Abstract
OBJECTIVE
Mobile health (mHealth) applications (apps) help individuals achieve healthy lifestyle behaviors. We assessed feasibility, acceptability, and efficacy of an mHealth app, Prosper, in helping adolescents with prediabetes track and achieve lifestyle modifications.
RESEARCH DESIGN AND METHODS
This prospective pilot study enrolled adolescents aged 13–17 years with prediabetes. Participants downloaded the Prosper app and use was followed for 3 months after a prediabetes visit. App use, check-ins, and habits data were analyzed; surveys assessed feasibility, acceptability, and participant-reported efficacy. Primary outcomes were use and feasibility or acceptability of the app. Secondary outcomes included changes in BMI and hemoglobin A1c (HbA1c). Caregivers and providers were also surveyed.
RESULTS
The study enrolled 25 participants (median age 15.1 years; 76% female; 64% identified as Black adolescents; 76% identifed as non-Hispanic; and 56% had Medicaid insurance). Participants completed 2,057 habits, 575 check-ins, and 114 journals overall. The median number of check-ins per participant was 4 (interquartile range 2–18), and 36% completed ≥12 check-ins. Most participants (68%) set at least 2 habits, with a maximum of 14 habits. High satisfaction was reported by 74–87%. Most found the app easy to use (95.7–100%), helpful for tracking nutrition (61–78%) and exercise (56.5–86%), and easy to integrate into daily routines (52–73%). Baseline median BMI was 37.7 kg/m2 (interquartile range 32.0–43.3) and HbA1c was 5.7% (interquartile range 5.6–5.8), without change noted after 3 months.
CONCLUSIONS
Adolescents reported high satisfaction with the Prosper app and found it easy to use, helpful for tracking nutrition and exercise goals, and easy to incorporate into daily routines. mHealth tools may support adolescent lifestyle modifications between clinical visits with broad scalability.
Introduction
One in five Americans has diabetes, and prediabetes is surging at alarming rates in adult and pediatric populations (1). According to the Centers for Disease Control and Prevention, current trends indicate that more than one in four children have prediabetes, with estimates approaching a prevalence of 36.3% (2,3). Children with prediabetes often progress to having youth-onset type 2 diabetes (T2D), a more progressive form of diabetes than adult T2D; therefore, it is recommended they be screened annually for T2D (4,5). Given the high rates of early diabetes complications associated with progression to youth-onset T2D, early identification, close monitoring, and implementation of lifestyle modifications in children with prediabetes are essential (6).
For those with prediabetes, lifestyle modifications are effective at preventing or reversing progression to T2D. The Diabetes Prevention Program demonstrated that lifestyle intervention (consisting of a healthy diet and moderate intensive exercise) reduced progression to T2D by 58% among adults with prediabetes (7). Although the rate of progression from pediatric prediabetes to T2D is not well established, adolescents with obesity and impaired glucose tolerance with weight maintenance are more likely to have improved glucose tolerance compared with those with weight gain (8,9). Suggested lifestyle interventions by the American Diabetes Association for youth with prediabetes include dietary modifications to increase higher-quality nutrient-rich foods and reduce refined and processed foods, moderate to vigorous physical activity for at least 60 min daily, and at least a 7–10% decrease in excess body weight (10,11).
In the age of digital health technology, mobile health (mHealth) applications (apps) can help individuals implement lifestyle changes and promote healthy behaviors. In adults with prediabetes, mHealth apps are associated with weight loss and reduced hemoglobin A1c (HbA1c) levels (12). Benefits of mHealth apps for adults with prediabetes included logging meals, recording weigh-ins, and access to articles, group posts, and messages to a coach (12). Additionally, digital self-monitoring of diet and physical activity has been shown to lead to positive behavior changes, including weight loss, increased physical activity, and improved nutrition (13,14). Recently, an app with a fully automated artificial intelligence–led program for diabetes prevention was shown to be a viable alternative to a diabetes prevention program led by human coaches in the adult prediabetes population (15). In adolescent studies of weight management, personal electronic devices and digital self-monitoring are considered more functional and convenient than traditional pen-and-paper methods and lead to enhanced self- monitoring adherence and improved outcomes for pediatric weight management (12,16).
