Journal of Obesity & Metabolic Syndrome

Search

Article

September, 2022 | Vol.31 No.3

J Obes Metab Syndr 2022; 31(3): 201-207

Published online September 30, 2022 https://doi.org/10.7570/jomes22027

Copyright © Korean Society for the Study of Obesity.

Effectiveness of Information and Communications Technology-Based Interventions for Obesity and Metabolic Syndrome

Sunyoung Kim1,2, Sang Youl Rhee2,3,* , Sungyoung Lee4, Committee of IT-convergence Treatment of Metabolic Syndrome, the Korean Society for the Study of Obesity

1Department of Family Medicine, College of Medicine, Kyung Hee University, Seoul; 2Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Seoul; 3Department of Endocrinology and Metabolism, College of Medicine, Kyung Hee University, Seoul; 4Department of Computer Science and Engineering, College of Software, Kyung Hee University, Suwon, Korea

Correspondence to:
Sang Youl Rhee
https://orcid.org/0000-0003-0119-5818
Department of Endocrinology and Metabolism, College of Medicine, Kyung Hee University, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Korea
Tel: +82-2-958-8200
Fax: +82-2-968-1848
E-mail: rheesy@khu.ac.kr

Received: March 18, 2022; Reviewed : May 1, 2022; Accepted: July 1, 2022

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

The rapid increase in the obese population is a problem indicates the need for measures to prevent and treat obesity. Since the early 2000s, worldwide digital health has focused on obesity management. Information and communication technology (ICT)-based obesity intervention can be an efficient method for treating obesity and metabolic syndrome, has no time limitations, and is an inexpensive and easily accessible treatment modality for both physicians and patients. Previous studies have confirmed the effects of ICT-based interventions for obesity and metabolic syndrome management for behavioral improvement in lifestyle modification. In addition, ICT-based interventions in obese and metabolic syndrome patients are recommended as part of a comprehensive strategy for weight loss and maintenance. The Committee of IT-convergence Treatment of Metabolic Syndrome was established by the Korean Society for the Study of Obesity in 2021, and had been gathering theoretical and clinical evidence in digital therapeutics fields and developing new methods for managing obesity and metabolic syndrome. As part of this effort, if the “obesity management prototype” is commercialized, it will be available for convenient treatment of individuals with obesity and metabolic syndrome.

Keywords: Cognitive behavioral therapy, Digital technology, Obesity, Telecommunication, Weight loss

Obesity is a significant health problem that is associated with numerous health issues, including cardiovascular disease (heart disease, stroke), type 2 diabetes, musculoskeletal problems, psychosocial disorders, and cancer.1 In 2016, the World Health Organization estimated that more than 650 million adults were obese, which is 13% of the population.2 According to the 2020 Obesity Fact Sheet created by the Korean Society for the Study of Obesity (KSSO), the prevalence of obesity in Korea continuously increased between 2009 (32.6%) and 2018 (38.5%) among the overall population and in both sexes.3 Such a rapid increase in the obese population is a social burden, so measures to prevent and treat obesity are essential.

To prevent obesity from overwhelming clinical practice, patients with obesity should be included in multidisciplinary programs with combined treatment options (diet, physical activity, cognitive-behavioral, pharmacological, and surgical).4,5 However, combined treatment options have often led to suboptimal outcomes due to difficulties in access, cost, lack of personnel and resources, patient treatment adherence, and long-term efficacy of these options. Moreover, about one-third of patients regain the lost weight after the first year of starting these lifestyle interventions.6 Therefore, the challenge of treating obesity is not only to achieve weight loss, but also to maintain it.7

Recently, there has been commitment to developing and implementing digital health technologies for assessment, prevention, and treatment of disease and to promote health and well-being worldwide.8 Digital health is defined broadly as a form in which the use of information and communication technology (ICT) is combined with improving or enabling health and healthcare.9 Since 2020 during the global coronavirus disease 2019 (COVID-19) pandemic, ICT-based interventions have experienced growth in many countries, such as the United States, Europe, China, and Japan. In line with this, ICT-based interventions for obesity have been suggested to be more suitable treatment options.10 The Committee of IT-convergence Treatment of Obesity and Metabolic Syndrome in KSSO (Director Sang Youl Rhee) was established in February 2021. The goals of this committee are as follows: (1) presenting academic rationale for IT-convergence technology for effective treatment of obesity and metabolic syndrome, (2) standardization of digital treatment (DTx) and presenting a general-purpose DTx algorithm, (3) proposal of policy direction for digital dissemination, and (4) establishing a multi-institutional/-disciplinary industry-academic collaboration network for dissemination and supply of treatment techniques for IT-convergence obesity and metabolic syndrome.

