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Effect of community-based lifestyle interventions on weight loss and cardiometabolic risk factors in obese elderly in China: A randomized controlled trial
Experimental Gerontology 128 (2019) 110749
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Experimental Gerontology journal homepage: www.elsevier.com/locate/expgero
Effect of community-based lifestyle interventions on weight loss and cardiometabolic risk factors in obese elderly in China: A randomized controlled trial
T
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Ruixue Cai, Jianqian Chao , Dan Li, Man Zhang, Lingyan Kong, Yingpeng Wang Key Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
A R T I C LE I N FO
A B S T R A C T
Section Editor: Holly M Brown-Borg
Purpose: We aimed to assess the effect of community-based lifestyle interventions on weight loss and cardiometabolic risk factors among obese older adults, and to explore the potential factors that impede weight loss during lifestyle interventions. Materials and methods: A 2-arm parallel randomized controlled trial was conducted from 2013 through 2016 in the community health service centers in Nanjing, China. Four hundred and eighty obese older adults were randomly assigned to receive a 24-month lifestyle intervention (242 participants) or usual care (238 participants). The intervention group received a community-based behavioral lifestyle intervention program, which targeted weight loss through dietary changes and increased physical activity, with a combination mode of intervention delivery. Results: Weight loss was statistically significant at the end of the intervention with a mean reduction of 0.03 ± 2.51 kg in the control group and 3.22 ± 3.43 kg in the intervention group (p < .001). In the intervention group, 41.1% of participants achieved the target of 5% weight loss significantly (p < .001). Participants in the intervention group had significantly greater improvements in cardiometabolic risk factors. Multivariable logistic regression showed that female, living alone, and having more comorbidities were barriers to weight loss during the intervention. Conclusions: This study demonstrated that community-based lifestyle interventions are effective for managing weight and improving cardiometabolic risk factors in obese older adults.
Keywords: Older adults Lifestyle intervention Obesity Weight-loss Cardiometabolic risk factors
1. Introduction The prevalence of overweight and obesity is high and is increasing rapidly worldwide (Di Cesare et al., 2016; Ng et al., 2014). Nowadays, obesity affects over 771 million global population, and 1.30 billion population are in the level of overweight (Ezzati et al., 2017). Obesity is a major public health concern not only in many high-income countries, but also in China. China has the largest number of people affected by obesity in the world, with approximately 42% of adults and 16% of children being obese or overweight (Wang et al., 2017). The prevalence of overweight and obesity has increased steadily over the past 20 years, surpassing that of many developed countries. Obesity is particularly prevalent among older people. In the USA, 68.6% of population aged 60 years and older were overweight or obese (BMI ≥ 25) and 30.5% were obese in 2006 (Houston et al., 2009), while the prevalence of obesity reached 41.0% in 2016 (Hales et al.,
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2017). One of key drivers is the sharply growing number of older people worldwide in the recent years. Population of older adults (≥60 years) worldwide has reached 962 million and is expected to double by 2050. Particularly, China has more older adults (65 years and older) than any other country (Zhao et al., 2014) due to the large population base and the rapidly growing percentage of the aging population. The convergence of the increase in both the global elderly population and in the prevalence of obesity itself contributes to the major issue of obesity in the elderly. The rising pandemic of obesity indicates that, worldwide, populations are experiencing an increasingly burden of chronic health conditions. Obesity is highly associated with higher risk of many health problems and diseases, including diabetes (Kodama et al., 2014), cardiovascular disease (Lu et al., 2014), and cancer (Arnold et al., 2015). People, especially older people, who are obese have a higher mortality rather than those who are not obese (Di Angelantonio et al., 2016;
Corresponding author at: School of Public Health, Southeast University, NO.87, Dingjiaqiao Road, Gulou District, Nanjing City, Jiangsu Province, China. E-mail address: [email protected] (J. Chao).
https://doi.org/10.1016/j.exger.2019.110749 Received 23 April 2019; Received in revised form 29 September 2019; Accepted 4 October 2019 Available online 20 October 2019 0531-5565/ © 2019 Elsevier Inc. All rights reserved.
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Afshin et al., 2017). Additionally, among older adults, obesity is associated with cognitive impairment (Xiang and An, 2015), functional limitation (Dowd and Zajacova, 2015), impaired mental conditions (Luppino et al., 2010), and economic burden (Agha and Agha, 2017). Weight loss can alleviate or reverse many of these problems (Ryan and Yockey, 2017), but weight loss is hard for people to achieve. Bariatric surgery has proven to be effective in people with severe obesity (Lee and Cha, 2016; Fisher et al., 2018); however, the low operation ratio of bariatric surgery makes it less feasible to solve the rapidly growing prevalence of obesity. Moreover, due to the increased risk of bariatric surgery and longer hospital stays in the elderly, obese older individuals need to undergo rigorous evaluation to determine their suitability for surgery (Batsis and Dolkart, 2015). Therefore, obese older people need more safer ways to lose weight, stay healthy, and prevent related diseases. Lifestyle interventions for weight loss among adults who are obese have been proven to result in a meaningful weight loss (5% or more of the initial body weight) and a decreased rate of diabetes (LeBlanc et al., 2018; Waters et al., 2013). However, the benefit in regard to other related health problems, such as cardiometabolic risk factors, is less clear. For older adults, the important subpopulation, there remains a lack of evidence regarding the appropriate therapeutic approach. Furthermore, relatively little attention has been paid to the issue of weight management among this population due to the complexity of the topic. Therefore, the purpose of this study is to assess the effect of community-based lifestyle interventions on weight loss and cardiometabolic risk factor reduction in obese older adults and to explore the factors that may be barriers to weight loss during lifestyle interventions.
