Examining Diet Quality and Sleep Duration in Overweight/Obese Adults in a Weight Loss Intervention

Statement of the Problem: The rate of increased body mass index (BMI) in the adult US population has been alarming within recent decades. Decreased sleep duration has been associated with higher BMI and lower diet quality. BMI and diet quality have been found to be associated with one another as well. The average US adult diet quality score has been indicated as moderately low, which is often associated with higher BMI. In order to confront these alarming rates, weight loss interventions have been researched. A strategy often seen in weight loss success is self-monitoring. Wearable devices, such as the Eat Less Move More (ELMM) device, are able to aid in self-monitoring of physical activity and eating patterns. However, such technology is still emerging, therefore little has been studied regarding the effect such a device may have on weight loss, diet quality, or sleep duration. Objective: As sleep duration (SD) and dietary quality (DQ) have been associated with each other, and with weight in previous research, this study explored SD and DQ and their relationship as outcomes of a novel randomized controlled trial weight loss intervention for overweight/obese adults. Methods: This study is a secondary data analysis of an 8-week intervention with and without the ELMM device for tracking steps, bites, and eating rate on weight loss. Experimental (Ex, n=37) and control (Cx, n=35) groups were mostly female (62.2%, 68.6%) and white (70.3%, 65.7%), and similar in age (37±16; 39±14yrs) and BMI (31.2±3.5, 31.5±3.0). Both groups received a workbook at week 0 that introduced nutrition-related topics during the 8 weeks. Outcomes included weight, kcal intake, SD, and DQ. These data were captured week 0 and 8 during in-lab visits and phone interviews. SD was collected through the 7-Day Physical Activity Recall. Dietary data were collected through three 24-hour dietary recalls at week 0 and 8 (6 recalls total). DQ was calculated using the 2015 HEI scoring algorithm through SAS. Outcomes were examined via paired t-tests and 2-way repeated measures ANOVA; all analyzed as completers analyses. Results: A significant time by group interaction was observed for mean kcal consumed (F=4.03, p=0.049, Eta Sq=0.061). However, no significant time by group interactions were found for weight loss, SD or DQ. Significant within-group changes were found for total kcals consumed and weight loss from week 0 to 8 for Ex (-300.1kcal/day, p=0.013; 1.0g, p=0.03), but not for Cx (-19.5kcal/day, p=0.82; -0.6kg, p=0.07).


Introduction
In 2011-2012, the average US adult diet quality score was 58.27 out of a possible 100 points, indicating moderately low diet quality 1 . Lower diet quality is positively associated with overweight and obesity 2 . Over the past few decades, there has been a precipitous rise in obesity rates among adults 3 . As these rates have increased, there has been a decrease of 1-2 hours per night in average reported sleep duration 4 , restricted sleep is also associated with lower diet quality and increased BMI 5 . Therefore it is not surprising that over two thirds of the adult population in the US are overweight or obese 6 .
In an effort to address this, many types of weight loss interventions, such as caloric restriction and alternate day-fasting 7 , have been researched to reduce the prevalence of these conditions. Factors that are important in weight loss include diet quality and physical activity (PA), as well as energy intake.
A recent 18-month weight loss and weight maintenance intervention 8 (n=197) assessed diet quality scores based on the 2010 Healthy Eating Index (HEI). From 0 to 6 months in the study, HEI scores increased by 20.3 points out of 100 possible points, mean weight reduced by 14.3%, and moderate to vigorous PA increased from 16.9 min/d to 35.9 min/d within the sample. Although this study found that weight loss was associated with increased PA and improved diet quality, these findings need to be replicated in other types of weight loss interventions.
Sleep duration is also related to diet quality score and weight status. Decreased sleep duration has been associated with a higher body mass index (BMI) and lower diet quality 9,10 . Two cross-sectional studies have found a positive correlation between sleep quality and duration, and diet quality 9,11 . However, to the best of our knowledge, the relationships between sleep duration and diet quality have yet to be investigated during a weight loss intervention. Many studies have explored the relationship between sleep duration and weight gain 12,13,14 or weight gain prevention 15 . O'Brien and colleagues 16 observed change in sleep duration as an outcome of a weight loss intervention although the intervention did not include sleep. However, O'Brien et al. 16 did not assess dietary quality. Other studies considered sleep duration as a predictor 14,15 of weight loss rather than an outcome of an intervention.
Self-monitoring of PA and eating behaviors have been effective for weight loss 19,20 . Wearable ambulatory bite-counter devices, such as the Eat Less Move More (ELMM) device, also known as the Bite Counter, have the potential to assist in such selfmonitoring of activity and eating patterns 21 . The ELMM Study is one of the first studies to test the device for its potential influence on weight loss. The design of the study employs the Social Cognitive Theory, a behavioral theory that is based on the construct of reciprocal determinism 22 in which personal, behavioral, and environmental factors all influence and are influenced by one another.
The ELMM Study collected data on diet quality, sleep duration, PA, and BMI in overweight and obese adults within Rhode Island. The purpose of the current study is to observe dietary quality and sleep duration as outcomes of a self-monitoring and workbook-based weight loss intervention. The primary hypothesis is that the diet quality of the subjects with the wearable device would improve more than in the participants who did not receive the device. The secondary hypothesis is that sleep duration of the subjects in the experimental group would improve more than in the participants who did not receive the device. We investigated change in energy intake and weight between the two groups, as well as relationships of diet quality, sleep duration, and PA.

