Abstract
Introduction
This article is our annual attempt to highlight the key papers written between July 1, 2020, and June 30, 2021, in which digital health technologies were used to prevent or treat diabetes mellitus. This article will mainly address interventions called digital therapeutics, while other articles will address telemedicine (remote patient–provider visits). Notably, some interventions might easily be categorized as both; we include some limited discussion of telemedical and text‐messaging interventions to the degree that they meet the definition of a digital therapeutic.
Digital therapeutics (DTx) deliver evidence‐based therapeutic interventions to patients that are driven by high-quality software programs to prevent, manage, or treat a medical disorder or disease. They are used independently or together with medications, devices, or other therapies to optimize patient care and health outcomes (DTx Alliance; https://dtxalliance.org/aboutdtx/).
A few new themes related to digital therapeutics have emerged this year. The field has been affected by limited resource investment in clinical trials and by a lack of rigor in study designs. Fortunately, the field shows signs of slowly gathering steam with some regulatory and quality entities endorsing minimum standards for the design of clinical trials and for the levels of evidence required to demonstrate efficacy for a digital therapeutic. Another theme has also emerged: it is becoming increasingly difficult to think about digital therapeutics as isolated interventions. They are increasingly studied and delivered as integrated components of broader care delivery platforms that include coaching, peer support, remote patient monitoring (which each involves a telehealth component), and person‐to‐person or text message–driven behavioral modification programs. Next, we have observed an increasing number of digital health trials that are going “virtual,” with many study procedures handled remotely. This is, of course, partly a response to the SARS‐CoV2 pandemic; we also expect to see that digital therapeutics increasingly become essential tools to support participant retention, study procedure engagement, and data collection for drug and device trials in this new era. Finally, governments and employers have increasingly embraced payment models for digital therapeutics, or for remote patient monitoring, which almost ubiquitously involves the use of a digital therapeutic that accompanies a connected self‐monitoring device such as a (continuous) glucose monitor, blood pressure cuff, or weigh scale. Two government‐driven examples include the emergence and expanded use of new billable codes for remote patient monitoring from the Centers for Medicare Services in the United States and for Digitale Gesundheitsanwendungen (DiGA) from the Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM) in Germany.
Key Articles Reviewed for the Article
Böhm AK, Jensen ML, Sørensen MR, Stargardt T
Yang X, Kovarik CL
Duncan MJ, Fenton S, Brown WJ, Collins CE, Glozier N, Kolt GS, Holliday EG, Morgan PJ, Murawski B, Plotnikoff RC, Rayward AT, Stamatakis E, Vandelanotte C, Burrows TL
Yew TW, Chi C, Chan SY, van Dam RM, Whitton C, Lim CS, Foong PS, Fransisca W, Teoh CL, Chen J, Ho‐Lim ST, Lim SL, Ong KW, Ong PH, Tai BC, Tai ES
Gershkowitz BD, Hillert CJ, Crotty BH
Cho SMJ, Lee JH, Shim JS, Yeom H, Lee SJ, Jeon YW, Kim HC
Eberle C, Löhnert M, Stichling S
Valeriani F, Protano C, Marotta D, Liguori G, Romano Spica V, Valerio G, Vitali M, Gallè F
Lee J, Bae S, Park D, Kim Y, Park J
Hamaya R, Fukuda H, Takebayashi M, Mori M, Matsushim, Nakano K, Miyake K, Tani Y, Yokokawa H
Forsyth JR, Chase H, Roberts NW, Armitage LC, Farmer AJ
Franc S, Hanaire H, Benhamou PY, Schaepelynck P, Catargi B, Farret A, Fontaine P, Guerci B, Reznik Y, Jeandidier N, Penfornis A, Borot S, Chaillous L, Serusclat P, Kherbachi Y, d'Orsay G, Detournay B, Simon P, Charpentier G
Eze ND, Mateus C, Cravo Oliveira Hashiguchi T
Horstman CM, Ryan DH, Aronne LJ, Apovian CM, Foreyt JP, Tuttle HM, Williamson DA
Osborn CY, Hirsch A, Sears LE, Heyman M, Raymond J, Huddleston B, Dachis J
Araya R, Menezes PR, Claro HG, Brandt LR, Daley KL, Quayle J, Diez‐Canseco F, Peters TJ, Vera Cruz D, Toyama M, Aschar S, Hidalgo‐Padilla L, Martins H, Cavero V, Rocha T, Scotton G, de Almeida Lopes IF, Begale M, Mohr DC, Miranda JJ
Presley C, Agne A, Shelton T, Oster R, Cherrington A
Mayberry LS, Berg CA, Greevy RA, Nelson LA, Bergner EM, Wallston KA, Harper KJ, Elasy TA
Represas‐Carrera FJ, Martínez‐Ques ÁA, Clavería A
Sandborg J, Söderström E, Henriksson P, Bendtsen M, Henström M, Leppänen M, Maddison R, Migueles JH, Blomberg M, Löf M
Real‐World Evidence of User Engagement with Mobile Health for Diabetes Management: Longitudinal Observational Study
Böhm AK1, Jensen ML2, Sørensen MR2, Stargardt T1
1Hamburg Center for Health Economics, University of Hamburg, Hamburg, Germany; 2Novo Nordisk A/S, Søborg, Denmark
Background
Patient support apps have risen in popularity and provide novel opportunities for self‐management of diabetes. Such apps offer patients the opportunity to play an active role in monitoring their condition, thereby increasing their own treatment responsibility. Although many health apps require active user engagement to be effective, there is little evidence exploring engagement with mobile health (mHealth).
Objective
This study aims to analyze the extent to which users engage with mHealth for diabetes and identifies patient characteristics that are associated with engagement.
Methods
The analysis is based on real‐world data obtained by Novo Nordisk's Cornerstones4Care Powered by Glooko diabetes support app. The authors assessed user engagement as the number of active days and using measures expressing the persistence, longevity, and regularity of interaction within the first 180 days of use. Beta regressions were estimated to assess the associations between user characteristics and engagement outcomes for each module of the app.
Results
A total of 9,051 individuals began use after registration and could be observed for 180 days. Among these, 55.39% (5,013/9,051) used the app for one specific purpose. The average user activity ratio varied from 0.05 (medication and food) to 0.55 (continuous glucose monitoring), depending on the module of the app. If the modules required manual data entries, the authors found that average user engagement was lower, although the initial uptake was higher for these modules. Regression analyses further revealed that although more women used the app (2,075/3,649, 56.86%), they engaged significantly less with it. Older people and users who were recently diagnosed tended to use the app more actively.
Conclusions
Strategies to increase or sustain the use of apps and availability of health data may target the mode of data collection and content design and should consider privacy concerns of the users at the same time. User engagement was determined by various user characteristics, indicating that particular patient groups should be targeted or assisted when integrating apps into the self‐management of their disease.
Comment
This study, while reporting on the unique engagement characteristics of user interactions with a specific diabetes‐related digital therapeutic, provides useful and somewhat generalizable information. The study used Cornerstones4Care (C4C) Powered by the Glooko mHealth app and was funded by Novo Nordisk A/S. The app has been available since June 2017, and approximately 30,000 users installed the app by October 2019 and provided consent to use the data for research purposes. After providing consent, 42.79% (12,685/29,643) of the users initiated use.
The authors advanced the field of user engagement research by developing engagement metrics on a theoretical framework that defined user engagement with technology as a process comprising four distinct stages: point of engagement, period of sustained engagement, disengagement, and reengagement.
Key findings included average user activity ratio, which varied from 0.05 (medication and food) to 0.55 (continuous glucose monitoring), depending on the module of the app. Average user engagement was lower if modules required manual data entries, although the initial uptake was higher for these modules. More women used the app (2,075/3,649; 56.86%), although they engaged significantly less with it. Older people and users who were recently diagnosed tended to use the app more actively.
One area not addressed was how a person's outcomes associated with the way that the participant learned about the program. This knowledge would help to determine how to minimize the financial and administrative costs to get a participant to actually start the program beyond just demonstrating interest. In this case, over 53% of participants who signed consent did not use the app. This is a typical dropout rate, which is one of the major challenges associated with digital therapeutics.
This study can provide much useful information and a research approach that can help move forward the entire field of digital therapeutics—something that is greatly needed in the increasingly digital world in which we live and work.
A Systematic Review of Mobile Health Interventions in China: Identifying Gaps in Care
Yang X1,2, Kovarik CL2
1Department of Social Medicine, China Medical University, P.R. China; 2Department of Dermatology, Perelman School of Medicine, University of PA
Introduction
Mobile health has a promising future in the healthcare system in most developed countries. An unprecedented opportunity has arisen due to China's rapidly developing mobile technology infrastructure, with the potential for the wide adoption of mobile health interventions in the delivery of effective and timely healthcare services. However, there is little data on the current extent of the mobile health landscape in China. The aim of this study was to systematically review the existing mobile health initiatives in China, characterize the technology used, disease categories targeted, location of the end user (urban versus rural), and examine the potential effects of mobile health on health system strengthening in China. Furthermore, we identified gaps in development and evaluation of the effectiveness of mobile health interventions.
Methods
The authors conducted a systematic review of the literature published from December 18, 2015, to April 3, 2019. This review yielded 2,863 articles from English and Chinese retrieval database and trial registries, including PubMed, EMBASE, China National Knowledge of Infrastructure, and World Health Organization International Clinical Trials Registry Platform. Studies were included if they used mobile health to support patient healthcare outcomes.
