Caregiver inclusion influence on adolescent acceptance and engagement of an mHealth app, a randomized controlled trial

Introduction

Adolescent obesity is a major health concern. Between 2017 and 2020, 22.2% of U.S. adolescents aged 12–19 years had obesity with the rates of overweight or obesity increased 5.2% among 12 to 15 years old and 3.1% among 16 to 17 years old by 2021 (1,2). Adolescent obesity is highly correlated with future adult obesity, an estimated 80% of adolescents with obesity are predicted to continue to have obesity into adulthood (3). Obesity can have many negative health repercussions, such as cardiovascular, pulmonary, and liver disease, dyslipidemia, type 2 diabetes, some types of cancer, low self-esteem, depression, and premature death (3,4).

Because of the heavy burden of obesity on patients and the health-care system, it is imperative that measures are taken to decrease the obesity rate. Prevention programs may be an important step towards reducing obesity rates. Early treatment in childhood and adolescence is important as some evidence suggests that long-term weight loss may be sustained if weight management efforts are started in children as compared to adults (5). The causes of obesity are complex and multifactorial including individual biology, structural inequalities, and environment (6). Because these factors are difficult to modify, obesity interventions often focus on modifiable risk factors. One possibly modifiable factor is the home environment, which is controlled by caregivers and may contribute to obesogenic lifestyle behaviors such as poor nutrition and lack of physical activity (7). Thus, interventions that focus on improving the health of the home environment particularly ones that include the caregiver have shown promising evidence in reducing childhood obesity (8).

Parent-based interventions may work for younger children because of the child’s lack control of their home environment. This leads them to be highly influenced by their caregivers’ actions (9). For young children, their caregivers tend to have more control over the nutrition and physical activity in the household. This means that this parental influence is age-dependent and is likely stronger than parental influence experienced during adolescence (9). However, with adolescents who more independently make lifestyle choices, the literature is less clear on the impact of parental involvement in obesity interventions. A 2020 systematic review found mixed results on the influence that parents have on adolescent obesity interventions (10). Janicke et al. found that when the intervention (n=93 adolescents) involved both parent and adolescent compared to only one member of the dyad, it resulted in greater adolescent weight loss (11). Conversely, Jelalian et al. showed, in a randomized controlled trial (RCT) (n=49 adolescents), a larger body mass index (BMI) decreases among adolescents who received the standard intervention as compared to those who received the intervention with enhanced parent participation (12).

A possible reason for the discrepancy in the literature between childhood and adolescence is that adolescence is a time of developmental change. During this stage, adolescents may start to exhibit opposition to authority as well as experience increased independence as they expand their own personal responsibility (13). Adolescents are likely influenced by interactions outside of the family unit, such as peers and media. These factors make the inclusion of parental involvement to maximize results in adolescent obesity interventions unclear (13). Additional studies are required to understand the role that parents play in adolescent acceptance and effectiveness of obesity prevention and management interventions.

Recent literature has shown significant promise in using mobile health applications (mHealth apps) as an intervention for adolescent obesity. These apps have been shown to be effective in adults in a 2015 meta-analysis by Flores Mateo et al. which showed significant positive impacts on body weight and BMI in fitness and diet mHealth app users as compared to controls (14). However, there is some uncertainty regarding the utility of mHealth apps in reducing childhood and adolescent obesity. Some authors express concern that the increase in screen time that mHealth apps cause may have negative impacts on the users. Excessive screen time may be associated with poor sleep quality and anxiety and excessive app use may impact child and adolescent social and emotional development (15). Despite these concerns, some evidence suggests that mHealth apps have the potential to be an effective method for delivering lifestyle interventions to children and adolescents because of the appeal of gamification elements and the ability to deliver an intervention in a more comfortable and natural environment (16). A 2017 meta-analysis by Fedele et al. found that fitness mHealth interventions had a small, but significant aggregate effect size [n=37; Cohen d =0.22; 95% confidence interval (CI): 0.14–0.29]. The average age of children in the review was 11 years (17). A study of preschool age children (average age of 4.5 years) by Nystrom et al. found that use of their fitness mHealth app showed an increase in dietary and physical activity measured, such as produce consumption and engagement in physical activity, respectively [odds ratio (OR) 1.99, P=0.008] (18).

