Study Results
Results pending
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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Recruitment Status
COMPLETED
Clinical Phase
NA
Total Enrollment
148 participants
Study Classification
INTERVENTIONAL
Study Start Date
2022-03-20
Study Completion Date
2024-12-20
Brief Summary
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The goals of this study is to examine whether treatment preferences shift after receiving a clients' decision aids about psychotherapy in digital and in in-person format.
Detailed Description
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1.1 The importance of clients' Preference and Acceptability While introducing and implementing E-mental health service, clients' preference and acceptability should not be neglected. It is increasingly acknowledged that acceptability should be considered when designing, evaluating, and implementing novel healthcare interventions. Treatment acceptability has also been framed as a key factor for successful dissemination and implementation of any new health service model, because A given treatment may be clinically effective, yet unacceptable for clients and patients(Kaltenthaler et al., 2008; Wallin, Mattsson, \&Olsson, 2016). Besides, clients' preference and acceptability of may not merely influence satisfaction, but also have significant implications for adherence and outcome (Gelhorn, Sexton, \&Classi, 2011). For example, according to a meta-analytic review across various treatment formats, individuals who matched with preferred treatment had a higher chance of showing improvements and were almost half as likely to drop out of treatment compared to those whose preferred choice of treatment were not offered(Swift \&Callahan, 2009). Previous studies also suggested that preferences and acceptability are significantly related to important process and outcomes of treatment such as service initiation, adherence, compliance, engagement, and the development of working alliance (Gelhorn et al., 2011). Thus, not only did the German National Care Guideline (S-3-Guideline, 2nd edition) for unipolar depression recommends a seven-step model of shared decision-making for health care service institutes and demands that the assessment and consideration of client preferences should be an indispensable step within the decision making process(German Medical Association, 2015). This year NICE has also published new guidelines recommending shared decision making be part of everyday practice across all healthcare settings(National Institute for Clinical Excellence, 2021).
Nevertheless, in the field of E-mental health for depression, studies investigating treatment preferences are not abundant and have predominantly focused on contrasting the choice between psychotherapy and pharmacological therapy (Raue, Schulberg, Heo, Klimstra, \&Bruce, 2009; Steidtmann et al., 2012). The homogenous scope of studies leaves new treatment modality like the use of technology aside, although there were still a handful of articles on this topic. For example, Renn et. al (2019) found that 44.5% of participants preferred in-person psychotherapy and 25.6% preferred self-guided digital treatment; March et. al (2018) found a significant proportion of respondents (39.6%) endorsed intentions to use e-mental health services if experiencing mental health difficulties in the future; yet Musiat, Goldstone, \&Tarrier (2014) reported a low likelihood of using computerized treatments for mental health in the future, and contrarily McCall, Sison, Burnett, Beahm, \&Hadjistavropoulos (2020) found that vast majority of participants (93%) reported that they would access E-mental health service if they needed help with mental health problems. Although most studies reported that substantial amount of people indicates a willingness to use digital intervention, face-to-face psychotherapy, still, appeared to be the more preferred option(Renn et al., 2019). Thus, more research is needed to ascertain why people with depressive symptoms maintained a preference for in-person psychotherapy as opposed to the equally effective (Andersson, Titov, Dear, Rozental, \&Carlbring, 2019) and yet cheaper options in digital form(Axelsson, Andersson, Ljótsson, \&Hedman-Lagerlöf, 2018), and to examine what determinants facilitate its acceptance. Nevertheless, most of the current evidence on E-preference and acceptability were based on community sample. Not using samples with depressive symptoms above clinical threshold may limit the ecological validity of any conclusions drawn, as participants had to "imagine" whether they would use the services if they were undergoing a depressive episode, which could be cognitively demanding.
In fact, integration of digital treatment into health care systems is no small investment, especially when advanced techniques in computational and data science are increasingly incorporated in the development of the E-treatments(Chien et al., 2020). It is thus worthwhile to study both the "within" treatment determinant of E-service acceptance, and "between" treatment determinants of E-service as a preferred option so as to facilitate dissemination in real world setting. Understanding the "within" treatment determinants could help us formulate general direction in marketing E-mental health service for those who are in contemplation of trying E-mental health, while understanding the "between" treatment determinant could inform our direction in direct-to-consumer marketing or social campaigns (Baumeister et al., 2014) that aim at increasing the market share of E-mental health services by convincing traditional service preferers to use E-mental health services. Eventually, with more potential service users who are flexible in treatment modality, or prefer E-service, facilitation of dissemination of E-mental health service could be achieved by creating increased "pull demand", such that demand are created from consumers and to be responded by clinical providers, decision makers or stakeholders(Santucci, McHugh, \&Barlow, 2012).