Fig. 1. Flow diagram of the progress of the randomized controlled trial.
through dietary changes and increased physical activity, with a combination mode of intervention delivery. The mixed delivery mode including group-based and individualbased interventions, was used to support weight loss. The group-based intervention provided classroom-style sessions for 2 h every two weeks in the first 12 months and every month from month 13 through month 24 to impart health knowledge by the clinicians in communities. These sessions included not only basic health knowledge, but also specific guidance regarding physical activity and diet. The individual-based intervention offered health evaluation, individualized counseling sessions with ongoing telephone support, and health promoting materials. The intervention components focused on diet and exercise as well as encouragement of self-monitoring. In terms of diet, participants met with dietitians who instructed the participants on how to modify their diet to achieve their weight loss goals. Individual advice was given, which included intake of appropriate energy; reduction of pickled food, high-fat food and high-sugar food; and inclusion of more cereal, vegetables and fruits. In addition, participants were provided Dietary Guidelines for Chinese Residents and food scales. The physical activity intervention included two aspects: more moderate exercise and less sedentary behavior. A tailored exercise program based on an earlier evaluation was implemented to increase physical activity. Participants were instructed to perform moderateintensity exercise for at least 150 min per week (e.g., walking, cycling), as recommended by the WHO (2010). Moreover, community clinicians gave sessions to help participants recognize the hazards of prolonged sedentary behavior (Vella et al., 2018; Rosique-Esteban et al., 2018) and encouraged participants to reduce their sitting time. Some materials and tools were provided to participants in the intervention group to enhance their health self-management ability, including health behavior handouts for elderly individuals developed by our research group, and a self-monitoring card that records information about weight, waist circumference, daily diet and physical activity.
2. Materials and methods 2.1. Study design and participants A 2-arm, 24-month parallel randomized controlled trial (RCT) was conducted in the community health service centers in Nanjing, China. All participants were recruited from the urban communities in Nanjing between September and December 2013 by screening individuals to identify older adults (aged ≥60 years) with obesity. The inclusion criteria for the subjects were as follows: aged 60 years and over; obese, defined as body mass index greater than or equal to 28 kg/m2, using the Chinese BMI classification standard developed by the WGOC (Chen and Lu, 2004); and local permanent resident. The exclusion criteria were: cognitive defects, severe psychological disorders or mental illnesses that could affected adherence to the study; cancer, recent cardiovascular disease and other severe chronic diseases that could seriously reduce the ability to participate in the study; participating in or had participated in other trials within the past 30 days (Nakanishi et al., 1995). A total of 480 eligible older adults were randomly assigned to receive a 24-month lifestyle intervention (intervention group) or usual care (control group) at a 1:1 ratio using a random number table. Flow diagram of the progress of the randomized controlled trial is shown in Fig. 1. The study was reviewed and approved by the Ethics Committee of Clinical Research of Zhongda Hospital, Affiliated to Southeast University. All participants provided written informed consent before enrolment. 2.2. Intervention Participants in the control group received usual care, including a two-hour education session every two months to impart basic health knowledge, such as the dangers of obesity and the benefits of lifestyle changes. The intervention group participated in a community-based behavioral lifestyle intervention program, which targeted weight loss
2.3. Outcomes/measurements All measurements were conducted by trained data collectors using the same equipment and standard protocols at baseline, 18 months, and 2
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24 months. Self-reported sociodemographic characteristics (i.e., sex, age, education, resident status) and comorbidity status were collected at the beginning of the study. The primary outcome measure was weight change at 24 months post baseline. Weight and height (in meters) were measured while the participants were without shoes on a calibrated digital body weight scale. The proportion of individuals who achieved clinically significant weight loss of ≥5% from baseline was also examined as a target. The secondary outcomes were cardiometabolic risk factors, including waist circumference, blood pressure, fasting blood sugar, total cholesterol, triglycerides, HDL cholesterol, and LDL cholesterol. As an indicator of abdominal adiposity, waist circumference (in centimeters) was measured using a non-stretchable tape with the participants in a standardized standing position. Blood pressure was measured on the right upper arm with participants in the sitting position using a mercury sphygmomanometer. Blood pressure was measured three times, and the average value was used as the outcome. Fasting blood sugar, total cholesterol, triglycerides, HDL cholesterol and LDL cholesterol were measured by the clinical test center of the hospital.
Table 1 General characteristics of the intervention and control groups at baseline.
2.4. Statistical analysis Chi-square tests and t-tests were used to compare the general characteristics between the intervention and control groups before the intervention. A chi-square test was used for categorical variables, and a t-test was used for normally distributed continuous variables. Linear mixed-effect models were used to compare changes between groups and group × time, while the generalized estimating equation models were used for categorical variables. A multivariable logistic regression analysis was conducted to examine the associative factors affecting the effect of the intervention. Significant variables (p < .05) that remained in the final model were reported. Regression results are presented as odds ratios (ORs) and 95% confidence intervals (CIs). All analyses were performed using SPSS 19.0 (SPSS Inc., Chicago, IL, USA), and a pvalue < .05 indicated statistical significance. This study was designed to have 90% power (α = 0.05) to detect a 5% difference in weight change at month 24, allowing for 15% attrition; therefore, 238 participants per group (476 total participants) were required.
Variables
Intervention group
Control group
p
Men, n(%) Age, years Education, n(%) Primary school or lower Middle school College or higher Resident status, n(%) Living alone Living with spouse Living with children Number of comorbidities, n(%) 0 1–2 ≥3 Weight, kg Waist circumference, cm BMI, kg/m2 Systolic blood pressure, mm Hg Diastolic blood pressure, mm Hg Fasting blood glucose, mmol/L Total cholesterol, mmol/L Triglycerides, mmol/L HDL cholesterol, mmol/L LDL cholesterol, mmol/L
121 (50.0) 66.84 ± 5.32
102 (42.9) 66.86 ± 4.73
0.117 0.975 0.905
40 (16.5) 167 (69.0) 35 (14.5)
43 (18.1) 161 (67.6) 34 (14.3)
27 (11.2) 189 (78.1) 26 (10.7)
27 (11.3) 189 (79.4) 22 (9.2)
102 (42.1) 120 (49.6) 20 (8.3) 78.65 ± 5.47 88.23 ± 5.31 30.01 ± 1.77 135.19 ± 16.45 79.57 ± 9.42 6.11 ± 0.94 4.97 ± 0.88 1.69 ± 0.64 1.12 ± 0.20 3.30 ± 0.79
88 (37.0) 133 (55.9) 17 (7.1) 78.35 ± 5.17 88.42 ± 6.18 30.12 ± 1.81 135.64 ± 17.31 79.48 ± 10.20 6.05 ± 1.10 5.01 ± 0.90 1.70 ± 0.72 1.14 ± 0.17 3.35 ± 0.87
0.861
0.385
0.543 0.726 0.503 0.768 0.918 0.509 0.620 0.859 0.250 0.536
Data are presented as the mean ± SD, and n (%).
than or equal to their baseline weight and the percentages of participants who had lost 5% or more of their baseline weight. A 5% weight loss through the intervention was one of the targets set. A higher number of participants reached this goal in the interventional group than in the control group at 24 months (41.1% vs 13.9%), and the difference was significant (p < .001). 3.3. Changes in cardiometabolic risk factors between groups Table 3 shows changes in cardiometabolic risk factors between groups. Participants who received community-based lifestyle intervention had significantly greater improvements in waist circumference, systolic blood pressure, triglycerides and HDL cholesterol levels at month 24 than participants in the control group (p < .05). There was no significant measurable change in diastolic blood pressure, fasting blood glucose, total cholesterol and LDL cholesterol levels at month 24 between the intervention group and the control group. Significant group × time interactions for waist circumference, systolic blood pressure, triglycerides and HDL cholesterol were found.
