Personalized game-based digital intervention for relieving depression and anxiety symptoms: a pilot RCT

Participants and procedure

This study was a pilot randomized controlled trial (RCT) with one experimental group and two control groups (an active control and a no-intervention control). As the study is a pilot trial, the sample size was determined to be 70 for each group based on feasibility and practicality. The study was registered at clinicaltrials.gov (NCT06301555) on March 4th, 2023. The study was approved by the Ethics Committee of the First Hospital of China Medical University (KLS 2023#53).

Participants were recruited from the Internet by posting advertisements on Chinese social media, forums, and classifieds websites. The advertisements invited individuals who thought they were experiencing depression and/or anxiety symptoms to participate. Upon clicking the advertisement page, participants were redirected to a page that collects demographic information (age and sex) as well as PHQ-9 and GAD-7 assessments. A detailed questionnaire asked the participants for their vision acuity (after correction), psychiatric history, current psychological or pharmacological treatments, cognitive impairments, substance use, and pregnancy or postpartum status. Participants were not required to have a clinical diagnosis of depression and/or anxiety disorders. The inclusion criteria include: (1) having a PHQ-9 score \(\ge 10\) or a GAD-7 score \(\ge 8\) upon enrollment; (2) owning an Android or iOS smartphone and being fluent in smartphone use; (3) having normal vision after correction. Exclusion criteria include (1) clinical diagnoses of severe psychiatric conditions such as schizophrenia or bipolar disorder; (2) at high risk of suicide or self-harm; (3) having significant cognitive impairments; (4) active substance abuse or dependence; (5) currently undergoing other psychological or pharmacological treatments for depression or anxiety; (6) pregnant individuals or those with postpartum depression, and those with serious physical health conditions that could impact mental health. Eligible participants were randomly assigned to the RL algorithm group, the no-intervention control group, and the no-algorithm active control group. All participants provided informed consent before participation. The randomization was performed using a computer-generated random sequence to ensure equal chances of assignment and avoid selection bias.

Intervention design

The DTx intervention program was crafted to resemble a role-playing game where the user navigates a character to explore the virtual world, complete quests and challenges, and interact with non-player characters (NPCs). To enhance user engagement, we have incorporated a questioning system. Periodically, users would receive quests that challenge them to complete a suite of training modules encompassing CBT, Visual Search Attention Training (VSAT), Approach Bias Modification (ApBM), and Cognitive Bias Modification Interpretation (CBM-I). The intervention was designed to last four weeks. Participants were encouraged to engage with the intervention by completing quests, which were released on a daily basis. New quests would only become available once the participant had completed the currently active ones. The users would receive a push notification if their quests of the day have not been completed by 7 pm. This design aimed to promote consistent engagement, with the expectation that participants would use the app daily to progress through the intervention. The gamification approach in the intervention program fosters a sense of accomplishment and progression, ultimately increasing motivation and adherence to the intervention. As an added incentive, we rewarded users with achievement badges, experience points, and reward points (which can be redeemed for outfits and equipment for their character) upon completion of their quests. In the RL algorithm group, the personalization of the quests was achieved through a proprietary reinforcement learning algorithm that maximizes user engagement. In the No Algorithm group, the quests were predetermined as a fixed sequence.

The game incorporated concepts, knowledge, and techniques in cognitive behavioral therapy into the interactions with the NPCs. Within the game, the player would encounter NPCs and converse with them to learn about CBT and use CBT concepts and techniques. The game also allowed for role-playing scenarios where the player could interact with the NPCs and apply CBT skills in a safe, virtual environment to build confidence for real-life situations. The game also included combat scenes, where the player controls the main character to combat various monsters representing negative emotions, thoughts, and behaviors. The combats leveraged CBM-I, VSAT, and ApBM to correct biased cognitive patterns (see Fig. 1 for the screenshots of the modules). CBM-I focuses on altering biased interpretations of ambiguous information using training exercises that promote more positive and healthy interpretations⁹.VSAT is an attentional training designed to enhance individuals’ abilities to control their attention and to visually search for information, and it can significantly improve the attention of individuals who suffer from depression and anxiety, particularly in terms of their ability to focus on positive information^11,12,16. ApBM aims to reduce approach tendencies by modifying the patients’ action tendencies toward positive stimuli⁵. The design of the CBT and CBM-I modules incorporated examples from real-world social interactions, such as workplace communications and interactions within intimate relationships and with family members. These modules not only provided in-game practice of cognitive restructuring and interpretation corrections but also included strategies in the form of cards to encourage participants to apply these skills in their daily lives. For instance, after completing a module on reframing negative thoughts, participants were encouraged to reflect on recent real-life situations where they could apply the same strategy. Additionally, behavioral activation and journaling tasks were provided to help them record how they used these skills in real-world interactions. By embedding these real-world examples and encouraging reflection and practice beyond the game environment, the intervention aimed to promote the generalization of new cognitive and behavioral strategies. This approach ensured participants could develop greater confidence in using these skills independently.

a On the left: screenshot of CBM-I; b inthe middle: screenshot of VSAT; c on the right: screenshot of ApBM.