Apart from the readiness for E-mental health among the general population, it is also important to understand if the population who are "hard to reach" are also ready for E-mental health service, given digital mental health interventions are often suggested to be able to increase reach and access to special groups who may less well served by traditional mental health services (for example, people with financial difficulties(Andrade et al., 2014), men with depression or endorse masculinity norm(Seidler, Dawes, Rice, Oliffe, \&Dhillon, 2016), and people with high level of stigma(Clement et al., 2015)). It is often assumed that E-mental health interventions are associated with a number of benefits over traditional face-to-face care(P.Musiat \&Tarrier, 2014). While it may be theoretically true that e-mental health interventions increased anonymity, increased convenience with regards to time and location of treatment, reduced treatment cost and certain attitudinal barriers (Andersson et al., 2019; Spurgeon \&Wright, 2010), it is unclear whether these "added benefits" enable individuals carrying the "hard to reach" characteristics prefer or accept E-service. It has also been suggested that the current evidence base for these "collateral outcomes" is sparse (P.Musiat \&Tarrier, 2014), and the benefit of digital health interventions should be based on evidence, otherwise the "hard to reach" may left unreached when they are assumed to be reached by digital health interventions.
1.2 The applicability of Decision Aids (DAs) in clarify preference of psychotherapies Another important and yet unexplored issue on clients' preference of E-mental health service for depression is the applicability of Decision Aids (DAs). Making decision of health management, especially a preference sensitive one, requires skills. Decision makers of health services first need to acquire information of available option, then they have to identify, understand, and evaluate the options, and finally they need to select the best option with the consideration of personal situations and values. In the last decade, active participation of clients and patients in the decision making process regarding their health care has been increasingly advocated(Berry, Beckham, Dettman, \&Mead, 2014). One of the influential conceptual models proposed within client-centered perspective of health care is the shared decision-making model. Shared decision-making model is a process of joint deliberation and collaboration between the health service providers and the clients in order to reach a consensus about treatment decisions. In this dyadic interaction, health service providers offer technical information about the disease or health condition, the benefits, and risks of the available therapeutic options, whereas the clients or patients provide information about their beliefs, concerns, values, and preferences about the consequences of those options(Joseph-Williams, Elwyn, \&Edwards, 2014). Shared decision-making model is especially relevant when evidence indicated that available treatments showed a similar balance between benefits and risks, and when there is potential trade-off between different attributes of treatment options. In light of the above model, patients decision aids (DAs) are designed to promote and facilitate shared decision-making and help clients to make informed choices(Coulter et al., 2013). These materials are developed in different formats (e.g., paper and pen instruments, videos, audio, website and interactive software), and can be used alone by the client or in interaction with the health service providers. DAs include explanations about treatment options, describing the benefits and harms based on the scientific evidence, and characteristics of health service based on local situations. They also encourage patients to think about their own values and preferences regarding the benefits, risks, and different aspects of the different treatment options, and how the choices could influence their lives and well-being(Fagerlin et al., 2013). Recent systematic reviews show that DAs are effective in improving patients' knowledge about available treatments, and reduced decisional conflict (i.e., uncertainty about the course of action to take). They also have shown to reduce the proportion of people who were passive and undecisive in decision making after deliberation(Stacey et al., 2017).
In the specific area of depressive disorders, results show that a majority of people with depression are interested in receiving information about their illness and participating in shared and informed decision making(Loh et al., 2004; Perestelo-Perez et al., 2017). Unfortunately, studies found that people with depression often perceived a lesser involvement in decisions than they desire (Delas Cuevas, Peñate, \&deRivera, 2014; Patel \&Bakken, 2010). Moreover, despite this unmet demand, and while DAs had been widely and successfully adopted in the multiple arenas of physical health (such as, breast cancer treatment(Savelberg et al., 2017), HIV preexposure prophylaxis(Sewell et al., 2021), colon cancer screening(Miller et al., 2011)(see figure 1), and smoking cessation(Gültzow, Smit, Hudales, Dirksen, \&Hoving, 2020)), there have been very few studies that have assessed the effectiveness of DAs in the field of depressive disorders. To our best knowledge no study has included E-mental health service in DAs for depression even when psychotherapy in E-format had been recommended by NICE for over a decade(Nice, 2009), and the effects of DAs on preference of psychological treatments and decisional conflict remain largely unknown.
1.3 Study Goal and Objective The goal of this study is to examine whether treatment preferences shift after receiving a clients' decision aids about psychotherapy in digital and in in-person format.
