Digital Therapeutics for Obesity and Eating-Related Problems

Meelim Kim, Hyung Jin Choi, Meelim Kim, Hyung Jin Choi

Abstract

In recent years, digital technologies have rapidly advanced and are being applied to remedy medical problems. These technologies allow us to monitor and manage our physical and mental health in our daily lives. Since lifestyle modification is the cornerstone of the management of obesity and eating behavior problems, digital therapeutics (DTx) represent a powerful and easily accessible treatment modality. This review discusses the critical issues to consider for enhancing the efficacy of DTx in future development initiatives. To competently adapt and expand public access to DTx, it is important for various stakeholders, including health professionals, patients, and guardians, to collaborate with other industry partners and policy-makers in the ecosystem.

Keywords: Digital healthcare; Feeding behavior; Obesity.

Conflict of interest statement

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

Figures

Fig. 1
Fig. 1
Interaction between mental and physical health for lifestyle modification via digital therapeutics.
Fig. 2
Fig. 2
Major considerations and main issues for digital therapeutics. RCT, randomized controlled trial.
Fig. 3
Fig. 3
Future perspectives for the ecological environment of digital therapeutics.

References

    1. Digital Therapeutics Alliance. A new category of medicine [Internet] Arlington: Digital Therapeutics Alliance; 2020. [cited 2021 Feb 23]. Available from: .
    1. Patel NA, Butte AJ. Characteristics and challenges of the clinical pipeline of digital therapeutics. NPJ Digit Med. 2020;3:159.
    1. 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;34:1934–42.
    1. Murphy R, Straebler S, Cooper Z, Fairburn CG. Cognitive behavioral therapy for eating disorders. Psychiatr Clin North Am. 2010;33:611–27.
    1. Wilhelm S, Weingarden H, Ladis I, Braddick V, Shin J, Jacobson NC. Cognitive-behavioral therapy in the digital age: presidential address. Behav Ther. 2020;51:1–14.
    1. Lattie EG, Schueller SM, Sargent E, Stiles-Shields C, Tomasino KN, Corden ME, et al. Uptake and usage of IntelliCare: a publicly available suite of mental health and well-being apps. Internet Interv. 2016;4:152–8.
    1. Castelnuovo G, Pietrabissa G, Manzoni GM, Cattivelli R, Rossi A, Novelli M, et al. Cognitive behavioral therapy to aid weight loss in obese patients: current perspectives. Psychol Res Behav Manag. 2017;10:165–73.
    1. Wadden TA, Tronieri JS, Butryn ML. Lifestyle modification approaches for the treatment of obesity in adults. Am Psychol. 2020;75:235–51.
    1. Garabedian LF, Ross-Degnan D, Wharam JF. Mobile phone and smartphone technologies for diabetes care and self-management. Curr Diab Rep. 2015;15:109.
    1. Holtz B, Lauckner C. Diabetes management via mobile phones: a systematic review. Telemed J E Health. 2012;18:175–84.
    1. 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;50:1524–32.
    1. 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;42:709–14.
    1. Quinn CC, Shardell MD, Terrin ML, Barr EA, Park D, Shaikh F, et al. Mobile diabetes intervention for glycemic control in 45- to 64-year-old persons with type 2 diabetes. J Appl Gerontol. 2016;35:227–43.
    1. 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.
    1. 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.
    1. 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;13:668–76.
    1. Fitzsimmons-Craft EE, Taylor CB, Graham AK, Sadeh-Sharvit S, Balantekin KN, Eichen DM, 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.
    1. Spring B, Pellegrini CA, Pfammatter A, Duncan JM, Pictor A, McFadden HG, et al. Effects of an abbreviated obesity intervention supported by mobile technology: the ENGAGED randomized clinical trial. Obesity (Silver Spring) 2017;25:1191–8.
    1. Spring B, Pellegrini C, McFadden HG, Pfammatter AF, Stump TK, Siddique J, 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.
    1. Kim EK, Kwak SH, Jung HS, Koo BK, Moon MK, Lim S, 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.
    1. Lowe DA, Wu N, Rohdin-Bibby L, Moore AH, Kelly N, Liu YE, 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;180:1–9.
    1. 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.
    1. 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.
    1. Chakraborty B, Collins LM, Strecher VJ, Murphy SA. Developing multicomponent interventions using fractional factorial designs. Stat Med. 2009;28:2687–708.
    1. Collins LM, Murphy SA, Strecher V. The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. Am J Prev Med. 2007;32(5 Suppl):S112–8.
    1. Hekler EB, Rivera DE, Martin CA, Phatak SS, Freigoun MT, Korinek E, et al. Tutorial for using control systems engineering to optimize adaptive mobile health interventions. J Med Internet Res. 2018;20:e214.
    1. Liao P, Klasnja P, Tewari A, Murphy SA. Sample size calculations for micro-randomized trials in mHealth. Stat Med. 2016;35:1944–71.
    1. Torous J, Michalak EE, O’Brien HL. Digital health and engagement-looking behind the measures and methods. JAMA Netw Open. 2020;3:e2010918.
    1. 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;15:104–16.
    1. Jain SH, Powers BW, Hawkins JB, Brownstein JS. The digital phenotype. Nat Biotechnol. 2015;33:462–3.
    1. Lee KH, Yoo S, Shin H, Baek RM, Chung CY, Hwang H. Development of digital dashboard system for medical practice: maximizing efficiency of medical information retrieval and communication. Stud Health Technol Inform. 2013;192:1091.
    1. Lindberg J, Bhatt R, Ferm A. Older people and rural eHealth: perceptions of caring relations and their effects on engagement in digital primary health care. Scand J Caring Sci. 2021 Jan 14; doi: 10.1111/scs.12953. [Epub].
    1. Kang HY, Kim HR. Impact of blended learning on learning outcomes in the public healthcare education course: a review of flipped classroom with team-based learning. BMC Med Educ. 2021;21:78.
    1. Lee JE, Lee DH, Oh TJ, Kim KM, Choi SH, Lim S, et al. Clinical feasibility of monitoring resting heart rate using a wearable activity tracker in patients with thyrotoxicosis: prospective longitudinal observational study. JMIR Mhealth Uhealth. 2018;6:e159.
    1. Addante F, Gaetani F, Patrono L, Sancarlo D, Sergi I, Vergari G. An innovative AAL system based on IoT technologies for patients with sarcopenia. Sensors (Basel) 2019;19:4951.

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