Study Design
This is a secondary data analysis using data from the ELMM Study, an 8-week randomized controlled trial. Subjects within this study were recruited with fliers, mass emails, and classroom announcements at University of Rhode Island, with the overlying goal of recruiting students and faculty. In order to participate, potential participants were required to meet the following criteria: non-smoking, 18-60 years of age, not currently pregnant or lactating, BMI between 27-37 kg/m 2 , no history of metabolic disease or documented eating disorders, and not currently taking medications that may affect appetite. Seventy-seven participants were recruited, 64 retained.
This project required three individual lab visits (baseline, Week 0, and Week 8) for each participant as well as a phone screening to determine eligibility. At the baseline visit informed consent (Appendix B) was obtained, height and weight measured to confirm BMI, and portion size booklets were provided as reference for future unannounced 24-hour dietary recalls. Two multiple pass 24-hour dietary recalls were collected by phone before each visit. The Week 0 visit required a 10-hour fast prior to visit commencement, which included another set of anthropometric measurements and a test breakfast that was served in lab on a universal eating monitor. After the breakfast, a 24-hour dietary recall and a 7-Day Physical Activity Recall (7-Day PAR) were conducted in-lab. These measurements were taken pre-and post-study.
Randomization of participants occurred after inclusion criteria was met and based on the random selection of the previous participant (the first was determined by coin toss). Participants in both groups were then introduced to the intervention workbook. This workbook was created at the URI and has yet been published; it focuses on reducing eating rate and increasing PA, as well as reducing the energy density of food and reducing liquid kcal consumption. The experimental group was provided the ELMM in addition to the workbook and shown how to use the device. The Week 8 visit included the same measurements and tests as Week 0, and the experimental group returned their device. Subjects were progressively compensated for their time within the study, $160 total.

Anthropometric and Demographic Data
Anthropometric measures were conducted according to standard procedures 23 .
Height and weight were both measured in duplicate and averaged. Height was measured using a digital wall-mounted stadiometer (SECA 240, Hamburg, Germany) and rounded to 0.1 cm. Weight was measured in duplicate using a digital scale (SECA 700, Hamburg, Germany) and rounded to 0.1 kg. Trained team members collected these measurements for each participant during their Week 0 and Week 8 visits. BMI, calculated as kg/m 2 using height and weight, is an indicator of health and adiposity 24 , and is used descriptively. Weight, measured in kg, was used to determine weight change pre-and post-study. Data on sex, race, ethnicity, and age were collected during the phonescreening process, conducted by a trained team member.

