1. Introduction
In the United States, about one in five people live in a designated rural area [1]. Adults living in rural communities exhibit elevated rates of obesity compared to urban residents at 39.6% and 33.4%, respectively [1,2]. In the state of Ohio, 23.7% of the population is rural, and 40.8% of rural Ohioans have obesity [3]. As adiposity is linked to the most pervasive chronic diseases and is a risk factor for many of the most prevalent cancer types [4,5,6,7,8,9], it is not surprising that cancer incidence and mortality are disproportionately higher in rural areas compared to non-rural [10]. In fact, although cancer incidence and mortality have been declining nationally, this trend has not been observed in rural populations for lung, prostate, breast, and colorectal cancers [10,11]. Consequently, it is essential to investigate how to engage rural communities to ameliorate these trends through innovative and evidence-based translational research efforts targeting modifiable determinants of lifestyle behavior change.
The factors contributing to rural health disparities are complex. Evidence suggests that individuals in rural areas engage in more disease-risk behaviors including physical inactivity, unhealthy diet, and lower rates of vaccination and cancer screening [12]. Social determinants of health such as access to quality health care and health literacy represent additional barriers to the successful self-management of one’s health in rural areas. Furthermore, access to in-person, community-based weight management programming remains limited. For example, the national Diabetes Prevention Program has been implemented across the U.S.; however, less than 15% of rural counties had access to the program, compared to 48% in urban counties [13].
Indeed, although the effectiveness of lifestyle weight management interventions has long been established, studies have primarily been conducted in controlled settings with participants recruited from urban areas. To date, few research studies have reported the effects of fully remote, telephone-based lifestyle weight management counseling interventions in rural-dwelling adults with overweight and obesity. Of the relevant extant trials, individual and group telephone counseling have been compared [14], as well as individual telephone versus group face-to-face counseling [15], and group telephone versus individual and group face-to-face counseling implemented in primary care clinics [16]. Overall, considerable heterogeneity in scientific methodology limits the conclusions that can be made regarding the effect of telephone counseling alone on weight loss in rural adults. Alternatively, telephone counseling has been extensively evaluated in extended-care trials of weight loss maintenance with results suggesting that both individual and group-based telephone counseling may be efficacious methods of mitigating weight regain following primary intervention [15,17]. However, all of the trials to date have focused on the outcome of weight loss with few trials reporting changes in other clinically relevant outcomes such as glucose and lipids [14,15,16,17]. Critically, no other studies in overweight or obese rural adults have reported effects of telephone counseling intervention on change in body composition or inflammatory markers.
Given the disparities in overweight and obesity, significant barriers to accessing weight management programs, and dearth of scientific study in rural populations, it is important to examine alternative approaches to delivering and evaluating lifestyle weight management interventions. Telephone-based counseling is one method that may increase reach and scalability to better serve rural populations. Therefore, we conducted the Home-based Exercise in Rural Ohio (HERO) study. HERO was a randomized feasibility trial of a 15-week individual telephone-based behavioral counseling intervention versus an active control condition among overweight or obese adults living in rural Ohio counties. We used a point-of-care assessment approach to collect data on all study outcomes. In this paper, we report on the observed changes in body weight, body composition, blood lipids, and inflammatory markers. We hypothesized that the 15-week intervention would yield improvements in these outcomes. Results from this study will be used to subsequently optimize the intervention approach for development of a future large-scale comparative efficacy study.
2. Materials and Methods
2.1. Participants
The HERO study was a pilot study using a single-blind, two-group randomized controlled design. The objective of the pilot study was to establish the feasibility of conducting a 15-week lifestyle intervention by weekly phone calls for rural individuals who had overweight/obesity. Detailed study methods and feasibility were previously published [18]. Briefly, we used Facebook advertising, flyers, and a list of participants from a previous study to recruit participants. Participants were eligible if they: lived in a rural Ohio county; BMI ≥ 25 kg/m2; age between 20 and 64 years; not in any weight loss intervention or reach physical activity guidelines; able to walk two city blocks; and able to speak and read English. Individuals who had a prior cancer diagnosis or severe medical conditions that would preclude physical activity and dietary modifications, or who were pregnant or nursing, or were unable to give informed consent were excluded from the study. Individuals who reported health conditions were asked to obtain medical clearance from their doctors before the initial in-person visit.
After completing the online screening survey, eligible individuals were scheduled for the initial in-person visit at the nearest participating local community center for informed consent and the baseline assessment. Using computer-generated random allocation sequence, 40 participants were 2:1 randomized to (1) lifestyle intervention group or (2) active control group. The project manager was the only staff who had access to the group allocation, and who assigned participants according to their allocation. The study was approved by The Ohio State University Institutional Review Board (2021C0033) and registered on ClinicalTrial.gov as NCT05040152. All participants provided written informed consent prior to any study activities.
