A power analysis was conducted to estimate the sample size needed to detect the main effects and interactions in this study. Based on an effect size of 0.3 [20], a power of 0.80, and an alpha of .05, the initial calculation suggested that 126 participants would be required. Some researchers have reported high dropout rates with online training. For instance, an online MoodGym study reported a dropout rate of 73.9% for research participants and 99.2% for public registrants [42], and completion rates for internet-based depression treatment programs have varied from 43% to 99% [43]. To account for a potential dropout rate of 50%, which is commonly observed in online interventions, we planned to recruit 250 participants.

From January 2021 to April 2021, participants were recruited by posting recruitment posters on various public social media platforms, including WeChat Moment, RED app, Douban Forum, and other online forums. Interested individuals were directed to an online questionnaire, where they could register by scanning the QR code on the recruitment poster and providing basic demographic information. A total of 328 participants voluntarily enrolled in this study.

The inclusion criteria were as follows: (1) aged 18 years or older; (2) possessed intact reading and comprehension abilities; (3) had the capacity to provide informed consent; and (4) had access to the internet and basic smartphone literacy. Exclusion criteria included (1) current use of psychotropic medications, (2) severe visual and auditory impairments that hindered the comprehension of audio content or receipt of information, and (3) concurrent participation in other psychosocial intervention programs.

To enhance data representativeness, recruitment efforts spanned multiple platforms and were conducted at different times of the day to capture a diverse participant pool. Additionally, we used the neutral phrase “Peace Journey” in recruitment materials to mitigate self-selection bias.

Finally, 81 individuals who were unreachable after registration were excluded, leaving 247 eligible participants enrolled for randomization and the subsequent intervention.

After the initial registration, potential participants were invited to join a WeChat group for further communication. Preliminary screening was conducted based on the predefined inclusion and exclusion criteria. Eligible participants were invited to participate in subsequent research, were informed of the entire research process, and signed an electronic informed consent form. Baseline data were collected after receiving the informed consent form.

The unit of randomization in this study was the individual. Using computer-generated random numbers, we randomly assigned all eligible participants to either the ISSMT group or the wait-list control group. A statistician generated the randomization sequence, and baseline data were collected after the randomization. Investigators remained blinded to the randomization process. Notably, the investigation of online mindfulness training by Krusche et al [34] revealed a dropout rate of 79.4% for the training group, contrasting with only 35.9% for the wait-list control group. To balance the retained sample size in the 2 arms and adhere to statistical standards, we randomly assigned participants to groups at a ratio of 2.5:1 during randomization.

Ultimately, 175 participants were allocated to the ISSMT group, while 72 were assigned to the wait-list control group. Those in the control group received the same mindfulness training after completion of the formal study.

To ensure data quality and reliability throughout the study, we implemented multiple control measures. Standardized protocols were followed during data collection to reduce variability. During data cleaning, responses were rigorously screened, and entries displaying excessive duplication or failing to pass attention-check items were excluded. Specifically, attention checks included prompts such as “Please select the option ‘Moderately agree’ for this question,” allowing us to effectively identify inattentive respondents. During the data collection phase, we regularly monitored participants’ completion status and their engagement with the intervention (see the Intervention Procedure section for specifics) to maintain data integrity. These comprehensive monitoring and engagement strategies ensured data integrity and minimized the risk of dropout, enhancing the robustness and reliability of our study findings.

Descriptive statistics summarized participants’ demographic information and baseline outcome variables across different groups. To assess group differences, we used t tests for continuous variables and chi-square tests for categorical variables.

To analyze training effects in this longitudinal study, we used linear mixed models [48,49]. This approach accounts for both random and fixed effects, making it well-suited to handle missing data, which we assumed to be missing at random [50]. Specifically, compound symmetric covariance and maximum likelihood estimation were used.