ICT-based interventions include web-based platforms (e.g., websites), mobile devices (e.g., cell phones), or wearable devices (e.g., pedometers, accelerometers, Fitbits, and Apple/Galaxy watches).11 Web-based platforms provide education on nutrition and physical activity, self-monitoring of targeted behaviors, goal setting, and more. Mobile devices allow people to use text messages and smartphone applications to monitor their food intake and weight. In the past, wearable technology has been used to set and evaluate the target amount of physical activity as measured by tools such as a pedometer or accelerometer; recently, wearables (e.g., Fitbit, Apple Watch, and Galaxy Watch) have also been used to monitor sleep patterns and other health-related activities.12

The most significant advantages of ICT-based interventions are that they can effectively provide feedback via direct communication and build alliances13 while improving patient convenience and reducing cost and time input.14,15 ICT-based interventions can be used at any time of day to help communicate with healthcare experts to support effective and efficient interventions. Web-based or smartphone application-based management of obesity can improve compliance with interventions. In addition, it can provide opportunities for solving problems related to weight loss and maintenance, including long-term efficacy.16 In particular, ICT-based interventions can compensate for the lack of psychological and behavioral counseling provided by healthcare experts, which can help to achieve and maintain the goal of lifestyle modification for overweight and obesity treatment.17,18 ICT-based interventions that use these advantages effectively enable weight loss and maintenance, and previous studies have shown the benefits of ICT-based interventions.14,19,20 Such effects have been confirmed through studies conducted in Korea.21,22

In the past, ICT-based interventions for obesity treatment have been considered a supplementary means of obesity management through lifestyle modification. Recently, ICT-based obesity treatment has emerged as a parallel or central means of obesity intervention as a part of DTx. Further, randomized controlled clinical trials (RCTs), long-term observational studies, and cost-effectiveness studies to ensure sufficient evidence are warranted.

ICT-based interventions using telephone, internet, smartphone applications, and wearable devices for obesity management effectively support weight loss and weight loss maintenance.23 In a meta-analysis from 2019, the weight loss was significantly higher when a web-based intervention was implemented than when not used (–2.14 kg; 95% confidence interval [CI], –2.65 to –1.64).24 However, this result does not provide conclusive evidence that an ICT-based intervention can be used as an alternative to existing face-to-face programs. Few studies have compared the effects of weight loss and weight maintenance using therapy based on ICT through phones, the internet, and smartphone apps versus conventional behavioral therapy through face-to-face methods.11,20

As a result of a meta-analysis of 23 RCTs that compared the effects of ICT-based interventions and existing management on weight loss, a significant effect (–0.68 kg, P=0.03) of ICT-based interventions was confirmed.17 In addition, when ICT-based weight loss and conventional (face-to-face) interventions were combined, superior weight loss (–1.93 kg; 95% CI, –2.71 to –1.15; P<0.001) was obtained compared to that of ICT-based intervention alone.17 However, a meta-analysis published in 2019, showed that the weight loss effect was inferior when using only ICT-based interventions compared to conventional (face-to-face) behavioral therapy (0.82 kg; 95% CI, 0.06–1.59).24 In addition, in a meta-analysis from 2021, ICT-based interventions had an effect on weight loss but it was not statistically significant compared to that of general treatment (–0.56 kg; 95% CI, –3.74 to 4.59; P=0.786).25

Table 1 summarizes the results of existing studies comparing the effects of obesity treatment on information and communication-based interventions with those of face-to-face treatment.26 To confirm the effect size, comparative advantage, and other factors related to ICT-based interventions compared to conventional (face-to-face) behavioral therapy for weight loss and maintenance, additional studies are needed.

In Korea, when patients were treated for obesity using a mobile app, the average weight loss was –2.73 kg,27 and a combined approach using both an ICT-based intervention and conventional cognitive behavioral therapy showed a more effective weight loss effect than the existing face-to-face treatment (–3.4% vs. –0.7%).28 Overseas, an intensive contact web-based program showed a remarkable effect on weight loss (mean, –4.31 kg; 95% CI, –5.22 to –3.41).29 These results suggest that ICT-based interventions can be most effective when provided multi-dimensionally and accompanied by therapist feedback and support.

The KSSO established the Committee of IT-convergence Treatment of Metabolic Syndrome in 2021, and is working to secure theoretical and academic evidence in related fields by developing new obesity and metabolic syndrome management methods. In particular, the committee is making efforts to improve the completeness of digital health solutions for obesity and metabolic syndrome management, including detailed individual weight goals, individualized interventions, continuous motivation, and maximized possibility of weight loss. The committee has developed a patent-pending prototype that collects and analyzes information for each algorithm and provides appropriate intervention information or methods customized by subject (Fig. 1).

