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Internet-delivered psychological treatments (IDPTs) are built on evidence-based psychological treatment models, such as cognitive behavioral therapy, and are adjusted for internet use. The use of internet technologies has the potential to increase access to evidence-based mental health services for a larger proportion of the population with the use of fewer resources. However, despite extensive evidence that internet interventions can be effective in the treatment of mental health disorders, user adherence to such internet intervention is suboptimal.
This review aimed to (1) inspect and identify the adaptive elements of IDPT for mental health disorders, (2) examine how system adaptation influences the efficacy of IDPT on mental health treatments, (3) identify the information architecture, adaptive dimensions, and strategies for implementing these interventions for mental illness, and (4) use the findings to create a conceptual framework that provides better user adherence and adaptiveness in IDPT for mental health issues.
The review followed the guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The research databases Medline (PubMed), ACM Digital Library, PsycINFO, CINAHL, and Cochrane were searched for studies dating from January 2000 to January 2020. Based on predetermined selection criteria, data from eligible studies were analyzed.
A total of 3341 studies were initially identified based on the inclusion criteria. Following a review of the title, abstract, and full text, 31 studies that fulfilled the inclusion criteria were selected, most of which described attempts to tailor interventions for mental health disorders. The most common adaptive elements were feedback messages to patients from therapists and intervention content. However, how these elements contribute to the efficacy of IDPT in mental health were not reported. The most common information architecture used by studies was tunnel-based, although a number of studies did not report the choice of information architecture used. Rule-based strategies were the most common adaptive strategies used by these studies. All of the studies were broadly grouped into two adaptive dimensions based on user preferences or using performance measures, such as psychometric tests.
Several studies suggest that adaptive IDPT has the potential to enhance intervention outcomes and increase user adherence. There is a lack of studies reporting design elements, adaptive elements, and adaptive strategies in IDPT systems. Hence, focused research on adaptive IDPT systems and clinical trials to assess their effectiveness are needed.
Research accounts for internet-delivered psychological treatment (IDPT) as a useful therapeutic tool [
Some studies have found that providing therapist contact for online guidance and support during interventions increases adherence and effect sizes [
A systematic review by Christensen et al [
Most of the research examining the causes of low user adherence to IDPT has discovered that the reasons associated with patients were about personal and interpersonal competencies, and lack of resources rather than the diagnosis or health problem severity [
In this paper, we propose that in addition to these two factors (perception of treatment and personal situations), a third factor is contributing to user adherence: the adaptiveness of the IDPT system. There are two perspectives here: adaptiveness and information architecture (IA) [
To the best of our knowledge, limited research has examined the experience of nonadherence in the IDPT system based on IA and adaptiveness as affecting factors. In this study, we focus on reviewing the adaptive elements and IA in the current IDPT systems used for the treatment of mental illness. Our review shows that several different terms are being used to describe similar IDPT systems. Interventions involving the internet as the communication mechanism are referred to as web-based treatments, web-based interventions, online treatment, computerized psychotherapy, e-therapy, eHealth, internet-based cognitive behavioral therapy, digital interventions, web app–based psychotherapy treatments, therapeutic web-based interventions, eHealth interventions [
We conducted the review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [
We searched the databases recommended by Cochrane [
We included studies in which the articles met the following inclusion criteria: (1) discussed an intervention delivered through the internet (web- or mobile-based), (2) attempted to provide adaptive (dynamic, tailored, flexible) interventions by using adaptive strategies, (3) targeted a mental health disorder defined by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) [
The selection of studies took place in three phases based on the review of the title, keywords, abstract, and full text. Title and abstract screening were carried out blinded for author, journal, and date of publication. Any doubtful papers were included in the next phase, and disagreement was resolved through discussion. After identifying 3341 relevant papers in the initial database search, 372 duplicate papers were removed, and 2969 unique papers remained. In the screening step, the resulting list of 2969 papers was reviewed independently by the same two authors according to inclusion and exclusion criteria. By reviewing the title, abstract, and keywords, 105 eligible papers were retrieved. Two main reasons for the substantial exclusions were (1) the search engine returned the results containing any of the search terms, although they were logically connected, and (2) most of the papers were related to mental health without any reference to IDPT. Full texts were evaluated to determine the eligibility of the remaining papers. The full texts of the 105 eligible papers were assessed independently by the same authors. Any discrepancies between the authors regarding the selection of the papers were resolved through discussion. In total, 74 papers were excluded in this round, and the selection process led to the inclusion of 31 papers, as illustrated in
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram for this systematic review.