Although studies using mHealth apps in adult populations with prediabetes and T2D have been associated with reductions in participants’ weight and HbA1c levels, there are limited data on utilization of mHealth apps for adolescents with prediabetes (12). In this study, we assessed the feasibility, acceptability, and efficacy of an mHealth app called Prosper to help adolescents with prediabetes track and achieve lifestyle modifications, and we assessed the effect of its use on BMI and HbA1c levels.
Research Design and Methods
Study Design
This was an investigator-initiated prospective pilot study conducted at the Johns Hopkins Children’s Center (Baltimore, MD) from 23 April 2024 to 16 December 2025. This study was approved by the Johns Hopkins Institutional Review Board (approval IRB00362014). Written informed consent and assent were obtained from all participants and their caregivers. An electronic informed consent was obtained for physicians and nurse practitioners caring for the participants.
Participants
Adolescents with prediabetes who were aged 13–17 years old (per Children’s Online Privacy Protection Rule minimum age for app use guidelines) (17) and clinically followed at the Johns Hopkins Pediatric Endocrinology Clinic for prediabetes were recruited. Eligibility criteria included 1) a diagnosis of prediabetes based upon at least one HbA1c level of 5.7–6.4% in the past year, 2) BMI ≥ 85th percentile for age and sex, and 3) smartphone use in the past 2 weeks. Participants were excluded if English was not their primary language (because surveys were only in English) or if they had prediabetes related to early type 1 diabetes, cystic fibrosis, or genetic forms of diabetes. Additionally, caregivers and medical providers of the adolescents who participated filled out a survey at study completion.
Study Procedures
Adolescents were recruited for participation during routine clinic visits. After obtaining caregiver consent and participant assent, participants were assisted by the research team to 1) download Uneo Health's Prosper App (Uneo, Inc., Davidsonville, MD) on their personal devices, 2) input the lifestyle management goals and habits aligned with specific goals provided to them by their pediatric endocrine health care provider at their prediabetes clinic visit, and 3) join the Hopkins Community on the app, where the research coordinator posted motivational health- and lifestyle-related posts once or twice per week. Participants were instructed to turn on daily alerts to complete daily goals and check-ins. Within the app, participants can create new, custom habits tailored to their health goals and set reminders to support daily completion. Participants can complete a habit multiple times if it is a recurrent habit. In addition to habit tracking, participants could complete daily check-ins, in which they describe their mood and associated emotions or lifestyle factors, with further options to journal, record sleep, and document exercise.
When participants completed both habit completions and check-ins, they moved up a level within the app and received “moonstones,” allowing them to unlock new themes and personalization features. The “Insights” section of the app displays a summary of users’ reflections, mood trends over time, and their progress on selected habits, helping them visualize changes in their well-being over the prior month. Beyond individual tracking, the app offered access to Johns Hopkins group-specific posts created by the study coordinators. The “Resources” tab includes curated videos on general well-being, fitness, nutrition, and mental health, giving participants additional educational content to support self-management.
Study Metrics
At the enrollment visit, participants completed a baseline survey that assessed 1) demographics, 2) questions about their internet, smartphone, and app usage, particularly regarding fitness/wellness or health, and 3) current lifestyle choices, including exercise, sleep, nutrition, and screen time. At 1 week, 1 month, and 3 months after enrollment, participants answered follow-up surveys to assess app feasibility or usage, as well as lifestyle changes sent by e-mail, text message, or electronic medical record direct message (MyChart; Epic Systems, Verona, WI). All surveys were administered through Research Electronic Data Capture (REDCap), a Health Insurance Portability and Accountability Act (HIPPA)-compliant software (18,19). Caregivers completed a survey assessing caregiver perception of app acceptability, usability, and relationship to their adolescent’s lifestyle changes at the ≥3-month follow-up visit.
Participant weight, height, BMI, blood pressure, recent metabolic laboratory results (i.e., HbA1c, lipid levels, and hepatic function), medications, outcome of visit, and insurance type were collected from electronic chart review at baseline and at a follow-up visit at least 3 months after the baseline visit. Data extraction was independently verified by two study team members. User data were provided by the app developers, including frequency of check-ins, habits created and completed, and overall app engagement by participants (determined by inclusion in Hopkins Community on the app). Of note, the app developers only had access to deidentified data that could be paired to participant results by the study team based on their app start date.
