Beyond Efficacy- Eliciting Preference for Face-to-face and Internet-based Psychotherapy Among People With Depression

Depression Clinical Trial

Official title: Beyond Efficacy- Eliciting Preference for Face-to-face and Internet-based Psychotherapy Among People With Depression: Using Discrete Choice Experiment to Advance Future Service Implementation

Status Recruiting

Clinical Trial Summary

The aim of this study is to strengthen the evidence base of clients' preferences of psychotherapy and to close the described literature gaps so as to inform public health resource reallocation and implementation of psychological services. The investigators aim to address the following research questions: 1. Which psychological service attributes exert the most influence on the service use decisions? 2. Can respondents be represented by latent classes on the basis of similar preference profiles? 3. Which attributes exert the most influence on the service utilization decisions of each latent class? 4. Will people with depression adopt Internet-based psychotherapy considering the long waiting time and high cost of conventional face-to-face psychotherapy? To systematically address the above questions, specific research objectives are defined as follows: 1. to examine the relative importance of a series of characteristics of psychological services (e.g., delivery modality, waiting time, out-of-pocket service fee, anonymity and referral methods) on the choices of psychological service using DCE. 2. to identify segments of people with depression with different service preferences using latent class model, because the relative weighting of service preferences may vary with demographic (e.g., gender, age, socioeconomic status, depression severity) and psychological characteristics (e.g., help seeking stigma); and 3. since DCEs need to be translated into improved services to be truly useful, and with reference to emerging interest in the development of digital mental health service for people with depression to solve the issue of long waiting time and cost in face-to-face psychotherapy, using a series of statistical simulations, we aim to estimate the percentage of people with depression in each latent class segment who would use psychotherapy with pre-defined treatment attributes packages.

