vertical axis were included for analysis in the present study. A valid day was defined as a day with at least 10 hours of recorded activity, and periods with ≥ 90 minutes of consecutive zeros were categorized as non-wear time [38]. Data were included in the analyses when a participant had at least five valid days, including at least one weekend day. Activity categories were defined with the cut points of Barnett and colleagues [39] and of Aguilar-Farias and colleagues [40] both developed specifically for older adults and using the Low Frequency Extension filter, as in the current study. Values <25 counts/min were categorised as ST [40] and values ≥1013 counts/min were categorised as MVPA [39].

Covariates and moderators

Age, gender, education (primary school versus higher), marital status (married or cohabiting versus widowed or other), neighbourhood-level SES (high versus low) and study site (Hong Kong or Ghent) were included as covariates in the analyses of the main effects of ST and MVPA on BMI. All these variables, except for marital status were also considered as moderators of ST and MVPA associations with BMI. The socio-demographic information (age, gender, education and marital status) was assessed with an interview-administered questionnaire in both study sites [30,31]. Neighbourhood-level SES was assessed using census-based median annual household income data.

Objectively-assessed food outlet density was included as a moderator in the regression models. Objective food outlet density was defined as the number of food outlets/km2, assessed in a 1-km street network buffer around each participant’s residential address using GIS. Because increases in dwelling density lead to increases in food outlet density and dwelling density is potentially related to BMI [41], objective dwelling density was included as an additional covariate (potential confounder) in the models with food outlet density as a moderator. Objective dwelling density was defined as the number of dwellings/km2, assessed in a 1-km street network buffer using GIS.

Data analysis

Descriptive statistics of sample characteristics (participant demographics, objective neighbourhood attributes, activity levels, and BMI) were computed. We also tested the significance (p<0.05) of the differences in these characteristics between the two samples using, as appropriate, generalised linear models and multinomial logistic regression with robust standard errors accounting for clustering at the administrative unit level.

To account for clustering at the administrative unit level and the possibility of curvilinear associations, generalised additive mixed models (GAMMs) [44] were used to estimate confounder-adjusted associations of ST and MVPA with BMI and the interaction effects of ST and MVPA on BMI [24]. We also examined whether these associations and interactions were moderated by gender, education, neighbourhood-level SES, food outlet density and study site by including 2- and 3-way interaction terms in the GAMMs, as appropriate. Separate sets of GAMMS for continuous BMI and BMI categorised as normal/underweight or overweight/obese were estimated. Gaussian variance and identity link functions were used to model associations of ST and MVPA with continuous BMI data, whereas binomial variance and logit link functions were used to model associations of ST and MVPA with BMI as a categorical variable [42]. Statistically significant (p<0.05) 3-way interactions were further investigated by first probing 2-way interactions (e.g., ST by food outlet density interaction) at meaningful values of the third interaction-term variable (e.g., Ghent and Hong Kong for study site). Then, if a 2-way interaction term indicated at least a weak moderating effect (p<0.10) [43] (e.g., ST by food outlet density interaction effect in Ghent), the 2-way interaction was probed by estimating the associations of the activity variable (e.g., ST) with BMI in the specific subgroup of participants (e.g., those living in Ghent) at meaningful values of the moderator (e.g., ST associations with BMI at mean-1SD, mean and mean+1SD values of food outlet density in participants from Ghent). Curvilinearity of associations was tested by comparing the Akaike Information Criterion (AIC) values of GAMMs with linear regression terms for ST and MVPA versus GAMMs with ST and MVPA modelled using thin plate smooth terms [44]. Evidence of curvilinearity was deemed sufficient if the model with smooth terms yielded a > 5-unit smaller AIC than the model with linear effects [45]. Analyses were conducted in R version 3.6.1 (https://www.R-project.org) using the packages ‘mgcv [46], ‘gmodels’ [47], and multcomp [48].

Results

Participant characteristics

Participant characteristics are presented in Table 1. Mean age was 75 years and slightly higher in the Hong Kong sample (p = 0.025). The total sample (n = 829) consisted of 61% female participants, 69% and 54% in Hong Kong and Ghent, respectively (p<0.001). The sample from Ghent had a higher level of education than the Hong Kong sample (p<0.001), with 75% versus 47% having an educational level higher than primary school. Having a car in the household was much more prevalent in Ghent (81%) than in Hong Kong (29%) (p<0.001). The mean ST per day was 414 minutes, with the Ghent cohort accumulating an average of 16 more minutes per day than the Hong Kong cohort (p = 0.795). For the total cohort, the average MVPA was 56 minutes per day, with Hong Kong participants engaging in approximately 26 minutes more per day than their Ghent counterparts (p<0.001). Mean BMI was 23 kg/m2 and, on average, ~ 1kg/m2 lower in Ghent (p<0.001). Because of the different cut-off values for categorising overweight/obesity in Asian and European older adults [33], the distribution of BMI categories was very different across sites with 57% of those in Hong Kong being overweight/obese as compared to 24% in Ghent (p<0.001).

Table 1. Sample characteristics.

ALECS & BEPAS (n = 829) ALECS (n = 402) BEPAS (n = 427) p-value (difference between samples)
Participant demographics
Age (years; mean ± SD) (n = 828) 74.83 ± 6.18 75.55 ± 6.15 74.16 ± 6.14 0.025
Gender (% female) (n = 828) 61.35 68.91 54.10 <0.001
Education (% above primary school) (n = 829) 61.04 46.77 74.47 <0.001
Marital status (% married or cohabiting) (n = 828) 64.66 62.94 66.28 0.284
Car in the household (% yes) (n = 827) 55.49 28.86 80.56 <0.001
Neighbourhood SES (low/high, % low) (n = 828) 51.15 50.75 51.52 0.951
Objective neighbourhood attributes (mean ± SD, n = 829) (GIS)
Dwelling density (1 km buffer, households/km2) 21,545.64 ± 21,210.69 36,316.04 ± 21,489.54 7,640.018 ± 6,328.31 <0.001
Food outlet density (1 km buffer, outlets/km2) 39.73 ± 52.22 26.41 ± 15.17 52.27 ± 68.98 0.103
Measures of activity (n = 829)
Sedentary time (average minutes/day) 414.13 ± 98.01 404.94 ± 98.11 422.78 ± 97.25 0.795
MVPA (average minutes/day) 55.78 ± 37.50 69.00 ± 36.31 43.33 ± 34.24 <0.001
Measures of weight status (n = 820)
BMI (kg/m2) 22.96 ± 3.58 23.49 ± 3.34 22.46 ± 3.73 <0.001
BMI categorised (%) <0.001
Normal or underweight 59.88 43.28 75.84
Overweight 30.85 41.79 20.33
Obese 9.27 14.93 3.83
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SES = socioeconomic status; SD = standard deviation; GIS = Geographic Information Systems; MVPA = moderate-to-vigorous physical activity; BMI = body mass index; BMI categories in Ghent: normal/underweight < 25 kg/m2, overweight 25—≤ 29.9 kg/m2, obese ≥ 30 kg/m2; BMI categories in Hong Kong: normal/underweight < 23 kg/m2, overweight 23—≤ 26.9 kg/m2, obese ≥ 27 kg/m2.

Associations of ST, MVPA and co-variates with BMI and being overweight/obese (Table 2)

Table 2. Associations of ST, MVPA and co-variates with BMI and being overweight/obese.