2.2. Intervention
Details of the theory-based lifestyle intervention, including the behavior change techniques and mechanisms of action can be found elsewhere [18]. Participants randomized to the lifestyle intervention group received health counseling sessions via phone calls (30–45 min/session, delivered by health coaches who were exercise physiologists or had experience with behavioral intervention) for 15 weeks. In each session, the health coach provided instructions for healthy eating and increasing physical activity. The targeted lifestyle modifications consisted of (1) a gradually progressed reduction in caloric intake (500–1000 kcal/day) to reach an individualized caloric goal (1200–1800 kcal/day); and (2) simultaneously increasing both aerobic physical activity and resistance exercise to meet the physical activity guidelines [19]. Health coaches also provided recommendations for participants to meet dietary guidelines through MyPlate (
Individuals in the active control group were provided brochures with the American Institute of Cancer Research (AICR) physical activity and dietary guidelines during the initial in-person visit [21]. Participants were encouraged to meet the physical activity and dietary guidelines. After completing the 15-week measures, the active control group received the identical lifestyle modification manual that had been initially provided to the lifestyle intervention group at baseline.
2.3. Measurement
Baseline and 15-week measurements were obtained by trained staff members who were blinded to group assignment. Demographic characteristics including age, sex, and race–ethnicity were self-reported. Physical activity was objectively measured using the activPALTM micro activity monitor (PAL Technologies Ltd., Glasgow, Scotland, UK). Participants were provided verbal and written instructions for wearing the activPALTM. They were asked to wear the device 24 h per day for seven consecutive days after each in-person assessment.
2.4. Anthropometric Assessment and Body Composition
Body weight (kg) and height (cm) were assessed using a digital scale and scale-mounted stadiometer to calculate BMI (kg/m2). Body composition, including fat mass (lb), percent fat, lean mass (lb), percent lean mass, as well as visceral and subcutaneous fat mass (lb), was measured using a three-dimensional (3D) body scanner, Styku S100 (Styku L.L.C., Los Angeles, CA, USA). The Styku S100 scanner is a portable body scanner, which offers advantages for measuring body composition in rural and field study settings. Styku S100 scanner uses an infrared camera built into a stationary tower and a 360-degree rotating platform to capture body circumference and volumes. Using proprietary formulas, Styku software (version 4.3) calculates body fat percentage, lean mass percentage, and bone mass among other body composition measures, all of which are reliable measures compared with dual-energy X-ray absorptiometry [22].
2.5. Lipid Profile
Participants underwent fasting (≥8 h) blood sampling for lipid profiles. Prior to blood draw, urine specific gravity (USG) was assessed by a light refractometer (Reichert™, Buffalo, NY, USA). In instances where USG > 1.025, participants were instructed to consume a minimum of 250 mL of water until they achieved proper hydration. Lipid profiles including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were acquired by fingerstick and analyzed using Cholestech LDX (Hayward, CA, USA). The Cholestech LDX has previously established good accuracy and precision in estimating whole-blood lipids and was used reliably as a point-of-care assessment for blood analyses in ambulatory settings [23,24].
2.6. Inflammatory Markers
Blood drawings (fasting ≥ 8 h) were performed by a trained phlebotomist after the finger stick, using antecubital puncture via butterfly needle (21G BD Eppendorf, Swedesboro, NJ, USA). Venous blood was collected in plasma EDTA (BD Vacutainer®, 10 mL, Swedesboro, NJ, USA), inverted 8 times, and centrifuged immediately after the blood draw (1500× g, 10 min, 4 °C). Plasma was aliquoted into 1000 μL screwcap cryovials and stored at −80 °C in a portable freezer until arrival to the laboratory center. C-reactive protein (CRP, mg/L), Interleukin-6 (IL-6; pg/mL), and Tumor Necrosis Factor alpha (TNF-α; pg/mL) were later assayed using the Meso QuickPlex SQ 120 sandwich immunoassay-VPlex Kit (Meso Scale Discovery, Rockville, MD, USA). For CRP, the lower limits of detection (LLD) was 1.33 mg/L; intra- and inter-assay coefficients were 2.9% and 7.9%, respectively. For IL-6, the LLD was 0.06 pg/mL; intra- and inter-assay coefficients were 4% and 6.4%, respectively. For TNF-α, the LLD was 0.04 pg/mL; intra- and inter-assay coefficients were 2.8% and 7.9%, respectively.
2.7. Statistical Analysis
For descriptive statistics at baseline, counts and proportions were reported for categorical variables and means ± standard deviations were reported for continuous variables. All inferential analyses were conducted on an intention-to-treat basis. Changes in body weight, body composition (fat mass, %fat, lean mass, %lean), lipids (TC, TG, HDL-C, LDL-C), and inflammatory markers (CRP, IL-6, TNF-α) were calculated from baseline to 15 weeks in the two groups using linear mixed-effects regression models. This statistical approach included all available data and accounted for the correlation between repeated measures [25]. Interaction terms between group and time were computed as fixed-effects in the regression model. We report estimates with least-squares mean (LS Mean) ± standard error (SE) based on this preliminary data to inform future studies. The level of significance was p < 0.05. The statistical analyses were performed from January to March 2023 using Stata version 17.0 (StataCorp, LLC., College Station, TX, USA).