3. Results 3.1. Baseline characteristics of the study participants The baseline characteristics of the intervention and control groups are shown in Table 1. The mean age ( ± SD) was 66.84 ± 5.32 years and 66.86 ± 4.73 years, with a mean body weight of 78.65 ± 5.47 kg and 78.35 ± 5.17 kg and a mean BMI of 30.01 ± 1.77 and 30.12 ± 1.81 (intervention and control group, respectively). The differences in demographics or characteristics contributing to outcomes between the intervention and the control groups were not statistically significant. Approximately 88.43% and 87.39% of the participants in the intervention and control groups, respectively, completed the assessment at 24 months. The main reasons for missed visits at the 24-month assessment were moving, hospitalization, withdrawal and death.
3.4. Factors affecting the effect of the intervention Multivariable logistic regression was used to analyze the factors associated with the effect of the intervention. Whether the participant reached the target achievement of 5% weight loss was used as the dependent variable. The independent variables included sex, age, education, resident status, comorbidity status, and adjustment for BMI at baseline. The results of the multivariable logistic regression analysis are presented in Table 4. The obese older adults in the intervention group who were female, living alone, and having more comorbidities were less responsive to the intervention (p < .05).
3.2. Weight loss Body weight changes were assessed at 18 and 24 months (Table 2). Participants in the intervention group lost a mean of 3.22 ± 3.43 kg, and those in the control group lost a mean of 0.03 ± 2.51 kg at month 24. The difference in weight loss between groups was statistically significant (p < .001). Significant group × time interactions for weight loss were found. Fig. 2 shows the categorical weight loss among the participants: the percentages of participants whose weight at 18 and 24 months was less
4. Discussion The principal finding of this study was that the 24-month community-based lifestyle interventions resulted in a significant weight loss in obese older adults. This loss, which was accompanied by ameliorations in cardiometabolic risk factors, was achieved via the combination of moderate dietary restriction, increased physical activity and reduced 3
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Table 2 Changes in weight between groups. Variables
Change in weight, kg Month 18 Month 24 Change in weight, % Month 18 Month 24 Weight ≤ baseline, n(%) Month 18 Month 24 Weight loss ≥5%, n(%) Month 18 Month 24
Intervention group
Control group
p value Group
Group × time
−2.48 ± 2.94 −3.22 ± 3.43
−0.02 ± 2.29 −0.03 ± 2.51
< 0.001 < 0.001
< 0.001
−0.03 ± 0.04 −0.04 ± 0.04
0.00 ± 0.03 0.00 ± 0.03
< 0.001 < 0.001
< 0.001
161 (75.2) 164 (76.6)
84 (40.4) 77 (37.0)
< 0.001 < 0.001
0.052
72 (33.6) 88 (41.1)
11 (5.3) 29 (13.9)
< 0.001 < 0.001
0.145
Data are presented as the mean ± SD and n (%).
Better understanding of the factors that affect lifestyle intervention efficacy in older adults is necessary to make the strategy comprehensively in this population. A systematic review reported that poor motivation, lack of time, complications of obesity or other chronic diseases, negative emotions, and lack of pleasantness of exercise act as barriers to lifestyle change (Burgess et al., 2017). Our results show that being female, living alone, and having more comorbidities were barriers to weight loss during the intervention. Therefore, older adults with these characteristics should be given more attention and careful consideration when designing and conducting the intervention strategy. A limitation of the present study is that measurements of muscle and bone density were lacking, so we could not identify the potential adverse effects of our study. Additionally, our study did not evaluate the actual dietary intake, exercise type, or energy expenditure, and these factors can influence the results. Further research is needed to clarify the effectiveness of different diets and physical activity strategies on overweight or obese older adults.
sedentary behavior. Evidence-based data to guide the behavioral treatment of obese older adults is limited (Batsis et al., 2017; Locher et al., 2016) and tends to focus on severe obese adults. Several randomized controlled clinical trials have emphasized the beneficial effects of dietary interventions (Normandin et al., 2015; O'Connor et al., 2018), some studies have focused on physical activity interventions (Villareal et al., 2017; Matson et al., 2018), and others have paid attention to the interventions combining diet and activity (Nicklas et al., 2015; Goodpaster et al., 2010). All of the interventions have demonstrated the efficacy of lifestyle interventions that aim to improve diet, physical activity, or a combination of both for reducing body weight and providing positive effects on physical function and other health outcomes among older adults. A weight loss of at least 3% to 10% of the initial body weight was selected as the primary intervention object among the obese population in many RCTs (Goodpaster et al., 2010; Wing, 2004; Wadden et al., 2011). In our study, 41.4% of participants in the intervention group accomplished the target of losing 5% or more of their initial body weight, which is a widespread standard for clinically significant weight loss recommended by the US Food and Drug Administration (LeBlanc et al., 2018). This is in accordance with other studies that have reported clinically significant weight loss in obese older individuals (Villareal et al., 2006). The results of this study indicate that the intervention facilitated general improvement in cardiometabolic risk factors. Participants in the intervention group experienced significant changes in waist circumference, systolic blood pressure, triglycerides and HDL cholesterol at month 24. Moreover, no significant changes in diastolic blood pressure, fasting blood glucose, total cholesterol and LDL cholesterol were observed. These findings were consistent with the results of previous randomized controlled clinical trials (Salas-Salvado et al., 2018; Sato et al., 2007).
5. Conclusion Community-based lifestyle interventions can lead to clinically significant and meaningful weight loss and improvements in cardiometabolic risk factors among obese older adults. These findings indicate that our nonsurgical approach of modifying dietary habits, increasing physical activity and reducing sedentary behavior is feasible and effective for geriatric obese persons.