BMI (kg.m-2) Being overweight/obese
b 95% CIs p-value OR 95% CIs p-value
Activity Variables
 Sedentary time (1 hr/day) 0.493 0.299, 0.686 <0.001 *** 1.264 1.109, 1.441 <0.001 ***
 MVPA (10 min/day) -0.019 -0.104, 0.065 0.651 1.006 0.951, 1.064 0.837
Covariates
 Gender (ref: male) -0.958 -1.497, -0.420 <0.001*** 0.571 0.399, 0.818 0.002**
 Age (yrs) -0.041 -0.083, 0.001 0.058 0.983 0.956, 1.010 0.212
 Education (ref: primary or lower) -0.015 -0.539, 0.510 0.957 0.904 0.656, 1.266 0.557
 Marital status (Married/cohabiting) -0.221 -0.745, 0.304 0.409 0.994 0.704, 1.402 0.971
 Area SES (ref: low neighbourhood SES) -0.567 -1.114, -0.024 0.041* 0.861 0.630, 1.178 0.349
 Study site (ref: Hong Kong) -1.074 -1.700, -0.448 <0.001*** 0.235 0.159, 0.346 <0.001***
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b = regression coefficient; OR = odds ratio; CIs = confidence intervals; MVPA = moderate-to-vigorous physical activity; SES = socioeconomic status

While the associations of MVPA with BMI and being overweight/obese were not significant, those of ST were positive, linear and strong. On average, compared to males, females had a significantly lower BMI and were less likely to be overweight/obese. High neighbourhood SES was associated with a lower BMI. Study site was a significant correlate of BMI and being overweight/obese, with participants from Ghent having lower BMI and being less likely to be classified as overweight/obese than the participants from Hong Kong.

Moderating effects (2- and 3-way interactions) on associations of ST and MVPA with BMI and being overweight/obese (Table 3)

Table 3. 2- and 3-way moderating effects on ST and MVPA.

Regression terms BMI (kg.m-2) Being overweight/obese
B 95% CIs p-value OR 95% CIs p-value
2-way interactions
1 ST (1 hr/day) * MVPA (10 min/day) -0.024 -0.059, 0.012 0.189 0.996 0.974, 1.019 0.738
2 ST (1 hr/day) * Study site [ref: Hong Kong] 0.044 -0.246, 0.333 0.768 0.991 0.816, 1.203 0.925
3 MVPA (10 min/day) * Study site [ref: Hong Kong] 0.028 -0.106, 0.163 0.680 1.007 0.920, 1.102 0.880
4 ST (1 hr/day) * Food outlet density -0.0003 -0.0032, 0.0027 0.862 0.9987 0.9967, 1.0007 0.196
5 MVPA (10 min/day) *Food outlet density 0.0007 -0.0004, -0.0018 0.213 1.00006 0.99926, 1.00087 0.878
6 ST (1 hr/day) * Gender [ref: male] 0.093 -0.201, 0.388 0.534 1.014 0.836, 1.231 0.886
7 MVPA (10 min/day) * Gender [ref: male] 0.019 -0.108, 0.147 0.766 0.981 0.902, 1.066 0.651
8 ST (1 hr/day) * Neigh. SES [ref: low] -0.039 -0.329, 0.251 0.792 1.095 0.904, 1.327 0.353
9 MVPA (10 min/day) * Neigh. SES [ref: low] 0.079 -0.050, 0.208 0.229 1.034 0.951, 1.125 0.431
10 ST (1 hr/day) * Education [ref: primary or lower] -0.017 -0.315, 0.281 0.912 0.988 0.812, 1.203 0.906
11 MVPA (10 min/day) * Education [ref: primary or lower] -0.047 -0.176, 0.082 0.474 1.002 0.922, 1.090 0.954
3-way interactions
1 ST (1 hr/day) * MVPA (10 min/day) * Study site [ref: Hong Kong] 0.117 0.038, 0.196 0.004** 1.077 1.015, 1.142 0.015*
ST*MVPA interaction in Ghent 0.044 -0.017, 0.106 0.156 1.046 0.995, 1.099 0.076
 ST @ MVPA mean-1SD 1.151 0.947, 1.399 0.156
 ST @ MVPA mean 1.362 1.119, 1.658 0.002**
 ST @ MVPA mean+1SD 1.611 1.160, 2.238 0.004**
ST*MVPA interaction in Hong Kong -0.073 -0.123, -0.023 0.004** 0.971 0.941, 1.003 0.076
 ST @ MVPA mean-1SD 0.846 0.483, 1.210 <0.001*** 1.482 1.168, 1.880 0.001***
 ST @ MVPA mean 0.573 0.310, 0.835 <0.001*** 1.329 1.119, 1.558 0.001***
 ST @ MVPA mean+1SD 0.299 0.026, 0.572 0.032* 1.191 0.999, 1.422 0.052
2 ST (1 hr/day) * Gender [ref: male] * Study site [ref: Hong Kong] -0.172 -0.765, 0.420 0.567 0701 0.470, 1.046 0.082
3 MVPA (10 min/day) * Gender [ref: male] * Study site [ref: Hong Kong] -0.234 -0.511, 0.044 0.099 0.896 0.733, 1.096 0.285
4 ST (1 hr/day) * MVPA (10 min/day) * Gender [ref: male] -0.016 -0.089, 0.057 0.662 1.010 0.964, 1.059 0.681
5 ST (1 hr/day) * Neigh. SES [ref: low] * Study site [ref: Hong Kong] 0.427 -0.157, 1.010 0.151 1.480 0.992, 2.208 0.055
6 MVPA (10 min/day) * Neigh. SES [ref: low] * Study site [ref: Hong Kong] -0.219 -0.493, 0.054 0.116 0.864 0.717, 1.041 0.125
7 ST (1 hr/day) * MVPA (10 min/day) * Neigh. SES [ref: low] 0.019 -0.054, 0.093 0.604 1.022 0.974, 1.074 0.363
8 ST (1 hr/day) * Education [ref: primary or lower] * Study site [ref: Hong Kong] 0.664 0.055, 1.273 0.033* 1.590 1.047, 2.414 0.029*
ST*Education interaction in Ghent 0.322 -0.121, 0.765 0.154 1.286 0.935, 1.769 0.121
ST*Education interaction in Hong Kong -0.342 -0.755, -0.060 0.100 0.809 0.610, 1.034 0.099
 ST @ Primary or lower education 0.670 0.336, 1.004 <0.001*** 1.446 1.154, 1.812 0.001***
 ST @ Secondary or higher education 0.328 0.025, 0.630 0.034* 1.160 0.956, 1.416 0.106
9 MVPA (10 min/day) * Education [ref: primary or lower] * Study site [ref: Hong Kong] 0.032 -0.280, 0.344 0.839 0.979 0.781, 1.228 0.856
10 ST (1 hr/day) * MVPA (10 min/day) * Education [ref: primary or lower] -0.064 -0.138, 0.010 0.090 0.971 0.924, 1.021 0.246
11 ST (1 hr/day) * Food outlet density (1 km buffer) * Study site [ref: Hong Kong] -0.0022 -0.0152, 0.0108 0.738 0.991 0.982, 0.999 0.049*
ST*Food outlet density interaction in Ghent 0.998 0.996, 1.000 0.082
 ST @ Food outlet density mean-1SD 1.422 1.142, 1.771 0.002**
 ST @ Food outlet density mean 1.288 1.087, 1.527 0.003**
 ST @ Food outlet density mean+1SD 1.167 0.970, 1.405 0.102
ST*Food outlet density interaction in Hong Kong 1.007 0.998, 1.113 0.111
12 MVPA (10 min/day) * Food outlet density (1 km buffer) * Study site [ref: Hong Kong] 0.0009 -0.0066, 0.0084 0.814 1.004 0.999, 1.008 0.142
13 ST (1 hr/day) * MVPA (10 min/day) * Food outlet density (1 km buffer) 0.00045 -0.00033, 0.00123 0.257 1.00007 0.99950, 1.00064 0.807
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BMI = body mass index; b = regression coefficient; CIs = confidence intervals; OR = odds ratio; ref = reference category; ST = sedentary time; MVPA = moderate-to-vigorous physical activity; Neigh. SES = neighbourhood socio-economic status

Only significant 3-way interactions (p<0.05) were further investigated by probing 2-way interactions within the third interaction variable, for example, Ghent and Hong Kong for site. Where these 2-way interactions were of at least weak significance (p<0.1), associations were examined to determine any significant differences at different levels of the interaction, e.g., ST associations with MVPA at mean-1SD, mean and mean+1SD.