Diet Quality Measures
Two multiple pass 24-hour dietary recalls (Appendix D) were administered by a trained member of the research team over the phone prior to the Week 0 and 8 visits.
Additionally, one 24-hour dietary recall was conducted in-person during each visit (6 recalls total). One recall reflects weekend intake and two recalls reflect weekday intakes.
This trained team member recorded brand names, portion sizes, and quantities of foods per participant recollection with the assistance of the portion size estimator handbook (Fred Hutchinson Research Center, Seattle, WA, 2015) provided at the baseline visit.
These recalls were then analyzed using the validated 25 Automated Self-Administered 24-Hour Dietary Recall (ASA24) 26 in order to measure energy intake and calculate cups or weighted ounces per HEI scoring category.
These data were used to calculate the HEI score in order to indicate diet quality pre-and post-intervention. The 2015 HEI utilizes a one to 100 score to indicate diet quality; a higher number indicates a higher quality diet. HEI scores are calculated based on 13 components, each ranging from 0 to 20 points: total fruit, whole fruit, total vegetables, greens and beans, whole grains, dairy, total protein foods, seafood and plant proteins, fatty acids (polyunsaturated fatty acid + monounsaturated fatty acid-to-saturated fatty acid ratio), refined grains, sodium, added sugars, and saturated fat. Each recall conducted within the ELMM Study was given an HEI score and three-day averages of the HEI scores were used to calculate overall participant diet quality pre-and postintervention. A change in this score from pre-to post-intervention indicates an increase or decrease in diet quality. The scores were calculated through the 2015 HEI algorithm within SAS (SAS Institute Inc. version 9.4, Cary, NC, 2013).

PA and Sleep Duration Measures
During the Week 0 and Week 8 visits, participants were interviewed about the frequency, duration, type, and intensity of PA within the past 7-day time period. This interview used a standard protocol with the validated 7-Day PAR 27 (Appendix C). PA was measured in minutes per week, as averaged at each Week 0 and Week 8 visits. The reported 7-day PA categories of "moderate", "hard", and "very hard" activities were summed to produce a moderate-to-vigorous PA measure per subject, per week. These data were analyzed as a continuous variable. The 7-Day PAR also measures the approximate number of hours of sleep they received each night during that 7-day time period; this value was used for sleep duration. In order to obtain average hours of sleep, sleep duration means were calculated at Week 0 and 8. Analyses were conducted with sleep measured as a continuous variable.

Statistical Analysis
The statistical package SPSS (IBM version 24.0 SPSS Inc, Chicago, IL, 2016) was used for all analyses. All data were normally distributed. Outliers were identified but were not excluded as there were no significant differences in data when tests were repeated. To assess the time by group interaction of sleep duration and dietary quality, 2way repeated measure ANOVA were conducted. Further 2-way repeated measures ANOVA were conducted on mean weight, PA, and average kcal consumed. Baseline independent t-tests were conducted on all outcomes of interest. Paired t-tests were run on variables for within group differences. Pearson's correlations were run with SD and HEI scores for 1) mean weight, 2) moderate-to-vigorous PA, 3) average kcal consumed, 4) HEI component scores. Acceptance of significance was identified as p<0.05.

Diet Quality and Sleep Duration
As shown in Table 2, the hypothesis that dietary quality will increase more in the experimental group than the control from pre-to post-intervention was not supported.  (Table 3) revealed no significant association between change of sleep duration and dietary quality from pre-to post-intervention (r=-0.172, p=0.185).

HEI Components
Further paired t-tests were run for all described HEI components: total fruits, whole fruits, total vegetables, greens and beans, whole grains, dairy, total protein foods, seafood and plant proteins, fatty acids, refined grains, sodium, added sugars, and saturated fats. Significance determined for the variables were based on a Bonferroni correction due to multiple comparisons. Within Table 4, paired t-tests were run on the whole sample for each component and yielded significance for saturated fat (p=0.005).
Within group time interactions were assessed as well, and found significance for saturated fat within the experimental group (p=0.009).

Energy Intake, Weight Loss, and PA
Two-way repeated measures ANOVA found a significant time by group interaction with average kcal consumed (F=4.0, p=0.049, Eta Sq=0.061) seen in Figure 1, but in no other outcomes including weight loss or PA. It is important to note that baseline average kcal consumption did not significantly vary (p=0.285). Paired t-tests demonstrated within experimental group significance for mean weight loss (p=0.030) and average kcal consumed (p=0.013), but not for PA (p=0.053). The control group did not demonstrate within-group significance for those variables.