Funding This work was supported by National Natural Science Foundation of China [Grant number 81872711 and 81273189].
Fig. 2. Categorical weight loss. 4
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Table 3 Changes in cardiovascular risk factors. Variables
Intervention group
Waist circumference, cm Month 18 Month 24 Systolic blood pressure, mm Hg Month 18 Month 24 Diastolic blood pressure, mm Hg Month 18 Month 24 Fasting blood glucose, mmol/L Month 18 Month 24 Total cholesterol, mmol/L Month 18 Month 24 Triglycerides, mmol/L Month 18 Month 24 HDL cholesterol, mmol/L Month 18 Month 24 LDL cholesterol, mmol/L Month 18 Month 24
Control group
p value Group
Group × time
< 0.001 < 0.001
< 0.001
−2.64 ± 4.47 −3.81 ± 5.72
−0.13 ± 3.29 −0.07 ± 3.53
−5.10 ± 12.66 −6.84 ± 12.97
−2.25 ± 13.64 −3.51 ± 14.70
0.027 0.014
0.010
0.97 ± 9.10 1.29 ± 8.76
1.95 ± 9.17 1.53 ± 11.11
0.268 0.805
0.930
−0.08 ± 1.04 −0.16 ± 1.05
−0.01 ± 0.99 −0.05 ± 1.01
0.478 0.269
0.210
0.06 ± 0.63 0.01 ± 0.75
−0.04 ± 0.97 −0.01 ± 1.02
0.235 0.801
0.808
−0.04 ± 0.49 −0.16 ± 0.40
−0.00 ± 0.81 −0.02 ± 0.80
0.547 0.022
0.007
0.11 ± 0.21 0.13 ± 0.20
0.06 ± 0.30 0.08 ± 0.29
0.089 0.037
0.034
−0.01 ± 0.85 −0.03 ± 0.89
−0.02 ± 0.74 −0.02 ± 0.84
0.894 0.951
0.902
Data are presented as the mean ± SD. Di Angelantonio, E., Bhupathiraju, S. N., Wormser, D., Gao, P., Kaptoge, S., de Gonzalez, A. B., … Global, B. M. I. M. C. (2016). Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet, 388(10046), 776–786. doi:https://doi.org/10.1016/s0140-6736(16) 30175-1. Di Cesare, M., Bentham, J., Stevens, G.A., Zhou, B., Danaei, G., Lu, Y., ... NCD, N. C. D. R. F. C, 2016. 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 387 (10026), 1377–1396. Dowd, J.B., Zajacova, A., 2015. Long-term obesity and physical functioning in older Americans. Int. J. Obes. 39 (3), 502–507. https://doi.org/10.1038/ijo.2014.150. Ezzati, M., Bentham, J., Di Cesare, M., Bilano, V., Bixby, H., Zhou, B., ... RisC, N.C.D., 2017. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet 390 (10113), 2627–2642. https://doi.org/10.1016/s0140-6736(17)32129-3. Fisher, D.P., Johnson, E., Haneuse, S., Arterburn, D., Coleman, K.J., O'Connor, P.J., ... Sidney, S., 2018. Association between bariatric surgery and macrovascular disease outcomes in patients with type 2 diabetes and severe obesity. JAMA 320 (15), 1570–1582. https://doi.org/10.1001/jama.2018.14619. Goodpaster, B.H., DeLany, J.P., Otto, A.D., Kuller, L., Vockley, J., South-Paul, J.E., ... Jakicic, J.M., 2010. Effects of diet and physical activity interventions on weight loss and cardiometabolic risk factors in severely obese adults a randomized trial. JAMA 304 (16), 1795–1802. https://doi.org/10.1001/jama.2010.1505. Hales, C.M., Carroll, M.D., Fryar, C.D., Ogden, C.L., 2017. Prevalence of obesity among adults and youth: United States, 2015–2016. NCHS Data Brief (288), 1–8. Houston, D.K., Nicklas, B.J., Zizza, C.A., 2009. Weighty concerns: the growing prevalence of obesity among older adults. J. Am. Diet. Assoc. 109 (11), 1886–1895. https://doi. org/10.1016/j.jada.2009.08.014. Kodama, S., Horikawa, C., Fujihara, K., Yoshizawa, S., Yachi, Y., Tanaka, S., ... Sone, H., 2014. Quantitative relationship between body weight gain in adulthood and incident type 2 diabetes: a meta-analysis. Obes. Rev. 15 (3), 202–214. https://doi.org/10. 1111/obr.12129. LeBlanc, E.S., Patnode, C.D., Webber, E.M., Redmond, N., Rushkin, M., O'Connor, E.A., 2018. Behavioral and pharmacotherapy weight loss interventions to prevent obesityrelated morbidity and mortality in adults updated evidence report and systematic review for the US preventive services task force. JAMA 320 (11), 1172–1191. https:// doi.org/10.1001/jama.2018.7777. Lee, G.K., Cha, Y.M., 2016. Cardiovascular benefits of bariatric surgery. Trends Cardiovasc. Med. 26 (3), 280–289. https://doi.org/10.1016/j.tcm.2015.07.006. Locher, J.L., Goldsby, T.U., Goss, A.M., Kilgore, M.L., Gower, B., Ard, J.D., 2016. Calorie restriction in overweight older adults: do benefits exceed potential risks? Exp. Gerontol. 86, 4–13. https://doi.org/10.1016/j.exger.2016.03.009. Lu, Y., Hajifathalian, K., Ezzati, M., Woodward, M., Rimm, E.B., Danaei, G., Global Burden Metab Risk Factors, C, 2014. Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: a pooled analysis of 97 prospective cohorts with 1.8 million participants. Lancet 383 (9921), 970–983. https://doi.org/10.1016/s0140-6736(13)61836-x. Luppino, F.S., de Wit, L.M., Bouvy, P.F., Stijnen, T., Cuijpers, P., Penninx, B., Zitman, F.G.,
Table 4 Multivariable logistic regression analysis of factors associated with the effect of the intervention. Variables
Group
Sex
Male Female Living alone Living with spouse Living with children 0 1–2 ≥3
Resident status
Number of comorbidities
Wald
p
OR
12.86
< 0.001
2.54
95% CI
0.111
1.00 0.32 1.00 2.27
0.83–6.23
4.95
0.026
4.09
1.18–14.18
0.19 4.49
0.665 0.034
1.00 1.14 0.23
0.62–2.10 0.06–0.90
0.17–0.60
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Experimental Gerontology journal homepage: www.elsevier.com/locate/expgero
Effect of community-based lifestyle interventions on weight loss and cardiometabolic risk factors in obese elderly in China: A randomized controlled trial
T
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Ruixue Cai, Jianqian Chao , Dan Li, Man Zhang, Lingyan Kong, Yingpeng Wang Key Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
A R T I C LE I N FO
A B S T R A C T
Section Editor: Holly M Brown-Borg
Purpose: We aimed to assess the effect of community-based lifestyle interventions on weight loss and cardiometabolic risk factors among obese older adults, and to explore the potential factors that impede weight loss during lifestyle interventions. Materials and methods: A 2-arm parallel randomized controlled trial was conducted from 2013 through 2016 in the community health service centers in Nanjing, China. Four hundred and eighty obese older adults were randomly assigned to receive a 24-month lifestyle intervention (242 participants) or usual care (238 participants). The intervention group received a community-based behavioral lifestyle intervention program, which targeted weight loss through dietary changes and increased physical activity, with a combination mode of intervention delivery. Results: Weight loss was statistically significant at the end of the intervention with a mean reduction of 0.03 ± 2.51 kg in the control group and 3.22 ± 3.43 kg in the intervention group (p < .001). In the intervention group, 41.1% of participants achieved the target of 5% weight loss significantly (p < .001). Participants in the intervention group had significantly greater improvements in cardiometabolic risk factors. Multivariable logistic regression showed that female, living alone, and having more comorbidities were barriers to weight loss during the intervention. Conclusions: This study demonstrated that community-based lifestyle interventions are effective for managing weight and improving cardiometabolic risk factors in obese older adults.
