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Original Investigation |

Effects of Television Viewing Reduction on Energy Intake and Expenditure in Overweight and Obese Adults:  A Randomized Controlled Trial FREE

Jennifer J. Otten, PhD, RD; Katherine E. Jones, MS; Benjamin Littenberg, MD; Jean Harvey-Berino, PhD, RD
[+] Author Affiliations

Author Affiliations: Departments of Nutrition and Food Sciences (Drs Otten and Harvey-Berino and Ms Jones) and Medicine and Nursing (Dr Littenberg), University of Vermont, Burlington. Dr Otten is now with Stanford Prevention Research Center, Stanford University School of Medicine, Palo Alto, California.


Arch Intern Med. 2009;169(22):2109-2115. doi:10.1001/archinternmed.2009.430.
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Published online

Background  The average adult watches almost 5 hours of television (TV) per day, an amount associated with increased risks for obesity. This trial examines the effects of TV reduction on energy intake (EI), energy expenditure (EE), energy balance, body mass index (BMI), (calculated as weight in kilograms divided by height in meters squared), and sleep in overweight and obese adults.

Methods  Randomized controlled trial of 36 adults with a BMI of 25 to 50 who self-reported a minimum of 3 h/d of TV viewing. Participants were enrolled in home-based protocols from January through July 2008. After a 3-week observation phase, participants were stratified by BMI and randomized to an observation-only control group (n = 16) or an intervention group (n = 20) for 3 additional weeks. The intervention consisted of reducing TV viewing by 50% of each participant's objectively measured baseline enforced by an electronic lock-out system.

Results  Although not statistically significant, both groups reduced their EI (−125 kcal/d [95% CI, −303 to 52] vs −38 [95% CI, −265 to 190]) (P = .52) for intervention and control group participants, respectively, where CI indicates confidence interval. The intervention group significantly increased EE (119 kcal/d [95% CI, 23 to 215]) compared with controls (−95 kcal/d [95% CI, −254 to 65]) (P = .02). Energy balance was negative in the intervention group between phases (−244 kcal/d [95% CI, −459 to −30]) but positive in controls (57 kcal/d [95% CI, −216 to 330]) (P = .07). The intervention group showed a greater reduction in BMI (−0.25 [95% CI, −0.45 to −0.05] vs −0.06 [95% CI, −0.43 to 0.31] in controls) (P = .33). There was no change in sleep.

Conclusion  Reducing TV viewing in our sample produced a statistically significant increase in EE but no apparent change in EI after 3 weeks of intervention.

Trial Registration  clinicaltrials.gov Identifier: NCT00622050

Figures in this Article

Public health efforts to prevent and reduce obesity in adults have largely focused on modifying physical activity and diet, but interest is growing in strategies that decrease sedentary behavior. Sedentary behaviors are activities that do not increase energy expenditure (EE) considerably above resting.1 One can be highly sedentary and also meet or exceed physical activity recommendations.1 High levels of sedentary behaviors are associated with increased risks of obesity and metabolic syndrome, independent of physical activity.2,3

The predominant contributor to sedentary behavior in the United States, and the third most time-consuming activity following sleep and work in adults, is watching television (TV).4 Numerous studies have shown positive associations between TV viewing time and risk for obesity, type 2 diabetes mellitus, and cardiovascular disease markers.58 Compared with other sedentary activities such as reading, writing, talking on the telephone, or desk work, TV viewing expends less energy.9

The average adult watches TV almost 5 h/d.10 According to the US Census Bureau, over the course of 1 year the average adult will spend over 65 days in front of the TV.11 Because TV consumes such a large amount of daily leisure time, it is plausible that reducing viewing time may allow time for more active endeavors or reduce opportunities for concurrent or advertisement-triggered energy intake (EI).6,12,13 It is also possible that TV viewing may displace sleep in adults. Emerging research has suggested a link between chronic sleep deprivation and obesity.1416

Research in children has shown that interventions focused on TV reduction can improve body weight, body composition, and decrease EI.1721 Similar studies have not been conducted in adults.

Our primary aim was to examine the effects of reducing TV viewing time on EI in overweight and obese adults. Our secondary aim was to examine the effects of the intervention on EE, energy balance, body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared), and sleep.