3. Results
A total of 40 participants were recruited between November and December, 2021, and completed intervention and final data collection between March and May 2022 (Figure 1) [18]. Baseline characteristics of study participants are presented in Table 1. Briefly, 35 of 40 participants (88%) completed the 15-week online survey. Among participants from the lifestyle intervention group, 22 out of 27 (82%) completed the 15-week intervention. Body composition and biomarker data at 15-week follow-up were available on 19 of 27 (70.4%) participants in the lifestyle intervention group and 9 of 13 (69.2%) participants in the active control. No adverse events were reported.
Anthropometric and body composition outcomes are presented in Table 2. Compared with the control group, the lifestyle intervention group lost 2.84 ± 1.93 kg weight (Figure 2, p = 0.14) and reduced BMI by 0.85 ± 0.71 kg/m2 (p = 0.23). Specifically, participants in the lifestyle intervention lost 4.93 kg body weight (p < 0.001) and reduced BMI by 1.56 kg/m2 (p < 0.001), whereas participants in the control group did not have significant changes in body weight or BMI. Among participants who completed the 15-week body composition assessment, eight (42%) participants from the lifestyle intervention group attained clinically significant weight loss (≥5%), and 2 participants (22%) from the active control group attained ≥ 5% weight loss. There were no significant changes in fat mass, percent body fat, lean mass, percent lean, visceral fat mass, and subcutaneous fat mass in either group (Table 2).
Fasting lipids and inflammatory biomarker outcomes are presented in Table 3. Comparing the lifestyle intervention to the control group, we observed between-group differences in total cholesterol (Figure 3, −18.70 ± 7.84 mg/dL, p = 0.017) and LDL-C (−17.05 ± 8.12 mg/dL, p = 0.036). Although we observed a large magnitude of the difference in change between groups in triglycerides (−25.04 ± 23.88 mg/dL), it was not statistically significant (p = 0.29). Participants in the lifestyle intervention substantially lowered total cholesterol (−13.97 ± 4.71 mg/dL, p = 0.003), triglycerides (−44.95 ± 13.87 mg/dL, p = 0.001), and HDL-C (−5.50 ± 1.65 mg/dL, p = 0.001), while participants in the control group reduced HDL-C (−6.46 ± 2.19 mg/dL, p = 0.003) and increased LDL-C (16.27 ± 6.58 mg/dL, p = 0.013).
In terms of inflammatory biomarkers, no significant between-group differences in CRP, IL-6, or TNF-α were observed. No significant changes in IL-6 and TNF-α were observed in either group; however, CRP demonstrated a within-group reduction by 1.73 ± 0.89 mg/L (p = 0.052) in the lifestyle intervention and a significant reduction of 2.96 ± 1.20 mg/L (p = 0.014) in the control group.
4. Discussion
Overall, the main aim of the current study was to quantify the intervention effect of a telephone-based lifestyle intervention that combined exercise and dietary modification, in comparison to an active control group, among Ohio residents living in rural counties who had overweight or obesity. Our findings suggest that a 15-week telephone-based lifestyle intervention, compared to the health education control group, can provide weight loss benefit and favorable changes in fasting blood biomarkers, such as total cholesterol.
The telephone-based lifestyle intervention yielded significant weight loss in the intervention group, with an average of 4.9 kg lost between baseline and 15-week follow-up, which was greater than a recent meta-analysis that reported 1.8 kg weight loss in most rural weight loss trials [26]. Furthermore, 42% of participants from the lifestyle intervention group attained clinically significant weight loss (≥5%), which is aligned with results from other studies using similar intervention methods [26].
It is worth emphasizing that both groups had reductions in weight over the duration of the study, with the active control group experiencing a 2.1 kg loss of body weight at 15 weeks. The weight loss in the control group could be explained by them receiving the educational brochure which included physical activity and dietary guidelines. Two participants also acquired personal trainers to help them meet the exercise goals. Although the between-group differences in body weight were insignificant, the magnitude of the weight loss exhibited in our lifestyle intervention group is promising, especially over a short period of 15 weeks. In an urban study among women with higher risk of breast cancer, Hartman and colleagues found that self-monitoring combined with telephone-based counseling yielded significant weight loss, approximately 4.4 kg, over a 6-month study duration compared to 0.8 kg in the control group [27]. The HERO study utilized a similar approach to Hartman et al., but within a shortened timeframe in a rural setting, which demonstrates the potential for implementing a remotely accessible lifestyle intervention with a shorter duration in rural areas to achieve similar weight loss results.