In the future, the KSSO aims to identify and improve additional problems through peer evaluation in conjunction with various expert groups and distribute the findings through an easily accessed open platform (Fig. 2). Furthermore, the committee plans to link this health promotion program to an institutional medical system through multicenter RCTs and real-world studies.

ICT-based interventions have several advantages in weight loss and maintenance in obese/metabolic syndrome patients and can be recommended as part of a comprehensive strategy. The obesity prototype is an ICT-based intervention for obesity and metabolic syndromes that is being developed and supplemented by the Committee of IT-convergence Treatment of Metabolic Syndrome for easy and convenient use in obesity and metabolic syndrome treatment.

The authors would like to thank President Ki-jin Kim, Chairman Chang-Beom Lee, and all executives of the Korean Society for the Study of Obesity. In addition, the corresponding author especially thanks Professor Emeriti Young Seol Kim and Young Gil Choi from Kyung Hee University for their exceptional teaching and inspiration.

Study concept and design: SK and SYR; drafting of the manuscript: SK; critical revision of the manuscript: SK, SYR, and Korean Society for the Study of Obesity; administrative, technical, or material support: all authors; and study supervision: RSY.

Fig. 1. The feedback system of intervention evolution for obesity management. MET, metabolic equivalent of task; PA, physical activity; BMR, basal metabolic rate.
Fig. 2. The basic user interface of a smartphone application. OMS, obesity management system.

Studies on the effectiveness of online obesity behavioral therapy26,29

Study Duration (mo) Intervention Control Men Age (yr) BMI (kg/m2) Weight change Effect
Shuger et al.30 9 PA monitoring device SC 197 (19) 46.8 ± 10.8 33.3 ± 5.2 SWA: –3.55 kg vs. SC: –0.9 kg (no significance) No difference
van Wier et al.31 6 Web-based intervention Control group 1,386 (67) 43 ± 8.6 29.6 ± 3.5 Internet: –1.9 kg vs. control: –1.0 kg (P = 0.112) No difference
Allen et al.32 6 SP application IC 68 (22) 44.9 ± 11.1 34.3 ± 3.9 SP: –1.8 ± 3.7 kg vs. IC: –2.5 ± 4.1 kg (P = 0.89) No difference
Steinberg et al.33 6 Smart scale on SP Text message 91 (25) 44 ± 11 32.15 ± 3.8 Intervention: –6.55% vs. control: –0.35% (P < 0.001) Superior
Harvey-Berino et al.34 6 Web-based intervention Teleconsultation 481 (80) 46.6 ± 9.9 35.7 ± 5.6 Internet: –5.5 kg vs. in person: –8.0 kg (P < 0.01) Inferior
Sullivan et al.35 3/6 Web-based class (VR) Face to face 20 (15) 31.1 ± 3.6 32.8 ± 5.1 Weight loss: VR: –7.6 % vs. FTF: –10.8% (P < 0.05), weight maintenance: VR: 14.0% vs. FTF: 9.5% (P < 0.05) Inferior/small
Blomfield et al.36 6 Website CalorieKing Program materials 159 (100) 47.5 ± 11.0 32.7 ± 3.5 Online: –5.8 ± 5.3 kg vs. resource: –4.4 ± 4.7 kg (P > 0.05) No difference
O’Brien et al.37 4 Web-based management Personal feedback 289 (41) 41.6 ± 10.2 32.4 ± 3.6 Control: 0.4 ± 2.4 kg vs. basic: –2.2 ± 3.4 kg (P < 0.001) Superior
Thomas et al.38 12 Web-based intervention PA monitoring 271 (22.5) 33.9 ± 3.7 33.9 ± 3.7 WWOAL: –1.6 kg vs. control: –1.2 kg (P > 0.26) No difference

Values are presented as number (%) or mean± standard deviation.

BMI, body mass index; PA, physical activity; SC, standard care; SWA, SenseWear Armband; SP, smartphone; IC, intensive counseling; VR, clinical delivered via virtual reality; FTF, face to face weight management clinic; WWOAL,

Weight Watchers Online plus ActiveLink.

