Author(s):
- Rhiannon Berry
- Aikaterini Kassavou
- Stephen Sutton
Abstract:
Establish whether digital self-monitoring of diet and physical activity is effective at supporting weight loss, increasing physical activity and improving eating behavior in adults with obesity or overweight, and determine the intervention components that might explain variations in its effectiveness. A systematic search of MEDLINE, Embase, PsycINFO, Web of Science, Scopus, Cinahl, and CENTRAL identified 4068 studies, of which 12 randomized controlled trials were eligible and included in the review. A random-effect meta-analysis evaluated intervention effectiveness and subgroup analyses tested for effective intervention content. Twelve studies were included in the review and meta-analysis. Digital self-monitoring of both diet and physical activity had a statistically significant effect at supporting weight loss (mean difference [MD] = -2.87 [95% CI −3.78, −1.96], P < 0.001, I2 = 69%), improving moderate physical activity (standardized mean difference [SMD] = 0.44 [95% CI 0.26, 0.62], P < 0.001, I2 = 0%), and reducing calorie intake (MD = −181.71 [95% CI −304.72, −58.70], P < 0.01, I2 = 0%). Tailored interventions were significantly more effective than nontailored interventions (x2 = 12.92, P < 0.001). Digital self-monitoring of physical activity and diet is an effective intervention to support weight loss in adults with obesity or overweight. This effect is significantly associated with tailored advice. Future studies should use rigorous designs to explore intervention effectiveness to support weight loss as an adjunct to weight management services.
Documentation:
https://doi.org/10.1111/obr.13306
References:
Blüher M. Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol. 2019; 15(5): 288- 298. https://doi.org/10.1038/s41574-019-0176-8
CrossrefPubMedWeb of Science®Google Scholar 2 World Health Organization Website. Obesity and overweight. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. Accessed December 1, 2020.
Google Scholar 3Ward ZJ, Bleich SN, Cradock AL, et al. Projected U.S. state-level prevalence of adult obesity and severe obesity. N Engl J Med. 2019; 381(25): 2440- 2450. https://doi.org/10.1056/nejmsa1909301
CrossrefPubMedWeb of Science®Google Scholar 4Wang YC, McPherson K, Marsh T, Gortmaker SL, Brown M. Health and economic burden of the projected obesity trends in the USA and the UK. Lancet. 2011; 378(9793): 815- 825. https://doi.org/10.1016/S0140-6736(11)60814-3
CrossrefPubMedWeb of Science®Google Scholar 5Caussy C, Pattou F, Wallet F, et al. Prevalence of obesity among adult inpatients with COVID-19 in France. Lancet Diabetes Endocrinol. 2020; S2213-8587(20): 30160- 30161. https://doi.org/10.1016/S2213-8587(20)30160-1
Google Scholar 6Schrauwen P, Westerterp KR. The role of high-fat diets and physical activity in the regulation of body weight. Br J Nutr. 2000; 84: 417- 427. https://doi.org/10.1017/S0007114500001720
CrossrefCASPubMedWeb of Science®Google Scholar 7 NICE Pathways. Lifestyle weight management services for overweight or obese adults overview. https://pathways.nice.org.uk/pathways/lifestyle-weight-management-services-for-overweight-or-obese-adults. Accessed September 16, 2020.
Google Scholar 8Dombrowski SU, Avenell A, Sniehotta FF. Behavioural interventions for obese adults with additional risk factors for morbidity: systematic review of effects on behaviour, weight and disease risk factors. Obes Facts. 2010; 3(6): 377- 396. https://doi.org/10.1159/000323076
CrossrefPubMedWeb of Science®Google Scholar 9Krebs P, Duncan DT. Health app use among US mobile phone owners: a national survey. JMIR Mhealth Uhealth. 2015; 3(4):e101. https://doi.org/10.2196/mhealth.4924
CrossrefPubMedWeb of Science®Google Scholar 10Teixeira PJ, Marques MM. Health behavior change for obesity management. Obes Facts. 2018; 10(6): 666- 673. https://doi.org/10.1159/000484933
CrossrefWeb of Science®Google Scholar 11Michie S, Richardson M, Johnston M, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013; 46(1): 81- 95. https://doi.org/10.1007/s12160-013-9486-6
CrossrefPubMedWeb of Science®Google Scholar 12Helsel DL, Jakicic JM, Otto AD. Comparison of techniques for self-monitoring eating and exercise behaviors on weight loss in a correspondence-based intervention. J Am Diet Assoc. 2007; 107(10): 1807- 1810. https://doi.org/10.1016/j.jada.2007.07.014
CrossrefPubMedWeb of Science®Google Scholar 13Neve M, Morgan PJ, Jones PR, Collins CE. Effectiveness of web-based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: a systematic review with meta-analysis. Obes Rev. 2010; 11(4): 306- 321. https://doi.org/10.1111/j.1467-789X.2009.00646.x
Wiley Online LibraryCASPubMedWeb of Science®Google Scholar 14Beleigoli AM, Andrade AQ, Cançado AG, Paulo MNL, Diniz MDFH, Ribeiro AL. Web-based digital health interventions for weight loss and lifestyle habit changes in overweight and obese adults: systematic review and meta-analysis. J Med Internet Res. 2019; 21(1):e298. https://doi.org/10.2196/jmir.9609
CrossrefPubMedWeb of Science®Google Scholar 15Hutchesson MJ, Rollo ME, Krukowski R, et al. eHealth interventions for the prevention and treatment of overweight and obesity in adults: a systematic review with meta-analysis. Obes Rev. 2015; 16(5): 376- 392. https://doi.org/10.1111/obr.12268
Wiley Online LibraryCASPubMedWeb of Science®Google Scholar 16Schoeppe S, Alley S, Van Lippevelde W, et al. Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review. Int J Behav Nutr Phys Act. 2016; 13(1): 127. https://doi.org/10.1186/s12966-016-0454-y
CrossrefPubMedWeb of Science®Google Scholar 17Patel ML, Wakayama LN, Bennett GG. Self-monitoring via digital health in weight loss interventions: a systematic review among adults with overweight or obesity. Obesity. 2021; 29: 478- 499. https://doi.org/10.1002/oby.23088
Wiley Online LibraryPubMedWeb of Science®Google Scholar 18 WHO|Obesity. World Health Organization Website. https://www.who.int/topics/obesity/en/. Accessed August 7, 2020.