Data from the included studies were extracted, verified, and tabulated for review by the authors. From the selected studies, we chose to obtain the main adaptive elements, adaptive strategies used, adaptive dimension, and actor involved in adaptation.
For purposes of transparency and reproducibility of our study, we have published the resulting data, code, and procedures on GitHub [
A significant number of the included studies addressed depression (n=11) and anxiety disorder (n=7), followed by general mental health issues (n=8), such as well-being, mindfulness, and goal achievement. Furthermore, some studies reported the use of adaptiveness in other areas such as insomnia (n=2), social psychology (n=1), attention deficit hyperactivity disorder (n=2), posttraumatic stress disorder (n=2), suicidality (n=2), and substance misuse (n=1). The full list of types of mental health problems addressed in the relevant studies is presented in
Types of mental illness for which an adaptive system was built.
Mental illnesses | Study references |
Depression | Tsiakas et al, 2015 [ |
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Levin et al, 2018 [ |
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Burns et al, 2011 [ |
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Rebar et al, 2016 [ |
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Malins et al, 2020 [ |
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Van Gemert-Pijnen et al, 2014 [ |
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Lillevoll et al, 2014 [ |
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Achtyes et al, 2015 [ |
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Wallert et al, 2018 [ |
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Kop et al, 2014 [ |
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D’Alfonso et al, 2017 [ |
Anxiety disorder | Tsiakas et al, 2015 [ |
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Levin et al, 2018 [ |
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Walter et al, 2007 [ |
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Batterham et al, 2017 [ |
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Malins et al, 2020 [ |
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Achtyes et al, 2015 [ |
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Wallert et al, 2018 [ |
Insomnia | Forsell et al, 2019 [ |
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Erten-Uyumaz et al, 2019 [ |
Substance use | Batterham et al, 2017 [ |
Suicidality | Delgado-Gomez et al, 2016 [ |
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Batterham et al, 2017 [ |
Social psychology | Rachuri et al, 2010 [ |
Bipolar disorder | Dodd et al, 2017 [ |
Stress | Konrad et al, 2015 [ |
Posttraumatic stress disorder | Tielman et al, 2019 [ |
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Eisen et al, 2016 [ |
Smoking cessation | Lagoa et al, 2014 [ |
Attention deficit hyperactivity disorder | Nahum-Shani et al, 2012 [ |
General mental health | Iorfino et al, 2019 [ |
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Bannink et al, 2012 [ |
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Berrouiguet et al, 2018 [ |
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Ketelaar et al, 2014 [ |
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Coyle et al, 2010 [ |
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Kitagawa et al, 2020 [ |
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van Os et al, 2017 [ |
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van de Ven et al, 2017 [ |
Based on our findings, the communication media used to administer internet-facilitated interventions to patients can be classified into three categories: web apps, mobile apps, and computer games. A significant number of the included studies were based on web apps [
IA is concerned with the art and science of organizing and labelling components of web apps, intranets, software, and online communities to enhance their usability and accessibility. IA plays a vital role in web app development, and a good architecture can improve the ability of employees and customers to find information and decrease the app’s maintenance cost [
Types of information architecture used in the reviewed studies.