A systematic review by Bonvicini et al. found limited effectiveness for using obesity management mHealth apps in treating childhood and adolescent obesity (7). This systematic analysis included 20 studies, mostly of younger children. Three of the twenty studies (Shen 2020, Wingo 2020, and Clarke 2019) included children 13 years of age or older, and of those, only one (Shen 2020) excluded children younger than 13 years (19-21). Thus, the inclusion of a wide age and developmental stages of pediatric participants makes it difficult to determine whether mHealth apps are effective in treating obesity in children, adolescents, or both.

Studies of mHealth apps that focus on adolescents and include the parent or caregiver are especially limited within the current literature. Some studies have shown a positive impact of parental involvement in mHealth apps at younger ages. Liu et al.’s 2022 study on children aged 8–10 years, using a fitness and diet mHealth app, found that increased parental engagement was correlated with a decrease in BMI from baseline level during the intervention period as well as improvements in several behavior parameters (22). Wong et al.’s 2020 study of children aged 6–15 years (mean age of 10 years) found that a mHealth app that promoted partner exercises between a parent and child showed a positive correlation with sustaining an exercise routine (23). Additionally, Fedele et al.’s 2017 meta-analysis that included 29,822 participants with an average adolescent age of 11.35 years, found those studies that included caregivers produced larger effect sizes (n=16 studies; Cohen d =0.28; 95% CI: 0.18–0.39) than those that did not (n=21 studies; Cohen d =0.13; 95% CI: 0.02–0.25) (17). These results suggest that the age of the child (or adolescent) is likely a factor in whether parental participation can increase app use and improve outcomes (24). However, Gulec and Smahel found that there is “…currently a lack of evidence for the role of parents in their adolescent children’s adoption of mHealth apps” (25). No studies were found in the current literature specifically about the role of parents or caregivers in adolescent mHealth interventions that did not also include younger children. Therefore, there is a need to elucidate whether caregiver inclusion in adolescent obesity mHealth apps can increase the effectiveness of these apps and, if so, how to best implement caregiver inclusion.

Research aims

To address these knowledge gaps, we conducted an RCT using the novel CommitFit mHealth app. We developed this app to engage adolescents and adult caregivers to set and achieve health behavior goals through gamification techniques. The purpose of this study is to evaluate three research aims: (I) determine whether adolescents find it acceptable to include caregivers in their mHealth lifestyle interventions; (II) evaluate whether caregiver engagement in mHealth lifestyle interventions is positively correlated with adolescent CommitFit mHealth app engagement based on their logging; and (III) explore whether including caregivers in adolescent mHealth lifestyle interventions increases the adolescent’s self-perceived motivation to improve their health behavior. This study was conducted as part of a larger study evaluating the effectiveness of CommitFit on improving adolescent and caregiver health behaviors and weight-related outcomes. Only results which evaluate caregiver inclusion are included in this manuscript. Additional publications detailing the primary outcomes of this study are forthcoming. For the purposes of this study, we will use the term “caregiver” rather than “parent” to describe the adult caring for the adolescent to not exclude non-biological caregivers, but these terms are often used interchangeably in the literature. We present this article in accordance with the CONSORT reporting checklist (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-97/rc).

Methods

The RCT included a total of 30 adolescent-caregiver dyads (60 participants total) who were randomized into one of three study arms with 10 dyads each: wait-list control, CommitFit, or CommitFit$. Each dyad was randomly assigned using allocation concealment via opaque envelopes. All dyads, regardless of their study arm, attended three clinic-based study visits: a baseline enrollment and 3- and 4-month follow-up visits at a Univeristy of Missouri family medicine clinic. The wait-list control group was not given access to the CommitFit app until after the 4-month visit. Both intervention groups were given access to CommitFit mHealth app for the duration of the study. The CommitFit$ group adolescents received an extra financial incentive of $0.05 per point they earned in the app for the first 3 months. At the 3- and 4-month visits, the intervention (CommitFit and CommitFit$) both adolescent and caregiver participants completed surveys. In the gamification survey, questions were asked to gauge the adolescent’s acceptance of the caregiver’s involvement and the caregiver’s impact on the adolescent’s motivation and engagement with the app. Another survey evaluated the adolescent’s motivation for behavior change. We reviewed the use of the CommitFit mHealth app using analytic software that was programmed for the app (26). The program recorded which goals were selected and achieved, and how often adolescent and caregiver participants logged these behaviors (%logged). A full description of study protocol if available through JMIR research protocols (27). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by institutional review board of the University of Missouri (IRB #2092610) and informed consent was obtained from all participants and legal guardians. This RCT titled CommitFit App to Facilitate Health Behavior Change in Clinic Adolescents has been registered with ClinicalTrials.gov with registration #NCT06985251.

