Empowered With Movement to Prevent Obesity and Weight Regain
Phase: NA
Conditions: Obesity, Weight Loss, Sedentary Lifestyle
Sponsor: Wake Forest University Health Sciences

 
 
 
 
 
Title:  EMPOWER (Empowered with Movement to Prevent Obesity & Weight Regain) 
NCT02923674 
IRB Approval Date:  8/3/2020 
 

 
 
 
 
 
Study Title: EMPOWER (Empowered with Movement to Prevent Obesity & Weight Regain) 
 
Co-Principal Investigators: Barbara Nicklas, PhD and Jack Rejeski, PhD 
Co-investigator(s): Jason Fanning, PhD, Mary Lyles, MD, Iris Leng, PhD, Jonathan Burdette, 
MD and Maria Theresa Opina, MD 
 
Sponsor or funding source: NIH/NIA 
 
Background, Rationale and Context 
The latest obesity treatment guidelines for all ages recommend an intensive lifestyle intervention 
involving behavioral counseling, caloric restriction, and regular exercise to achieve weight loss.1 
While this approach elicits moderate weight loss over a short period of time (typically 6-12 mos), 
weight regain is the norm.2 Regain of lost weight may be especially detrimental for older adults 
who are prone to preferentially store excess calories as fat, rather than lean mass;3-5 thus, it is 
imperative that WL interventions for older adults focus on the challenge of weight regain.  
 
The current recommendation to prevent weight regain is performance of a high volume of physical 
activity/exercise (200-300 min/wk),1,6 an approach that has not been highly successful, especially 
in reduced-weight older adults as they are likely less able to adhere to this recommendation.7,8 A 
key biological adaptation to weight loss is a decrease in total energy expenditure, mainly through 
declines in non-exercise activities of daily living (spontaneous physical activity, SPA).9-11 SPA does 
not include structured exercise and is primarily influenced by sedentary behavior (SB; defined as 
sitting or lying down12). Both the duration and the number of breaks in SB are consistently 
associated with multiple adverse health outcomes, including weight gain, independent of exercise 
behavior.13-30 This study will test an innovative, scalable, and acceptable behavioral intervention 
that targets this known risk factor for weight gain. We found that this intervention is feasible and 
well-tolerated by older adults; if proven to be effective against weight regain, it would add 
important scientific input for advancing treatment guidelines.  
 
We hypothesize that, in older adults, intervening on SB will be more effective for preventing weight 
regain than the conventional approach of intervening on exercise behavior. This is supported by 
evidence showing: 1) decreased SPA/increased SB is a consistent biological adaptation to weight 
loss31-39 and this adaptation predicts weight regain;40 2) as our data show, reducing SB with a self-
monitoring intervention enhances weight loss and may prevent weight regain41; 3) interventions 
targeting increased exercise are less tolerated and sustained over time in older, obese adults42-50 
who may be more likely to adhere to a SB intervention; 4) initiation of exercise in older adults can 
lead to compensatory increases in SB,51-66 thus reducing total energy expenditure and limiting its 
efficacy for preventing weight regain; and 5) compensatory eating occurs in many individuals who 
exercise,56,67,68 though the effects of altering SB on appetite and energy intake are not known.    
Objectives 
Our pilot data provide evidence that a novel, highly acceptable, behavioral intervention (SitLess) 
that focuses on increased awareness of SB employing accelerometry-based self-monitoring, that 
is reinforced with other self-regulatory strategies, improves weight loss during treatment and 
prevents weight regain during a short (5-month) follow-up.41 The main goal of this study is to 
definitively test the efficacy of this intervention for longer-term maintenance of lost weight. 
This will be an 18-month trial in up to 189 obese (BMI=30-45 kg/m2) older (65-85 years) adults 
randomized to one of three treatment arms, all with a caloric restriction intervention for weight loss 
(WL) plus: 1) moderate-intensity aerobic exercise (WL+EX); 2) a SB intervention (WL+SitLess); or 

 
 
 
 
 
3) (WL+EX+SitLess). Participants will undergo a 9-month weight loss intervention involving a 6-
month intensive phase with decreasing contact from months 7 to 9, and a minimal contact, self-
managed, 9-month follow-up phase to address these aims and hypotheses: 
Aim 1: To determine whether addition of an intervention that targets sedentary behavior to a 
standard WL intervention that only targets EX results in a larger 18-month reduction in body weight 
in older, obese adults.  
Primary hypothesis: WL+EX+SitLess will have lower 18-mo body weight than either WL+EX or 
WL+SitLess. 
Secondary hypothesis:  WL+SitLess will have lower 18-mo body weight than WL+EX. 
 
Aim 2: To compare the effects of the interventions on volume and pattern of sedentary behavior 
and physical activity and determine if these factors predict weight regain during the 9-month follow-
up phase.  
Hypothesis: WL+EX+SitLess will have higher total physical activity energy expenditure, less 
sedentary behavior, more breaks in sedentary behavior, and more minutes of light and moderate-
vigorous activity averaged across follow-up than either WL+EX or WL+SitLess.  
Hypothesis: Irrespective of treatment arm, greater activity energy expenditure and fewer minutes 
of sedentary behavior after the WL phase will be predictive of less weight regain during follow-up.   
 
Aim 3: To evaluate treatment effects on clinical outcomes (body composition, functional fitness, 
cardiometabolic risk, fatigue, appetite) and social cognitive measures.  
Hypothesis: WL+EX+SitLess will improve these tertiary outcomes more than either WL+EX or 
WL+SitLess. 
 
Methods and Measures 
Study Overview: We will use a 3-group design in up to189 older (65-85 years), obese (BMI=30-45 
kg/m2), sedentary men and women, all of whom will undergo a 9-month WL intervention (6-mo 
intensive phase and 3-mo reduced contact phase), followed by a 9-month self-managed follow-up 
phase with minimal contact, to test our overall hypothesis that intervening on SB will enhance 
long-term WL in this age group. The diet element of the intervention is identical across groups, 
but groups differ by activity intervention: 1) structured, moderate-intensity, aerobic exercise 
(WL+EX); 2) intervening on SB throughout the day (WL+SitLess); or 3) (WL+EX+SitLess).  
 
Recruitment of study participants: Our recruitment goals include: 50% women and 20% minority. 
We will recruit these individuals using community-based recruitment strategies, including 
newspaper and radio advertisements, and mass mailings. Specific inclusion/exclusion criteria 
(see table) are in place to eliminate those that may be adversely affected by the 
interventions or that are not able to comply with the interventions. To reduce adverse 
effects of dietary WL we exclude individuals that are non-obese, those with recent WL, those who 
have low bone density, are being treated for osteoporosis, or taking medication that may affect 
bone density, and/or those who have deficient levels of vitamin D (25 hydroxyvitamin D level < 20 
ng/mL) who are not taking a calcium/vitamin D supplement.   We also exclude anyone who 
cannot walk unassisted.  
 
 
Participant screening and randomization: All individuals who respond to our recruitment 
strategies will call a toll-free phone number and a recruiter will describe the study and perform a 
brief screen for general eligibility. They will be asked about their age, weight, height, current 
physical activity habits, body weight in the past year, medical history, current medications, and 

 
 
 
 
 
eligibility to participate. All participants must conform to the study’s inclusion/exclusion criteria 
(see table below). 
 
 
 
 
 
Participant Inclusion/Exclusion criteria 
Criteria 
Inclusion 
Exclusion 
Assessment 
Age 
65-85 years  
 
Self-report 
Obesity status 
BMI=30-45 kg/m2 
 
Measured on scale 
Functional status 
 
Dependent on cane or walker 
Self-report 
Weight stability 
Weight stable—no loss or 
gain (±5%) in past 6 
months 
Reported unintentional or intentional weight loss 
or gain of >5% in past 6 months 
Self-report 
 
Physical activity status 
Sedentary 
Participation in regular resistance training and/or 
> 20 mins/day of aerobic exercise in past 6 
months 
Self-report 
Cognitive status   
 
Cognitive impairment (MoCA score <22) 
Questionnaire 
Orthopedic status  
No 
contraindication 
for 
safe 
and 
optimal 
participation in exercise 
training 
Osteoporosis (t-score < -2.3 on hip or spine 
scan); Severe arthritis, or other musculoskeletal 
disorder; Joint replacement or other orthopedic 
surgery in past 6 mos; joint replacement or other 
orthopedic surgery planned in next 2 years 
DXA scan (at V2) 
 
Self-report 
on 
Medical
History form, or medication
use 
(Self-reported
medications will be cross-
referenced 
to 
have 
a
medical condition listed on
the Medical History form)  
Comorbidity/ 
health status 
Approved for participation 
by Medical Director  
--Uncontrolled resting hypertension (>160/90 
mmHg); 
--Current or recent past (within 1 year) severe 
symptomatic heart disease, uncontrolled angina, 
stroke, chronic respiratory disease other than 
asthma or COPD, any disease requiring oxygen 
use, neurological or hematological disease; 
cancer requiring treatment in past year, except 
non-melanoma skin cancers 
BP measurement 
 
 
Self-report on  
Medical History form 
 
 
 
 

 
 
 
 
 
*Orlistat (Xenical), Belviq, Contrave, Saxenda, Phentermine (Adipex/Suprenza), Qsymia 
 
**Fosamax (Alendronate), Miacalcin (Calcitonin), Boniva (Ibandronate Sodium), Evista (Raloxifene), Actonel (Atelvia/Risedronate), 
Forteo (Teriparatide), Reclast (Zoledronic Acid) (Zometa), Prolia (Denosumab/Xgeva, Protelos (Strontium Ranelate)  
 
 
 
 
Those who pass phone screening will be scheduled for an in-clinic screening visit which is 
conducted in the early morning following an overnight (at least 8 hrs) fast which includes 
--Serious conduction disorder, new Q waves or 
ST-segment depression (>3 mm), or uncontrolled 
arrhythmia  
--Room air SpO2 (oxygen saturation) at rest or 
with 
exercise 
qualifying 
for 
supplementary 
oxygen (SpO2≤88%)  
--Abnormal kidney or liver function (2x upper limit 
of normal); 
--eGFR <45 mL/min/1.73m2 
--Anemia (Hb<13 g/dL in men/ <12 g/dL in 
women); 
-- Uncontrolled diabetes (fasting glucose 
>140 mg/dl); 
--Deficient levels of vitamin D (25 hydroxyvitamin 
D level < 20 ng/mL) in those not taking a vitamin 
D supplement; 
--Smoker (No nicotine in past yr) 
--No heavy alcohol use (>14 drinks/week) 
--Unstable severe depression 
 