After all participants completed the study, physicians and nurse practitioners who were care providers of individuals in the study completed a survey to evaluate the provider’s opinion on the app’s effectiveness in supporting clinical care goals.
The primary outcomes were use and feasibility and acceptability of the app; secondary outcomes included changes in BMI and HbA1c.
Statistical Analysis
As a pilot study, there was no goal sample size calculated. Summary statistics are reported as median and interquartile range (IQR) for continuous variables and as counts and percentages for categorical variables. Positive responses (very easy/easy, very satisfied/satisfied, very helpful/helpful, and very likely/likely) to survey questions were grouped together for the analysis. We defined a “superuser" as a participant who created more than two habits, or completed more than four habits, or completed more than 12 check-ins with the app. Logistic regression was used to evaluate odds of a participant being a superuser, by sociodemographic markers of age, sex, race, ethnicity, household income, insurance status, highest caregiver education status, and cellular data availability during loss of internet. All analyses were conducted using Stata (version 18.0; StataCorp).
Data and Resource Availability
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available, due to privacy or ethical restrictions.
Results
Participant Demographics and Clinical Characteristics
A total of 33 individuals were approached for the study. Of these, 26 were consented, and 25 were enrolled in the study and downloaded the app. One individual did not have a legal guardian to provide consent at the visit, and six individuals declined participation (Supplementary Fig. 1). Of the 25 participants enrolled, 22 had a follow-up visit during which objective metrics were collected (Supplementary Fig. 1).
As shown in Table 1, the median age of participants was 15.1 years, 76% were female, 64% idenitfied as Black, 76% indicated they were of non-Hispanic or Latino ethnicity, 56% had Medicaid insurance, and 84% of participants’ caregivers had a high school education or higher. At the baseline visit, median BMI was 37.7 kg/m2 (IQR 32.0–43.3) and HbA1c was 5.7% (IQR 5.6–5.8). There were no notable changes in BMI or HbA1c during the study (Table 2).
Table 1.
Participant demographics (N = 25)
| Age, median (IQR), years | 15.1 (13.9–17.1) |
| Female sex | 19 (76) |
| Race | |
| White or Caucasian | 4 (16) |
| Black or African American | 16 (64) |
| Asian or Pacific Islander | 3 (12) |
| Multiracial or biracial | 1 (4) |
| Other | 1 (4) |
| Ethnicity | |
| Hispanic or Latino | 4 (16) |
| Non-Hispanic or Latino | 19 (76) |
| Prefer not to share | 2 (8) |
| Insurance | |
| Medicaid | 14 (56) |
| Commercial | 11 (44) |
| Highest level of caregiver’s education | |
| Some high school | 2 (8) |
| High school/GED degree | 7 (28) |
| Associate’s degree | 2 (8) |
| Undergraduate degree | 5 (20) |
| Postgraduate degree | 7 (28) |
| Unknown | 2 (8) |
| Household income, USD | |
| <50,000 | 7 (28) |
| 50,000–100,000 | 6 (24) |
| ≥100,000 | 9 (36) |
| Unknown | 3 (12) |
| Cellular data availability during loss of internet | 16 (64) |
*Data are n (%) unless otherwise indicated.
Table 2.
Participant baseline and follow-up clinical characteristics
| Baseline, median (IQR) | Baseline (n) | ≥3 Months, median (IQR) | ≥3 Months (n) | |
|---|---|---|---|---|
| BMI (kg/m2) | 37.7 (32.0–43.3) | 25 | 37.6 (31.4–44.0) | 22 |
| BMI z score | 2.49 (2.11–3.37) | 25 | 2.45 (1.97–3.43) | 22 |
| HbA1c (%) at the visit | 5.7 (5.6–5.8) | 25 | 5.8 (5.5–5.9) | 21 |
| HbA1c (%) over the past 2 years* | 5.8 (5.7–5.8) | 25 | – | – |
| Total cholesterol (mg/dL) | 163.5 (144.0–187.0) | 22 | 183.0 (149.0–192.0) | 11 |
| Triglycerides (mg/dL) | 95.5 (55.0–155.0) | 22 | 102.0 (71.0–143.0) | 11 |
| HDL cholesterol (mg/dL) | 40.5 (36.0–52.0) | 22 | 39.0 (33.0–52.0) | 11 |
| LDL cholesterol (mg/dL) | 93.5 (81.0–115.0) | 22 | 109 (90.0–130.0) | 11 |
| AST (units/L) | 14.0 (12.0–18.0) | 19 | 17.0 (15.0–20.0) | 17 |
| ALT (units/L) | 14.0 (11.0–18.0) | 20 | 17.0 (10.0–19.0) | 17 |
*Available only at baseline visit.
