Diabetes Prevention and Weight Loss with a Fully Automated Behavioral Intervention by Email, Web, and Mobile Phone: A Randomized Controlled Trial Among Persons with Prediabetes

Abstract

BackgroundOne-third of US adults, 86 million people, have prediabetes. Two-thirds of adults are overweight or obese and at risk for diabetes. Effective and affordable interventions are needed that can reach these 86 million, and others at high risk, to reduce their progression to diagnosed diabetes.ObjectiveThe aim was to evaluate the effectiveness of a fully automated algorithm-driven behavioral intervention for diabetes prevention, Alive-PD, delivered via the Web, Internet, mobile phone, and automated phone calls.MethodsAlive-PD provided tailored behavioral support for improvements in physical activity, eating habits, and factors such as weight loss, stress, and sleep. Weekly emails suggested small-step goals and linked to an individual Web page with tools for tracking, coaching, social support through virtual teams, competition, and health information. A mobile phone app and automated phone calls provided further support. The trial randomly assigned 339 persons to the Alive-PD intervention (n=163) or a 6-month wait-list usual-care control group (n=176). Participants were eligible if either fasting glucose or glycated hemoglobin A1c (HbA1c) was in the prediabetic range. Primary outcome measures were changes in fasting glucose and HbA1c at 6 months. Secondary outcome measures included clinic-measured changes in body weight, body mass index (BMI), waist circumference, triglyceride/high-density lipoprotein cholesterol (TG/HDL) ratio, and Framingham diabetes risk score. Analysis was by intention-to-treat.ResultsParticipants’ mean age was 55 (SD 8.9) years, mean BMI was 31.2 (SD 4.4) kg/m2, and 68.7% (233/339) were male. Mean fasting glucose was in the prediabetic range (mean 109.9, SD 8.4 mg/dL), whereas the mean HbA1c was 5.6% (SD 0.3), in the normal range. In intention-to-treat analyses, Alive-PD participants achieved significantly greater reductions than controls in fasting glucose (mean –7.36 mg/dL, 95% CI –7.85 to –6.87 vs mean –2.19, 95% CI –2.64 to –1.73, P<.001), HbA1c (mean –0.26%, 95% CI –0.27 to –0.24 vs mean –0.18%, 95% CI –0.19 to –0.16, P<.001), and body weight (mean –3.26 kg, 95% CI –3.26 to –3.25 vs mean –1.26 kg, 95% CI –1.27 to –1.26, P<.001). Reductions in BMI, waist circumference, and TG/HDL were also significantly greater in Alive-PD participants than in the control group. At 6 months, the Alive-PD group reduced their Framingham 8-year diabetes risk from 16% to 11%, significantly more than the control group (P<.001). Participation and retention was good; intervention participants interacted with the program a median of 17 (IQR 14) of 24 weeks and 71.1% (116/163) were still interacting with the program in month 6.ConclusionsAlive-PD improved glycemic control, body weight, BMI, waist circumference, TG/HDL ratio, and diabetes risk. As a fully automated system, the program has high potential for scalability and could potentially reach many of the 86 million US adults who have prediabetes as well as other at-risk groups.Trial RegistrationClinicaltrials.gov NCT01479062; https://clinicaltrials.gov/ct2...

Lifestyle interventions based on the diabetes prevention program delivered via eHealth: A systematic review and meta-analysis

“…Among the 22 studies, the majority were randomized controlled trials (n=13) (Ackermann et al, 2014; Azar et al, 2015; Bennett et al, 2010; Block et al, 2015; Estabrooks and Smith-Ray, 2008; Fischer et al, 2016; Fukuoka et al, 2015; Leahey et al, 2014; Ma et al, 2013; Tate et al, 2003; Thomas et al, 2015; Wing et al, 2010). Other study designs were single group intervention studies (n=5) (McTigue et al, 2009; Pagoto et al, 2015; Sepah et al, 2014; Skoyen et al, 2015; Thomas and Wing, 2013), non-randomized controlled trials (n=2) (Kramer et al, 2010; Vadheim et al, 2010), a comparative effectiveness trial (n=1) (Piatt et al, 2013), and a retrospective cohort study (n=1) (Ahrendt et al, 2014).…”

“…The methodological quality ratings ranged from 3 to 9 on a scale of 0 to 10. Thirteen of the studies were scored as ≥ 6/10 on the methodological quality scale, indicating that they had a lower risk of bias (Tate et al, 2003; Estabrooks and Smith-Ray, 2008; McTigue et al, 2009; Bennett et al, 2010; Wing et al, 2010 (study1&2); Ma et al, 2013; Ackermann et al, 2014; Leahey et al, 2014; Block et al, 2015; Fukuoka et al, 2015; Thomas et al, 2015; Fischer et al, 2016). Details of the methodological quality scoring available upon request.…”

“…Samples per intervention group ranged from 12 to 220 participants (Table 1). Eligible individuals were recruited through the health system and clinics (n=12) (Ahrendt et al, 2014; Azar et al, 2015; Bennett et al, 2010; Block et al, 2015; Estabrooks and Smith-Ray, 2008; Fischer et al, 2016; Kramer et al, 2010; Ma et al, 2013; McTigue et al, 2009; Skoyen et al, 2015; Thomas and Wing, 2013; Thomas et al, 2015), advertisements in local communities (eg. TV, radio, newspaper) (n=6) (Ackermann et al, 2014; Fukuoka et al, 2015; Pagoto et al, 2015; Piatt et al, 2013; Tate et al, 2003; Vadheim et al, 2010), a statewide wellness program (n=3) (Leahey et al, 2014; Wing et al, 2010), and online (n=1) (Sepah et al, 2014).…”

Self-Monitoring Utilization Patterns Among Individuals in an Incentivized Program for Healthy Behaviors

“…However, these conditions (and others) are often poorly controlled. In a number of cases, automated interventions have shown health benefits: a prior study showed that a fully automated behavioral intervention leveraging Web, mobile, and automated mobile phone calls significantly improved glycemic control, body weight, and diabetes risk among prediabetics [36]; another showed that a physical activity intervention consisting of automated weekly exercise scheduling reminders, a message board to share their experience with others, and feedback on their level of physical activity increased and maintained levels of physical activity in healthy adults [37]; and another showed that a fully automated smoking cessation program using email, Web, interactive voice response, and short messaging service was associated with abstinence rates without the use of nicotine replacement therapy [38]. However, even with access to the latest technologies to monitor any biometric or condition, engagement, which leads to behavior change, is key.…”

Expert Consensus Survey on Digital Health Tools for Patients With Serious Mental Illness: Optimizing for User Characteristics and User Support (Preprint)

“…Digital health tools (DHTs) have the potential to empower patients, improve access to care, enhance communication between patients and providers, improve health outcomes, and make healthcare processes and decisions more efficient and cost-effective [2][3][4][5]. DHTs are increasingly being adopted for use independently or in the context of traditional care and have been effective for monitoring and improving physical health in patients with a variety of conditions, including diabetes and cardiovascular disease [6][7][8][9]. Interest in applying digital technologies to psychiatric practice has been increasing since the early 2000s, and recommendations for future research in this area were issued in 2013 by a technical expert panel convened by the Agency for Healthcare Research and Quality and the National Institute of Mental Health [10][11][12].…”