Discussion
In this 8-week workbook-based weight loss intervention, total dietary quality score and sleep duration did not change within or between groups, nor were they associated with one another. It is likely that significance was not seen for the primary and secondary hypotheses due to high variability in both dietary quality (2.0+9.9) and sleep duration (0.1+0.9 hours/day). Total HEI scores and sleep duration still increased in both groups. The lack of effect is consistent with the short-term Salley 28 29 , using a cross-sectional probability sample and self-report data from a community-based cohort study of Hispanics/Latinos in the US, found no relation between diet quality (as assessed by the 2010 Alternative-HEI) and sleep duration. Similar to the results of this study in terms of sleep duration (as assessed by the Pittsburgh Sleep Quality Index), a study in overweight and obese women 16 also found no within group significance. The intervention consisted of weekly 1-hour group meetings that provided a caloric-restriction recommendation, a fat gram goal, PA activity goals for a progressive increase of PA to 200min/week, sample meal plans and vouchers for meal replacement products, and were taught behavioral skills targeting eating habits and PA levels. However, the control group was given four 1-hour group sessions in which information regarding weight loss, PA, and healthy eating habits was provided.
O'Brien et al. did not find significance with sleep as a predictor of weight loss or a significant difference with the experimental group with sleep. Unlike O'Brien et al., the present secondary data analysis did not limit participant gender and included influencers of dietary quality within the workbook provided to both groups, which has been associated with sleep duration 9 . One reason by which significance was not found in the O'Brien et al. study may be due to lack of a sleep component within the intervention, this may have also been the case in the present study as well. However, the subjects within this study had little or no room for improvement in regard to their sleep duration at baseline (Ex: 7.5+1.0;Cx: 7.6+0.9 hours/day); therefore, there likely would not have been a significant improvement in sleep even if the intervention had a sleep adequacy component incorporated.
While the hypotheses have been rejected, various aspects of the intervention, specifically the nutrition workbook, yielded success as evidenced by weight loss seen in both groups (Ex: -1.0+2.4; Cx: -0.6+1.8kg). The one between group difference found was for change in mean kcal intake. Experimental group subjects reduced kcal more than the control group. Thus, it can be suggested that participants with the ELMM, through its bite-counting algorithm, were able to more closely monitor their energy intake or at least be more aware of their intake than the control group. Success was also found in the sample through improved HEI component scores, specifically reduced saturated fat consumption (+0.94 component score, pre-to post-intervention), indicated by a higher component score, as well as increased fatty acid score (+0.82 component score, pre-to post-intervention).
It can be suggested that these improved food categories and scores may have encouraged weight loss success and decreased caloric intake in the sample through their satiating effects. Throughout the literature 7,30 , reducing kcal has been correlated with weight loss and employed in many weight loss interventions. Lower kcal consumption through increasing nutrient dense foods, as seen within this study, and weight loss have been connected to satiety 31 , indicated by lower serum levels of ghrelin, and higher peptide YY, and glucagon-like peptide-1 32 . Dietary factors that have been found to influence satiety biomarkers are fat 33 , protein 34 , and fiber 35 . Recently, the type of fat consumed has been explored further in relation to satiety. Increasing monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs), both found in fatty fish, nuts, and olive oil, has been linked with higher satiety 32,36 . The suggested mechanism that drives this is the potential suppressive effect on ghrelin that MUFAs and PUFAs have compared to saturated fat. This mechanism may act as an alternative reason why there was significant weight loss success in the experimental group as opposed to simply assuming the ELMM device yielded this success.
While this study has taken some initial steps in progressing the literature in weight loss, there are some limitations to consider. First, the self-reported data from participants, namely the dietary and PA data, may not be entirely accurate. It has been identified within the literature 37,38 that overweight and obese samples may underreport dietary data more than samples with a healthy BMI. Additionally, while sleep duration was a secondary outcome of this analysis, it was not incorporated in the intervention like aspects of dietary quality and PA were. Therefore, if research is to continue regarding sleep and weight loss, it may be necessary to include sleep within the intervention.
However, our purpose was to determine if sleep would change as a result of the intervention. There are also many environmental and biological factors that effect sleep that were unable to be controlled for this study such as depression 39 , anxiety 40 , stress 41 , sleep medications, and obstructive sleep apnea 42 . Further, the 7-Day PAR is not validated for sleep duration. The 7-Day PAR is intended for estimating total energy expenditure; sleep duration is collected with this tool for the purpose of calculating resting metabolic rate to aid in approximating total energy expenditure. While there are limitations, this study has considerable strengths.
This analysis is the first of its kind to observe dietary quality within the context of a weight loss study using the ELMM. Prior literature 43,44 regarding this device collected dietary data for the purpose of exploring energy intake, but did not dietary quality. As a result, this study has produced novel findings unique to the ELMM being used within a weight loss intervention. While this is not the first study 43,44 to include a nutrition intervention with the ELMM for weight loss outcomes, it is the first study to utilize a workbook-based intervention. Further, approximately six dietary recalls were collected for each participant who completed the study, this is substantially more dietary data than prior studies 29,45 . Additionally, the randomized controlled trial design that this study models is the gold standard for research, and there was high homogeneity amongst the two groups.