We implemented both intention-to-treat (ITT) and per-protocol (PP) analyses [51]. ITT analysis includes all randomized participants, maintaining the benefits of randomization regardless of treatment adherence. PP analysis includes participants who completed the assigned treatment. In this study, the ITT analysis included participants who completed baseline measurements and participated in at least one course. The PP analysis focused on the participants who completed at least 75% of their assigned courses (ie, at least 10 sessions), which this study defined as completion of training. This approach enables a clear assessment of the actual treatment impact. Together, these methods provide a comprehensive view of the intervention’s effectiveness, with ITT generally yielding more conservative estimates.

Additionally, we evaluated the following effects: the time effect captures changes in measured outcomes across different time points, reflecting how participants’ performance evolved over the training duration. The group effect indicates differences in outcomes between treatment and control groups, illustrating the intervention’s impact. Finally, the time × group interaction examines how changes over time may differ across groups, elucidating the differential effectiveness of the intervention [52].

All analyses were conducted using SPSS 20.0 (IBM Corp) and incorporated age, gender, and other baseline variables as covariates. Statistical significance was determined using 2-sided tests, with a threshold of P<.05.

A total of 328 individuals voluntarily signed up to participate. Of these 328 individuals, 247 (75.3%) met the inclusion criteria and were randomized into the study. Data from 42 people who did not engage in any subsequent courses were excluded, leaving 205 eligible participants (133 in the training group and 72 in the wait-list control group). For the purposes of this study, individuals who completed more than 75% of the courses (ie, participation in at least 10 courses) were considered to have completed the training. A total of 81 participants (81/131, 60.9%) in the training group met this criterion (see Figure 1). No adverse events were reported during the study. Finally, data from 133 individuals in the training group and 72 individuals in the wait-list control group were included in the ITT analysis, while data from 81 and 72 participants, respectively, were included in the PP analysis.

The basic demographic information of the participants is shown in Table 2. The average ages of the participants in the training group and wait-list control group were 24.80 (SD 6.31) years and 24.60 (SD 6.62) years, respectively, with no statistically significant difference. There were also no significant differences between the 2 groups in terms of gender, education, or baseline mindfulness and depressive symptoms.

According to the ITT analysis, the scores for each outcome variable at each measurement time point are presented in Table 3. At baseline, there was no significant difference in mindfulness scores between the training group and the wait-list control group. The results of the mixed linear model indicated significant main effects of time (F_3,515.19=29.48; P<.001), group (F_1,214.78=10.20; P=.002), and the time × group interaction (F_3,515.25=7.05; P<.001). The results suggest that the online mindfulness training significantly improved the participants’ mindfulness. The effect size for the change in mindfulness level was 0.44.

As shown in Table 3, the training group demonstrated a significant increase in mindfulness starting from the first week, with continued significant improvements thereafter, whereas no significant changes were observed in the wait-list control group. The trends in mindfulness level changes at each time point are illustrated in Figure 2A.

According to the PP analysis, the scores for each outcome variable at each measurement time point are presented in Table 4. At baseline, there was no significant difference in mindfulness scores between the training group and wait-list control group. The results of the mixed linear model revealed significant main effects of time (F_3,419.57 =29.31; P<.001), group (F_1,158.47=11.21; P=.001), and the time × group interaction (F_3,419.64=7.67; P<.001). These results indicate that the online mindfulness training significantly improved the participants’ mindfulness level. The effect size for the change in mindfulness level was 0.55.

As shown in Table 4, the mindfulness level in the training group showed a marginally significant increase starting from the first week and continued to increase significantly thereafter, while no significant changes were observed in the wait-list control group. The trends in mindfulness level changes at each time point are depicted in Figure 3A.

According to the ITT analysis, the scores for each outcome variable at each measurement time point are presented in Table 3. At baseline, there was no significant difference in depression scores between the training group and wait-list control group. The results of the mixed linear model revealed significant effects of time (F_3,514.77=38.48; P<.001), group (F_1,207.10=9.31; P=.003), and the time × group interaction (F_3,514.87=3.87; P=.009). These findings indicate that ISSMT can significantly reduce depressive symptoms, with an effect size of 0.50 for improving depression.

As shown in Table 3, depressive symptoms in the training group improved significantly from the first week and continued to improve significantly thereafter, whereas no significant changes were observed in the wait-list control group. The trends in the depression changes at each time point are illustrated in Figure 2B.