Only a few significant moderators of ST or MVPA associations with BMI or being overweight/obese were found. Study site moderated the interaction between ST and MVPA on both BMI and being overweight/obese. Specifically, a significant ST by MVPA interaction on BMI was found in Hong Kong only. In Hong Kong, the positive association between ST and BMI was significantly stronger at lower (i.e., mean -1SD) than higher (i.e., mean +1SD) values of MVPA. A similar effect was observed with respect to being overweight/obese. Hong Kong older adults with low levels of MVPA (e.g., ~18 minutes per day) were 48% more likely to be overweight/obese if they increased their ST by 1 hour a day. In contrast, those engaging in high levels of MVPA (~90 minutes per day) were only 19% more likely to be overweight/obese if they increased their ST by 1 hour per day. In Ghent, an opposite trend was observed, with participants engaging in more MVPA tending to be more susceptible to being overweight/obese as a result of accumulating more ST.

Study site also moderated the interaction between ST and education, whereby the Hong Kong but not the Ghent sample tended to show a two-way ST by education interaction on BMI and being overweight/obese. The positive associations of ST with BMI and being overweight/obese were stronger in Hong Kong older adults with primary or lower education than in those with secondary or higher education.

Finally, the number of food outlets within one kilometre from a resident’s home tended to moderate the association between ST and being overweight/obese in Ghent, but not in Hong Kong. Associations of ST with being overweight/obese in Ghent were weaker in participants with a higher food outlet density in their neighbourhood than in those with a lower food outlet density.

Discussion

This observational study, conducted in two culturally and geographically very diverse regions, aimed to examine the main and interaction effects of objectively-assessed ST and MVPA with weight outcomes in older adults. Furthermore, potential moderating effects of gender, educational level, food outlet density and neighbourhood-level SES were studied. Finally, the generalizability of all associations and interactions across study sites was examined. This study revealed some interesting and surprising findings, which are summarized and discussed below.

ST was linearly and positively related to BMI and the odds of being overweight or obese in the overall sample, while MVPA was not. The overall-sample analyses including the two-way interaction between MVPA and ST showed no interactions between these behaviours on weight outcomes. Furthermore, none of the other two-way interactions were significant, meaning that the included socio-demographic characteristics and objective neighbourhood characteristics did not moderate the associations of MVPA, ST with the weight outcomes. So, in the overall sample, the non-significant association of MVPA as well as the linear positive association of ST with BMI and being overweight or obese, were generalisable across genders, educational levels, neighbourhood-level SES and across different densities of food outlets in the neighbourhood. However, an interesting and remarkable finding was that three site-specific findings were identified, confirming the complexity of the relation between PA, ST and weight outcomes.

The absence of a main effect of MVPA on weight outcomes in this sample of Hong Kong and Ghent older adults is surprising. Many other cross-sectional and longitudinal studies in older adults showed that MVPA is associated with a lower risk of overweight and obesity [15,49,50]. No definite explanation for our finding can be given, but it is important to note that the participants in both study sites were quite active. A large proportion of the older adults, both in Hong Kong (90.0%) and in Ghent (68.9%) reached the health guideline of 150 minutes of MVPA per week. This limited variability in MVPA might have contributed to not finding an association with the weight outcomes. Furthermore, other accelerometer cut-points were used to define MVPA in previous studies [15,50], possibly leading to different associations.

The robust association found between ST and the weight outcomes, independent of the levels of PA, is promising. When confirmed in future studies with a stronger study design, this may imply that a focus on limiting ST as a preventive strategy against overweight and obesity could be effective in older adults, especially in those who are physically incapable of being highly active. Naturally, in light of the co-dependency of PA, ST and sleep within a 24-hour cycle, it is necessary to determine by which behaviour ST ideally should be replaced. Recent evidence from studies examining the theoretical [51] or real-life [50] effect of behavioural substitution in older adults, shows that reallocation of ST to different intensities of PA can have a positive effect on obesity markers. However, current evidence is strongest for reallocation to MVPA. Dumuid and colleagues [51] found a theoretical decrease of -0.7 BMI points if 15 minutes of ST were replaced by 15 minutes of MVPA, and concluded that reallocation of ST to light-intensity PA had no impact on older adults’ BMI. In contrast, a seven-year longitudinal study in older women concluded that an increase in light-intensity PA or MVPA at the expense of ST was related to a lower BMI and percentage body fat [50]. However, also in that study, the largest change in BMI and percentage body fat was found when sedentary behaviour was longitudinally replaced by MVPA. More longitudinal studies in larger and more diverse groups of older adults are needed before concrete recommendations can be given regarding replacing ST by light- or moderate-intensity PA to prevent overweight or obesity.

Interestingly, although main effects of ST on the weight outcomes were identified in the total sample, site-specific moderating effects of educational level and food outlet density were revealed. In Hong Kong, the association between ST and the weight outcomes was stronger in lower-educated than in higher-educated participants, while no moderating effect of educational level was found in Ghent. In general, lower-educated individuals often have a less healthy lifestyle than their higher-educated counterparts [52,53]. Thus, it is possible that higher-educated Hong Kong older adults might have compensated for the negative effects of ST by following a healthier diet. In fact, educational attainment was one of the two strongest predictors of dietary quality in Chinese older adults [54]. Lack of nutritional knowledge, lack of cooking skills and apathy toward nutrition messages have been identified as potential reasons for the unhealthy diet among educationally and economically disadvantaged people [55]. The fact that educational attainment was not a significant moderator of the ST-weight outcomes associations in Ghent may be due to the much higher level of education among participants from Ghent (~75% with above primary education) than from Hong Kong (~47% with above primary education). In Ghent, a positive association between ST and being overweight/obese was found in participants with a low or mean food outlet density in their neighbourhood and not in those with a high food outlet density. This seems to be logical, as limited access to healthy food options has been associated with less healthy dietary intake, which is in turn associated with higher odds of being overweight or obese [27,28]. The fact that no such moderating effect was found in Hong Kong might be due to Hong Kong residents living in neighbourhoods with few food outlets being able to easily access via public transport destination-rich neighbourhoods with many food options [56].

A site-specific interaction effect of PA and ST on BMI was identified. In Hong Kong, the positive association between ST and BMI was stronger in older adults with lower levels of MVPA than in those with higher levels of MVPA. In contrast, in Ghent, the positive association between ST and the odds of being overweight or obese was unexpectedly stronger in older adults with higher instead of lower levels of MVPA. This suggests that only in Hong Kong older adults, higher levels of MVPA seem to be protective against the positive association between ST and BMI. In Ghent, no such potentially protective effect of MVPA was identified, even a reverse (‘unprotective’) association was found. Overall, all these moderating effects confirm the complexity of some of the examined associations. It can be cautiously suggested that, although an overall association between ST and weight outcomes was found, strategies to limit ST might have stronger effects in specific subgroups located in specific regions (e.g. lower-educated adults in Hong Kong, those living in neighbourhoods with low food outlet density in Ghent). Consequently, strategies to prevent overweight and obesity through a focus on ST might not be equally effective across regions. Furthermore, the results suggest that unassessed country-specific variables (e.g. dietary patterns, perceptions of the built environment) may play a key confounding role. Future international studies combining data of multiple geographically and culturally-diverse countries are strongly encouraged to further disentangle these complex associations.

Finally, potential curvilinearity of the relationships of PA and ST with weight outcomes was examined. A previous multi-country study in adults found a curvilinear association of MVPA with BMI and the odds of being overweight or obese, where a linear negative relation levelled off at higher levels of MVPA [24]. No such associations were found here. The relation between ST and weight status was consistently linear, implying that higher levels of ST are consistently related to higher levels of BMI and higher odds of overweight and obesity. If confirmed in future studies also taking into account potential curvilinearity in their analyses, this may help to shape recommendations regarding limiting ST in older adults in order to prevent overweight or obesity.

A first strength of this study was the objective assessment of PA and ST. Most previous studies only included self-reported measures of ST, often limited to TV viewing [15]. Second, weight and height were also objectively assessed in both samples. It is known that individuals (mainly those with overweight or obesity) tend to underreport their weight when self-assessment is used [57]. Third, comparable study protocols were used in two culturally and geographically diverse countries. This study also has some weaknesses. First, this was an observational, cross-sectional study, precluding any conclusions regarding causality of the results. Second, mainly active older adults participated in the study. Since study participation was voluntarily, it is possible that mainly motivated and active older adults consented to participate, especially in Ghent, which had a lower response rate than Hong Kong. This may have influenced the findings and limits the generalisability of the current results. Third, although it is known that dietary intake is an important precursor of overweight and obesity, dietary intake was not assessed in this study. Two proxy measures (neighbourhood-level SES and food outlet density) that have been associated with (un)healthy food intake were included as moderators, but the study would have been stronger if dietary intake had been assessed directly. Finally, although accelerometers measure PA and ST objectively, only the behaviour in one specific week was assessed. This specific week might not be representative of a habitual week (e.g. due to bad weather or physical complaints during that week), and this might have biased the results.