Participant App Usage
Of the 25 participants, 24 had at least one habit set. The median number of habits set per participant was 2 (IQR 1–3), with 68% of participants inputting at least two habits. The maximum number of habits created by a participant was 14. Overall, participants set up a total of 78 habits, actively interacted with 56 of those habits, and, in total across the study, completed habits 720 times. The median number of times a habit was completed was 4 (IQR 0–10). Four participants completed habits more than 100 times. The top habits recorded in the app were face washing, brushing teeth, taking medications, exercising, and doing something creative. Habit completion decreased for most users during the study period (Supplementary Fig. 2). However, one participant completed habits 56 times per month for all 3 months, and another increased from completing habits 13 times in the first month to completing habits 73 times in the third month.
In addition to tracking habits, 24 of the 25 participants also completed check-ins. During the 3-month study period, the median number of check-ins per participant was 4 (IQR 2–18) with a range of 0–97 check-ins. A third of participants (36%) completed 12 or more check-ins in total in the 3-month study period for a once-weekly average check-in. For most users, check-ins decreased as the study period progressed (Supplementary Fig. 2). However, two participants consistently checked in every day for their entire study participation. Whether a participant was a superuser was only associated with insurance status and not age, sex, ethnicity, household income, highest caregiver education status, or cellular data availability during loss of internet. Participants with commercial health insurance were 6.4 times more likely to be a superuser in completing habits compared with those receiving Medicaid (odds ratio 6.42; 95% CI 1.09–37.73; P = 0.04).
Across the 386 completed check-ins during the study period, participants reported feeling “good” or “great” for 54.4% of check-ins, okay for 38.6%, and "bad” or “awful” for 7% of the check-ins. The top three emotions associated with good or great moods were “happy,” “relaxed,” and “excited,” due to three drivers: friends, family, and sleep. The top three emotions associated with bad or awful moods were “tired,” “stressed,” and “anxious,” due to three drivers: school, health, and sleep. Six individuals also used the journaling feature in the app and completed morning, midday, and evening reflections.
After their respective study completion, six participants continued to actively use the app, completing habits, daily check-ins, and journal entries. Including this post-study app usage, from the beginning of the study to the end of data collection, the 25 participants completed 2,057 habits, 575 check-ins, and 114 journal entries (Table 3 and Supplementary Table 1).
Table 3.
App activity overview
| Metric | App usage per participant during study period, median (IQR) | App usage for all participants during study period (n) | Overall app usage per participant, median (IQR) | Overall app usage for all participants (n) |
|---|---|---|---|---|
| Check-ins | ||||
| Total check-ins | 4 (2–18) | 386 | 4 (2–19) | 575 |
| Good/great mood check-ins | 3 (1–11) | 210 | 3 (1–11) | 314 |
| Okay mood check-ins | 1 (0–4) | 149 | 2 (0–4) | 233 |
| Awful/bad mood check-ins | 0 (0–1) | 27 | 0 (0–1) | 28 |
| Habits | ||||
| Total habits created | 2 (1–3) | 78 | 2 (1–3) | 78 |
| Habits interacted with | 1 (0–3) | 56 | 1 (0–3) | 56 |
| Study habits completed | 4 (0–10) | 720 | 4 (0–10) | 2,057 |
| Journal entries | ||||
| Journal entries completed | 0 (0–0) | 104 | 0 (0–0) | 114 |
Study-period counts represent app engagement during the 3 months participants were enrolled; overall app usage includes all usage recorded through 16 December 2025, reflecting activity beyond the study window.