Nevertheless, in the field of E-mental health for depression, studies investigating treatment preferences are not abundant and have predominantly focused on contrasting the choice between psychotherapy and pharmacological therapy (Raue, Schulberg, Heo, Klimstra, \&Bruce, 2009; Steidtmann et al., 2012). The homogenous scope of studies leaves new treatment modality like the use of technology aside, although there were still a handful of articles on this topic. For example, Renn et. al (2019) found that 44.5% of participants preferred in-person psychotherapy and 25.6% preferred self-guided digital treatment; March et. al (2018) found a significant proportion of respondents (39.6%) endorsed intentions to use e-mental health services if experiencing mental health difficulties in the future; yet Musiat, Goldstone, \&Tarrier (2014) reported a low likelihood of using computerized treatments for mental health in the future, and contrarily McCall, Sison, Burnett, Beahm, \&Hadjistavropoulos (2020) found that vast majority of participants (93%) reported that they would access E-mental health service if they needed help with mental health problems. Although most studies reported that substantial amount of people indicates a willingness to use digital intervention, face-to-face psychotherapy, still, appeared to be the more preferred option(Renn et al., 2019). Thus, more research is needed to ascertain why people with depressive symptoms maintained a preference for in-person psychotherapy as opposed to the equally effective (Andersson, Titov, Dear, Rozental, \&Carlbring, 2019) and yet cheaper options in digital form(Axelsson, Andersson, Ljótsson, \&Hedman-Lagerlöf, 2018), and to examine what determinants facilitate its acceptance. Nevertheless, most of the current evidence on E-preference and acceptability were based on community sample. Not using samples with depressive symptoms above clinical threshold may limit the ecological validity of any conclusions drawn, as participants had to "imagine" whether they would use the services if they were undergoing a depressive episode, which could be cognitively demanding.
In fact, integration of digital treatment into health care systems is no small investment, especially when advanced techniques in computational and data science are increasingly incorporated in the development of the E-treatments(Chien et al., 2020). It is thus worthwhile to study both the "within" treatment determinant of E-service acceptance, and "between" treatment determinants of E-service as a preferred option so as to facilitate dissemination in real world setting. Understanding the "within" treatment determinants could help us formulate general direction in marketing E-mental health service for those who are in contemplation of trying E-mental health, while understanding the "between" treatment determinant could inform our direction in direct-to-consumer marketing or social campaigns (Baumeister et al., 2014) that aim at increasing the market share of E-mental health services by convincing traditional service preferers to use E-mental health services. Eventually, with more potential service users who are flexible in treatment modality, or prefer E-service, facilitation of dissemination of E-mental health service could be achieved by creating increased "pull demand", such that demand are created from consumers and to be responded by clinical providers, decision makers or stakeholders(Santucci, McHugh, \&Barlow, 2012).
Apart from the readiness for E-mental health among the general population, it is also important to understand if the population who are "hard to reach" are also ready for E-mental health service, given digital mental health interventions are often suggested to be able to increase reach and access to special groups who may less well served by traditional mental health services (for example, people with financial difficulties(Andrade et al., 2014), men with depression or endorse masculinity norm(Seidler, Dawes, Rice, Oliffe, \&Dhillon, 2016), and people with high level of stigma(Clement et al., 2015)). It is often assumed that E-mental health interventions are associated with a number of benefits over traditional face-to-face care(P.Musiat \&Tarrier, 2014). While it may be theoretically true that e-mental health interventions increased anonymity, increased convenience with regards to time and location of treatment, reduced treatment cost and certain attitudinal barriers (Andersson et al., 2019; Spurgeon \&Wright, 2010), it is unclear whether these "added benefits" enable individuals carrying the "hard to reach" characteristics prefer or accept E-service. It has also been suggested that the current evidence base for these "collateral outcomes" is sparse (P.Musiat \&Tarrier, 2014), and the benefit of digital health interventions should be based on evidence, otherwise the "hard to reach" may left unreached when they are assumed to be reached by digital health interventions.