Conclusion

In conclusion, this study showed that ST was linearly and positively related to BMI and the odds of being overweight/obese in Hong Kong and Belgian older adults, while MVPA was not. This finding was generalizable across men and women, high- and low-educated individuals, those living in high- and low-SES neighbourhoods and with high and low access to healthy food options. If confirmed in future longitudinal studies, it can be carefully suggested that a focus on limiting ST as a preventive strategy against overweight and obesity might be effective in older adults, especially in those physically incapable of being very active. However, some country-specific effects were found, confirming the cultural dependency and complexity of the associations between PA, ST and weight outcomes. Future longitudinal international studies including older adults from multiple geographically and culturally-diverse regions assessing PA, ST, weight outcomes and dietary intake should be encouraged to further disentangle the complex associations. Such studies are needed to refine the recommendations regarding ST and PA in older adults in light of preventing overweight and obesity.

Supporting information

S1 Data

(SAV)

Click here for additional data file. (48KB, sav)

Data Availability

Hong Kong data cannot be shared publicly because of ethics restrictions, as the participants consented to provide their data to the project investigators only. Data are available from the University of Hong Kong (contact via Ester Cerin; ecerin@hku.hk) for researchers who meet the criteria for access to confidential data upon approval of the Human Research Ethics Committee of the University of Hong Kong (hrec_data@hku.hk). Belgian data are publicly available. All relevant Belgian data are within the manuscript and its Supporting Information files.

Funding Statement

The Hong Kong study received a General Research Fund grant from the University Grant Committee (Hong Kong) (HKU 741511H). EC is supported by an Australian Research Council Future Fellowship (FT3 #140100085). DVD and JVC are supported by Research Foundation Flanders (FWO). The funding bodies had no input in study design and the collection, analysis and interpretation of data and the writing of the article and the decision to submit it for publication. All authors are independent from the funding bodies.

