Association between consistent weight gain tracking and gestational weight gain: Secondary analysis of a randomized trial

Christine M Olson, Myla S Strawderman, Meredith L Graham, Christine M Olson, Myla S Strawderman, Meredith L Graham

Abstract

Objective: The effective components of interventions for reducing excessive gestational weight gain (GWG) remain to be identified. This study investigated the sociodemographic, physical, psychosocial, and environmental correlates of online GWG tracking and its independent association with GWG outcomes.

Methods: Eight hundred ninety-eight women in the intervention arms of a randomized trial assessing the effectiveness of an integrated online and mobile phone behavioral intervention to decrease the prevalence of excessive GWG were included in this secondary analysis. Data were analyzed using χ2 analysis and modified Poisson and linear regression approaches.

Results: Only 16.5% of low-income (Medicaid-eligible) women consistently tracked GWG, as did 34.2% of not-low-income women. More highly educated, older, and white women were more likely to be consistent GWG trackers. Among not-low-income women, consistent GWG tracking was associated with 2.35 kg less GWG (95% CI: -3.23 to -1.46 kg; P < 0.0001) and a reduced risk of excessive GWG (RR 0.73; 95% CI: 0.59 to 0.89; P = 0.002).

Conclusions: Electronic tracking of GWG is an effective component of electronic and mobile health interventions aiming to decrease the prevalence of excessive GWG in not-low-income women. Income group-specific motivators are needed to increase the prevalence of GWG tracking.

Trial registration: ClinicalTrials.gov NCT01331564.

Conflict of interest statement

DISCLOSURE: None of the authors have any conflicts of interest to disclosure.

© 2017 The Obesity Society.

Figures

Figure 1
Figure 1
Variables from the Integrative Model of Behavioral Prediction (19) considered for models of consistent tracking and weight outcomes.
Figure 2
Figure 2
Total gestational weight gain (GWG) in kg by consistency of use of weight gain tracker among low (Medicaid eligible) and not- low income women. a (KW) is the level of significance for the Kruskal-Wallis chi-square test assessing whether the total amount of gestational weight gain varies by consistency of use of the weight gain tracker.
Figure 3
Figure 3
Percent with excessive total gestational weight gain (GWG) by consistency of use of weight gain tracker among low (Medicaid eligible) and not-low income women. a P(MH) is the level of significance for the Mantel-Haenszel chi-square test assessing whether the rate of excessive total gestational weigh gain is linearly related to the consistency of use of the tracker

References

    1. Grivell RM, O’Brien CM, Dodd JM. Managing obesity in pregnancy: A change in focus from harm minimization to prevention. Seminars in Reproductive Medicine. 2016;34(2):e-38–48.
    1. IOM (Institute of Medicine), National Research Council (NRC) Weight Gain During Pregnancy: Reexamining the Guidelines. Washington, D.C.: The National Academies Press; 2009.
    1. Viswanathan M, Siega-Riz AM, Moos M-K, Deierlein A, Mumford A, Knaack J, et al. Outcomes of Maternal Weight Gain. Rockville, MD: Agency for Healthcare Research and Quality; 2008. (Evidence Report/Technology Assessment No 168).
    1. Muktabhant B, Lawrie T, Lumbiganon P, Laopaiboon M. Diet or exercise, or both, for preventing excessive weight gain in pregnancy. Cochrane Database of Systematic Reviews. 2015:CD007145.
    1. Murray E. Web-based interventions for behavior change and self-management: potential, pitfalls, and progress. Medicine 2.0. 2012;1:e3.
    1. Harris J, Felix L, Miners A, Murray E, Michie S, et al. Adaptive e-learning to improve dietary behaviour: a systematic review and cost-effectiveness analysis. Health Technol Assess. 2011;15 (37):1–160.
    1. Arem H, Irwin J. A review of web-based weight loss interventions in adults. Obes Rev. 2011;12(5):e256–42.
    1. Kodama S, Saito K, Tanaka S, Horikawa C, Fujiwara K, Hirasawa R, et al. Effect of web-based lifestyle modification on weight control: a meta-analysis. Int J Obes. 2012;36:675–685.
    1. Soltani H, Arden MA, Duxbury AMS, Fair FJ. An analysis of behaviour change techniques used in a sample of gestational weight management trials. (Ariticle No 1085916).J Preg. 2016
    1. Jensen MD, Ryan DH, Donato KA, Apovian CM, Ard JD, Comuzzie AG, et al. Guidelines (2013) for managing overweight and obesity in adults. Obesity. 2014;22(S2):S1–S410.
    1. Zheng Y, Klem ML, Sereika SM, Danford CA, Eving LJ, Burke LE. Welf-weighing in weight management: a systematic literature review. Obesity. 2015;23(2):256–265.
    1. Burke LE, Conroy MB, Sereika SM, Elci OU, Styn MA, Acharya SD, et al. The effect of electronic self-monitoring on weight loss and dietary intake: a randomized behavioral weight loss trial. Obesity. 2011;19(2):338–344.
    1. Harrison CL, Teede HJ, Lombard CB. How effective is self-weighing in the setting of a lifestyle intervention to reduce gestational weight gain and pospartum weight retention? Aust NZ J Obstet Gynaecol. 2014;54(4):382–5.
    1. Jeffries K, Shub A, Walker SP, Hiscock R, Permezel M. Reducing excessive weight gain in pregnancy: a randomised controlled trial. Med J Aust. 2009;191:429–433.
    1. McCarthy EA, Walker SP, Ugoni A, Lappas J, Leong O, Shub A. Self-weighing and simple dietary advice for overweight and obese pregnant women to reduce obstetric coomplications without impact on quality of life: a randomised trial. BJOG. 2016;123:965–973.
    1. Graham ML, Strawderman MS, Demment M, Olson CM. Does usage of an ehealth intervention reduce the risk of excessive gestational weight gain? Secondary analysis from a randomized controlled trial. J Med Internet Res. 2017;19(1):e6.
    1. Lytle LA, Svetkey LP, Patrick K, Belle SH, Fernandez ID, Jakicic JM, et al. The EARLY trials: a consortium of studies targeting weight control in young adults. Transl Behav Med. 2014;4:304–313.
    1. Fernandez I, Groth S, Reschke J, Graham M, Strawderman M, Olson C. Electronically-mediated weight interventions for pregnant and postpartum women. Study design and baseline characteristics. Contemp Clin Trials. 2015;43:63–74.
    1. Fishbein M, Yzer MC. Using theory to design effective health behavior interventions. Communication Theory. 2003;13:164–183.
    1. Fogg B. Persuasive Technology Using computers to change what we think and do. San Francisco, CA: Morgan Kaufmann; 2002.
    1. Graham M, Uesegi K, Niederdeppe J, Gay G, Olson CM. The theory, development, and implementation of an e-Intervention to prevent excessive gestational weight gain: e-Moms Roc. Telemed J E Health. 2014;20:1–8.
    1. Kendall A, Olson CM, Frongillo EA., Jr Evaluation of psychosocial measures for understanding weight-related behaviors in pregnant women. Ann Behav Med. 2001;23(2):50–58.
    1. Graham J. Missing data analysis: making it work in the real world. Ann Rev Psychol. 2009;60:549–576.
    1. Zou G. A modified Poisson regression approach to prospective studies with binary data. Am J Epidem. 2004;159:702–706.

Source: PubMed

Patrocinadores y Colaboradores

Condiciones médicas

Intervenciones de drogas

3
Suscribir