Information architectures | Study references |
Tunnel-based IA | Iorfino et al, 2019 [ |
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Konrad et al, 2015 [ |
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Batterham et al, 2017 [ |
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Kitagawa et al, 2020 [ |
Hybrid IA | D’Alfonso et al, 2017 [ |
Matrix IA | Levin et al, 2018 [ |
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Lagoa et al, 2014 [ |
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Van Gemert-Pijnen et al, 2014 [ |
Hierarchical IA | Tielman et al, 2019 [ |
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Bannink et al, 2012 [ |
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Berrouiguet et al, 2018 [ |
Not clear/not reported | Coyle et al, 2010 [ |
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Tsiakas et al, 2015 [ |
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Delgado-Gomez et al, 2016 [ |
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Walter et al, 2007 [ |
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Bannink et al, 2012 [ |
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Burns et al, 2011 [ |
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Rebar et al, 2016 [ |
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Ketelaar et al, 2014 [ |
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Malins et al, 2020 [ |
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Kitagawa et al, 2020 [ |
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van Os et al, 2017 [ |
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Lillevoll et al, 2014 [ |
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Dodd et al, 2017 [ |
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Achtyes et al, 2015 [ |
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Wallert et al, 2018 [ |
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Forsell et al, 2019 [ |
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Erten-Uyumaz et al, 2019 [ |
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Kop et al, 2014 [ |
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van de Ven et al, 2017 [ |
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Rachuri et al, 2010 [ |
The analysis of
Adaptive elements are the main components that are personalized for the user. As reported in a previous study [
Numerous studies (9/31, 29%) reported adapting the content of the intervention. However, most of these studies did not explicitly report the type of content, level of complexity, or modality (audio, video, presentation, pictures, assignments, activities, and assessments). Knowledge of the modalities of the content and their associated complexity provides insight into how interventions could be adapted and personalized for patients.
Another notable observation is that several studies (11/31, 35%) used feedbacks as adaptive elements. Numerous studies described the process of adaptive feedback in different forms, including sending personalized motivational messages [
Types of adaptive elements identified from the relevant studies.
Main adaptive elements | Study references |
Intervention content | Iorfino et al, 2019 [ |
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Lagoa et al, 2014 [ |
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Batterham et al, 2017 [ |
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Rebar et al, 2016 [ |
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Coyle et al, 2010 [ |
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Nahum-Shani et al, 2012 [ |
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Van Gemert-Pijnen et al, 2014 [ |
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D’Alfonso et al, 2017 [ |
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Kop et al, 2014 [ |
Content presentation | Iorfino et al, 2019 [ |
Feedback message, support | Iorfino et al, 2019 [ |
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Tielman et al, 2019 [ |
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Bannink et al, 2012 [ |
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Batterham et al, 2017 [ |
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Burns et al, 2011 [ |
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Ketelaar et al, 2014 [ |
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Malins et al, 2020 [ |
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Kitagawa et al, 2019 [ |
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Van Gemert-Pijnen et al, 2014 [ |
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Dodd et al, 2017 [ |
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van de Ven et al, 2017 [ |
Assessment tests | Iorfino et al, 2019 [ |
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van Os et al, 2017 [ |
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Van Gemert-Pijnen et al, 2014 [ |
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Achtyes et al, 2015 [ |
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Delgado-Gomez et al, 2016 [ |
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Walter et al, 2007 [ |
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Eisen et al, 2016 [ |
Behavioral activities (sleep pattern) | Erten-Uyumaz et al, 2019 [ |
Reminder messages (SMS text messages, emails, phone calls) | Burns et al, 2011 [ |
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Lillevoll et al, 2014 [ |
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Batterham et al, 2017 [ |
Exercises | Levin et al, 2018 [ |
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Konrad et al, 2015 [ |
Reports | Iorfino et al, 2019 [ |
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Burns et al, 2011 [ |
Not clear | Tsiakas et al, 2015 [ |
The way an adaptive system changes its behaviors depends on a multitude of factors: (1) users’ data and preferences, (2) goals of the intervention, (3) measures, (4) adaptation actors, and (5) adaptation strategies. We refer to these aspects as the dimensions of the adaptive IDPT system [
Dimensions considered for adaptation in the relevant studies.
