CD-ROM Nutrient Analysis Database Assists Self-monitoring Behavior of Active Duty Air Force Personnel Receiving Nutrition Counseling for Weight Loss

2001 ◽  
Vol 101 (9) ◽  
pp. 1041-1046 ◽  
Author(s):  
MAJ JANE E HEETDERKS-COX ◽  
BETTY B ALFORD ◽  
CAROLYN M BEDNAR ◽  
CYNTHIA J HEISS ◽  
LISA A TAUAI ◽  
...  
Keyword(s):  
Weight Loss ◽  
Air Force ◽  
Active Duty ◽  
Nutrition Counseling ◽  
Nutrient Analysis ◽  
Self Monitoring ◽  
Cd Rom ◽  
Monitoring Behavior

scholarly journals Weight Loss Intervention Impact on the Physical Fitness Test Scores of Air Force Service Members

2019 ◽  
Vol 185 (5-6) ◽  
pp. e781-e787
Author(s):  
Kinsey Pebley ◽  
Alexis Beauvais ◽  
Leslie A Gladney ◽  
Mehmet Kocak ◽  
Robert C Klesges Klesges ◽  
...  
Keyword(s):  
Weight Loss ◽  
Air Force ◽  
Test Scores ◽  
Active Duty ◽  
Institutional Review Board ◽  
Abdominal Circumference ◽  
Health Concern ◽  
Weight Loss Intervention ◽  
Fitness Test ◽  
Institutional Review

Abstract Introduction Overweight and obesity are a major public health concern in the United States, including among active duty military personnel. Approximately 51% of active duty personnel are classified as overweight and 15% are classified as obese. This may impact military readiness. The current study aimed to determine if a weight loss intervention impacted fitness test scores among Air Force personnel. Materials and Methods From 2014 to 2016, 204 Air Force members with overweight/obesity were randomized into either a Self-paced or counselor-initiated arm in a weight loss program. Study procedures were approved by the Institutional Review Board of the 59th Medical Wing in San Antonio and were acknowledged by the Institutional Review Board at the University of Tennessee Health Science Center. Fitness test scores from before, during, and after the intervention were used to determine if the intervention resulted in improvements in overall fitness test ratings and scores on individual components of the test. Results Participants who lost at least 5% of their weight had better fitness ratings during the intervention compared to individuals who did not lose 5%. However, in the overall sample, fitness ratings worsened from preintervention to during the intervention, and from during to postintervention. Participants with overweight had better aerobic scores pre- and postintervention as well as better abdominal circumference scores and better fitness test ratings preintervention, during the intervention and postintervention compared to participants with obesity. Conclusions Behavioral weight management interventions that achieve 5% weight loss may help improve military fitness test ratings.

scholarly journals Describing Transitions in Adherence to Physical Activity Self-monitoring and Goal Attainment in an Online Behavioral Weight Loss Program: Secondary Analysis of a Randomized Controlled Trial (Preprint)

2021 ◽  
Author(s):  
Melissa Lee Stansbury ◽  
Jean R Harvey ◽  
Rebecca A Krukowski ◽  
Christine A Pellegrini ◽  
Xuewen Wang ◽  
...  
Keyword(s):  
Physical Activity ◽  
Weight Loss ◽  
Goal Attainment ◽  
Vigorous Physical Activity ◽  
Secondary Analysis ◽  
Individual Characteristics ◽  
Behavioral Weight Loss ◽  
Self Monitoring ◽  
Program Goal ◽  
Monitoring Behavior