References

  • 1.Finucane MM, Stevens GA, Cowan MJ, Goodarz D, Lin JK, Paciorek CJ et al. on behalf of the Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet 2011; 377: 557–567. 10.1016/S0140-6736(10)62037-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.NCD Rick Factor Collaboration (NCD-RisC). Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet 2016; 387: 1377–1396. Erratum in: Lancet 2016; 387: 1998. 10.1016/S0140-6736(16)30054-X [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Peralta M, Ramos M, Lipert A, Martins J, Marques A. Prevalence and trends of overweight and obesity in older adults from 10 European countries from 2005 to 2013. Scand J Public Health 2018; 46: 522–529. 10.1177/1403494818764810 [DOI] [PubMed] [Google Scholar]
  • 4.Howel D. Waist Circumference and Abdominal Obesity among Older Adults: Patterns, Prevalence and Trends. PLoS ONE 2012; 7(10): e48528 10.1371/journal.pone.0048528 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Kivipelto M, Ngandu T, Fratiglioni L, Viitanen M, Kareholt I, Winblad B, et al. Obesity and vascular risk factors at midlife and the risk of dementia and Alzheimer disease. Arch Neurol. 2005; 62: 1556–1560. 10.1001/archneur.62.10.1556 [DOI] [PubMed] [Google Scholar]
  • 6.Orces CH, Montalvan M, Tettamanti D. Prevalence of abdominal obesity and its association with cardio metabolic risk factors among older adults in Ecuador. Diabetes Metab Syndr. 2017;11: S727–33. 10.1016/j.dsx.2017.05.006 [DOI] [PubMed] [Google Scholar]
  • 7.Rao DP, Patel P, Roberts KC, Thompson W. Obesity and healthy aging: social, functional and mental well-being among older Canadians. Health Promot Chronic Dis Prev Can. 2018; 38: 437–444. 10.24095/hpcdp.38.12.01 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Wullems JA, Verschueren SMP, Degens H, Morse CI, Onambélé GL. A review of the assessment and prevalence of sedentariness in older adults, its physiology/health impact and non-exercise mobility counter-measures. Biogerontology 2016; 17: 547–565. 10.1007/s10522-016-9640-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Sun F, Norman IJ, While AE. Physical activity in older people: A systematic review. BMC Public Health 2013; 13: 449 10.1186/1471-2458-13-449 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Kuhle CL, Steffen MW, Anderson PJ, Murad MH. Effect of exercise on anthropometric measures and serum lipids in older individuals: a systematic review and meta-analysis. BMJ Open 2014; 4: e005283 10.1136/bmjopen-2014-005283 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Rosique-Esteban N, Babio N, Díaz-López A, Romaguera D, Alfredo Martínez J, Sanchez VM, et al. Leisure-time physical activity at moderate and high intensity is associated with parameters of body composition, muscle strength and sarcopenia in aged adults with obesity and metabolic syndrome from the PREDIMED-Plus study. Clin Nutr. 2018;2018:1–8. [DOI] [PubMed] [Google Scholar]
  • 12.Golubic R, Wijndaele K, Sharp SJ, Simmons RK, Griffin SJ, Wareham NJ, et al. Physical activity, sedentary time and gain in overall and central body fat: 7-year follow-up of the ProActive trial cohort. Int J Obes 2015; 39: 142–148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Inoue S, Sugiyama T, Takamiya T, Oka K, Owen N, Shimomitsu T. Television viewing time is associated with overweight/obesity among older adults, independent of meeting physical activity and health guidelines. J Epidemiol. 2012;22(1):50–6. 10.2188/jea.je20110054 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Wanner M, Richard A, Martin B, Faeh D, Rohrmann S. Associations between self-reported and objectively measured physical activity, sedentary behaviour and overweight/obesity in NHANES 2003–2006. Int J Obes. 2017;41(1):186–93. [DOI] [PubMed] [Google Scholar]
  • 15.Cabanas-Sanchez V, De la Camara M, Sadarangani KP, Higueras-Fresnillo S, Martinez-Gomez D. Associations of daily activities measured by a pattern-recognition activity monitor with overall and abdominal obesity in older people: the IMPACT65+ study. Int J Obes. 2019; 43: 2545–2554. [DOI] [PubMed] [Google Scholar]
  • 16.Nunnally JC. Psychometric theory. 2nd edition New York: Mc-Graw-Hill; 1978. [Google Scholar]
  • 17.Cleland C, Ferguson S, Ellis G, Hunter RF. Validity of the International Physical Activity Questionnaire (IPAQ) for assessing moderate-to-vigorous physical activity and sedentary behaviour of older adults in the United Kingdom. BMC Med Res Methodol. 2018; 18: 176 10.1186/s12874-018-0642-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Clark BK, Healy GN, Winkle EAH, Gardiner PA, Sugiyama T, Dunstan DW, et al. Relationship of television time with accelerometer-derived sedentary time: NHANES. Med Sci Sports Exerc. 2011; 43: 822–828. 10.1249/MSS.0b013e3182019510 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Suminski RR, Patterson F, Perkett M, Heinrich KM, Carlos Poston WS. The association between television viewing and percent body fat in adults varies as a function of physical activity and sex. BMC Public Health. 2019; 19: 736 10.1186/s12889-019-7107-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Stamatakis E, Hirani V, Rennie K. Moderate-to-vigorous physical activity and sedentary behaviours in relation to body mass index-defined and waist-circumference-defined obesity. Br J Nutr. 2008; 101: 765–773. 10.1017/S0007114508035939 [DOI] [PubMed] [Google Scholar]
  • 21.Tucker LA, Tucker JM. Television viewing and obesity in 300 women: evaluation of the pathways of energy intake and physical activity. Obesity (Silver Spring). 2011; 19: 1950–1956. [DOI] [PubMed] [Google Scholar]
  • 22.Maher CA, Mire E, Harrington DM, Staiano AE, Katzmarzyk PT. The independent and combined associations of physical activity and sedentary behaviour with obesity in adults: NHANES 2003–06. Obesity (Silver Spring). 2013; 21: E730–E737. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Menai M, Charreire H, Kess-Guyot E, Andreeva VA, Hercberg S, Galan P, et al. Determining the association between types of sedentary behaviours and cardiometabolic risk factors: a 6-year longitudinal study of French adults. Diabetes Metab. 2016; 42: 112–121. 10.1016/j.diabet.2015.08.004 [DOI] [PubMed] [Google Scholar]
  • 24.Van Dyck D, Cerin E, De Bourdeaudhuij I, Hinckson E, Reis RS, Davey R, et al. International study of objectively measured physical activity and sedentary time with body mass index and obesity: IPEN adult study. Int J Obes. 2015. February;39(2):199–207. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Evenson KR, Buchner DM, Morland KB. Objective measurement of physical activity and sedentary behavior among US adults aged 60 years or older. Prev Chronic Dis. 2012; 9: E26 [PMC free article] [PubMed] [Google Scholar]
  • 26.Sallis JF, Owen N, Fisher EB. Ecological models of health behaviour In: Glanz K, Rimer K, Viswanath K, editors. Health behaviour and health education: theory, research and practice, 4th ed San Francisco: Jossey-Bass; 2008. pp. 465–486. [Google Scholar]
  • 27.Harnack LJ, Schmitz KH. The role of nutrition and physical activity in the obesity epidemic Obesity epidemiology: from aetiology to public health. New York: Oxford University Press Inc; 2010. pp 91–104. [Google Scholar]
  • 28.Rahmanian E, Gasevic D, Vukmirovich I, Lear SA. The association between the built environment and dietary intake—a systematic review. Asia Pac J Clin Nutr. 2014; 23: 183–196. 10.6133/apjcn.2014.23.2.08 [DOI] [PubMed] [Google Scholar]
  • 29.Gosliner W, Brown DM, Sun BC, Woodward-Lopez G, Crawford PB. Availability, quality and price of produce in low-income neighbourhood food stores in California raise equity issues. Public Health Nutr. 2018; 21: 1639–1648. 10.1017/S1368980018000058 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Cerin E, Sit CH, Zhang CJ, Barnett A, Cheung MM, Lai PC, et al. Neighbourhood environment, physical activity, quality of life and depressive symptoms in Hong Kong older adults: a protocol for an observational study. BMJ Open. 2016; 6: e010384 10.1136/bmjopen-2015-010384 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Van Holle V, Van Cauwenberg J, Van Dyck D, Deforche B, Van de Weghe N, De Bourdeaudhuij I. Relationship between neighborhood walkability and older adults' physical activity: results from the Belgian Environmental Physical Activity Study in Seniors (BEPAS Seniors). Int J Behav Nutr Phys Act. 2014; 11: 110 10.1186/s12966-014-0110-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Hong Kong Government—Personal Data Ordinance. 2015. Retrieved 31 September 2019 https://www.pcpd.org.hk/english/data_privacy_law/ordinance_at_a_Glance/ordinance.html
  • 33.WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004; 363: 157–63. Erratum in: Lancet. 2004; 363: 902. 10.1016/S0140-6736(03)15268-3 [DOI] [PubMed] [Google Scholar]
  • 34.Copeland JL, Esliger DW. Accelerometer assessment of physical activity in active, healthy older adults. J Aging Phys Act. 2009, 17: 17–30. 10.1123/japa.17.1.17 [DOI] [PubMed] [Google Scholar]
  • 35.Pruitt LA, Ph D, Glynn NW, King AC, Guralnik JM, Aiken EK, et al. Use of accelerometry to measure physical activity in older adults at risk for mobility disability. J Aging Phys Act. 2008; 16: 416–434 10.1123/japa.16.4.416 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Davis MG, Fox KR, Hillsdon M, Sharp DJ, Coulson JC, Thompson JL. Objectively measured physical activity in a diverse sample of older urban UK adults. Med Sci Sports Exerc. 2011; 43: 647–654. 10.1249/MSS.0b013e3181f36196 [DOI] [PubMed] [Google Scholar]
  • 37.Cerin E, Barnett A, Cheung MC, Sit CHP, Macfarlane DJ, Chan WM. Reliability and validity of the international physical activity questionnaire—long form in a sample of Hong Kong urban older adults: does neighborhood of residence matter? J Aging Phys Act. 2012; 20: 402–420. 10.1123/japa.20.4.402 [DOI] [PubMed] [Google Scholar]
  • 38.Choi L, Ward SC, Schnelle JF, Buchowski MS. Assessment of wear/nonwear time classification algorithms for triaxial accelerometer. Med Sci Sports Exerc. 2012; 44: 2009–2016. 10.1249/MSS.0b013e318258cb36 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Barnett A, Van den Hoek D, Barnett D, Cerin E. Measuring moderate-intensity walking in older adults using the Actigraph accelerometer. BMC Geriatrics. 2016; 16: 211 10.1186/s12877-016-0380-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Aguilar-Farias N, Brown WJ, Peeters GMEE. Actigraph GT3X+ cut-points for identifying sedentary behaviour in older adults in free-living environments. J Sci Med Sport. 2014; 17: 293–299. 10.1016/j.jsams.2013.07.002 [DOI] [PubMed] [Google Scholar]
  • 41.De Bourdeaudhuij I, Van Dyck D, Salvo D, Davey R, Reis RS, Schofield G, et al. International study of perceived neighbourhood environmental attributes and Body Mass Index: IPEN Adult study in 12 countries. Int J Behav Nutr Phys Act. 2015; 12: 62 10.1186/s12966-015-0228-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Cerin E, Zhang CJ, Barnett A, Sit CH, Cheung MM, Johnston JM, et al. Associations of objectively-assessed neighborhood characteristics with older adults' total physical activity and sedentary time in an ultra-dense urban environment: Findings from the ALECS study. Health Place. 2016; 42: 1–10. 10.1016/j.healthplace.2016.08.009 [DOI] [PubMed] [Google Scholar]
  • 43.Bland M. An Introduction to Medical Statistics. 3rd ed Oxford: Oxford University Press; 2000. [Google Scholar]
  • 44.Wood SN. Generalized Additive Models: An Introduction with R. Boca Raton: Chapman & Hall; 2006. [Google Scholar]
  • 45.Cerin E, Conway TL, Adams MA, Barnett A, Cain KL, Owen N, et al. Objectively-assessed neighbourhood destination accessibility and physical activity in adults from 10 countries: An analysis of moderators and perceptions as mediators. Soc Sci Med. 2018; 211: 282–293. 10.1016/j.socscimed.2018.06.034 [DOI] [PubMed] [Google Scholar]
  • 46.Zhang CJP, Barnett A, Sit CHP, Lai PC, Johnston JM, Lee RSY, et al. Cross-sectional associations of objectively assessed neighbourhood attributes with depressive symptoms in older adults of an ultra- dense urban environment: the Hong Kong ALECS study. BMJ Open. 2018; 8(3): e020480 10.1136/bmjopen-2017-020480 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Warnes GR. Gmodels: Various R Programming Tools for Model Fitting. R Package Version 2.15.3. 2012. 〈http://CRAN.R-project.org/package=gmodels〉.
  • 48.Hothorn T, Bretz F, Westfall P. Simultaneous inference in general parametric models. Biom J 2008; 50: 346–363. 10.1002/bimj.200810425 [DOI] [PubMed] [Google Scholar]
  • 49.McPhee JS, French DP, Jackson D, Nazroo J, Pendleton N, Degens H. Physical activity in older age: perspectives for healthy ageing and frailty. Biogerontology 2016; 17: 567–580. 10.1007/s10522-016-9641-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Pelclova J, Stefelova N, Dumuid D, Pedisic Z, Hron K, Gaba A, et al. Are longitudinal reallocations of time between movement behaviours associated with adiposity among elderly women? A compositional isotemporal substitution analysis. Int J Obes (Lond). 2020; Forthcoming. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Dumuid D, Lewis LK, Olds TS, Maher C, Bondarenko C, Norton L. Relationships between older adults’ use of time and cardio-respiratory fitness, obesity and cardio-metabolic risk: a compositional isotemporal substitution analysis. Maturitas. 2018; 110: 104–110. 10.1016/j.maturitas.2018.02.003 [DOI] [PubMed] [Google Scholar]
  • 52.Bono F, Matranga D. Socioeconomic inequality in non-communicable diseases in Europe between 2004 and 2015: evidence from the SHARE survey. Eur J Public Health. 2019; 29: 105–110. 10.1093/eurpub/cky165 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Kollia N, Caballero FF, Sanchez-Niubo A, Tyrovolas S, Ayuso-Mateos JL, Haro JM, et al. Social determinants, health status and 10-year mortality among 10,906 older adults from the English longitudinal study of aging: the ATHLOS project. BMC Public Health. 2018; 18: 1357 10.1186/s12889-018-6288-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Xu X, Hall J, Byles J, Shi Z. Assessing dietary quality of older Chinese people using the Chinese Diet Balance Index (DBI). PloS ONE. 2015; 10: e0121618 10.1371/journal.pone.0121618 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Darmon N, Drewnowski A. Does social class predict diet quality? Am J Clin Nutr. 2008; 87: 1107–1117. 10.1093/ajcn/87.5.1107 [DOI] [PubMed] [Google Scholar]
  • 56.Cerin E, Sit CH, Barnett A, Johnston JM, Cheung MC, Chan WM. Ageing in an ultra-dense metropolis: perceived neighbourhood characteristics and utilitarian walking in Hong Kong elders. Public Health Nutr. 2014; 17: 225–232. 10.1017/S1368980012003862 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Cawley J, Maclean JC, Hammer M, Wintfeld N. Reporting error in weight and its implications for bias in economic models. Econ Hum Biol. 2015;19: 27–44. 10.1016/j.ehb.2015.07.001 [DOI] [PubMed] [Google Scholar]