Adaptation dimensions | Study references | |
User data and preferences (user context, needs, and location) | Iorfino et al, 2019 [ |
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|
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Delgado-Gomez et al, 2016 [ |
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Tielman et al, 2019 [ |
|
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Lagoa et al, 2014 [ |
|
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Walter et al, 2007 [ |
|
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Eisen et al, 2016 [ |
|
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Van Gemert-Pijnen et al, 2014 [ |
|
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Dodd et al, 2017 [ |
|
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Forsell et al, 2019 [ |
|
|
Erten-Uyumaz et al, 2019 [ |
|
|
Kop et al, 2014 [ |
|
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van de Ven et al, 2017 [ |
|
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Rachuri et al, 2010 [ |
|
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D’Alfonso et al, 2017 [ |
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|
|
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Psychometric tests/screening | Tsiakas et al, 2015 [ |
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Levin et al, 2018 [ |
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Bannink et al, 2012 [ |
|
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Batterham et al, 2017 [ |
|
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Berrouiguet et al, 2018 [ |
|
|
Burns et al, 2011 [ |
|
|
Rebar et al, 2016 [ |
|
|
Ketelaar et al, 2014 [ |
|
|
Coyle et al, 2010 [ |
|
|
Malins et al, 2020 [ |
|
|
Nahum-Shani et al, 2012 [ |
|
|
Kitagawa et al, 2019 [ |
|
|
Van Gemert-Pijnen et al, 2014 [ |
|
|
Achtyes et al, 2015 [ |
|
|
Wallert et al, 2018 [ |
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User behavior analysis based on interaction data | Berrouiguet et al, 2018 [ |
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Burns et al, 2011 [ |
Goals of intervention | Konrad et al, 2015 [ |
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Not clear | van Os et al, 2017 [ |
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Lillevoll et al, 2014 [ |
The relevant studies were mainly grouped into two clusters based on the choice of adaptive dimensions: user preferences (13/31, 42%) or outcome measures (14/31, 45%). Only 1 study used a goal-based adaptive dimension. Among the studies using user preferences, some studies [
The adaptive strategy indicates the techniques used to tailor the intervention. In a recent study [
Types of adaptive strategies found in the relevant studies.
Types of adaptive strategies | Study references |
Rule-based strategies | Iorfino et al, 2019 [ |
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Tsiakas et al, 2015 [ |
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Levin et al, 2018 [ |
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Delgado-Gomez et al, 2016 [ |
|
Tielman et al, 2019 [ |
|
Walter et al, 2007 [ |
|
Eisen et al, 2016 [ |
|
Bannink et al, 2012 [ |
|
Konrad et al, 2015 [ |
|
Batterham et al, 2017 [ |
|
Rebar et al, 2016 [ |
|
Ketelaar et al, 2014 [ |
|
Coyle et al, 2010 [ |
|
Malins et al, 2020 [ |
|
Nahum-Shani et al, 2012 [ |
|
Kitagawa et al, 2019 [ |
|
van Os et al, 2017 [ |
|
Van Gemert-Pijnen et al, 2014 [ |
|
Lillevoll et al, 2014 [ |
|
van de Ven et al, 2017 [ |
Predictive algorithm- or machine learning–based strategies | Tsiakas et al, 2015 [ |
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Lagoa et al, 2014 [ |
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Berrouiguet et al, 2018 [ |
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Burns et al, 2011 [ |
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Nahum-Shani et al, 2012 [ |
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Wallert et al, 2018 [ |
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Rachuri et al, 2010 [ |
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Erten-Uyumaz et al, 2019 [ |
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Kop et al, 2014 [ |
Recommendation-based strategies | D’Alfonso et al, 2017 [ |
General or unclear strategy | Dodd et al, 2017 [ |
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Achtyes et al, 2015 [ |
The list of strategies includes rule-based strategies, predictive algorithm-based strategies, and recommendation-based strategies. As expected, a significant number of studies (20/31, 65%) used some form of rule-based adaptation mechanism. For example, some studies [
In general, this systematic review shows that tailoring interventions according to patients’ needs and preferences has a positive effect on user adherence and hence treatment outcomes. Several studies reported that the personalization of interventions [
A total of 3341 studies were initially identified based on the inclusion criteria. Following a review of the title, abstract, and full text, 31 studies that fulfilled the inclusion criteria remained, most of which attempted to tailor interventions for mental illnesses. Approximately 68% (21/31) of the studies had a first author with a health care background. The most common adaptive elements were feedback messages to patients from therapists (11/31, 35%) and intervention content (9/31, 29%). However, how these elements contribute to the efficacy of IDPT in mental illness was not reported. The most common IA used was tunnel-based IA (4/31, 13%), while many studies (20/31, 65%) did not report the IA used. The rule-based technique was the most common adaptive strategy used in these studies (20/31, 65%). All the studies were broadly grouped into two adaptive dimensions based on user preferences or using performance measures such as psychometric tests.
