Towards adaptive technology in routine mental health care

The need for adaptive technology

Currently, most of the available evidence-based online psychological interventions consist of ‘treatment packages’ characterized by uniform composition, intensity, and delivery procedures for all regardless of needs, goals and motivation across potential participants (one size fits all).^79,28,18 Consequently, about 20%–30% of the patients undergoing psychological treatments do not receive therapy that corresponds with their needs. ¹⁸ To address this challenge, studies in psychotherapy have investigated ‘what works for whom?’. ¹⁹ In IDPT pre-treatment information (e.g., age gender and symptom level) have been extensively studied. ²⁰ Nevertheless, more than 30 years of research has pointed to few consistent pre-treatment factors predicting treatment effects or treatment failures. ²¹ A more recent approach to the ‘what works for whom?’ question searches for in-treatment factors/mediators/proximal outcomes (such as knowledge acquisition, treatment adherence or engagement with the intervention) preceding distal treatment outcomes, such as reduced symptoms and increased quality of life). The in-treatment and proximal outcomes, such as adherence, may trigger adaptive elements, such as prompts. Non-adherence is frequent in IDPTs, it is not unusual with drop out rates as high as 50%. ²² In INTROMAT we will design adaptive IDPTs to reduce dropout and increase adherence and engagement among patients.

Research methods for adaptive treatments

In order to study the effects of proximal and distal outcomes, we need to supplement the current randomized controlled trials (RCT’s), the gold standard for studying predefined pre-treatment packages, with ‘Sequential Multiple Assignment Randomized Trials (SMART).^23–25. In SMART, the patients may be randomly presented with adaptive elements multiple times in one trial. The randomization can be based on active or passive data such as symptoms across time or engagement with the program. This approach allows us to study the effects of adaptive treatment strategies on the sequencing of treatment content (psycho-education vs exercises), prompts (frequency), or format (video/audio/ text). The PBA ²⁶ is a method for designing interventions that focus on adapting the intervention to the users’ needs thereby increasing its persuasiveness, feasibility and relevance. It’s focus is on user needs, their contexts, and perceptions of intervention elements in order to promote acceptability, usability, and user satisfaction. The PBA also identifies ‘guiding principles’ that inspire and help the intervention designer to address key context-specific behavioural issues in the intervention.

Towards a new infrastructure for adaptive IDPT

Current IT-infrastructures for mental health research are primarily developed for health researchers studying the effects of ‘treatment packages’ using ‘historical’ post-trial data in RCTs. Despite the strong correlation between treatment outcome and user adherence, ¹⁶ there are almost no systematic studies available reporting on the usability of the technology of the IDPTs. ²⁷ Furthermore, there is also a lack of studies systematically collecting and analysing user data to investigate possibilities for implementing IDPTs that can better respond to the needs of each user. ²⁸ Hence, by using the existing IDPT infrastructure, it is almost impossible to implement and evaluate the principles of SMART and PBA.

In order to facilitate for intelligent use of data during RCTs and online treatments, there is a need for infrastructure with the following characteristics:

The infrastructure should incorporates powerful analytic tools that can collect, store, analyse and feed data back to the user at the right time and in appropriate formats.

Reference Architecture

The proposed RA is based on the study presented in the work of Mukhiya et al. ³⁷ As shown in Figure 2, the architecture consists of four different layers: consumer architecture, requirement architecture, technical architecture, and artefacts produced in the INTROMAT project.

Library of reusable IDPT content:

During the INTROMAT project, we developed and validated seven IDPT interventions (see Methods section). Initially, the interventions were built as isolated IDPTs by different teams of experts in psychology and IT. During the project, we experienced the need for reuse and modification of existing treatment components. Hence, we developed a library of IDPT intervention contents to fetch interventions, their modules, and their tasks. The library is accessible through an API. A critical purpose is to facilitate the extraction of treatment materials such that content can be accessed from different platforms, such as web applications or mobile applications. In the work of Fuglestad ⁴⁰ we demonstrate a dimensional modelling approach to the ontological organization of IDPT components based on the user profiling dimensions of the open IDPT framework. We focused on two use cases for this approach, namely (a) facilitating reuse of treatment materials and (b) adapting existing treatment materials to user needs.

Artefacts produced

The last layer maps the artefacts produced with the RA. The artefacts are listed in Table 1. The primary idea is to list the artefacts and map them into the proposed RA. Interested readers will find the problem these artefacts solve, the study method, results and evaluation process of each of these artefacts in the studies referred to. We briefly introduce the clinical setting of these artefacts below (the index represents the row number in Table 1):

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Table 1.