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PONE-D-20-03534

Main and interacting effects of physical activity and sedentary time on older adults’ BMI: the moderating roles of socio-demographic and environmental attributes

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Reviewer #2: Yes

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Reviewer #1: Review of the manuscript

Main and interacting effects of physical activity and sedentary time on older adults’ BMI: the moderating roles of socio-demographic and environmental attributes.

General comments:

The manuscript is interesting! The authors have taken already sampled data and made new use of the data to evaluate the relationship between MVPA and sedentary activity.

Methods and material:

Good methods, well described, and useable, I only have a few comments and questions.

The authors are using Actigraphs, and describe the cut points for MVPA and sedentary activity, they have defined non-wear time as 90 minutes? Is there a reference for this, it seems a bit high, personally I would have chosen 60 minutes, but it could be because older adults are less agile might be very still over longer time span than younger people, therefore it might be OKAY, but I would like the authors to use 60 minutes instead of 90 minutes to see if it changes the accelerometer wear time and thereby the results of their analyzes, they might have to exclude some participants as they do not get time enough to be used in the analyzes.

Data analyzes are performed using a method that is not used often, but seems to be good for the objective. But when the authors use a rarely used method, I would like to explain why they are using GAMM, from how I understand the literature GAMM are usually used for longitudinal data as GAMM are especially good for estimating trend see e.g. [1]. So please explain why you chose to use GAMM, and what the advantage is compared to e.g. GEE or mixed linear models.

Minor problem: line 235, you cite Wood, but without a reference number.

Results:

It seems that the authors are using a kind of substitution model, but their analyzes are difficult to follow. They state that with lower amount of MVPA then an increase in sedentary activity had a higher likelihood of generating increased BMI or obesity. But as amount of Sedentary activity is highly associated with MVPA (negatively) and including both in the same analysis could be problematic. The participants “only” have 100% time or 24 hour a day, now if they increase ST then they have to reduce either MVPA or low activity, thus when the authors make an assumption of increase of both MVPA and ST activity, it can only come from low activity, now how likely is to increase both ST and MVPA? To my knowledge this has been addressed by Heidemann et al [2] see https://www.ncbi.nlm.nih.gov/pubmed/23452342, the approach will enable a graphical illustration that will it easier to understand the authors point.

Discussion:

Short and to the point, good.

I miss a mentioning or discussion of one important weakness of accelerometry, we measure for a given time, and have the assumption that the activity we measure is the habitual activity! This might not be so, we know that weather and seasons have a large impact on physical activity, so the authors need to address this weakness, al though I do agree that it is a strength they have been using accelerometers rather than questionnaires.

Conclusion of review: Interesting cross sectional study that raises new questions and hypotheses, with a few additions and changes it should be ready for publication.

1. Shadish WR, Zuur AF, Sullivan KJ: Using generalized additive (mixed) models to analyze single case designs. J Sch Psychol 2014, 52(2):149-178.

2. Heidemann M, Molgaard C, Husby S, Schou AJ, Klakk H, Moller NC, Holst R, Wedderkopp N: The intensity of physical activity influences bone mineral accrual in childhood: the childhood health, activity and motor performance school (the CHAMPS) study, Denmark. BMC pediatrics 2013, 13:32.

Reviewer #2: In the current manuscript, Dyck et al assessed the main and interacting effects of PA and ST with BMI and overweight/obese in Hong Kong and Ghent older adults. They found that ST was linearly and positively related to weight outcomes in Hong Kong and Belgian older adults, while MVPA was not. Although this study have important public health implications, but there are some limitations to the study. The manuscript could be strengthened by addressing the following issues and then reconsidering the potential for publication:

1. The author found the positive association between ST and BMI was significantly stronger at lower than higher values of MVPA in Hong Kong older adults but an opposite trend in Belgian population. Table 1 should compares baseline characteristics of the study participants from Hong Kong and those from Ghent using relevant statistical methods. Knowing differences between these groups helps the reader understand the results of this article.

2. What is the explanation for why the study participants in Hong Kong were mostly women?

3. The author adopted a targeted obesity standard for the Hong Kong population, is there a targeted obesity standard for the Belgian population?

4. A large proportion of the older adults, both in Hong Kong (90.0%) and in Ghent (68.9%) reached the health guideline of 150 minutes of MVPA per week. The author could divide these participants into moderate physical activity group and heavy physical activity group to further explore the association of ST and PA with weight outcomes.

5. Please rephrase with a focus on the public health implications of your findings in terms of its practical utility taking current practices and clinical feasibility into account in Conclusions.

**********

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Reviewer #1: Yes: Niels Wedderkopp

Reviewer #2: No

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PLoS One. 2020 Jul 9;15(7):e0235833. doi: 10.1371/journal.pone.0235833.r002

Author response to Decision Letter 0

11 Jun 2020

Comments of reviewer #1

Main and interacting effects of physical activity and sedentary time on older adults’ BMI: the moderating roles of socio-demographic and environmental attributes.

General comments:

The manuscript is interesting! The authors have taken already sampled data and made new use of the data to evaluate the relationship between MVPA and sedentary activity.

Thank you for your positive evaluation of the manuscript.

Methods and material:

Good methods, well described, and useable, I only have a few comments and questions.

1. The authors are using Actigraphs, and describe the cut points for MVPA and sedentary activity, they have defined non-wear time as 90 minutes? Is there a reference for this, it seems a bit high, personally I would have chosen 60 minutes, but it could be because older adults are less agile might be very still over longer time span than younger people, therefore it might be OKAY, but I would like the authors to use 60 minutes instead of 90 minutes to see if it changes the accelerometer wear time and thereby the results of their analyses, they might have to exclude some participants as they do not get time enough to be used in the analyses.

In the manuscript, we refer to the paper of Choi and colleagues (reference 38) to justify our decision to define accelerometer non-wear time as >90 minutes of consecutive zeros. In that paper, the accuracy of accelerometer wear/non-wear classification in older adults was examined by using two time-windows (60 minutes and 90 minutes). Both wrist-worn and waist-worn Actigraph accelerometers were used in that study. Results showed that the 90-minute time window was more accurate than the 60-minute time window to classify wear/non-wear time in free-living older adults. Based on findings from validation studies in older adults, other researchers have used even higher cut-off values to determine non-wear time in older adults, such as >100 minutes of consecutive zeros (Cerin et al., 2016; Davis et al., 2011; Davis and Fox, 2007).

