Prescription digital therapeutics in obesity management: Present and future
Prescription digital therapeutics (PDTs) are emerging as innovative solutions in the management of obesity, complementing traditional methods such as lifestyle interventions, pharmacotherapy, and surgery. This mini-review explores the current landscape and future potential of PDTs in obesity management. We begin by defining PDTs and examining key players and products in the market. These products exemplify the integration of artificial intelligence -driven personalized coaching and real-time data tracking to enhance user engagement and treatment efficacy. Clinical evidence supporting the effectiveness of PDTs in promoting weight loss and improving metabolic health is discussed, with an emphasis on the comparative studies with traditional interventions. The review also addresses challenges such as regulatory hurdles, user adherence, data privacy, and accessibility issues. Looking forward, advancements in technology, personalized medicine, and better integration with healthcare systems are poised to further enhance the impact of PDTs. This article underscores the potential of PDTs to revolutionize obesity management and calls for continued innovation and research in this field.
- Bray GA, Frühbeck G, Ryan DH, Wilding JPH. Management of obesity. Lancet. 2016;387(10031):1947-1956. doi: 10.1016/S0140-6736(16)00271-3
- Boutari C, Mantzoros CS. A 2022 update on the epidemiology of obesity and a call to action: As its twin COVID-19 pandemic appears to be receding, the obesity and dysmetabolism pandemic continues to rage on. Metabolism. 2022;133:155217. doi: 10.1016/j.metabol.2022.155217
- Wadden TA, Tronieri JS, Butryn ML. Lifestyle modification approaches for the treatment of obesity in adults. Am Psychol. 2020;75(2):235-251. doi: 10.1037/amp0000517
- Digital Therapeutics Alliance. A New Category of Medicine. Arlington: Digital Therapeutics Alliance; 2020. Available from: https://dtxalliance.org [Last accessed on 2021 Feb 23].
- Patel NA, Butte AJ. Characteristics and challenges of the clinical pipeline of digital therapeutics. NPJ Digit Med. 2020;3:159. doi: 10.1038/s41746-020-00370-8
- Quinn CC, Shardell MD, Terrin ML, Barr EA, Ballew SH, Gruber-Baldini AL. Cluster-randomized trial of a mobile phone personalized behavioral intervention for blood glucose control. Diabetes Care. 2011;34:1934-1942. doi: 10.2337/dc11-0366
- Wang C, Lee C, Shin H. Digital therapeutics from bench to bedside. NPJ Digit Med. 2023;6(1):38. doi: 10.1038/s41746-023-00777-z
- European Data Protection Supervisor. Digital Therapeutics (DTx); 2022. Available from: https://edps.europa.eu/ press-publications/publications/techsonar/digital-therapeuticsdtx_en [Last accessed on 2024 Jun 02].
- Ministry of Food and Drug Safety (Korea). Guideline on Review and Approval of Digital Therapeutics (For Industry); 2020. Available from: https://www.mfds.go.kr/ eng/brd/m_40/view.do?seq=72624&srchfr=&srchto =&s r chword=&s r chtp=&itm_ s e q _1=0& itm_ seq_2=0&multi_itm_seq=0&company_cd=&company_ nm=&page=1 [Last accessed on 2024 Jun 02]. 10. Digital Therapeutics Alliance. Digital Therapeutics Definition and Core Principles; 2019. Available from: https://dtxalliance. org [Last accessed on 2024 Jun 02]. 11. Dang A, Arora D, Rane P. Role of digital therapeutics and the changing future of healthcare. J Family Med Prim Care. 2020;9(5):2207-2213. doi: 10.4103/jfmpc.jfmpc_105_20
- Lottes AE, Cavanaugh KJ, Chan YY, et al. Navigating the regulatory pathway for medical devices-a conversation with the FDA, clinicians, researchers, and industry experts. J Cardiovasc Transl Res. 2022;15(5):927-943. doi: 10.1007/s12265-022-10232-1
- Kepplinger EE. FDA’s expedited approval mechanisms for new drug products. Biotechnol Law Rep. 2015;34(1):15-37. doi: 10.1089/blr.2015.9999
- Burns L, Le Roux N, Kalesnik-Orszulak R, et al. Real-world evidence for regulatory decision-making: Updated guidance from around the world. Front Med (Lausanne). 2023;10:1236462. doi: 10.3389/fmed.2023.1236462
- Castelnuovo G, Pietrabissa G, Manzoni GM, et al. Cognitive behavioral therapy to aid weight loss in obese patients: Current perspectives. Psychol Res Behav Manag. 2017;10:165-73. doi: 10.2147/PRBM.S113278
- Garabedian LF, Ross-Degnan D, Wharam JF. Mobile phone and smartphone technologies for diabetes care and selfmanagement. Curr Diab Rep. 2015;15:109. doi: 10.1007/s11892-015-0680-8
- Holtz B, Lauckner C. Diabetes management via mobile phones: A systematic review. Telemed J E Health. 2012;18:175-184. doi: 10.1089/tmj.2008.0099