Acceptability and Feasibility
As shown in Fig. 1, all 25 participants completed the baseline survey. Follow-up completion rates remained high, with 23 participants completing week 1 survey, 22 completing the month 1 survey, and 23 completing the month 3 survey. Overall, participants reported high satisfaction with the app and found it easy to use, helpful for tracking nutrition and exercise goals, and generally easy to incorporate into their daily routines. However, these ratings declined over time. The rating for “Ease of app” use remained consistently high (95.7% at week 1, 100% at month 1, and 95.7% at month 3). In contrast, “Ease of integrating the app into daily routines” declined (65.2% at week 1, 72.7% at month 1, and 52.2% at month 3). “Satisfaction with using the app” to track progress toward goals decreased from 87% at week 1 to 81.8% at month 1 and 73.9% at month 3. Similarly, perceived “Helpfulness for nutrition” tracking declined (78.3% at week 1, 77.3% at month 1, and 60.9% at month 3), as did “Helpfulness for exercise” tracking (82.6% at week 1, 86.4% at month 1, and 56.5% at month 3). Participants’ “Likelihood of continuing to use the app” after the study also decreased over time, from 90.5% at week 1 to 72.7% at month 1 and 65.2% at month 3.
Figure 1.
Change in app ratings over time. The y-axis shows percentage of participants rating positively.
Caregiver and Provider Feedback
Overall, caregivers of participants reported high satisfaction with adolescents’ experience and ease of using the app. Among the 21 caregivers who completed the survey (of 25 total), 76.2% were satisfied with adolescents’ experience and stated they would recommend the app to others. “Ease of use” was reported by 80.9% of caregivers, and 71.4% found the app easy to incorporate into an adolescent’s routine. However, fewer caregivers (42.8%) reported that the participant in their care made the lifestyle changes that were recommended by their medical provider with the app use.
Of the five providers with participants in the study, one found the app helpful for motivating adolescents to make lifestyle changes, one found it helpful in achieving adolescent’s lifestyle goals, and three would recommend the app for individuals with prediabetes to help them achieve their lifestyle goals. Two providers reported neutral responses across all three questions. Notably, no providers expressed negative opinions of the app.
Conclusions
In this pilot study of a diverse cohort of adolescents with prediabetes, we showed that use of a digital mHealth app is acceptable to adolescents and their caregivers and can help them track their nutrition, exercise, and behavioral goals between clinic appointments. Overall, participants expressed high satisfaction with the app and reported it was easy to use, helpful for monitoring nutrition and physical activity goals, and readily incorporated into daily routines. Although there were no changes in weight and glycemic outcomes, there was steady use of the app by one-third of participants, and 25% of participants continued engagement with the app after completion of the study. Amid the growing public health challenge of overweight and obesity in youth and the increasing prevalence of prediabetes, electronic tools such as health behavior apps that support digital self-monitoring and lifestyle modification between clinical visits may help individuals maintain healthy behaviors and have broad applicability. In the U.S., where greater than 75% of adolescents own a smartphone and use it for more than 2.5 h/day, use of mHealth apps could be scalable to help teens achieve their health goals.
We observed a similar pattern to other studies with a subset of superusers (20,21). Six of the 25 participants continued to use the app even after the study ended. Although mHealth use has not been as well characterized in younger populations, findings from prior studies of adults suggest greater app use is associated with younger age, having health insurance, and higher educational attainment (22,23). In our sample, those with commercial insurance were more likely to be a superuser in habit completions. The lack of a similar association with income may reflect its more granular categorization, which could have limited our ability to detect a comparable effect. Participants in our cohort also set goals consistent with patterns documented in the literature, with medication-related reminders among the most frequently selected (20). All participants had at least one habit related to prediabetes management, but several also chose to create additional habits that reflected other aspects of their self-care. Logging a health behavior that is unrelated to prediabetes is a positive sign because it may demonstrate a person is at a higher stage of change for one behavior, which makes them more likely to be at a higher stage of change for additional health behaviors (24). Because our engagement metrics relied on check-ins and habit completions, it is possible that some participants were using the reminder functions or continuing their habits without actively marking them as complete, which may underestimate true engagement.
The digital self-monitoring provided with this app has the potential to lead to lifestyle modification behaviors, as suggested in previous studies (13,14). In addition, the presence of persistent users highlights that a subset of adolescents can maintain regular interaction with self-management tools. These findings point to opportunities to refine support strategies and app features to promote more sustained engagement in future iterations of the intervention. They also suggest that apps may be a helpful therapeutical adjunct that could be offered to all adolescents with prediabetes, because some individuals may respond significantly to this strategy to support their behavior change.