PARTICIPANTS

Participants were recruited primarily through newspaper articles, a local TV interview, listserves, and flyers posted throughout Chittenden County, Vermont. The flow of participant recruitment and screening is shown in Figure 1. Interested individuals (n = 143) were screened by telephone. Inclusion criteria included a BMI of 25 to 50, age 21 to 65 years, and self-reported TV viewing of 3 to 8 h/d. The interval of 3 to 8 hours was chosen to correspond to average viewing times of American adults and the range associated with obesity risk.6,10 We excluded respondents with factors that conflicted with study outcomes (pregnancy, breastfeeding, use of certain medications, or participation in another intervention related to energy balance or sleep), for whom TV lockout was impractical, with conflicts that interfered with the study schedule, with medical or pharmaceutical contraindications to participating, or with household members who would not agree to the intervention. Eligible participants were invited to attend an in-person orientation and screening to further describe the study protocol and demonstrate study equipment. At screening, study staff measured weight and height and confirmed BMI eligibility. Interested participants provided written informed consent.

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Figure 1.

Participant flow diagram. BMI indicates body mass index; TV, television.

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STUDY DESIGN AND PROTOCOL

This study was a randomized controlled trial to compare the effects of a TV viewing reduction intervention with an observation-only control. The protocol was approved by the University of Vermont committee on human research in the behavioral sciences. Participants were enrolled on a continuing basis from January through July 2008. Each 6-week protocol was divided into a 3-week observation-only phase (phase 1) followed by randomization into either a 3-week TV reduction intervention group or a 3-week observation-only control group (phase 2). Both groups participated in identical observation periods and assessment measures. During phase 2, the intervention consisted of reducing TV viewing by 50% from each participant's objectively measured phase 1 viewing time. We chose the durations of each phase and the 50% reduction in viewing time based on previous research conducted in children.21 Participants received $125 per phase for complete measures.

Phase 1

At the initiation of phase 1, study staff attached electronic monitors (BOB TV Time Manager; Hopscotch Technology, Boulder, Colorado) to all home TV sets. The monitors were attached by plugging the TV cord into the monitor, locking a plastic cover over the plug with a key, and plugging the monitor into the wall. Study staff retained the keys. The monitors could not be tampered with or removed without the knowledge of the study staff. Each household member received a unique 4-digit code to activate the monitors. The device recorded total minutes per day of viewing per participant code. Viewing time was measured as “screen time,” regardless of input source (ie, cable, videotape, DVD). Participants were instructed to continue their usual TV viewing habits and to enter their code when they were watching, even when others were present. During the initial home visit, participants received training according to standardized protocol in study equipment, sleep logs, and diet reporting. During week 3, participants wore SenseWear Pro 3 Armband (Bodymedia, Pittsburgh, Pennsylvania), a portable device that uses various physiological and movement parameters to measure EE for 7 days to measure their total estimated EE, kept sleep logs, and participated in three 24-hour dietary recalls via telephone. At the end of week 3, study staff conducted another home visit at which weight was measured, armband data were downloaded, sleep logs were collected, and daily totals from the TV monitors were recorded. Participants successfully completing phase 1 were then randomly assigned to the intervention or control group.

Phase 2

Eleven men and 25 women, aged 22 to 61 years, were randomized to intervention (n = 20) or control (n = 16) groups. For the intervention group, study staff placed a weekly limit on the participant code of 50% of objectively measured phase 1 viewing time per TV. When the limit was reached, the monitor shut the TV off and would not allow it to be turned on again until the next week, when the time limit was refreshed. Participants were informed of their weekly limit. No instruction was given as to how to allocate reduced viewing time, nor was instruction given on diet, sleep, or physical activity. Monitors were left on the control group’s TVs for continued observation and to control for effect of their presence. Controls were again instructed to continue usual TV viewing habits.

During week 6 of the study, all participants again wore armbands and kept sleep logs for 7 days. Study staff conducted three 24-hour dietary recalls via telephone. At the end of week 6, study staff conducted a final home visit at which weight was measured, armband data downloaded, TV monitors removed, and a brief poststudy interview conducted.

RANDOMIZATION PROCEDURES

Prior to study initiation, randomization assignment slips were inserted into opaque envelopes in separate strata for overweight (BMI, 25.0-29.9) and obese (BMI, 30.0-50.0) individuals. The envelopes were divided into blocks of 4 (2 for the controls, 2 for the intervention group), sealed, shuffled within each block, and numbered consecutively within each stratum. On scheduling the first home visit, study staff assigned the next randomization envelope within the stratum for the participant's BMI. Envelopes were not opened until completion of phase 1. Participants and research staff were blind to randomization assignment until the conclusion of phase 1 for each participant, when the envelope was opened. Participants who withdrew (n = 1) or were withdrawn (n = 3) were not randomized.

