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Overview of the expert consensus on the digital therapeutics in addictive-related disorders
  1. Wei Hao1,
  2. Xuyi Wang1,
  3. Dai Li2 and
  4. Gang Wang3
  1. 1 Mental Health Institute, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
  2. 2 Adai Technology Beijing Co, Beijing, China
  3. 3 Wuhan Mental Health Centre, Wuhan, Hubei, China
  1. Correspondence to Professor Wei Hao; weihao57{at}csu.edu.cn

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Background

Addictive disorders have gained worldwide attention. The Chinese Association of Drug Abuse Prevention and Treatment, along with the consensus panel on digital therapeutics (DTx) for addictive disorders, has published an expert consensus on DTx for addictive disorders.1 This consensus discusses and summarises the current research and application status of DTx for addictive disorders. It identifies its clinical value, application directions, research and development principles, and future prospects. As the consensus is published in Chinese, it may not be easily accessible to an international audience. To address this, we present here an overview of the expert consensus on DTx for addictive disorders in China. The recommendations from the consensus are summarised in table 1.

Table 1

Summary of the recommendations in the consensus

Substance-related and addictive disorders have a significant impact on both physical and mental health and represent a contributing factor to crime.2 According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, substance-related and addictive disorders refer to a broad range of substance-related disorders involving 10 distinct classes of drugs, including opioids, methamphetamines and alcohol.3 The detoxification process for substance-related and addictive disorders consists of three phases: physiological recovery to relieve withdrawal symptoms, psychological rehabilitation to reduce mental dependence and social function restoration to facilitate reintegration into daily life. The second phase, psychological rehabilitation, is often the most challenging.4 Conventional treatments such as pharmacotherapy, psychotherapy and physical therapy face various limitations, including limited effectiveness, a shortage of skilled professional staff, high costs, long treatment duration and high dropout rates.5 DTx, driven by recent digital advancements, have emerged as a potential solution to these challenges. They are convenient, easy to implement and affordable, addressing the uneven distribution of conventional healthcare resources.

DTx refer to digital technology measures supported by evidence-based medicine and driven by software and algorithms for the treatment, prevention, screening, evaluation and management of diseases.5 They have shown effectiveness in treating substance addictions and behavioural addictions.6 DTx for substance addictions are used in a variety of settings, including community rehabilitation centres and prisons. DTx can be used at different stages of intervention, from prevention education and treatment to follow-up management. Moreover, DTx can be used alone or alongside conventional treatment methods.7 DTx products, including reSET and reSET-O from Pear Therapeutics, as well as China’s ‘Happy Quit’, have been successfully applied to treat various addictive disorders.7 8

However, DTx for addictive disorders face several challenges. First, the efficacy of some DTx products is not established, requiring evaluation following evidence-based principles in randomised controlled trials. Second, DTx products may encounter issues with poor patient adherence. Third, there is a lack of research and development industry standards for DTx products. Lastly, the collection and processing of large amounts of data by DTx products raise concerns regarding information security and privacy.

DTx intervention components

Evidence-based psychological and behavioural interventions for addiction include behavioural contingency management (CM), cognitive–behavioural therapy (CBT), motivation enhancement therapy and cognitive rehabilitation therapy.1 CM is an approach that increases positive behaviour over time, thereby reducing drug use and improving occupational and social support.9 10 DTx, like reSET and Wangli Harbor, effectively apply CM treatment as an incentive system that combines CM methodology with drug therapy to improve outcomes.7 8 CBT is effective in various addiction treatments and is a primary component of DTx products. It focuses on identifying and changing negative emotions, behaviours and thoughts related to addiction, and providing coping methods.11 Motivation enhancement therapy, which focuses on patients’ motivation, can help improve patients’ mental health and quality of life, particularly in cases of opioid addiction.12 Cognitive rehabilitation therapy uses digital technology like artificial intelligence and virtual reality to correct addiction-related cognitive biases and improve cognitive deficits.8 Additionally, DTx for addiction can integrate multiple psychotherapy techniques, taking into account the external social environment, interpersonal relationships and individual negative emotions causing poor withdrawal effects, prolonged treatment and frequent relapses. Techniques such as exposure therapy, aversion therapy and interpersonal therapy are designed to improve negative emotions, boost treatment confidence and compliance, and consolidate treatment effects.

