The DIPPAO study is a 2-arm parallel prospective randomized controlled trial conducted in France. Full details of the study methods have been reported previously [27]. This trial aimed to compare the effectiveness, cost-effectiveness, and psychological mechanisms of the Kiplin program in comparison with face-to-face, supervised PA sessions among patients treated for obesity or T2D. The authors are solely responsible for the design and conduct of this study; all analyses, drafting, and editing of the paper; and its final contents. In this paper, we report the primary and secondary clinical outcomes along with the cost-utility analysis; the psychological mechanisms results are reported separately. There were no significant deviations from the prespecified protocol during the trial. The reporting follows the Consolidated Standards of Reporting Trials (CONSORT) guidelines [28] and the Template for Intervention Description and Replication checklist [29].

Full details of the outcome measures, including their reliability and validity, have been reported previously [27]. At baseline, participants provided demographic data, including date of birth, sex, and highest level of education completed. The primary outcome was the change in daily step count, assessed at high resolution using the Garmin Vivofit, a wearable activity tracker with validated accuracy under various walking conditions [32], from baseline to 1-week postintervention. Additionally, the change in daily steps in the follow-up period from baseline was also evaluated.

The 3 experimental visits included the following clinical outcomes. BMI was calculated as body mass (kg) divided by height squared (m²). Body composition, including fat and lean mass, was assessed using bioelectrical impedance analysis with the multifrequency segmented body composition analyzer (Tanita MC780; Tanita). Moderate-to-vigorous PA (MVPA), light PA (LPA), and sedentary time were measured over 7 consecutive days using a triaxial accelerometer (ActiGraph GT3x). Cardiorespiratory fitness was assessed via the 6-minute walk test (6MWT). Muscular strength of the upper limbs was assessed via handgrip strength using a dynamometer (Takei Grip-D; Takei) with a series of 3 handgrip test measurements for right and left hands in the seated position. Muscular strength of lower limbs was assessed through maximum knee extension torque using an isokinetic dynamometer at speeds of 30 °/s, 60 °/s, and 120 °/s. Quality of life was measured via the EQ-5D-5L questionnaire [33], which evaluates the following 5 dimensions: mobility, autonomy of the person, current activity, pain and discomfort, and anxiety and depression.

For both groups, the number of APA sessions attended was assessed. For the Kiplin group, app engagement was collected and comprised participation in the games, app usage frequency, and the number of messages sent.

The health economic evaluation was conducted through a cost-utility analysis incorporating the (1) identification and valuation of costs and (2) measurement of utility using the EQ-5D questionnaire. The analysis was performed from the health insurance perspective considering only direct medical costs. The time horizon for this evaluation extended from baseline (T0) to the follow-up assessment at T2.

The trial was designed to demonstrate a difference equivalent of an effect size of d=0.6 on our primary outcome (daily steps) for 80% power and a 2-sided type I error at 0.05. In idiographic designs, statistical experts [34] recommend a minimum of 835 observations for adequate power (.80) at level 1. With 2058 (98 days × 21 participants) observations, we had adequate power to detect within-person changes in the Kiplin condition [35].

All analyses followed a modified intention-to-treat principle, incorporating participants with complete data for the primary or secondary outcomes at the initial 2 experimental visits. This pragmatic analytical approach aimed to enhance internal validity by assessing treatment effects among participants who received the intervention while acknowledging that the necessary exclusions could attenuate some benefits of randomization. Analyses were performed using R (R Foundation for Statistical Computing). Baseline variables are reported as numbers and percentages for categorical variables and means with SDs for continuous variables. According to the CONSORT 2010 statement [28], group differences in baseline variables were not compared using significance testing. Two authors directly accessed and verified the underlying data reported in the manuscript, including one who had no affiliation with the Kiplin company.