Results
A total of 1,129 full‐text articles were assessed, with 338 included in this study. The review found that most studies targeted client education and behavior change via applications (apps) (65.4%), including WeChat and text messaging (short text messages) (19.8%) to improve patient medical treatment outcomes such as compliance and appointment reminders. The most common disease‐specific mobile health interventions focused primarily on chronic disease management and behavior change in cardiology (13.3%), endocrinology/diabetes (12.1%), behavioral health (11.8%), oncology (11.2%), and neurology (6.8%). The mobile health interventions related to nutrition (0.6%) and chronic respiratory diseases (1.6%) are underrepresented in mobile health in comparison to the burden of disease in China. The majority (90.0%) of the mobile health interventions were conducted exclusively in urban areas, with few opportunities reaching rural populations.
Conclusions
Overall, mobile health has a promising future in China, with recent rapid growth in initiatives. The majority of studies reviewed are focused on education and behavior change in the realm of chronic diseases and target patients in urban areas. The imbalance in mobile health between urban and rural areas, as well as between population disease spectrum and health service delivery, pose substantial dilemmas. However, mobile health may be redirected to correct this imbalance, possibly improving access to healthcare services, and filling the gaps with the aim of improving health equity for the underserved populations in China.
Comment
This study is a systematic review of the Chinese experience with digital therapeutics and is not generalizable to any other country or culture. Not surprisingly, the vast majority of reports were for mobile health interventions designed to improve outcomes for people with chronic disease not focusing on the delivery of care. The complexity of healthcare delivery makes the planning, developing, testing, and demonstrating value very hard, even in a centralized healthcare delivery system. The disparity between urban and rural seen in the study is typical around the world, including in developed countries secondary to lack of access to the technology and Internet connections needed to participate. It is hoped that will be solved in the coming years.
Efficacy of a Multi‐Component m‐Health Weight‐Loss Intervention in Overweight and Obese Adults: A Randomised Controlled Trial
Duncan MJ1,2, Fenton S1,2, Brown WJ3, Collins CE2,4, Glozier N5, Kolt GS6, Holliday EG1, Morgan PJ2,7, Murawski B1,2, Plotnikoff RC2,7, Rayward AT2,7, Stamatakis E8, Vandelanotte C9, Burrows TL2,4
1School of Medicine & Public Health, Faculty of Health and Medicine, The University of Newcastle, University Drive, Callaghan, NSW, Australia; 2Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle, University Drive, Callaghan, NSW, Australia; 3School of Human Movement and Nutrition Sciences, The University of Queensland, St Lucia, QLD, Australia; 4School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, University Drive, Callaghan, NSW, Australia; 5Brain and Mind Centre, Central Clinical School, The University of Sydney, Camperdown, NSW, Australia; 6School of Health Sciences, Western Sydney University, Penrith, NSW, Australia; 7School of Education, The University of Newcastle, University Drive, Callaghan, NSW, Australia; 8Charles Perkins Centre, Faculty of Medicine and Health, School of Health Sciences, Sydney, Australia; 9Physical Activity Research Group, Appleton Institute, School of Health, Medical and Applied Science, Central Queensland University, Rockhampton, QLD, Australia
Background
This study compared the efficacy of two multicomponent mhealth interventions with a waitlist control group on body weight (primary outcome), and secondary outcomes of cardiovascular risk factors, lifestyle behaviors, and mental health.
Methods
This study employed a three‐arm randomized controlled trial (Enhanced: physical activity, diet, sleep; Traditional: physical activity, diet; Control) with assessments conducted at baseline, 6, and 12 months. Participants (n=116) were overweight or obese adults aged 19–65 (M=44.5 [SD=10.5]). The 6‐month intervention was delivered via a Smartphone app providing educational materials, goal‐setting, self‐monitoring, and feedback, and also included one face‐to‐face dietary consultation, a Fitbit, and scales. The trial was prospectively registered and conducted between May 2017 and September 2018. Group differences on primary and secondary outcomes were examined between the Pooled Intervention groups (Pooled Intervention=Enhanced and Traditional) and Control groups, and then between Enhanced and Traditional groups.
Results
A total of 19 participants (16.4%) formally withdrew from the trial. Compared with the Control group, the average body weight of the Pooled Intervention group did not differ at 6 months (between‐group difference=−0.92, [95% CI −3.33, 1.48]) or 12 months (0.00, [95% CI −2.62, 2.62]). Compared with the Control group, the Pooled Intervention group significantly increased resistance training (OR=7.83, [95% CI 1.08, 56.63]) and reduced energy intake at 6 months (−1037.03, [−2028.84, −45.22]), and improved insomnia symptoms at 12 months (−2.59, [−4.79, −0.39]). Compared with the Traditional group, the Enhanced group had increased waist circumferences (2.69, [0.20, 5.18]) and sedentary time at 6 months (105.66, [30.83, 180.48]), and improved bedtime variability at 12 months (−1.08, [−1.86, −0.29]). No other significant differences were observed between groups.
Conclusions
Relative to Controls, the Pooled Intervention groups did not differ on body weight. However, the Pooled Intervention groups did improve resistance training, and reduced energy intake and insomnia symptom severity. There was no apparent additional weight loss when targeting improvements in physical activity, diet, and sleep in combination compared with physical activity and diet.
Comment
This small but well‐designed randomized control trial tried to determine the impact from a digital therapeutic targeting behavior change in adults with obesity. As seen in most relatively short‐term interventions (6 months in this study), it is difficult to show sustainable weight loss. It is encouraging to see changes in behaviors such as increased resistance training, reduced caloric intake, and improved preparation for sleep. Study results like this give us hope that more effective interventions will emerge.
A Randomized Controlled Trial to Evaluate the Effects of a Smartphone Application–Based Lifestyle Coaching Program on Gestational Weight Gain, Glycemic Control, and Maternal and Neonatal Outcomes in Women with Gestational Diabetes Mellitus: The SMART‐GDM Study
Yew TW,1,2 Chi C3, Chan SY3,4, van Dam RM5, Whitton C5, Lim CS5, Foong PS5, Fransisca W2, Teoh CL6, Chen J6, Ho‐Lim ST6, Lim SL7, Ong KW7, Ong PH8, Tai BC5, Tai ES1,2,5
1Department of Medicine, National University Hospital, Singapore; 2Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 3Department of Obstetrics and Gynecology, National University Hospital, Singapore; 4Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 5Saw Swee Hock School of Public Health, National University of Singapore, Singapore; 6Department of Nursing, National University Hospital, Singapore; 7Department of Dietetics, National University Hospital, Singapore; 8Department of Dietetics, National University Hospital, Singapore; 9Singapore Institute of Technology, Singapore
Objective
SMART‐GDM examined whether Habits‐GDM, a Smartphone application (app) coaching program, can prevent excessive gestational weight gain (EGWG) and improve glycemic control and maternal and neonatal outcomes in gestational diabetes mellitus (GDM).
Research design and methods
In this randomized controlled trial, women diagnosed with GDM between 12 and 30 weeks were randomly assigned to usual care (control) or to additional support from Habits‐GDM that integrated dietary, physical activity, weight, and glucose monitoring (intervention). The proportion of participants with EGWG was the primary outcome. Secondary outcomes included absolute gestational weight gain (GWG), glycemic control, and maternal, delivery, and neonatal outcomes.
Results
In total, 340 women were randomized (170 intervention, 170 control; mean±SD age 32.0±4.2 years; mean BMI 25.6±5.6 kg/m2). No statistically significant differences existed in the proportions of women with EGWG, absolute GWG, or maternal and delivery outcomes between experimental groups. Average glucose readings were lower in the intervention group (mean difference −0.15 mmol/L [95% CI −0.26; −0.03], P=0.011) as were the proportions of glucose above targets (premeal: 17.9% vs 23.3%, odds ratio 0.68 [95% CI 0.53; 0.87], P=0.003; 2‐h postmeal: 19.9% vs 50%, 0.54 [0.42; 0.70], P<0.001). When regarded as a composite, although this was not prespecified, overall neonatal complications including birth trauma, neonatal hypoglycemia, hyperbilirubinemia, respiratory distress, neonatal intensive care unit admission, and perinatal death were significantly lower in the intervention group (38.1% vs 53.7%, 0.53 [0.34; 0.84], P =0.006).
Conclusions
Habits‐GDM resulted in better maternal glycemic control and composite neonatal outcomes (not specified) when added to usual care. However, use of the app did not reduce EGWG among women with GDM.
Comment
This study reports on the digital therapeutic's ability to impact excessive weight gain in women with gestational diabetes. While they were unable to document such an impact, the intervention improved maternal glycemic control (e.g., less hyperglycemia) and was associated with fewer overall neonatal complications (including birth trauma, neonatal hypoglycemia, hyperbilirubinemia, respiratory distress, neonatal intensive care unit admission, and perinatal death). Demonstrating decreased neonatal complications is the obvious goal of maternal interventions and makes this study quite important. It is hoped that larger studies will not only confirm these findings but will also demonstrate the components of the interventions which have the most power to influence the most valuable outcomes.
Digital Coaching Strategies to Facilitate Behavioral Change in Type 2 Diabetes: A Systematic Review
Gershkowitz BD, Hillert CJ, Crotty BH
Medical College of Wisconsin, WI
Background
In this systematic review, the authors focus on the clinical impact of digital tools for providing health coaching, education, and facilitating behavior in patients with prediabetes or type 2 diabetes. Their approach was designed to provide insights for clinicians and healthcare systems that are considering adopting such digital tools.
Methods
We searched the CINAHL, Scopus, and Ovid/MEDLINE databases using PRISMA guidelines for studies that reported digital coaching strategies for management and prevention of type 2 diabetes published from January 2014 to June 2019. Articles were reviewed by two independent blinded reviewers. A total of 21 articles met inclusion criteria.