DTx screening and assessment components

Digital screening and assessment tools for addiction are a crucial component of DTx for addictive disorders. Digital evaluations, which may include online adaptive scale assessments or objective assessments, can capture real-time physiological data from patients.13 By combining these data with artificial intelligence algorithms, these tools can offer deeper insights into addictive behaviours and addiction mechanisms, thereby improving assessment accuracy.14 One study found that a machine learning classification model could accurately distinguish between the electroencephalogram and electrodermal responses of individuals with methamphetamine addiction and those without, with 90% accuracy.15 By using electroencephalography and machine learning algorithms, the model could also identify if individuals with methamphetamine addiction were in a craving scenario with 92% accuracy.16 Digital assessment tools can facilitate timely problem identification, personalised treatment plans, progress monitoring, treatment effect evaluations and scientific research advancements. Hence, DTx for addictive disorders should integrate assessment and screening functions.

Evaluation criteria for DTx

DTx for addictive disorders need thorough review and testing before clinical use. In the development and application process, several factors should be evaluated, including quality, efficacy, compliance, accuracy, treatment content, adverse reactions and privacy security. The results should be published in peer-reviewed academic journals to ensure transparency and credibility. The suggested standards in the consensus include passing national quality inspection and meeting product standards, as well as demonstrating effectiveness through large-sample, multicentre trials and achieving comparable with or better results than conventional treatments in terms of effectiveness, convenience and safety. Patient compliance should not be significantly lower than conventional therapies. For accuracy, assessments should ideally achieve a rate of 85% or above. Treatment methodologies must be evidence-based, with continuous monitoring of adverse reactions. Furthermore, DTx products must comply with the highest information system protection standards to ensure data security and patient privacy.

Ethics and security

Models and algorithms in DTx for addictive disorders collect and process patient data. This process must be handled ethically and securely. Key guidelines for protecting privacy and maintaining data security include obtaining informed consent from patients, anonymizing personal information, using encryption technologies, and implementing strict access controls. It is also recommended to follow the principle of data minimisation, that is, only collecting data necessary for treatment, along with establishing a defined data retention period and conducting regular audits for data security.1 Furthermore, it is crucial to ensure compliance with relevant regulations and emphasise patient education about self-protection and appropriate software use.

Vision and conclusions

DTx is a recommended non-pharmacological treatment for addiction, which can be used in various stages of treatment and in different settings. Success in real-world applications of DTx depends on several factors, including improved training for medical staff, increased public awareness, implementation of pilot projects in public hospitals and primary care institutions, and enhancement of screening assessment accuracy to support personalised treatment using clinical data. Integrating DTx and conventional therapy is crucial for improving treatment efficiency and expanding coverage.6 17 A more closely coordinated system can deliver higher-quality care for addictive disorders. Ethical standards, particularly those regarding patient privacy and security, must be strictly followed. Future development of DTx should adhere to industry standards and policies, improve product quality, expand usage scopes, and ensure local and cultural adaptability while managing potential risks from technology.

Ethics statements

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References

Wei Hao graduated with a bachelor's degree from Anhui Medical University, China in 1982. He obtained a master's degree and a PhD degree at Hunan Medical University, China in 1987 and 1997, respectively. He currently is a professor in the Department of Psychiatry and the Deputy Director of the Mental Health Institute at the Central South University, Changsha, China. He serves as the Director of the World Health Organization (WHO) Collaborating Centre for Psychosocial Factors, Substance Abuse and Health. Prof. Hao is a member of WHO Expert Advisory Panel on Drug Dependence and Alcohol Problems (2006–present); a member of the Working Group on the Classification of Substance Abuse for the eleventh revision of the International Classification of Diseases (ICD-11), WHO (2011–present), and the President of Chinese Association of Drug Abuse Prevention and Treatment (2015–present). His main research interests include psychiatry and addictions.


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Footnotes

  • Contributors XW and WH drafted the manuscript. DL, GW, along with XW and WH, critically reviewed the content and provided guidance and support throughout the writing process.

  • Funding This study was supported by the National Key Research and Development Program of China (grant 2023YFC3304200).

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.