Google Scholar 19Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan—a web and mobile app for systematic reviews. Syst Rev. 2016; 5(1): 210. https://doi.org/10.1186/s13643-016-0384-4
CrossrefPubMedWeb of Science®Google Scholar 20 Cochrane Bias. Cochrane Bias Website. RoB 2: a revised Cochrane risk-of-bias tool for randomized trials. https://methods.cochrane.org/bias/resources/rob-2-revised-cochrane-risk-bias-tool-randomized-trials. Accessed September 16, 2020.
Google Scholar 21 RevMan|Cochrane Training. Cochrane Training Website. https://training.cochrane.org/online-learning/core-software-cochrane-reviews/revman. Accessed September 14, 2020.
Google Scholar 22 Cochrane Training. Cochrane Training Website. Chapter 6: choosing effect measures and computing estimates of effect. https://training.cochrane.org/handbook/current/chapter-06. Accessed September 14, 2020.
Google Scholar 23Borenstein M, Hedges L, Higgins JPT, Rothstein H. Meta-analysis fixed effect vs. random effects. In: M Bornstein, L Hedges, JPT Higgins, H Rothstein, eds. Introduction to Meta-Analysis. John Wiley & Sons, Inc; 2009: 77- 86. https://doi.org/10.1002/9780470743386.ch13
Wiley Online LibraryGoogle Scholar 24Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Br Med J. 2003; 327(7414): 557- 560. https://doi.org/10.1136/bmj.327.7414.557
CrossrefPubMedGoogle Scholar 25Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009; 339(7716): 332- 336. https://doi.org/10.1136/bmj.b2535
Google Scholar 26Mehring M, Haag M, Linde K, et al. Effects of a general practice guided web-based weight reduction program—results of a cluster-randomized controlled trial. BMC Fam Pract. 2013; 14(1): 1- 8. https://doi.org/10.1186/1471-2296-14-76
CrossrefPubMedWeb of Science®Google Scholar 27Allen JK, Stephens J, Dennison Himmelfarb CR, Stewart KJ, Hauck S. Randomized controlled pilot study testing use of smartphone technology for obesity treatment. J Obes. 2013; 2013: 1- 7. https://doi.org/10.1155/2013/151597
CrossrefGoogle Scholar 28Cadmus-Bertram L, Nelson SH, Hartman S, Patterson RE, Parker BA, Pierce JP. Randomized trial of a phone-and web-based weight loss program for women at elevated breast cancer risk: the HELP Study HHS Public Access. J Behav Med. 2016; 39(4): 551- 559. https://doi.org/10.1007/s10865-016-9735-9
CrossrefPubMedWeb of Science®Google Scholar 29Collins CE, Morgan PJ, Jones P, et al. A 12-week commercial web-based weight-loss program for overweight and obese adults: randomized controlled trial comparing basic versus enhanced features. J Med Internet Res. 2012; 14(2): 128- 143. https://doi.org/10.2196/jmir.1980
CrossrefWeb of Science®Google Scholar 30Fukuoka Y, Gay CL, Joiner KL, Vittinghoff E. A novel diabetes prevention intervention using a mobile app: a randomized controlled trial with overweight adults at risk. Am J Prev Med. 2015; 49(2): 223- 237. https://doi.org/10.1016/j.amepre.2015.01.003
CrossrefPubMedWeb of Science®Google Scholar 31Glasgow RE, Kurz D, King D, et al. Outcomes of minimal and moderate support versions of an internet-based diabetes self-management support program. J Gen Intern Med. 2010; 25(12): 1315- 1322. https://doi.org/10.1007/s11606-010-1480-0
CrossrefPubMedWeb of Science®Google Scholar 32Hartman SJ, Nelson SH, Cadmus-Bertram LA, Patterson RE, Parker BA, Pierce JP. Technology- and phone-based weight loss intervention: pilot RCT in women at elevated breast cancer risk. Am J Prev Med. 2016; 51(5): 714- 721. https://doi.org/10.1016/j.amepre.2016.06.024
CrossrefPubMedWeb of Science®Google Scholar 33Hutchesson M, Callister R, Morgan P, et al. A targeted and tailored eHealth weight loss program for young women: the Be Positive Be Healthe Randomized Controlled Trial. Healthcare. 2018; 6(2):39. https://doi.org/10.3390/healthcare6020039