Changes in daily step count were assessed between baseline and intervention periods as well as between baseline and follow-up using linear mixed-effects models. These models accounted for the nested data structure and included fixed effects for group, time, and group × time interaction. Because plots of the residuals and estimated trajectories suggested substantial nonlinearity in step count change over time, we compared polynomial extensions of the time effect (quadratic, cubic, and quartic) by minimum Akaike information criterion (AIC) and Bayesian information criterion (BIC). The quadratic model provided the best fit and is presented in the Results section. Random intercepts for participants and random linear slopes for repeated measures at the participant level were included. As an additional analysis, we treated measurement occasion as a categorical “period” factor (baseline, intervention, follow-up) rather than as a continuous measure of days. In this model, we included fixed effects for group, period, and the group × period interaction with the same random effects structure.

Adjusted models were further fitted and incorporated fixed effects for group, period, group × period interaction, age, baseline PA levels, BMI, season, and number of completed APA sessions – factors previously identified as significant predictors of intervention effects [30]. Nonwear days, defined as days with fewer than 1000 steps, were treated as missing data. All models were performed using the lmerTest package [36], and contrast analyses were conducted using the emmeans package [37]. Diamond comparison plots were drawn with the R package ufs [38].

In a complementary analysis of the primary outcome, an idiographic approach was used to analyze daily step counts for each participant separately using generalized additive models (GAMs) [39]. GAMs are an extension of generalized linear mixed models that allow the estimation of smoothly varying trends where the relationship between the covariates and the response is modeled using smooth functions [40]. GAMs are particularly well-suited to the modeling of time series data with 1 level of measurement (ie, repeated measurements nested within 1 individual), as they can accommodate the inclusion of autocorrelated error terms [41]. Nonlinearity was assessed via the effective degrees of freedom (edf) of smoothing terms, with edf ≥3 indicating nonlinearity [41]. GAMs were computed using the mgcv package [42], and the visreg package [43] was used for model visualization.

Continuous secondary outcomes were analyzed using mixed-effect models (including the group, time, and group × time interaction terms as fixed effects) to assess changes across 9 months (ie, considering the 3 measurement points). As statistical power was computed for the primary outcome, secondary outcome analyses were classified as exploratory [44].

Between June 22, 2021, and October 6, 2022, 57 patients were screened for eligibility, with 50 meeting the inclusion criteria and subsequently randomized to either the Kiplin group (n=25) or usual care group (n=25; Figure 2). Participants who completed at least 2 assessments (modified intention-to-treat sample) had a mean age of 47.7 (SD 13.3) years and a mean BMI of 40.0 (SD 7.14) kg/m², and 31 were women (31/42, 74%). A total of 13 participants had T2D (13/42, 31%). At baseline, the average daily step count was 6584 (SD 3787) steps per day. No substantial differences in baseline characteristics were observed between groups (Table 1). The descriptive statistics of all the randomized participants are available in Table S2 in Multimedia Appendix 1.

Among the 50 randomized participants, 8 withdrew or were lost before program completion (3 in the intervention group and 5 in the control group), and 17 were lost during the follow-up period (Figure 1). Of a possible 274 days, participants logged steps for a median of 158 (IQR 162) days throughout the study, with 80% (34/42) of participants wearing their activity monitor for at least 75% of days. In the Kiplin condition, participants attended an average of 14.68 (SD 6.14) of 22 possible APA sessions, yielding a completion ratio of 0.67 (SD 0.28). They engaged in an average of 2.6 (SD 0.71) of a possible 3 PA games, corresponding to a mean of 36.6 (SD 10.02) days in-game; sent an average of 14.2 (SD 15.93) messages; and logged into the app on 88.12 (SD 43.63) times on average. In contrast, patients receiving usual care attended an average of 30.27 (SD 5.88) of 36 APA sessions, with a completion ratio of 0.84 (SD 0.16).

Table 2 presents the results of the secondary outcome measures. Within-group comparisons showed significant improvements from baseline to 9 months in lower limb muscle strength (ß=6.68, 95% CI 1.40 to 11.96; P=.01) and a significant decrease in BMI at 3 months (ß=−0.51, 95% CI −0.98 to −0.04; P=.03) for the Kiplin group. In contrast, the control group showed significant improvements in quality of life at 3 months (ß=0.08, 95% CI 0.01 to 0.15; P=.02) and in 6MWT and lower limb muscle strength at both 3 months (ß=38.94, 95% CI 18.96 to 58.93; P<.001, and ß=9.27, 95% CI 4.15 to 14.40; P=.001, respectively) and 9 months (ß=25.41, 95% CI 2.04 to 48.78; P=.03, and ß=7.59, 95% CI 1.80 to 13.39; P=.01, respectively). Results of the mixed-effect models revealed no significant group × time interactions for secondary outcomes, except for a significant interaction in MVPA change from baseline favoring the Kiplin group (ß=5.69, 95% CI 0.61 to 10.77; P=.03). Figure 6 provides a visual comparison of changes between conditions with effect size estimates. Graphs depicting the evolution of secondary outcomes are available in Figure S2 in Multimedia Appendix 1.