CommitFit the mHealth app

The CommitFit mHealth app (26,28,29) was developed to use gamification techniques to motivate adolescents and their caregivers to set and achieve health behavior goals and is grounded in behavioral economics (30), self-determination (31), and social cognitive theories (32). The user chooses one or two of the five health behavior goal options to log over a selected time-period (1 to 4 weeks). The goal options include increasing water intake, increasing fruit and vegetable consumption, increasing physical activity, decreasing sugary drinks, and increasing overnight sleep. Participants first enter their baseline level for the chosen health behavior and a recommendation is made by the app (two levels above their current level). For example, if a user reported no fruit and vegetable consumption at baseline, CommitFit would recommend setting a goal of two fruit and vegetable servings per day. Users are not required to choose this recommended goal level and are encouraged to self-select their own level. The CommitFit mHealth app has a programmed maximum threshold for each behavior based on current evidence-based clinical practice guidelines to prevent excessive or harmful health behaviors (33). The app can be set to provide two daily notification reminders to prompt users to log their health behavior. One point can be earned each time a participant logs a goal, with one extra point given if the user achieves their goal level for the day. Six points are given when a goal period is completed, and ten points are added if the average of the logged behaviors during their selected goal period is equal to or better than the goal set. The app displays a leaderboard that ranks users based on their total points and by their points per week.

To promote collaboration, the adolescents and their caregivers are given the option to form a dyad team. These teams receive team points, which is the average points of the two independent family members and can compete against other teams on the team leaderboards. Although intervention families weren’t required to form teams, they were instructed how to do so at the baseline visit and encouraged to form teams by the research staff. The CommitFit mHealth app also allows players to earn CommitFit coins which can be used to buy gear and additional clothing options for their customizable avatars. In addition to these gamification techniques, the app also contains educational resources including tips to achieve their health behavior goals and provides a progress graph showing their logged behaviors over time (27).

Recruitment and consent

Participants were recruited through lists of adolescent patients who were followed by at a University of Missouri family medicine or pediatric primary care physician (PCP) at the clinic where the study took place. PCPs first reviewed the list of potential adolescents and removed any they felt did not meet study inclusion criteria. Caregivers of the remaining adolescents were emailed information about the study and then called for baseline screening and to schedule an intake visit, if they met inclusion/exclusion criteria and were interested in participating.

Participants were invited to enroll in the study if the adolescent was 13–15 years old and the caregiver was 18 years old or older. All participants were required to speak English fluently and read English at the 6^th grade level or higher. Furthermore, all participants were required to be familiar with Apple© iOS devices and caregivers had to be able to access emails for surveys and educational materials. Exclusion criteria included a previous or current diagnosis of an eating disorder for any participant and/or either severe or uncontrolled anxiety or depression in the adolescent. Interested dyads who met screening criteria, were scheduled for a baseline intake visit in the clinic where they completed informed consent (caregiver for themselves and the adolescent) and assent (adolescent) and were enrolled in the study.

Surveys completed by the intervention group included a gamification survey and motivations for behavior change survey. The surveys were developed or adapted for this study by the research team which included two family medicine physicians (A.B. and Richelle Koopman) as well as two health psychologists (C.L. and K.K.). The language used in the questionnaires was carefully chosen to be intentionally neutral with alternative positive and negative framing to increase accuracy and reduce the social desirability bias. After development, the survey was tested internally by the research team to assess the survey’s accuracy and clarity.