CrossrefWeb of Science®Google Scholar 34Pellegrini CA, Verba SD, Otto AD, Helsel DL, Davis KK, Jakicic JM. The comparison of a technology-based system and an in-person behavioral weight loss intervention. Obesity. 2012; 20(2): 356- 363. https://doi.org/10.1038/oby.2011.13
Wiley Online LibraryPubMedWeb of Science®Google Scholar 35Stephens JD, Yager AM, Allen J. Smartphone technology and text messaging for weight loss in young adults: a randomized controlled trial. J Cardiovasc Nurs. 2017; 32(1): 39- 46. https://doi.org/10.1097/JCN.0000000000000307
CrossrefPubMedWeb of Science®Google Scholar 36Watson S, Woodside JV, Ware LJ, et al. Effect of a web-based behavior change program on weight loss and cardiovascular risk factors in overweight and obese adults at high risk of developing cardiovascular disease: randomized controlled trial. J Med Internet Res. 2015; 17(7):e177. https://doi.org/10.2196/jmir.3828
CrossrefPubMedWeb of Science®Google Scholar 37Morgan PJ, Lubans DR, Collins CE, Warren JM, Callister R. The SHED-IT randomized controlled trial: evaluation of an internet-based weight-loss program for men. Obesity. 2009; 17(11): 2025- 2032. https://doi.org/10.1038/oby.2009.85
Wiley Online LibraryPubMedWeb of Science®Google Scholar 38Richardson M, Garner P, Donegan S. Interpretation of subgroup analysis in systematic reviews: a tutorial. Clin Epidemiol Glob Health. 2019; 7(2): 192- 198. https://doi.org/10.1016/j.cegh.2018.05.005
CrossrefWeb of Science®Google Scholar 39Akobeng AK. Understanding randomised controlled trials. Arch Dis Child. 2005; 90(8): 840- 844. https://doi.org/10.1136/adc.2004.058222
CrossrefCASPubMedWeb of Science®Google Scholar 40Karanicolas PJ, Farrokhyar F, Bhandari M. Practical tips for surgical research: blinding: who, what, when, why, how? Can J Surg. 2010; 53(5): 345- 348. Accessed October 12, 2020
PubMedWeb of Science®Google Scholar 41Kim DD, Basu A. Estimating the medical care costs of obesity in the United States: systematic review, meta-analysis, and empirical analysis. Value Health. 2016; 19(5): 602- 613. https://doi.org/10.1016/j.jval.2016.02.008
CrossrefPubMedWeb of Science®Google Scholar 42 Adult Obesity Causes & Consequences|Overweight & Obesity|CDC. Centers for Disease Control and Prevention Website. https://www.cdc.gov/obesity/adult/causes.html. Accessed December 2, 2020.
Google Scholar 43Agha M, Agha R. The rising prevalence of obesity. Int J Surg Oncol. 2017; 2(7):e17. https://doi.org/10.1097/ij9.0000000000000017
CrossrefWeb of Science®Google Scholar 44 NHLBI Obesity. Education initiative expert panel on the identification, evaluation, and treatment of obesity in adults (US). Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: the evidence report. Bethesda (MD): National Heart, Lung, and Blood Institute; 1998. Chapter 2.
Google Scholar 45Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. 2016; 7(2): 254- 267. https://doi.org/10.1007/s13142-016-0453-1
CrossrefWeb of Science®Google Scholar 46Turner-McGrievy GM, Dunn CG, Wilcox S, et al. Defining adherence to mobile dietary self-monitoring and assessing tracking over time: tracking at least two eating occasions per day is best marker of adherence within two different mobile health randomized weight loss interventions. J Acad Nutr. 2019; 119(9): 1516- 1524. https://doi.org/10.1016/j.jand.2019.03.012
CrossrefPubMedWeb of Science®Google Scholar 47Jensen MD, Ryan DH, Apovian CM, et al. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation. 2014; 129(25 suppl 2): S102- S138. https://doi.org/10.1161/01.cir.0000437739.71477.ee
CrossrefPubMedWeb of Science®Google Scholar 48Kozak AT, Buscemi J, Hawkins MAW, et al. Technology-based interventions for weight management: current randomized controlled trial evidence and future directions. J Behav Med. 2017; 40(1): 99- 111. https://doi.org/10.1007/s10865-016-9805-z