A list of software artefacts that have been developed in the INTROMAT project.

Index	Artefacts	Aim	Technology	Data Store/Share	Research Contribution
1	Timeout App (ADHD)	To monitor arousal levels in adults with ADHD and use the data to deliver timely prompts to perform a skill-building exercise.	Empatica E4 wearable wristwatch was used to capture real-time physiological signals.	Data captured from wearable devices were sent to IBM cloud for processing via Telenor Shepherd Internet of Things (IoT) infrastructure.	A moving average algorithm was developed to detect significant changes in arousal and send alerts to patients. 41
2	VR App (Social Anxiety)	To create an in vitro home-based exposure scenario for adolescents with fear of public speaking.	A VR app and Empatica E4 were used to capture patient’s physiological signals during the VR therapeutic intervention.	Data captured from sensor devices were normalized and sent to data analytics in the Weka framework.	ML were applied to detect correlation of stress with physiological signals. 60 Participatory design was applied to design VR scenarios. 41
3	Motor activity monitor (Bipolar Disorder)	To monitor motor activity with the long-term goal to early detection of new manic and depressive phases.	Actigraph watch was used to capture bipolar patients’ motor activity signals on a daily basis.	14 days of motor activity data from 23 depressed patients and 32 healthy controls were captured and analysed in the Weka framework.	Different ML algorithms were applied to analyse the actigraph data to predict significant mode change.42,61
4	Chatbots for: 1) Self-assessment and 2) Group therapy (ADHD)	1) To provide self-assessment for adults with ADHD and 2) To investigate if chatbots for peer support in ADHD group therapy.	1) A chatbot was used to capture ADHD symptoms. 2) A low fidelity chatbot prototype was used for capturing user experience data	1) Chatbot data were sent to IBM Watson for analysis. 2) UX data from test and control group where collected and analysed in IBM Watson.	1) IBM Watson was used for detecting ADHD symptoms in chatbot data. 45 2) RtD applied to investigate if chatbots could facilitate peer support among adults with ADHD. 62
5	Patient dashboards for therapists (Depression)	To support therapists in guided IDPT to manage their work, and understand and prioritize their patients.	A dashboard was developed to present a generated view of patients’ treatments and interaction data to support therapist’s interaction with the system.	Data from the clinic management system and self-assessments are used for real-time presentation of patients’ interaction and symptom development.	IDPT treatment patterns was identified and represented by time series, interactive spider charts introduced, 46 further reusable visual components presented 47
6	OpenIDPT Framework	To create an open-source framework for building adaptive IDPT System.	A web application framework is created that can be extended and used for several similar health care cases.	It collects user profile data to personal adaption of interventions. Data are available in HL7 FHIR and GraphQL format.	Adaptation strategies can be decided by the therapists or an analytical server can be integrated for adaptation.48,37,28
7	Self-assessment App	To provide self-assessment for adults with mental health	A mobile application that provided psychometric questionnaire in HL7 FHIR format	Data captured from the mobile application was delivered using HL7 FHIR format for further study.	The open-source app can be used to build any self-assessment questionnaire and supports interoperability. 49
8	Gynea intervention (Cancer Survivors)	To design IDPT targeting women’s psycho-social needs during the follow-up period after treatment for gynecological cancer	Internet intervention including psycho-educational information, multimedia content, exercises, and weekly follow-up with a nurse	Semi-structured interviews was applied to evaluate the patients experience of the audiovisual narratives.	User Centered Design (UCD) 78 was applied to create audiovisual narratives to support reflection on cancer journeys 50
9	PRIORI and MAThyS App (Bipolar Disorder)	To collect mobile phone speech data from regular calls by out of hospital bipolar patients and analyse them to detect mood changes.	The PRIORI mobile phone app collects speech data from regular calls by out of hospital patients, and the MAThyS web app is used to collect weekly self-reports from PRIORI app users.	The PRIORI and MAThyS data were forwarded to the ‘Services for sensitive data’ (TSD), a secure data storage at the University of Oslo, the data were made available for data analytics	The MAThyS data will be used to label the speech data collected by the PRIORI app. 52 The labeled data will be used to train models to estimate the mood by classifying the speech data 51

INTROMAT: INTROducing Mental health through Adaptive Technology; IDPT: internet-delivered psychological treatment; PRIORI: Predicting Individual Outcomes for Rapid Intervention; MAThys: Multidimensional Assessment of Thymic States.

