Game-Based Digital Intervention for Relieving Depression and Anxiety Symptoms

NCT06301555 | Completed

• 1 other identifier: db2023
• Study Type: interventional
• Target: 223
• Locations: 1 country
• Sites: 1

Brief Summary

Objective: To collect preliminary data and assess the preliminary effectiveness of a game-based digital therapeutics (DTx) intervention for individuals with symptoms of anxiety and depression, and to investigate whether reinforcement learning (RL) can personalize the intervention and enhance effectiveness. Design: Randomized controlled trial with three arms. Setting: Internet-based recruitment and delivery of the intervention. Participants: 223 individuals with symptoms of anxiety and depression, aged between 18 and 50 years. Interventions: Participants were randomly assigned to one of three groups: game-based DTx with RL algorithm (RL algorithm group), game-based DTx without RL algorithm (no algorithm group), and a blank control group. Main Outcomes and Measures: The primary outcomes were reductions in symptoms of anxiety and depression, measured using the Patient Health Questionnaire-9 and Generalized Anxiety Disorder 7-item scales. Response rates and rates of recovery, as well as the impact of demographic variables, were also examined.

Conditions

Depression; Anxiety

Outcome Measures

Primary Outcomes (2)

• PHQ9 Response

  A user is said to responded to the intervention if the PHQ9 score dropped no less than 50% compared to the baseline

  Immeidately after intervention

• GAD7 Response

  A user is said to responded to the intervention if the PHQ9 score dropped no less than 50% compared to the baseline

  Immeidately after intervention

Study Arms (3)

Blank Control

NO INTERVENTION

No intervention

RL Algorithm Intervention

EXPERIMENTAL

Behavioral: Reinforcement learning algorithm powered cognitive and behavioral intervention

No Algorithm Active Control

ACTIVE COMPARATOR

Behavioral: cognitive and behavioral intervention

Interventions

Reinforcement learning algorithm powered cognitive and behavioral intervention BEHAVIORAL

Reinforcement learning algorithm powered cognitive and behavioral intervention

RL Algorithm Intervention

cognitive and behavioral intervention BEHAVIORAL

cognitive and behavioral intervention

No Algorithm Active Control

Eligibility Criteria

• Age: 18 Years - 60 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64)

You may qualify if:

• age between 18 and 60 years;
• having a PHQ-9 score or a GAD-7 score upon enrollment;
• being able to operate a smartphone fluently;
• having normal vision after correction

You may not qualify if:

• clinical diagnoses of severe psychiatric conditions such as schizophrenia or bipolar disorder;
• at high risk of suicide or self-harm;
• having significant cognitive impairments;
• active substance abuse or dependence;
• currently undergoing other psychological or pharmacological treatments for depression or anxiety;
• pregnant individuals or those with postpartum depression, and those with serious physical health conditions that could impact mental health.

Sponsors & Collaborators

• Adai Technology (Beijing) Co., Ltd.: lead

Study Sites (1)

the First Hospital of China Medical University

Shenyang, Liaoning, China

Location

Related Publications (1)

• Shao X, Liu L, Zhu X, Tian C, Li D, Zhang L, Liu X, Liu Y, Zhu G, Li L. Personalized game-based digital intervention for relieving depression and anxiety symptoms: a pilot RCT. Npj Ment Health Res. 2025 Jul 3;4(1):27. doi: 10.1038/s44184-025-00141-x.

  PMID: 40610644 DERIVED

MeSH Terms

Conditions

Depression; Anxiety Disorders

Interventions

Behavior Therapy

Condition Hierarchy (Ancestors)

Behavioral Symptoms; Behavior; Mental Disorders

Intervention Hierarchy (Ancestors)

Psychotherapy; Behavioral Disciplines and Activities

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: TREATMENT
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 4, 2024
• First Posted: March 8, 2024
• Study Start: November 1, 2023
• Primary Completion: January 1, 2024
• Study Completion: February 1, 2024
• Last Updated: March 8, 2024

Record last verified: 2024-03

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)