According to the PP analysis, the scores for each outcome variable at each time point are presented in Table 4. At baseline, there were no significant differences in depression scores between the training group and wait-list control group. The results of the mixed linear model revealed significant effects of time (F_3,420.91=38.58; P<.001), group (F_1,157.77=10.11; P=.002), and the time × group interaction (F_3,421.03=3.65; P=.01). These findings indicate that online mindfulness training can significantly reduce depressive symptoms. The effect size for improving depression was 0.53.

As shown in Table 4, the depression score in the training group improved significantly from the first week and continued to improve significantly thereafter, while no significant changes were observed in the wait-list control group. The trends in the depressive symptom changes at each time point are shown in Figure 3B.

The average Intervention Satisfaction Scale score for item 1 (“Overall, I liked the two-week course practice”) was 3.77, and the average score for item 2 (“I would recommend this course to others”) was 3.52, exceeding the average. At the same time, 87% (77/89) of the participants said they thought the course was helpful to them, and 72% (64/89) of the participants said they liked this online training method.

Depression is a leading global public health problem, with enormous demand for mental health services but limited access to mental health services. The emergence of telehealth solutions offers a promising approach to bridging this service gap. However, despite the potential, there are currently few studies in China that have focused on short-term online mindfulness training, with the majority of online studies adhering to the traditional 8-week mindfulness training framework [53]. In response, we developed a 2-week ISSMT course based on the MAT model. Additionally, we created an online platform for program delivery. The results from our RCT indicate that, compared with the wait-list control group, participants in the training group had increased mindfulness levels and decreased depressive symptoms. Furthermore, participants expressed high acceptance of this training format. This study provides empirical evidence supporting the feasibility and effectiveness of ISSMT for Chinese adults. We recommend that future research explore the integration of this type of training into psychosocial service systems to enhance mental health.

Although the effectiveness of traditional 8-week mindfulness training has been reported in some studies, concerns remain regarding the extended training duration. Scholars have noted that longer training periods may lead to higher dropout rates and reduced participant motivation, thereby affecting treatment adherence [32]. Adherence is a critical determinant of treatment outcome, with approximately 50% of treatment failures attributable to nonadherence [54]. In a study delivering online psychosocial interventions, high dropout rates have been observed, reaching up to 73.9% among research participants and as high as 99.2% among public registrants [42]. This study introduces an ISSMT program that can be delivered online and demonstrated a notably lower dropout rate (<40%) as well as effective implementation. We believe the adapted training program, customized delivery platform, and quality control measures used in this study can serve as a valuable reference for further practices in delivering online mindfulness training and other psychosocial interventions in China.

Although previous studies have reported inconsistent results for short-term online mindfulness training [34-36], our study found that ISSMT significantly reduced depressive symptoms in the general population. Specifically, no significant differences in depressive symptoms were observed between the 2 groups at baseline or at week 1. However, by weeks 2 and 3, the severity of depressive symptoms in the training group was significantly lower than that in the control group. Although previous research suggests that brief mindfulness meditation sessions can improve immediate emotional states (eg, a single 10-minute mindfulness meditation training can reduce the intensity of negative affect in response to emotional stimuli [55]), our findings indicated that certain “doses” of training may be necessary to ensure the effectiveness of internet-delivered training. Furthermore, the emphasis on acceptance in MAT may be a key factor for sustaining the intervention’s effects [25].

There are several strengths of the ISSMT program delivered in the study. First, the effect sizes for the improvement in mindfulness and reduction in depressive symptoms exceeded those reported in other similar studies [56], with these benefits maintained at follow-up. This suggests that some participants may have continued practicing mindfulness mediation in their daily life after the trial concluded, further supporting the feasibility and long-term potential of the ISSMT program for the Chinese population.

Moreover, the dropout rate in this study was lower than in other similar online training programs. High dropout rates are a well-documented challenge for internet-based psychological training, with reported rates ranging from 73.9% to 99.2% in other studies [42,43,57,58]. Another review reported that an additional 0% to 18% of participants were lost between the posttraining assessment and follow-up assessment [59]. In contrast, our study achieved a completion rate of 60.9%, reflecting a considerably lower dropout rate and confirming the acceptability of this training format.