Timeout App (ADHD): This study uses the Empatica E4 wearable to capture physiological signals to monitor arousal levels in adults with ADHD. The IBM Cloud was used to preprocess the captured data and to produce timely prompts to deliver skill-building exercises to the participants. ⁴¹

Technical architecture considerations

This section outlines the technical considerations that we advocate as a requirement for building adaptive and scalable infrastructure in routine mental health care.

Handling of privacy and security

Privacy and security are of the highest importance when working with any health data, particularly within the domain of mental health. In the INTROMAT project, the clinical research trials had to get approval from the regional ethical committee for health research and follow the required ethical research standards. Moreover, the infrastructure should implement the state of the art techniques for privacy and security.

Due to the tight integration of actual treatment with interventions and the sensitive nature of the information involved, it is required to apply a structured approach to information management to maintain a high level of security throughout all infrastructure. Providing all services needed to research projects within one environment enables us to ensure adequate security without significantly restricting the freedom of interdisciplinary research initiatives. The following capabilities are vital to ensure privacy and security in such an environment:

Authentication: Authentication and access control is performed for all users trying to access resources within the infrastructure. The authentication layer supports external users with identities administered by third parties and national authentication services. According to Norwegian law, users must provide at least two authentication factors to acquire system access. All IoT devices, such as medical devices and wearables, have to be authenticated to access the infrastructure. An automated process will handle the registration of new IoT devices.

There are two different approaches regarding privacy-preserving data analysis. In the first alternative, the (processed) data are shared in a central location. However, we need to take the necessary measures to preserve the patients’ privacy in the dataset. This approach is generally known as privacy-preserving data sharing. Perturbation-based techniques, along with anonymization approaches, propose solutions for privacy-preserving data sharing, for example,^67–71. However, the trade-off between privacy and data utility in such approaches renders them impractical in many applications.

In the second alternative, we conduct privacy-preserving data analysis without sharing the data, for example, in the literary works.^72–76 Several mining algorithms can learn machine-learning models by receiving partial information about the data in the learning process instead of raw data. The party/parties holding data can participate in the learning process by merely sharing partial information instead of the raw data. Moreover, specific approaches adopt secure multi-party computation techniques for limiting the sharing of partial information. Hence, such techniques provide solutions for mining without sharing data or sensitive information and, consequently, preserve data subjects’ privacy. Privacy-preserving distributed machine learning approaches address the learning tasks without sharing the data while preserving the patients’ privacy.

Adapting interventions

We aim to deliver adaptive interventions based on the patients’ needs and preferences and in the context of any given situation. To do this, we need to create an adaptive IDPT system that can personalize psychological treatments based on a patient’s needs, contexts and preferences. An adaptive system needs to have a detailed profile of users to comprehend their status, requirements, contexts and priorities. User preferences, progress and needs are dynamic. Hence, it is essential to create, maintain, and update the user model. An adaptive system accumulates the user model data into two distinct approaches: (a) implicitly by observing user interactions and (b) explicitly requesting direct input from the user. This process is referred to as user profiling. The essence of the adaptation effect that a system can deliver depends on the user model’s information. A comprehensive profile is a primary basis for adapting an intervention in an IDPT context. Figure 3 adopted from the study ⁴⁸ depicts why user profiling is essential for creating adaptive content. Users interact with the intervention system and generate a large number of interaction logs. The analytics server stores, analyses, and processes these interaction logs to create a comprehensive user profile. Major user profile components include interests, knowledge, background, goals, individual traits, user context, user history and other demographic data. ³⁷ These user profiles serve as input for recommendation engines, AI-based predictive algorithms, and rule-based engines to create adapted content. Once a user profile is maintained, we can adapt content using several predictive algorithms, recommendation engines, or rule-based engines.

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Figure 3.

The figure depicts why user profiling is essential for creating adaptive content. ⁴⁸

User profiling, as aforementioned, could be built explicitly by asking the patients or using implicit interaction log data. For example, the analytics layer, as depicted in Figure 2 uses several AI-based approaches to extract user profile information.

Topics for future research

IDPT is still an emerging technology where, until now, the development has mainly been driven by health care practitioners in cooperation with the IT industry. In the INTROMAT project, we have experienced how MBSE, HCI and AI research can be used to improve today’s IDPT systems. We envision further research potentials in the IDPT domain and propose the following problems as candidates for future research:

More efficient data sharing and collaborations: We have experienced that treatment data are locked down in the current IDPT systems and not available for data analysis and ML. Hence, there is a need for timely privacy-preserving data sharing and data analytics to adapt the IDPT systems to the users’ needs.