Increased adiposity elevates the risk of cancer by the negative impacts on metabolic and inflammatory processes [6,7,8]. Recognizing the link between obesity and cancer risk, optimal weight loss would be a result of a decrease in fat mass and a concomitant increase or maintenance of lean mass. However, participants in our study did not yield significant changes in any body composition measures. The novel 3D body scanner showed validity and reliability in repeated measures [22]. However, the BMI of our study participants was greater than the samples in the validation studies, which may lead to insensitivity of detecting the changes in this study. A recently published pilot study examined the changes in body composition measured by bioelectrical impedance after a 26-week technology-based intervention with rural older adults [28]. The authors attributed a 10.1-pound average decrease in body weight to a decrease in fat mass and preservation of muscle mass through diet and exercise behavioral modifications. In addition to the smaller magnitude of weight loss, other factors may also explain the lack of changes in body composition in the current study, such as reduction in water weight and the actual magnitude of dietary modification, as well as engagement in the prescribed resistance training. Future studies should consider including various measures to examine how these factors influence changes in body composition from lifestyle interventions. It is also possible that longer intervention duration, including physical activity and dietary modification, may lead to substantial changes in body composition. Due to the short study period, participants were not able to experience significant change. Studies that include longer study duration with multiple assessments of body composition may help us understand when changes in body composition occur and how that aligns with changes in weight, lipids, and other disease factors.
Elevated levels of total cholesterol and triglycerides as well as low levels of HDL are associated with risk of cancers, suggesting that reducing total cholesterol and improving HDL may potentially reduce cancer risk [29]. Our study showed that total cholesterol and triglycerides were significantly reduced among the lifestyle intervention group, which highlights the potential of implementing a similar intervention approach to rural populations to reduce cancer risk.
Another important aspect of reducing obesity-related cancer risk is improved inflammatory biomarkers. Healthy lifestyle, including leisure time physical activity, healthy diet, and weight loss can improve energy balance and may reduce obesity-related inflammation, such as IL-6, TNF-α, and CRP [30,31,32]. Nevertheless, we found that both groups had improvement in CRP, but not in IL-6 or TNF-α. This might be explained by the fact that both groups experienced weight loss at 15 weeks, which may lead to a reduction in systematic inflammation. Further evaluation of the relationship between changes in physical activity, diet intake, and lipid and inflammatory profiles is warranted to help identify the optimal dose and intensity of lifestyle modifications in cancer prevention.
There are several notable strengths in our study. First, this was a randomized controlled pilot study, which by design, balanced the baseline confounding. We also kept staff blinded for the baseline and 15-week assessment to reduce measurement bias. Most importantly, we are, to date, the first study utilizing a portable 3D body scanner for body composition assessment in rural settings. Together with the portable lipid analyzer, we provided a point-of-care approach that was able to show participants their body composition and lipid results at the local study site. This approach may encourage participants to maintain a healthy lifestyle after study completion.
This study has several limitations. Due to the pilot study design, we conducted an exploratory analysis to quantify the intervention effect of a 15-week telephone-based intervention on weight, body composition, fasting blood lipids, and inflammatory biomarkers. We intend to use these findings as pilot data to support the design of a large-scale efficacy trial to demonstrate the metabolic benefits from weight loss interventions in rural populations with overweight/obesity. Therefore, the interpretation of these pilot study findings should be taken with caution. Additionally, participants who were interested and enrolled in the HERO study may not represent the general rural population with overweight/obesity. Moreover, although we established the feasibility of the lifestyle intervention in rural Ohio, we experienced some dropouts (17.5%). Compared to those who completed the study, those who dropped out had a higher BMI at baseline (Table S1). Future research on the development of optimal retention strategies in weight loss interventions for this population is needed. Researchers may consider utilizing group teleconference to improve connection with participants and increase group engagement. To understand the effect of lifestyle intervention on systematic inflammation, future studies may consider extending the duration of the intervention, to allow sufficient time to evaluate the intervention efficacy on inflammation.
In conclusion, our pilot study offers important preliminary data and valuable experience in delivering a telephone-based lifestyle intervention for individuals with overweight/obesity in rural Ohio. Our findings demonstrate that a 15-week telephone-based lifestyle intervention may offer health benefits and reduce cancer-risk factors in rural populations. Future large-scale randomized trials are needed to examine the efficacy of remotely accessible weight loss programs in rural populations, with the goal to reduce obesity-related disparities.
X.Z.—conceptualization, methodology, data curation, investigation, formal analysis, writing—original draft, writing—review and editing, funding acquisition; V.R.D., Z.L.C. and M.K.—methodology, data curation, investigation, resources, writing—original draft; J.B.—data curation, investigation, resources, project administration, writing—review and editing; A.B. and M.L.K.—methodology, data curation, investigation, resources, writing—review and editing; A.B.S.—conceptualization, methodology, formal analysis, writing—review and editing; A.S.F.—supervision, writing—review and editing; E.D.P.—conceptualization, methodology, resources, supervision, writing-original draft, writing—review and editing; B.C.F.—methodology, resources, supervision, writing—review and editing, funding acquisition. All authors have read and agreed to the published version of the manuscript.
This study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of The Ohio State University (Study# 2021C0033, 5 October 2021).
Informed consent was obtained from all subjects involved in the study.
The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research, supporting data are not available.