OUTCOME MEASURES

The primary outcome was change in EI due to the short duration of the study and to compare with previous research in children by Epstein et al.21 Energy intake was assessed via three 24-hour recalls (2 weekday, 1 weekend day) collected at the ends of phase 1 and phase 2 using the US Department of Agriculture (USDA) automated multiple pass method (AMPM; version 1.2.1, USDA, Beltsville, Maryland). The AMPM is a research-based, multiple-pass, computerized method that collects interviewer-administrated food recalls.2225 Studies show significantly improved reporting of food intakes using this method compared with other available methods, with mean EI reported at only 11% below mean objectively measured EE at group levels.22,23 Staff members were trained in this method by USDA staff. The staff member who administered the telephone interviews was blinded to group assignment to the extent possible. Participants were told to consume their usual diet and instructed on how to report their diet recall using the USDA Food Model Booklet that came with the software package and common household measures. Collected food intake data were processed using USDA SurveyNet software (version 3.15; Beltsville) and then entered into the USDA Food and Nutrient Database for Dietary Studies (FNDDS) (version 1.0; USDA, Beltsville) to calculate energy and nutrient intake. A random subsample of diet recalls was double-coded for quality assurance (r = 0.976; P < .001).

Weight was measured at baseline and the end of each phase to the nearest 0.1 pound on a calibrated Healthometer Professional floor scale (Sunbeam Products, Bridgeview, Illinois). Participants were weighed in street clothes without shoes. A portable stadiometer (Invicta, Leicester, England) graduated in 0.1-cm intervals was used to measure height.

Energy expenditure was collected using the SenseWear Pro 3 armband, which is worn on the right upper arm against the skin over the triceps muscle. It uses a 2-axis accelerometer, heat flux, galvanic skin response, skin temperature, near-body ambient temperature, sex, age, height, weight, smoking status, and handedness to calculate daily total estimated EE, measured EE, measured active EE, measured sedentary EE, and steps. Its design allows precise measure of on- and off-body time, which is reported in hours:minutes per day. The armband has high agreement with doubly labeled water for measuring total daily EE (intraclass correlation [ICC] = 0.81; r2 = 0.74; P < .01).26 In a field study comparing the armband and 3 other activity monitors against indirect calorimetry, the armband was found to provide the best estimate for total EE (ICC = 0.73 [95% CI, 0.44-0.88], where CI indicates confidence interval).27

Participants were trained in armband placement and were instructed to wear it during waking hours over a minimum of 7 consecutive days28 except for activities where it might be immersed in water. Armbands were set to record activity in 1-minute epochs.28 Data were downloaded using software provided by the manufacturer (INNERVIEW, version 6.1; Bodymedia, Pittsburgh, Pennsylvania). Days in which the armband was worn for less than 10 hours (n = 29 of 236 in the control group, n = 25 of 288 in the intervention group) were excluded from the final analysis.28 Results from analysis of EE with and without these days were similar. To reduce between-day variation, energy balance per person was calculated by subtracting the mean daily total estimated EE from the mean daily EI.29

Sleep, naps, and awakenings were collected via paper sleep logs on the days the armband was worn.30 Participants were instructed to record their time to bed, time of sleep start, time of sleep end, time out of bed, naps, awakenings, and their duration.31,32 Total bedtime sleep time was calculated as time of sleep start to time of sleep end minus awakening time. Total sleep time was calculated as total bedtime sleep plus naps.

Demographic variables were reported via questionnaire at baseline. A brief qualitative interview was conducted at study completion to discuss adherence to the protocol, effects of study on outside TV and videotape or DVD watching, computer use, other household members' viewing time, and intentional or unintentional use of other household members' codes during the study.

STATISTICAL ANALYSIS

Comparisons of baseline data between the 2 groups were performed using independent samples t tests for continuous variables that met assumptions of normality, Wilcoxon rank sum tests for continuous distributions that were not normally distributed, and Fisher exact tests for categorical data. Outcomes were assessed by independent samples t tests comparing the mean within-participant change in the intervention group with that of the control group. All tests were 2-sided. P < .05 was required for statistical significance. We hypothesized that the intervention would be associated with a significant decrease in EI in the intervention arm compared with controls. Secondary outcomes were EE, energy balance, sleep, and BMI. For EE and sleep, we hypothesized an increase in the intervention group and no change in the control group. For BMI, we hypothesized a decrease in the intervention group and no change in the control group. For energy balance, we hypothesized a negative energy balance in the intervention group and no change in the control group. Data were analyzed using Stata (version10.0; StataCorp, College Station, Texas) and SPSS (version 15.0; SPSS Inc, Chicago, Illinois) statistical software.

Power was calculated with the goal of detecting at least a 300 kcal reduction in EI in the intervention group vs no change in EI in the control group. We assumed a 2-sided t test, an intraindividual standard deviation of 300 kcal,33 α of .05, and 80% power to estimate the need for 17 individuals per group.