The cost per patient in the usual care group was €491.92 (US $566.80) compared with €568.74 (US $655.31) in the Kiplin group, resulting in a cost differential (surplus of Kiplin compared with the control group) of €76.82 (US $88.51). Quality-adjusted life years (QALYs) did not significantly differ between groups and time points. The QALYs calculated from the EQ-5D questionnaire were, on average, rather good at baseline, with averages of 0.62 at T0 and 0.67 at T1 and T2.

The results of this RCT revealed that a group-based gamified digital intervention significantly increased daily PA compared with a traditional supervised, face-to-face APA program. Interestingly, this significant difference was observed both during the 3-month program (+1085 daily steps) and during follow-up periods (ie, 6 months postintervention: +1775 daily steps). These results remained consistent irrespective of adjustments for potential confounding factors, reinforcing the robustness of the findings.

In terms of trajectory, participants in the usual care group followed a U-shaped trajectory: their daily steps declined during the intervention and then returned to baseline by the program’s end, which is consistent with our hypothesis and the compensation mechanisms often observed between supervised and leisure-time PA in APA programs [27]. In contrast, Kiplin participants exhibited an inverted U-shaped pattern, with a sharp early surge in steps that gradually decelerated over time, suggesting the intervention was most potent at the outset.

Our findings are consistent with previous research showing that gamified digital interventions can lead to meaningful real-world improvements in daily step counts, with effects lasting several months after the intervention [14,30,45]. We observed that the intervention’s impact was strongest at the beginning of the program, aligning with evidence that shorter gamified interventions tend to produce the largest gains [14,30]. Although the follow-up period in our study was longer than the average duration reported in previous research, the effect size 6 months postintervention (834 additional daily steps on average) observed in this study was greater than that of earlier trials [14].

To the best of our knowledge, no gamified digital program has previously been compared with usual care in the management of obesity and T2D. However, a prior meta-analysis comparing digital health interventions with minimal intervention or usual care in the context of cardiac rehabilitation found no significant effect on objectively measured PA [46]. In contrast, our study demonstrated that the digital intervention promoted greater increases in both daily step counts and MVPA compared with usual care. This effectiveness for MVPA is also novel, as previous meta-analyses did not report significant effects of gamified interventions on such outcomes [14].

These results are particularly promising for several reasons. Where current traditional face-to-face, supervised APA programs often face challenges for driving sustained increases in PA [26], the Kiplin program addresses this gap effectively. Notably, the effect size observed during the intervention was maintained throughout the 6-month follow-up, underscoring the sustainability of the PA behavior change. The benefits of improving daily PA are now well-recognized [47]. A recent meta-analysis revealed that taking more steps per day was associated with a progressively lower risk of all-cause mortality, regardless of age, health status, or intensity [48]. This suggests that the observed behavioral changes in the Kiplin group could have significant long-term health implications.

Nevertheless, in parallel to these results, idiographic analyses revealed substantial variability in individual responses within the Kiplin condition. More especially, GAM models showed (1) significant between- and within-person variability during the intervention, with some participants displaying highly nonlinear patterns while others showed linear trends, and (2) divergent responses to the intervention, with several participants experiencing no significant changes across time. These findings introduce important nuances to our results, indicating that digital interventions may not be suitable for every individual. This underscores the need for effective screening methods to identify patients who are most likely to benefit. Indeed, we can assume that factors such as the stage of behavior change, the acceptability of the technologies [49], or some physiological characteristics [50] could play a critical role in determining whether a digital or in-person program would be more appropriate.