Keywords: Older adults Lifestyle intervention Obesity Weight-loss Cardiometabolic risk factors
1. Introduction The prevalence of overweight and obesity is high and is increasing rapidly worldwide (Di Cesare et al., 2016; Ng et al., 2014). Nowadays, obesity affects over 771 million global population, and 1.30 billion population are in the level of overweight (Ezzati et al., 2017). Obesity is a major public health concern not only in many high-income countries, but also in China. China has the largest number of people affected by obesity in the world, with approximately 42% of adults and 16% of children being obese or overweight (Wang et al., 2017). The prevalence of overweight and obesity has increased steadily over the past 20 years, surpassing that of many developed countries. Obesity is particularly prevalent among older people. In the USA, 68.6% of population aged 60 years and older were overweight or obese (BMI ≥ 25) and 30.5% were obese in 2006 (Houston et al., 2009), while the prevalence of obesity reached 41.0% in 2016 (Hales et al.,
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2017). One of key drivers is the sharply growing number of older people worldwide in the recent years. Population of older adults (≥60 years) worldwide has reached 962 million and is expected to double by 2050. Particularly, China has more older adults (65 years and older) than any other country (Zhao et al., 2014) due to the large population base and the rapidly growing percentage of the aging population. The convergence of the increase in both the global elderly population and in the prevalence of obesity itself contributes to the major issue of obesity in the elderly. The rising pandemic of obesity indicates that, worldwide, populations are experiencing an increasingly burden of chronic health conditions. Obesity is highly associated with higher risk of many health problems and diseases, including diabetes (Kodama et al., 2014), cardiovascular disease (Lu et al., 2014), and cancer (Arnold et al., 2015). People, especially older people, who are obese have a higher mortality rather than those who are not obese (Di Angelantonio et al., 2016;
Corresponding author at: School of Public Health, Southeast University, NO.87, Dingjiaqiao Road, Gulou District, Nanjing City, Jiangsu Province, China. E-mail address: [email protected] (J. Chao).
https://doi.org/10.1016/j.exger.2019.110749 Received 23 April 2019; Received in revised form 29 September 2019; Accepted 4 October 2019 Available online 20 October 2019 0531-5565/ © 2019 Elsevier Inc. All rights reserved.
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Afshin et al., 2017). Additionally, among older adults, obesity is associated with cognitive impairment (Xiang and An, 2015), functional limitation (Dowd and Zajacova, 2015), impaired mental conditions (Luppino et al., 2010), and economic burden (Agha and Agha, 2017). Weight loss can alleviate or reverse many of these problems (Ryan and Yockey, 2017), but weight loss is hard for people to achieve. Bariatric surgery has proven to be effective in people with severe obesity (Lee and Cha, 2016; Fisher et al., 2018); however, the low operation ratio of bariatric surgery makes it less feasible to solve the rapidly growing prevalence of obesity. Moreover, due to the increased risk of bariatric surgery and longer hospital stays in the elderly, obese older individuals need to undergo rigorous evaluation to determine their suitability for surgery (Batsis and Dolkart, 2015). Therefore, obese older people need more safer ways to lose weight, stay healthy, and prevent related diseases. Lifestyle interventions for weight loss among adults who are obese have been proven to result in a meaningful weight loss (5% or more of the initial body weight) and a decreased rate of diabetes (LeBlanc et al., 2018; Waters et al., 2013). However, the benefit in regard to other related health problems, such as cardiometabolic risk factors, is less clear. For older adults, the important subpopulation, there remains a lack of evidence regarding the appropriate therapeutic approach. Furthermore, relatively little attention has been paid to the issue of weight management among this population due to the complexity of the topic. Therefore, the purpose of this study is to assess the effect of community-based lifestyle interventions on weight loss and cardiometabolic risk factor reduction in obese older adults and to explore the factors that may be barriers to weight loss during lifestyle interventions.