Results
The authors found that 20 of 21 studies in our analysis showed statistically significant improvements in at least one measure of diabetes control including HbA1c, weight loss, fasting blood glucose, and BMI. Studies that reported weight loss percentage from baseline at 1 year reported values ranging from −3.04% to −8.98%, similar to outcomes with traditional coaching in the Diabetes Prevention Program (N=4). Also of note, all the studies that included a comparison group of in‐person or telephone‐based coaching showed statistically better or similar outcomes in the digital coaching group (N=5).
Conclusions
Evidence from this systematic review suggests that digital health coaching offers a promising strategy for long‐term management and prevention of type 2 diabetes in diverse populations with similar benefits to in‐person or telephone‐based health coaching. The authors argue that digital coaching offers a promising solution to the rapid increase in diabetes prevalence, especially because of its potential to treat large numbers of individuals in diverse locations.
Comment
This review of digital therapeutics for type 2 diabetes gives a good overview of the current (January 2014 to June 2019) state of published behavior change interventions. The reader of the Yearbook can benefit from reading this paper to get a sense of the different approaches being taken at this time. I agree with the author's conclusion that these approaches can impact large numbers of participants, with the caveat that they must be able to get to the people who will benefit. The business models required to sustainably bring successful interventions to scale not only need effective interventions, but also need to demonstrate that they can get people to show up, participate, and stay engaged enough to experience the dose effect from the intervention required for it to be successful. A major challenge indeed.
Effect of Smartphone‐Based Lifestyle Coaching App on Community‐Dwelling Population with Moderate Metabolic Abnormalities: Randomized Controlled Trial
Cho SMJ1, Lee JH2, Shim JS3, Yeom H2, Lee SJ4, Jeon YW1, Kim HC1,3
1Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea; 2Department of Medicine, the Graduate School of Yonsei University, Seoul, Republic of Korea; 3Cardiovascular and Metabolic Diseases Etiology Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea; 4Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
Background
Metabolic disorders are established precursors to cardiovascular diseases, yet they can be readily prevented with sustained lifestyle modifications.
Objective
The authors assessed a Smartphone‐based weight management app and its effectiveness on metabolic parameters in adults at high‐risk, yet without physician diagnosis nor pharmacological treatment for metabolic syndrome, in a community setting.
Methods
In this three‐arm parallel‐group, single‐blind, randomized controlled trial, the authors recruited participants aged 30 to 59 years with at least two conditions defined by the Third Report of the National Cholesterol Education Program expert panel (abdominal obesity, high blood pressure, high triglycerides, low high‐density lipoprotein cholesterol, and high fasting glucose level). Participants were randomly assigned (1:1:1) by block randomization to either the nonuser group (control), the app‐based diet and exercise self‐logging group (app only), or the app‐based self‐logging and personalized coaching from professional dieticians and exercise coordinators group (app with personalized coaching). Assessments were performed at baseline, week 6, week 12, and week 24. Change in systolic blood pressure (between baseline and follow‐up assessments) was the primary outcome. Secondary outcomes included the following: changes in diastolic blood pressure, body weight, body fat mass, waist circumference, homeostatic model of assessment of insulin resistance, triglyceride level, and high‐density lipoprotein cholesterol level between baseline and follow‐up assessments. The intention‐to‐treat principle was used for analysis.
Results
Between October 28, 2017, and May 28, 2018, 160 participants took part in the baseline screening examination. Participants (129/160, 80.6%) who satisfied the eligibility criteria were assigned to control (n=41), app only (n=45), or app with personalized coaching (n=43) groups. In each group, systolic blood pressure showed decreasing trends from baseline (control: mean −10.95, SD 2.09 mmHg; app only: mean −7.29, SD 1.83 mmHg; app with personalized coaching: mean −7.19, SD 1.66 mmHg), yet without significant difference among the groups (app only: P=.19; app with personalized coaching: P=.16). Instead, those in the app with personalized coaching group had greater body weight reductions (control: mean −0.12, SD 0.30 kg; app only: mean − 0.35, SD 0.36 kg, P=.67; app with personalized coaching: mean −0.96, SD 0.37 kg; P=.08), specifically by body fat mass reduction (control: mean −0.13, SD 0.34 kg; app only: mean −0.64, SD 0.38 kg, P=.22; app with personalized coaching: mean −0.79, SD 0.38 kg; P=.08).
Conclusions
A combination of diet and exercise self‐logging, and persistent lifestyle modification coaching was ineffective in lowering systolic blood pressure but effective in patients' losing weight and reducing body fat mass. These results warrant future implementation studies of similar models of care on a broader scale in the context of primary prevention.
Comment
This short‐term (6-month) primary prevention study demonstrated that an app designed for weight loss, with or without additional personalized coaching, was able to improve weight loss outcomes for participants with metabolic syndrome. Unfortunately, there was no lowering of the frequency of hypertension, the targeted outcome. It is encouraging to see some positive results and the lack of improvement in blood pressure control may just be the multifactorial pathogenies of hypertension seen in metabolic syndrome. Future study is required.
Effectiveness of Disease‐Specific mHealth Apps in Patients with Diabetes Mellitus: Scoping Review
Eberle C, Löhnert M, Stichling S
Medicine with Specialization in Internal Medicine and General Medicine, Hochschule Fulda‐University of Applied Sciences, Fulda, Germany
Background
According to the World Health Organization, the worldwide prevalence of diabetes mellitus (DM) is increasing dramatically, with DM comprising a large part of the global burden of disease. At the same time, the ongoing digitalization occurring in society today offers novel possibilities to address this challenge, such as the creation of mobile health (mHealth) apps. However, while a great variety of DM‐specific mHealth apps exist, the evidence in terms of their clinical effectiveness is still limited.
Objective
This review aimed to evaluate the clinical effectiveness of mHealth apps in DM management by analyzing health‐related outcomes in patients diagnosed with type 1 DM (T1DM), type 2 DM (T2DM), and gestational DM.
Methods
The authors performed a scoping review. A systematic literature search was conducted in MEDLINE (PubMed), Cochrane Library, EMBASE, CINAHL, and Web of Science Core Collection databases for studies published between January 2008 and October 2020. The studies were categorized by outcomes and type of DM. In addition, the authors carried out a meta‐analysis to determine the impact of DM‐specific mHealth apps on the management of HbA1c.
Results
In total, 27 studies comprising 2,887 patients were included. We analyzed 19 randomized controlled trials, 1 randomized crossover trial, 1 exploratory study, 1 observational study, and 5 pre‐post design studies. Overall, there was a clear improvement in HbA1c values in patients diagnosed with T1DM and T2DM. In addition, positive tendencies toward improved self‐care and self‐efficacy as a result of mHealth app use were found. The meta‐analysis revealed an effect size, compared with usual care, of a mean difference of −0.54% (95% CI −0.8 to −0.28) for T2DM and −0.63% (95% CI −0.93 to −0.32) for T1DM.
Conclusions
In general, mHealth apps were effective in enhancing DM management. Results from the DM‐specific mHealth apps included improved glycemic control by significantly reducing HbA1c values in patients with T1DM and T2DM patients. However, further research is needed in terms of clinical effectiveness.
Comment
This review specifically looking at the effectiveness of digital therapeutics on diabetes outcomes provides additional support for the future of mHealth apps. While a number of good studies were found (N=27), the field needs considerably more and larger studies if it is to be accepted as standard of care and reimbursable by the various payers in the ecosystem.
Exergames in Childhood Obesity Treatment: A Systematic Review
Valeriani F1, Protano C2, Marotta D2, Liguori G3, Romano Spica V1, Valerio G3, Vitali M2, Gallè F3
1Department of Movement, Human, and Health Sciences, University of Rome “Foro Italico”, 00135 Rome, Italy; 2Department of Public Health and Infectious Diseases, “Sapienza” University of Rome, Rome, Italy; 3Department of Movement Sciences and Wellbeing, University of Naples “Parthenope”, Naples, Italy
Background
In the last decade, active video games (exergames) have been proposed in obesity prevention and treatment as a potential tool to increase physical activity. This review was aimed to assess the possible role of exergames in reducing weight‐related outcomes among overweight/obese children and/or adolescents.
Methods
The databases PubMed, Scopus, Web of Science, and SPORTDiscus were searched to detect controlled studies involving healthy overweight/obese children and adolescents in interventions based exclusively on exergames. Out of a total of 648 articles found, 10 met the inclusion criteria and were included in the review. The included studies differ for duration, setting and type of intervention, frequency of active game sessions, and outcomes considered.
Results
Out of the 10 studies, 7 reported better outcomes in children and adolescents involved in the interventions, including significant differences between groups in 4 of the studies. Three studies found better outcomes in control groups.
Conclusions
These results suggest a possible positive effect of active video games on weight‐related outcomes in obese children and adolescents. However, further research remains necessary to define if active video games can be effectively used in childhood obesity treatment, and if so, what may be the most effective approach. The potentiality of the new digital media in this field should be explored.
Comment
Making weight loss interventions engaging and fun is a challenge for developers of digital therapeutics. This review highlights some of the progress with gamification of physical activity interventions. That challenge will be to not only make the games engaging; I suspect they must be embedded in broader behavior change intervention to give the dose effect needed to change ingrained behaviors. For elements of the healthcare system to cover the program, outcomes beyond increased physical activity will be needed. For prevention‐oriented services, the sophistication of the outcome measures may be lower, but that remains to be seen.