Of 143 individuals who expressed interest in participating, 62 were eligible for further screening and 36 were randomized. All 36 completed the study and were included in the analysis. There were no missing measures for TV viewing, EI, BMI, or sleep. Armband compliance was high. Four participants in phase 1 (2 controls and 2 from the intervention group) and 2 in phase 2 (both controls) wore the armband less than the scheduled 7 days. There were no significant differences in number of days of usable armband data (>10 hours) between groups in either phase. Usable armband days (mean [SD], number of days) in phase 1 were 6.8 (1.7) in the intervention group vs 6.9 (1.7) in the control group and, in phase 2, 6.8 (1.0) in the intervention group vs 6.1 (1.4) in the control group. As shown in Table 1, there were no significant differences between groups at baseline.

Table Graphic Jump LocationTable 1. Selected Baseline Characteristics by Groupa

In phase 2, daily TV viewing (mean [SD], hours/day) decreased by more than the 50% goal in the intervention group (4.8 [2.5] in phase 1 vs 1.8 [1.1] in phase 2; P < .001). The controls also reduced their TV viewing in phase 2 (5.3 [2.8] in phase 1 vs 4.5 [2.2] in phase 2; P < .001).

Changes in outcome measures are shown in Table 2 and Figure 2. Both groups reduced their EI in phase 2: (−125 kcal/d [95% CI, −303 to 52]) in the intervention group vs (−38 kcal/d [95% CI, −265 to 190) in controls. The difference in change in EI between groups was not significant (P = .52).

Place holder to copy figure label and caption
Figure 2.

Effects of reducing television viewing time on outcome measures. A, Change in energy intake; B, change in energy expenditure; C, change in body mass index (BMI); and D, change in energy balance. Each box represents change as phase 2 minus phase 1 by outcome measure and arm. BMI indicates body mass index. Boxes contain 50% of data with the inside horizontal line representing the median value; whiskers contain 100% of data, except for extreme values shown as individual data points (open circles). *represents statistical significance.

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Table Graphic Jump LocationTable 2. Changes in Primary and Secondary Outcome Measures After Intervention

The intervention group significantly increased total daily estimated EE in phase 2 (119 kcal/d [95% CI, 23 to 215]) compared with controls (−95 [95% CI, −254 to 65]) (P = .02). Those in the intervention group had a significant decrease in time spent in sedentary activities compared with a nonsignificant increase in controls (P = .04), as shown in Table 2.

The intervention group showed a negative energy balance between phases (−244 kcal/d [95% CI, −459 to −30]) compared with a positive energy balance in controls between phases (57 kcal/d [95% CI, −216 to 330]). The difference in change between groups was not statistically significant (P = .07).

Although not significant, BMI between phase 1 and phase 2 showed greater reduction in the intervention group (−0.25 kg/m2 [95% CI,−0.45 to −0.05] vs −0.06 [95% CI, −0.43 to 0.31] in controls; P = .33). Interestingly, the negative energy balance in the intervention group corresponds to the approximately 1.5 lb (0.66 kg) mean weight loss from phase 1 to phase 2. As shown in Table 1 and Table 2, there was minimal change within or between groups for bedtime sleep, number of awakenings, or total sleep.

The principal finding from this randomized controlled trial is that a TV viewing reduction produced a nonsignificant decrease in EI in both groups and a significant increase in total daily EE in intervention participants compared with controls. There was a change in energy balance with a negative mean energy balance in the intervention group compared with a positive mean energy balance in controls, although the change was not significant. The intervention group showed a greater reduction in BMI, consistent with other measured outcomes, but this change also was not significant. (See Table 2 for P values.)

A recent task force report34 supports small behavior changes as a more sustainable, long-term approach to help address the obesity epidemic. It has been estimated that combined increases in EE and decreases in EI equaling only 100 kcal/d could prevent the gradual weight gain observed in most of the population.35,36 In our sample, reducing TV viewing by half was a behavioral change sufficient to produce a significant mean increase in EE of 119 kcal/d and a negative mean energy balance of 244 kcal/d.

Our findings are similar in outcome to those in children but contrast in mechanism. A 2-year randomized trial on the effects of a 50% reduction in TV and computer use in children produced significantly greater reductions in EI and BMI trajectory compared with controls and no changes in physical activity.19 Obese children enrolled in a year-long clinical study of reductions in sedentary behavior (ie, TV/videotape, computer, video games) showed decreases in percentage of overweight and EI compared with a physical activity reinforced and combined arm but no changes in fitness.18 A shorter experimental study21 in youth showed that 50% reductions in sedentary behaviors have resulted in significant decreases in EI in association with targeted behaviors, but no change in activity. A randomized, school-based, TV viewing reduction intervention in third- and fourth-grade students slowed increase in BMI and reduced the number of meals eaten in front of the TV but caused no change in physical activity.17

This suggests that adults may differ from children in how they respond to reductions in sedentary behaviors. Although studies6,12,13 in children and adults suggest that high levels of TV viewing are associated with increased EI and with greater advertisement-triggered food demand, it is possible that children may be more susceptible to these stimuli than adults. It may also be possible that routine eating behaviors and patterns are more established in adults than children and thus will not change over 3 weeks, although activity patterns may change more readily.