BACKGROUND Standard behavioral weight loss interventions often set uniform physical activity (PA) goals and promote PA self-monitoring; however, adherence remains a challenge and recommendations may not accommodate all individuals. Identifying patterns of PA goal attainment and self-monitoring behavior will offer a deeper understanding of how individuals adhere to different types of commonly prescribed PA recommendations (ie., minutes of moderate-to-vigorous physical activity [MVPA] and daily steps) and guide future recommendations for improved intervention effectiveness. OBJECTIVE This study examined weekly patterns of adherence to steps-based and minutes-based PA goals and self-monitoring behavior during a 6-month online behavioral weight loss intervention. METHODS Participants were prescribed weekly PA goals for steps (7,000 to 10,000 steps/day) and minutes of MVPA (50 to 200 minutes/week) as part of a lifestyle program. Goals gradually increased during the initial 2 months, followed by 4 months of fixed goals. PA was self-reported daily on the study website. For each week, participants were categorized as “adherent” if they self-monitored their PA and met the program PA goal, “suboptimally adherent” if they self-monitored but did not meet the program goal, or “nonadherent” if they did not self-monitor. The probability of transitioning into a less adherent status was examined using multinomial logistic regression. RESULTS Individuals (N=212) were predominantly middle-aged females with obesity, and 31.6% self-identified as a racial/ethnic minority. Initially, 34.4% were categorized as “adherent” to steps-based goals (51.9% “suboptimally adherent” and 13.7% “nonadherent”), and there was a high probability of either remaining “suboptimally adherent” from week-to-week or transitioning to a “nonadherent” status. On the other hand, 70.3% of individuals started out “adherent” to minutes-based goals (16.0% “suboptimally adherent” and 13.7% “nonadherent”), with “suboptimally adherent” seen as the most variable status. During the graded goal phase, individuals were more likely to transition to a less adherent status for minutes-based goals (OR 1.39, 95% CI 1.31-1.48) compared to steps-based goals (OR 1.24, 95% CI 1.17-1.30); however, no differences were seen during the fixed goal phase (minutes-based goals: OR 1.06, 95% CI 1.05, 1.08 versus steps-based goals: OR 1.07, 95% CI 1.05, 1.08). CONCLUSIONS States of vulnerability to poor PA adherence can emerge rapidly and early in obesity treatment. There is a window of opportunity within the initial two months to bring more people towards “adherent” behavior, especially those who fail to meet the prescribed goals but engage in self-monitoring. While this study describes the probability of adhering to steps-based and minutes-based targets, it will be prudent to determine how individual characteristics and contextual states relate to these behavioral patterns, which can inform how best to adapt interventions. CLINICALTRIAL This study was a secondary analysis of a pre-registered randomized trial (Trial Registration: ClinicalTrials.gov NCT02688621).

Lose your weight with online buddies: behavioral contagion in an online weight-loss community

2019 ◽  
Vol 33 (1) ◽  
pp. 22-36
Author(s):  
Xiaolong Song ◽  
Jiahua Jin ◽  
Yi-Hung Liu ◽  
Xiangbin Yan
Keyword(s):  
Weight Loss ◽  
Peer Influence ◽  
Network Structure ◽  
Online Social Networks ◽  
Behavioral Changes ◽  
Contagion Effect ◽  
Social Contacts ◽  
Self Monitoring ◽  
Content Type ◽  
Monitoring Behavior

Purpose A question of interest is whether online social networks are effective in promoting behavioral changes and weight loss. The purpose of this paper is to examine the contagion effect of an online buddy network on individuals’ self-monitoring behavior. Design/methodology/approach This study collects data from an online weight-loss community and constructs an online buddy network. This study compares the effects of the network structure of the buddy network and the actor attributes when predicting self-monitoring performance by employing the auto-logistic actor attribute models. Findings This study confirms the contagion effect on weigh-in behavior in the online buddy network. The contagion effect is significantly predictive when controlling for actor attribute and other network structure effects. Originality/value There is limited evidence that one’s weight-related behavior can be affected by online social contacts. This study contributes to the literature on peer influence on health by examining the contagion effect on weight-related behavior between online buddies. The findings can assist in designing peer-based interventions to harness influence from online social contacts for weight loss.

scholarly journals Characterizing Self-Monitoring Behavior and Its Association With Physical Activity and Weight Loss Maintenance

2018 ◽  
pp. 155982761879055 ◽  
Author(s):  
Kara L. Gavin ◽  
Nancy E. Sherwood ◽  
Julian Wolfson ◽  
Mark A. Pereira ◽  
Jennifer A. Linde
Keyword(s):  
Physical Activity ◽  
Weight Loss ◽  
Weight Loss Maintenance ◽  
Self Monitoring ◽  
Monitoring Behavior

scholarly journals Characteristics Associated With Participation in a Behavioral Weight Loss Randomized Control Trial in the U.S. Military

2018 ◽  
Vol 184 (3-4) ◽  
pp. e120-e126 ◽  
Author(s):  
Margaret C Fahey ◽  
Marion E Hare ◽  
Gerald W Talcott ◽  
Mehmet Kocak ◽  
Ann Hryshko-Mullen ◽  
...  
Keyword(s):  
Weight Loss ◽  
Air Force ◽  
Active Duty ◽  
College Degree ◽  
Behavioral Weight Loss ◽  
Randomized Controlled ◽  
Institutional Review ◽  
Active Duty Personnel ◽  
Weight Loss Interventions ◽  
The U.S