The decline in app use in our study cohort over time is consistent with prior literature on mHealth app usage. A systematic review of studies focused on adolescents and young adults using health apps for type 1 diabetes and other chronic disease management showed similar patterns, with usage declining as the study period progressed (25). Among adolescents undergoing hematopoietic cell transplantation, engagement with the health information technology platform fell to 58% by week 4 (26). In a separate study on heart disease management apps (Just TRAC It!), 63% of participants reported not having used one or more of the apps at the 6-month mark (27). Adult diabetes apps show comparable patterns. Nearly 70% of Cornerstones4Care users discontinued module engagement after the first week, and retention in GlucoNote declined from 35% at 4 weeks to 22% at 12 weeks (28,29). Notably, an app designed to support adolescents with type 1 diabetes that also involved parents demonstrated higher sustained uptake, with 84% of the participants characterized as medium or high users, but usage still declined as the study progressed (21).
Adolescents with prediabetes are at risk for development of youth-onset T2D, which is more aggressive compared with adult-onset T2D (5). mHealth apps can help users achieve recommended lifestyle changes (namely, improved nutrition and increased physical activity) to reduce risk of progression from prediabetes to youth-onset T2D (27). One systemic review and meta-analysis evaluating 440 clinical trials of mHealth apps for health behavior change in healthy children and children and adolescents with overweight or obesity revealed improvements in physical activity and changes in fat and vegetable intake, as well as BMI and body weight, with greatest effect in physical activity behavior change with shorter interventions (<8 weeks) (30). In a small trial (n = 40 participants) involving a wearable fitness tracker and text messages for adolescents with overweight or obesity showed improvements in BMI, blood pressure, physical and sedentary activity, and nutrition (31). In a pilot study of adolescents with prediabetes, a virtual peer-led diabetes prevention program over 12 weeks focused on healthy eating and lifestyle modifications, using goal setting and problem-solving techniques, demonstrated feasibility and acceptability (32). In focus groups with adolescents with prediabetes and T2D, health-related motivational text messages were favorably considered a helpful reminder system to improve health-related behaviors (26,33,34). Despite a variety of mHealth apps used in these studies, adolescents overall had positive changes and viewed these interventions favorably. Because the population of youth who progress to youth-onset T2D has highest prevalence in minoritized racial and ethnic groups and lower socioeconomic status households and have been difficult to recruit for clinical trials, it is reassuring that youth with prediabetes are responsive to digital interventions in the few studies conducted with this population (5). Although our pilot study did not show changes in BMI or other metabolic indices, the app was highly acceptable to both adolescents and their caregivers and easy to implement, which could be a sustainable between-visit intervention for busy health care providers to suggest to adolescents and families (31).
This study has several strengths, including inclusion of a diverse population of adolescents with prediabetes, a population that is rarely specifically studied. Although our study population may not have the same demographics as youth with prediabetes in the United States, it is representative of youth who progress to youth-onset T2D, because we had a high percentage of Black or African American participants—the group with the highest prevalence of youth-onset T2D in the U.S. (35,36). However, it is possible these data may not be generalizable to practices with different demographics or to other countries. As a pilot study, our data are limited by the small sample size and absence of a control group; as such, the study was not well powered to detect our secondary outcomes of metabolic changes or further sociodemographic differences among app users. There was also potential self-selection bias, because participants enrolled may have been more motivated or already engaged in healthier habits. Additionally, caregiver restrictions on an adolescent’s technology use or limited internet or cellular data may have precluded some participants' app usage. Despite these limitations, as medical technology advances and smart devices become more widely accessible, our findings provide relevant data about app use, acceptability, and feasibility in youth with prediabetes that can inform future studies and guide targeted interventions.
In summary, adolescents with prediabetes found using an app easy, feasible, and helpful in tracking their lifestyle modification goals for healthy eating and exercise. Given the widespread use of smartphones among adolescents, a mobile app to support healthy lifestyle behaviors between clinic visits may be widely applicable and scalable. Future studies involving larger cohorts and longer follow-up are needed to assess the clinical impact and broader applicability of this approach.
This article contains supplementary material online at https://doi.org/10.2337/figshare.32249199.
Acknowledgments
Article Information
Acknowledgments
The authors thank the participants and their caregivers for their participation.