The Effectiveness of a Monetary Reimbursement Model for Weight Reduction via a Smartphone Application: A Preliminary Retrospective Study
Lee J1,2, Bae S3, Park D1, Kim Y1,4, Park J5
1Noom Inc, Seoul, Korea; 2Ingenium College of Liberal Arts, KwangWoon University, Seoul, Korea; 3office of Research, Chung‐Ang University, Seoul, Korea; 4Department of Biomedical Systems Information, Yonsei University College of Medicine, Seoul, Korea; 5Department of Social Work and Counseling, Catholic University of Pusan, Pusan, Korea
Background
Weight loss for obese populations has been a challenging subject. Numerous mobile applications exist to address weight loss, but the low retention rate is a barrier for the intervention. This is a retrospective study, aiming to investigate the effectiveness of financial incentives to achieve weight loss via a monetary reimbursement model on a Smartphone application.
Methods
Participants voluntarily purchased a 16‐week mobile weight loss application program, and those who logged food intake three times a day received monetary reimbursement up to the full amount they initially paid. The authors analyzed health‐related information and logged in‐app activities from participants (N=2,803) including age, sex, weight, food intake, and physical activity on their mobile healthcare application, Noom, from January 2017 to April 2019. The authors used analysis of covariance (ANCOVA) to compare differences between groups who succeeded and failed at food logging, controlling for baseline BMI.
Results
Participants who completed the food logging successfully for 16 weeks (N=1,565) lost significantly more weight than those who failed at food logging (N=1,238, F=56.0, P<0.001). In addition, participants who logged their food intake successfully exercised more (F=41.5, P<0.001), read more in‐app articles (F=120.7, P<0.001), and consumed more quantity of healthy foods (F=12.8, P<0.001).
Conclusions
Encouraging participants to monitor their health‐related behaviors regularly with monetary reimbursement is an effective tool for weight reduction.
Comment
This retrospective study using the weight loss app from Noom demonstrated the positive impact of financial incentives on the posting to the app of food eaten three times a day for 24 weeks. While this behavior was associated with increased weight loss and other health‐promoting behaviors (e.g., increased exercise, more reading of in‐app articles, consumption of healthier foods), the study should only claim association and not causation as is the stated conclusion. It is a great start to understanding this very complex dynamic. A large enough randomized controlled trial would provide more certainty to the conclusion.
Effects of an mHealth App (Kencom) with Integrated Functions for Healthy Lifestyles on Physical Activity Levels and Cardiovascular Risk Biomarkers: Observational Study of 12,602 Users
Hamaya R1,2, Fukuda H3,4, Takebayashi M5, Mori M6, Matsushim6, Nakano K6, Miyake K6, Tani Y6, Yokokawa H3
1Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; 2Department of Epidemiology, Harvard University, Boston, MA; 3Department of General Medicine, School of Medicine, Juntendo University, Tokyo, Japan; 4Department of Advanced Preventive Medicine and Health Literacy, Graduate School of Medicine, Juntendo University, Tokyo, Japan; 5Graduate School of Health Science, Aomori University of Health and Welfare, Aomori, Japan; 6DeSC Healthcare Inc., Tokyo, Japan
Background
Mobile health (mHealth) apps are considered to be potentially powerful tools for improving lifestyles and preventing cardiovascular disease (CVD), although only few have undergone large, well‐designed epidemiological research. A novel mHealth app, kencom, features integrated functions for healthy lifestyles such as monitoring daily health/step data, providing tailored health information, or facilitating physical activity through group‐based game events. The app is linked to large‐scale Japanese insurance claims databases and annual health checkup databases, thus comprising a large longitudinal cohort.
Objective
The authors aimed to assess kenkom's effects on physical activity levels and CVD risk factors such as obesity, hypertension, dyslipidemia, and diabetes mellitus in a large population in Japan.
Methods
Daily step count, annual health checkup data, and insurance claim data of the kencom users were integrated within the kencom system. The authors analyzed steps by comparing the 1‐year average daily step count before and after kencom registration. In the CVD risk analysis, changes in CVD biomarkers following kencom registration were evaluated among the users grouped into the quintile according to their change in step count.
Results
A total of 12,602 kencom users were included for the step analysis and 5,473 for the CVD risk analysis. Participants were generally healthy, with a mean age of 44.1 (SD 10.2) years. The daily step count significantly increased following kencom registration by a mean of 510 steps/day (P<.001). In particular, participation in “Arukatsu” events held twice a year within the app was associated with a remarkable increase in step counts. In the CVD risk analysis, the users of the highest quintile in daily step change had, compared with those of the lowest quartile, a significant reduction in weight (−0.92 kg, P<.001), low‐density lipoprotein cholesterol (−2.78 mg/dL, P=.004), HbA1c (−0.04%, P=.004), and increase in high‐density lipoprotein cholesterol (+1.91 mg/dL, P<.001) after adjustment of confounders.
Conclusions
The use of the kencom app was significantly associated with enhanced physical activity, which might lead to weight loss and improvement in lipid profile. The app's framework successfully integrated Japanese health data from multiple data sources to generate a large, longitudinal data set.
Comment
Japan's kencom is a novel mHealth app with integrated functions for healthy lifestyles such as monitoring daily health/step data, providing tailored health information, or facilitating physical activity through group‐based game events. The app is linked to large‐scale Japanese insurance claims databases and annual health checkup databases, thus providing a large user base for this study.
This observational analysis demonstrated that a low‐intensity intervention is able to influence participant behaviors leading to increased physical activity using step counts as the metric and improved outcomes for those with cardiovascular risk factors.
This population health approach demonstrates that carefully designed opportunities to enroll large numbers of participants into a database allows health‐promoting services and interventions to thrive. The fact that the database is from insurance claims and annual checkups covered by the insurance companies increases the likelihood that the payer will see direct benefit from proven effective approaches. The ability to market the services to individuals enrolled in the database markedly increases the success of direct‐to‐consumer outreach and leads to a low customer/participant acquisition cost. This is the missing key to nearly all digital therapeutics.
Application of the National Institute for Health and Care Excellence Evidence Standards Framework for Digital Health Technologies in Assessing Mobile‐Delivered Technologies for the Self‐Management of Type 2 Diabetes Mellitus: Scoping Review
Forsyth JR1, Chase H1, Roberts NW2, Armitage LC3, Farmer AJ3
1Medical Sciences Division, University of Oxford, Oxford, UK; 2Bodleian Health Care Libraries, University of Oxford, Oxford, UK; 3Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
Background
Digital health technologies (DHTs) are playing a growing role in the management of chronic health conditions, specifically type 2 diabetes. It is increasingly important that health technologies meet the evidence standards for healthcare settings. In 2019, the National Institute for Health and Care Excellence (NICE) published the NICE Evidence Standards Framework for DHTs. This standards framework provides guidance for evaluating the effectiveness and economic value of DHTs in healthcare settings in the United Kingdom.
Objective
The aim of this study is to assess whether scientific articles on DHTs for the self‐management of type 2 diabetes mellitus report the evidence suggested for implementation in clinical practice, as described in the NICE Evidence Standards Framework for DHTs.
Methods
The authors performed a scoping review of published articles and searched five databases to identify systematic reviews and primary studies of mobile device–delivered DHTs that provide self‐management support for adults with type 2 diabetes mellitus. The evidence reported within articles was assessed against standards described in the NICE framework.
Results
The database search yielded 715 systematic reviews, of which 45 were relevant and together included 59 eligible primary studies. Within these, 39 unique technologies were discussed. Using the NICE framework, 13 technologies met best practice standards, 3 met minimum standards only, and 23 technologies did not meet minimum standards.
Conclusions
After assessing peer‐reviewed publications, the authors concluded that over half of the identified DHTs did not appear to meet the minimum evidence standards recommended by the NICE framework. The most prevalent reasons for studies of DHTs not meeting these evidence standards included the absence of a comparator group, no previous justification of sample size, no measurable improvement in condition‐related outcomes, and a lack of statistical data analysis. The information provided in this report will enable researchers and digital health developers to address these limitations when designing, delivering, and reporting digital health technology research in the future.
Comment
In 2019, the National Institute for Health and Care Excellence (NICE) published evidence standards for digital health technologies. The intent was to provide stakeholders guidance in assessing whether research studies that evaluate these technologies are providing sufficient evidence of efficacy or effectiveness. Other organizations also provide guidelines on evaluating the use of digital health technologies. These include the WHO, the Food and Drug Administration in the United States, and the National Health Service in England. The NICE guideline, however, is the only quality standard that acknowledges a technology's functionality in the evaluation. The authors of this review article identify peer‐reviewed publications on mobile digital health technologies to support self‐management of type 2 diabetes. They further place each technology into an “intervention tier” based on the technology's function. Finally, they examine whether the evidence reported meets the NICE framework level of evidence for that particular tier. The review is essentially a how‐to guide for readers who wish to critically evaluate the evidence related to digital health technologies. If you need a guidepost for that task, look no further.