  1. Abdelaal M, le Roux CW, Docherty NG. Morbidity and mortality associated with obesity. Ann Transl Med 2017;5:161.
    Pubmed KoreaMed CrossRef
  2. World Health Organization. Obesity and overweight fact sheets. Geneva: World Health Organization; 2016.
  3. Nam GE, Kim YH, Han K, Jung JH, Rhee EJ, Lee WY, et al. Obesity fact sheet in Korea, 2020: prevalence of obesity by obesity class from 2009 to 2018. J Obes Metab Syndr 2021;30:141-8.
    Pubmed KoreaMed CrossRef
  4. Jahangiry L, Shojaeizadeh D, Abbasalizad Farhangi M, Yaseri M, Mohammad K, Najafi M, et al. Interactive web-based lifestyle intervention and metabolic syndrome: findings from the Red Ruby (a randomized controlled trial). Trials 2015;16:418.
    Pubmed KoreaMed CrossRef
  5. Burguera B, Jesús Tur J, Escudero AJ, Alos M, Pagán A, Cortés B, et al. An intensive lifestyle intervention is an effective treatment of morbid obesity: the TRAMOMTANA study-a two-year randomized controlled clinical trial. Int J Endocrinol 2015;2015:194696.
    Pubmed KoreaMed CrossRef
  6. Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: 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):S102-38.
    KoreaMed CrossRef
  7. Montesi L, El Ghoch M, Brodosi L, Calugi S, Marchesini G, Dalle Grave R. Long-term weight loss maintenance for obesity: a multidisciplinary approach. Diabetes Metab Syndr Obes 2016;9:37-46.
    Pubmed KoreaMed CrossRef
  8. World Health Organization. Global strategy on digital health 2020–2025. Geneva: World Health Organization; 2020.
  9. Pagliari C, Sloan D, Gregor P, Sullivan F, Detmer D, Kahan JP, et al. What is eHealth (4): a scoping exercise to map the field. J Med Internet Res 2005;7:e9.
    Pubmed KoreaMed CrossRef
  10. Oosterveen E, Tzelepis F, Ashton L, Hutchesson MJ. A systematic review of eHealth behavioral interventions targeting smoking, nutrition, alcohol, physical activity and/or obesity for young adults. Prev Med 2017;99:197-206.
    Pubmed CrossRef
  11. Raaijmakers LC, Pouwels S, Berghuis KA, Nienhuijs SW. Technology-based interventions in the treatment of overweight and obesity: a systematic review. Appetite 2015;95:138-51.
    Pubmed CrossRef
  12. McDonough DJ, Su X, Gao Z. Health wearable devices for weight and BMI reduction in individuals with overweight/obesity and chronic comorbidities: systematic review and network meta-analysis. Br J Sports Med 2021;55:917-25.
    Pubmed CrossRef
  13. Piccinini-Vallis H, Adamo K, Bell R, Pereira L, Nerenberg K. Canadian adult obesity clinical practice guidelines: weight management over the reproductive years for adult women living with obesity. Edmonton: Obesity Canada; 2021.
  14. Gudzune KA, Doshi RS, Mehta AK, Chaudhry ZW, Jacobs DK, Vakil RM, et al. Efficacy of commercial weight-loss programs: an updated systematic review. Ann Intern Med 2015;162:501-12.
    Pubmed KoreaMed CrossRef
  15. Finkelstein EA and Kruger E. Meta- and cost-effectiveness analysis of commercial weight loss strategies. Obesity (Silver Spring) 2014;22:1942-51.
    Pubmed CrossRef
  16. Coons MJ, Demott A, Buscemi J, Duncan JM, Pellegrini CA, Steglitz J, et al. Technology interventions to curb obesity: a systematic review of the current literature. Curr Cardiovasc Risk Rep 2012;6:120-34.
    Pubmed KoreaMed CrossRef
  17. 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 (Lond) 2012;36:675-85.
    Pubmed CrossRef
  18. Huang J, Yu H, Marin E, Brock S, Carden D, Davis T. Physicians' weight loss counseling in two public hospital primary care clinics. Acad Med 2004;79:156-61.
    Pubmed CrossRef
  19. Rao G, Burke LE, Spring BJ, Ewing LJ, Turk M, Lichtenstein AH, et al. New and emerging weight management strategies for busy ambulatory settings: a scientific statement from the American Heart Association endorsed by the Society of Behavioral Medicine. Circulation 2011;124:1182-203.
    Pubmed CrossRef
  20. Cheatham SW, Stull KR, Fantigrassi M, Motel I. The efficacy of wearable activity tracking technology as part of a weight loss program: a systematic review. J Sports Med Phys Fitness 2018;58:534-48.
    Pubmed CrossRef