 
Resting ECG 
 
 
Self-report 
 
 
Metabolic 
panel/CBC
screening blood test 
 
 
 
 
 
Self-report 
 
CES-D 
 
Medication use 
 
Regular use of: growth hormones, oral steroids, 
weight loss medications* or prescription 
osteoporosis medications* 
Self-report and medicatio
inventory 
Research participation 
Willing to provide informed 
consent; Agree to all study 
procedures 
and 
assessments; 
Able 
to 
provide own transportation 
to 
study 
visits 
and 
intervention 
Current participation in other research study 
Self-report 

 
 
 
 
 
refraining from food, beverages (with the exception of water), alcohol and caffeine. Before any 
data collection, participants provide written informed consent and complete a HIPAA 
authorization form in accordance with our IRB policy. At this visit, weight and height will be 
measured to calculate BMI. Individuals who meet the BMI criteria will undergo a medical history 
and a cognitive test (MoCA). The medical history (including blood pressure, review of 
medications, and a fasting blood draw for metabolic panel, vitamin D level, and blood cell count) 
will exclude those with recent history of coronary heart disease, cancer, liver or renal disease, 
severe pulmonary disease, gross physical impairment, uncontrolled hypertension, or any 
contraindications to exercise. The fasting blood profile will exclude those with uncontrolled 
diabetes,69 anemia, or liver, renal, or hematological dysfunction. Individuals with abnormal 
results at screening will be referred to their physicians. Prior to randomization, eligible 
participants will be asked if they would like to participate in an MRI ancillary study. This is visit 
2.1 listed in the table on page 7.  Refusal to participate in the ancillary study will not exclude 
them from the main (parent) study.  See Appendix A for details of the MRI ancillary study. 
Eligible participants will be randomized to treatment using a web-based randomization scheme 
developed by the biostatistician, stratified by sex with random block sizes.  
  
Study Interventions 
Weight Loss intervention: 9-mo WL phase—All participants will undergo a dietary WL 
intervention designed to elicit behavioral changes leading to decreased caloric intake sufficient to 
yield a ~10% loss of initial body mass. The WL intervention includes nutrition education, state-of-
the-art behavioral methods for promoting WL, and strategies that optimize self-regulation.8, 70, 71 
During the intensive phase of WL (months 1-6), participants will meet in weekly group sessions 
delivered by either the RD or staff trained on specific elements of the intervention with individual 
sessions possible on an as needed basis. These group meetings will transition to bimonthly group 
meetings during months 7-9 and to monthly follow-up phone/email contact during the follow-up 
phase (months 10-18). 
 
Individual goals for caloric intake will be prescribed to achieve an energy deficit of ~400 kcal/d 
from weight maintenance energy requirements (resting energy expenditure x activity factor of 1.3). 
The macronutrient goal will target an intake range of 25-30% from protein, 20-35% fat, and 45-
55% carbohydrates. The education and counseling component of the intervention will emphasize 
instruction in making healthy, lower-calorie food choices while teaching changes in eating habits to 
lower caloric intake. Specific emphasis will be placed on the acquisition and use of self-
management skills, particularly self-monitoring of diet and weight.  
 
Throughout the 9-month WL phase, participants will be asked to track their food and beverage 
intake daily (via a paper diary or an internet-based diary)) for at least the first 3 months and then 
on a reduced schedule as warranted. These will be reviewed weekly by the RD. In addition, body 
weight is measured and recorded at all group sessions. If it is evident, either through review of diet 
records or inadequate WL that participants are struggling to meet their WL goals or nutrient needs 
then they will be asked to meet with the RD for additional assessment and intervention. During the 
final month of the WL treatment, emphasis will be placed on transitioning to self-managed weight 
maintenance. Particular emphasis is placed on regular self-monitoring of body weight and 
discussion of how to deal with relapse. 
 
9-mo Follow-up phase—During this phase, participants will receive a monthly phone call or 
email contact, for the purpose of emphasizing study adherence and continuity of self-regulatory 
skills for monitoring diet and body weight. 

 
 
 
 
 
SitLess intervention: 9-mo WL phase—Participants assigned to the WL+SitLess and 
WL+EX+SitLess groups will be encouraged and taught to reduce sedentary behavior (SB) during 
waking hours. The SitLess treatment targets increases in postural shifts and light, spontaneous 
physical activity (SPA) (MET level <3).41, 72 The intervention is individually tailored due to varied 
physical/social environments along with the heterogeneity inherent in the daily lives and capacities 
of older adults. The primary goal is to increase the number of postural shifts across the day by 
25% and to increase light activity to a minimum of 3000 steps each day with specific goals being 
collaborative, progressive, and based, in part, on baselines levels of light activity to optimize 
motivation and to accomodate individual differences. As a general rule, we discourage bouts of SB 
that are >30 minutes at any one time; however, we also realize that this may not always be 
possible. To faciltiate achieving their goals, participants receive 6 vibrating alerts across the course 
of the day from a Fitbit watch. We ask participants to use these cued reminders as a means of 
monitoring their progress for light activity goals by checking steps accumulated on their Fitbit 
watch, viewing their daily postural shifts on the study website, and to reflect as to whether they are 
increasing postural shifts as planned. The alerts also become a cue to increase postural shifts and 
SPA if they are falling short of their goals. 
 
The Fitbit is connected via bluetooth to a mobile device and data are uploaded automatically to a 
central website that can be viewed by individual participants and the interventionist. Each 
participant only sees his/her own data, while the interventionist has access to data on all 
participants and can encourage them via phone or email if goals are not being met or the device is 
malfunctioning. The study website is used: 1) to track self-monitoring, 2) to assist in motivating 
participants via graphic feedback on their short- and long-term progress, and 3) as a vehicle for 
reinforcing and modifying participants’ goals. The website also provides an additional means of 
communication between participants and intervention staff. 
 
The Fitbit and website are used to induce behavior change over the course of the intervention; 
participants begin wearing the Fitbit daily for the first 9 months and then wear time is tailored to 
each individual for the remainder of the study, however, we ask that all participants use the devices 
at least 2 days each week as described above. 
 
The SitLess intervention counseling sessions occur in conjunction with the WL sessions and follow 
the same contact schedule to achieve treatment goals. Participants meet weekly during the first 6 
months and bimonthly during months 7-9. These sessions will focus on: (1) reviewing data from 
the activity monitors, (2) reinforcement for observed changes in behavior, and (3) motivational 
interviewing and problem solving to increase commitment to change and assist with overcoming 
barriers.73 The goal is to build commitment to the use of the devices and to link the feedback to 
improvements in personally-valued outcomes in their daily lives. 
 
9-mo Follow-up phase—During this phase, participants will receive a monthly phone call or 
email contact for the purpose of emphasizing adherence to the SitLess goals and the Fitbit 
activity tracking and associated website.  
 
 
 
Aerobic exercise intervention: 9-mo WL phase—Participants in the WL+EX and 
WL+EX+SitLess treatment arms will be asked to perform structured aerobic exercise (mostly 
treadmill walking) of moderate-intensity for 4-5 days/week, progressing to a duration of 200 

 
 
 
 
 
min/week. Participants will attend center-based sessions for at least 3 days/week during the first 
intensive 6-month phase and at least one day/week during the second 3-month transition phase 
(months 7-9), with recording of home-based exercise for the other 2-4 days/week. The rationale 
for, background education on exercise training, and monitoring of study progress occurs in 
conjunction with the WL sessions in a manner consistent with the SitLess intervention. The level of 
exercise is based on current public health recommendations from the American Heart Association 
and American College of Sports Medicine (ACSM) for optimizing cardiovascular fitness in older 
adults,74 and the ACSM recommendation for appropriate physical activity strategies for WL.75  
 
The supervised exercise sessions will take place in our research facility on motorized treadmills 
under the direction of an exercise interventionist. Participants will keep a log of their treadmill 
speed, grade, exercise duration, and the amount of energy expended for each exercise session 
which is used for feedback and reinforcement. All participants will be instructed to wear proper 
footwear, will be familiarized with the treadmills prior to use and will be instructed in fall prevention 
safety. Participants will warm-up by walking for 3-5 min at a slow pace and will then walk at an 
intensity of 65-70% of heart rate reserve (HRR, assessed during the VO2peak test). In general, the 
duration of supervised walking exercise will progress from 15-20 min at 50% HRR the 1st week to 
40-50 min at 65-70% HRR by the end of the 6th week and thereafter. Each walking session will end 
with a 3-5 min cool-down followed by 5 min of large muscle flexibility exercise. At least two HR 
readings (by HR monitors) will be taken during each supervised walking session to monitor 
compliance to the prescribed intensity. Speed and grade will be adjusted individually to insure that 
participants exercise at their prescribed exercise intensity.  
The exercise prescription for the home-based sessions will allow the participant to perform over 
ground walking, or treadmill walking, elliptical or stationary bike if available. They will be 
counseled to exercise at a moderate-intensity as determined by a rating of 13-15 using Borg’s 
Rating of Perceived Exertion.76  
Throughout the 18 month intervention period, those in the EX treatment arms will also wear a 
Fitbit device that will be integrated with the study website. Here, participants will view receive 
daily feedback relative to sustained bouts of physical activity (i.e., greater than 10 consecutive 
minutes of movement). As in the SitLess intervention, participants begin wearing the Fitbit daily 
for the first 9 months and then wear time is tailored to each individual for the remainder of the 
study, however, we ask that participants use the devices at least 2 days each week. 
9-mo Follow-up phase—During this phase, participants will receive a monthly phone call or 
email contact for the purpose of emphasizing adherence to their moderate-intensity exercise 
goal of 5-6 days/week to maintain a volume of 200 min/week.   
Participants in all three groups will receive a Fitbit activity monitor, prior to the start of intervention, 
to assist with self-monitoring of their prescribed activity (e.g., aerobic exercise and/or decreased 
daily SB). As stated above, they will be encouraged to wear the Fitbit daily during the 9-month WL 
phase, and to wear it at least twice/week during the 9-month follow-up phase. If the monitor is lost, 
stolen or damaged they will be provided with a replacement. If a participant withdraws from the 
study, they will be asked to return the Fitbit. After completion of the last study visit, participants will 
be allowed to keep the Fitbit. 
 
In addition, the study interventions require the use of a smartphone to upload data from the Fitbit 
and to access the study website to receive encouragement and motivation. If a participant does 
not own a smartphone or their smartphone is not compatible with our applications, the study will 
provide them with a smartphone and a phone/data plan for the length of their participation in the 

 
 
 
 
 
study. Participants will be expected to keep the phone with them daily and to take care not to lose 
or damage it. Participants will be instructed that the phone is for study use only.  If the phone is 
lost, stolen or damaged they will be provided with a replacement. If a participant withdraws from 
the study, they will be asked to return the smartphone and it will be deactivated immediately. After 
completion of the last study visit (V8), participants will return the smartphones and they will be 
deactivated. 
 