DIABEO System Combining a Mobile App Software with and Without Telemonitoring Versus Standard Care: A Randomized Controlled Trial in Diabetes Patients Poorly Controlled with a Basal‐Bolus Insulin Regimen
Franc S1, Hanaire H2, Benhamou PY3, Schaepelynck P4, Catargi B5, Farret A6, Fontaine P7, Guerci B8, Reznik Y9, Jeandidier N10, Penfornis A2,11, Borot S12, Chaillous L13, Serusclat P14, Kherbachi Y15, d'Orsay G16, Detournay B17, Simon P18, Charpentier G1
1Department of Diabetes, Sud‐Francilien Hospital, Corbeil‐Essonnes, and Centre d'étude et de Recherche pour l'Intensification du Traitement du Diabète (CERITD), Evry, France; 2Department of Diabetology, Metabolic Diseases and Nutrition, CHU Toulouse, University of Toulouse, Toulouse, France; 3Department of Diabetology, Pôle DigiDune, University Hospital, Grenoble, France; 4Department of Nutrition‐Endocrinology‐Metabolic Disorders, Marseille University Hospital, Sainte Marguerite Hospital, Marseille, France; 5Department of Endocrinology and Diabetes, University Hospital, Bordeaux, France; 6Department of Endocrinology, Diabetes and Nutrition, University Hospital, Montpellier, France; 7Department of Diabetology, University Hospital, Lille, France; 8Endocrinology‐Diabetes Care Unit, University of Lorraine, Vandoeuvre Lès Nancy, France; 9Department of Endocrinology, University of Caen Côte de Nacre Regional Hospital Center, Caen, France; 10Department of Endocrinology, Diabetes and Nutrition, CHU of Strasbourg, Strasbourg, France; 11University Paris‐Sud, University Paris‐Sud, Orsay, France; 12Centre Hospitalier Universitaire Jean Minjoz, Service d'Endocrinologie‐Métabolisme et Diabétologie‐Nutrition, Besançon, France; 13CHU de Nantes–Hospital Laennec, Saint‐Herblain, France; 14Endocrinology, Diabetology and Nutrition, Clinique Portes du Sud, Venissieux, France; 15Sanofi‐Diabetes, Gentilly, France; 16Voluntis, Suresnes, France; 17CEMKA‐EVAL, Bourg‐la‐Reine, France; 18National Association of Telemedicine, Evry, France
This manuscript is also discussed in article on Decision Support Systems and Closed-Loop, page XX
Background
The DIABEO system (DS) is a telemedicine solution that combines a mobile app for patients with a web portal for healthcare providers. DS allows real‐time monitoring of basal‐bolus insulin therapy as well as therapeutic decision‐making, integrating both basal and bolus dose calculation. Real‐life studies have shown a very low rate of use of mobile health applications by patients. Therefore, we conducted a large randomized controlled trial study to investigate the efficacy of DS in conditions close to real life (TELESAGE study).
Methods
TELESAGE was a multicenter, randomized, open study with three parallel arms: arm 1 (standard care), arm 2 (DIABEO alone), and arm 3 (DIABEO+telemonitoring by trained nurses). The primary outcome assessed the reduction in HbA1c levels after a 12‐month follow‐up.
Results
Six hundred sixty‐five patients were included in the study. Participants who used DIABEO once or more times a day (DIABEO users) showed a significant and meaningful reduction of HbA1c versus standard care after a 12‐month follow‐up: mean difference − 0.41% for arm 2–arm 1 (P =0.001) and − 0.51% for arm 3–arm 1 (P ≤ 0.001). DIABEO users included 25.1% of participants in arm 2 and 37.6% in arm 3. In the intention‐to‐treat population, HbA1c changes and incidence of hypoglycemia were comparable between arms.
Conclusions
A clinical and statistically significant reduction in HbA1c levels was found in those patients who used DIABEO at least once a day.
Comment
This article describes the TeleSAGE study, which was a large (N=665) randomized controlled trial to investigate the efficacy of the DIABEO system in conditions close to real life. The trial included treatment arms involving standard care, software alone, and software plus telemonitoring by trained nurses. The DIABEO system includes a mobile app to support diabetes self‐management for patients and a web portal for healthcare providers. Those who used the system one or more times per day showed a significant reduction in HbA1c after 12 months, with a mean reduction of −.41% and −.51% for the two treatment arms. Unfortunately, individuals with a baseline HbA1c > 9.5% did not appear to experience a benefit in sensitivity analysis. One notable (and all‐too‐common) finding in this article is the strikingly low number of individuals who actually used the system in the absence of a telemedical intervention. Participants were much more engaged if they were part of an active remote patient monitoring intervention. The reduction in HbA1c that was observed was strongly dependent on how frequently participants used the DIABEO application. One might conclude from this and other evidence in the literature that a digital health app to promote self‐monitoring may not drive a high enough payoff to users for them to engage with it, unless it is part of an organized telemonitoring (i.e., remote patient monitoring) program. And the payoff for healthcare providers, healthy systems, and payers? Increased engagement just might drive improvement in glycemic control. Now we just need some user‐centered design for those with HbA1c > 9.5%.
Telemedicine in the OECD: An Umbrella Review of Clinical and Cost‐Effectiveness, Patient Experience and Implementation
Eze ND1, Mateus C1, Cravo Oliveira Hashiguchi T2
1Division of Health Research, Health Economics at Lancaster, Lancaster University, Lancaster, UK; 2Health Division Organisation for Economic Co‐operation and Development, Directorate for Employment, Labour and Social Affairs, Paris, France
Introduction
Patients and policy-makers alike have high expectations for the use of digital technologies as tools to improve healthcare service quality at a sustainable cost. Many countries within the Organisation for Economic Co‐operation and Development (OECD) are investing in telemedicine initiatives, and a large and growing body of peer‐reviewed studies on the topic has developed as a consequence. Nonetheless, telemedicine is still not used at scale within the OECD. The authors sought to provide a snapshot of the evidence on the use of telemedicine in the OECD. Their umbrella review of systematic reviews summarizes findings on four areas of policy relevance: clinical and cost‐effectiveness, patient experience, and implementation.
Methods
This review followed a prior written, unregistered protocol. Four databases (PubMed/MEDLINE, CRD, and Cochrane Library) were searched for systematic reviews or meta‐analyses published between January 2014 and February 2019. Based on the inclusion criteria, 98 systematic reviews were selected for analysis. Due to substantial heterogeneity, a meta‐analysis was not conducted. The quality of included reviews was assessed using the AMSTAR 2 tool.
Results
Most reviews (n=53) focused on effectiveness, followed by clinical effectiveness (n=18), implementation (n=17), and patient experience (n=15). Eighty‐three percent of clinical effectiveness reviews found telemedicine at least as effective as face‐to‐face care, and 39% of cost‐effectiveness reviews found telemedicine to be cost‐saving or cost‐effective. Patients reported high acceptance of telemedicine. The most common barriers to implementation were usability and lack of reimbursement. However, the methodological quality of most reviews was low to critically low, which limits generalizability and applicability of findings.
Conclusion
Telemedicine interventions can improve glycemic control in diabetic patients; reduce mortality and hospitalization due to chronic heart failure; help patients manage pain and increase their physical activity; improve mental health, diet quality and nutrition; and reduce exacerbations associated with respiratory diseases like asthma. Telemedicine may be a less effective way to deliver care in certain disease and specialty areas. While evidence exists that telemedicine can be cost‐effective, poor quality and reporting standards hinder the authors' ability to generalize about the cost‐effectiveness. This umbrella review also finds that patients report high levels of acceptance and satisfaction with telemedicine interventions, but that important barriers to wider use remain.
Comment
This review is an umbrella review of 98 reviews performed on “telemedical interventions.” Why include it in the section on digital health technologies? Because features that support the three major forms of telemedicine (real-time communication, remote monitoring, and store-and-forward telehealth) are being integrated into mHealth and eHealth (web) applications, which increasingly seek to fulfill all the requirements of remotely delivered healthcare. The article focuses specifically on interventions that promote synchronous or asynchronous communications between patients and healthcare providers. Communication‐promoting interventions are being used to monitor foot ulcer healing, to prevent or delay type 2 diabetes in at‐risk populations, and to provide remote monitoring and behavioral intervention during weight loss interventions. The major theme of the reviewed articles is that telemedical interventions can achieve similar or better outcomes compared to face‐to‐face interventions for glycemic control and other outcomes in diabetes. Similar conclusions were drawn for cardiovascular disease outcomes. The authors specifically summarize findings related to clinical outcomes, cost‐effectiveness, patient experience, and also implementation in clinical care. They conclude that 83% of effectiveness reviews have found that telemedicine interventions are at least as effective as face‐to‐face care. For diabetes management, all of the included reviews found that telemedicine interventions were effective. Among cost‐effectiveness reviews, 39% concluded that communication‐promoting interventions were cost‐effective or cost‐saving, while 28% found that they may be cost‐effective or cost‐saving, and only 22% found that they were not cost‐effective compared to usual care. I predict that in the coming years we will increasingly lose the distinction between “telehealth” and “digital health” technologies as the latter increasingly incorporate features to support communication.
Return on Investment: Medical Savings of an Employer‐Sponsored Digital Intensive Lifestyle Intervention, Weight Loss
Horstman CM1, Ryan DH2, Aronne LJ3, Apovian CM4, Foreyt JP5, Tuttle HM1, Williamson DA2
1Rally Health, Minneapolis, MN; 2Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA; 3Division of Endocrinology, Diabetes and Metabolism, Comprehensive Weight Control Center, Weill Cornell Medicine, New York, NY; 4Section for Endocrinology, Diabetes, Nutrition and Weight Management, Nutrition and Weight Management Center, Boston Medical Center, School of Medicine, Boston University, Boston, MA; 5Behavioral Medicine Research Center, Baylor College of Medicine, Houston, TX
Objective
This objective of this study was to determine the medical cost impact and return on investment (ROI) of a large, commercial, digital, weight‐management intensive lifestyle intervention (ILI) program (Real Appeal).
Methods
Participants in this program were compared with a control group matched by age, sex, geographic region, health risk, baseline medical costs, and chronic conditions. Medical costs were defined as the total amount paid for all medical expenses, inclusive of both the insurers' and the study participants' responsibility.