Fig. 1. Flow diagram of the progress of the randomized controlled trial.
through dietary changes and increased physical activity, with a combination mode of intervention delivery. The mixed delivery mode including group-based and individualbased interventions, was used to support weight loss. The group-based intervention provided classroom-style sessions for 2 h every two weeks in the first 12 months and every month from month 13 through month 24 to impart health knowledge by the clinicians in communities. These sessions included not only basic health knowledge, but also specific guidance regarding physical activity and diet. The individual-based intervention offered health evaluation, individualized counseling sessions with ongoing telephone support, and health promoting materials. The intervention components focused on diet and exercise as well as encouragement of self-monitoring. In terms of diet, participants met with dietitians who instructed the participants on how to modify their diet to achieve their weight loss goals. Individual advice was given, which included intake of appropriate energy; reduction of pickled food, high-fat food and high-sugar food; and inclusion of more cereal, vegetables and fruits. In addition, participants were provided Dietary Guidelines for Chinese Residents and food scales. The physical activity intervention included two aspects: more moderate exercise and less sedentary behavior. A tailored exercise program based on an earlier evaluation was implemented to increase physical activity. Participants were instructed to perform moderateintensity exercise for at least 150 min per week (e.g., walking, cycling), as recommended by the WHO (2010). Moreover, community clinicians gave sessions to help participants recognize the hazards of prolonged sedentary behavior (Vella et al., 2018; Rosique-Esteban et al., 2018) and encouraged participants to reduce their sitting time. Some materials and tools were provided to participants in the intervention group to enhance their health self-management ability, including health behavior handouts for elderly individuals developed by our research group, and a self-monitoring card that records information about weight, waist circumference, daily diet and physical activity.
2. Materials and methods 2.1. Study design and participants A 2-arm, 24-month parallel randomized controlled trial (RCT) was conducted in the community health service centers in Nanjing, China. All participants were recruited from the urban communities in Nanjing between September and December 2013 by screening individuals to identify older adults (aged ≥60 years) with obesity. The inclusion criteria for the subjects were as follows: aged 60 years and over; obese, defined as body mass index greater than or equal to 28 kg/m2, using the Chinese BMI classification standard developed by the WGOC (Chen and Lu, 2004); and local permanent resident. The exclusion criteria were: cognitive defects, severe psychological disorders or mental illnesses that could affected adherence to the study; cancer, recent cardiovascular disease and other severe chronic diseases that could seriously reduce the ability to participate in the study; participating in or had participated in other trials within the past 30 days (Nakanishi et al., 1995). A total of 480 eligible older adults were randomly assigned to receive a 24-month lifestyle intervention (intervention group) or usual care (control group) at a 1:1 ratio using a random number table. Flow diagram of the progress of the randomized controlled trial is shown in Fig. 1. The study was reviewed and approved by the Ethics Committee of Clinical Research of Zhongda Hospital, Affiliated to Southeast University. All participants provided written informed consent before enrolment. 2.2. Intervention Participants in the control group received usual care, including a two-hour education session every two months to impart basic health knowledge, such as the dangers of obesity and the benefits of lifestyle changes. The intervention group participated in a community-based behavioral lifestyle intervention program, which targeted weight loss
2.3. Outcomes/measurements All measurements were conducted by trained data collectors using the same equipment and standard protocols at baseline, 18 months, and 2
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24 months. Self-reported sociodemographic characteristics (i.e., sex, age, education, resident status) and comorbidity status were collected at the beginning of the study. The primary outcome measure was weight change at 24 months post baseline. Weight and height (in meters) were measured while the participants were without shoes on a calibrated digital body weight scale. The proportion of individuals who achieved clinically significant weight loss of ≥5% from baseline was also examined as a target. The secondary outcomes were cardiometabolic risk factors, including waist circumference, blood pressure, fasting blood sugar, total cholesterol, triglycerides, HDL cholesterol, and LDL cholesterol. As an indicator of abdominal adiposity, waist circumference (in centimeters) was measured using a non-stretchable tape with the participants in a standardized standing position. Blood pressure was measured on the right upper arm with participants in the sitting position using a mercury sphygmomanometer. Blood pressure was measured three times, and the average value was used as the outcome. Fasting blood sugar, total cholesterol, triglycerides, HDL cholesterol and LDL cholesterol were measured by the clinical test center of the hospital.
Table 1 General characteristics of the intervention and control groups at baseline.
2.4. Statistical analysis Chi-square tests and t-tests were used to compare the general characteristics between the intervention and control groups before the intervention. A chi-square test was used for categorical variables, and a t-test was used for normally distributed continuous variables. Linear mixed-effect models were used to compare changes between groups and group × time, while the generalized estimating equation models were used for categorical variables. A multivariable logistic regression analysis was conducted to examine the associative factors affecting the effect of the intervention. Significant variables (p < .05) that remained in the final model were reported. Regression results are presented as odds ratios (ORs) and 95% confidence intervals (CIs). All analyses were performed using SPSS 19.0 (SPSS Inc., Chicago, IL, USA), and a pvalue < .05 indicated statistical significance. This study was designed to have 90% power (α = 0.05) to detect a 5% difference in weight change at month 24, allowing for 15% attrition; therefore, 238 participants per group (476 total participants) were required.
Variables
Intervention group
Control group
p
Men, n(%) Age, years Education, n(%) Primary school or lower Middle school College or higher Resident status, n(%) Living alone Living with spouse Living with children Number of comorbidities, n(%) 0 1–2 ≥3 Weight, kg Waist circumference, cm BMI, kg/m2 Systolic blood pressure, mm Hg Diastolic blood pressure, mm Hg Fasting blood glucose, mmol/L Total cholesterol, mmol/L Triglycerides, mmol/L HDL cholesterol, mmol/L LDL cholesterol, mmol/L
121 (50.0) 66.84 ± 5.32
102 (42.9) 66.86 ± 4.73
0.117 0.975 0.905
40 (16.5) 167 (69.0) 35 (14.5)
43 (18.1) 161 (67.6) 34 (14.3)
27 (11.2) 189 (78.1) 26 (10.7)
27 (11.3) 189 (79.4) 22 (9.2)
102 (42.1) 120 (49.6) 20 (8.3) 78.65 ± 5.47 88.23 ± 5.31 30.01 ± 1.77 135.19 ± 16.45 79.57 ± 9.42 6.11 ± 0.94 4.97 ± 0.88 1.69 ± 0.64 1.12 ± 0.20 3.30 ± 0.79
88 (37.0) 133 (55.9) 17 (7.1) 78.35 ± 5.17 88.42 ± 6.18 30.12 ± 1.81 135.64 ± 17.31 79.48 ± 10.20 6.05 ± 1.10 5.01 ± 0.90 1.70 ± 0.72 1.14 ± 0.17 3.35 ± 0.87
0.861
0.385
0.543 0.726 0.503 0.768 0.918 0.509 0.620 0.859 0.250 0.536
Data are presented as the mean ± SD, and n (%).
than or equal to their baseline weight and the percentages of participants who had lost 5% or more of their baseline weight. A 5% weight loss through the intervention was one of the targets set. A higher number of participants reached this goal in the interventional group than in the control group at 24 months (41.1% vs 13.9%), and the difference was significant (p < .001). 3.3. Changes in cardiometabolic risk factors between groups Table 3 shows changes in cardiometabolic risk factors between groups. Participants who received community-based lifestyle intervention had significantly greater improvements in waist circumference, systolic blood pressure, triglycerides and HDL cholesterol levels at month 24 than participants in the control group (p < .05). There was no significant measurable change in diastolic blood pressure, fasting blood glucose, total cholesterol and LDL cholesterol levels at month 24 between the intervention group and the control group. Significant group × time interactions for waist circumference, systolic blood pressure, triglycerides and HDL cholesterol were found.