 
Schedule of assessment visits 
Assessments at baseline, after the intensive WL phase (6-months), after the entire 9-month WL 
intervention (9-months) and at study end (18-months) include: the primary outcome of body 
weight, as well as physical activity energy expenditure, light, moderate and vigorous activity 
minutes, and sedentary behavior minutes and breaks. The other secondary outcome 
assessments (RMR, physical function and measures of cardiometabolic risk) will be performed 
at baseline, after the intensive WL phase (6-months), and at study end (18-months) (See 
Table).  At follow up, Visit 7 and 8 may be combined to help with participant burden and some 
questionnaires will be administered over the phone to limit the time in the clinic.   
 
The nature, purpose, and risks of all tests will be explained to participants prior to obtaining their 
written consent and prior to each test. All examiners will be blinded to participant treatment 
assignment and are trained in the standardized conduct of all tests. Standardized written 
instructions will be provided.  
 

 
 
 
 
 
EMPOWER Measurements
PSV
V1
V2
V3
Inter.
V4
V5
Trans
V6
Maint
V7
V8
Other
fast
fast
fast
fast
Weeks
(-12)- (-8) (-8) - (-2) (-4) - 0 1-24
25-28
25-28
26-37
37-40
37-76
74-77
74-77
Questionnaires
Phone Screener
x
Informed consent/I &E
x
Demographics
x
Medical history 
x
Medications
x
x
x
x
x
x
x
x
MOCA
x
Adverse Events
x
x
x
x
x
x
x
CES-D
x
FACIT questionnaire
x
x
x
PROMIS
x
x
x
Pittsburgh Fatigability
x
x
x
SF-36
x
x
x
DSST
x
Walking Self-Efficacy
x
x
x
Sat with Physical Function
x
x
x
Self-Efficacy for Managing Eating
x
x
x
x
x
x
Experiences in Close Relationships
x
x
x
MAAS- Mindfulness
x
x
x
Power of Food
x
x
x
Perceived Stress
x
x
x
Food Cravings
x
x
x
Technology Survey
x
x
Intervention Survey
x
x
Physical Exams and Physical Performance Measures
Vital signs
x
Anthropometric Measures
x
x
x
eSPPB
x
x
x
Treadmill Fatigue
x
x
VO2 Peak
x
x
Hand Grip Test
x
x
x
RMR
x
x
x
Activity Monitor
x
x
x
x
EMA
x
x
Weight
x
x
x
x
x
x
x
400 meter Walk- Fast
x
x
x
Walking Efficiency
x
x
Radiology/Imaging Tests
DXA (WB)
x
x
x
DXA (hip)
x
x
x
DXA (spine)
x
x
x
Phlebotomy
Lipid Panel
x
x
x
CBC
x
Metabolic Panel
x
Insulin
x
x
x
HbA1c
x
x
x
Glucose
x
x
EDTA- Plasma Storage
x
x
x
Red- Serum Storage
x
x
x
Blood Processing Form
x
x
x
x
Blood Collection Form
x
x
x
x
Other
Medical Approval Form
x
Randomization
x
Participant Status Form
x
 

 
 
 
 
 
 
 
 
Study Measure(s) 
1. Baseline demographic data will be recorded based on participant self-report. Medical 
information on prior and existing co-morbidities and hospitalizations will be ascertained by self-
report and confirmed by direct query of the participant at the screening visit. We will also record 
medication use by asking participants to bring in all medications (including nutritional supplements).  
 
2. Adverse events will be assessed by asking participants at each assessment visit, and during 
monthly phone or email contact with participants during the 9-month follow-up phase, to report 
any health changes, injuries, etc. and these will be recorded using the Adverse Event Form.  
 
3. Body weight and composition: Body weight (the study’s Primary Outcome) will be measured 
in our clinic on the same scale. Body composition, bone density, visceral fat will be measured by 
dual-energy X-ray absorptiometry (DXA; Hologic Discovery W, Bedford, MA). All scans will be 
performed and analyzed by our trained DXA technician who is certified by the International 
Society for Clinical Densitometry. Bone mineral density (BMD) will be measured using specific 
scans of the anterior-posterior spine (L1-L4) and proximal femur. Visceral fat area will be 
measured from the whole-body scan in a 5-cm wide region across the entire abdomen just 
above the iliac crest at a level approximate with the 4th lumbar vertebrae.77 
 
4. Resting metabolic rate will be assessed to determine participant’s weight-maintaining 
energy needs for prescribing the 400 kcal/d energy deficit for WL and will be measured again at 
6- and 18-months to determine its response to the interventions. RMR will be measured in the 
morning after an overnight fast (8 hour minimum of no food, beverage, alcohol, caffeine or 
strenuous exercise) by indirect calorimetry.41 Upon arrival, subjects are asked to lie quietly for 
20-30 min before testing. Measurement of oxygen consumption and carbon dioxide production 
are collected continuously for at least 30 min and RMR is calculated using the Weir equation.78 
 
5. Sedentary behavior, minutes of light and moderate-vigorous physical activity, and energy 
expenditure: We will use the ActivPAL®TM monitor to assess treatment effects on daily SB and 
physical activity.79-84 Participants will be asked to wear the devices continuously (they are water 
resistant) for 7 consecutive days at each time point. Data will be downloaded at the end of each 
7-day period and cleaned and summarized for statistical analyses. 
 
6. Physical function measures to be conducted include: 1) Expanded Short Physical 
Performance Battery (SPPB), which is a measure of lower-extremity function consisting of 
walking speed, balance, and repeated chair stands.85 2) 400-meter walk, which is a measure of 
aerobic endurance;86, 87 participants are instructed to complete the 400m distance (on a flat 
indoor surface) as quickly as possible at a maintainable pace and the time to complete the walk 
is recorded in seconds. 3) Perceived Fatigue/Exertion will assess perceived fatigability at a 
given workload. Participants walk for 5 min at 0% grade at a fixed, comfortable speed of 2.0 
mph, and heart rate and perceived exertion are recorded at midpoint and end. 4)Walking 
efficiency, will be assessed as the number of calories used for walking at 2.0 mph using a 
steady state exercise test on a treadmill.  This test will be performed before the assessment of 
VO2 peak and serves as a warm-up to acclimate participants to the treadmill. 5) Peak aerobic 
capacity (VO2peak) will be measured during a graded exercise test to exhaustion and used for 
prescription of exercise intensity (at baseline) and as an outcome measure of fitness. 

 
 
 
 
 
Additionally, a baseline, room air, oxygen saturation (SpO2) will be recorded prior to the start of 
the graded exercise test and at peak aerobic capacity. The graded exercise test during the 
screening visit will also be used as screening for participants who would otherwise qualify for 
supplementary oxygen.  We have extensive experience measuring VO2peak in older persons.88-
90 
 
7. Psychosocial Assessments: Since attachment-related behavior is an important facet of group-
mediated interventions and known to be associated with responsiveness to behavior change, we 
will assess anxiety and avoidance experienced in close relationships.91 As social cognitive 
measures, we will assess self-efficacy expectations related to physical activity92 and eating 
behavior,93 and outcome expectations for physical function and appearance.94 Perceived stress 
will be evaluated with a short-form of the perceived stress scale,95 trait mindfulness with the 
MAAS,96 and control in resisting food using the power of food scale.97  
 
8. Craving Assessment: One tertiary goal of EMPOWER is to examine whether appetite and 
craving for desired foods may change over the course of the study. At baseline and follow-up 
visits, and following an overnight fast, we will administer a multidimensional state measure of 
craving.98 The measure of craving provides subscales that assess the desire to consume highly 
valued foods, the positive reinforcement resulting from their consumption, the relief from negative 
feeling states that these foods provide, lack of control related to food consumption, and hunger. 
 
9. Cardiometabolic risk factors: A fasting venous blood sample will be collected at baseline, 6-
months and 18-months to measure lipoprotein lipids, C-reactive protein, and HbA1c, glucose and 
insulin (all will be measured by LabCorp). Vitamin D will only be collected at baseline.  For those 
participants that agree to blood storage, plasma and serum will also be extracted from whole blood 
samples and stored at -80oC for future assays. We will also assess treatment effects on seated 
blood pressure, which will be assessed in the right arm using an automated sphygmomanometer 
after resting quietly for 10-15 minutes 
 
10. Fatigue measures: The following fatigue questionnaires will be administered: 1) PROMIS 
Short-form 8a; 2) FACIT Fatigue Scale; and 3) Pittsburgh Fatigability Scale. 
 
11. Cognitive function will be assessed during screening using the Montreal Cognitive 
Assessment (MoCA); participants must score ≥22 to be eligible. We will also assess 
psychomotor speed, attention, and working memory using the Digit Symbol Substitution Test 
(DSST). Participants are given a series of numbered symbols and then asked to draw the 
appropriate symbols below a list of random numbers. The score is the number of correctly made 
matches in 2 minutes (120 seconds).  
12. Ecological Momentary Assessment: Increasingly researchers are recognizing the large 
degree of variability within the individual, and within the day, and the role this variability has on 
intervention efficacy. Therefore, ecological momentary assessment (EMA) will be used to study 
within-person and within-day interactions between sleep quality and duration (assessed via 
items from the Pittsburgh sleep quality inventory), feeling states (i.e., calmness, fatigue, 
relaxation, tiredness, assessed via items from the exercise-induced feelings inventory), affect 
(assessed via the feelings scale), level of hunger (assessed by asking current level of hunger), 
and craving for food (assessed by asking current craving for favorite meals or snack foods). 
Prior to the start of the intervention and following the initial 9-month intervention period, 
participants will complete week-long daily assessments.  These data will align with ActivPAL 
monitoring. Because data collected via the study EMA website are time-stamped, they will be 
time-matched to the hourly ActivPAL data. Participants will be instructed to complete 
assessments upon waking up in the morning and immediately prior to going to bed at night. 

 
 
 
 
 
Additionally, each participant will receive up to five daily text message prompts with links to 
secure web-based questionnaires. Each assessment will take the average user less than two 
minutes to complete, and will contain all items with the exception of sleep items, which will be 
assessed only during the first assessment of the day. 
13. A Technology survey and Intervention survey will be administered at 9 months and 18 
months assessment visits to gain insight on the participants opinion of the technology and group 
dynamic used in this study. 
 
