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critique the research design of the attached article using the following outline: (5-6 double-spaced pages in length IN APA 7TH EDITION STYLE) • Identify the Independent and dependent variables. (x --> y) • Explain what makes them so • Identify the unit of analysis • Explain what makes it so • Identify the relationship between the Independent and Dependent Variables (direct or indirect). Explain why • Identify whether the author(s) used an experimental design or quasi-experimental (or some combination or other) • Carefully and thoughtfully explain why you think so • Describe what methodology they used in detail: Identify whether the author(s) use a quantitative or qualitative research design • Discuss the methods the author(s) used in detail. (qualitative/descriptive case study method, meta-analysis, cross-sectional analysis method, longitudinal design method, time series method, panel design method, and so on) • Identify strengths of the methodology, research design, methods that the authors used • Identify weaknesses of the methodology, research design, methods the authors used • Carefully lay out potential threats or concerns regarding internal validity and how you propose to account for them • Carefully lay out potential threats or concerns regarding external validity and how you propose to account for them Conclusion: What specific recommendations would you make to increase the validity of the research design BE SURE TO: Accurately identify IV and DVs in the article. Accurately explain why do you think which one is IV and DV. • Accurately identify and explain the relationship between variables as directional or inverted. • What methodology is used and accurately describe. What are the weaknesses of the design and accurately describe and provide alternative strategy? See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/309695159 Lifestyle correlates of overweight in adults: A hierarchical approach (the SPOTLIGHT project) Article in International Journal of Behavioral Nutrition and Physical Activity · December 2016 DOI: 10.1186/s12966-016-0439-x CITATIONS 4 READS 67 13 authors, including: Some of the authors of this publication are also working on these related projects: PRO GREENS View project SPOTLIGHT project View project Thierry Feuillet Université de Vincennes - Paris 8 78 PUBLICATIONS 288 CITATIONS SEE PROFILE Joreintje Mackenbach VU University Medical Center 47 PUBLICATIONS 407 CITATIONS SEE PROFILE Keti Glonti Paris Descartes, CPSC 48 PUBLICATIONS 283 CITATIONS SEE PROFILE Harry Rutter London School of Hygiene and Tropical Medi… 144 PUBLICATIONS 2,442 CITATIONS SEE PROFILE All content following this page was uploaded by Jeroen Lakerveld on 08 December 2016. The user has requested enhancement of the downloaded file. RESEARCH Open Access Lifestyle correlates of overweight in adults: a hierarchical approach (the SPOTLIGHT project) Célina Roda1, Hélène Charreire1,2, Thierry Feuillet1, Joreintje D. Mackenbach3, Sofie Compernolle4, Ketevan Glonti5, Helga Bárdos6, Harry Rutter5, Martin McKee5, Johannes Brug3, Ilse De Bourdeaudhuij4, Jeroen Lakerveld3 and Jean-Michel Oppert1,7* Abstract Background: Obesity-related lifestyle behaviors usually co-exist but few studies have examined their simultaneous relation with body weight. This study aimed to identify the hierarchy of lifestyle-related behaviors associated with being overweight in adults, and to examine subgroups so identified. Methods: Data were obtained from a cross-sectional survey conducted across 60 urban neighborhoods in 5 European urban regions between February and September 2014. Data on socio-demographics, physical activity, sedentary behaviors, eating habits, smoking, alcohol consumption, and sleep duration were collected by questionnaire. Participants also reported their weight and height. A recursive partitioning tree approach (CART) was applied to identify both main correlates of overweight and lifestyle subgroups. Results: In 5295 adults, mean (SD) body mass index (BMI) was 25.2 (4.5) kg/m2, and 46.0 % were overweight (BMI ≥25 kg/m2). CART analysis showed that among all lifestyle-related behaviors examined, the first identified correlate was sitting time while watching