Gamification survey

The purpose of the gamification survey was to evaluate the role of competition and family social dynamics. The gamification survey included 16 questions. Adolescent participants were asked to respond to questions with a number on a 1–100 scale with 1 being completely disagree, 100 being completely agree, and 50 being neutral. A score of 0–39 was considered unfavorable, 40–59 was considered neutral, 60–100 was considered favorable. The survey also inquired about the user’s opinion on whether competing as a team increased motivation to use the CommitFit app (Cronbach’s alpha 0.74 for adolescents at 120 days) and how adolescents felt about having their caregivers also using the app.

Motivations for behavior change survey

The purpose of the motivations for behavior change survey was to explore external and internal factors that influence reasons for healthy lifestyle change in adolescents and caregivers. External influences could include a friend, peer, or caregiver, whereas examples of internal influences would include the desire to be healthy or participation in sports. The questions were answered by the adolescents using a scale of 0 to 3 with 0= not at all, 1= sometimes, 2= often, and 3= much or most of the time. This survey had previously been developed by one of the health psychologists (K.K.) for use in her weight management clinic but was adapted by the research team for the purposes of this study.

App usage

Participant use of the app was tracked over time (up to 6 months) with the use of analytic software developed by the research team. The software measured user activity of the CommitFit app including how often the app was accessed, how often and which goals were set, and how often and which levels for each health behavior was logged. This allowed us to calculate and evaluate %logged which is the total logged behavior divided by possible days to log behavior. Users were divided into three groups based on their app use: high loggers with an average of 4 out of 7 days a week (57% logging rate or greater), moderate loggers with an average of 2–3 times a week (28–56% logging rate), or low loggers with an average of less than 1 time per week (<28% logging rate). These stratifications were based on patterns that emerged during the study.

Statistical analysis

To determine the baseline demographics and study outcomes, descriptive statistics were used. For analysis that required use of the CommitFit app (gamification survey, users analytics), two intervention groups (CommitFit and CommitFit$) were included (n=20 dyads). For analysis that could also include controls (baseline demographics, motivation for behavior change) three groups (CommitFit, CommitFit$, and control) were included (n=30 dyads), Chi-squared analysis or two-sided t-test/analysis of variance (ANOVA) were used to compare group differences. Correlation was evaluated using Pearson correlation coefficient. All analyses were done using SAS version 9.4 (SAS institute, Cary, NC, USA). All participants were analyzed using their original group placement.

Results

Baseline demographics

Out of the 89 families that were contacted via email or phone call, a total of 30 dyads were enrolled with a recruitment rate of 33% in 10 weeks (this is shown in Figure 1). Adolescent participants had a mean age of 14.3 years, while caregiver participants had a mean age of 44.9 years. Both groups had more females (60% for adolescents, 86.67% for caregivers). Caregivers in our sample had a high level of education (with 83.33% having at least a bachelor’s degree) and had a high-income level (with 50% making $90,000 or more per year) (this is shown in Table 1). For comparison, in Boone County, Missouri, where the study was conducted, 51.5% of adult residents had a bachelor’s degree or higher and the median annual household income was $62,561 in 2022 (34).

Figure 1 Enrollment and analysis flow chart. Participant tracking for enrollment, allocation, follow-up, and analysis.

Gamification survey

Adolescents felt that using CommitFit had an overall positive impact on their health behavior in general with a total score of 64.1 out of 100. Adolescent’s opinion of their caregivers also using the app was favorable with an average score of 71.9 out of 100 [standard deviation (SD) 22.3] (Research Aim 1. Results are displayed in Table 2).

In relation to gamification, adolescents in both groups had a positive opinion that competing with others on the leaderboard helped motivate them to log their behaviors and try to achieve their CommitFit goals with a score of 62.1. There was a significant difference (P=0.04) between the two intervention study arms with the CommitFit group being neutral (48.7) and the CommitFit$ group having a more favorable opinion of the leaderboards (75.5).

Adolescents in both groups felt neutral that competing together as a team with their caregiver helped motivate them to log and achieve their CommitFit goals with a score of 51.8. The CommitFit$ group was more positive (63.1) compared to the CommitFit group (40.5), but this difference was not statistically significant (P=0.10) (results are shown in Table 2).