Statistical Analyses 
 Sample size: The sample size chosen for this study (n=180; 60/group) provides >85% statistical 
power (with estimated 51 completers per group, see Table) to definitively test our Primary 
Hypothesis. In our pilot project, the adjusted mean group weight difference after 10 months (5 
months of weight loss and 5 months of follow-up) was 3.6 kg (95% CI= 1.2-6.0 kg). Thus, with the 
longer 18-month time frame in the proposed study, we anticipate there could be a mean group 
difference between WL+EX+SitLess and WL+EX or WL+SitLess of ~4 kg; however, we will have 
power to detect a mean group difference as little as 2.7 kg. This corresponds to 3.0-4.4% of the 
baseline weight for a 90 kg person and is considered clinically meaningful. We also anticipate that 
the WL+SitLess group will regain less weight than WL+EX, and will have >85% power to detect a 
mean group difference as small as 2.3 kg (not adjusted for multiple comparisons). Based on our 
prior 18-month exercise and diet trials in similar populations,8,70 we expect no more than 15% 
drop-out. In addition, with 51/grp, we have at least 80% power to detect effect sizes of 0.49-0.52 
for our Aim 2 outcomes (physical activity energy expenditure, and minutes of SB and SPA), under 
ANCOVA with repeated measures (to estimate the time-averaged difference in response), 
assuming a compound symmetry covariance structure, and effect sizes of 0.24-0.49 for our Aim 3 
outcomes under ANCOVA models as for the primary aim.  
 
Database management: All data except those generated from the activity monitors will be 
entered into the web-based study database as collected. Our data entry system will protect 
confidentiality and data security and the use of text containing identifying information will be 
avoided. Activity monitor data will be downloaded to a server and the summary data merged 
with other study data. Final analysis datasets will be stored in SAS. All data will undergo range 
checks at the time of data entry and will be examined periodically by histograms and bivariate 
scatterplots to check for inconsistencies, unusual data needing further verification, and outliers. 
Plots of longitudinal observations will be used to inspect for unusual changes that need to be 
verified against source documents.  
 
Data analyses: Initial analyses will follow the “intent-to-treat” principle in which data from all 
randomized participants are analyzed according to their randomized groups. We will document 
withdrawal and compliance in detail. We will attempt to identify baseline covariates that predict 
Table 5.  Power to detect significant differences in mean change (α=0.05 level; 2-sided test, Bonferroni 
adjustment for multiple comparisons) between groups using ANCOVA, adjusting for sex; Baseline SD and pre-
post correlations (adjusted for sex) were calculated using data from obese, older persons enrolled in our pilot. 
Outcome  Group Comparison 
Pre-Post 
Correlation 
SD  Mean 18-mo  
grp difference  
Effect Size 
(diff/SD) 
Power 
 
51/grp (85% 
retention) 
Weight 
(kg) 
WL+EX+SitLess vs. 
WL+EX & WL+SitLess 
(α=0.025) 
0.87 
8.2 
2.7 
0.33 
 
0.87 
4 
0.49 
 
>0.99 
WL+SitLess  
vs. WL+EX (α=0.05) 
0.87 
8.2 
2.3 
0.28 
 
0.81 
2.5 
0.30 
 
0.87 

 
 
 
 
 
attrition and compliance, and if such covariates can be identified, the analyses may need to 
incorporate stratification by these factors to decrease bias. Aim 1 hypothesis will be tested using 
analysis of covariance (ANCOVA) to compare group differences in 18-mo body weight, adjusted 
for baseline weight, gender (used to stratify randomization), and treatment arm. Differences in 
least-squares means and associated 95% confidence intervals will be calculated. Bonferroni 
adjustment will be used to account for multiple comparisons.  
 
Secondary analyses (Aims 2 & 3)—Hypotheses of Aim 2 secondary outcomes will be tested using 
ANCOVA with repeated measures, where within subject correlations will be accounted for by using 
a compound symmetry covariance structure. Differences in least-squares means and associated 
95% confidence intervals will be calculated. Group differences in breaks in SB will be tested using 
a generalized linear model with repeated measures, since a Poisson distribution is a better fit for 
such count data. Linear regression will be used to examine whether total physical activity energy 
expenditure and sedentary behavior at 6-months is predictive of less weight regain at 18-months, 
adjusted for baseline body weight and gender.  
  
Hypotheses of our Aim 3 secondary outcomes will be tested at the 0.05 significance level using 
ANCOVA techniques similar to those used for the primary hypothesis. All initial models will be 
adjusted only for gender and baseline variable value; however, specific factors (e.g., age, race, 
initial body weight or composition, medication or supplement use, etc.) that may influence the 
measured responses to the interventions will be evaluated and possibly included in secondary 
analyses. We expect the treatments to similarly affect responses in men and women and between 
races. Nevertheless, we will assess whether there is an interaction between gender and/or race 
and treatment group, and will report this as part of our secondary results. We will also consider 
conducting analyses adjusted for intervention compliance and process measures (such as 
average attendance at the exercise sessions, amount of initial weight lost, social cognitive/group 
process measures, etc.) to examine the extent to which these factors moderate/alter the effect of 
the intervention on the outcomes.  
Human Subjects Protection 
Subject Recruitment Methods: We will recruit individuals using community-based recruitment 
strategies including newspaper ads and mass mailings. We will also advertise in the Aging 
Center’s VITAL newsletter and participate in community outreach events.  
Informed Consent: The informed consent process will follow the procedures of the WFSM 
Institutional Review Board. The potential participant is mailed the informed consent form and 
asked to read it before their initial screening visit. The form is written in simple easy to 
understand language.  At first contact with the prospective participant during the initial screening 
visit, the study staff member will explain the purpose, methods and extent of the study. We 
require study staff to review all of the key aspects of the study verbally with the potential 
participants and to question potential participants to ascertain whether s/he has understood the 
information. Potential participants are encouraged to ask questions regarding the consent form 
and the study. Those who are illiterate or have impaired vision must have the consent read to 
them, followed by opportunity for questions and discussion. A copy of the signed and dated 
consent form will be given to participants, and the original document will be placed in their 
individual study file, which will be stored in a secure location. In compliance with the Health 
Insurance Portability and Accountability Act (HIPAA) and the Standards for Privacy of 
Individually Identifiable Health Information of the Department of Health and Human Services, we 
will access personal health information only after obtaining informed consent. 
 
Potential Study Risks: There are inherent potential risks to human subjects who participate in 
any research study and the potential risks to study participants in this study are listed below. 

 
 
 
 
 
Any injuries or illnesses (adverse events) during the course of a participant’s enrollment in the 
study are monitored regularly as described in the Data Safety Monitoring Plan.  
Assessment risks: 
• DXA scans: There are risks with exposure to radiation from the whole body, anterior-posterior 
spine, and proximal femur DXA scans. The total radiation dose is 45 mRem for baseline and 
follow-up scans. The potential long-term risk from these radiation doses is uncertain, but these 
doses have never been associated with any definite adverse effects. Thus, the risk to study 
participants, if any, is estimated to be slight. 
 
• Physical function tests: There is a small risk of injury during the physical function tests, such 
as muscle strains or pulls, falls, or joint injury. Risks will be minimized by having 
experienced/trained staff conducting these assessments. A warm-up and range of motion 
practice will be conducted before testing. If a participant reports pain, dizziness, or other 
medical problem, the test will be terminated.   
 
• Blood draw: There is a risk of discomfort, bruising, and/or infection at the sight of puncture for 
blood drawing. To minimize these risks, blood will be drawn only by trained and experienced 
phlebotomists who will minimize the discomfort as much as possible and will use good clinical 
practice procedures to reduce risk of infection. 
 
• Maximal exercise testing is a common procedure with risks, including fainting, dizziness, 
chest pain, irregular heartbeat, or heart attack, although the latter is extremely rare (<1 death in 
10,000 tests) in people with no history of heart disease. The test is monitored by an exercise 
physiologist and will be stopped if problems occur. Blood pressure, heart rate and rhythm, and 
breathing will be closely monitored before, throughout, and after the test.  
 
Intervention risks: 
1) Risks of weight loss at any age include the concomitant loss of lean (muscle and bone) tissue 
along with fat mass loss. However, the clinical impact of this muscle and bone loss is not 
known. Since most adults with obesity have a higher muscle mass and bone density, this risk is 
mitigated by excluding individuals who are non-obese and less likely to benefit from weight loss 
and by excluding those with a bone density t-score less than -2.3. In addition, we will assess 
each participant’s 25 hydroxyvitamin D blood level at baseline, 6- and 18-months; if found to be 
deficient (< 20 ng/mL) a participant will be referred to his/her primary care physician for follow-
up assessment and supplementation before beginning the study intervention.      
During the behavioral weight loss classes, we will also provide nutrition education regarding 
adequate intake of calcium and Vitamin D, including encouraging use of dietary supplements. 
Participants will be counseled to consume, either using food or dietary supplements, at least 
1300 mg of calcium and 1000 IU of Vitamin D daily.  
 
Finally, if any participant experiences a bone density decline (either at hip or spine) of ≥10%, or 
has a t-score of < -2.5, at the 6-month DXA assessment, she/he will be counseled to stop their 
caloric restriction and will be referred to his/her primary care physician.   
 
2) The risks of the structured exercise intervention may include: 1) musculoskeletal 
complications and muscle soreness in the early phases of the intervention, 2) an increased fall 
risk, and 3) increased risk of hypoglycemia or hyperglycemia, especially in participants with type 
II diabetes on insulin or short-acting insulin secretagogues.  
 

 
 
 
 
 
Musculoskeletal and fall risks are minimized since all center-based exercise sessions will be 
supervised by trained exercise staff who will instruct participants in proper treadmill walking and 
balance safety, proper footwear, and in selection of safe walking areas outside of the center.  
 
Hypoglycemia/hyperglycemia risks will be mitigated by following the recommendations of the 
American College of Sports Medicine and American Diabetes Association for safety precautions 
of exercise in patients with diabetes.99 Specifically, risks will be reduced by educating 
participants of this risk, by discussing signs and symptoms for them to be aware of prior to, 
during, and after an exercise bout, and by encouraging proper nutrition and hydration prior to 
exercise. For center-based sessions, we will have easily digestible snacks, glucose tablets, and 
juice in the exercise center and we will encourage them to have one of these carbohydrate 
sources accessible during their home-based sessions. Participants who use insulin or short-
acting insulin secretagogues for blood glucose control will have their blood glucose checked via 
glucometer before and after exercise at the supervised sessions and will be encouraged to do 
this for their home-based exercise. If a participant’s glucose is <100 mg/dl we will require them 
to consume a snack containing about 15 grams of carbohydrate prior to exercise; if it is >300 
mg/dL they will be encouraged to hydrate and postpone exercise until their blood glucose level 
is below 300 ng/dL. We will also encourage participants to monitor their glucose after exercise 
to determine if food consumption or medication changes should be made. In some cases, we 
may ask a participant to consult with their physician about reducing medication dosage prior to 
an exercise session.      
 