Future Implications and Conclusions
While this intervention did not yield a significant effect on either total dietary quality or sleep duration, these aspects of health should continue to be explored within weight loss interventions. The workbook created for this study can be further improved by incorporating a sleep component in order for change in sleep to be better assessed with weight loss. Additionally, the eating rate portion of the workbook can be expanded to include more information on satiety cues and information on the foods that promote satiety. The potential health benefits posed from self-monitoring, such as significantly lower kcal consumption and weight loss, are further indicated within this study. These findings within the HEI component scores add to the budding research regarding satiety and types of fat.

9.
Haghighatdoost F, Karimi G, Esmaillzadeh A, Azadbakht L. Sleep deprivation is associated with lower diet quality indices and higher rate of general and central obesity among young female students in Iran. Nutrition. 2012;28 (11)(12)

BMI and Weight Loss
Overweight and obesity, as indicated by a higher BMI, pose many threats to health such as hypertension, dyslipidemia, and diabetes 6  These data indicate that weight loss may be achieved through prepackaged low-energy foods leading to a higher quality diet and increasing PA.
A different, but similarly structured, study 45  (ASA24) database was used. ASA24 is typically used as a self-administered recall tool 63 , and has been validated as such 25 , but studies can utilize this database for retroactive data entry in order to get the desired outputs for the scoring algorithm due to its accessibility and reliability. Once the outputs are obtained, they are run through the HEI algorithm, typically through the statistical software, SAS.

Physical Activity
As described previously, PA is an important variable within weight loss interventions. PA is categorized by intensity of activity, from moderate to vigorous intensity 64  OSA typically leads to sleep deprivation due to periodic sleep interruptions throughout a sleep opportunity. Another factor that may affect sleep duration is depression.
Within the literature 39 , it has been discussed that short or long sleep duration may be predictive or increase relative risk of depression. There are a few mechanisms that are believed to drive relative risk. One explanation is that restrictive sleep contributes to daytime tiredness 70 , which has been found to be a predictor of depression 71 82 . It has been inferred that the association between higher BMI and decreased sleep duration is due to increased total kcal intake, or increased time and opportunities for eating, rather than decreased PA 12 .
It is important to note that there is a "U" shaped association between sleep duration and BMI, as well as most other poor health outcomes. There is a certain point in which restrictive 83  week prior to experimentation, all participants were given a 9-hour sleep opportunity.
Three days prior to the study, participants were provided a diet based on their estimated needs (metabolic rate x 1.5 activity factor) and instructed to only eat the food provided and nothing else, other than water. After the week of 9-hour sleep opportunity, the participants were given a 5-hour sleep opportunity for 5 days, in order to simulate a workweek. After this period of inadequate sleep, subjects were transitioned back to 9hour sleep opportunities. It was found that inadequate sleep duration only increased energy expenditure by ~5%, however, energy intake went in excess of energy needed for weight maintenance. Ultimately, inadequate sleep led to a 0.82 kg (±0.47) weight gain amongst total participants 85 . However, when participants were transitioned from inadequate to adequate sleep, a -0.03 kg (±0.5) weight loss was observed 85 . These data indicate that sleep duration has physiological and behavioral mechanisms that effect energy balance.
Sleep restriction and its effect on weight loss has also been studied by other researchers 86,87 . A 14-day randomized control trial 87  such as the actigraphy 91 . However, the 7-Day PAR was used within the initial study that this secondary data analysis is using for analyses and has not been validated for sleep duration against the actigraphy. Ultimately, self-reported measures of sleep duration have been previously studied against objective measures, therefore the 7-Day PAR is an appropriate tool to measure self-reported sleep duration.