Abstract Introduction Effective recruitment and subsequent enrollment of diverse populations is often a challenge in randomized controlled trials, especially those focused on weight loss. In the civilian literature, individuals identified as racial and ethnic minorities, men, and younger and older adults are poorly represented in weight loss interventions. There are limited weight loss trials within military populations, and to our knowledge, none reported participant characteristics associated with enrollment. There may be unique motives and barriers for active duty personnel for enrollment in weight management trials. Given substantial costs and consequences of overweight and obesity in the U.S. military, identifying predictors and limitations to diverse enrollment can inform future interventions within this population. The study aims to describe the recruitment, screening, and enrollment process of a military weight loss intervention. Demographic and lifestyle characteristics of military personnel lost between screening and randomization are compared to characteristics of personnel randomized in the study and characteristics of the Air Force in general. Materials and Methods The Fit Blue study, a randomized controlled behavioral weight loss trial for active duty personnel, was approved by the Institutional Review Board of the Wilford Hall Ambulatory Surgical Center in San Antonio, TX, USA and acknowledged by the Institutional Review Board at the University of Tennessee Health Science Center. Logistic regressions compared participant demographics, anthropometric data, and health behaviors between personnel that attended a screening visit but were not randomized and those randomized. Multivariable models were constructed for the likelihood of being randomized using a liberal entry and stay criteria of 0.10 for the p-values in a stepwise variable selection algorithm. Descriptive statistics compared the randomized Fit Blue cohort demographics to those of the U.S. Air Force Results In univariate analyses, older age (p < 0.02), having a college degree or higher (p < 0.007) and higher military rank (p < 0.02) were associated with completing the randomization process. The randomized cohort reported a lower percentage of total daily kilocalories for fat compared to the non-randomized cohort (p = 0.033). The non-randomized cohort reported more total minutes and intensity of physical activity (p = 0.073). In the multivariate model, only those with a college degree or higher were 3.2 times more likely to go onto randomization. (OR = 3.2, 95% CI = 2.0, 5.6, p < 0.0001). The Fit Blue study included a higher representation of personnel who identified as African American (19.4% versus 15.0%) and Hispanic/Latino (22.7% versus 14.3%) compared with the U.S. Air Force in general; however, men were underrepresented (49.4% versus 80.0%). TABLE I.Comparisons of Demographic Characteristics of Randomized Fit Blue Cohort to Screened Non-Randomized CohortFit Blue Randomized Participants (N = 248)Non-Randomized Cohort (N = 111)All Screened Participants (N = 359)p-ValueSex N (%)0.73 Male122 (49.2)52 (46.8)174 (48.5) Female126 (50.8)59 (53.2)183 (51.5)Age Mean (±SD) years34 (±7.5)32 (±6.7)33 (±7.3)0.02Race N (%)0.89 African American49 (19.8)22 (19.8)71 (19.8) Caucasian163 (65.7)75 (67.6)238 (66.3) Other36 (14.5)14 (12.2)50 (13.9)Ethnicity N (%)0.59 Hispanic/Latino56 (22.6)28 (25.2)84 (23.4) Non-Hispanic/Latino192 (77.4)83 (74.8)275 (76.6)Education N (%)<0.0001 Less than college degree123 (49.6)82 (73.9)205 (57.1) College degree or greater125 (50.4)29 (26.1)154 (42.9)Marital status N (%)0.83 Single/never married40 (16.1)20 (18)60 (16.7) Married/living as married169 (68.1)72 (64.9)241 (67.1) Separated/divorced39 (15.7)19 (17.1)58 (16.2)Number of additional adults in household N (%)0.82 046 (18.5)22 (19.8)68 (18.9) 1162 (65.3)73 (65.8)235 (65.5) 231 (12.5)14 (12.6)45 (12.5) 3 or more9 (3.6)2 (1.8)11 (3.1)Number of children in household N (%)0.56 091 (36.7)37 (33.3)128 (35.7) 159 (23.8)23 (20.7)82 (22.8) 257 (23)26 (23.4)83 (23.1) 3 or more41 (16.5)25 (22.5)66 (18.4)Years in service mean (± SD)12 (±6.6)11 (±6.1)12 (±6.4)0.20Military gradeaN (%)0.02 E1–E434 (13.7)19 (17.1)53 (14.8) E5–E6105 (42.3)58 (52.3)163 (45.4) E7–E952 (21)21 (18.9)73 (20.3) O1–O317 (6.9)9 (8.1)26 (7.2) O4–O639 (15.7)4 (3.6)43 (12)Branch0.68 Army4 (1.6)1 (0.9)5 (1.4) Air Force234 (94.4)105 (94.6)339 (94.4) Navy8 (3.2)5 (4.5)13 (3.6) Marine Corp2 (0.8)0 (0.0)2 (0.6)BMI (m2/kg) N (%)30.6 (±2.7)30.4 (±2.9)30.6 (±2.8)BMI category N (%)0.76 Overweight115 (46.4)52 (48.1)167 (46.9) Obese133 (53.6)56 (51.9)189 (53.1)aMilitary ranking; Enlisted (E) categories: E1–E4 (enlisted), E5–E6 (non-commissioned officers), E7–E9 (senior non-commissioned officers) and two Officer categories (O): O1–O3 (Company Grade Officer) and O4–O6 (Field Grade Officer); standard deviation (SD).Table II.Comparisons of Anthropometric Characteristics of Randomized Fit Blue Cohort to Screened Non-Randomized CohortFit Blue Randomized Participants (N = 248)Non-Randomized Cohort (N = 111)All Screened Participants (N = 359)p-ValuePhysical activity Total physical activity2525 (±3218)2840 (±2541)2621 (±3028)0.027 (mean (±SD) minutes per week) Total sedentary physical activity5046 (±239)472 (±221)494 (±234)0.35 (mean (±SD) minutes per week) Vigorous physical activity34 (±145)54 (±152)40 (±147)0.036 (mean (±SD) minutes per week)Dietary intake Total sweetened beverages (kcal per day)165 (±206)152.9 (±166)160.8 (±194)0.80 Fruit and vegetable consumption (cups per day)3 (±1)3 (±1)3 (±1)0.52 Dietary fat (% total kcal)35 (±4)34 (±4)35 (±4)0.033 Conclusions Accounting for all influencing characteristics, higher educational status was the only independent predictor of randomization. Perhaps, highly educated personnel are more invested in a military career, and thus, more concerned with consequences of failing required fitness tests. Thus, it may be important for future weight loss interventions to focus recruitment on less-educated personnel. Results suggest that weight loss interventions within a military population offer a unique opportunity to recruit a higher prevalence of males and individuals who identify as racial or ethnic minorities which are populations commonly underrepresented in weight loss research.