television, followed by smoking status. Different combinations of lifestyle-related behaviors (prolonged daily television viewing, former smoking, short sleep, lower vegetable consumption, and lower physical activity) were associated with a higher likelihood of being overweight, revealing 10 subgroups. Members of four subgroups with overweight prevalence >50 % were mainly males, older adults, with lower education, and living in greener neighborhoods with low residential density. Conclusion: Sedentary behavior while watching television was identified as the most important correlate of being overweight. Delineating the hierarchy of correlates provides a better understanding of lifestyle-related behavior combinations which may assist in targeting preventative strategies aimed at tackling obesity. Keywords: CART, Eating habits, Lifestyle-related behaviors, Obesity, Physical activity, Sedentary behavior, Sleep, Smoking status, Television viewing * Correspondence: [email protected] 1Équipe de Recherche en Épidémiologie Nutritionnelle (EREN), Université Paris 13, Centre de Recherche en Épidémiologie et Statistiques, Inserm (U1153), Inra (U1125), Cnam, COMUE Sorbonne Paris Cité, Bobigny F-93017, France 7Sorbonne Universités, Université Pierre et Marie Curie, Université Paris 06, Institute of Cardiometabolism and Nutrition, Department of Nutrition, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France Full list of author information is available at the end of the article © The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Roda et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:114 DOI 10.1186/s12966-016-0439-x Background Excess body weight is determined by multiple factors acting in combination, including genetic, metabolic and behavioral factors, as well as more upstream socio- economic influences and built environment characteris- tics [1]. Those that are modifiable provide important potential targets for preventive interventions [2]. Diet and physical activity are recognized as the most prox- imal determinants of energy balance [3] but there is growing recognition of the role of sedentary behaviors (e.g., sitting time), independent of physical activity [4–7]. The influences of smoking and alcohol intake on body weight are also well documented [8–10]. More recently, a role has also been suggested for sleep duration [11–13]. The inter-relationship of these obesity-related lifestyle behaviors has stimulated interest in co-occurrence pat- terns [14, 15]. Several studies have used explorative data-driven methods, such as cluster analysis or latent class analysis to examine the relations between diet, physical activity, and sedentary behaviors, independently of the health outcome of interest [6, 16, 17]. Smoking status and alcohol consumption have been included in some analyses [18–20]. The variety of methodologies used make it difficult to ascertain how these factors cor- relate with each other and what this means for body weight and health. Additionally, previous studies have not considered contextual factors such as socio- economic characteristics and the built environment, in- creasingly recognized as major upstream determinants of overweight [21]. A recursive partitioning method—the classification and regression tree (CART) approach [22]—makes it possible to examine how a set of risk factors jointly in- fluence the risk of an outcome such as overweight. This approach has previously been used to assess the risk of overweight in children [23, 24] and the risk of reduced mobility in older obese adults [25]. This study sought to identify the hierarchy of lifestyle- related behaviors associated with overweight in Euro- pean adults, and to examine how subgroups identified differed by socio-demographic and built environment characteristics. Methods Study design and sampling This study, part of the EU-funded SPOTLIGHT project [26], was conducted in five European urban regions: Ghent and suburbs (Belgium), Paris and inner suburbs (France), Budapest and suburbs (Hungary), the Randstad (a conurbation including Amsterdam, Rotterdam, the Hague and Utrecht in the Netherlands) and Greater London (United Kingdom). Sampling of neighborhoods and recruitment of participants have been described in detail elsewhere [27]. Briefly, neighborhood sampling was based on a combination of residential density and socio-economic status (SES) data at the neighborhood level. This resulted in four pre-specified neighborhood types: low SES/low residential density, low SES/high residential density, high SES/low residential density and high SES/high residential density. In each country, three neighborhoods of each neighborhood type were ran- domly sampled (i.e. 12 neighborhoods per country, 60 neighborhoods in total). Subsequently, adult inhabitants (≥18 years) were invited to participate in a survey. A total of 6037 individuals participated in the study be- tween February and September 2014. The study was ap- proved by the corresponding local ethics committees of participating countries and all participants in the survey provided informed consent. Measures Body mass index Body mass index (BMI) was calculated by dividing self- reported weight (kg) by the square of the self-reported height (m2). Adults were categorized as overweight if their BMI was ≥25 kg/m2 [1]. Socio-demographic data Socio-demographic variables included age, gender and educational level (defined as ‘lower’ [from less than pri- mary to higher secondary education] and ‘higher’ [col- lege or university level] to allow comparison between country-specific education systems). Physical activity Physical activity during the last 7 days was documented using questions from the long version of the validated International Physical Activity Questionnaire (IPAQ) [28]. Good reliability (Spearman correlation coefficients ranged from 0.46 to 0.96) and acceptable criterion valid- ity (median ρ of about 0.30) have been found for this questionnaire in a 12 country study [28]. Transport- related and leisure time physical activity were estimated (in minutes per day − min/d) by multiplying the fre- quency (number of days in the last 7 days) and duration (average time/d). Sedentary behavior The validated Marshall questionnaire was used to collect sedentary behavior data during the last 7 days [29]. Acceptable criterion validity (Spearman correlation coefficient greater than or equal to 0.50 for watching TV, and using a computer at home during weekdays) has been demonstrated. Lowest validity coefficients were found for other leisure-time activities and transport- related sedentary behaviors during weekend days (correl- ation coefficients ranged from 0.15 to 0.42) [29]. Time spent (min/d) sedentary for travel, television (TV), Roda et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:114 Page 2 of 12 computer and other leisure time activities (e.g., socializ- ing, movies but not including TV and computer use) was averaged over a week. Eating habits Current eating habits were assessed using common food frequency questions on consumption of fruit, vegetables, fish, sweets, fast-food, sugar-sweetened beverages, and alcohol. Response options were ‘once a week or less’, ‘2 times a week’, ‘3 times a week’, ‘4 times a week’, ‘5 times a week’, ‘6 times a week’, ‘7 times a week’, ‘twice a day’, and ‘more than twice a day’. Smoking status Participants reported their smoking status: current, former or never. Sleep duration Participants provided information on their hours of sleep during an average night. The response options ranged from 4 to 16 h/night (in half-hour intervals). Neighborhood clusters Four neighborhood clusters were previously identified based on data related to food and physical activity fea- tures of the built environment collected by a Google Street View-based virtual audit performed in 59 study neighborhoods [30]. The clusters were labeled: cluster 1 (n = 33) ‘green neighborhoods with low residential dens- ity’, cluster 2 (n = 16) ‘neighborhoods supportive of active mobility’, cluster 3 (n = 7) ‘high residential density neigh- borhoods with food and recreational facilities’, and clus- ter 4 (n = 3) ‘high residential density neighborhoods with low level of aesthetics’. Data analysis CART approach Recursive partitioning was used to identify the hierarchy and combinations of all lifestyle-related behaviors de- scribed in the Measures section that best differentiated overweight (≥25 kg/m2) vs. non-overweight (<25 kg/m2) participants. Recursive partitioning is an algorithm