Reinforcement learning is a machine learning framework for sequential decision-making problems in which an agent must choose an action given contextual information (also known as state). Each action is associated with a probability distribution that governs the generation of rewards, which is influenced by the contextual information. The agent’s goal is to learn, given contextual information, which action should be chosen to maximize the accumulated rewards¹⁷.

In the DTx intervention program, each quest corresponded to an action, which was a suite of training modules. Each action contained a different set of modules to simulate different dosages and compositions. A weighted sum of the app usage time and reductions in PHQ-9 and GAD-7 scores was used as the reward for the RL model. The usage time reflected the engagement and adherence of the participant to the intervention program, while the reductions in PHQ-9 and GAD-7 scores indicated an improvement in symptoms of anxiety and/or depression. The weight of each component can be adjusted to prioritize certain outcomes, depending on the goals of the intervention. For example, if increasing engagement is a primary objective, a higher weight can be assigned to the usage time component. Conversely, if symptom improvement is the main focus, greater emphasis can be placed on the reduction in PHQ-9 and GAD-7 scores. By using this reward structure, the reinforcement learning algorithm can learn to make decisions that maximize the overall effectiveness of the intervention program. The contextual information used in the RL algorithm included demographic information (e.g., age, gender), initial PHQ-9 and GAD-7 scores, usage patterns (e.g., cumulative usage time of each component), and performance on the various training modules. By incorporating this information, the RL algorithm can personalize treatment plans and optimize the training modules offered to each user based on their characteristics. Overall, our algorithm allows for a dynamic and personalized intervention that maximizes user engagement and efficacy in treating depression and anxiety.

Outcome measures

The Patient Health Questionnaire-9¹⁸ (PHQ-9) was used to assess symptoms of depression at baseline and post-intervention, with a cut-off point of ≥10 indicating that the patient is a clinical case in a primary care population. The 7-item Generalized Anxiety Disorder scale (GAD-7) was used to assess symptoms of anxiety at baseline and post-intervention¹⁹. A cut-off point of GAD-7 ≥ 8 indicated that the patient is a clinical case in a primary care population. These cut-off points followed the Improving Access to Psychological Therapies program of the UK’s National Health Service. For PHQ-9 and GAD-7, we used the standard definitions of response, i.e., \(\ge 50 \%\) reduction, to define whether a participant responds to the intervention. We followed the definition used in the Improving Access to Psychological Therapies program of the UK’s National Health Service to define recovery based on the PHQ-9 and GAD-7 scores²⁰. A patient was considered recovered if they were a clinical case at the start of treatment (\(\ge 10\) on PHQ-9 and/or \(\ge 8\) on GAD-7) but fell below the threshold to be considered a clinical case at post-intervention. The post-intervention assessments took place at the end of the 4-week intervention.

Statistical analysis

All statistical analyses were conducted using R version 4.3.2²¹. Statistical significance was set at p < 0.05, and all tests were two-tailed. A complete case analysis was performed using the data from participants who had completed the intervention as well as PHQ-9 and GAD-7 assessments at both pre-intervention and post-intervention time points. An intend-to-treat analysis was performed using all eligible and randomized participants regardless of their adherence and assessment completion. Missing assessment data were imputed using multiple imputations.

Using the complete case data, we performed three multivariate logistic regression analyses to predict the three binary outcomes of interest, namely, PHQ-9 response, GAD-7 response, and recovery. The predictor variables were the baseline PHQ-9 and GAD-7 scores, age, sex (female as the reference level), and group (no-intervention control as the reference level). The odds ratios, their 95% confidence intervals, and p-values were calculated and reported.

For the intend-to-treat analysis, we performed a bootstrapped multiple imputation to impute the missing variables of the eligible and randomized participants (n = 223) using the bootImpute R package (ver 1.2.1)²². For each bootstrap sample, we imputed the missing variables by group using the available variables. The number of bootstrap samples was set to 2000, and each bootstrap sample was imputed 5 times. We estimated the response rates (PHQ-9 and GAD-7) as well as the IAPT recovery rate using the bootstrapped multiple imputed data and calculated the standard errors as well as the 95% confidence intervals.