Television viewing decreased significantly in both groups in phase 2. Participants watched even fewer hours than their restriction allowed. It was not clear why this occurred, although seasonal changes in viewing habits (ie, from winter to spring or spring to summer) emerged as a possible reason in some poststudy interviews.

Limitations of this study include the short duration, which may not have provided enough time for behaviors to stabilize. However, results from a 3-week sedentary behavior reduction in children were able to predict the direction in results of a similar intervention extended over 2 years in children.19,21 Study participants were overweight or obese adults, and, thus, results may not generalize to all weight ranges. However, our sample is similar to national samples for EI, EE, number of household TVs, and TV viewing time.10,23Another limitation of the study was that participants were able to use other nonparticipant codes to watch TV. Self-reported compliance suggests this was infrequent and accidental in all but 2 cases where individuals reported episodes of cheating totaling less than 5 hours over phase 2. It is possible that computer time, which was not objectively measured, increased in response to the study. Three participants reported increases in computer use in response to the reduction. However, all 3 reported an increase of 1 hour or less per day, an amount insufficient to completely replace eliminated TV time.

In conclusion, restricting TV viewing time in overweight and obese adults produces increases in EE and a trend toward negative energy balance. The intervention did not significantly change EI, BMI, or amount of sleep. To our knowledge, this is the first study to measure the effects of a TV reduction intervention in adults. Reducing TV viewing should be further explored as a method to reduce and prevent obesity in adults.

Correspondence: Jennifer J. Otten, PhD, RD, Stanford University School of Medicine, Stanford Prevention Research Center, 1070 Arastradero Rd, Ste 100, Mail Code 5559, Palo Alto, CA 94304 (jotten@stanford.edu).

Accepted for Publication: September 15, 2009.

Author Contributions: Dr Otten had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of data analysis. Study concept and design: Otten, Jones, Littenberg, and Harvey-Berino. Acquisition of data: Otten and Jones. Analysis and interpretation of data: Otten, Jones, and Littenberg. Drafting of the manuscript: Otten and Harvey-Berino. Critical revision of the manuscript for important intellectual content: Otten, Jones, Littenberg, and Harvey-Berino. Statistical analysis: Littenberg. Obtained funding: Otten and Harvey-Berino. Administrative, technical, and material support: Otten and Jones. Study supervision: Littenberg and Harvey-Berino.

Financial Disclosure: None reported.

Funding/Support: This project was supported in part by USDA Hatch Act Funds to Dr Harvey-Berino and by grants from the National Institutes of Health to Dr Littenberg (K30 RR 022260 and 1 K24 DK068380).

Role of the Sponsor: None of the funding sources were involved in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.

Additional Contributions: Bridget Shea, BS, assisted with data collection; Margaret Dunn-Carver, BS, with data entry; Alan Howard, MS, with data analysis; and Paul Buzzell, BS, with the diet recall process (all are at the University of Vermont, Burlington). We thank the individuals in the TView study for their participation.