We agree that a 60-minute window is more optimal for use in a general adult population, but because of the fact that older adults indeed may be sedentary (but still wearing the accelerometer) for longer periods, using 60 minutes as a cut-off value does not seem to be optimal in this age group. Consequently, based on the results of the study of Choi et al, we decided to keep this cut-off value.

Cerin E, Zhang CJ, Barnett A, et al. Associations of objectively-assessed neighborhood characteristics with older adults' total physical activity and sedentary time in an ultra-dense urban environment: Findings from the ALECS study. Health Place. 2016;42:1‐10.

Choi L, Ward SC, Schnelle JF, Buchowski MS. Assessment of wear/nonwear time classification algorithms for triaxial accelerometer. Med Sci Sports Exerc. 2012; 44: 2009-2016.

Davis MG, Fox KR, Hillsdon M, Sharp DJ, Coulson JC, Thompson JL. Objectively measured physical activity in a diverse sample of older urban UK adults. Med Sci Sports Exerc. 2011;43(4):647‐654.

Davis MG, Fox KR. Physical activity patterns assessed by accelerometry in older people. Eur J Appl Physiol. 2007;100(5):581‐589.

2. Data analyses are performed using a method that is not used often, but seems to be good for the objective. But when the authors use a rarely used method, I would like to explain why they are using GAMM, from how I understand the literature GAMM are usually used for longitudinal data as GAMM are especially good for estimating trend see e.g. [1]. So please explain why you chose to use GAMM, and what the advantage is compared to e.g. GEE or mixed linear models.

Generalized Additive Mixed Regression Models (GAMMs) were chosen because of the following reasons:

- mixed models can account for multiple levels of clustering (e.g. individuals in neighbourhoods in countries)

- generalized models can model non-normally distributed outcomes like physical activity

- additive models can explore the shape (e.g. curvilinearity) of the associations between explanatory variables and outcomes.

So, the added value of using GAMMs instead of linear mixed models is mainly the fact that potential non-linearity of associations can be examined, and that non-normally distributed outcomes can be included. Also compared to GEE models, GAMMs have the added value that they can identify non-linear associations.

In the Data Analyses section we explain the added value of using GAMMs (page 10).

“To account for clustering at the administrative unit level and the possibility of curvilinear associations, generalised additive mixed models (GAMMs) [44] were used…”

3. Minor problem: line 235, you cite Wood, but without a reference number.

Thank you for noticing this, the reference number for Wood, 2006 is [44]. We changed this in the manuscript.

4. Results:

It seems that the authors are using a kind of substitution model, but their analyses are difficult to follow. They state that with lower amount of MVPA then an increase in sedentary activity had a higher likelihood of generating increased BMI or obesity. But as amount of Sedentary activity is highly associated with MVPA (negatively) and including both in the same analysis could be problematic. The participants “only” have 100% time or 24 hour a day, now if they increase ST then they have to reduce either MVPA or low activity, thus when the authors make an assumption of increase of both MVPA and ST activity, it can only come from low activity, now how likely is to increase both ST and MVPA? To my knowledge this has been addressed by Heidemann et al [2] see https://www.ncbi.nlm.nih.gov/pubmed/23452342, the approach will enable a graphical illustration that will it easier to understand the authors point.

To explain our analyses, we would like to refer to our answer to comment 2 of this reviewer. We did not use a substitution model, but regression models (GAMMs) to examine the associations of MVPA and ST with BMI and the odds of being overweight/obese. Furthermore, because the data were not longitudinal, nor did we use a substitution model, we cannot (and did not) make statements about ‘increases’ or ‘decreases’ in ST or PA, but only about ‘higher’ and ‘lower’ levels of these behaviours. So, we did not state that an ‘increase’ in ST leads to a higher likelihood of overweight/obesity. What our findings do show, is that in Hong Kong older adults, there was an interaction between ST and MVPA to explain BMI and being overweight/obese. So, higher levels of ST were associated with a higher BMI, and the association was strongest in individuals with lower values of MVPA. In Ghent, an opposite trend was revealed, with stronger associations between ST and BMI in those older adults with higher levels of MVPA. Based on these results, we do not assume an increase of both MVPA and ST, and nowhere in the Discussion section did we suggest this. We do mention that both MVPA and ST can be high in individuals. This has been shown in previous research as well, and is called the ‘active couch potato’ phenomenon (Owen et al, 2010).

Nonetheless, we do agree that individuals only have 24 hours in a day, and that PA, ST and sleep are co-dependent. Consequently, we based our suggestions in the Discussion on these assumptions. On page 20, we suggest that limiting ST as a preventive strategy against overweight and obesity could be effective in older adults. Furthermore, we suggest that reallocation of ST to PA of different intensities (light-intensity and MVPA) can have a positive effect on obesity markers, with evidence being strongest for MVPA. This suggestion is based on existing literature, mentioned on page 20 of the manuscript.

We hope that this explanation clarifies the Results for reviewer #1.

Owen N, Healy GN, Matthews CE, Dunstan DW. Too much sitting: the population health science of sedentary behavior. Exerc Sport Sci Rev 2010; 38: 105-113.

5. Discussion:

Short and to the point, good. I miss a mentioning or discussion of one important weakness of accelerometry, we measure for a given time, and have the assumption that the activity we measure is the habitual activity! This might not be so, we know that weather and seasons have a large impact on physical activity, so the authors need to address this weakness, although I do agree that it is a strength they have been using accelerometers rather than questionnaires.

We added this to the Limitations on page 24.

“Finally, although accelerometers measure PA and ST objectively, only the behaviour in one specific week was assessed. This specific week might not be representative of a habitual week (e.g. due to bad weather or physical complaints during that week), and this might have biased the results.”

Conclusion of review: Interesting cross sectional study that raises new questions and hypotheses, with a few additions and changes it should be ready for publication.

Thank you once again for your positive and helpful feedback.

1. Shadish WR, Zuur AF, Sullivan KJ: Using generalized additive (mixed) models to analyze single case designs. J Sch Psychol 2014, 52(2):149-178.

2. Heidemann M, Molgaard C, Husby S, Schou AJ, Klakk H, Moller NC, Holst R, Wedderkopp N: The intensity of physical activity influences bone mineral accrual in childhood: the childhood health, activity and motor performance school (the CHAMPS) study, Denmark. BMC pediatrics 2013, 13:32.

Comments of reviewer #2

In the current manuscript, Dyck et al assessed the main and interacting effects of PA and ST with BMI and overweight/obese in Hong Kong and Ghent older adults. They found that ST was linearly and positively related to weight outcomes in Hong Kong and Belgian older adults, while MVPA was not. Although this study have important public health implications, but there are some limitations to the study. The manuscript could be strengthened by addressing the following issues and then reconsidering the potential for publication.

We would like to thank reviewer 2 for his/her positive evaluation of the manuscript.

1. The author found the positive association between ST and BMI was significantly stronger at lower than higher values of MVPA in Hong Kong older adults but an opposite trend in Belgian population. Table 1 should compares baseline characteristics of the study participants from Hong Kong and those from Ghent using relevant statistical methods. Knowing differences between these groups helps the reader understand the results of this article.

We have now reported the results of a formal comparison of baseline characteristics between Hong Kong and Ghent participants in Table 1. However, we wish to note that these analyses cannot explain the observed between-sample differences in moderating effects of MVPA on the association between ST and BMI. In fact, the estimates of the moderating effect of MVPA were adjusted for all baseline characteristics. On page 10, we have now stated:

“Descriptive statistics of sample characteristics (participant demographics, objective neighbourhood attributes, activity levels, and BMI) were computed. We also tested the significance (p<0.05) of the differences in these characteristics between the two samples using, as appropriate, generalised linear models and multinomial logistic regression with robust standard errors accounting for clustering at the administrative unit level.”

Furthermore, we added the baseline comparisons (p-values) in the Results (page 11).