  21. Chin SO, Keum C, Woo J, Park J, Choi HJ, Woo JT, et al. Successful weight reduction and maintenance by using a smartphone application in those with overweight and obesity. Sci Rep 2016;6:34563.
    Pubmed KoreaMed CrossRef
  22. Han M and Rhee SY. Effect of adherence to smartphone app use on the long-term effectiveness of weight loss in developing and OECD countries: retrospective cohort study. JMIR Mhealth Uhealth 2021;9:e13496.
    Pubmed KoreaMed CrossRef
  23. Afshin A, Babalola D, Mclean M, Yu Z, Ma W, Chen CY, et al. Information technology and lifestyle: a systematic evaluation of internet and mobile interventions for improving diet, physical activity, obesity, tobacco, and alcohol use. J Am Heart Assoc 2016;5:e003058.
    Pubmed KoreaMed CrossRef
  24. Beleigoli AM, Andrade AQ, Cançado AG, Paulo MN, Diniz MF, 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:e298.
    Pubmed KoreaMed CrossRef
  25. Jahangiry L and Farhangi MA. Obesity paradigm and web-based weight loss programs: an updated systematic review and meta-analysis of randomized controlled trials. J Health Popul Nutr 2021;40:16.
    Pubmed KoreaMed CrossRef
  26. Hutchesson MJ, Rollo ME, Krukowski R, Ells L, Harvey J, Morgan PJ, et al. eHealth interventions for the prevention and treatment of overweight and obesity in adults: a systematic review with meta-analysis. Obes Rev 2015;16:376-92.
    Pubmed CrossRef
  27. Kim Y, Oh B, Shin HY. Effect of mHealth with offline antiobesity treatment in a community-based weight management program: cross-sectional study. JMIR Mhealth Uhealth 2020;8:e13273.
    Pubmed KoreaMed CrossRef
  28. Kim M, Kim Y, Go Y, Lee S, Na M, Lee Y, et al. Multidimensional cognitive behavioral therapy for obesity applied by psychologists using a digital platform: open-label randomized controlled trial. JMIR Mhealth Uhealth 2020;8:e14817.
    Pubmed KoreaMed CrossRef
  29. Varela C, Oda-Montecinos C, Andrés A, Saldaña C. Effectiveness of web-based feedback interventions for people with overweight and obesity: systematic review and network meta-analysis of randomized controlled trials. J Eat Disord 2021;9:75.
    Pubmed KoreaMed CrossRef
  30. Shuger SL, Barry VW, Sui X, McClain A, Hand GA, Wilcox S, et al. Electronic feedback in a diet-and physical activity-based lifestyle intervention for weight loss: a randomized controlled trial. Int J Behav Nutr Phys Act 2011;8:41.
    Pubmed KoreaMed CrossRef
  31. van Wier MF, Dekkers JC, Hendriksen IJ, Heymans MW, Ariëns GA, Pronk NP, et al. Effectiveness of phone and e-mail lifestyle counseling for long term weight control among overweight employees. J Occup Environ Med 2011;53:680-6.
    Pubmed CrossRef
  32. Allen 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;2013:151597.
    Pubmed KoreaMed CrossRef
  33. Steinberg DM, Tate DF, Bennett GG, Ennett S, Samuel-Hodge C, Ward DS. The efficacy of a daily self-weighing weight loss intervention using smart scales and e-mail. Obesity (Silver Spring) 2013;21:1789-97.
    Pubmed KoreaMed CrossRef
  34. Harvey-Berino J, West D, Krukowski R, Prewitt E, VanBiervliet A, Ashikaga T, et al. Internet delivered behavioral obesity treatment. Prev Med 2010;51:123-8.
    Pubmed KoreaMed CrossRef
  35. Sullivan DK, Goetz JR, Gibson CA, Washburn RA, Smith BK, Lee J, et al. Improving weight maintenance using virtual reality (Second Life). J Nutr Educ Behav 2013;45:264-8.
    Pubmed CrossRef
  36. Blomfield RL, Collins CE, Hutchesson MJ, Young MD, Jensen ME, Callister R, et al. Impact of self-help weight loss resources with or without online support on the dietary intake of overweight and obese men: the SHED-IT randomised controlled trial. Obes Res Clin Pract 2014;8:e476-87.
    Pubmed CrossRef
  37. O'Brien KM, Hutchesson MJ, Jensen M, Morgan P, Callister R, Collins CE. Participants in an online weight loss program can improve diet quality during weight loss: a randomized controlled trial. Nutr J 2014;13:82.
    Pubmed KoreaMed CrossRef
  38. Thomas JG, Raynor HA, Bond DS, Luke AK, Cardoso CC, Foster GD, et al. Weight loss in weight watchers online with and without an activity tracking device compared to control: a randomized trial. Obesity (Silver Spring) 2017;25:1014-21.
    Pubmed CrossRef