Pate  RRO'Neill  JRLobelo  F The evolving definition of “sedentary.” Exerc Sport Sci Rev 2008;36 (4) 173- 178
PubMed
Wijndaele  KDuvigneaud  NMatton  L  et al.  Sedentary behaviour, physical activity and a continuous metabolic syndrome risk score in adults. Eur J Clin Nutr 2009;63 (3) 421- 429
PubMed
Sugiyama  THealy  GNDunstan  DWSalmon  JOwen  N Joint associations of multiple leisure-time sedentary behaviours and physical activity with obesity in Australian adults. Int J Behav Nutr Phys Act 2008;5 (5) 35
PubMed
US Bureau of Labor and Statistics, American time use survey summary: 2007. http://www.bls.gov/tus/home.htm. Accessed July 17, 2008
Bertrais  SBeyeme-Ondoua  JPCzernichow  SGalan  PHercberg  SOppert  JM Sedentary behaviors, physical activity, and metabolic syndrome in middle-aged French subjects. Obes Res 2005;13 (5) 936- 944
PubMed
Foster  JAGore  SAWest  DS Altering TV viewing habits: an unexplored strategy for adult obesity intervention? Am J Health Behav 2006;30 (1) 3- 14
PubMed
Hu  FBLi  TYColditz  GAWillett  WCManson  JE Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. JAMA 2003;289 (14) 1785- 1791
PubMed
Jakes  RWDay  NEKhaw  KT  et al.  Television viewing and low participation in vigorous recreation are independently associated with obesity and markers of cardiovascular disease risk: EPIC-Norfolk population-based study. Eur J Clin Nutr 2003;57 (9) 1089- 1096
PubMed
Ainsworth  BEHaskell  WLWhitt  MC  et al.  Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 2000;32 (9) ((suppl)) S498- S504
PubMed
The Nielsen Co, Americans can't get enough of their screen time [news release]; November 24, 2008. http://www.nielsenmedia.com/nc/portal/site/Public/menuitem.55dc65b4a7d5adff3f65936147a062a0/?vgnextoid=e6db9c9ba2ecd110VgnVCM100000ac0a260aRCRD. Accessed January 14, 2009
US Census Bureau, Statistical Abstract of the United States: 2007. http://www.census.gov/compendia/statab/. Accessed December 18, 2007
Stroebele  Nde Castro  JM Television viewing is associated with an increase in meal frequency in humans. Appetite 2004;42 (1) 111- 113
PubMed
Gore  SAFoster  JADiLillo  VGKirk  KSmith West  D Television viewing and snacking. Eat Behav 2003;4 (4) 399- 405
PubMed
Magee  CAIverson  DCHuang  XFCaputi  P A link between chronic sleep restriction and obesity: methodological considerations. Public Health 2008;122 (12) 1373- 1381
PubMed
Patel  SRMalhotra  AWhite  DPGottlieb  DJHu  FB Association between reduced sleep and weight gain in women. Am J Epidemiol 2006;164 (10) 947- 954
PubMed
Vgontzas  ANBixler  EOTan  TLKantner  DMartin  LFKales  A Obesity without sleep apnea is associated with daytime sleepiness. Arch Intern Med 1998;158 (12) 1333- 1337
PubMed
Robinson  TN Reducing children's television viewing to prevent obesity: a randomized controlled trial. JAMA 1999;282 (16) 1561- 1567
PubMed
Epstein  LHPaluch  RAGordy  CCDorn  J Decreasing sedentary behaviors in treating pediatric obesity. Arch Pediatr Adolesc Med 2000;154 (3) 220- 226
PubMed
Epstein  LHRoemmich  JNRobinson  JL  et al.  A randomized trial of the effects of reducing television viewing and computer use on body mass index in young children. Arch Pediatr Adolesc Med 2008;162 (3) 239- 245
PubMed
Epstein  LHValoski  AMVara  LS  et al.  Effects of decreasing sedentary behavior and increasing activity on weight change in obese children. Health Psychol 1995;14 (2) 109- 115
PubMed
Epstein  LHPaluch  RAConsalvi  ARiordan  KScholl  T Effects of manipulating sedentary behavior on physical activity and food intake. J Pediatr 2002;140 (3) 334- 339
PubMed
Blanton  CAMoshfegh  AJBaer  DJKretsch  MJ The USDA automated multiple-pass method accurately estimates group total energy and nutrient intake. J Nutr 2006;136 (10) 2594- 2599
PubMed
Moshfegh  AJRhodes  DGBaer  DJ  et al.  The US Department of Agriculture automated multiple-pass method reduces bias in the collection of energy intakes. Am J Clin Nutr 2008;88 (2) 324- 332
PubMed
Conway  JMIngwersen  LAMoshfegh  AJ Accuracy of dietary recall using the USDA five-step multiple-pass method in men: an observational validation study. J Am Diet Assoc 2004;104 (4) 595- 603
PubMed
Conway  JMIngwersen  LAVinyard  BTMoshfegh  AJ Effectiveness of the US Department of Agriculture 5-step multiple-pass method in assessing food intake in obese and nonobese women. Am J Clin Nutr 2003;77 (5) 1171- 1178
PubMed
St-Onge  MMignault  DAllison  DBRabasa-Lhoret  R Evaluation of a portable device to measure daily energy expenditure in free-living adults. Am J Clin Nutr 2007;85 (3) 742- 749
PubMed
Berntsen  SHageberg  RAandstad  A  et al.  Validity of physical activity monitors in adults participating in free living activities [published online July 15, 2008]. Br J Sports Med
PubMed10.1136/bjsm.2008.048868
Trost  SGMcIver  KLPate  RR Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc 2005;37 (11) ((suppl)) S531- S543
PubMed
Borrelli  R Collection of food intake data: a reappraisal of criteria for judging the methods. Br J Nutr 1990;63 (3) 411- 417
PubMed
Wilson  KGWatson  STCurrie  SR Daily diary and ambulatory activity monitoring of sleep in patients with insomnia associated with chronic musculoskeletal pain. Pain 1998;75 (1) 75- 84
PubMed
Lauderdale  DSKnutson  KLYan  LL  et al.  Objectively measured sleep characteristics among early-middle-aged adults: the CARDIA study. Am J Epidemiol 2006;164 (1) 5- 16
PubMed
Liu  XForbes  EERyan  NDRofey  DHannon  TSDahl  RE Rapid eye movement sleep in relation to overweight in children and adolescents. Arch Gen Psychiatry 2008;65 (8) 924- 932
PubMed
Andersson  IRossner  S The Gustaf study: repeated, telephone-administered 24-hour dietary recalls of obese and normal-weight men: energy and macronutrient intake and distribution over the days of the week. J Am Diet Assoc 1996;96 (7) 686- 692
PubMed
Hill  JO Can a small-changes approach help address the obesity epidemic? a report of the Joint Task Force of the American Society for Nutrition, Institute of Food Technologists, and International Food Information Council. Am J Clin Nutr 2009;89 (2) 477- 484
PubMed
Hill  JOWyatt  HRReed  GWPeters  JC Obesity and the environment: where do we go from here? Science 2003;299 (5608) 853- 855
PubMed
Hill  JOThompson  HWyatt  H Weight maintenance: what's missing? J Am Diet Assoc 2005;105 (5) ((suppl 1)) S63- S66
PubMed