We acknowledge Justin Brown and Jennifer Ligibel for sharing intervention contents from the Healthy Living program. We appreciate support from the rural community centers (Pike county YMCA, Jackson county Health Department, and Hardin county YMCA). This work could not have been completed without assistance from students from the Exercise Behavioral Medicine Lab at OSU. This research was supported by the Analytical & Development Laboratory, Clinical Research Center/Center for Clinical Research Management of The Ohio State University Wexner Medical Center and The Ohio State University College of Medicine in Columbus, Ohio.
Electra Paskett would like to disclose that she has grant funding for work outside of this project from the Merck Foundation, Genentech, and Pfizer. The authors declare no potential conflict of interest.
Footnotes
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Baseline Characteristics of Participants by Study Group (N = 40).
| Lifestyle Intervention |
Control |
|
|---|---|---|
| Age, years | 49 ± 10 | 51 ± 9 |
| Sex, n (%) | ||
| Female, n (%) | 24 (89%) | 12 (92%) |
| Male, n (%) | 3 (11%) | 1 (8%) |
| BMI, kg/m2 | 36.7 ± 5.82 | 35.8 ± 5.49 |
| Physical Activity, MET-h/day | 33.3 ± 0.93 | 34.05 ± 1.29 |
Anthropometric and body composition outcomes at baseline and change over 15 weeks.
| Baseline | ∆ Baseline to 15-Week | ∆ from Control | ||||
|---|---|---|---|---|---|---|
| Mean ± SD | LS Mean ± SE | p-Value | LS Mean ± SE | p-Value | ||
| Anthropometric Outcomes | ||||||
| Weight (kg) | ||||||
| Control | 96.20 ± 15.58 | −2.09 ± 1.58 | 0.19 | |||
| Intervention | 103.20 ± 18.37 | −4.93 ± 1.12 | <0.001 | −2.84 ± 1.93 | 0.14 | |
| BMI (kg/m2) | ||||||
| Control | 35.77 ± 5.49 | −0.71 ± 0.58 | 0.22 | |||
| Intervention | 36.69 ± 5.82 | −1.56 ± 0.41 | <0.001 | −0.85 ± 0.71 | 0.23 | |
| Waist Circumference (in) | ||||||
| Control | 38.18 ± 5.16 | −0.24 ± 0.79 | 0.76 | |||
| Intervention | 38.98 ± 4.71 | −0.25 ± 0.54 | 0.65 | −0.007 ± 0.96 | 0.99 | |
| Waist-to-hip ratio | ||||||
| Control | 0.90 ± 0.08 | −0.012 ± 0.011 | 0.24 | |||
| Intervention | 0.87 ± 0.06 | −0.001 ± 0.007 | 0.87 | 0.011 ± 0.013 | 0.39 | |
| Body Composition Outcomes | ||||||
| Fat (lb) | ||||||
| Control | 86.89 ± 19.23 | 0.23 ± 3.46 | 0.95 | |||
| Intervention | 92.02 ± 19.36 | 0.11 ± 2.38 | 0.97 | 0.12 ± 4.20 | 0.98 | |
| % Fat | ||||||
| Control | 41.21 ± 4.50 | −0.089 ± 0.84 | 0.92 | |||
| Intervention | 41.33 ± 5.06 | 0.27 ± 0.58 | 0.64 | 0.36 ± 1.02 | 0.73 | |
| Lean (lb) | ||||||
| Control | 117.68 ± 18.36 | 0.71 ± 2.30 | 0.70 | |||
| Intervention | 125.41 ± 29.17 | −1.80 ± 1.59 | 0.13 | −2.50 ± 2.80 | 0.37 | |
| % Lean | ||||||
| Control | 56.28 ± 4.26 | 0.084 ± 0.81 | 0.92 | |||
| Intervention | 56.13 ± 4.91 | −0.27 ± 0.56 | 0.63 | −0.36 ± 0.99 | 0.72 | |
| Visceral fat mass (lb) | ||||||
| Control | 1.77 ± 0.63 | −0.019 ± 0.100 | 0.85 | |||
| Intervention | 1.74 ± 0.44 | 0.039 ± 0.069 | 0.57 | 0.058 ± 0.12 | 0.63 | |
| Subcutaneous fat mass (lb) | ||||||
| Control | 5.34 ± 1.26 | 0.033 ± 0.20 | 0.72 | |||
| Intervention | 5.63 ± 1.06 | 0.015 ± 0.14 | 0.65 | −0.017 ± 0.24 | 0.94 | |
∆: change.