3) Risks of the SitLess intervention are minimal, but also may include an enhanced fall risk. This 
will be minimized by reviewing environmental factors that are known to increase fall risk.  
 
Confidentiality and Privacy 
Confidentiality will be protected by collecting only information needed to assess study 
outcomes, minimizing to the fullest extent possible the collection of any information that could 
directly identify study participants, and maintaining all study information in a secure manner. To 
help ensure subject privacy and confidentiality, only a unique study identifier will appear on the 
data collection form. Any collected patient identifying information corresponding to the unique 
study identifier will be maintained on a separate master log. The master log will be kept secure, 
with access limited to designated study personnel. Following data collection subject identifying 
information will be destroyed at the earliest opportunity, consistent with data validation and 
study design, producing an anonymous analytical data set. Data access will be limited to study 
staff. Data and records will be kept locked and secured, with any computer data password 
protected. No reference to any individual participant will appear in reports, presentations, or 
publications that may arise from the study. 
 
Data and Safety Monitoring 
The co-PIs, along with the study physician, will be responsible for the overall monitoring of the 
data and safety of study participants. In addition, all clinical intervention studies conducted by 
Aging Center investigators are monitored by the WFU Claude Pepper Older Americans 
Independence Center’s NIH-approved Data Safety Monitoring Board (DSMB). Finally, the 
external NIA-appointed Data and Safety Monitoring Board (DSMB) will have overall 
responsibility of independently monitoring all aspects of the study, including those that require 
access to any un-blinded data. This committee acts in an advisory capacity to the NIA to 
monitor participant safety, evaluate the progress of the study, review procedures for maintaining 
the confidentiality of data, and the quality of data collection, management, and analyses. The 

 
 
 
 
 
DSMB will have access to all study data, documents and progress information, and will be 
notified of all changes that are made to the protocol.  
 
Reporting of Unanticipated Problems, Adverse Events or Deviations 
Any major AE, i.e., any serious injury, including all SAEs, will be recorded and reported to the 
co-PIs immediately after completing any and all actions that are necessary to protect the 
subject’s health and safety. Minor AEs will be recorded and reported within seven days. A 
description of the event, and the date and location of the event will be recorded on the AE 
Reporting Form. The PIs and the Medical Director will meet quarterly, or as needed, to review 
all reported events and these will be compiled and reported in aggregate to the DSMB at each 
biannual meeting of the DSMB. 
 
Within 24 hours of notification of any SAE, the PIs will report the event to the WFSM Institutional 
Review Board (IRB). If the event occurred as a direct result of participation in this study, an 
amendment will be made to the consent form and the co-PIs will request new IRB approval. The 
SAE will also be reported within 24 hours to the NIA and Chair of the DSMB via a verbal phone 
call, or by a report submitted by fax or email.  
 
Use of biological samples by other investigators 
Biological samples may be used by investigators other than the investigators of the current 
study. The use will be limited to non-commercial purposes. The names and other personal 
identifiers of the study participants will not be sent to any recipients of the blood samples. 
Storage and disposal of biological material 
Blood samples will be stored at Wake Forest University Medical Center for up to twenty years 
after the end of the trial at which time the samples will be destroyed. Biological specimens will 
be stored in locked -70oC alarmed freezers located in a locked room. The lab coordinator and 
the PIs have access to the keys of the freezers. All the specimens will have numerical study IDs 
with no personal identifiers of the participants. These are stored under the Pepper Center 
Tissue Repository (IRB#1219). 
 
 
 
References 
 
 
1. Executive summary: Guidelines (2013) for the management of overweight and obesity in adults: a 
report of the American College of Cardiology/American Heart Association Task Force on Practice 
Guidelines and the Obesity Society published by the Obesity Society and American College of 
Cardiology/American Heart Association Task Force on Practice Guidelines. Based on a systematic review 
from the The Obesity Expert Panel, 2013. Obesity (Silver Spring) 2014;22 Suppl 2:S5-39. 
 
2. MacLean PS, Wing RR, Davidson T et al. NIH working group report: Innovative research to improve 
maintenance of weight loss. Obesity (Silver Spring) 2015;23(1):7-15. 
 
3. Lee JS, Visser M, Tylavsky FA et al. Weight loss and regain and effects on body composition: the 
Health, Aging, and Body Composition Study. J Gerontol A Biol Sci Med Sci 2010;65(1):78-83. 
 
4. Beavers KM, Lyles MF, Davis CC, Wang X, Beavers DP, Nicklas BJ. Is lost lean mass from intentional 
weight loss recovered during weight regain in postmenopausal women? Am J Clin Nutr 2011;94(3):767-
774. 

 
 
 
 
 
 
5. Villalon KL, Gozansky WS, Van Pelt RE et al. A Losing Battle: Weight Regain Does Not Restore 
Weight Loss-Induced Bone Loss in Postmenopausal Women. Obesity (Silver Spring) 2011;10. 
 
6. Donnelly JE, Blair SN, Jakicic JM, Manore MM, Rankin JW, Smith BK. American College of Sports 
Medicine Position Stand. Appropriate physical activity intervention strategies for weight loss and 
prevention of weight regain for adults. Med Sci Sports Exerc 2009;41(2):459-471. 
 
7. Messier SP, Loeser RF, Miller GD et al. Exercise and dietary weight loss in overweight and obese older 
adults with knee osteoarthritis: the Arthritis, Diet, and Activity Promotion Trial. Arthritis Rheum 
2004;50(5):1501-1510. 
 
8. Rejeski WJ, Brubaker PH, Goff DC, Jr. et al. Translating weight loss and physical activity programs into 
the community to preserve mobility in older, obese adults in poor cardiovascular health. Arch Intern Med 
2011;171(10):880-886. 
 
9. Levine JA, Kotz CM. NEAT--non-exercise activity thermogenesis--egocentric &amp; geocentric 
environmental factors vs. biological regulation. Acta Physiol Scand 2005;184(4):309-318. 
 
10. Levine J, Melanson EL, Westerterp KR, Hill JO. Measurement of the components of nonexercise 
activity thermogenesis. Am J Physiol Endocrinol Metab 2001;281(4):E670-E675. 
 
11. Garland T, Jr., Schutz H, Chappell MA et al. The biological control of voluntary exercise, spontaneous 
physical activity and daily energy expenditure in relation to obesity: human and rodent perspectives. J 
Exp Biol 2011;214(Pt 2):206-229. 
 
12. Sedentary Behaviour RN. Letter to the editor: standardized use of the terms "sedentary" and 
"sedentary behaviours". Appl Physiol Nutr Metab 2012;37(3):540-542. 
 
13. Lyden K, Keadle SK, Staudenmayer J, Braun B, Freedson PS. Discrete features of sedentary 
behavior impact cardiometabolic risk factors. Med Sci Sports Exerc 2015;47(5):1079-1086. 
 
14. Ekblom O, Ekblom-Bak E, Rosengren A, Hallsten M, Bergstrom G, Borjesson M. Cardiorespiratory 
Fitness, Sedentary Behaviour and Physical Activity Are Independently Associated with the Metabolic 
Syndrome, Results from the SCAPIS Pilot Study. PLoS One 2015;10(6):e0131586. 
 
15. Biswas A, Oh PI, Faulkner GE et al. Sedentary Time and Its Association With Risk for Disease 
Incidence, Mortality, and Hospitalization in Adults: A Systematic Review and Meta-analysis. Ann Intern 
Med 2015;20;162(2):123-132. 
 
16. Carson V, Wong SL, Winkler E, Healy GN, Colley RC, Tremblay MS. Patterns of sedentary time and 
cardiometabolic risk among Canadian adults. Prev Med 2014;65:23-7. doi: 10.1016/j.ypmed.2014.04.005. 
Epub;2014 Apr 13:23-27. 
 
17. Schmid D, Leitzmann MF. Television viewing and time spent sedentary in relation to cancer risk: a 
meta-analysis. J Natl Cancer Inst 2014;106(7):dju098. 
 
18. de Rezende LF, Rodrigues LM, Rey-Lopez JP, Matsudo VK, Luiz OC. Sedentary behavior and health 
outcomes: an overview of systematic reviews. PLoS One 2014;9(8):e105620. 
 
19. Shuval K, Finley CE, Barlow CE, Gabriel KP, Leonard D, Kohl HW, III. Sedentary behavior, 
cardiorespiratory fitness, physical activity, and cardiometabolic risk in men: the cooper center longitudinal 
study. Mayo Clin Proc 2014;89(8):1052-1062. 

 
 
 
 
 
 
20. Henson J, Yates T, Biddle SJ et al. Associations of objectively measured sedentary behaviour and 
physical activity with markers of cardiometabolic health. Diabetologia 2013;56(5):1012-1020. 
 
21. Heinonen I, Helajarvi H, Pahkala K et al. Sedentary behaviours and obesity in adults: the 
Cardiovascular Risk in Young Finns Study. BMJ Open 2013;20;3(6):e002901. 
 
22. Chomistek AK, Manson JE, Stefanick ML et al. Relationship of sedentary behavior and physical 
activity to incident cardiovascular disease: results from the Women's Health Initiative. J Am Coll Cardiol 
2013;61(23):2346-2354. 
 
23. Gennuso KP, Gangnon RE, Matthews CE, Thraen-Borowski KM, Colbert LH. Sedentary behavior, 
physical activity, and markers of health in older adults. Med Sci Sports Exerc 2013;45(8):1493-1500. 
 
24. Wilmot EG, Edwardson CL, Achana FA et al. Sedentary time in adults and the association with 
diabetes, cardiovascular disease and death: systematic review and meta-analysis. Diabetologia 
2012;55(11):2895-2905. 
 
25. Santos DA, Silva AM, Baptista F et al. Sedentary behavior and physical activity are independently 
related to functional fitness in older adults. Exp Gerontol 2012;47(12):908-912. 
 
26. Dogra S, Stathokostas L. Sedentary behavior and physical activity are independent predictors of 
successful aging in middle-aged and older adults. J Aging Res 2012;2012:190654.  
 