Sleep Duration and Diet Quality
To date, very little has been studied on the relationship between sleep duration and diet quality, assessed by validated diet quality indices. There are two cross-sectional studies that have researched these variables. Stern 9 . Both of these studies reflect that sleep duration is postively correlated with diet quality. However, there has been one study that reported no correlation between sleep duration and diet quality.
Within a study 29 using a cross-sectional probablity sample and self-report data from a community-based cohort study of hispanics/latinos in the US, the Hispanic Similar to these food items, the amino acid, tryptophan (found in turkey meat and other foods), has been said to improve or induce sleep. It has been found, that in clinical doses of typtophan, the liteature supports that trptophan may induce or improve sleep based upon the underlying mechanism this amino acid plays in sleep and alertness 95 . In addition to tryptophan, B vitamins and minerals, such as magnesium, may effect sleep based upon their influence on the secretion of melatonin, which also effects sleep and alertness 95 .
Despite these findings, there can be no conclusions drawn from the current body of evidence in regard to certain food items defintively improving sleep quality or duration.
Regardless of sleep duration effecting diet quality or the alternative, these variables have yet to be adequately studied before and after a weight loss intervention.

Wearable Bite Counter Device and Self-Monitoring
Wearable devices are used to automatically and quantitatively monitor eating activities and PA 28 . Such devices can be used to self-monitor bite count and aid users monitor and control eating rate 21,28 . Self-monitoring requires an individual to record dietary intake or PA in order for that person to regulate or be aware of their behaviors 96 .
Self-monitoring has consistently been shown to aid in weight loss 19 , especially when combined with goal setting. The intentional act of self-monitoring heightens selfawareness of food consumption and daily activity that otherwise may have been overlooked, allowing the individual to adjust these behaviors in order to achieve a goal 97 .
Technologies, such as the ELMM, have been developed in order to make self-monitoring eating activities, energy intake, and PA more attainable and user friendly.
As a result, wearable devices such as the ELMM have been implemented in weight loss interventions. The ELMM is an emerging device within the wearable device community, with the first generation debuting in 2011 and the second version, now available to the public, in 2015. As this device is still developing, little has been studied regarding behavior change, such as improved diet quality, with the application of a wearable device in free-living scenarios.
The ELMM device, also known as the Bite Counter, is a wearable ambulatory device that tracks bites of food taken and number of steps throughout the day. The device is able to track number of bites by the wrist motions on the wrist the device is worn via gyroscope and an algorithm 98 . This device has been validated for bite and step counting purposes in free-living 99 and lab-based 100 scenarios. However, it has been shown that this device may underestimate number of bites with most foods and beverages, and over records number of bites when cutting food with a knife and fork 100 . Another limitation identified within the literature is that the device is not entirely automated, the user is required to turn "on" and "off" the bite counting feature 21  Therefore, it is necessary to analyze the association of diet quality with a wearable selfmonitoring device and a nutrition education intervention. It may be possible that the introduction of a device may help improve the diet quality of an individual with the addition of a nutrition education piece. However, diet quality has yet been observed in a weight loss intervention utilizing the ELMM device.

Conclusion
The rate at which average adult BMI is increasing within the US needs to continue to be confronted. One of the only ways to reduce the incidence of overweight and obesity is to healthfully promote behaviors that would lead provide those individuals with an unhealthy BMI or weight to a state of negative energy balance. You have been invited to take part in a research project described below. The researcher will explain the project to you in detail. You should feel free to ask questions. If you have more questions later, Kathleen Melanson, the person primarily responsible for this study {Phone: (401) 874-4477}, will discuss them with you. You must be between the ages of eighteen and sixty years old to participate in this study.

Exclusionary criteria • Smokers
• BMI of less than 27 mg/kg2 or greater than 37 mg/kg2 • Age of less than 18 or greater than 60 years • Documented eating disorder • Chronic metabolic disease, such as diabetes or kidney disease • Use of prescription or over-the-counter medications that affect appetite or energy expenditure • Pregnant or lactating women