App usage

Intervention adolescents logged into the app more often (234 times) than caregivers (153 times). Of the 20 intervention adolescents, 6 were considered high loggers (≥57% logging rate), 11 were moderate loggers (28–56% logging rate), and the remaining 3 were low loggers (<28% logging rate), while caregivers were overall less engaged with the CommitFit app (5 high loggers, 5 moderate loggers, and 10 low loggers). When considering logging rates by the goal set, 60% of teens (9/15) who set a goal of increasing exercise were high loggers (i.e., logged in ≥57% of the time to report on a goal accomplishment), as compared to only 36% of caregivers (5/14) who had the same goal (results are shown in Table 3).

There was a positive correlation between caregiver app engagement and adolescent app engagement (r=0.65, P=0.002). Of the 5 caregivers that were high loggers, 80% of their adolescents were also high loggers with the remaining adolescents being moderate loggers. Similarly, of the 5 moderate logging caregivers, 60% of the adolescents were also moderate loggers with the remaining two being high loggers. Lastly, of the 10 caregivers that were low loggers 70% had moderate logging adolescents. None of the low logging caregivers had an adolescent who was a high logger (results are shown in Table 3 and represented in Figure 2). This suggests that caregiver app engagement was positively correlated with adolescent app engagements (Research Aim 2).

Figure 2 Relationship of logging behaviors between adolescents and caregivers. How adolescent logging behaviors correspond to caregiver logging behaviors. Users were divided into three groups based on their app use: high loggers with an average of 4 out of 7 days a week (57% logging rate or greater), moderate loggers with an average of 2–3 times a week (28–56% logging rate), or low loggers with an average of less than 1 time per week (<28% logging rate).

Motivations for behavior change survey

In general, adolescents stated they were not very motivated by their caregivers’ approval when trying to be healthier with all overall scores at each time-period being between 0 (none of the time) and 1 (sometimes). When comparing differences in behavior motivation scores over time, a statistically significant difference was seen at 120 days between the CommitFit group which stated they sometimes (x=1.10) were motivated by their caregivers as compared to the waitlist control group (x=0.20) and the CommitFit$ group (x=0.30) (P=0.01). When examining the impact of friends and other family members on adolescent’s motivation for behavior change, there were no statistically significant differences between any of the study arms (this is show in Table 4) (Research Aim 3).

Correlation between gamification survey and app usage

There was a statistically significant correlation between the adolescents who gave a higher rating to “Competing together as a team helped motivate me to log and try to achieve my CommitFit goal?” and the adolescents that had a higher rate of logging their goals (r=0.45, P=0.04). A similar, though nonsignificant, correlation was also seen for caregivers (r=0.42, P=0.06).

Discussion

Not only do adolescent obesity interventions need to appeal to the developmental changes that occur during adolescence, but they must also compete for their attention and retain their interest long enough for the intervention to be effective to modify health behaviors. In our increasingly digital world, mHealth apps have the potential to capture and engage adolescent attention and, thus, be an effective vehicle to promote healthier behavior changes. Overall, adolescents in our study had favorable opinions of the CommitFit app. This is consistent with previous studies which indicated that mHealth apps can have a positive influence on health behaviors (17,18).

Including caregivers in adolescent obesity interventions is a subject that has received mixed results within the literature. We could find no articles that directly asked adolescents how they feel about including their caregivers in obesity management and prevention lifestyle intervention. This is a major deficit in the current literature and important to the efficacy of interventions that include both adolescents and caregivers because if they do not accept the caregiver’s inclusion, they will not engage in treatment. It is possible that because of adolescents’ increasing desire for independence, they might not want their caregivers involved. Yet, the adolescents that were included in this study showed a strong favorability (overall score of 71.9) of caregiver inclusion, which did not vary between the CommitFit groups (P=0.46). Therefore, adolescents seem to find caregiver involvement in the CommitFit mHealth lifestyle intervention acceptable, even favorable, although this does vary by individual (SD =22.3). These results suggest that including caregivers in adolescent mHealth lifestyle interventions, if done thoughtfully, can be acceptable to adolescents (Specific Aim 1).

Adolescent use and engagement in using mHealth apps may be influenced by team play with their caregivers and by caregiver engagement with the app. From tracking logging, we found that the 10 caregivers who were high-to-moderate loggers also had high-to-moderate logging adolescents. Our results suggest that caregiver engagement and adolescent engagement are correlated (Specific Aim 2). Based on this analysis, however, the direction of the relationship cannot be ascertained. It is possible that high adolescent engagement resulted in increased caregiver engagement and logging with CommitFit, or that both groups were both inherently engaged at baseline. These results do support that adolescents do not appear to be less accepting or engaged to use CommitFit when their caregivers also use it, which is an important result to consider for mHealth apps designed for families.