Lipids and inflammatory biomarker outcomes at baseline and change over 15 weeks.
| Baseline | ∆ Baseline to 15-Week | ∆ from Control | ||||
|---|---|---|---|---|---|---|
| Mean ± SD | LS Mean ± SE | p-Value | LS Mean ± SE | p-Value | ||
| Lipids | ||||||
| Total cholesterol (mg/dL) | ||||||
| Control | 191.92 ± 35.37 | 4.73 ± 6.27 | 0.45 | |||
| Intervention | 200.30 ± 32.72 | −13.97 ± 4.71 | 0.003 | −18.70 ± 7.84 | 0.017 | |
| Triglycerides (mg/dL) | ||||||
| Control | 138.75 ± 59.49 | −19.92 ± 19.43 | 0.31 | |||
| Intervention | 168.61 ± 95.50 | −44.95 ± 13.87 | 0.001 | −25.04 ± 23.88 | 0.29 | |
| HDL-C (mg/dL) | ||||||
| Control | 56.08 ± 19.41 | −6.46 ± 2.19 | 0.003 | |||
| Intervention | 50.57 ± 12.46 | −5.50 ± 1.65 | 0.001 | 0.96 ± 2.74 | 0.73 | |
| LDL-C (mg/dL) | ||||||
| Control | 115.75 ± 31.36 | 16.27 ± 6.58 | 0.013 | |||
| Intervention | 116.86 ± 30.58 | −0.77 ± 4.76 | 0.87 | −17.05 ± 8.12 | 0.036 | |
| Inflammatory Biomarkers | ||||||
| CRP (mg/L) | ||||||
| Control | 10.06 ± 6.42 | −2.96 ± 1.20 | 0.014 | |||
| Intervention | 9.82 ± 7.31 | −1.73 ± 0.89 | 0.052 | 1.23 ± 1.49 | 0.41 | |
| IL-6 (pg/mL) | ||||||
| Control | 1.01 ± 0.55 | −0.18 ± 0.15 | 0.25 | |||
| Intervention | 1.13 ± 0.50 | −0.084 ± 0.11 | 0.46 | 0.09 ± 0.19 | 0.63 | |
| TNF-α (pg/mL) | ||||||
| Control | 0.82 ± 0.56 | −0.14 ± 0.11 | 0.20 | |||
| Intervention | 0.84 ± 0.44 | −0.050 ± 0.081 | 0.54 | 0.09 ± 0.14 | 0.49 | |
∆: change.
Supplementary Materials
The following supporting information can be downloaded at:
References
1. Befort, C.A.; Nazir, N.; Perri, M.G. Prevalence of obesity among adults from rural and urban areas of the United States: Findings from NHANES (2005–2008). J. Rural. Health; 2012; 28, pp. 392-397. [DOI: https://dx.doi.org/10.1111/j.1748-0361.2012.00411.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23083085]
2. NCD Risk Factor Collaboration (NCD-RisC). Rising rural body-mass index is the main driver of the global obesity epidemic in adults. Nature; 2019; 569, pp. 260-264. [DOI: https://dx.doi.org/10.1038/s41586-019-1171-x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31068725]
3. US Census Bureau. 2020 Census Urban and Rural Classification and Urban Area Criteria; US Census Bureau: Washington, DC, USA, 2020.
4. Ligibel, J.A.; Alfano, C.M.; Courneya, K.S.; Demark-Wahnefried, W.; Burger, R.A.; Chlebowski, R.T.; Fabian, C.J.; Gucalp, A.; Hershman, D.L.; Hudson, M.M. et al. American Society of Clinical Oncology position statement on obesity and cancer. J. Clin. Oncol.; 2014; 32, 3568. [DOI: https://dx.doi.org/10.1200/JCO.2014.58.4680] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25273035]
5. Hursting, S.D.; DiGiovanni, J.; Dannenberg, A.J.; Azrad, M.; LeRoith, D.; Demark-Wahnefried, W.; Kakarala, M.; Brodie, A.; Berger, N.A. Obesity, energy balance, and cancer: New opportunities for prevention. Cancer Prev. Res.; 2012; 5, pp. 1260-1272. [DOI: https://dx.doi.org/10.1158/1940-6207.CAPR-12-0140] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23034147]
6. Iyengar, N.M.; Hudis, C.A.; Dannenberg, A.J. Obesity and cancer: Local and systemic mechanisms. Annu. Rev. Med.; 2015; 66, pp. 297-309. [DOI: https://dx.doi.org/10.1146/annurev-med-050913-022228]
7. Lauby-Secretan, B.; Scoccianti, C.; Loomis, D.; Grosse, Y.; Bianchini, F.; Straif, K. Body fatness and cancer—Viewpoint of the IARC Working Group. N. Engl. J. Med.; 2016; 375, pp. 794-798. [DOI: https://dx.doi.org/10.1056/NEJMsr1606602]
8. Bray, G.; Kim, K.; Wilding, J. World Obesity Federation. Obesity: A chronic relapsing progressive disease process. A position statement of the World Obesity Federation. Obes. Rev.; 2017; 18, pp. 715-723. [DOI: https://dx.doi.org/10.1111/obr.12551]
9. Bhaskaran, K.; Douglas, I.; Forbes, H.; dos-Santos-Silva, I.; Leon, D.A.; Smeeth, L. Body-mass index and risk of 22 specific cancers: A population-based cohort study of 5–24 million UK adults. Lancet; 2014; 384, pp. 755-765. [DOI: https://dx.doi.org/10.1016/S0140-6736(14)60892-8]