3. Results 3.1. Baseline characteristics of the study participants The baseline characteristics of the intervention and control groups are shown in Table 1. The mean age ( ± SD) was 66.84 ± 5.32 years and 66.86 ± 4.73 years, with a mean body weight of 78.65 ± 5.47 kg and 78.35 ± 5.17 kg and a mean BMI of 30.01 ± 1.77 and 30.12 ± 1.81 (intervention and control group, respectively). The differences in demographics or characteristics contributing to outcomes between the intervention and the control groups were not statistically significant. Approximately 88.43% and 87.39% of the participants in the intervention and control groups, respectively, completed the assessment at 24 months. The main reasons for missed visits at the 24-month assessment were moving, hospitalization, withdrawal and death.
3.4. Factors affecting the effect of the intervention Multivariable logistic regression was used to analyze the factors associated with the effect of the intervention. Whether the participant reached the target achievement of 5% weight loss was used as the dependent variable. The independent variables included sex, age, education, resident status, comorbidity status, and adjustment for BMI at baseline. The results of the multivariable logistic regression analysis are presented in Table 4. The obese older adults in the intervention group who were female, living alone, and having more comorbidities were less responsive to the intervention (p < .05).
3.2. Weight loss Body weight changes were assessed at 18 and 24 months (Table 2). Participants in the intervention group lost a mean of 3.22 ± 3.43 kg, and those in the control group lost a mean of 0.03 ± 2.51 kg at month 24. The difference in weight loss between groups was statistically significant (p < .001). Significant group × time interactions for weight loss were found. Fig. 2 shows the categorical weight loss among the participants: the percentages of participants whose weight at 18 and 24 months was less
4. Discussion The principal finding of this study was that the 24-month community-based lifestyle interventions resulted in a significant weight loss in obese older adults. This loss, which was accompanied by ameliorations in cardiometabolic risk factors, was achieved via the combination of moderate dietary restriction, increased physical activity and reduced 3
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Table 2 Changes in weight between groups. Variables
Change in weight, kg Month 18 Month 24 Change in weight, % Month 18 Month 24 Weight ≤ baseline, n(%) Month 18 Month 24 Weight loss ≥5%, n(%) Month 18 Month 24
Intervention group
Control group
p value Group
Group × time
−2.48 ± 2.94 −3.22 ± 3.43
−0.02 ± 2.29 −0.03 ± 2.51
< 0.001 < 0.001
< 0.001
−0.03 ± 0.04 −0.04 ± 0.04
0.00 ± 0.03 0.00 ± 0.03
< 0.001 < 0.001
< 0.001
161 (75.2) 164 (76.6)
84 (40.4) 77 (37.0)
< 0.001 < 0.001
0.052
72 (33.6) 88 (41.1)
11 (5.3) 29 (13.9)
< 0.001 < 0.001
0.145
Data are presented as the mean ± SD and n (%).
Better understanding of the factors that affect lifestyle intervention efficacy in older adults is necessary to make the strategy comprehensively in this population. A systematic review reported that poor motivation, lack of time, complications of obesity or other chronic diseases, negative emotions, and lack of pleasantness of exercise act as barriers to lifestyle change (Burgess et al., 2017). Our results show that being female, living alone, and having more comorbidities were barriers to weight loss during the intervention. Therefore, older adults with these characteristics should be given more attention and careful consideration when designing and conducting the intervention strategy. A limitation of the present study is that measurements of muscle and bone density were lacking, so we could not identify the potential adverse effects of our study. Additionally, our study did not evaluate the actual dietary intake, exercise type, or energy expenditure, and these factors can influence the results. Further research is needed to clarify the effectiveness of different diets and physical activity strategies on overweight or obese older adults.
sedentary behavior. Evidence-based data to guide the behavioral treatment of obese older adults is limited (Batsis et al., 2017; Locher et al., 2016) and tends to focus on severe obese adults. Several randomized controlled clinical trials have emphasized the beneficial effects of dietary interventions (Normandin et al., 2015; O'Connor et al., 2018), some studies have focused on physical activity interventions (Villareal et al., 2017; Matson et al., 2018), and others have paid attention to the interventions combining diet and activity (Nicklas et al., 2015; Goodpaster et al., 2010). All of the interventions have demonstrated the efficacy of lifestyle interventions that aim to improve diet, physical activity, or a combination of both for reducing body weight and providing positive effects on physical function and other health outcomes among older adults. A weight loss of at least 3% to 10% of the initial body weight was selected as the primary intervention object among the obese population in many RCTs (Goodpaster et al., 2010; Wing, 2004; Wadden et al., 2011). In our study, 41.4% of participants in the intervention group accomplished the target of losing 5% or more of their initial body weight, which is a widespread standard for clinically significant weight loss recommended by the US Food and Drug Administration (LeBlanc et al., 2018). This is in accordance with other studies that have reported clinically significant weight loss in obese older individuals (Villareal et al., 2006). The results of this study indicate that the intervention facilitated general improvement in cardiometabolic risk factors. Participants in the intervention group experienced significant changes in waist circumference, systolic blood pressure, triglycerides and HDL cholesterol at month 24. Moreover, no significant changes in diastolic blood pressure, fasting blood glucose, total cholesterol and LDL cholesterol were observed. These findings were consistent with the results of previous randomized controlled clinical trials (Salas-Salvado et al., 2018; Sato et al., 2007).
5. Conclusion Community-based lifestyle interventions can lead to clinically significant and meaningful weight loss and improvements in cardiometabolic risk factors among obese older adults. These findings indicate that our nonsurgical approach of modifying dietary habits, increasing physical activity and reducing sedentary behavior is feasible and effective for geriatric obese persons.
Funding This work was supported by National Natural Science Foundation of China [Grant number 81872711 and 81273189].