China’s large internet user base underscores the considerable potential of digital health interventions on a global scale, particularly within significant internet economies like China. According to the 54th “Statistical Report on the Development of China’s Internet” released by the China Internet Network Information Center, as of August 2024, the number of internet users in China was 1.099 billion, with an internet penetration rate of 78% [60].

The convenience and accessibility of online training programs make them an important supplement to offline training, helping to bridge the gap between limited medical resources and the vast mental health service needs in China [61]. Despite a more than twofold increase in the national ratio of psychiatrists, including accredited and assistant psychiatrists, per 100,000 population from 2002 to 2020 (from 1.51 to 3.19) and for accredited psychiatrists specifically (from 1.20 to 2.89) [62], there remains a significant gap between the demand for mental health services and the available resources. This disparity is exacerbated by the uneven distribution of mental health professionals, with a concentration in major cities and the northeast and east regions of China [62]. In light of these challenges, this study developed and validated an online mindfulness training program that effectively overcomes the geographical limitations of traditional face-to-face interventions, underscoring its significance for areas with constrained medical resources.

From a policy perspective, the empirical evidence presented in this study offers a valuable benchmark for authorities and stakeholders when shaping mental health policies. Our results demonstrate that digital mental health interventions not only deliver effective therapeutic outcomes but also garner high user satisfaction. Based on our knowledge, China has begun pilot initiatives to incorporate psychological therapies into public health insurance in certain regions, including Guangdong, Jiangsu, and Beijing. Although traditional psychotherapy is known for its extended treatment periods and challenges in efficacy monitoring, this study shows that a 2-week intervention can yield significant benefits. This finding supports the proposition for policymakers to integrate comparable online intervention models into national or regional mental health frameworks. Integrating such models would enhance the provision of accessible and efficient mental health services to the population.

It is noteworthy that this study did not account for the effects of group-specific differences, leaving uncertainties whether the findings extend to high-risk populations for depression, such as older adults and pregnant women. Older adults may face unique physiological and psychological challenges, including cognitive decline and difficulties with technology use [63], that may influence their acceptance of and response to online interventions. For pregnant women, prior research has shown that those with high levels of neuroticism demonstrate greater improvements in maladaptive cognitive emotion regulation strategies following mindfulness interventions compared with those with moderate or low neuroticism [64]. This suggests variability in response to mindfulness interventions based on neuroticism levels. Therefore, caution is advised when generalizing these findings to these specific populations.

A couple of limitations should be mentioned for this study. First, the main intervention effects were based primarily on self-report data. Further research may combine a broader range of measures, such as behavioral and biological measures. Second, a wait-list control design was used, and using an active control intervention in future studies would provide stronger support for the specific effects of the program. Additionally, this study did not segment participants by specific demographic categories. Certain groups, such as older adults and pregnant women, are recognized as higher-risk populations for depression, and the absence of targeted analysis for these groups may limit the generalizability of our findings. Future research should consider implementing strategies to increase the inclusion of high-risk groups, ensuring a broader and more diverse sample. For instance, offline recruitment methods or targeted online advertisements could be used to reach these specific populations. These enhancements would strengthen the representativeness of the sample and improve the external validity of the findings.

The 2-week ISSMT course based on the MAT demonstrated high feasibility and effectiveness for enhancing mindfulness levels and alleviating depressive symptoms. Given China’s substantial potential user base and the disparity between limited mental health resources and the significant demand for services, this digital health intervention could play a pivotal role in expanding access to mental health support. Future research should prioritize the development of personalized online mindfulness programs that consider individual differences such as age, gender, and daily emotional fluctuations. Integrating artificial intelligence [65], wearable technology, and diverse health data may further refine the precision and efficacy of these interventions. Additionally, exploring virtual reality [66] and natural language processing could enable more immersive and engaging user experiences, potentially enhancing therapeutic outcomes.