of the CART nonparametric statistical method [22]. This approach has been used in different research fields, such as genetic epidemiology [31], and produced greater homogeneity in subgroups than has been achieved with other ap- proaches, such as regression models [32]. Recursive par- titioning is a step-by-step process by which a decision tree is built by either splitting or not splitting each node of the tree into two daughter nodes. Each possible split among all variables present at each node is considered. The tree is constructed by the algorithm asking a se- quence of hierarchical Boolean (yes/no) questions (e.g., is Xi ≤ θj ?, where Xi is a candidate variable, and θj is a cut-off) generating descendant nodes [33]. The cut-off in the candidate variable that produced the maximal dif- ferentiation between individuals is retained, and used to split the sample into two subgroups (i.e. two daughter nodes). This process is repeated for each new subgroup found. Every variable is a potential candidate at each stage in growing the tree, so some variables may appear several times, using different cut-offs. The best way to split the data is determined by the Gini impurity index. This index ranges from 0 (pure node, i.e. all observations within the node assigned to a single target class—e.g., a node with a class distribution [0;1]) to 1 (impure node, i.e. mixed target classes—e.g., a node with a class distri- bution [0.5;0.5]). The complete tree is pruned by a se- quential node-splitting process to avoid over-fitting the data; a sequence of sub-trees is generated and compared. The optimum tree is obtained using both cross- validation and cost-complexity pruning method. The cost-complexity pruning method assesses the balance between misclassification costs and complexity of the sub-tree. Additionally, each terminal node was set to re- quire a minimum of 200 subjects. Lifestyle subgroups Characteristics of the subgroups identified through the CART analysis were compared. All variables included in the CART analysis were considered, in addition to socio- demographic and built environment characteristics (i.e. urban region, neighborhood type—pre-specified neigh- borhood type, and residential density and SES levels ex- amined separately—and neighborhood cluster). Chi-squared tests, and Kruskal-Wallis tests with post- hoc Bonferroni-Dunn test were used to examine differ- ences between subgroups. Multilevel regression analyses Because participants were nested within neighborhoods, the likelihood of being overweight for each partitioning variable was estimated by a multilevel logistic regression model (neighborhood identifier included as a random ef- fect) adjusted for potential confounders (gender, age, education level, and neighborhood type). Statistical analyses were performed using R version 3.2 [34] (‘R-part’ package [35]), and STATA software (release 13.0; Stata Corporation, College Station, TX, USA). Results Characteristics of the study population Results are given for 5295 individuals for whom BMI was available. The study population comprised 55.8 % females, with a mean (standard deviation-SD) age of 51.7 (16.4) years; 54 % were highly educated. Mean BMI was 25.2 (4.5) kg/m2, and 46.0 % adults were overweight. Roda et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:114 Page 3 of 12 Compared to non-overweight subjects, overweight adults were more likely to be male, older, less educated, former smokers, short sleepers, less physically active, eating less fruit and vegetables, and spending more time sitting, especially when viewing TV. The prevalence of overweight ranged from 38.3 % in Greater Paris to 53.2 % in Greater Budapest (Table 1). CART analysis The final tree contained 10 nodes (i.e. 10 subgroups) and had a classification error of 35.4 %. The 6 variables that were retained as the most important for discrimin- ating overweight status were in the following order: sed- entary time while watching TV, smoking status, sleep duration, leisure time physical activity, and vegetable in- take (Fig. 1). The odds of being overweight were 61 % (41–85 %) higher for those reporting longer time watching TV (≥142 min/d) than others. Longer time spent watching TV (≥142 min/d) and be- ing a former smoker were