Duality of Interest
R.M.W. previously served in an unpaid advisory role to Uneo Inc. (“Uneo Health”), the company that developed the mobile application evaluated in this study. R.M.W. reports receiving research support from Lilly Diabetes, Novo Nordisk, and Sanofi unrelated to the submitted work. No other potential conflicts of interest relevant to this article were reported.
No financial compensation or contractual relationship existed with Uneo Health, and the company had no role in study design, analysis, interpretation of results, or manuscript preparation.
Author Contributions
C.W., N.P., D.P., and S.N.M. collected data. C.W. completed the statistical analysis. C.W. and N.P. created figures and tables. C.W., N.P., H.W., R.M.W., and T.A.H. wrote the manuscript. R.M.W. and T.A.H. conceived of the study. All authors made critical contributions to the manuscript and edited, reviewed, and approved the manuscript. R.M.W. and T.A.H. are the guarantors of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Supporting information
References
- 1. Wagenknecht LE, Lawrence JM, Isom S, et al. ; SEARCH for Diabetes in Youth study. Trends in incidence of youth-onset type 1 and type 2 diabetes in the USA, 2002–18: results from the population-based SEARCH for Diabetes in Youth study. Lancet Diabetes Endocrinol 2023;11:242–250 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Ouyang A, Hu K, Chen L. Trends and risk factors of diabetes and prediabetes in US adolescents, 1999–2020. Diabetes Res Clin Pract 2024;207:111022. [DOI] [PubMed] [Google Scholar]
- 3. Andes LJ, Cheng YJ, Rolka DB, et al. Prevalence of prediabetes among adolescents and young adults in the United States, 2005–2016. JAMA Pediatr 2020;174:e194498. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Magge SN, Silverstein J, Elder D, et al. Evaluation and treatment of prediabetes in youth. J Pediatr 2020;219:11–22 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Hitt TA, Hannon TS, Magge SN. Approach to the patient: youth-onset type 2 diabetes. J Clin Endocrinol Metab 2023;109:245–255 [DOI] [PubMed] [Google Scholar]
- 6. Tuso P. Prediabetes and lifestyle modification: time to prevent a preventable disease. Perm J 2014;18:88–93 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Knowler WC, Barrett-Connor E, Fowler SE, et al. ; Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002;346:393–403 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Savoye M, Shaw M, Dziura J, et al. Effects of a weight management program on body composition and metabolic parameters in overweight children: a randomized controlled trial. JAMA 2007;297:2697–2704 [DOI] [PubMed] [Google Scholar]
- 9. Kleber M, Lass N, Papcke S, et al. One-year follow-up of untreated obese white children and adolescents with impaired glucose tolerance: high conversion rate to normal glucose tolerance. Diabet Med 2010;27:516–521 [DOI] [PubMed] [Google Scholar]
- 10. American Diabetes Association Professional Practice Committee for Diabetes . 5. Facilitating positive health behaviors and well-being to improve health outcomes: Standards of Care in Diabetes—2026. Diabetes Care 2025;49:S89–S131 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. American Diabetes Association Professional Practice Committee for Diabetes . 14. Children and adolescents: Standards of Care in Diabetes—2025. Diabetes Care 2024;48:S283–S305 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Toro-Ramos T, Michaelides A, Anton M, et al. Mobile delivery of the diabetes prevention program in people with prediabetes: randomized controlled trial. JMIR Mhealth Uhealth 2020;8:e17842 20200708 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Berry R, Kassavou A, Sutton S. Does self-monitoring diet and physical activity behaviors using digital technology support adults with obesity or overweight to lose weight? A systematic literature review with meta-analysis. Obes Rev 2021;22:e13306. [DOI] [PubMed] [Google Scholar]
- 14. Patel ML, Wakayama LN, Bennett GG. Self-monitoring via digital health in weight loss interventions: a systematic review among adults with overweight or obesity. Obesity (Silver Spring) 2021;29:478–499 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Mathioudakis N, Lalani B, Abusamaan MS, et al.; AI-DPP Study Group . An AI-powered lifestyle intervention vs human coaching in the diabetes prevention program: a randomized clinical trial. JAMA 2025;334:2079–2089 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Darling KE, Sato AF. Systematic review and meta-analysis examining the effectiveness of mobile health technologies in using self-monitoring for pediatric weight management. Child Obes 2017;13:347–355 [DOI] [PubMed] [Google Scholar]