Figures

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Figure 1.

Participant flow diagram. BMI indicates body mass index; TV, television.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.

Effects of reducing television viewing time on outcome measures. A, Change in energy intake; B, change in energy expenditure; C, change in body mass index (BMI); and D, change in energy balance. Each box represents change as phase 2 minus phase 1 by outcome measure and arm. BMI indicates body mass index. Boxes contain 50% of data with the inside horizontal line representing the median value; whiskers contain 100% of data, except for extreme values shown as individual data points (open circles). *represents statistical significance.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Selected Baseline Characteristics by Groupa
Table Graphic Jump LocationTable 2. Changes in Primary and Secondary Outcome Measures After Intervention

References

Pate  RRO'Neill  JRLobelo  F The evolving definition of “sedentary.” Exerc Sport Sci Rev 2008;36 (4) 173- 178
PubMed
Wijndaele  KDuvigneaud  NMatton  L  et al.  Sedentary behaviour, physical activity and a continuous metabolic syndrome risk score in adults. Eur J Clin Nutr 2009;63 (3) 421- 429
PubMed
Sugiyama  THealy  GNDunstan  DWSalmon  JOwen  N Joint associations of multiple leisure-time sedentary behaviours and physical activity with obesity in Australian adults. Int J Behav Nutr Phys Act 2008;5 (5) 35
PubMed
US Bureau of Labor and Statistics, American time use survey summary: 2007. http://www.bls.gov/tus/home.htm. Accessed July 17, 2008
Bertrais  SBeyeme-Ondoua  JPCzernichow  SGalan  PHercberg  SOppert  JM Sedentary behaviors, physical activity, and metabolic syndrome in middle-aged French subjects. Obes Res 2005;13 (5) 936- 944
PubMed
Foster  JAGore  SAWest  DS Altering TV viewing habits: an unexplored strategy for adult obesity intervention? Am J Health Behav 2006;30 (1) 3- 14
PubMed
Hu  FBLi  TYColditz  GAWillett  WCManson  JE Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. JAMA 2003;289 (14) 1785- 1791
PubMed
Jakes  RWDay  NEKhaw  KT  et al.  Television viewing and low participation in vigorous recreation are independently associated with obesity and markers of cardiovascular disease risk: EPIC-Norfolk population-based study. Eur J Clin Nutr 2003;57 (9) 1089- 1096
PubMed
Ainsworth  BEHaskell  WLWhitt  MC  et al.  Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 2000;32 (9) ((suppl)) S498- S504
PubMed
The Nielsen Co, Americans can't get enough of their screen time [news release]; November 24, 2008. http://www.nielsenmedia.com/nc/portal/site/Public/menuitem.55dc65b4a7d5adff3f65936147a062a0/?vgnextoid=e6db9c9ba2ecd110VgnVCM100000ac0a260aRCRD. Accessed January 14, 2009
US Census Bureau, Statistical Abstract of the United States: 2007. http://www.census.gov/compendia/statab/. Accessed December 18, 2007
Stroebele  Nde Castro  JM Television viewing is associated with an increase in meal frequency in humans. Appetite 2004;42 (1) 111- 113
PubMed
Gore  SAFoster  JADiLillo  VGKirk  KSmith West  D Television viewing and snacking. Eat Behav 2003;4 (4) 399- 405
PubMed
Magee  CAIverson  DCHuang  XFCaputi  P A link between chronic sleep restriction and obesity: methodological considerations. Public Health 2008;122 (12) 1373- 1381
PubMed
Patel  SRMalhotra  AWhite  DPGottlieb  DJHu  FB Association between reduced sleep and weight gain in women. Am J Epidemiol 2006;164 (10) 947- 954
PubMed
Vgontzas  ANBixler  EOTan  TLKantner  DMartin  LFKales  A Obesity without sleep apnea is associated with daytime sleepiness. Arch Intern Med 1998;158 (12) 1333- 1337
PubMed
Robinson  TN Reducing children's television viewing to prevent obesity: a randomized controlled trial. JAMA 1999;282 (16) 1561- 1567
PubMed
Epstein  LHPaluch  RAGordy  CCDorn  J Decreasing sedentary behaviors in treating pediatric obesity. Arch Pediatr Adolesc Med 2000;154 (3) 220- 226
PubMed
Epstein  LHRoemmich  JNRobinson  JL  et al.  A randomized trial of the effects of reducing television viewing and computer use on body mass index in young children. Arch Pediatr Adolesc Med 2008;162 (3) 239- 245
PubMed
Epstein  LHValoski  AMVara  LS  et al.  Effects of decreasing sedentary behavior and increasing activity on weight change in obese children. Health Psychol 1995;14 (2) 109- 115
PubMed
Epstein  LHPaluch  RAConsalvi  ARiordan  KScholl  T Effects of manipulating sedentary behavior on physical activity and food intake. J Pediatr 2002;140 (3) 334- 339
PubMed
Blanton  CAMoshfegh  AJBaer  DJKretsch  MJ The USDA automated multiple-pass method accurately estimates group total energy and nutrient intake. J Nutr 2006;136 (10) 2594- 2599
PubMed
Moshfegh  AJRhodes  DGBaer  DJ  et al.  The US Department of Agriculture automated multiple-pass method reduces bias in the collection of energy intakes. Am J Clin Nutr 2008;88 (2) 324- 332
PubMed
Conway  JMIngwersen  LAMoshfegh  AJ Accuracy of dietary recall using the USDA five-step multiple-pass method in men: an observational validation study. J Am Diet Assoc 2004;104 (4) 595- 603
PubMed
Conway  JMIngwersen  LAVinyard  BTMoshfegh  AJ Effectiveness of the US Department of Agriculture 5-step multiple-pass method in assessing food intake in obese and nonobese women. Am J Clin Nutr 2003;77 (5) 1171- 1178
PubMed
St-Onge  MMignault  DAllison  DBRabasa-Lhoret  R Evaluation of a portable device to measure daily energy expenditure in free-living adults. Am J Clin Nutr 2007;85 (3) 742- 749
PubMed
Berntsen  SHageberg  RAandstad  A  et al.  Validity of physical activity monitors in adults participating in free living activities [published online July 15, 2008]. Br J Sports Med
PubMed10.1136/bjsm.2008.048868
Trost  SGMcIver  KLPate  RR Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc 2005;37 (11) ((suppl)) S531- S543
PubMed
Borrelli  R Collection of food intake data: a reappraisal of criteria for judging the methods. Br J Nutr 1990;63 (3) 411- 417
PubMed
Wilson  KGWatson  STCurrie  SR Daily diary and ambulatory activity monitoring of sleep in patients with insomnia associated with chronic musculoskeletal pain. Pain 1998;75 (1) 75- 84
PubMed
Lauderdale  DSKnutson  KLYan  LL  et al.  Objectively measured sleep characteristics among early-middle-aged adults: the CARDIA study. Am J Epidemiol 2006;164 (1) 5- 16
PubMed
Liu  XForbes  EERyan  NDRofey  DHannon  TSDahl  RE Rapid eye movement sleep in relation to overweight in children and adolescents. Arch Gen Psychiatry 2008;65 (8) 924- 932
PubMed
Andersson  IRossner  S The Gustaf study: repeated, telephone-administered 24-hour dietary recalls of obese and normal-weight men: energy and macronutrient intake and distribution over the days of the week. J Am Diet Assoc 1996;96 (7) 686- 692
PubMed
Hill  JO Can a small-changes approach help address the obesity epidemic? a report of the Joint Task Force of the American Society for Nutrition, Institute of Food Technologists, and International Food Information Council. Am J Clin Nutr 2009;89 (2) 477- 484
PubMed
Hill  JOWyatt  HRReed  GWPeters  JC Obesity and the environment: where do we go from here? Science 2003;299 (5608) 853- 855
PubMed
Hill  JOThompson  HWyatt  H Weight maintenance: what's missing? J Am Diet Assoc 2005;105 (5) ((suppl 1)) S63- S66
PubMed

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