Clinical Trial Description

1.1 Depression and Internet-based Psychotherapy as a way to improve Access of Care Depression is a common mental disorder with a prevalence of 8.8%. It is associated with a significant decrement of quality of life, ranked as the 3rd cause of burden of disease worldwide by WHO and will be ranked 1st by 2030(1). Nonetheless, 55.3% people with depression were unwilling to seek professional help for depression(2), and the median duration of delay in treatment ranges from 2 to 8 years(3,4). Although a preference for psychotherapy over pharmacological treatment was consistently evident(5), barriers to traditional face-to-face psychotherapy such as inconvenient location, long waiting time, high costs, low perceived need, preference to self-reliance, self-stigma, and feelings of embarrassment (6) deter many to access and adhere to treatment. Worse still, the number of individuals with mental disorders has far outnumbered the resources available in traditional models of delivering psychotherapy(7). Thus, effective psychotherapies need to be made increasingly available in a more cost-effective way using the Internet as an alternative service modality(8). Research suggests that the less labor-intensive Internet-based psychotherapies appear to be an effective strategy in increasing psychosocial services and reducing burden to the healthcare system. Numerous clinical trials have demonstrated the efficacy of Internet-based psychotherapies in treating depression. A systematic review of 32 randomized controlled trials evaluating Internet-based cognitive behavioral therapy for symptoms of depression in 5642 participants found a mean effect size of g=0.67 (8). A recent meta-analysis also found that Internet-based cognitive behavioral psychotherapy has comparable effects to traditional face-to-face psychotherapy, even when they are compared directly to each other(9). 1.2 Beyond Efficacy, the Importance of Preference in Client-Centered Services Despite the growing evidence on the equivalence of efficacy in using the Internet to deliver psychotherapy as compared to its face-to-face counterpart, an essential prerequisite to actualize the full potential of any effective treatment is that individuals are willing to utilize it. This proposition is in accordance with the Diffusion of Innovation Theory and The Unified Theory of Acceptance and Use of Technology; potential users need to have an adequate level of service preference and acceptance prior to the initiation and uptake of the novel treatment(10).With the awareness of translational issues (e.g., poor uptake) occurring from efficacy trials to the real world and the recognition that healthcare interventions are valued for more than just clinical health outcomes(11), research in understanding what patients* care in health services had a ten-fold increase in the last two decades(12). In fact, involving patients' preferences in the determination of best care not only leads to better service implementation, it also promote policy decisions that embrace patient values(13). Unmet preferences partly explain the sub-optimal uptake and poor adherence of psychotherapy for major depressive disorder(14,15). Addressing client preferences for psychotherapies could help overcome the treatment gap by guiding policy decisions and restructuring psychological services offered in the society(11). Characterizing and prioritizing potential service users' demand for evidence-based psychotherapies could provide insights to such significant and practical questions as "To whom should Internet-based psychotherapy be implemented?", "How should resources be allocated between Internet-based and face-to-face services to commensurate with users' preferences?" "Can the new service modality reach people who have high levels of stigma or prefer managing their mental health anonymously through an online program?(16)". Unsurprisingly, research found that accounting for patients' preference in service planning could lead to improved service utilization and decreased dropout(17). The UK National Health Services also explicitly stated its "services must reflect, and should be coordinated around and tailored to, the needs and preferences of patients". Furthermore, respect of users' autonomy and self-determination is recognized in international Code of Ethics including those of the American and the Australian Psychological Society. Patients' preference was also identified as one of the six aims for high-quality new health system for the 21st century in the Institute of Medicine report, Crossing the Quality Chasm. 1.3 Using Discrete Choice Experiment (DCE) to understand client preferences To date, client preferences in psychological services have primarily been assessed using qualitative methods or quantitative measures of acceptability or attitudes(18). Studies suggested the mean preference strength of Internet-based psychotherapy was lower than traditional psychotherapy, yet comparable to that of medication. A substantial portion of people with depression (38%) reported likely or very likely to consider Internet-based interventions (18). For those preferring traditional face-to-face approaches, more than a third of them indicated that they are likely to use online services in the future(19). In a recent preference study in Canada, although most postsecondary students preferred face-to-face psychotherapy over the digital mental health service, when students considered potential waiting periods, a greater percentage of students opted for Internet-based psychotherapy over waiting for face-to-face psychotherapy (20). Although preference studies using simple Likert scale ratings provided certain insight into desirable service, they lacked details on the underlying attributes that drive user preferences, and they did not quantitatively assess the relative importance of each attribute (e.g., 'Is the treatment modality, waiting duration, or cost the most important?') or the degree to which clients would be willing to trade-off one treatment characteristic for another (e.g., 'Would clients be willing to receive services via the Internet as opposed to in-person if it reduces cost/waiting time?'). Without this information, researchers and policy makers have limited guidance on how best to design best services that align with client preferences. An alternative to preference studies is discrete choice experiment (DCE), which can be used to quantitatively, experimentally and systematically understand clients' preference. DCE is a valid, reliable, and widely applied survey methodology for eliciting patients' preferences for health care(21). DCE is grounded in random utility theory that posits individuals choosing attributes that maximize the option utility. In DCE, respondents will be asked to complete a series of hypothetical choice tasks (see Figure 2 for example). Within each task, the respondents select their preferred option from alternatives with varying service characteristics. DCE is analogous to real-world decision making and offer advantages over traditional survey methods in reducing superficial decision-making, halo effect and social desirability bias, and allowing a better understanding of the underlying reasons of choice behaviors(22). Ratings often produce high scores across favorable attributes, reflecting general desire for services that encompass all positive characteristics. However, the scarcity of resources often necessitates trade-offs between attributes, and DCE could provide important information to inform services decision making on what service attributes are to be prioritized for improvement(21). Notably, DCE is simple to administer, with studies showing individuals with severe mental illnesses capable of completing DCE and making rational decisions (23), and choice methods has been found to adequately predict actual behavior(24). In fact, the United States Food and Drug Administration has endorsed DCEs as the 'favored' method for assessing patients view on harm and benefit profile of health care products. Similarly, data derived from DCEs also played a significant role in informing and supporting health care decision making and regulation(25). In the field of academia, researchers have applied DCEs to identify attributes that impact patient preferences for breast cancer screening programs (26), HIV prevention (27), rheumatoid arthritis treatment(28), and vaccination(24). Given DCE can capture the relative importance of the studied attributes, intervention researchers and policy makers can prioritize the changes related to the most important attribute in psychotherapy in service development and implementation. 1.4 The Research Gap: What about relative significance of important service attributes? Hitherto, only a handful of DCEs have been conducted on psychological services. Prioritizing services on the basis of clinical efficacy and cost-effectiveness often overlooks other important factors relevant to the clients. Capturing clients' choices in psychotherapy in light of the new development in Internet-based interventions is of paramount importance and urgency to optimize service implementation(11). To the best of our knowledge, only one study to date has explicitly examined how people with depression trade off the pros and cons of psychological service options with varying service attributes(29). However, the study adopted forced choice tasks without an opt-out alternative, making choice sets less realistic and at risk of preference weight over-estimation (30,31). Moreover, no existing DCE on psychological services has incorporated all the three main treatment attributes for depression, which are outcome (e.g., effectiveness), process (e.g., treatment modality, waiting time), and cost attributes(32) 1.5 Study Aim The aim of this study is to strengthen the evidence base of clients' preferences of psychotherapy and to close the described literature gaps so as to inform public health resource reallocation and implementation of psychological services. The investigators aim to address the following research questions: ‣ Which psychological service attributes exert the most influence on the service use decisions? ‣ Can respondents be represented by latent classes on the basis of similar preference profiles? Which attributes exert the most influence on the service utilization decisions of each latent class? ‣ Will people with depression adopt Internet-based psychotherapy considering the long waiting time and high cost of conventional face-to-face psychotherapy? To systematically address the above questions, specific research objectives are defined as follow: 1. to examine the relative importance of a series of characteristics of psychological services (e.g., delivery modality, waiting time, out-of-pocket service fee, anonymity and referral methods) on the choices of psychological service using DCE. 2. to identify segments of people with depression with different service preferences using latent class model, because the relative weighting of service preferences may vary with demographic (e.g., gender, age, socioeconomic status, depression severity) and psychological characteristics (e.g., help seeking stigma); and 3. since DCEs need to be translated into improved services to be truly useful, and with reference to emerging interest in the development of digital mental health service for people with depression to solve the issue of long waiting time and cost in face-to-face psychotherapy, using a series of statistical simulations, the investigators aim to estimate the percentage of people with depression in each latent class segment who would use psychotherapy with pre-defined treatment attributes packages. Afterwards, the investigators will invite at least 20 participants with at least mild level of depressive symptoms to participate in individual interviews. The interviews will be in semi-structured format. ;

Related Conditions & MeSH terms

• Depression
• Depressive Disorder

• NCT number: NCT05449561
• Study type: Observational
• Source: Chinese University of Hong Kong
• Contact: Kelly Chan
• Phone: +852 95706418
• Email: kellychan@cuhk.edu.hk
• Status: Recruiting
• Start date: July 26, 2022
• Completion date: July 26, 2023