10. Blake, K.D.; Moss, J.L.; Gaysynsky, A.; Srinivasan, S.; Croyle, R.T. Making the Case for Investment in Rural Cancer Control: An Analysis of Rural Cancer Incidence, Mortality, and Funding TrendsMaking the Case for Investment in Rural Cancer Control. Cancer Epidemiol. Biomark. Prev.; 2017; 26, pp. 992-997. [DOI: https://dx.doi.org/10.1158/1055-9965.EPI-17-0092]
11. Lewis-Thames, M.W.; Langston, M.E.; Khan, S.; Han, Y.; Fuzzell, L.; Xu, S.; Moore, J.X. Racial and ethnic differences in rural-urban trends in 5-year survival of patients with lung, prostate, breast, and colorectal cancers: 1975–2011 Surveillance, Epidemiology, and End Results (SEER). JAMA Netw. Open; 2022; 5, e2212246. [DOI: https://dx.doi.org/10.1001/jamanetworkopen.2022.12246]
12. Zahnd, W.E.; James, A.S.; Jenkins, W.D.; Izadi, S.R.; Fogleman, A.J.; Steward, D.E.; Colditz, G.A.; Brard, L. Rural–urban differences in cancer incidence and trends in the United States. Cancer Epidemiol. Biomark. Prev.; 2017; 27, pp. 1265-1274. [DOI: https://dx.doi.org/10.1158/1055-9965.EPI-17-0430] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28751476]
13. Ariel-Donges, A.H.; Gordon, E.L.; Dixon, B.N.; Eastman, A.J.; Bauman, V.; Ross, K.M.; Perri, M.G. Rural/urban disparities in access to the National Diabetes Prevention Program. Transl. Behav. Med.; 2020; 10, pp. 1554-1558. [DOI: https://dx.doi.org/10.1093/tbm/ibz098] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31228199]
14. Befort, C.A.; Donnelly, J.E.; Sullivan, D.K.; Ellerbeck, E.F.; Perri, M.G. Group versus individual phone-based obesity treatment for rural women. Eat Behav.; 2010; 11, pp. 11-17. [DOI: https://dx.doi.org/10.1016/j.eatbeh.2009.08.002] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19962115]
15. Perri, M.G.; Limacher, M.C.; Durning, P.E.; Janicke, D.M.; Lutes, L.D.; Bobroff, L.B.; Dale, M.S.; Daniels, M.J.; Radcliff, T.A.; Martin, A.D. Extended-care programs for weight management in rural communities: The treatment of obesity in underserved rural settings (TOURS) randomized trial. Arch. Intern. Med.; 2008; 168, pp. 2347-2354. [DOI: https://dx.doi.org/10.1001/archinte.168.21.2347]
16. Befort, C.A.; Van Wormer, J.J.; Desouza, C.; Ellerbeck, E.F.; Gajewski, B.; Kimminau, K.S.; Greiner, K.A.; Perri, M.G.; Brown, A.R.; Pathak, R.D. et al. Effect of Behavioral Therapy with In-Clinic or Telephone Group Visits vs In-Clinic Individual Visits on Weight Loss Among Patients with Obesity in Rural Clinical Practice: A Randomized Clinical Trial. JAMA; 2021; 325, pp. 363-372. [DOI: https://dx.doi.org/10.1001/jama.2020.25855]
17. Perri, M.G.; Shankar, M.N.; Daniels, M.J.; Durning, P.E.; Ross, K.M.; Limacher, M.C.; Janicke, D.M.; Martin, A.D.; Dhara, K.; Bobroff, L.B. et al. Effect of Telehealth Extended Care for Maintenance of Weight Loss in Rural US Communities: A Randomized Clinical Trial. JAMA Netw. Open; 2020; 3, e206764. [DOI: https://dx.doi.org/10.1001/jamanetworkopen.2020.6764]
18. Zhang, X.; Chaplow, Z.L.; Bowman, J.; Shoben, A.; Felix, A.S.; DeScenza, V.R.; Kilar, M.; Focht, B.C.; Paskett, E.D. The feasibility of a telephone-based weight loss intervention in rural Ohio: A pilot study. PLoS ONE; 2023; 18, e0282719. [DOI: https://dx.doi.org/10.1371/journal.pone.0282719]
19. Committee PAGA. Physical Activity Guidelines Advisory Committee Scientific Report; US Department of Health and Human Services: Washington, DC, USA, 2018; Volume 2018, pp. F2-F33.
20. Dietary Guidelines for Americans, 2020–2025. U.S. Department of Agriculture and U.S. Department of Health and Human Services; 9th ed. 2020; Available online: https://www.dietaryguidelines.gov/sites/default/files/2020-12/Dietary_Guidelines_for_Americans_2020-2025.pdf (accessed on 10 August 2023).
21. Diet, Nutrition, Physical Activity and Cancer: A Global Perspective. Continuous Update Project Expert Report 2018: World Cancer Research Fund. American Institute for Cancer Research 2018. Available online: https://www.wcrf.org/wp-content/uploads/2021/02/Summary-of-Third-Expert-Report-2018.pdf (accessed on 10 August 2023).