Predicting the potential for active duty success of rehabilitated Air Force prisoners.

1967 ◽  
Author(s):  
Thomas H. Smith ◽  
C. Deene Gott ◽  
Robert A. Bottenberg
Keyword(s):  
Air Force ◽  
Active Duty

Spouse Abuse Perpetration among Active Duty Air Force Personnel Who Have Deployed During OEF/OIF

2012 ◽  
Author(s):  
Wendy Travis ◽  
Mandy M. Rabenhorst ◽  
Randy J. McCarthy ◽  
Joel S. Milner ◽  
Rachel E. Foster ◽  
...  
Keyword(s):  
Air Force ◽  
Spouse Abuse ◽  
Active Duty

scholarly journals Long-Term Evaluation of Counselor Efficiency at Providing Feedback in a Technology-Based Behavioral Weight Loss Intervention (Preprint)

2020 ◽  
Author(s):  
Margaret Fahey ◽  
Robert C. Klesges ◽  
Mehmet Kocak ◽  
Leslie Gladney ◽  
Gerald W. Talcott ◽  
...  
Keyword(s):  
Weight Loss ◽  
Electronic Mail ◽  
Detailed Examination ◽  
Monitoring Data ◽  
Weight Loss Intervention ◽  
Behavioral Weight Loss ◽  
Self Monitoring ◽  
Future Technology ◽  
Study Implementation ◽  
Weight Loss Interventions

BACKGROUND Feedback for participants’ self-monitoring is a crucial, and costly, component of technology-based weight loss interventions. Detailed examination of interventionist time when reviewing and providing feedback for online self-monitoring data is unknown. OBJECTIVE Study purpose was to longitudinally examine time counselors spent providing feedback on participant self-monitoring data (i.e., diet, physical activity, weight) in a 12-month technology-based weight loss intervention. We hypothesized that counselors would deliver feedback to participants more quickly over time. METHODS Time counselors (N=10) spent reviewing and providing feedback to participants via electronic mail (e-email) was longitudinally examined for all counselors across the three years of study implementation. Descriptives were observed for counselor feedback duration across counselors by 12 annual quarters (i.e., three-month periods). Differences in overall duration times by each consecutive annual quarter were analyzed using Wilcoxon-Mann-Whitney tests. RESULTS There was a decrease in counselor feedback duration from first to second quarter [Mean (M) = 53 to 46 minutes], and from second to third (M= 46 to 30). A trend suggested a decrease from third to fourth quarters (M = 30 to 26), but no changes were found in subsequent quarters. Consistent with hypothesis, counselors increased their efficiency in providing feedback. Across 12-months, mean time counselors needed to review participant self-monitoring and provide feedback decreased from 53 to 26 minutes. CONCLUSIONS Counselors needed increasingly less time to review online self-monitoring data and provide feedback after the initial nine months of study implementation. Results inform counselor costs for future technology-based behavioral weight loss interventions. For example, regardless of increasing counselor efficiency, 25-30 minutes per feedback message is a high cost for interventions. One possibility for reducing costs would be generating computer-automated feedback. CLINICALTRIAL NCT02063178

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