Description of the project:
This study will involve research using the Bite Counter, a device that counts the number of bites of food taken during a meal. The purpose of this research study is to determine the effects of wearing the Bite Counter on weight, body composition, lean body mass and fitness level. The amount of time required for participation is about 8 hours in total, in 3 lab visits over approximately 8 weeks. It also involves a total of 4 telephone interviews about diet and activity over the 8 weeks.
What will be done? If you decide to take part in this study, here is what will happen over the course of three visits (the first visit will be approximately 45 minutes and the second and third visits will be approximately two and a half hours), totaling a lab time commitment of about 8 hours: You will first complete a participant screening over the phone to determine if you meet the inclusion criteria.
❖ During the first visit to the lab, a researcher will sit with you to review the informed consent form, and answer your questions. Your height and weight will be taken to confirm that the measurements you provided us in the phone screening are accurate. These measurements will be used to determine if you meet the body mass index (BMI) criteria for the study. You will be assigned to one of two groups in the study: one group will receive the weight loss intervention, and the other will receive the weight loss intervention and the Bite Counter. Please note that you may not be assigned to the group with the Bite Counter; however, your participation in the study is just as important. You will be asked to give a 24 hour dietary recall as well as a 24 hour physical activity recall.
❖ During the following week before visit two, you will be contacted via telephone and asked to give two 24-hour dietary recalls and two 24-hour physical activity recalls over the phone.
❖ For lab visit two, you will come to the lab after a 10 hour, overnight fast. After your blood pressure has been measured, your height, weight and waist circumference measurements will be taken again, and body composition will be tested using the Bod Pod following standardized procedures.* You will then have your blood pressure taken using standardized procedures, have a finger stick blood sample taken to measure your fasting glucose and blood lipid levels, and you will then be served a test breakfast in the lab. After the meal, you will be asked to fill out two questionnaires, and you will be asked to give in-person 24hour dietary and physical activity recalls. You will then be introduced to the weight loss intervention. Finally, you will be asked to perform a standardized three minute step fitness test.
❖ Visit two will be scheduled after visit one depending on the time frame relating to the female menstrual cycle if applicable. During the last week of the intervention, you will again be contacted via telephone and asked to give two 24-hour dietary recalls and two 24-hour physical activity recalls over the phone.
❖ Visit three will take place eight weeks after visit two. For lab visit three, you will come to the lab after a 10 hour, overnight fast. After your blood pressure has been measured, your height, weight and waist circumference measurements will be taken again, and body composition will be tested using the Bod Pod following standardized procedures. You will then have a finger stick blood sample taken to measure your fasting glucose and blood lipid levels, and you will then be served a test breakfast in the lab. After the meal, you will be asked to fill out two questionnaires, and you will be asked to give in-person 24-hour dietary and physical activity recalls. Finally, you will be asked to perform a standardized three minute step fitness test.
❖ The Bod Pod is a research tool that can measure body composition by way of air displacement plethysmography. You will be asked to come into the lab in comfortable clothes with a swimsuit or fitted exercise clothes so that the Bod Pod can more accurately analyze your body composition. You will be asked to sit inside the Bod Pod for a few minutes while the measurements are taken, and the researcher will remain in the room with you the entire time.

Risks or discomfort:
There are minimal risks for the following procedures: questionnaires, consumption of a test meal, measures of height, weight, waist circumference, food intake, and appetite. Some minor discomfort may occur with those who are afraid of confined spaces when sitting in the Bod Pod for body composition testing. If you feel uncomfortable, the test will cease and you can exit the Bod Pod. The blood pressure cuff may cause a feeling of pressure on the upper arm. The finger prick may result in some slight, short term discomfort. Even though trained, experienced personnel will perform the blood draw using sterile technique, it is possible that minor bruising and infection may occur.

Benefits of this study:
The potential benefits to this research study also include obtaining data that may be insightful to eating habits, and potential mechanisms to lose weight. Participants will also receive their own physical and dietary measurements, including body composition results. The potential benefits to society include the possibility of further validation of a wearable device that will potentially help individuals control their eating rate, food intake and physical activity, thereby leading to a helpful, sustainable, low-effort way to achieve healthy weight loss. The research has the potential to provide a valuable piece to weight loss programs, and may help address the need for long-term, sustainable results.

Confidentiality:
Your part in this study is confidential. The information you provide to us will be identified using a code, not your name. This information, which includes a paper copy of each informed consent form, will be stored in a locked file cabinet in the Energy Balance Lab in Fogarty Hall, to which only the researchers and research assistants will have a key. In addition, the Energy Balance Lab is locked when lab researchers and assistants are not present and only researchers and assistants possess a key to the lab. The electronic version of any private information will be stored on the computer in the lab to which only lab researchers and assistants have the login and password information.
This study is using an investigational device; therefore please be advised that the Food and Drug Administration has the privilege of inspecting study data with your identifying information.
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