1.2 The applicability of Decision Aids (DAs) in clarify preference of psychotherapies Another important and yet unexplored issue on clients' preference of E-mental health service for depression is the applicability of Decision Aids (DAs). Making decision of health management, especially a preference sensitive one, requires skills. Decision makers of health services first need to acquire information of available option, then they have to identify, understand, and evaluate the options, and finally they need to select the best option with the consideration of personal situations and values. In the last decade, active participation of clients and patients in the decision making process regarding their health care has been increasingly advocated(Berry, Beckham, Dettman, \&Mead, 2014). One of the influential conceptual models proposed within client-centered perspective of health care is the shared decision-making model. Shared decision-making model is a process of joint deliberation and collaboration between the health service providers and the clients in order to reach a consensus about treatment decisions. In this dyadic interaction, health service providers offer technical information about the disease or health condition, the benefits, and risks of the available therapeutic options, whereas the clients or patients provide information about their beliefs, concerns, values, and preferences about the consequences of those options(Joseph-Williams, Elwyn, \&Edwards, 2014). Shared decision-making model is especially relevant when evidence indicated that available treatments showed a similar balance between benefits and risks, and when there is potential trade-off between different attributes of treatment options. In light of the above model, patients decision aids (DAs) are designed to promote and facilitate shared decision-making and help clients to make informed choices(Coulter et al., 2013). These materials are developed in different formats (e.g., paper and pen instruments, videos, audio, website and interactive software), and can be used alone by the client or in interaction with the health service providers. DAs include explanations about treatment options, describing the benefits and harms based on the scientific evidence, and characteristics of health service based on local situations. They also encourage patients to think about their own values and preferences regarding the benefits, risks, and different aspects of the different treatment options, and how the choices could influence their lives and well-being(Fagerlin et al., 2013). Recent systematic reviews show that DAs are effective in improving patients' knowledge about available treatments, and reduced decisional conflict (i.e., uncertainty about the course of action to take). They also have shown to reduce the proportion of people who were passive and undecisive in decision making after deliberation(Stacey et al., 2017).
In the specific area of depressive disorders, results show that a majority of people with depression are interested in receiving information about their illness and participating in shared and informed decision making(Loh et al., 2004; Perestelo-Perez et al., 2017). Unfortunately, studies found that people with depression often perceived a lesser involvement in decisions than they desire (Delas Cuevas, Peñate, \&deRivera, 2014; Patel \&Bakken, 2010). Moreover, despite this unmet demand, and while DAs had been widely and successfully adopted in the multiple arenas of physical health (such as, breast cancer treatment(Savelberg et al., 2017), HIV preexposure prophylaxis(Sewell et al., 2021), colon cancer screening(Miller et al., 2011)(see figure 1), and smoking cessation(Gültzow, Smit, Hudales, Dirksen, \&Hoving, 2020)), there have been very few studies that have assessed the effectiveness of DAs in the field of depressive disorders. To our best knowledge no study has included E-mental health service in DAs for depression even when psychotherapy in E-format had been recommended by NICE for over a decade(Nice, 2009), and the effects of DAs on preference of psychological treatments and decisional conflict remain largely unknown.
1.3 Study Goal and Objective The goal of this study is to examine whether treatment preferences shift after receiving a clients' decision aids about psychotherapy in digital and in in-person format.
Conditions
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Depression
Psychotherapies
Keywords
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Depression
Online psychotherapies
psychotherapies
Decision aid
Study Design
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Allocation Method
RANDOMIZED
Intervention Model
PARALLEL
Primary Study Purpose
HEALTH_SERVICES_RESEARCH
Blinding Strategy
NONE
Study Groups
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Experimental group
Participants in the experimental group will be expected use the decision aid developed in this study. They will be assessed at two different time points:
(1) before intervention (T0) and (2) post-intervention (T1).
Group Type
EXPERIMENTAL
Decision Aid
Intervention Type
OTHER
Participants in the experimental group would be asked to use the decision aid developed by the study. The decision aid tool will ask participants their preferences on traditional face-to-face and online psychotherapies and how they rank different treatment attributes.
Attention control group
The control group participants will be asked to search information related to "Depression" and "therapies for Depression" online. They will be assessed at two different time points:
(1) at baseline (T0) and (2) after searching for information (T1).
Group Type
NO_INTERVENTION
No interventions assigned to this group
Interventions
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Decision Aid
Participants in the experimental group would be asked to use the decision aid developed by the study. The decision aid tool will ask participants their preferences on traditional face-to-face and online psychotherapies and how they rank different treatment attributes.
Intervention Type
OTHER
Eligibility Criteria
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Inclusion Criteria
* Being 18 years of age or older;
* With at least mild to moderate depressive symptoms (defined as having a cut-off score of 10 or above based on the PHQ-9, Patient Health Questionnaire-9)
* Being Chinese speaking
* With at least mild to moderate depressive symptoms (defined as having a cut-off score of 10 or above based on the PHQ-9, Patient Health Questionnaire-9)
* Being Chinese speaking
Minimum Eligible Age
18 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
No
Sponsors
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Chinese University of Hong Kong
OTHER
Sponsor Role
lead
Responsible Party
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Winnie W.S. MAK
Professor
Responsibility Role
PRINCIPAL_INVESTIGATOR
Locations
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Department of Psychology
Hong Kong, , Hong Kong
Site Status
Countries
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Hong Kong
References
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DERIVED
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
SBRE- 20-275
Identifier Type: -
Identifier Source: org_study_id