important correlates of over- weight. Current or non-smokers who spent a long time watching TV and were less physically active during leis- ure time were also at risk of being overweight. Among adults watching less TV (<142 min/d) and be- ing former smokers, those who were short sleepers (<7 h/night) were more likely to be overweight com- pared to long sleepers. Protective factors against being overweight among current and non-smokers included: short time watching TV, being physically active during leisure time, and eating vegetables every day. Lifestyle subgroups Table 2 shows the characteristics of the subgroups identi- fied by CART. The proportion of overweight subjects ranged from 20 % (Subgroup 1) to 65.4 % (Subgroup 10). Overall, participants from the various subgroups differed in terms of lifestyle-related behaviors as well as socio- demographic and built environment characteristics. Subgroup 1 (n = 315, mean [SD] BMI: 22.7 [3.4] kg/m2) consisted of the youngest (40.8 [13.6] years-old), and highly educated participants (78.4 %). This subgroup re- ported the lowest time spent watching TV (mean [SD]: 5.2 [7.9] min/day, median: 0 min/day), the highest mean frequency of eating fruits and vegetables. The highest per- centage of participants living in neighborhoods that were characterized by high SES and high residential density was observed in this subgroup, as was the lowest percentage of participants living in ‘green neighborhoods with low resi- dential density’. In 4 subgroups (7, 8, 9, and 10), overweight prevalence was >50 %. Members lived mainly in low SES neighbor- hoods. Subgroup 7 grouped less physically active individ- uals, who ate fruits, vegetables, and fish less frequently. Subgroup 8 members were short sleepers. The greatest percentage of individuals living in low residential neigh- borhoods was reported in this subgroup. Subgroup 9 in- cluded the greatest percentage of current smokers, individuals who reported long mean time watching TV (mean [SD]: 306.0 [131.3] min/day, median: 257 min/ day), and high mean consumption of sugar-sweetened beverages (4.9 [5.7] times/week, median: 3.0 times/week). Subgroup 10 (n = 676, mean [SD] BMI = 27.2 [5.0] kg/m2) included mainly males, older (59.6 [14.4] years-old) and low educated adults (64.5 %), who re- ported high alcohol consumption and living in ‘green neighborhoods with low residential density’. Discussion This study investigated the hierarchy and combination of lifestyle-related behaviors in relation to the prevalence of overweight in European adults. Prolonged sitting while watching TV, being a former smoker, short sleep, lower levels of physical activity and lower vegetable con- sumption were the lifestyle-behaviors that identified the subgroups with highest likelihood of being overweight. High-risk subgroups included mainly males, older and less well educated adults living in greener neighborhoods with low residential density. Although it is well recognized that overweight and obesity are multifactorial in origin [1, 2], few studies have examined the joint relation of lifestyle-related behaviors with overweight in adults. In this study, a hier- archy of lifestyle-related behaviors in identifying sub- groups at risk was established through a visual chart showing how risk factors are inter-related. The tree indi- cated that the most important factor was sitting while watching TV. This variable appeared several times at dif- ferent levels of the tree, underlying its importance. The variable that followed was smoking status, in both tree branches, and no additional variable appeared to explain the risk for overweight in former smokers (among those with longer duration of watching TV), suggesting its very high impact. Sleep duration, leisure time physical activity and vegetable intake appeared at later stages in the tree, suggesting they would have less importance compared to sedentary behavior and smoking status. Re- lations between the lifestyle-related behaviors and over- weight status were confirmed in multilevel regression analyses taking into account potential confounding fac- tors. The findings also suggested nonlinear relations be- tween lifestyle-related behaviors and overweight. Indeed, subgroups who watched TV a lot (>180 min/d) had