27. Knight JA. Physical inactivity: associated diseases and disorders. Ann Clin Lab Sci 2012;42(3):320-
337. 
 
28. Chastin SF, Ferriolli E, Stephens NA, Fearon KC, Greig C. Relationship between sedentary 
behaviour, physical activity, muscle quality and body composition in healthy older adults. Age Ageing 
2012;41(1):111-114. 
 
29. Thorp AA, Owen N, Neuhaus M, Dunstan DW. Sedentary behaviors and subsequent health outcomes 
in adults a systematic review of longitudinal studies, 1996-2011. Am J Prev Med 2011;41(2):207-215. 
 
30. Hamilton MT, Hamilton DG, Zderic TW. Role of low energy expenditure and sitting in obesity, 
metabolic syndrome, type 2 diabetes, and cardiovascular disease. Diabetes 2007;56(11):2655-2667. 
 
31. Gorsky RD, Calloway DH. Activity pattern changes with decreases in food energy intake. Hum Biol 
1983;55(3):577-586. 
 
32. Weigle DS, Sande KJ, Iverius PH, Monsen ER, Brunzell JD. Weight loss leads to a marked decrease 
in non-resting energy expenditure in ambulatory human subjects. Metabolism 1988;37(930-936). 
 
33. de Groot LC, van Es AJ, van Raaij JM, Vogt JE, Hautvast JG. Energy metabolism of overweight 
women 1 mo and 1 y after an 8-wk slimming period. Am J Clin Nutr 1990;51(4):578-583. 
 
34. Leibel R, Rosenbaum M, Hirsch J. Changes in energy expenditure resulting from altered body weight. 
N Engl J Med 1995;332:621-628. 
 
35. Weyer C, Walford RL, Harper IT et al. Energy metabolism after 2 y of energy restriction: the biosphere 
2 experiment. Am J Clin Nutr 2000;72(4):946-953. 
 
36. Martin CK, Heilbronn LK, de JL et al. Effect of calorie restriction on resting metabolic rate and 
spontaneous physical activity. Obesity (Silver Spring) 2007;15(12):2964-2973. 

 
 
 
 
 
 
37. Redman LM, Heilbronn LK, Martin CK et al. Metabolic and behavioral compensations in response to 
caloric restriction: implications for the maintenance of weight loss. PLoS One 2009;4(2):e4377. 
 
38. Bonomi AG, Soenen S, Goris AH, Westerterp KR. Weight-loss induced changes in physical activity 
and activity energy expenditure in overweight and obese subjects before and after energy restriction. 
PLoS One 2013;8(3):e59641. 
 
39. Martin CK, Das SK, Lindblad L et al. Effect of calorie restriction on the free-living physical activity 
levels of nonobese humans: results of three randomized trials. J Appl Physiol (1985 ) 2011;110(4):956-
963. 
 
40. Wang X, Lyles MF, You T, Berry MJ, Rejeski WJ, Nicklas BJ. Weight regain is related to decreases in 
physical activity during weight loss. Med Sci Sports Exerc 2008;40(10):1781-1788. 
 
41. Nicklas BJ, Gaukstern JE, Legault C, Leng I, Rejeski WJ. Intervening on spontaneous physical 
activity to prevent weight regain in older adults: design of a randomized, clinical trial. Contemp Clin Trials 
2012;33(2):450-455. 
 
42. Weyer C, Linkeschowa R, Heise T, Giesen HT, Spraul M. Implications of the traditional and the new 
ACSM physical activity recommendations on weight reduction in dietary treated obese subjects. Int J 
Obes Relat Metab Disord 1998;22(11):1071-1078. 
 
43. Fogelholm M, Kukkonen-Harjula K. Does physical activity prevent weight gain--a systematic review. 
Obes Rev 2000;1(2):95-111. 
 
44. Costello E, Kafchinski M, Vrazel J, Sullivan P. Motivators, barriers, and beliefs regarding physical 
activity in an older adult population. J Geriatr Phys Ther 2011;34(3):138-147. 
 
45. Borg P, Kukkonen-Harjula K, Fogelholm M, Pasanen M. Effects of walking or resistance training on 
weight loss maintenance in obese, middle-aged men: a randomized trial. Int J Obes Relat Metab Disord 
2002;26(5):676-683. 
 
46. Schutzer KA, Graves BS. Barriers and motivations to exercise in older adults. Prev Med 
2004;39(5):1056-1061. 
 
47. Davis JN, Hodges VA, Gillham MB. Physical activity compliance: differences between 
overweight/obese and normal-weight adults. Obesity (Silver Spring) 2006;14(12):2259-2265. 
 
48. Ekkekakis P, Lind E. Exercise does not feel the same when you are overweight: the impact of self-
selected and imposed intensity on affect and exertion. Int J Obes (Lond) 2006;30(4):652-660. 
 
49. Forkan R, Pumper B, Smyth N, Wirkkala H, Ciol MA, Shumway-Cook A. Exercise adherence following 
physical therapy intervention in older adults with impaired balance. Phys Ther 2006;86(3):401-410. 
 
50. Bethancourt HJ, Rosenberg DE, Beatty T, Arterburn DE. Barriers to and facilitators of physical activity 
program use among older adults. Clin Med Res 2014;12(1-2):10-20. 
 
51. Goran MI, Poehlman ET. Endurance training does not enhance total energy expenditure in healthy 
elderly persons. Am J Physiol 1992;263(5 Pt 1):E950-E957. 
 
52. Morio B, Montaurier C, Pickering G et al. Effects of 14 weeks of progressive endurance training on 
energy expenditure in elderly people. Br J Nutr 1998;80(6):511-519. 

 
 
 
 
 
 
53. Meijer EP, Westerterp KR, Verstappen FT. Effect of exercise training on total daily physical activity in 
elderly humans. Eur J Appl Physiol Occup Physiol 1999;80(1):16-21. 
 
54. Meijer EP, Westerterp KR, Verstappen FT. Effect of exercise training on physical activity and 
substrate utilization in the elderly. Int J Sports Med 2000;21(7):499-504. 
 
55. Westerterp KR. Impacts of vigorous and non-vigorous activity on daily energy expenditure. Proc Nutr 
Soc 2003;62(3):645-650. 
 
56. King NA, Caudwell P, Hopkins M et al. Metabolic and behavioral compensatory responses to exercise 
interventions: barriers to weight loss. Obesity (Silver Spring) 2007;15(6):1373-1383. 
 
57. Fabene PF, Mariotti R, Navarro MG, Chakir A, Zancanaro C. Forced mild physical training-induced 
effects on cognitive and locomotory behavior in old mice. J Nutr Health Aging 2008;12(6):388-390. 
 
58. Westerterp KR. Physical activity as determinant of daily energy expenditure. Physiol Behav 
2008;93(4-5):1039-1043. 
 
59. Colley RC, Hills AP, King NA, Byrne NM. Exercise-induced energy expenditure: implications for 
exercise prescription and obesity. Patient Educ Couns 2010;79(3):327-332. 
 
60. Manthou E, Gill JM, Wright A, Malkova D. Behavioral compensatory adjustments to exercise training 
in overweight women. Med Sci Sports Exerc 2010;42(6):1121-1128. 
 
61. Hanlon B, Larson MJ, Bailey BW, Lecheminant JD. Neural response to pictures of food after exercise 
in normal-weight and obese women. Med Sci Sports Exerc 2012;44(10):1864-1870. 
 
62. Di BA, Ripari P, Bucci I et al. Walking training in postmenopause: effects on both spontaneous 
physical activity and training-induced body adaptations. Menopause 2012;19(1):23-32. 
 
63. Wang X, Nicklas BJ. Acute impact of moderate-intensity and vigorous-intensity exercise bouts on 
daily physical activity energy expenditure in postmenopausal women. J Obes 2011;2011. 
 
64. Hunter GR, Bickel CS, Fisher G, Neumeier WH, McCarthy JP. Combined aerobic and strength 
training and energy expenditure in older women. Med Sci Sports Exerc 2013;45(7):1386-1393. 
 
65. Wasenius N, Venojarvi M, Manderoos S et al. Unfavorable influence of structured exercise program 
on total leisure-time physical activity. Scand J Med Sci Sports 2014;24(2):404-413. 
 
66. Wasenius N, Venojarvi M, Manderoos S et al. The effect of structured exercise intervention on 
intensity and volume of total physical activity. J Sports Sci Med 2014;13(4):829-835. 
 
67. Melanson EL, Keadle SK, Donnelly JE, Braun B, King NA. Resistance to exercise-induced weight 
loss: compensatory behavioral adaptations. Med Sci Sports Exerc 2013;45(8):1600-1609. 
 
68. Turner JE, Markovitch D, Betts JA, Thompson D. Nonprescribed physical activity energy expenditure 
is maintained with structured exercise and implicates a compensatory increase in energy intake. Am J 
Clin Nutr 2010;92(5):1009-1016. 
 
69. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes 
Care 2002;25(90001):S5-S20. 

 
 
 
 
 
 
70. Messier SP, Mihalko SL, Legault C et al. Effects of intensive diet and exercise on knee joint loads, 
inflammation, and clinical outcomes among overweight and obese adults with knee osteoarthritis: the 
IDEA randomized clinical trial. JAMA 2013;310(12):1263-1273. 
 
71. Rejeski WJ, Ip EH, Bertoni AG et al. Lifestyle change and mobility in obese adults with type 2 
diabetes. N Engl J Med 2012;366(13):1209-1217. 
 
72. Nicklas BJ, Gaukstern JE, Beavers KM, Newman JC, Leng X, Rejeski WJ. Self-monitoring of 
spontaneous physical activity and sedentary behavior to prevent weight regain in older adults. Obesity 
(Silver Spring) 2014;22(6):1406-1412. 
 
73. Perri MG, Nezu AM, McKelvey WF, Shermer RL, Renjilian DA, Viegener BJ. Relapse prevention 
training and problem-solving therapy in the long-term management of obesity. J Consult Clin Psychol 
2001;69(4):722-726. 
 
74. Nelson ME, Rejeski WJ, Blair SN et al. Physical Activity and Public Health in Older Adults: 
Recommendation from the American College of Sports Medicine and the American Heart Association. 
Med Sci Sports Exerc 2007;39(8):1435-1445. 
 
75. Donnelly JE, Blair SN, Jakicic JM, Manore MM, Rankin JW, Smith BK. American College of Sports 
Medicine Position Stand. Appropriate physical activity intervention strategies for weight loss and 
prevention of weight regain for adults. Med Sci Sports Exerc 2009;41(2):459-471. 
 