22. Bourgeois, B.; Ng, B.K.; Latimer, D.; Stannard, C.R.; Romeo, L.; Li, X.; Shepherd, J.A.; Heymsfield, S.B. Clinically applicable optical imaging technology for body size and shape analysis: Comparison of systems differing in design. Eur. J. Clin. Nutr.; 2017; 71, pp. 1329-1335. [DOI: https://dx.doi.org/10.1038/ejcn.2017.142]
23. Santee, J. Accuracy and precision of the Cholestech LDX System in monitoring blood lipid levels. Am. J. Health Syst. Pharm.; 2002; 59, pp. 1774-1779. [DOI: https://dx.doi.org/10.1093/ajhp/59.18.1774]
24. Parikh, P.; Mochari, H.; Mosca, L. Clinical utility of a fingerstick technology to identify individuals with abnormal blood lipids and high-sensitivity C-reactive protein levels. Am. J. Health Promot.; 2009; 23, pp. 279-282. [DOI: https://dx.doi.org/10.4278/ajhp.071221140]
25. Fitzmaurice, G.M.; Laird, N.M.; Ware, J.H. Applied Longitudinal Analysis; John Wiley & Sons: Hoboken, NJ, USA, 2012.
26. Porter, G.C.; Laumb, K.; Michaud, T.; Brito, F.; Petreca, D.; Schwieger, G.; Bartee, T.; Yeary, K.H.K.; Estabrooks, P.A. Understanding the impact of rural weight loss interventions: A systematic review and meta-analysis. Obes. Rev.; 2019; 20, pp. 713-724. [DOI: https://dx.doi.org/10.1111/obr.12825] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30633845]
27. Hartman, S.J.; Nelson, S.H.; Cadmus-Bertram, L.A.; Patterson, R.E.; Parker, B.A.; Pierce, J.P. Technology-and phone-based weight loss intervention: Pilot RCT in women at elevated breast cancer risk. Am. J. Prev. Med.; 2016; 51, pp. 714-721. [DOI: https://dx.doi.org/10.1016/j.amepre.2016.06.024] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27593420]
28. Batsis, J.A.; Shirazi, D.; Petersen, C.L.; Roderka, M.N.; Lynch, D.; Jimenez, D.; Cook, S.B. Changes in body composition in older adults after a technology-based weight loss intervention. J. Frailty Aging; 2022; 11, pp. 151-155. [DOI: https://dx.doi.org/10.14283/jfa.2022.15] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35441191]
29. Melvin, J.C.; Holmberg, L.; Rohrmann, S.; Loda, M.; Van Hemelrijck, M. Serum lipid profiles and cancer risk in the context of obesity: Four meta-analyses. J. Cancer Epidemiol.; 2013; 2013, 823849. [DOI: https://dx.doi.org/10.1155/2013/823849]
30. Pedersen, L.R.; Olsen, R.H.; Anholm, C.; Astrup, A.; Eugen-Olsen, J.; Fenger, M.; Simonsen, L.; Walzem, R.L.; Haugaard, S.B.; Prescott, E. Effects of 1 year of exercise training versus combined exercise training and weight loss on body composition, low-grade inflammation and lipids in overweight patients with coronary artery disease: A randomized trial. Cardiovasc. Diabetol.; 2019; 18, 127. [DOI: https://dx.doi.org/10.1186/s12933-019-0934-x]
31. Imayama, I.; Ulrich, C.M.; Alfano, C.M.; Wang, C.; Xiao, L.; Wener, M.H.; Campbell, K.L.; Duggan, C.; Foster-Schubert, K.E.; Kong, A. et al. Effects of a caloric restriction weight loss diet and exercise on inflammatory biomarkers in overweight/obese postmenopausal women: A randomized controlled trial. Cancer Res.; 2012; 72, pp. 2314-2326. [DOI: https://dx.doi.org/10.1158/0008-5472.CAN-11-3092]
32. Fisher, G.; Hyatt, T.C.; Hunter, G.R.; Oster, R.A.; Desmond, R.A.; Gower, B.A. Effect of Diet with and Without Exercise Training on Markers of Inflammation and Fat Distribution in Overweight Women. Obesity; 2011; 19, pp. 1131-1136. [DOI: https://dx.doi.org/10.1038/oby.2010.310]
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; Chaplow, Zachary L 3
; Kilar, Megan 3 ; Bowman, Jessica 3 ; Buga, Alex 3
; Kackley, Madison L 3
; Shoben, Abigail B 4 ; Felix, Ashley S 5 ; Paskett, Electra D 6 ; Focht, Brian C 3 1 Division of Cancer Prevention and Control, Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA;
2 Kinesiology, College of Agriculture, Health, and Natural Resource, University of Connecticut, Storrs, CT 06268, USA;
3 Kinesiology, Department of Human Sciences, The Ohio State University, Columbus, OH 43210, USA;
4 Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH 43210, USA;
5 Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH 43210, USA;
6 Division of Cancer Prevention and Control, Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA;