[Skip to Content]
Access to paid content on this site is currently suspended due to excessive activity being detected from your IP address 50.16.17.16. Please contact the publisher to request reinstatement.
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Article
December 2006

YOUTHA Health Plan–Based Lifestyle Intervention Increases Bone Mineral Density in Adolescent Girls

Author Affiliations

Author Affiliations: Center for Health Research, Kaiser Permanente Northwest, Portland, Ore (Drs DeBar, Vuckovic, and Stevens, and Mr Dickerson); University of Arizona College of Medicine, Tucson (Drs Ritenbaugh and Aickin); Oregon Health & Science University, Portland (Drs Orwoll, Elliot, and Moe); Washington State University, Vancouver (Dr Irving).

†Deceased.

Arch Pediatr Adolesc Med. 2006;160(12):1269-1276. doi:10.1001/archpedi.160.12.1269
Abstract

Objective  To test the efficacy of a health plan–based lifestyle intervention to increase bone mineral density in adolescent girls.

Design  Two-year randomized, controlled trial.

Setting  Large health maintenance organization.

Participants  Girls 14 to 16 years old with body mass index below the national median.

Intervention  Behavioral intervention (bimonthly group meetings, quarterly coaching telephone calls, and weekly self-monitoring) designed to improve diet and increase physical activity.

Main Outcome Measures  Total bone mineral density was measured by dual-energy x-ray absorptiometry. Behavioral outcomes included intake of calcium, vitamin D, soda, and fruits and vegetables; high-impact and strength-training physical activity; measures of strength and fitness; and biomarkers (osteocalcin and naltrexone).

Results  Compared with control subjects, girls in the intervention group had significantly higher bone mineral density in the spine and trochanter regions during the first study year, which was maintained during the second study year. The naltrexone biomarker demonstrated a greater relative decrease in the intervention group compared with the control group, with nonsignificant changes in osteocalcin consistent with more bone building in the intervention group. Participants in the intervention group reported significantly greater consumption of calcium in both study years, vitamin D in the first year, and fruits and vegetables in both years. We found no effect on soda consumption or target exercise rates.

Conclusions  A comprehensive health care–based lifestyle intervention can effectively improve dietary intake and increase bone mineral gains in adolescent girls. To our knowledge, this study is the first to significantly improve bone mass in adolescent girls in a non–school-based intervention.

Trial Registration  ClinicalTrials.gov Identifier: NCT00067600.

The primary prevention of osteoporosis is an important public health target. Almost half of all women in the United States older than 50 years demonstrate low bone density (osteopenia).1 An estimated 1.3 million osteoporosis-related fractures occur each year in the United States, with annual costs of approximately $13.8 billion.2 One determinant of lifetime osteopenia and osteoporosis risk is low bone mineral density (BMD). Because 90% of peak bone mass is acquired by age 18 years,35 interventions to maximize BMD in youth may decrease the incidence of osteopenia later in life. For this reason, the National Institute of Child Health and Health Development recently requested applications (RFA: HD-97-006) for the prevention of adult osteoporosis by targeting BMD in youth.

Although a substantial component of osteoporosis risk is genetic,6,7 both diet and physical activity are important modifiers of bone accrual.3 Several controlled trials have found that increasing calcium intake increases BMD in youth.810 Other dietary factors also may maximize the retention of calcium in bones, but few randomized trials have examined these factors in adolescents. Studies suggest that greater fruit and vegetable intake is important for bone health11,12 and is associated with higher BMD.13,14 In addition, studies15,16 suggest that achievement of peak bone mass in adolescent girls is contingent on adequate vitamin D intake. Further, consuming caffeinated beverages, particularly colas, increases risk of bone fracture.17,18 Finally, many studies have suggested that increasing weight-bearing activity increases BMD in children and adolescents.1924

Although much of the research on building healthy bones in youth has targeted younger children,19,21,23,25,26 adolescents may be an equally important target population. Eating and exercise patterns established in adolescence may be more likely to be sustained into adulthood than similar efforts aimed at younger children.27,28 Gains in bone mass are most rapid during adolescence, with as much as 51% of peak bone mass accumulated during pubertal growth.29,30 Interventions to prevent osteoporosis are particularly important in adolescent girls, because they are at a higher risk of developing osteoporosis in adulthood than males.31 Recent reports suggest that vigorous exercise declines in adolescents,32 which makes this time key for intervention.

Preventive interventions conducted in youth generally have involved calcium supplementation, controlled feeding trials, or prescribed exercise in a controlled setting; that is, they have not emphasized sustainable behavioral practices and, thus, not represented community trials. Further, existing youth interventions are mainly school based,3336 largely overlooking the opportunities in other settings such as health care. Inasmuch as most children and adolescents (about 80%) visit a medical provider at least annually (76 million annual contacts with physicians37), such visits are a largely untapped setting in which to offer primary prevention programs. Pediatric patients are influenced by physician advice and are receptive to health behavior recommendations.38 Thus, adolescents may comply with targeted lifestyle interventions offered through health care settings more than with those offered in schools.

METHODS
OVERVIEW

This randomized controlled trial (YOUTH) tested the efficacy of a lifestyle intervention for increasing BMD in adolescent girls initially 14 to 16 years old. The goal of the intervention was to improve diet and increase physical activity. The 3 dietary targets were increasing dairy consumption, eating 8 servings of fruits and vegetables daily, and decreasing soft drink intake. The 2 primary physical activity targets were high-impact exercise and strength training.

SETTING

Kaiser Permanente Northwest is a nonprofit, group-model health maintenance organization (HMO) in the Portland, Ore, metropolitan region that provides comprehensive medical care to more than 440 000 members, including 15 768 female adolescents between 14 and 16 years of age. The research center is located within the HMO but conducts independent, public domain research. The HMO Human Subjects Protection Committee monitored and approved all study procedures.

STUDY POPULATION AND RECRUITMENT, SCREENING, AND RANDOMIZATION

We selected adolescent girls with body mass index below the national median to enrich our sample with girls at risk of low peak BMD.39,40 We also targeted potential participants by selecting for characteristics we expected would enhance adherence to the study (ie, younger girls [freshmen and sophomores], parent or guardian willing to participate in the study, and no indication of psychiatric or psychosocial disorders). We excluded potential participants with any apparent contraindication to the dietary or exercise portions of the intervention, including current or past disordered eating behavior. Potential participants were identified through the HMO's electronic medical record. Health plan member contracts with the HMO provide consent for use of their data in research. Members who met the selection criteria were mailed study invitations, followed by telephone calls from research staff. An informational meeting for interested families meeting study criteria preceded randomization. Eligible adolescent girls were randomized by a computer program developed by one of us (M.A.) into either the lifestyle intervention group or an attentional control group after baseline data collection (between September 1, 2000, and August 31, 2001). The project manager informed participants of group assignment to keep assessors blinded. Treatment group assignment was made by a design-adaptive randomization to minimize group imbalance on physical activity, calcium intake, age, and other factors.41,42 Design-adaptive randomization sequentially assigned girls to the control or intervention groups to achieve, at each step, the maximum balance of factors predictive of bone measurements, such as menarcheal age and participation in organized sports. To conceal allocation, the project biostatistician (M.A.) made allocations in response to project staff requests.

INTERVENTION

The YOUTH intervention emphasized adolescents actively developing strategies for healthy dietary and exercise practices that they could maintain in adulthood. Participants attended group and individual meetings, participated in activities, and received coaching telephone calls (Table 1). They also received psychoeducational information, recorded their diet and exercise goals and achievements, and kept in touch with their cohort through a Web-based study site. We combined elements especially for adolescents (peer-oriented, community-building activities) with those widely recognized as important in lifestyle interventions (individual tailoring). Further, our intervention drew heavily from both mentoring models and motivational interviewing or enhancement techniques.43 Because this trial involved a 2-year intervention and follow-up, we incorporated several adherence and retention components (Table 1). The intervention has been extensively described elsewhere.39

Table 1. 
Study Intervention and Adherence Components
Study Intervention and Adherence Components
ASSESSMENT

Staff who performed clinical and dietary and physical activity assessments were masked to the experimental condition of the participants. These assessors had no additional contact with participants.

BONE MINERAL DENSITY

Bone mineral density was measured using dual-energy x-ray absorptiometry (DEXA; QDR 2000, Hologic Inc, Waltham, Mass) at baseline and at 1- and 2-year follow-up. We assessed BMD and bone mineral content (BMC) for the total body and at specific sites: lumbar spine (L2 through L4), trochanter, femoral neck, and total hip. Independently determined in vivo precision (coefficient of variation) for total hip and lumbar spine in our laboratory were 1.4% and 1.7%, respectively. Phantom scans performed daily during the observation period revealed no change in DEXA machine performance. Our adolescent girls had mostly completed linear growth, and we anticipated little change in bone dimensions during follow-up. Thus, BMD was the primary outcome measure. Nevertheless, we also measured BMC, bone mineral apparent density (BMAD), and bone area. Because volumetric density (BMAD) is difficult to estimate using DEXA, we limited BMAD results to L2 through L4, for which there are established reference ranges for teenagers.44

BODY COMPOSITION AND PHYSICAL DEVELOPMENT

Certified technicians measured weight and height using a standardized protocol.45 Body mass index was calculated as weight in kilograms divided by height in meters squared. The total-body scan (DEXA) was used to measure lean and fat masses. We used years since menarche at baseline as our measure of sexual maturation because46 our minimum age requirement (14 years) meant that most subjects (97%) had reached menarche. Month and year of menarche was updated at every diet recall for those who had not reached menarche at baseline.

BIOMARKERS

We collected blood samples from participants at the beginning and end of the study to examine biochemical markers of bone formation (osteocalcin; Diagnostic Products Corp, Los Angeles, Calif) and bone resorption (N-terminal telopeptides; Ostex International, Inc, Seattle, Wash). Blood samples were drawn in the morning after overnight fasting and handled and assayed according to the manufacturer's specifications.

DIETARY INTAKE

Certified dietary interviewers used unannounced 24-hour telephone diet recalls to obtain data on all foods consumed, preparation method, and portion sizes. Participants were trained to estimate portion size using real food and food models at the screening visit, and received visual aids for estimating portion size of various foods. At baseline, we obtained data from 3 unannounced diet recalls for a 2-week period. Postrandomization, 1 recall was obtained every other month, targeting 4 weekdays and 2 weekend days per year to cover seasonal effects. The 6 dietary recalls in each year were averaged for analysis. Data were directly entered into the ESHA database (ESHA Food Processor, version 8.1, 2003; ESHA Research Inc, Salem, Ore). We limited the nutrient variables to the food group–based nutrition categories potentially relevant for bone mineral accrual: total calcium intake, in milligrams per day; total vitamin D, in international units per day; and fruits and vegetables, in servings per day. In addition, we adapted the ESHA program to output soda intake, in ounces per day, and vitamin supplementation.

WEIGHT-BEARING PHYSICAL ACTIVITY, STRENGTH, AND FITNESS

We used both laboratory and self-reported measures to assess weight-bearing physical activity, strength, and fitness. To determine weight-bearing physical activity, we adapted a 72-hour physical activity recall from the Previous Day Physical Activity Recall form47,48 and administered it like the dietary recalls. Because we examined activity most relevant for bone mineral accrual (high impact, spinal motion, and weight-loading activities), physical activity recall focused on exercise rather than usual daily activities or sedentary behaviors. We defined “high impact” as movement in which both feet were simultaneously off the ground (eg, jumping or running) and “strength training” as any activity that provided muscular resistance (eg, weight training and resistance band use).

Strength and fitness were assessed at the 3 annual clinic visits using standardized protocols and trained assessors. Assessments included hand grip (overall strength), Roman chair and sit-ups (lower back strength), and vertical jump (hip and upper thigh strength). We used sit-ups and vertical jump as representative strength measures.

OTHER STUDY MEASURES

At baseline and follow-up visits, girls completed questionnaires about potential moderators and mediators of outcomes. Only the demographic characteristics and the participant's osteoporosis risk are included here. We defined “adult osteoporosis risk” as the proportion of first- and second-degree relatives of the participant's parents (eg, their parents, aunts, and uncles) whom the parents identified as having hip fractures or osteoporosis. Teen participants also reported their self-perceived risk of osteoporosis later in life.

ANALYSIS

Statistical analyses were conducted using SAS Release 8.2 (SAS Institute Inc, Cary, NC) and STATA version 6.0 (StataCorp, College Station, Tex). Bone mineral density was our primary dependent variable, and the intervention effect was estimated as the adjusted (for baseline values) mean difference between the intervention and control conditions after years 1 and 2. We used a conditional change model and the Zellner seemingly unrelated regression models.4951 This approach uses joint estimates of several regression models. Baseline and change equations were estimated simultaneously because we expected that the 2 equations were not independent. Adjusting for the correlated errors generally leads to more efficient estimates of the coefficients and reduced standard errors in both equations than would result from the use of separate equation estimations. Treatment condition was the primary independent variable. All analyses were adjusted for baseline age, years since menarche, risk of adult osteoporosis, height, body mass index, and the respective bone mass variable. We analyzed the intervention's effects on bone mineral over the initial year of the intervention and across the entire 2-year period. All significance tests were 2-sided.

We used the same regression approach for secondary outcomes: changes in diet and physical activity. In addition, we examined behavioral (overall energy intake and overall physical activity) and anthropometric factors (weight, height, body mass index, and lean and fat masses) that were not targeted for behavioral change.

Of the 1063 girls originally contacted, 228 met the inclusion criteria, agreed to participate, and were randomized to either the intervention or the control group (Figure 1). Of those randomized, 210 (92%) underwent at least 1 bone mineral follow-up test. For those with missing values for the first-year follow-up DEXA measurement (n = 8), the data were imputed by averaging baseline and second-year DEXA values. One girl was excluded after a positive pregnancy screening, bringing the 1-year outcome analysis sample to 209. Two hundred girls had DEXA data at the 2-year follow-up; the sustainability analysis was limited to these girls. Blood was drawn in all girls for biomarker analyses. In a laboratory error, a box of samples was lost; all remaining paired samples (n = 130) were analyzed. We repeated all nonblood analyses with this biomarker subsample; patterns (direction and significance of results) were comparable to those of the entire sample (data not shown).

Figure 1.
Consolidated Standards of Reporting Trials (CONSORT) diagram. Participant flow through the clinical trial. BMI, body mass index (calculated as weight in kilograms divided by height in meters squared).

Consolidated Standards of Reporting Trials (CONSORT) diagram. Participant flow through the clinical trial. BMI, body mass index (calculated as weight in kilograms divided by height in meters squared).

RESULTS
DESCRIPTIVE CHARACTERISTICS

Baseline values for study participants (Table 2) showed they were mainly white (81%) and from middle- to upper-middle-income working homes. The participants were in the lower half of the body mass index distribution (20.6) per the selection criteria, and 97% had reached menarche at enrollment. At baseline, average consumption included 986 mg/d of calcium, 161 IU of vitamin D, 3.6 servings of fruits and vegetables, and less than 6 oz per day of soda. At baseline, total physical activity was 61.9 min/d (including 13.9 min/d of high-impact activity and 6.9 min/d of strength training), with 68.9% of participants reporting participation in organized team sports. No statistically significant differences were found for these variables between the intervention and control groups at baseline.

Table 2. 
Baseline to Year 1 and Year 2 Intervention Outcomes: Behavioral and Other Intermediary Factors*
Baseline to Year 1 and Year 2 Intervention Outcomes: Behavioral and Other Intermediary Factors*
INTERVENTION EFFECTS ON MAIN DIET AND EXERCISE TARGETS

The intervention had a substantial effect on the 3 main dietary targets but not on exercise (Table 2). Participants in the intervention group reported significantly higher consumption compared with those in the control group for calcium in both study years (adjusted mean difference [AMD], 216.6 and 241.3 mg, respectively; P<.001), vitamin D in the first year of the study (AMD, 34.3 IU; P = .02), and fruit and vegetable servings in both study years (AMD, 0.74 and 0.79 servings, respectively; P≤.01). We found no effect of the intervention on soda consumption or significant differences between the conditions in target exercise rates during either year.

INTERVENTION EFFECTS ON BONE MINERAL VARIABLES AND MARKERS OF BONE TURNOVER

Significantly higher BMD was found in the intervention group compared with the control group in the spine (AMD, 0.01; P < .001) and trochanter region (AMD, 0.007; P = .05) and a trend toward higher density in the total hip (AMD, 0.006; P = .08) after 1 year of intervention (Table 3 and Figure 2). We found no significant differences between the groups for BMD for the total body or the femoral neck region or for bone area or BMC for any of the bone regions. The 2 groups differed in spinal BMAD at the year 1 follow-up (AMD, 0.01; P = .001).

Figure 2.
Percent changes in bone mineral density during 2 years. *P <.01; †P <.05.

Percent changes in bone mineral density during 2 years. *P <.01; †P <.05.

Table 3. 
Baseline to Year 1 and Year 2 Bone Marker and Body Composition Outcomes*
Baseline to Year 1 and Year 2 Bone Marker and Body Composition Outcomes*

Data in Table 3 and Figure 2 suggest that the intervention effects on BMD in the spine (AMD, 0.01; P = .007) and trochanter region (AMD, 0.01; P = .03) were maintained during the second study year. During the second year, we observed no differences in bone areas in the 2 groups but significantly higher levels of BMC for the total body (AMD, 19.78; P = .43) and spine (AMD, 7.09; P = .03) in the intervention group compared with the control group. Also, the 2 groups differed in spinal BMAD at the year 2 follow-up (AMD, 0.01; P = .02). In addition, the N-terminal telopeptides biomarker demonstrated a larger relative decrease in the intervention group compared with the control group (AMD, 2.05; P = .02), with nonsignificant changes in osteocalcin. This combination is consistent with more net bone formation in participants in the intervention group.

COMMENT

The YOUTH health care–based lifestyle intervention increased BMD gains and improved dietary intake during a 2-year period. The intervention resulted in significant increases in BMD in the spine and femoral trochanter and increases in dietary calcium, vitamin D, and fruit and vegetable consumption. As expected, in adolescents who had essentially finished growing, we observed no changes in bone size, and the greater increase in BMD seemed to come from a greater accrual of BMC in the intervention group. Further, the biomarkers collected in the baseline and second-year follow-up visits were consistent with the observed bone mineral changes. Changes achieved in BMD and dietary behavior were achieved largely during the first year of the intervention. In the second year, the difference between groups was maintained and BMD and dietary behavior were not further improved in the intervention group. Finally, our retention rate for participants was 88% for the 2 years of the study.

Although we did not directly examine the cellular basis for the BMD changes, the maintenance of serum osteocalcin levels (a marker of osteoblastic function) with a relative reduction in N-terminal telopeptides levels (a marker of osteoclast activity) in the intervention group suggests that the intervention reduced bone resorption while allowing bone formation to continue. We would expect increases in fruit and vegetable intake to reduce dietary acid load, and fruits and vegetables have been associated with reduced bone resorption, maintained bone formation, and higher BMD.13 Similarly, increased calcium and vitamin D intake has been shown to reduce bone resorption. These findings suggest that the skeletal changes induced by the intervention are biologically credible and are likely to enhance bone strength.

The significant increase in BMD in the intervention group was associated with targeted dietary behaviors. This increase in BMD is especially significant because the dietary changes occurred in a community setting. The researchers had no control over the physical environment, and the individually targeted intervention did not affect the girls' peer groups. Other studies with calcium-related bone mineral changes have relied on supplementation rather than influence of adolescents' dietary behavior.52,53 One recent study targeting dietary calcium showed significant increases in dietary calcium but not associated bone mineral changes.54 In addition, studies targeting bone mineral changes have not emphasized other dietary factors that may contribute to BMD.1113 In this study, baseline calcium intake was already close to recommendations, whereas vitamin D and fruit and vegetable intake was below recommendations; this suggests the importance of vitamin D and fruits and vegetables in the outcomes. The improvements achieved in the intervention group in fruit and vegetable consumption (about 20% increase in year 1 and 26% overall increase by year 2; from 3.68 servings at baseline to 4.42 and 4.62 for follow-up years 1 and 2, respectively) exceeded changes achieved in school-based adolescent studies that have specifically examined fruit and vegetable consumption.55,56 The intervention did not significantly affect soda consumption; however, study participants reported drinking little soda.

Despite significant improvements in BMD and dietary targets, reported levels of physical activity and physiologic strength measures did not differ between the intervention and comparison groups. Although levels in individual girls varied substantially, overall trends suggested that physical activity declined in both study conditions. This finding mirrors reports of overall decline in physical activity during adolescence32,57,58 and a recent community trial that attempted to increase weight-bearing physical activity to promote bone mass gains in younger girls.54 Studies that have positively affected adolescent girls' physical activity were school-based interventions that enrolled girls in structured physical education classes53,5961 rather than relying on self-directed changes. In addition to this study's component of self-directed change, our study population reported an initially high level of physical activity: 69% of the girls participated in team sports. Since they were already active, this group may have been a particularly difficult population in which to increase or even shift physical activity. Finally, despite the decline in physical activity, we did not observe a commensurate decline in physical strength or fitness measures.

Although this study uniquely contributes to the previous research, this medical setting has some limitations. Our population was largely white, from middle- to upper-middle-income working families, and had relatively high levels of reported calcium consumption and physical activity at baseline. Therefore, the intervention might need adjustments in different populations. Further, some intervention elements, such as events for participant motivation and retention, may not be easily replicated in all medical settings. Another limitation is that health plans might have less participant contact than schools do. We addressed this limitation by providing a wide range of intervention components with both in-person and remote study contact to maximize participant exposure to the intervention. The effect on participants' dietary habits was more substantial than that achieved in most school-based interventions targeting these factors, although possible differences in participant socioeconomic status may have influenced the ease of achieving the dietary targets. Our results suggest that the dietary intervention designed to empower high school–aged girls to take charge of their health was reasonably successful. Conversely, we had more difficulty in achieving our physical activity targets than school-based interventions do. Health care settings may be best suited to helping adolescents achieve change in domains in which individual tailoring is important and the behavior is more individual; conversely, substantially increasing physical activity may be maximized with the built-in community and structure that school interventions provide.

In summary, the YOUTH project is one of very few preventive research interventions in adolescents conducted in a health plan setting. Information available to medical providers may provide ways of targeting such interventions (eg, a family history of hip fracture or osteoporosis). Our results suggest that a comprehensive health care–based lifestyle intervention can effectively increase bone mineral gains and improve dietary intake. Future research should examine what this magnitude of BMD gain means for adult osteoporosis risk. To our knowledge, this study is the first to significantly improve bone mass in adolescents in a non–school-based intervention emphasizing self-directed behavioral change.

Back to top
Article Information

Correspondence: Lynn L. DeBar, PhD, MPH, Center for Health Research, Kaiser Permanente Northwest, 3800 N Interstate Ave, Portland, OR 97227 (lynn.debar@kpchr.org).

Accepted for Publication: July 7, 2006.

Author Contributions: Dr DeBar had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: DeBar, Ritenbaugh, Aicken, Orwoll, Elliot, Vuckovic, Stevens, and Moe. Intervention design: Stevens. Acquisition of data: DeBar, Ritenbaugh, and Dickerson. Analysis and interpretation of data: Ritenbaugh, Aicken, Orwoll, Dickerson, Vuckovic, and Stevens. Drafting of the manuscript: DeBar, Ritenbaugh, and Aicken. Critical revision of the manuscript for important intellectual content: Ritenbaugh, Aicken, Orwoll, Elliot, Dickerson, Vuckovic, Stevens, and Moe. Statistical analysis: Aicken. Obtained funding: Ritenbaugh, Aicken, and Stevens. Administrative, technical, and material support: DeBar, Ritenbaugh, Orwoll, Elliot, Dickerson, Stevens, and Moe. Study supervision: DeBar and Ritenbaugh. Qualitative research: Vuckovic.

Financial Disclosure: None reported.

Funding/Support: This study was supported by grant R01-HD037744 to Dr DeBar from the National Institute of Child Health and Human Development.

Trial Registration: ClinicalTrials.gov Identifier: NCT00067600.

Acknowledgment: We thank Jen Coury, MA (Center for Health Research, Kaiser Permanente Northwest, Portland, Ore), for helpful comments about previous versions of this article and for editorial assistance; and Gina Keppel, BS, Chris Catlin, BS, Patty LeGarda, BS, Colleen Flattum, RD, Megan Porter, RD, Cynthia Roh, RD, Amanda Petrik, BS, and Pat Elmer, PhD, for assistance with this study.

Dedication: We dedicate this article to the memory of Dr Irving, who was an essential part of the conceptualization and realization of the project.

References
1.
Siris  ESMiller  PDBarrett-Connor  E  et al.  Identification and fracture outcomes of undiagnosed low bone mineral density in postmenopausal women: results from the National Osteoporosis Risk Assessment JAMA 2001;2862815- 2822
PubMedArticle
2.
US Preventive Services Task Force, Guide to Clinical Preventive Services.  Alexandria, Va International Medical Publishing1996;
3.
Teegarden  DProulx  WRMartin  BR  et al.  Peak bone mass in young women J Bone Miner Res 1995;10711- 715
PubMedArticle
4.
Bailey  DAMcKay  HAMirwald  RLCrocker  PRFaulkner  RA A six-year longitudinal study of the relationship of physical activity to bone mineral accrual in growing children: the University of Saskatchewan bone mineral accrual study J Bone Miner Res 1999;141672- 1679
PubMedArticle
5.
Bonjour  JPTheintz  GBuchs  BSlosman  DRizzoli  R Critical years and stages of puberty for spinal and femoral bone mass accumulation during adolescence J Clin Endocrinol Metab 1991;73555- 563
PubMedArticle
6.
Slemenda  CWChristian  JCWilliams  CJNorton  JAJohnston  CC  Jr Genetic determinants of bone mass in adult women: a reevaluation of the twin model and the potential importance of gene interaction on heritability estimates J Bone Miner Res 1991;6561- 567
PubMedArticle
7.
Smith  DMNance  WEKang  KWChristian  JCJohnston  CC  Jr Genetic factors in determining bone mass J Clin Invest 1973;522800- 2808
PubMedArticle
8.
Chan  GMHoffman  KMcMurry  M Effects of dairy products on bone and body composition in pubertal girls J Pediatr 1995;126551- 556
PubMedArticle
9.
Lloyd  TAndon  MBRollings  N  et al.  Calcium supplementation and bone mineral density in adolescent girls JAMA 1993;270841- 844
PubMedArticle
10.
Jackman  LAMillane  SSMartin  BR  et al.  Calcium retention in relation to calcium intake and postmenarcheal age in adolescent females Am J Clin Nutr 1997;66327- 333
PubMed
11.
Lemann  J  JrLitzow  JRLennon  EJ The effects of chronic acid loads in normal man: further evidence for the participation of bone mineral in the defense against chronic metabolic acidosis J Clin Invest 1966;451608- 1614
PubMedArticle
12.
Wachman  ABernstein  DS Diet and osteoporosis Lancet 1968;1958- 959
PubMedArticle
13.
New  SARobins  SPCampbell  MK  et al.  Dietary influences on bone mass and bone metabolism: further evidence of a positive link between fruit and vegetable consumption and bone health? Am J Clin Nutr 2000;71142- 151
PubMed
14.
Tucker  KLHannan  MTChen  HCupples  LAWilson  PWKiel  DP Potassium, magnesium, and fruit and vegetable intakes are associated with greater bone mineral density in elderly men and women Am J Clin Nutr 1999;69727- 736
PubMed
15.
Lehtonen-Veromaa  MKMottonen  TTNuotio  IOIrjala  KMLeino  AEViikari  JS Vitamin D and attainment of peak bone mass among peripubertal Finnish girls: a 3-y prospective study Am J Clin Nutr 2002;761446- 1453
PubMed
16.
Du  XZhu  KTrube  A  et al.  School-milk intervention trial enhances growth and bone mineral accretion in Chinese girls aged 10-12 years in Beijing Br J Nutr 2004;92159- 168[published correction appears in Br J Nutr2005;93571- 572
PubMedArticle
17.
Draper  HHScythes  CA Calcium, phosphorus, and osteoporosis Fed Proc 1981;402434- 2438
PubMed
18.
Wyshak  G Teenaged girls, carbonated beverage consumption, and bone fractures Arch Pediatr Adolesc Med 2000;154610- 613
PubMedArticle
19.
Fuchs  RKBauer  JJSnow  CM Jumping improves hip and lumbar spine bone mass in prepubescent children: a randomized controlled trial J Bone Miner Res 2001;16148- 156
PubMedArticle
20.
Gutin  BOwens  SOkuyama  TRiggs  SFerguson  MLitaker  M Effect of physical training and its cessation on percent fat and bone density of children with obesity Obes Res 1999;7208- 214
PubMedArticle
21.
Heinonen  ASievanen  HKannus  POja  PPasanen  MVuori  I High-impact exercise and bones of growing girls: a 9-month controlled trial Osteoporos Int 2000;111010- 1017
PubMedArticle
22.
McKay  HAPetit  MASchutz  RWPrior  JCBarr  SIKhan  KM Augmented trochanteric bone mineral density after modified physical education classes: a randomized school-based exercise intervention study in prepubescent and early pubescent children J Pediatr 2000;136156- 162
PubMedArticle
23.
Morris  FLNaughton  GAGibbs  JLCarlson  JSWark  JD Prospective ten-month exercise intervention in premenarcheal girls: positive effects on bone and lean mass J Bone Miner Res 1997;121453- 1462
PubMedArticle
24.
Snow-Harter  CBouxsein  MLLewis  BTCarter  DRMarcus  R Effects of resistance and endurance exercise on bone mineral status of young women: a randomized exercise intervention trial J Bone Miner Res 1992;7761- 769
PubMedArticle
25.
Bradney  MPearce  GNaughton  G  et al.  Moderate exercise during growth in prepubertal boys: changes in bone mass, size, volumetric density, and bone strength; a controlled prospective study J Bone Miner Res 1998;131814- 1821
PubMedArticle
26.
McKay  HAPetit  MAKhan  KMSchutz  RW Lifestyle determinants of bone mineral: a comparison between prepubertal Asian- and Caucasian-Canadian boys and girls Calcif Tissue Int 2000;66320- 324
PubMedArticle
27.
Petersen  ACLeffert  N Developmental issues influencing guidelines for adolescent health research: a review J Adolesc Health 1995;17298- 305
PubMedArticle
28.
Steinberg  LSilverberg  SB The vicissitudes of autonomy in early adolescence Child Dev 1986;57841- 851
PubMedArticle
29.
Gordon  CLHalton  JMAtkinson  SAWebber  CE The contributions of growth and puberty to peak bone mass Growth Dev Aging 1991;55257- 262
PubMed
30.
MacKelvie  KJKhan  KMMcKay  HA Is there a critical period for bone response to weight-bearing exercise in children and adolescents? a systematic review Br J Sports Med 2002;36250- 257
PubMedArticle
31.
Campion  JMMaricic  MJ Osteoporosis in men Am Fam Physician 2003;671521- 1526
PubMed
32.
Kimm  SYGlynn  NWKriska  AM  et al.  Decline in physical activity in black girls and white girls during adolescence N Engl J Med 2002;347709- 715
PubMedArticle
33.
MacKelvie  KJMcKay  HAPetit  MAMoran  OKhan  KM Bone mineral response to a 7-month randomized controlled, school-based jumping intervention in 121 prepubertal boys: associations with ethnicity and body mass index J Bone Miner Res 2002;17834- 844
PubMedArticle
34.
Perry  CLBishop  DBTaylor  G  et al.  Changing fruit and vegetable consumption among children: the 5-a-Day Power Plus program in St. Paul, Minnesota Am J Public Health 1998;88603- 609
PubMedArticle
35.
Petit  MAMcKay  HAMacKelvie  KJHeinonen  AKhan  KMBeck  TJ A randomized school-based jumping intervention confers site and maturity-specific benefits on bone structural properties in girls: a hip structural analysis study J Bone Miner Res 2002;17363- 372
PubMedArticle
36.
Reynolds  KDFranklin  FABinkley  D  et al.  Increasing the fruit and vegetable consumption of fourth-graders: results from the High 5 Project Prev Med 2000;30309- 319
PubMedArticle
37.
Sallis  JFPatrick  KFrank  EPratt  MWechsler  HGaluska  DA Interventions in health care settings to promote healthful eating and physical activity in children and adolescents Prev Med 2000;31(suppl 2)S112- S121Article
38.
Fleming  GV Pediatricians and health promotion practitioner goals for the year 2000 Patient Educ Counsel 1993;21143- 154Article
39.
Rollins  DImrhan  VCzajka-Narins  DMNichols  DL Lower bone mass detected at femoral neck and lumbar spine in lower-weight vs normal-weight small-boned women J Am Diet Assoc 2003;103742- 744
PubMedArticle
40.
Blum  MHarris  SSMust  APhillips  SMRand  WMDawson-Hughes  B Weight and body mass index at menarche are associated with premenopausal bone mass Osteoporos Int 2001;12588- 594
PubMedArticle
41.
Taves  DR Minimization: a new method of assigning patients to treatment and control groups Clin Pharmacol Ther 1974;15443- 453
PubMed
42.
Aickin  M Randomization, balance, and the validity and efficiency of design-adaptive allocation methods J Stat Plan Inference 2001;9497- 119Article
43.
Miller  WRRollnick  S Motivational Interviewing: Preparing People for Change. 2nd ed New York, NY Guilford Press2002;
44.
Bachrach  LK Dual energy X-ray absorptiometry (DEXA) measurements of bone density and body composition: promise and pitfalls J Pediatr Endocrinol Metab 2000;13(suppl 2)983- 988
PubMed
45.
Lohman  TGedRoche  AFedMartorell  Red Anthropometric Standardization Reference Manual.  Champaign, Ill Human Kinetics Books1998;
46.
Slemenda  CWReister  TKHui  SLMiller  JZChristian  JCJohnston  CC  Jr Influences on skeletal mineralization in children and adolescents: evidence for varying effects of sexual maturation and physical activity J Pediatr 1994;125201- 207
PubMedArticle
47.
Weston  ATPetosa  RPate  RR Validation of an instrument for measurement of physical activity in youth Med Sci Sports Exerc 1997;29138- 143
PubMedArticle
48.
Lee  KSTrost  SG Validity and reliability of the 3-day physical activity recall in Singaporean adolescents Res Q Exerc Sport 2005;76101- 106
PubMedArticle
49.
Zellner  A An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias J Am Stat Assoc 1962;57348- 368Article
50.
Zellner  AHuang  DS Further properties of efficient estimators for seemingly unrelated regression equations Int Econ Rev 1962;3300- 313Article
51.
Zellner  A Estimators for seemingly unrelated regression equations: some exact finite sample results J Am Stat Assoc 1963;58977- 992Article
52.
Matkovic  VGoel  PKBadenhop-Stevens  NE  et al.  Calcium supplementation and bone mineral density in females from childhood to young adulthood: a randomized controlled trial Am J Clin Nutr 2005;81175- 188
PubMed
53.
Stear  SJPrentice  AJones  SCCole  TJ Effect of a calcium and exercise intervention on the bone mineral status of 16-18-y-old adolescent girls Am J Clin Nutr 2003;77985- 992
PubMed
54.
French  SAStory  MFulkerson  JA  et al.  Increasing weight-bearing physical activity and calcium-rich foods to promote bone mass gains among 9-11-year-old girls: outcomes of the Cal-Girls study Int J Behav Nutr Phys Act 2005;28
PubMedArticle
55.
Lytle  LAMurray  DMPerry  CL  et al.  School-based approaches to affect adolescents' diets: results from the TEENS study Health Educ Behav 2004;31270- 287
PubMedArticle
56.
Nicklas  TAJohnson  CCMyers  LFarris  RPCunningham  A Outcomes of a high school program to increase fruit and vegetable consumption: Gimme 5, a fresh nutrition concept for students J Sch Health 1998;68248- 253
PubMedArticle
57.
Heath  GWPratt  MWarren  CWKann  L Physical activity patterns in American high school students: results from the 1990 Youth Risk Behavior Survey Arch Pediatr Adolesc Med 1994;1481131- 1136
PubMedArticle
58.
Aaron  DJKriska  AMDearwater  SR  et al.  The epidemiology of leisure physical activity in an adolescent population Med Sci Sports Exerc 1993;25847- 853
PubMedArticle
59.
Jamner  MSSpruijt-Metz  DBassin  SCooper  DM A controlled evaluation of a school-based intervention to promote physical activity among sedentary adolescent females: project FAB J Adolesc Health 2004;34279- 289
PubMedArticle
60.
Mcmurray  RGHarrell  JSBangdiwala  SIBradley  CBDeng  SLevine  A A school-based intervention can reduce body fat and blood pressure in young adolescents J Adolesc Health 2002;31125- 132
PubMedArticle
61.
Pate  RRWard  DSSaunders  RPFelton  GDishman  RKDowda  M Promotion of physical activity among high-school girls: a randomized controlled trial Am J Public Health 2005;951582- 1587
PubMedArticle
×