- Ku EJ, Park JI, Jeon HJ, Oh T, Choi HJ. Clinical efficacy and plausibility of a smartphone-based integrated online real-time diabetes care system via glucose and diet data management: A pilot study. Intern Med J. 2020;50:1524-1532. doi: 10.1111/imj.14738
- Guo H, Zhang Y, Li P, Zhou P, Chen LM, Li SY. Evaluating the effects of mobile health intervention on weight management, glycemic control and pregnancy outcomes in patients with gestational diabetes mellitus. J Endocrinol Invest. 2019;42:709-714. doi: 10.1007/s40618-018-0975-0
- Kirwan M, Vandelanotte C, Fenning A, Duncan MJ. Diabetes self-management smartphone application for adults with type 1 diabetes: Randomized controlled trial. J Med Internet Res. 2013;15:e235. doi: 10.2196/jmir.2588
- Kim M, Kim Y, Go 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. doi: 10.2196/14817
- Sepah SC, Jiang L, Ellis RJ, McDermott K, Peters AL. Engagement and outcomes in a digital diabetes prevention program: 3-year update. BMJ Open Diabetes Res Care. 2017;5(1):e000422. doi: 10.1136/bmjdrc-2017-000422
- May CN, Cox-Martin M, Ho AS, et al. Weight loss maintenance after a digital commercial behavior change program (Noom Weight): Observational cross-sectional survey study. Obes Sci Pract. 2023;9(5):443-451. doi: 10.1002/osp4.666
- Quinn CC, Clough SS, Minor JM, Lender D, Okafor MC, Gruber-Baldini A. WellDoc mobile diabetes management randomized controlled trial: Change in clinical and behavioral outcomes and patient and physician satisfaction. Diabetes Technol Ther. 2008;10(3):160-168. doi: 10.1089/dia.2008.0283
- Kumbara AB, Iyer AK, Green CR, et al. Impact of a combined continuous glucose monitoring-digital health solution on glucose metrics and self-management behavior for adults with type 2 diabetes: Real-world, observational study. JMIR Diabetes. 2023;8:e47638. doi: 10.2196/47638
- Levine BJ, Close KL, Gabbay RA. Reviewing U.S. Connected diabetes care: The newest member of the team. Diabetes Technol Ther. 2020;22:1-9.
- Arigo D, Jake-Schoffman DE, Wolin K, Beckjord E, Hekler EB, Pagoto SL. The history and future of digital health in the field of behavioral medicine. J Behav Med. 2019;42:67-83. doi: 10.1007/s10865-018-9966-z
- Kim M, Choi HJ. Digital therapeutics for obesity and eating-related problems. Endocrinol Metab (Seoul). 2021;36(2):220-228. doi: 10.3803/EnM.2021.107
- Spring B, Pellegrini CA, Pfammatter A, et al. Effects of an abbreviated obesity intervention supported by mobile technology: The ENGAGED randomized clinical trial. Obesity (Silver Spring). 2017;25:1191-1198. doi: 10.1002/oby.21842
- Nezami BT, Ward DS, Lytle LA, Ennett ST, Tate DF. A mHealth randomized controlled trial to reduce sugar-sweetened beverage intake in preschool-aged children. Pediatr Obes. 2018;13:668-676. doi: 10.1111/ijpo.12258
- Spring B, Pellegrini C, McFadden HG, et al. Multicomponent mHealth intervention for large, sustained change in multiple diet and activity risk behaviors: The Make Better Choices 2 randomized controlled trial. J Med Internet Res. 2018;20:e10528. doi: 10.2196/10528
- Kim EK, Kwak SH, Jung HS, et al. The effect of a smartphone-based, patient-centered diabetes care system in patients with type 2 diabetes: A randomized, controlled trial for 24 weeks. Diabetes Care. 2019;42:3-9. doi: 10.2337/dc17-2197
- Fitzsimmons-Craft EE, Taylor CB, Graham AK, et al. Effectiveness of a digital cognitive behavior therapy-guided self-help intervention for eating disorders in college women: A cluster randomized clinical trial. JAMA Netw Open. 2020;3:e2015633. doi: 10.1001/jamanetworkopen.2020.15633
- Lowe DA, Wu N, Rohdin-Bibby L, et al. Effects of time-restricted eating on weight loss and other metabolic parameters in women and men with overweight and obesity: The TREAT randomized clinical trial. JAMA Intern Med. 2020;180:1491-1499. doi: 10.1001/jamainternmed.2020.4153
- Torous J, Michalak EE, O’Brien HL. Digital health and engagement-looking behind the measures and methods. JAMA Netw Open. 2020;3:e2010918. doi: 10.1001/jamanetworkopen.2020.10918
- Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J. 2017;15:104-116. doi: 10.1016/j.csbj.2016.12.005
- Strombotne KL, Lum J, Pizer SD, Figueroa S, Frakt AB, Conlin PR. Clinical effectiveness and cost-impact after 2 years of a ketogenic diet and virtual coaching intervention for patients with diabetes. Diabetes Obes Metab. 2024;26:1016-1022. doi: 10.1111/dom.15401
- Kim HC, Lee H, Lee HH, et al. Korea Hypertension Fact Sheet 2023: Analysis of nationwide population-based data with a particular focus on hypertension in special populations. Clin Hypertens. 2024;30:7. doi: 10.1186/s40885-024-00262-z