Dyad teams were used to examine whether team dynamics could be a method to increase adolescent app motivation. Based on the gamification survey, the intervention adolescents rated that team competition with their caregivers had a neutral (51.8 out of 100) impact on their motivation for logging and achieving their CommitFit goals (this is shown in Table 2). These results suggest adolescents are not opposed to teaming up with their caregivers in mHealth lifestyle interventions, but they do not feel that competing with their caregiver is a strong motivator for healthy behavior change. When asked about the influence of competing with others on the leaderboard on logging and achieving health behavior goals, the CommitFit$ group reported a higher motivation than the CommitFit group (75.5 vs. 48.7, P=0.04). This difference suggests that the adolescents who were provided financial incentives may have more motivation initially to utilize a mHealth app. Once they are more regularly using the app, they may become more motivated by competition or gamification within the mHealth app than adolescents who never engaged with the app in a meaningful way. Additional studies are required to understand how to best motivate adolescent mHealth app use, both initially and over time.

To further explore how adolescents view their caregivers as motivation for healthy behavior changes (Specific Aim 3), we asked all adolescent participants how often they thought about being healthier because it would make their parents or caregivers happier (results are shown in Table 4). When asked at baseline, the adolescents in this study overall scored this question as 0.62 (between 0= not at all and 1= sometimes) with no statistical difference between the groups. At 120 days, a statistically significant difference developed between the CommitFit group (x=1.10) and the other groups (x=0.20 for the waitlist control and x=0.30 for the CommitFit$ group). These results may suggest that, for the CommitFit adolescents, caregiver inclusion may be correlated with increased adolescent motivation by their caregiver for healthy behavior changes over time, although this was still considered a small motivating factor. The CommitFit$ adolescents at 90 days showed an increase in their score of caregiver motivation for behavior change; in fact, this increase was larger than the increase in the CommitFit group. However, at 120 days, the CommitFit$ adolescents returned to their baseline level (µ=0.30). The reason for this change may be that at 90 days, the CommitFit$ adolescents were receiving financial incentives for earning points in the CommitFit app, while between 90–120 days the financial incentives were stopped. At 90 days, the CommitFit$ adolescents seemed to be more influenced by the leaderboards and gamification elements which included team dynamics and may have been more impacted by their caregivers. Additionally, all external motivation scores increased for the CommitFit$ group at 90 days, but then declined between 90 and 120 days. This could indicate that external motivation could have then waned as the monetary influence was removed. Given intrinsic motivation is considered more sustainable (35), external motivators that shift to more internal motivators, such as an adolescent wanting to maintain health behaviors for more energy and better sport performance, would support a more sustained outcome from an mHealth app intervention. At this stage interpretation of these trends is speculative, and additional quantitative and qualitative studies will be needed to further evaluate the relationship between initial financial incentives and internal versus external motivators for health behavior change and health outcomes.

To further examine the impact of dyad team dynamics of the CommitFit mHealth app, we compared those participants that gave a more favorable rating to the question “Competing together as a team helped motivate me to log and try to achieve my CommitFit goal?” with those that had a higher logging rate. A statistically significant correlation was seen with those adolescents who felt more motivated by being on a team having higher logging rates (P=0.04). A similar correlation was seen between caregivers who were more motivated by team competition and logging rates, although this was not statistically significant (P=0.06). These results suggest that concurrent use of mHealth apps by adolescents and their caregivers, particularly utilizing team play, could be a potential motivator for increasing app usage and engagement for both adolescents and their caregivers.

Our study had many strengths, including the use of group randomization, high recruitment and retention rate, and use of surveys examining the inclusion of caregivers and team dynamics. Additionally, the length of the three-month intervention and 4-month follow-up period to evaluate app use and opinions of caregiver inclusion, as well as the robust data collected from the user-analytic software, adds to the validity of the results. We were also able to examine the influence of financial incentives on app use both while they were being administered, and after they ended.