76. Borg GA. Perceived exertion: a note on &quot;history&quot; and methods. Med Sci Sports 
1973;5(2):90-93. 
 
77. Micklesfield LK, Goedecke JH, Punyanitya M, Wilson KE, Kelly TL. Dual-energy X-ray performs as 
well as clinical computed tomography for the measurement of visceral fat. Obesity (Silver Spring) 
2012;20(5):1109-1114. 
 
78. De Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. 
J Physiol London 1949;109:1-9. 
 
79. Steeves JA, Bowles HR, McClain JJ et al. Ability of thigh-worn ActiGraph and activPAL monitors to 
classify posture and motion. Med Sci Sports Exerc 2015;47(5):952-959. 
 
80. Barreira TV, Zderic TW, Schuna JM, Jr., Hamilton MT, Tudor-Locke C. Free-living activity counts-
derived breaks in sedentary time: Are they real transitions from sitting to standing? Gait Posture 
2015;42(1):70-72. 
 
81. Schneller MB, Pedersen MT, Gupta N, Aadahl M, Holtermann A. Validation of five minimally 
obstructive methods to estimate physical activity energy expenditure in young adults in semi-standardized 
settings. Sensors (Basel) 2015;15(3):6133-6151. 
 
82. Lee JA, Williams SM, Brown DD, Laurson KR. Concurrent validation of the Actigraph gt3x+, Polar 
Active accelerometer, Omron HJ-720 and Yamax Digiwalker SW-701 pedometer step counts in lab-based 
and free-living settings. J Sports Sci 2015;33(10):991-1000. 
 
83. Korshoj M, Skotte JH, Christiansen CS et al. Validity of the Acti4 software using ActiGraph 
GT3X+accelerometer for recording of arm and upper body inclination in simulated work tasks. 
Ergonomics 2014;57(2):247-253. 
 
84. Stemland I, Ingebrigtsen J, Christiansen CS et al. Validity of the Acti4 method for detection of physical 
activity types in free-living settings: comparison with video analysis. Ergonomics 2015;58(6):953-965. 

 
 
 
 
 
 
85. Guralnik JM, Ferrucci L, Pieper CF et al. Lower extremity function and subsequent disability: 
consistency across studies, predictive models, and value of gait speed alone compared with the short 
physical performance battery. J Gerontol A Biol Sci Med Sci 2000;55(4):M221-M231. 
 
86. Pettee Gabriel KK, Rankin RL, Lee C, Charlton ME, Swan PD, Ainsworth BE. Test-retest reliability 
and validity of the 400-meter walk test in healthy, middle-aged women. J Phys Act Health 2010;7(5):649-
657. 
 
87. Simonsick EM, Montgomery PS, Newman AB, Bauer DC, Harris T. Measuring fitness in healthy older 
adults: the Health ABC Long Distance Corridor Walk. J Am Geriatr Soc 2001;49(11):1544-1548. 
 
88. Nicklas BJ, Wang X, You T et al. Effect of exercise intensity on abdominal fat loss during calorie 
restriction in overweight and obese postmenopausal women: a randomized, controlled trial. Am J Clin 
Nutr 2009;89(4):1043-1052. 
 
89. Lynch N, Nicklas B,  DB, Dennis K, Goldberg A. Improvement in VO2max is associated with 
preferential loss of visceral fat in postmenopausal women. J Appl Physiol 2001. 
 
90. Misquita NA, Davis DC, Dobrovolny CL, Ryan AS, Dennis KE, Nicklas BJ. Applicability of maximal 
oxygen consumption criteria in obese, postmenopausal women. J Womens Health Gend Based Med 
2001;10(9):879-885.  
91. Lafontaine MF BA, Lussier L, Valois P, Shaver PR, Johnson SM. . Selecting the best items for a short-
form of the experiences in close relationships questionaire. European Journal of Psychological 
Assessment. In press. 
 
92. Brawley L, Rejeski WJ, Gaukstern JE, Ambrosius WT. Social cognitive changes following weight loss and 
physical activity interventions in obese, older adults in poor cardiovascular health. Ann.Behav.Med. 
2012;44(3):353-364. 
 
93. Clark MM, Abrams DB, Niaura RS, Eaton CA, Rossi JS. Self-Efficacy in Weight Management. Journal of 
Consulting and Clinical Psychology. 1991;59(5):739-744. 
 
94. Reboussin BA, Rejeski WJ, Martin KA, et al. Correlates of satisfaction with body function and body 
appearance in middle- and older aged adults: The Activity Counseling Trial (ACT). Psychology & Health. 
2000;15(2):239-254. 
 
95. Cohen S, Kamarck T, Mermelstein R. A Global Measure of Perceived Stress. Journal of Health and 
Social Behavior. 1983;24:385-396. 
 
96. Brown KW, Ryan RM. The benefits of being present: Mindfulness and its role in psychological well-being. 
Journal of Personality and Social Psychology. 2003;84(4):822-848. 
 
97. Lowe MR, Butryn ML, Didie ER, et al. The Power of Food Scale. A new measure of the psychological 
influence of the food environment. Appetite. 2009;53(1):114-118. 
 
98. Cepeda-Benito A, Gleaves DH, Williams TL, Erath SA. The development and validation of the state and 
trait food-cravings questionnaires. Behavior Therapy. 2000;31(1):151-173. 
99.  Colberg SR, Sigal RJ, Fernhall B, et al. Exercise and type 2 diabetes: the American College of Sports 
Medicine and the American Diabetes Association: Joint position statement. Diabetes Care. 
2010;33(12):e147–e167. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Appendix A 
Background, Rationale and Context 
Although the scientific premise of the EMPOWER Sitless intervention is based on research showing 
that sedentary behavior in aging has adverse effects on the regulation of body weight1,2 and functional 
decline3,4, recent evidence5,6 suggests there will be considerable within-treatment variability in weight 
loss during the intensive phase of treatment (the first 6-months) and in weight regain from 6- to 18-
months. For the past six years, we have been investigating whether or not there are brain-based 
phenotypes among aging adults that place certain individuals at high-risk for failing to lose 
weight when involved in structured programs of weight loss. Indeed, relative to older adults with 
a low self-reported drive to consume highly palatable foods, their high-scoring counterparts exhibited a 
shift in brain networks that is typical of other addictive behaviors following exposure to a short-term 
food restraint.7 Most recently, using baseline brain gray and white matter volume within a support 
vector machine learning model on older adults (n = 52) enrolled in an 18-month weight loss 

 
 
 
 
 
intervention, we were able to predict classification to the lower or upper half of the weight loss 
distribution with an average accuracy of 72.62%; a receiver operating characteristic analysis indicated 
that classification performance was robust based on an area under the curve of 0.82.11 In unpublished 
work, we have recently used functional network data and have increased prediction accuracy to 93%. 
 
The ancillary study to EMPOWER will enable us to acquire baseline brain magnetic resonance 
imaging (MRI) scans on a random subset of n = 90 participants (EMPOWER has a total n = 160).  We 
will test the robustness of the brain phenotypes identified in our existing work on an independent 
cohort, examining how well these phenotypes predict (a) weight loss during the first 6-month intensive 
phase and (b) weight regain from 6 months to 18 months of follow-up across all three treatment 
groups, and whether the predictive accuracy is similar between treatment groups. Furthermore, we are 
leveraging mobile technology in EMPOWER to collect ecological momentary assessment data on 
patterns of change in appetite, mood, and fatigue throughout the day both at baseline and again at the 
6-month assessment following the intensive phase of treatment. A secondary aim of this ancillary 
study is to examine whether daily fluctuations in appetite, mood, or fatigue mediate the relationship 
between baseline brain phenotypes and weight loss at 6-months and/or weight regain from 6 to 18 
months of follow-up. 
 
Primary Aim: To evaluate the classification accuracy of brain phenotypes based on both structural 
brain MRI scans and functional brain networks to predict baseline-adjusted weight loss at 6 months 
and weight regain from 6 to 18 months both as a main effect across the three treatment groups as well 
as between the three treatment groups. 
 
Secondary Aim: To examine whether the ability of both brain structure and functional brain networks 
to predict baseline-adjusted weight loss at 6 months and weight regain from 6 to 18 months across the 
three treatment groups as well as between the three treatment groups can be explained by variation in 
daily appetite, mood, and fatigue ratings assessed at both baseline and 6-months following the 
intensive phase of treatment. 
 
 
Methods and Measures 
Our main premise is that the structural features of gray matter and white matter in combination with 
functional brain networks constitute a brain phenotype for predicting the amount of weight that older 
adults lose following involvement in the intensive 6-month phase of a weight loss program and the 
amount of weight regained from 6 months until the 18-month closeout visit. We also hypothesize that 
the effects of this phenotype on weight loss and weight regain can be explained, in part, by self-
regulatory failure due to daily fluctuations in appetite, mood, and fatigue.  
A random subset of n = 90 participants (from the EMPOWER total n = 160) stratified by sex will be 
recruited for participation. All participants must be enrolled in the EMPOWER study and must not have 
contraindications for an MRI scan. All participants selected for this MRI sub-study will be asked to 
complete a separate informed consent. 
 
Protocol for collecting brain images: imaging data will be collected after medical approval to 
participate in the EMPOWER parent study but before beginning the intervention.  This baseline MRI 
scan will take place following an overnight fast by the participant. After completing a brief food craving 
and a regulation of eating questionnaire, participants will be positioned in the MRI scanner such that 
they can view an MR-compatible flat panel display positioned at the end of the MRI scanner. The 
screen is connected to the computer that will be used to present experimental stimuli. The scanning 
protocol involves a resting functional scan in which participants simply lie still with their attention 

 
 
 
 
 
focused on a cross that appears on the display screen. Following the resting scan, there is both a food 
cue trial and a neutral cue trial that are randomly ordered. During the food cue trial, participants are 
presented with a series of words representing their favorite snack foods (these are collected prior to 
the MRI scan) and are asked to create an image of each food in their minds-eye and to focus on the 
taste, smell, and pleasure of consuming each food. For the neutral cue trial, neutral stimuli, such the 
word “chair”, are presented and participants are asked to create an image of each object focusing on 
the shape, color, texture and overall appearance of each object visualized. Visual analogue scales are 
presented immediately following each scan to acquire ratings of state craving for their favorite foods, a 
rating on positive/negative affect and, following the visualization tasks, a rating of how vivid the images 
appeared in their minds-eye. 
 