Results
In the 3 years following program registration, the intent‐to‐treat (ITT) cohort had significantly lower medical expenditures than the matched controls, with an average of −$771 or 12% lower costs (P=0.002). Among 4,790 ITT participants, a total savings of $3,693,090 compared with total program costs of $1,639,961 translated into a 2.3:1 ROI. Program completers (n=3,990), who attended more sessions than the overall ITT group, had greater mean weight loss (−4.4%), greater cost savings (−$956 or 14%), and an ROI of 2.0:1 over the 3‐year time frame when compared with matched controls.
Conclusions
The findings showed that the digital weight‐management ILI was associated with a significantly positive ROI. Employers and payers who are willing to cover the cost of an ILI that produces both weight loss and demonstrated cost benefits can improve health and save money for their overweight or obese population.
Comment
The study performs a return‐on‐investment analysis for Real Appeal, an employment‐based eHealth weight management program administered by Rally Health. The program has a 1‐year curriculum modeled after the diabetes prevention program and Look Ahead program. The program is not incentivized. The study evaluated the medical costs associated with the intervention by measuring program engagement, outcomes data, and medical cost data. Intent‐to‐treat participants lost an average of 2.8% of body weight and 23% achieved 5% weight loss; among more active participants, 29% achieved a 5% weight loss. The authors found that the intent‐to‐treat cohort had significantly lower medical expenditures than matched controls with an average of $771—or 12%—lower costs. This translated to a total savings of $3,693,000 compared with the total program cost of $1.6 million (a 2.3:1 return on investment). Those who attended more sessions demonstrated greater cost savings. What is notable about this article is that participation in an employer‐offered benefit without incentives achieved these outcomes. Another notable feature is the fee structure; the program was delivered using a pay‐for‐performance structure. The program received reimbursement only for those participants who engaged with sessions and who were on track to lose 5% of their weight. This is a good example of real‐world evidence research pertaining to an eHealth‐driven behavioral intervention.
One Drop App with an Activity Tracker for Adults with Type 1 Diabetes: Randomized Controlled Trial
Osborn CY1,2, Hirsch A1, Sears LE1,3, Heyman M1,4, Raymond J5, Huddleston B,1 Dachis J1
1Informed Data Systems Inc, New York, NY; 2Lirio, Nashville, TN; 3Sarah Cannon Research Institute, Nashville, TN; 4Department of Psychiatry, University of California San Diego, San Diego, CA; 5Division of Endocrinology, Department of Pediatrics, University of Southern California, Los Angeles, CA
Background
In 2017, mobile app support for managing diabetes was available to 64% of the global population of adults with diabetes. One Drop's digital therapeutics solution includes an evidence‐based mobile app with global reach, a Bluetooth‐connected glucometer, and in‐app coaching from certified diabetes educators (CDEs). Among people with type 1 diabetes (T1D) and an estimated HbA1c level ≥ 7.5%, using One Drop for 3 months has been associated with an improved estimated HbA1c level of 22.2 mg/dL (−0.80%). However, the added value of integrated activity trackers is unknown.
Objective
The authors conducted a pragmatic, remotely administered randomized controlled trial to evaluate One Drop with a new‐to‐market activity tracker against One Drop only on the 3‐month HbA1c level of adults with T1D.
Methods
Social media advertisements and online newsletters were used to recruit adults (≥ 18 years old) diagnosed (≥ 1 year) with T1D, unfamiliar with One Drop's full solution and the activity tracker, with a laboratory HbA1c level ≥ 7%. Participants (N=99) were randomized to receive One Drop and the activity tracker or One Drop only at the start of the study. The One Drop only group received the activity tracker at the end of the study. Multiple imputation, performed separately by group, was used to correct for missing data. Analysis of covariance models, controlling for baseline HbA1c, were used to evaluate 3‐month HbA1c differences in intent‐to‐treat (ITT) and per protocol (PP) analyses.
Results
The enrolled sample (N=95) had a mean age of 41 (SD 11) years, was 73% female, 88% white, diagnosed for a mean of 20 (SD 11) years, and had a mean HbA1c level of 8.4% (SD 1.2%); 11% of the participants did not complete follow‐up. Analysis of covariance assumptions were met for the ITT and PP models. In ITT analysis, participants in the One Drop and activity tracker condition had a significantly lower 3‐month HbA1c level (mean 7.9%, SD 0.60%, 95% CI 7.8–8.2) than that of the participants in the One Drop only condition (mean 8.4%, SD 0.62%, 95% CI 8.2–8.5). In PP analysis, participants in the One Drop and activity tracker condition also had a significantly lower 3‐month HbA1c level (mean 7.9%, SD 0.59%, 95% CI 7.7–8.1) than that of participants in the One Drop only condition (mean 8.2%, SD 0.58%, 95% CI 8.0–8.4).
Conclusions
During the 3‐month study period, participants exposed to One Drop and the activity tracker had a significantly lower 3‐month HbA1c level compared to participants exposed to One Drop only during the same timeframe. One Drop and an activity tracker may work better together than alone in helping people with T1D.
Comment
The OneDrop platform includes a mobile app (for mobile phone and for Apple Watch), a Bluetooth‐connected glucometer, and in‐app coaching from CDEs. The present study was a randomized controlled trial of OneDrop plus an activity tracker versus OneDrop only in adults with T1D. HbA1c after 3 months was much lower in individuals using OneDrop plus an activity tracker. Eighty percent of individuals used the app and 38% used the in‐app coaching at least half the time or more. Buried in these results is the fact that individuals who used OneDrop only did not improve relative to their baseline. However, when the OneDrop mobile app was combined with an activity tracker, there was a significant reduction in HbA1c at 3 months relative to baseline and relative to OneDrop only. A significant limitation of the study is that authors provide no data or references on the validity of the tracker. Another fact that may limit generalizability of the findings is that the tracker is not a typical tracker. It includes built‐in GPS, music to accompany exercise, personalized workout recommendations, and personalized dynamic feedback. It will be interesting to know whether these findings translate to younger individuals with T1D and to populations with type 2 diabetes. Finally, the study highlights another trend: it leveraged a pragmatic and remote study design that allowed data collection to occur remotely. This is an increasing trend in clinical trials and clinical research that is enabled by some digital health applications.
Effect of a Digital Intervention on Depressive Symptoms in Patients with Comorbid Hypertension or Diabetes in Brazil and Peru: Two Randomized Clinical Trials
Araya R1,3, Menezes PR2, Claro HG2,4, Brandt LR5, Daley KL2, Quayle J2, Diez‐Canseco F5, Peters TJ6, Vera Cruz D2, Toyama M5, Aschar S2, Hidalgo‐Padilla L5, Martins H2, Cavero V5, Rocha T2, Scotton G2, de Almeida Lopes IF7, Begale M8, Mohr DC8, Miranda JJ5,9
1Centre for Global Mental Health, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; 2Population Mental Health Research Centre, Universidade de São Paulo, São Paulo, Brazil; 3Department of Preventive Medicine, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil; 4School of Nursing, Universidade Estadual de Campinas, Campinas, Brazil; 5CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru; 6Bristol Medical School, Faculty of Health Sciences, University of Bristol, Bristol, UK; 7Federal University of ABC, Engineering, Modeling and Applied Social Sciences Center (CECS), Santo André, Brazil; 8Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL; 9Department of Medicine, School of Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru
Importance
Depression is a leading contributor to disease burden around the world. Digital mental health interventions can address the treatment gap in low‐ and middle‐income countries, but the effectiveness in these countries is unknown.
Objective
The authors aimed to investigate the effectiveness of a digital mental health intervention in reducing depressive symptoms among people with diabetes and/or hypertension.
Design, setting, and participants
Participants with clinically significant depressive symptoms (Patient Health Questionnaire‐9 [PHQ‐9] score ≥ 10) who were being treated for hypertension and/or diabetes were enrolled in a cluster randomized clinical trial (RCT) at 20 sites in São Paulo, Brazil (N=880; from September 2016 to September 2017; final follow‐up, April 2018), and in an individual‐level RCT at seven sites in Lima, Peru (N=432; from January 2017 to September 2017; final follow‐up, March 2018).
Interventions
An 18‐session, low‐intensity, digital intervention was delivered over 6 weeks via a provided Smartphone, based on behavioral activation principles, and supported by nurse assistants (n = 440 participants in 10 clusters in São Paulo; n = 217 participants in Lima) vs enhanced usual care (n = 440 participants in 10 clusters in São Paulo; n = 215 participants in Lima).
Main outcomes and measures
A reduction of at least 50% from baseline in PHQ‐9 scores (range, 0–27; higher score indicates more severe depression) was the primary outcome at 3 months. As secondary outcomes, a reduction of at least 50% from baseline PHQ‐9 scores at 6 months was recorded.
Results
Among 880 patients cluster randomized in Brazil (mean age, 56.0 years; 761 [86.5%] women) and 432 patients individually randomized in Peru (mean age, 59.7 years; 352 [81.5%] women), 807 (91.7%) in Brazil and 426 (98.6%) in Peru completed at least one follow‐up assessment. The proportion of participants in São Paulo with a reduction in PHQ‐9 score of at least 50% at 3‐month follow‐up was 40.7% (159/391 participants) in the digital intervention group vs 28.6% (114/399 participants) in the enhanced usual care group (difference, 12.1 percentage points [95% CI, 5.5 to 18.7]; adjusted odds ratio [OR], 1.6 [95% CI, 1.2 to 2.2]; P = .001). In Lima, the proportion of participants with a reduction in PHQ‐9 score of at least 50% at 3‐month follow‐up was 52.7% (108/205 participants) in the digital intervention group vs 34.1% (70/205 participants) in the enhanced usual care group (difference, 18.6 percentage points [95% CI, 9.1 to 28.0]; adjusted OR, 2.1 [95% CI, 1.4 to 3.2]; P < .001). At 6‐month follow‐up, differences across groups were no longer statistically significant.
Conclusions
In two RCTs of patients with hypertension or diabetes and depressive symptoms in Brazil and Peru, a 6‐week‐long digital mental health intervention significantly improved depressive symptoms at 3 months when compared with enhanced usual care. However, the effects were not sustained at the 6‐month period, and the magnitude of the effect was small in the trial from Brazil.
Comment
This paper reports on two South American randomized controlled trials that evaluated a 6‐week, 18‐session, low‐intensity Smartphone intervention for depressive symptoms that is based on behavioral activation principles. The intervention appeared effective at 3 months, but the effect waned by 6 months. Studies of digital interventions have been limited in low‐ and middle‐income country settings, making this study an important investigation into feasibility of digital interventions in these settings in general. A limitation of the study was that individuals who were unable to read a computer tablet screen were excluded. The authors also noted variations in findings and adherence between sites (Lima and São Paulo), raising questions on replicability of successful interventions across different cultural and national settings. As digital health companies increasingly “go global,” it will be important to pay attention to the transferability of findings between countries, especially among low‐ and middle‐income countries.
Mobile‐Enhanced Peer Support for African Americans with Type 2 Diabetes: a Randomized Controlled Trial
Presley C1, Agne A1, Shelton T2, Oster R1, Cherrington A1
1Division of Preventive Medicine, Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL; 2Cooper Green Mercy Health Services, Birmingham, AL
Background
Although peer support has been shown to improve diabetes self‐management and control, no standard exists to link peer support interventions to clinical care.
Objective
The authors aimed to compare a community‐based diabetes self‐management education (DSME) plus mobile health (mHealth)‐enhanced peer support intervention to community‐based diabetes self‐management education (DSME) alone for African American adults with poorly controlled type 2 diabetes.
Design
The authors undertook a randomized controlled trial.
Participants
Participants included African American adults, age >19 years, receiving care within a safety‐net healthcare system in Jefferson County, Alabama, with a diagnosis of type 2 diabetes and a HbA1c (A1C) ≥7.5%.
Interventions
Participants in the intervention group received community‐based diabetes self‐management education (DSME) plus 6 months of mHealth‐enhanced peer support, including 12 weekly phone calls, followed by 3 monthly calls from community health workers, who used a novel web application to communicate with participants' healthcare teams. In the control group, participants received community‐based DSME alone.
Main measures
The primary outcome was HbA1c; secondary outcomes included diabetes distress, depressive symptoms, self‐efficacy or confidence in their ability to manage diabetes, and social support. The authors used mixed models repeated measures analyses to assess for between‐arm differences and baseline to follow‐up changes.
Key results
Of 120 participants who were randomized, 97 completed the study. Participants in intervention and control groups experienced clinically meaningful reduction in HbA1c, 10.1 (SD 1.7) to 9.6 (SD 1.9) and 9.8 (SD 1.7) to 9.1 (SD 1.9), respectively, P=0.004. Participants in the intervention group experienced a significantly larger reduction in diabetes distress compared to the control, 2.7 (SD 1.2) to 2.1 (1.0) versus 2.6 (SD 1.1) to 2.3 (SD 1.0), P=0.041.
Conclusions
Improved glycemic control resulted from community‐based DSME with and without peer support. Peer support linked to clinical care led to a larger reduction in diabetes distress, which has important implications for the overall well-being of adults with type 2 diabetes.
Comment
This study evaluated a community‐based diabetes self‐management education (DMSE) program that incorporated Diabetes Connect, an eHealth‐enhanced peer support intervention. The web application was designed specifically for community health workers to coordinate with the healthcare team. The DSME included 12 weekly phone calls and 3 monthly calls from community health workers who used the Diabetes Connect web application to manage their work. The intervention was targeted to African American adults with poorly controlled type 2 diabetes who received care in a safety net healthcare system. One‐hundred twenty participants with HbA1c > 7.5% were randomized, and 97 completed the study. The study showed that the intervention group and control group each achieved similar reductions in HbA1c but that the intervention group achieved a greater improvement in diabetes‐related distress. The intervention did not improve self‐efficacy, depressive symptoms, or quality of life. This article is an example of a digital health intervention that is primarily used by the healthcare team; it is also another example of the deep integration of digital health solutions into person‐to‐person‐driven healthcare delivery. One must imagine that the term “digital health” will one day be a thing of the past, as all healthcare adopts one or more digital components.
Mixed‐Methods Randomized Evaluation of FAMS: A Mobile Phone‐Delivered Intervention to Improve Family/Friend Involvement in Adults' Type 2 Diabetes Self‐Care
Mayberry LS1,2, Berg CA3, Greevy RA2,4, Nelson LA1,2, Bergner EM,1 Wallston KA2, Harper KJ1, Elasy TA1,2
1Department of Medicine, Division of General Internal Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN; 2Vanderbilt Center for Diabetes Translation Research, Vanderbilt University Medical Center, Nashville, TN; 3Department of Psychology, University of Utah, Salt Lake City, UT; 4Department of Biostatistics, Vanderbilt University, Nashville, TN
Background
Family and friends have both helpful and harmful effects on adults' diabetes self‐management. Family‐focused Add‐on to Motivate Self‐care (FAMS) is a mobile phone–delivered intervention designed to improve family/friend involvement, self‐efficacy, and self‐care via monthly phone coaching, texts tailored to goals, and the option to invite a support person to receive texts.
Purpose
The authors sought to evaluate how FAMS was received by a diverse group of adults with type 2 diabetes (T2D) and whether FAMS improved diabetes‐specific family/friend involvement (increased helpful and reduced harmful), diabetes self‐efficacy, and self‐care (diet and physical activity). The authors also assessed whether improvements in family/friend involvement mediated improvements in self‐efficacy and self‐care.
Methods
Participants were prospectively assigned to enhanced treatment as usual (control), an individualized text messaging intervention alone, or the individualized text messaging intervention plus FAMS for 6 months. Participants completed surveys at baseline, 3 months, and 6 months, and postintervention interviews. Between‐group and multiple mediator analyses followed intention‐to‐treat principles.
Results
The study maintained high retention, engagement, and fidelity. FAMS was well received and helped participants realize the value of involving family/friends in their care. Relative to control, FAMS participants had improved family/friend involvement, self‐efficacy, and diet (but not physical activity) at 3 and 6 months (all P<.05). Improvements in family/friend involvement mediated effects on self‐efficacy and diet for FAMS participants but not for the individualized intervention group.
Conclusions
The promise of effectively engaging patients' family and friends lies in sustained long‐term behavior change. This study demonstrates how content targeting helpful and harmful family/friend involvement can drive short‐term effects, and how it may represent a first step toward the goal of long‐term engagement.
Comment
The present study evaluates the digital health intervention—or really two interventions, Family‐focused Add‐on to Motivate Self‐care (FAMS) and REACH—against treatment as usual. REACH is a partially tailored text‐message intervention, while FAMS involves 6 monthly structured coaching sessions lasting 20–30 minutes, along with more tailored text messages. In REACH, text messages were tailored to address barriers to medication adherence and nontailored to address diet physical activity and blood glucose monitoring. In contrast, the messages in FAMS were also tailored to the participant's physical activity goals during coaching. FAMS leverages family systems theory, which attempts to sustain individual initiated behaviors through system change. The authors identify that not all family support is helpful; some can be harmful, which is a limitation of past research involving family support. The coaching in FAMS is unique in that it helps individuals identify both helpful and harmful interactions from family members, and the coaching is reinforced by messages from REACH that are tailored to the goals established in coaching sessions. Because the adoption of a new digital health technology involves change in one or more behaviors, it makes sense that one might want to promote the sustainability of the intervention's effect through positive feedback loops within the family. The authors found that FAMS+REACH and REACH alone resulted in improved diet and self‐efficacy at 6 months, but only FAMS was associated with improvements in family/friend involvement. They stopped short of evaluating this as a SMART (stepped) intervention. Presumably some individuals do not respond to REACH; it would be interesting to know if those individuals would benefit from the more intensive FAMS+REACH intervention. This is also an example of combining a mobile phone–driven intervention with a person‐to‐person care paradigm.
Effectiveness of Mobile Applications in Diabetic Patients' Healthy Lifestyles: A Review of Systematic Reviews
Represas‐Carrera FJ1, Martínez‐Ques ÁA2, Clavería A1
1Vigo Health Area, Galician Health Service (SERGAS), Galicia South Health Research Institute, Vigo, Spain. Spanish Primary Care Research Network (REDIAPP), Barcelona, Spain; 2Ourense Health Area, Galician Health Service (SERGAS), Galicia South Health Research Institute, Ourense, Spain
Objective
The authors had two main objectives: 1) To examine the mobile applications that address lifestyles to improve the metabolic control of adult patients with diabetes mellitus; and 2) Describe the characteristics of the used mobile applications, identify the healthy lifestyles they target, and describe any of their adverse effects.
Methods
The authors assessed systematic reviews. They included studies that used any mobile application to help patients improve diabetes mellitus self‐management by focusing on healthy lifestyles. To be eligible, studies needed to include a control group receiving regular care with no mobile devices. In May 2018, MEDLINE, Embase, Cochrane, LILACS, PsychINFO, CINAHL and Science Direct were searched, and then updated in June 2021. The methodological quality of the studies was assessed by the Amstar‐2 tool.
Results
As a first step, 804 articles were analyzed, out of which 17 systematic reviews were selected. Of these reviews, the methodological quality of seven was high or moderate. Interventions lasted 1–12 months. Twenty‐three different mobile applications were identified that were all related to eating and physical activity. Significant changes were noted in HbA1c values. No clear improvement was observed for weight/BMI, lipid profile, quality of life, or blood pressure. No adverse effects were found.
Conclusions
Managing the lifestyle of patients with diabetes using mobile applications improves short‐term glycemic control, but the long‐term results are not conclusive. The identified mobile applications focus on food and physical activity. Most are free. No adverse effects were identified from their use.
Comment
This article evaluates 17 systematic reviews, covering 23 different mobile applications designed to promote healthy eating and physical activity among individuals with diabetes. The authors noted significant changes are often reported for HbA1c but not for weight, lipids, quality of life, or blood pressure. The authors conclude that mobile apps to improve glycemic control are beneficial for improving HbA1c in the short term. There remains very little evidence, however, that they can improve glycemic control in the long term. The authors also note that very few studies describe the potential adverse effects associated with using a mobile app.
Effectiveness of a Smartphone App to Promote Healthy Weight Gain, Diet, and Physical Activity During Pregnancy (HealthyMoms): Randomized Controlled Trial
Sandborg J1,2, Söderström E,2 Henriksson P2, Bendtsen M2, Henström M1, Leppänen M1,3,4, Maddison R5, Migueles JH2,6, Blomberg M7,8, Löf M1,2,5
1Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden; 2Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden; 3Folkhälsan Research Center, Helsinki, Finland; 4Faculty of Medicine, University of Helsinki, Helsinki, Finland; 5Institute for Physical Activity and Nutrition, Deakin University, Burwood, Australia; 6PROFITH “PROmoting FITness and Health through physical activity” research group, Department of Physical Education and Sports, Faculty of Sport Sciences, Research Institute of Sport and Health, University of Granada, Granada, Spain; 7Department of Obstetrics and Gynecology, Linköping University, Linköping, Sweden; 8Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
Background
Excessive gestational weight gain (GWG) during pregnancy is a major public health concern and is associated with negative health outcomes for both mother and child. Scalable interventions are needed, and digital interventions have the potential to reach many women and promote healthy GWG. Most previous studies of digital interventions have been small pilot studies or have not included women from all BMI categories. The authors therefore examined the effectiveness of a Smartphone app in a large sample (n=305) covering all BMI categories.
Objective
The authors aimed to investigate the effectiveness of a 6‐month intervention (the HealthyMoms app) on GWG, body fat levels, dietary habits, moderate‐to‐vigorous physical activity (MVPA), glycemia, and insulin resistance in comparison to standard maternity care.
Methods
A two‐arm, parallel, randomized controlled trial was conducted. The authors recruited participants from maternity clinics in Östergötland, Sweden, focusing on women in early pregnancy. Eligible women who provided written informed consent completed baseline measures, before being randomized in a 1:1 ratio to either an intervention (n=152) or control group (n=153). The control group received standard maternity care while the intervention group received the HealthyMoms Smartphone app for 6 months (which includes multiple features, including information; push notifications; self‐monitoring; and feedback features for GWG, diet, and physical activity) in addition to standard care. Outcome measures were assessed at Linköping University Hospital at baseline (mean 13.9 [SD 0.7] gestational weeks) and follow‐up (mean 36.4 [SD 0.4] gestational weeks). The primary outcome was GWG and secondary outcomes were body fat levels (Bod Pod), dietary habits (Swedish Healthy Eating Index) using the web‐based 3‐day dietary record Riksmaten FLEX, MVPA using the ActiGraph wGT3x‐BT accelerometer, glycemia, and insulin resistance.
Results
The authors found no overall statistically significant effect on GWG (P=.62); however, the data indicate that the effect of the intervention differed by prepregnancy BMI, as women with overweight and obesity before pregnancy gained less weight in the intervention group as compared with the control group in the imputed analyses (−1.33 kg; 95% CI −2.92 to 0.26; P=.10) and completers‐only analyses (−1.67 kg; 95% CI −3.26 to −0.09; P=.031). Bayesian analyses showed that there was a 99% probability of any intervention effect on GWG among women with overweight and obesity, and an 81% probability that this effect was over 1 kg. The intervention group had higher scores for the Swedish Healthy Eating Index at follow‐up than the control group (0.27; 95% CI 0.05–0.50; P=.017). We observed no statistically significant differences in body fat levels, MVPA, glycemia, and insulin resistance between the intervention and control group at follow‐up (P≥.21).
Conclusions
Although the authors found no overall effect on GWG, the results demonstrate the potential of a Smartphone app (HealthyMoms) to promote healthy dietary and to decrease weight gain during pregnancy in women with overweight and obesity characteristics.
Comment
The Healthy Moms mHealth app was studied as a 6‐month intervention on gestational weight gain, dietary habits, and moderate‐to‐vigorous physical activity. The authors found no effect on gestational weight gain across the treatment cohort as a whole, but women with overweight and obesity prior to pregnancy gained less weight during the intervention compared to those in the control group. The intervention group also demonstrated improved scores on the Swedish Healthy Eating Index. The authors observed no differences in moderately vigorous physical activity, glycemic control, or other outcomes. The authors found that the intervention effect of this application was similar to that of traditional face‐to‐face counseling, suggesting that it could be more cost‐effective. One must again ask whether we are entering an era in which digital health interventions become the front‐line approach, with escalations of care occurring for those who fail to meet treatment targets with a digital intervention alone.
CLOSING REMARKS
Digital therapeutics: The growing revolution in healthcare
The global pandemic continues to reinforce the immediate need for digital interventions, digital health platforms, and care paradigms that leverage them. As the consumer service industry globally continues its long march toward providing what consumers want, when they want it, in the palms of their hands (think about preordering that coffee or arranging food delivery from your mobile phone), it only makes sense that healthcare has been asked to join the parade.
1. Reimbursement for remote patient monitoring and digital therapeutics continues to drive the rapid rise of a multibillion‐dollar industry.
2. The last several years have brought increased scrutiny related to digital privacy, yet multiple signs indicate that consumers and payers do not want that to come at the cost of data interoperability. We see signs of a shift in focus to patient autonomy over one's own health‐related data, with some government mandates for system interoperability. This can only serve to drive innovation in digital therapeutics that seek to “plug in” to electronic health record data or self‐management device data so that they can gather more context about the person's health journey. Bidirectional data flow with electronic medical records (EMRs) will make digital therapeutics more contextually relevant and personalized.
3. As researchers and companies seek to know not only what works in a digital therapeutic, but also what increases cost‐effectiveness of care, there is increasing focus on automation to limit the need for labor‐intensive person‐to‐person interactions.
4. Digital therapeutics are increasingly becoming components of larger care delivery interventions that might also include population management and person‐to‐person care components.
For the field to accelerate its growth, we will need to see more of the following:
1. More focus on patient‐centered design, with interventions specifically designed for the most vulnerable populations, populations of special interest, and globally diverse populations.
2. Increasing data interoperability between devices and digital therapeutics, and among various digital therapeutics.
3. Increased business partnerships between digital therapeutics companies and other hardware, software, or healthcare service companies, or with academic researchers.
4. Increased ease of bidirectional communication between digital therapeutics and electronic health record systems.
5. More rigorous clinical trial designs, including evaluation of individual components of digital therapeutics and evaluation of individual components of more comprehensive intervention programs that include a digital therapeutic. Clinical trial reporting should include more specificity regarding each digital therapeutic's targeted outcomes that matter; mechanism(s) of action; assessment of the engagement to get results (dose effects); approaches to minimize patient acquisition costs; economic analyses of return on investment; and ability to sustainably scale to large numbers of participants.
6. Personalization of mHealth and eHealth platforms and personalization of features within a platform. A platform should be designed to adapt to the needs of different personas, and should be designed to promote precision engagement from the user.
7. SMART trial designs which recognize that the most cost‐effective interventions will be those that begin with the least invasive, resource‐intensive, and burdensome approaches, and then use risk‐based approaches to intensify the treatment approach for those who do not respond to first‐tier approaches.
8. Increasing use of algorithms and machine learning/reinforcement learning methods to drive predictions, classification, and reinforcement learning to enable population risk stratification, just‐in‐time interventions, and improved health goal attainment.
Requirements for the digital therapeutic enterprise to be successful include:
1. Improved interventions designed with authentic input from the target population.
2. The development of innovative clinical trial designs that are better suited to evaluating the various components of digital health interventions, which are often multicomponent in nature.
3. Development of research methodologies and studies that demonstrate outcomes relevant to people with diabetes, providers, payers, community‐based organizations, and governments.
4. Creation of more and improved collaborations between developers of digital therapeutics and plans, providers, pharma/device companies, and employers.
5. Enhanced focus not only on the outcomes for those participants who enroll in a program but also on how to cost‐effectively identify and recruit the right person for the right intervention at the right time in their health journey.
6. Creation of a digital therapeutic ecosystem capable of not only offering high‐quality interventions but also able to be integrated into the core workflows used to provide direct medical health services.
7. Development of new/improved business models that not only encourage innovation but are also able to sustainably bring the interventions to scale.
Footnotes
Author Disclosure Statement
NK works for and has equity interest in ABk Ventures, Inc. (dba Canary Health). NK's son is an employee of and has equity interest in WW, the subject of the article regarding Ontrack. MAC is chief medical officer of Glooko, Inc. He has received research support from Dexcom and Abbott Diabetes Care. EM has no competing financial interests.