“Participant characteristics are presented in Table 1. Mean age was 75 years and slightly higher in the Hong Kong sample (p=0.025). The total sample (n=829) consisted of 61% female participants, 69% and 54% in Hong Kong and Ghent, respectively (p<0.001). The sample from Ghent had a higher level of education than the Hong Kong sample (p<0.001), with 75% versus 47% having an educational level higher than primary school. Having a car in the household was much more prevalent in Ghent (81%) than in Hong Kong (29%) (p<0.001). The mean ST per day was 414 minutes, with the Ghent cohort accumulating an average of 16 more minutes per day than the Hong Kong cohort (p=0.795). For the total cohort, the average MVPA was 56 minutes per day, with Hong Kong participants engaging in approximately 26 minutes more per day than their Ghent counterparts (p<0.001). Mean BMI was 23 kg/m2 and, on average, ~ 1kg/m2 lower in Ghent (p<0.001). Because of the different cut-off values for categorising overweight/obesity in Asian and European older adults [33], the distribution of BMI categories was very different across sites with 57% of those in Hong Kong being overweight/obese as compared to 24% in Ghent (p<0.001).”

2. What is the explanation for why the study participants in Hong Kong were mostly women?

The Hong Kong sample consisted of substantially more women than men for two reasons. First, there are more women than men among older Hong Kong Chinese 65 years and over (54%; Census and Statistics Department, Government of HK SAR, 2020). Second, men were less likely to consent participating in the study. Of the 29% potential participants who refused to join the study, approximately 70% were men. This is not unusual as many studies have found that women are more likely to participate in studies than men (e.g., Moore and Tarnai, 2002; Singer et al., 2000)

Census and Statistics Department, Hong Kong SAR. Populations estimates: Table 002 – Population by age group and sex. Available at: https://www.censtatd.gov.hk/hkstat/sub/sp150.jsp?tableID=002&ID=0&productType=8 (retrieved: 30 May 2020).

Moore DL, Tarnai J. Evaluating nonresponse error in mail surveys. In: Groves RM, Dillman DA, Eltinge JL, Little RJA (eds.), Survey Nonresponse. New York: John Wiley & Sons, 2002. (pp. 197–211).

Singer E, van Hoewyk J, Maher MP. Experiments with incentives in telephone surveys. Public Opinion Quarterly. 2000;64:171–188.

3. The author adopted a targeted obesity standard for the Hong Kong population, is there a targeted obesity standard for the Belgian population?

No, for the Belgian population, the WHO standards to define overweight and obesity were used (https://gateway.euro.who.int/en/indicators/mn_survey_19-cut-off-for-bmi-according-to-who-standards/). According to these cut offs, a BMI score of ≥25 kg/m² is defined as overweight and a BMI score of ≥30 kg/m² is defined as obesity. These WHO standards are used globally, so there was no need to adapt these for Belgian older adults.

However, as noted in the paper (WHO Expert Consultation, 2004), appropriate BMI cut offs for Asian populations have been developed and the WHO recommends to use these cut offs in research with Asian populations. Therefore, we used these adjusted cut offs to define overweight in the Hong Kong sample.

WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004; 363: 157-63. Erratum in: Lancet. 2004; 363: 902.

4. A large proportion of the older adults, both in Hong Kong (90.0%) and in Ghent (68.9%) reached the health guideline of 150 minutes of MVPA per week. The author could divide these participants into moderate physical activity group and heavy physical activity group to further explore the association of ST and PA with weight outcomes.

This is an interesting suggestion. However, we used the cut points of Barnett et al (2016) to define MVPA in our sample of older adults. Barnett and colleagues developed accelerometer-based MVPA cut points for older adults. They did not develop separate cut points for moderate and for vigorous PA. Consequently, we cannot divide the current sample into a moderate and a high-intensity PA group, to further explain the associations of ST and PA with weight outcomes.

We chose the cut points of Barnett and colleagues for several reasons.

1) They were developed specifically for older adults, while most other cut points were developed for adults. The resting metabolic rate of older adults is lower than of adults, so it is important to take this into account in calibration studies.

2) Other studies determining Actigraph cut points in older adults included other activities like household chores or light intensity exercises that typically occur infrequently in real life. Walking is a preferred activity for older adults to increase their levels of PA and is therefore the most suited activity to determine accelerometer-based cut points.

3) Overground walking was used instead of treadmill walking. When treadmill walking is used to determine cut points, ActiGraph counts are lower and energy expenditure is higher for a given walking speed compared with overground walking. Consequently, energy expenditure walking speed predicted by the accelerometer are overestimated in ream-life conditions.

Furthermore, according to studies that used ActiGraph accelerometers to measure PA, older adults tend to accumulate very minimal amounts of vigorous-intensity PA. For example, in a study conducted in Hong Kong, older adults accumulated, on average, only 1 minute (SD = 2.0) of vigorous-intensity PA in a week (Cerin et al., 2012). Similarly, a study conducted in Sweden found that the median daily minutes of accelerometry-assessed vigorous-intensity PA in older adults was 0 (Hurtig-Wennlöf et al., 2010).

For these reasons, we decided to use the cut points of Barnett and colleagues, and a distinction between moderate and vigorous PA cannot be made.

Barnett A, Van den Hoek D, Barnett D, Cerin E. Measuring moderate-intensity walking in older adults using the Actigraph accelerometer. BMC Geriatrics. 2016; 16: 211.

Cerin E, Barnett A, Cheung MC, Sit C HP, Macfarlane DJ, Chan WM. Reliability and validity of the IPAQ-L in a sample of Hong Kong urban older adults: does neighborhood of residence matter?. J Aging Phys Act. 2012;20(4):402‐420.

Hurtig-Wennlöf A, Hagströmer M, Olsson LA. The International Physical Activity Questionnaire modified for the elderly: aspects of validity and feasibility. Public Health Nutr. 2010;13(11):1847‐1854.

5. Please rephrase with a focus on the public health implications of your findings in terms of its practical utility taking current practices and clinical feasibility into account in Conclusions.

In the previous version of the manuscript, we indeed formulated a rather careful conclusion because this study is an observational study, so no assumptions regarding causality of the associations can be made. Furthermore, to our knowledge, this was one of the first studies examining the interaction effects between ST and MVPA on weight outcomes in older adults, so future studies should confirm our findings before firm recommendations can be made.

However, based on our findings (and like we did in the Discussion) we can carefully suggest that a focus on limiting ST as a preventive strategy against overweight and obesity may be effective in older adults, especially in those who are physically incapable of being highly active. Ideally, the ST should then be replaced by MVPA, but replacing it by light-intensity PA might already be beneficial. We added this to the Conclusion of the paper, but we did not add the suggestion regarding replacement by MVPA or light-intensity PA, because we did not examine this ourselves.

“In conclusion, this study showed that ST was linearly and positively related to BMI and the odds of being overweight/obese in Hong Kong and Belgian older adults, while MVPA was not. This finding was generalizable across men and women, high- and low-educated individuals, those living in high- and low-SES neighbourhoods and with high and low access to healthy food options. If confirmed in future longitudinal studies, it can be carefully suggested that a focus on limiting ST as a preventive strategy against overweight and obesity might be effective in older adults, especially in those physically incapable of being very active.”

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Decision Letter 1

Martin Senechal

24 Jun 2020

Main and interacting effects of physical activity and sedentary time on older adults’ BMI: the moderating roles of socio-demographic and environmental attributes

PONE-D-20-03534R1

Dear Dr. Van Dyck,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

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Reviewer #2: Yes

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The Authors have generally followed the advice form the reviewers, and if not have a good and valid explanation for why not. In my opinion the manuscript should be ready for publication.

Reviewer #2: (No Response)

**********

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Reviewer #1: Yes: Professor Niels Wedderkopp

Reviewer #2: No

Acceptance letter

Martin Senechal

30 Jun 2020

PONE-D-20-03534R1

Main and interacting effects of physical activity and sedentary time on older adults’ BMI: the moderating roles of socio-demographic and environmental attributes

Dear Dr. Van Dyck:

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    Data Availability Statement

    Hong Kong data cannot be shared publicly because of ethics restrictions, as the participants consented to provide their data to the project investigators only. Data are available from the University of Hong Kong (contact via Ester Cerin; ecerin@hku.hk) for researchers who meet the criteria for access to confidential data upon approval of the Human Research Ethics Committee of the University of Hong Kong (hrec_data@hku.hk). Belgian data are publicly available. All relevant Belgian data are within the manuscript and its Supporting Information files.

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