In addition, no statistical differences were observed in the secondary outcomes evaluated in this study, except for the MVPA change. Although these results should be interpreted with caution, as they are exploratory and stem from secondary analyses potentially without appropriate statistical power, they illustrate 2 different approaches to chronic disease management: behavior change facilitated through the Kiplin program versus functional and physical fitness improvement achieved through the traditional program.

According to the cost-utility analysis, the cost surplus of approximately €76 per patient generated by Kiplin is not significant for an unimpaired quality of life. The anticipated gains in QALYS could not be demonstrated, probably due to the limited sample size at T2 and the high baseline quality of life score reported by patients at T0. This suggests that the generic EQ-5D measurement tool, while useful for QALY transposability, may lack the specificity needed to detect quality of life changes in this population of patients. To provide a conclusive evaluation of Kiplin’s cost-effectiveness over usual care, future research should conduct more specific medical-economic analyses, incorporating quality of life metrics adapted to the patient population. Although digital interventions are often expected to be more cost-effective than in-person programs, the intervention in this study proved to be more expensive. The higher costs observed in the Kiplin group primarily reflect the fixed expenses associated with implementing a hybrid digital intervention, including software licensing, human resources, and equipment. Given the relatively small number of participants in our study, these fixed costs could not be offset through economies of scale. In a larger-scale implementation, the average cost per participant would likely decrease substantially, making the digital component more cost-efficient in practice. Nevertheless, although unexpected, our findings are consistent with previous studies that have not demonstrated clear evidence of cost-effectiveness [20,51] .

From a methodological perspective, the variability observed with the GAMs reflects a broader limitation of traditional RCTs, which typically focus on group-level differences, potentially overlooking meaningful interindividual variability. By relying solely on aggregate measures, RCTs may fail to capture distinct response patterns, limiting insights into underlying mechanisms and moderating factors that influence intervention effectiveness. In this line, idiographic approaches appear to be a valuable complement to traditional clinical experimental designs, and this study highlights the importance of combining both between- and within-person approaches for evaluating digital interventions, as well as the advantage of high-resolution behavior measurement.

However, the results should be interpreted in light of several limitations. First, although using wearables allowed for continuous daily step monitoring in real-world conditions, variations in wear time between groups at the day level cannot be excluded, as the Kiplin group may have been more incentivized to wear the device through gamification. Qualitative interviews (not reported in this paper) with the patients suggested that participants in both groups wore the devices consistently (since recharging was not required), but we lack objective data to confirm this. In addition, these activity monitors are not able to assess several forms of PA, such as cycling or ergometer exercise. Future studies using wearables with continuous heart rate tracking could better assess and control for wear time, and devices such as Motus or SENS may allow for large-scale, 24-hour movement behavior assessment [52], including cycling [53]. Second, results from secondary outcomes should be viewed with caution due to potentially insufficient power to detect these effects as the power test was computed for the primary outcome according to a large effect size. Third, we observed baseline differences in daily steps between the 2 conditions, which were driven by a few participants with unusually high activity for people with obesity or T2D, as our sample reflected real-world hospital admissions without activity-based selection criteria. Future studies could screen for initial PA to minimize such variability. Last, digital interventions consist of a complex interplay of interconnected components, making it challenging to isolate the specific influence of individual elements when the intervention is evaluated as a whole. To address this challenge, innovative research frameworks like the Multiphase Optimization Strategy (MOST) [54] or hybrid designs [55] could offer a promising avenue in future research for systematically isolating the intervention components and optimizing digital health interventions.

This study confirms the potential of digital health interventions to promote sustained changes in PA compared with usual care in participants with obesity and T2D. Although this behavior change has not led to superior clinical outcomes compared with usual care in this study, its continued persistence beyond the 6-month postintervention period could lead to more pronounced long-term benefits. To validate this hypothesis, larger clinical trials with extended follow-up durations are necessary. This study also had the originality of incorporating both between-group and within-person analyses of daily step counts. The findings indicate that, although the digital intervention effectively increased daily steps on average compared with usual care, the benefits were not uniform across all participants. This underscores the importance of patient screening and tailoring program content to individual needs. Future research should further investigate these considerations to optimize digital health intervention design and implementation.