lower odds of being overweight than subgroups who watched less TV (between 24 min/d and 142 min/d). Although it has been suggested that a combination of several sedentary behavior variables is appropriate to capture sedentary lifestyle [36], only TV viewing was Roda et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:114 Page 4 of 12 Table 1 Characteristics of the overall study population and according to weight status in the SPOTLIGHT study n (%) or median (IQR) Overall N = 5295 100 % Non-overweighta n = 2862 54.0 % Overweightb n = 2433 46.0 % p† Socio-demographic characteristics Gender, (n = 5246) Male 2316 (44.2) 1059 (37.3) 1257 (52.2) <0.001 Female 2930 (55.8) 1780 (62.7) 1150 (47.8) Age (in years), (n = 5256) 52.0 (26.0) 47.0 (27.0) 57.0 (23.0) <0.001 Education, (n = 5191) High level 2804 (54.0) 1756 (62.4) 1048 (44.1) <0.001 Low level 2387 (46.0) 1058 (37.6) 1329 (55.9) BMI (kg/m2), (n = 5295) 24.6 (5.5) 22.3 (2.9) 27.8 (4.2) <0.001 Lifestyle-related behaviors Smoking status, (n = 5247) Never 3049 (58.1) 1783 (62.8) 1266 (52.6) <0.001 Former 1464 (27.9) 637 (22.5) 827 (34.3) Current 734 (14.0) 418 (14.7) 316 (13.1) Physical activity Transport-related physical activity (min/d), (n = 5274) 26.0 (59.0) 27.0 (57.0) 26.0 (61.0) 0.012 Leisure-time physical activity (min/d), (n = 5274) 26.0 (44.0) 26.0 (44.0) 21.0 (47.0) <0.001 Sedentary behaviors Television time (min/d), (n = 4481) 137.0 (120.0) 120.0 (120.0) 154.0 (146.0) <0.001 Computer time (min/d), (n = 4358) 77.0 (103.0) 77.0 (98.0) 91.0 (108.0) <0.001 Other leisure sitting time (min/d), (n = 3942) 64.0 (112.0) 69.0 (112.0) 60.0 (129.0) 0.064 Transport-related sitting time (min/d), (n = 4100) 60.0 (73.0) 60.0 (71.0) 60.0 (74.0) <0.001 Eating habits Fruit intake (times per week), (n = 5198) 7.0 (3.0) 7.0 (3.0) 7.0 (3.0) <0.001 Vegetables intake (times per week), (n = 5253) 7.0 (2.0) 7.0 (1.0) 7.0 (2.0) <0.001 Fish intake (times per week), (n = 5187) 0.5 (1.5) 0.5 (1.5) 0.5 (1.5) 0.116 Fast-food intake (times per week), (n = 4803) 0.5 (0) 0.5 (0) 0.5 (0) 0.213 Sweets intake (times per week), (n = 5149) 3.0 (5.5) 3.0 (5.5) 3.0 (4.5) 0.004 Sugar-sweetened beverages consumption (times per week), (n = 5073) 2.0 (5.5) 2.0 (5.5) 2.0 (6.5) 0.349 Alcohol consumption (times per week), (n = 5011) 2.0 (5.5) 3.0 (5.5) 2.0 (5.5) 0.487 Sleep duration (hours/night), (n = 5269) 7.0 (1.5) 7.0 (1.5) 7.0 (2.0) <0.001 Environmental factors Urban region, (n = 5295) Ghent region 1651 (31.2) 850 (29.7) 801 (32.9) <0.001 Greater Paris 737 (13.9) 455 (15.9) 282 (11.6) Greater Budapest 824 (15.5) 386 (13.5) 438 (18.0) Randstad 1412 (26.7) 804 (28.1) 608 (25.0) Greater London 671 (12.7) 367 (12.8) 304 (12.5) Neighborhood type, (n = 5223) HSES/HRD 1269 (24.3) 725 (25.7) 544 (22.5) <0.001 LSES/HRD 1230 (23.5) 635 (22.5) 595 (24.8) HSES/LRD 1325 (25.4) 764 (27.1) 561 (23.5) LSES/LRD 1399 (26.8) 699 (24.7) 700 (29.2) Roda et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:114 Page 5 of 12 retained among several variables related to sedentary time. The greater importance of TV viewing has been previously suggested in cross-sectional studies [37–39]. Given the lack of evidence from prospective studies, the issue of bidirectional or reverse causality has been raised [40]. In the Nurses’ Health study, each 2 h/d increment in TV watching was associated with a 23 % [17–30 %] increased risk of obesity. However, the risk of developing obesity was attenuated after adjustment for baseline BMI [5]. These findings may suggest that, even at base- line, women who watched more TV were already on a trajectory to become obese [5]. Heavier individuals at baseline could have a preference for sedentary habits due to their higher body weight. TV viewing is not only an indicator of sedentary behavior but may represent a potential surrogate of other behaviors affecting the energy balance e.g., via increased snack- ing behavior [7, 41]. Table 1 Characteristics of the overall study population and according to weight status in the SPOTLIGHT study (Continued) Neighborhood cluster, (n = 4618) Green neighborhood with LRD 3022 (65.6) 1588 (63.2) 1434 (68.1) 0.001 Neighborhood supportive of active mobility 1150 (24.9) 648 (25.8) 502 (23.9) HRD neighborhood with food and recreational facilities 