Fig. 2. Categorical weight loss. 4
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Table 3 Changes in cardiovascular risk factors. Variables
Intervention group
Waist circumference, cm Month 18 Month 24 Systolic blood pressure, mm Hg Month 18 Month 24 Diastolic blood pressure, mm Hg Month 18 Month 24 Fasting blood glucose, mmol/L Month 18 Month 24 Total cholesterol, mmol/L Month 18 Month 24 Triglycerides, mmol/L Month 18 Month 24 HDL cholesterol, mmol/L Month 18 Month 24 LDL cholesterol, mmol/L Month 18 Month 24
Control group
p value Group
Group × time
< 0.001 < 0.001
< 0.001
−2.64 ± 4.47 −3.81 ± 5.72
−0.13 ± 3.29 −0.07 ± 3.53
−5.10 ± 12.66 −6.84 ± 12.97
−2.25 ± 13.64 −3.51 ± 14.70
0.027 0.014
0.010
0.97 ± 9.10 1.29 ± 8.76
1.95 ± 9.17 1.53 ± 11.11
0.268 0.805
0.930
−0.08 ± 1.04 −0.16 ± 1.05
−0.01 ± 0.99 −0.05 ± 1.01
0.478 0.269
0.210
0.06 ± 0.63 0.01 ± 0.75
−0.04 ± 0.97 −0.01 ± 1.02
0.235 0.801
0.808
−0.04 ± 0.49 −0.16 ± 0.40
−0.00 ± 0.81 −0.02 ± 0.80
0.547 0.022
0.007
0.11 ± 0.21 0.13 ± 0.20
0.06 ± 0.30 0.08 ± 0.29
0.089 0.037
0.034
−0.01 ± 0.85 −0.03 ± 0.89
−0.02 ± 0.74 −0.02 ± 0.84
0.894 0.951
0.902
Data are presented as the mean ± SD. Di Angelantonio, E., Bhupathiraju, S. N., Wormser, D., Gao, P., Kaptoge, S., de Gonzalez, A. B., … Global, B. M. I. M. C. (2016). Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet, 388(10046), 776–786. doi:https://doi.org/10.1016/s0140-6736(16) 30175-1. Di Cesare, M., Bentham, J., Stevens, G.A., Zhou, B., Danaei, G., Lu, Y., ... NCD, N. C. D. R. F. C, 2016. 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 387 (10026), 1377–1396. Dowd, J.B., Zajacova, A., 2015. Long-term obesity and physical functioning in older Americans. Int. J. Obes. 39 (3), 502–507. https://doi.org/10.1038/ijo.2014.150. Ezzati, M., Bentham, J., Di Cesare, M., Bilano, V., Bixby, H., Zhou, B., ... RisC, N.C.D., 2017. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet 390 (10113), 2627–2642. https://doi.org/10.1016/s0140-6736(17)32129-3. Fisher, D.P., Johnson, E., Haneuse, S., Arterburn, D., Coleman, K.J., O'Connor, P.J., ... Sidney, S., 2018. Association between bariatric surgery and macrovascular disease outcomes in patients with type 2 diabetes and severe obesity. JAMA 320 (15), 1570–1582. https://doi.org/10.1001/jama.2018.14619. Goodpaster, B.H., DeLany, J.P., Otto, A.D., Kuller, L., Vockley, J., South-Paul, J.E., ... Jakicic, J.M., 2010. Effects of diet and physical activity interventions on weight loss and cardiometabolic risk factors in severely obese adults a randomized trial. JAMA 304 (16), 1795–1802. https://doi.org/10.1001/jama.2010.1505. Hales, C.M., Carroll, M.D., Fryar, C.D., Ogden, C.L., 2017. Prevalence of obesity among adults and youth: United States, 2015–2016. NCHS Data Brief (288), 1–8. Houston, D.K., Nicklas, B.J., Zizza, C.A., 2009. Weighty concerns: the growing prevalence of obesity among older adults. J. Am. Diet. Assoc. 109 (11), 1886–1895. https://doi. org/10.1016/j.jada.2009.08.014. Kodama, S., Horikawa, C., Fujihara, K., Yoshizawa, S., Yachi, Y., Tanaka, S., ... Sone, H., 2014. Quantitative relationship between body weight gain in adulthood and incident type 2 diabetes: a meta-analysis. Obes. Rev. 15 (3), 202–214. https://doi.org/10. 1111/obr.12129. LeBlanc, E.S., Patnode, C.D., Webber, E.M., Redmond, N., Rushkin, M., O'Connor, E.A., 2018. Behavioral and pharmacotherapy weight loss interventions to prevent obesityrelated morbidity and mortality in adults updated evidence report and systematic review for the US preventive services task force. JAMA 320 (11), 1172–1191. https:// doi.org/10.1001/jama.2018.7777. Lee, G.K., Cha, Y.M., 2016. Cardiovascular benefits of bariatric surgery. Trends Cardiovasc. Med. 26 (3), 280–289. https://doi.org/10.1016/j.tcm.2015.07.006. Locher, J.L., Goldsby, T.U., Goss, A.M., Kilgore, M.L., Gower, B., Ard, J.D., 2016. Calorie restriction in overweight older adults: do benefits exceed potential risks? Exp. Gerontol. 86, 4–13. https://doi.org/10.1016/j.exger.2016.03.009. Lu, Y., Hajifathalian, K., Ezzati, M., Woodward, M., Rimm, E.B., Danaei, G., Global Burden Metab Risk Factors, C, 2014. Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: a pooled analysis of 97 prospective cohorts with 1.8 million participants. Lancet 383 (9921), 970–983. https://doi.org/10.1016/s0140-6736(13)61836-x. Luppino, F.S., de Wit, L.M., Bouvy, P.F., Stijnen, T., Cuijpers, P., Penninx, B., Zitman, F.G.,
Table 4 Multivariable logistic regression analysis of factors associated with the effect of the intervention. Variables
Group
Sex
Male Female Living alone Living with spouse Living with children 0 1–2 ≥3
Resident status
Number of comorbidities
Wald
p
OR
12.86
< 0.001
2.54
95% CI
0.111
1.00 0.32 1.00 2.27
0.83–6.23
4.95
0.026
4.09
1.18–14.18
0.19 4.49
0.665 0.034
1.00 1.14 0.23
0.62–2.10 0.06–0.90
0.17–0.60
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