Limitations and suggestions for future research

This pilot study had a small sample size (n=30 adolescent and caregiver dyads) which in some instances may have limited our ability to detect statistically significant group differences. The research team is planning a fully scaled RCT of CommitFit with financial incentives to confirm and expand results.

Another limitation of this study is that we only enrolled adolescents and caregivers as a dyad; no adolescents were enrolled who were not paired with a caregiver which excludes the ability to compare results without caregiver inclusion as a control. However, creating a team in CommitFit with their caregiver was optional. Therefore, adolescents did have the option to use the CommitFit app independently from their caregivers. Future studies could make caregiver co-participation in the app more standardized between intervention groups and add another control group without caregiver inclusion.

Our sample included caregivers who had a high socioeconomic and educational status and were predominately female. More research is needed to see if these results are generalizable to adolescents in other populations, including different educational and socioeconomic backgrounds, and with male caregivers. Additionally, our sample included adolescents and caregivers with healthy weights at baseline. It is possible that adolescents with obesity may be more motivated or have different barriers to change their health behaviors, which could influence their interaction on the app with their caregivers. Our next CommitFit study will include low-income families with overweight or obesity at baseline, and we intend to include more male caregivers.

Finally, in this study, we also showed that higher caregiver engagement with the app correlated with higher adolescent app engagement, but much is still unknown about the best way to utilize caregiver inclusion to motivate and sustain adolescent health behavior change. Further research, including larger studies with more in-depth dyadic data analysis, is needed to better understand the relationship between caregiver and adolescent mHealth app use and the role of these relationships on the effectiveness of mHealth obesity lifestyle interventions.

Conclusions

Results from this study suggest that including caregivers in adolescent mHealth interventions is acceptable to adolescents and may increase their engagement but it is unclear this leads to motivation for behavior change. Utilization of family-based team play and caregiver engagement has the potential to increase the effectiveness of mHealth apps by increasing adolescent engagement. Team play and caregiver involvement also yields the added benefit of engaging the caregiver to improve their own health behaviors, further increasing the health of the adolescent’s home environment.

This is the first study we could find that directly evaluated adolescent preferences regarding the inclusion of their adult caregiver in an mHealth lifestyle intervention. For an mHealth app to be effective at promoting healthy lifestyle behaviors, it must be something that adolescents find acceptable to use and have the desire to continue using. Our results show that adolescents consider including caregivers in mHealth lifestyle interventions acceptable. Additionally, caregiver engagement in mHealth lifestyle interventions is correlated with adolescent engagement in using the CommitFit app. Finally, our results suggest that including caregivers in adolescent mHealth lifestyle interventions has the potential to increase adolescent’s self-perceived motivation to use the app, although results were mixed. Therefore, if integrated thoughtfully, increased caregiver involvement in adolescent mHealth lifestyle interventions may increase adolescent engagement with and use of the mHealth app and thus potentially increasing the effectiveness of the mHealth intervention to result in health behavior change. Although CommitFit included five health behaviors, these principles and inclusion of caregivers are likely generalizable to other health behaviors and mHealth apps. However, additional research is required to understand the best approach to maximize the effectiveness of caregiver inclusion in mHealth adolescent interventions, and to further understand the best way to motivate sustained health behavior change in adolescents.

Acknowledgments

The research reported in this publication was supported by the University of Missouri Coulter Biomedical Accelerator. The content is solely the responsibility of the authors and does not necessarily represent the official views of the University of Missouri Coulter Biomedical Accelerator or the University of Missouri-Columbia. Once publications have been completed, the deidentified data will be shared through MoSpace, the University of Missouri institutional repository, if the sample size is large enough to protect participant anonymity. The authors would like to acknowledge Alex Henigman for her assistance with literature searchers and references management.

Footnote

Reporting Checklist: The authors have completed the CONSORT reporting checklist. Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-97/rc

Trial Protocol: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-97/tp

Data Sharing Statement: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-97/dss

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-97/prf

Funding: This work was made possible with support from Washington University in St Louis through the Center for Diabetes Translation Research (CDTR) [No. P30DK092950 from the National Institute of Diabetes and Digestive and Kidney Disease (NIDDK)]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the CDTR or NIDDK.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-97/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by institutional review board of the University of Missouri (IRB #2092610) and informed consent was obtained from all participants and legal guardians.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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