Our findings show that web apps, mobile apps, and computer games are the primary platforms used to facilitate interventions. Apart from these, other communication media include robotics, virtual reality (VR) [
Mode of delivery for internet-delivered psychological treatments (IDPTs). AR: augmented reality; mHealth: mobile health; VR: virtual reality.
Although a significant number of the studies failed to report which IA was used in their IDPT system, IA is still present in all software systems. Understanding the IA of a system helps a user to store, find, and interpret information readily, as IA is the design principle that is applied to making information discoverable and understandable. Finding the underlying IA of the IDPT system can help in making systems accessible and discoverable for end users, and knowledge about information design, structure, organization, and labelling can facilitate the development and evaluation phase. As explained in a previous study [
The primary context for building adaptive IDPT systems is to assist patients suffering from mental health disorders to learn about and recover from their illnesses. An IDPT system provides this information by using different media and elements such as text, video, audio, pictures, presentations, feedback, reminders, reports, and others. These elements have their format, structure, metadata, volume, and dynamism (such as frequency of updates). It is essential to understand these elements (contents described by Pakkala et al [
The adaptive dimension provides the context for an adaptive IDPT system to tailor its behavior. A common way to tailor the behavior of the IDPT system is based on input regarding user preferences, measures (psychometric tests, user behavior analysis, and others), and goals, as shown in
Adaptive strategies provide a mechanism to present the right content to the right people based on their needs and preferences. While our review findings reveal that the rule-based adaptive strategy is the most widely adopted practice, other strategies, especially machine learning, are becoming highly prevalent. Given the premise that we can capture every digital footprint of a user, resulting in a complex and comprehensive data set, there is a possibility of using sophisticated machine learning or deep learning algorithms on the one hand, but it also raises an essential question about privacy on the other. In general, to build an adaptive IDPT system, it is crucial to understand which adaptive strategies can be used. Based on the selected strategies, one needs to collect and store the data. No matter which adaptive strategy is chosen, the adaption in an IDPT system is an iterative cycle where data is collected and preprocessed; preprocessed data are then analyzed and, based on the results of the analysis, an action is taken to tailor the intervention. However, how the data are analyzed and the result is extracted affect the way an IDPT system is developed, the choice of IA, the method of data storage, and other parameters.
Although the integration of health care systems has emerged as a policy for several health care agencies, there is a large gap between current policy, program implementation efforts, and evidence for health care integration. The results of our review led us to list the following challenges in current IDPT practice.
There is a lack of a standardized definition of the health care system and proper taxonomy to allow the grouping of similar interventions. As mentioned in the introduction, the use of nonstandard terms to refer to the same system causes inconsistencies and makes it hard to draw conclusions. Based on this challenge, several researchers [
The outcome of trials of IDPT systems has demonstrated comparable results as face-to-face therapies. However, despite considerable attention to IDPTs, user adherence is low, and there is remarkably less literature on the underlying science of the field of IDPT system design and development [
Technology has matured to the point where several researchers envision the creation of automated, adaptive IDPT systems that work without much human involvement. However, there are controversies between what is possible and what is acceptable in adaptive systems. Hence, it requires careful consideration of both ethical and legal issues; focusing solely on technological and operational perspectives can lead to low value or utility for patients. As a result, both information and communication technology (ICT) researchers and medical practitioners must consider the capabilities, limitations, and needs of patients when designing adaptive systems. The primary objective of the adaptive IDPT system is to tailor the intervention based on user needs or any other adaptive dimensions. The adaptive IDPT system can understand the user’s needs by creating detailed user profiling. User profiling includes storage of the patient’s previous diagnosis, sensitive personal information, as well as the current status. Moreover, to maximize the benefits of data-driven adaptiveness, the adaptive IDPT system needs to store interaction data, including the time of login, the frequency of login, and the interaction with the system at the granular level (clicks, keystrokes). For example, the study by Van Gemert-Pijnen [
It is not easy to predict how technologies will develop over time and whether these technologies will continue adapting to clinical use. However, based on the results of this systematic review, we outline some implications and future directions in the field of IDPT system development and innovation.