265 (5.7) 162 (6.4) 103 (4.9) HRD neighborhood with low level of aesthetics 181 (3.9) 115 (4.6) 66 (3.1) Abbreviations: BMI body mass index, H- high-, IQR interquartile range, L- low-, RD residential density, SD standard deviation, SES socio-economic status aNon-overweight: BMI <25 kg/m2 bOverweight: BMI ≥25 kg/m2 †p-value from Chi-squared or Kruskal-Wallis test comparing overweight and non-overweight subjects Boldface indicates statistical significance Fig. 1 Recursive partitioning analysis (CART) of lifestyle-related behaviors for overweight status in SPOTLIGHT study (N = 5295). In dark grey are the identified subgroups with overweight prevalence above 50 %, and in light grey, those with overweight prevalence below 50 %. OR [95 %], odds ratios and confidence intervals at 95 % for each partitioning variable obtained by multilevel logistic regression model (dependent variable: overweight [yes/no], independent variables: partitioning variable identified by CART, gender, age, education, neighborhood type, and neighbor- hood identifier included as a random effect) are also provided. Abbreviations: h/n hours per night, min/d minutes per day, t/w times per week Roda et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:114 Page 6 of 12 Tab le 2 Pro files o f th e sub g ro up s id en tified b y recursive p artitio n in g an alysis (C A RT) in th e SPO TLIG H T stud y N = 5295,n (% ) o r m ed ian (IQ R) Sub g ro up 1 n = 315 (5.9) Sub g ro up 2 n = 1400 (26.4) Sub g ro up 3 n = 353 (6.7) Sub g ro up 4 n = 497 (9.4) Sub g ro up 5 n = 545 (10.3) Sub g ro up 6 n = 360 (6.8) Sub g ro up 7 n = 268 (5.1) Sub g ro up 8 n = 243 (4.6) Sub g ro up 9 n = 638 (12.0) Sub g ro up 10 n = 676 (12.8) p † So cio -d em o g rap h ic ch aracteristics G end er M ale 133 (42.5) 550 (39.5) 122 (34.8) 201 (40.5) 251 (46.5) 165 (46.3) 121 (45.3) 136 (57.1) 242 (38.7) 395 (59.2) < 0.001 Fem ale 180 (57.5) 842 (60.5) 229 (65.2) 295 (59.5) 289 (53.5) 191 (53.7) 146 (54.7) 102 (42.9) 384 (61.3) 272 (40.8) A g e (in years) 38.0 (20.0) a 47.0 (24.0) a,b 49.0 (25.5) a,c 52.0 (27.0) a,b ,d 51.5 (25.0) a,b ,e 60.5 (22.0) a,b ,c,d ,e ,f 45.0 (21.0) a,d ,e ,f,g 53.0 (24.0) a,b ,g ,h 57.0 (24.0) a,b ,c,d ,g ,i 63.0 (18.0) a,b ,c,d ,e ,g ,h ,i < 0.001 Ed ucatio n H ig h level 243 (78.4) 893 (65.3) 209 (60.6) 251 (51.3) 344 (63.9) 134 (37.6) 128 (50.2) 139 (58.4) 228 (36.1) 235 (35.5) < 0.001 Lo w level 67 (21.6) 474 (34.7) 136 (39.4) 238 (48.7) 194 (36.1) 222 (62.4) 127 (49.8) 99 (41.6) 403 (63.9) 427 (64.5) O verw eig h t 63 (20.0) 482 (34.4) 148 (41.9) 220 (44.3) 243 (44.6) 170 (47.2) 146 (54.5) 142 (58.4) 377 (59.1) 442 (65.4) < 0.001 BM I(kg /m 2) 22.3 (4.0) a 23.5 (4.9) a,b 24.0 (5.1) a,c 24.5 (5.4) a,b ,d 24.6 (4.9) a,b ,e 24.7 (5.3) a,b ,f 25.3 (5.2) a,b ,c,e ,g 25.5 (6.0) a,b ,c,d ,e 25.8 (6.1) a,b ,c,d ,e ,f,g ,h 26.7 (5.5) a,b ,c,d ,e ,f,g ,h < 0.001 Lifestyle-related b eh avio rs To b acco sm o ke status N o sm o ker 262 (83.4) 1 189 (85.7) 279 (79.7) 403 (81.9) 0 263 (74.7) 194 (74.3) 0 459 (73.2) 0 < 0.001 Fo rm er 0 0 0 0 545 (100) 0 0 243 (100) 0 676 (100) C urren t 52 (16.6) 198 (14.3) 71 (20.3) 89 (18.1) 0 89 (25.3) 67 (25.7) 0 168 (26.8) 0 Ph ysicalactivity Transport-related physicalactivity (m in/d) 26.0 (53.0) a 29.0 (52.0) b 13.0 (40.0) a,b ,c 26.0 (53.0) c,d 26.0 (55.0) c,e 77.0 (94.0) a,b ,c,d ,e ,f 9.0 (30.0) a,b ,d ,e ,f,g 20.0 (51.0) c,f,g ,h 19.0 (51.0) b ,c,f,g ,i 36.0 (93.0) b ,c,e ,f,g ,h ,i < 0.001 Leisure-tim e p h ysicalactivity (m in /d ) 26.0 (40.0) a 36.0 (39.0) a,b 0 (4.0) a,b ,c 26.0 (45.0) b ,c,d 26.0 (42.0) b ,c,e 86.0 (60.0) a,b ,c,d ,e ,f 0(4.0) a,b ,d ,e ,f,g 17.0 (53.0) b ,c,f,g ,h 9.0 (20.0) a,b ,c,d ,e ,f,g ,h ,i 26.0 (54.0) b ,c,d ,f,g ,i < 0.001 D om ain -sp ecific sed entary b eh avio rs Televisio n tim e (m in /d ) 0 (13.0) a 94.0 (60.0) a,b 90.0 (60.0) a,c 167.0 (26.0) a,b ,c,d 94.0 (60.0) a,d ,e 257.0 (120.0) a,b ,c,d ,e ,f 94.0 (60.0) a,d ,f,g 86.5 (64.0) a,d ,f,h 257.0 (120.0) a,b ,c,d ,e ,g ,h ,i 219.0 (120.0) a,b ,c,d ,e ,f,g ,i < 0.001 C o m p uter tim e (m in /d ) 77.0 (136.5) a 91.2 (89.3) a,b 60.0 (90.0) a,c 77.0 (94.0) b ,c,d 60.0 (81.0) a,e 129.0 (14.6) a,b ,c,d ,e ,f 77.0 (98.0) b ,c,e ,f,g 77.0 (73.5) b ,c,e ,f,h 120.0 (146.0) a,b ,c,d ,e ,g ,h 103.0 (120.0) a,b ,c,d ,e ,f,g …