- 17. Federal Trade Commission . Children’s Online Privacy Protection Rule. In: 16 CFR Part 312, Federal Register. Federal Trade Commission; June 23, 2025 [Google Scholar]
- 18. Harris PA, Taylor R, Minor BL, et al.; REDCap Consortium . The REDCap consortium: building an international community of software platform partners. J Biomed Inform 2019;95:103208. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Harris PA, Taylor R, Thielke R, et al. Research Electronic Data Capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009;42:377–381 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Davis SR, Peters D, Calvo RA, et al. A consumer designed smartphone app for young people with asthma: pilot of engagement and acceptability. J Asthma 2021;58:253–261 [DOI] [PubMed] [Google Scholar]
- 21. Holtz B, Mitchell KM, Holmstrom AJ, et al. An mHealth-based intervention for adolescents with type 1 diabetes and their parents: pilot feasibility and efficacy single-arm study. JMIR Mhealth Uhealth 2021;9:e23916. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Bhuyan SS, Lu N, Chandak A, et al. Use of mobile health applications for health-seeking behavior among US adults. J Med Syst 2016;40:153. [DOI] [PubMed] [Google Scholar]
- 23. Carroll JK, Moorhead A, Bond R, et al. Who uses mobile phone health apps and does use matter? A secondary data analytics approach. J Med Internet Res 2017;19:e125. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Lippke S, Nigg CR, Maddock JE. Health-promoting and health-risk behaviors: theory-driven analyses of multiple health behavior change in three international samples. Int J Behav Med 2012;19:1–13 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Li Z, Lu F, Wu J, et al. Usability and effectiveness of ehealth and mhealth interventions that support self-management and health care transition in adolescents and young adults with chronic disease: systematic review. J Med Internet Res 2024;26:e56556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Brookshire-Gay K, LaLonde L, Byrd M, et al. Health information technology utilization by adolescent and young adult aged inpatients undergoing hematopoietic cell transplantation. J Adolesc Young Adult Oncol 2021;10:100–104 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Han A, Gingrich J, Yaskina M, et al. Just TRAC it! Transitioning responsibly to adult care using smart phone technology. J Adolesc Health 2023;73:561–566 [DOI] [PubMed] [Google Scholar]
- 28. Böhm A-K, Jensen ML, Sørensen MR, et al. Real-world evidence of user engagement with mobile health for diabetes management: longitudinal observational study. JMIR Mhealth Uhealth 2020;8:e22212. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Yamaguchi S, Waki K, Nannya Y, et al. Usage patterns of GlucoNote, a self-management smartphone app, based on ResearchKit for patients with type 2 diabetes and prediabetes. JMIR Mhealth Uhealth 2019;7:e13204. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Singh B, Ahmed M, Staiano AE, et al. Lifestyle eHealth and mHealth interventions for children and adolescents: systematic umbrella review and meta-meta-analysis. J Med Internet Res 2025;27:e69065. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Chen J-L, Guedes CM, Cooper BA, et al. Short-term efficacy of an innovative mobile phone technology-based intervention for weight management for overweight and obese adolescents: pilot study. Interact J Med Res 2017;6:e12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Islam S, Elaiho C, Arniella G, et al. A pilot study to examine the feasibility and acceptability of a virtual adaptation of an in-person adolescent diabetes prevention program. Int J Environ Res Public Health 2022;19:12286. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Vajravelu ME, Hitt TA, Mak N, et al. Text messages and financial incentives to increase physical activity in adolescents with prediabetes and type 2 diabetes: web-based group interviews to inform intervention design. JMIR Diabetes 2022;7:e33082. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Sapre M, Elaiho CR, Brar Prayaga R, et al. The development of a text messaging platform to enhance a youth diabetes prevention program: observational process study. JMIR Form Res 2024;8:e45561. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Perng W, Conway R, Mayer-Davis E, et al. Youth-onset type 2 diabetes: the epidemiology of an awakening epidemic. Diabetes Care 2023;46:490–499 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Harrison C, Peyyety V, Rodriguez Gonzalez A, et al. Prediabetes prevalence by adverse social determinants of health in adolescents. JAMA Netw Open 2024;7:e2416088. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.