With an increasing trend in user adherence toward internet-delivered treatments on the one hand and the prevalence of the internet of things (IoT), with growth in ambient intelligence technology, on the other, there is an expectation that the IDPT system will flourish over time. A plethora of health care interventions delivered via the internet have a similar format, as most of them are based on psychoeducation. All such interventions attempt to create adaptive elements (see
We analyzed the state-of-the-art studies concerned with adapting psychological interventions. The analysis yielded the answers to the most critical questions, including (1) what are the essential elements that therapists wish to tailor? (2) what are the main dimensions in which these elements can be tailored to meet patients’ needs? and (3) what are the primary adaptive strategies used to trigger adaptation in those dimensions? Findings from the analysis helped to identify the essential variables that are associated with an adaptive system. As McGaghie et al [
While the current research evidence is fragmented about the benefit of an adaptive IDPT system on treatment outcomes, this review suggests that adaptive IDPT systems can benefit people with mental health issues in providing personalized psychoeducation. Such an education will help mental health patients to manage their illness. In addition, a high number of health care researchers have published about adaptive interventions, as shown in
The development of an adaptive IDPT system that increases user adherence and treatment outcomes requires more extensive research to establish clinical appropriateness. Given the potential benefit of the IDPT system for cost-effective delivery to the far-reaching population, further research should be conducted on how to personalize adaptive strategies. Furthermore, reporting back to the research community is the part of any discipline of transparency that keeps studies honest and accountable. In addition, it fits into the broader responsibilities of scientists to communicate their work and foster public understanding. Such understanding can be used by other researchers to gain insight into new research directions.
An immediate future task involves the creation of a conceptual framework for adaptive IDPT systems. In addition to this, we envision the development of domain-specific language that can model such an adaptive IDPT system. Furthermore, it is imperative from the review that there is a need for a comprehensive visual dashboard for therapists and patients where they can receive the intervention, monitor their symptoms, and manage their illness.
Given that the health ICT literature is quite diverse and extensive, the current study focused exclusively on internet-delivered interventions for mental health morbidities. Notwithstanding this limitation, this paper highlights the significance of the continued study of this intervention method. Another limitation is that our literature exploration only encompassed articles in the English language; therefore, it is plausible that some research conducted in other parts of the world and published in other languages were missed. A third limitation pertains to IDPT apps developed by industry that were not accessible for review. Hence, we have less knowledge about the adaptive elements involved in their architecture.
Adaptive psychological interventions tailor the type of content or tasks to individuals based on their needs and preferences in order to improve saliency and intervention efficacy. This systematic review describes the investigation and analysis of existing studies about adaptive psychological intervention delivered through the internet. The study outlines the main elements used in the process of adaptation, the IA used in the adaptive systems, the main dimensions of adaptation, and the main adaptive strategies. Based on these findings, we envision the development of a conceptual framework that researchers and clinicians can utilize to build adaptive models of several health care interventions.
The findings of our review indicate the use of web-based and mobile apps to deliver mental health interventions, such as for depression (most studied), anxiety, and others. However, a number of these studies did not report the IA used in their system, and those that did report mostly used tunnel-based systems. Similarly, several studies used rule-based adaptive strategies to adapt intervention based on performance measures such as psychometric tests. Feedback messages, reminders, and support were the most used adaptive element. Further study is required to explore the role of IA, adaptive elements, adaptive dimensions, and adaptive strategies in building a successful IDPT system. Knowledge about these core elements of the adaptive IDPT system can serve to create a conceptual framework that can be used for several health care interventions.
Search terms that were used to execute the search in different databases.
The main table that helped to extract the adaptive elements, adaptive strategies, information architecture, and other information about the internet-delivered psychological treatment system.
augmented reality
Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition
Fast Healthcare Interoperability Resources
Health Level Seven International
information architecture
information communication technology
internet-delivered psychological treatment
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
virtual reality
We owe a lot of thanks and gratitude to our colleagues for their help searching for publications, refining articles and cross-referencing the relevant papers. This publication is a part of the INTROducing Mental health through Adaptive Technology (INTROMAT) project (www.intromat.no), funded by the Norwegian Research Council (259293/o70). INTROMAT is a research and development project in Norway that employs adaptive technology for confronting mental health issues.
None declared.