Methods of data acquisition and processing: This project utilizes MRI data acquisition and 
processing techniques that our laboratory has used for the last decade. In brief, all scans will be 
performed on a research Siemens 3T Skyra MRI scanner with a 32-channel head coil utilizing our 
typical imaging protocol.  T1 / T2 / FLAIR structural scans, a blood-oxygen-level-dependent (BOLD)-
weighted resting scan, and BOLD-weighted trials of food cues and neutral cues will be collected.  
Diffusion Tensor Images (DTI) and Perfusion Images may also be acquired. The high-resolution (0.98 
x 0.98 x 1.0 mm) T1-weighted structural scans will be acquired in the sagittal plane using a single-shot 
3D MPRAGE GRAPPA2 sequence (TR = 2.3 seconds, TE = 2.99 ms, 192 slices). Both BOLD-
weighted image sequences will be acquired in the axial plane using an echo-planar imaging (EPI) 
sequence (resolution = 4 x 4 x 4 mm, TR = 2.0 seconds, TE = 25 ms, flip angle = 75˚, 35 slices per 
volume, 160 volumes).  
 
Anatomical Image processing: the T1-weighted structural images will be first segmented into gray 
matter (GM) and white matter (WM) images using statistical parametric mapping toolbox version 12 
(SPM12). Advanced normalization tools (ANTS) will then be used to warp the segmented images to 
standard space8. The warped images will be modulated with the Jacobian deformation field and 
masked using GM/WM masks provided by the International Consortium for Brain Mapping 
(http://www.loni.usc.edu/atlases). Finally, the images will be smoothed using ANTS median filter 
with a radius of 1.5 mm. The resulting images will serve as the inputs into a support vector classifier in 
order to classify subjects in the low and high weight loss groups.  
 
BOLD-weighted Scans: The resting state and food cue and neutral cue functional images will be 
processed using our typical protocol of realignment, slice-time correction, co-registration to the 
structural image, normalization, global signal adjustment, regression of signal from the superior 
sagittal sinus, and motion-correction.  The first 10 images of scans will be discarded to allow for signal 
equilibration. This will leave us with a volume of 147 time points. Functional volumes will be slice-time 
corrected, and then realigned to the first image. Confounding signals, including 6 rigid-body 
transformation parameters generated during the realignment process and 3 mean signals (whole-
brain, white matter, and CSF) will be regressed out of the functional data. The inverse of the 
deformation field resulting from ANTS registration will be applied to transform the Yale brain atlas9 to 
each subject’s native brain space. Unlike commonly used brain atlases which integrate functionally 
different areas into one region, the Yale atlas is a functional atlas which parcellates brain into finer 
functional unites (268 regions versus 90 regions of automated anatomical labeling (AAL)). Finally, the 
resulting atlas in the individuals’ brain space will be used to extract the mean time series for each 
region from the preprocessed functional images. Correlation matrices will then be thresholded to 
maintain edge density consistency across subjects. This process ensures that comparisons between 
networks are of equivalent density relative to the number of network nodes. This will be done using the 
formula N = Ks  10, with N equal to the number of nodes and K equal to average degree (number of 

 
 
 
 
 
edges per node in the network).  Networks will be thresholded at S = 2.5 based on work showing that 
brain networks fragment when S > 3 11 and that the reproducibility is highest with an S between 2 and 
312. Correlation values equal to or greater than the correlation coefficient solving the threshold formula 
are preserved and all others set to 0 to produce undirected, weighted networks. The resulting whole-
brain networks from each study participant will then serve as the basis for the machine learning 
algorithm testing.  
 
Data Analysis 
Support Vector Machine (SVM) Learning classification:  Our recently published SVM model was 
developed to classify weight loss success and failure in an elderly cohort.13 We will apply our 
published SVM model to the warped GM and WM images from the high resolution structural scans 
of the participants in the 3 arms of this study, and this SVM will classify the participants into 
successful and unsuccessful weight loss. In addition, we will apply our more recent machine 
learning hypergraph model that uses functional brain networks of the resting state and task-based 
functional brain network data. In addition, we will combine GM and WM images with resting state 
and task-based fMRI networks in a single machine learning model to compare the performance of 
this model with the models trained using structural or functional data, alone. We expect that this 
combined model will enhance prediction accuracy. 
 
Following this EMPOWER clinical trial, we will assess our machine learning models by determining 
the accuracy, sensitivity, and specificity in classifying the participants’ weight loss success/failure. 
Similar to our published analyses, we will also perform receiver operator characteristic (ROC) 
analyses to investigate how efficiently the classifiers performed at different decision thresholds. 
ROC curves plot sensitivity versus ‘1-specificity’ in a given classification decision threshold, and the 
area under the curve (AUC) is calculated to determine the effectiveness of the predictions and is 
considered as the best representative score to represent classification algorithm performance.  The 
AUC will be determined for the structural, resting state fMRI brain network, and task-based fMRI 
networks by themselves, as well as for the analysis combining these datasets in a single model. A 
one-way repeated measure analysis of variance (ANOVA) will be performed to evaluate for 
significant differences for the AUC between the 3 types of classifiers. 
 
Human Subject Protection 
Subject Recruitment Methods 
All study participants will be enrolled in the EMPOWER study and must not have contraindications 
for an MRI scan. All participants selected for this MRI sub-study will be asked to complete a 
separate informed consent. 
 
Informed Consent 
The participant will be seated in a quiet room with a copy of the consent form. After being given 
time to read the form, the PI or his designee will go through the document with them, and answer 
any questions they might have about the study. Participant consent will be documented. 
 
Potential Study Risks 
The risks associated with MRI scans are metal objects in or on the body and fear/anxiety 
associated with being in the MRI scanner. To reduce any risks associated with metal, we will screen 
all potential participants to exclude anyone with metallic or electronic implants. In addition, all 
individuals are screened prior to entering the MRI suite to ensure that they do not have any metallic 
objects on their person. This process is performed by the MRI technologist.  
  

 
 
 
 
 
To reduce fear and anxiety, the participants will be fully informed of exactly what will happen during 
the scan session at the informed consent meeting. They will be reminded that they are allowed to 
stop participating in the study at any time without any adverse consequences. Throughout the scan, 
the participant will be in communication with the MRI technologist. They will also have an 
emergency squeeze ball that alerts the technologist that they want the scan stopped. Should the 
participant decide to stop the MRI scan before completion, they will be allowed withdraw from the 
MRI portion of the study, but remain in the rest study. 
  
Confidentiality and Privacy 
Confidentiality will be protected by collecting only information needed to assess study outcomes, 
minimizing to the fullest extent possible the collection of any information that could directly identify 
subjects, and maintaining all study information in a secure manner.  To help ensure subject privacy 
and confidentiality, only a unique study identifier will appear on the data collection form.  Any 
collected participant identifying information corresponding to the unique study identifier will be 
maintained on a linkage file, store separately from the data.  The linkage file will be kept secure, 
with access limited to designated study personnel.  Data and records will be kept locked and 
secured, with any computer data password protected.  No reference to any individual participant will 
appear in reports, presentations, or publications that may arise from the study. 
 
Data and Safety Monitoring 
The MRI scans will be reviewed for potential abnormalities by a board certified neuroradiologist and 
all findings confirmed by study coinvestigator Dr. Jonathan Burdette, MD. Although the MRI scans 
collected for this study will not be of diagnostic quality, they might reveal incidental findings. 
Participants will be notified of such findings, and their primary care physician notified if requested by 
the participant. In the event that the participant does not have a primary care physician, they will be 
referred to the appropriate clinical practitioner based on the MRI findings. Follow up clinical 
evaluation would be required to validate any potential findings observed in the research MRI scans. 
Any medical costs associated with follow-up of an incidental finding will be the responsibility of the 
study participant and their parents. 
 
 
 
 
Reporting of Unanticipated Problems, Adverse Events or Deviations 
Any unanticipated problems, serious and unexpected adverse events, deviations or protocol 
changes will be promptly reported by the principal investigator or designated member of the 
research team to the IRB and sponsor or appropriate government agency if appropriate. 
 
 
References 
1. 
Levine JA, Vander Weg MW, Hill JO, Klesges RC. Non-exercise activity thermogenesis: the 
crouching tiger hidden dragon of societal weight gain. Arteriosclerosis, thrombosis, and 
vascular biology. 2006;26(4):729-736. 
 
2. 
Garland T, Jr., Schutz H, Chappell MA, et al. The biological control of voluntary exercise, 
spontaneous physical activity and daily energy expenditure in relation to obesity: human and 
rodent perspectives. J Exp Biol. 2011;214(Pt 2):206-229. 
 

 
 
 
 
 
3. 
Dogra S, Stathokostas L. Sedentary behavior and physical activity are independent 
predictors of successful aging in middle-aged and older adults. Journal of aging research. 
2012;2012:190654. 
 
4. 
Santos DA, Silva AM, Baptista F, et al. Sedentary behavior and physical activity are 
independently related to functional fitness in older adults. Experimental gerontology. 
2012;47(12):908-912. 
 
5. 
Dombrowski SU, Knittle K, Avenell A, Araujo-Soares V, Sniehotta FF. Long term 
maintenance of weight loss with non-surgical interventions in obese adults: systematic review 
and meta-analyses of randomised controlled trials. BMJ (Clinical research ed ). 
2014;348:g2646. 
 
6. 
MacLean PS, Wing RR, Davidson T, et al. NIH working group report: Innovative research to 
improve maintenance of weight loss. Obesity (Silver Spring, Md ). 2015;23(1):7-15. 
 
7. 
Rejeski WJ, Blumenthal TD, Miller GD, Lobe M, Davis C, Brown L. State craving, food 
availability, and reactivity to preferred snack foods. Appetite. 2010;54(1):77-83. 
 
8. 
Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC. A reproducible evaluation of 
ANTs similarity metric performance in brain image registration. Neuroimage. 
2011;54(3):2033-2044. 
 
9. 
Shen X, Tokoglu F, Papademetris X, Constable RT. Groupwise whole-brain parcellation from 
resting-state fMRI data for network node identification. Neuroimage. 2013;82:403-415. 
 
10. 
Watts DJ, Strogatz SH. Collective dynamics of 'small-world' networks. Nature. 
1998;393(6684):440-442. 
 
11. 
Hayasaka S, Laurienti PJ. Comparison of characteristics between region-and voxel-based 
network analyses in resting-state fMRI data. Neuroimage. 2010;50(2):499-508. 
 
12. 
Telesford QK, Burdette JH, Laurienti PJ. An exploration of graph metric reproducibility in 
complex brain networks. Frontiers in neuroscience. 2013;7:67. 
13. 
Mokhtari F, Paolini BM, Burdette JH, Marsh AP, Rejeski WJ, Laurienti PJ. Baseline gray- and 
white-matter volume predict successful weight loss in the elderly. Obesity (Silver Spring). 
2016;24(12):2475-2480.