Readiness for E-mental Health- Awareness, Acceptance, and Preference Towards Technology-Mediated Mental Health Treatment Among Individuals With Elevated Depressive Symptoms in Hongkong

Study Overview
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Brief Summary
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The goals of this study is as follow: to understand the acceptability/perception of seeking E-mental health service versus other options for depression in Hong Kong, to investigate the extent to which people preferring E-mental health service systemically differ from people preferring traditional face-to-face service, and the extent to which digital health interventions increase reach and access to groups who may less well served by traditional mental health services (e.g. people with financial difficulties, men with depression, people with high level of stigma. etc), and 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 Goals and Objectives Considering the above research gaps, the goals of this study is threefold, which include the following to understand the acceptability/perception of seeking E-mental health service versus other options for depression in Hong Kong, to investigate the extent to which people preferring E-mental health service systemically differ from people preferring traditional face-to-face service, and the extent to which digital health interventions increase reach and access to groups who may less well served by traditional mental health services (e.g. people with financial difficulties, men with depression, people with high level of stigma. etc), and to examine whether treatment preferences shift after receiving a clients' decision aids about psychotherapy in digital and in in-person format.

Official Title
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Readiness for E-mental Health- Awareness, Acceptance, and Preference Towards Technology-Mediated Mental Health Treatment Among Individuals With Elevated Depressive Symptoms in Hongkong: A Survey Study and Effect of Web-based Decision Aids on Preferences Shift

Conditions
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Psychotherapies, Depression

Intervention / Treatment
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* Other: Decision Aid


Participation Criteria
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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 Exclusion Criteria: • Self-reported mental disorders other than major depressive disorder will be excluded.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Health Services Research
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Experimental group<br>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). | Other: Decision Aid<br>* 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.<br>|
| No Intervention: Attention control group<br>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). | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Depression | The PHQ-9 provides a brief 9-item measure of current depression symptoms using a 4-point Likert scale ranging from 0 (not at all) to 3 (nearly every day). Participants will be asked to rate their past week depression symptoms. | at baseline | 
| Depression | The PHQ-9 provides a brief 9-item measure of current depression symptoms using a 4-point Likert scale ranging from 0 (not at all) to 3 (nearly every day). Participants will be asked to rate their past week depression symptoms. | Upon completion of intervention, around half an hour later from baseline | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| COVID Stress | To measure the distress associated with COVID-19, subscales of the COVID Stress Scales will be used. Instructions for the fear-related items were as follows: The following questions ask about various kinds of worries that you might have experienced over the past seven days… about the virus. Items will be rated on a 5-point scale ranging from 0 (not at all) to 4 (extremely). We used the term worries to assess feared (anticipated) outcomes. The traumatic stress items will be rated on a 5-point scale ranging from 0 (never) to 4 (almost always). The higher the score, the higher the COVID stress. | at baseline and upon completion of intervention, around half an hour later from baseline | 
| Electronic mental health Service Awareness | Electronic health readiness will be measured by the 7-item eHealth readiness scale. Items will adopt a 6-point Likert-type scale (1 = strongly disagree and 6 = strongly agree). Scores could range between 7 and 42. Sample items include I enjoy the challenge of figuring out the different functions of websites and web applications and I would be comfortable using an internet-connected device several times a week to participate in a lifestyle intervention online | at baseline and upon completion of intervention, around half an hour later from baseline | 
| Barriers to access to care | Barriers related to stigma and discrimination and other non-stigma related barriers were assessed using the Barriers to Access to Care Evaluation Scale. The BACE is a 30-item self-report instrument where respondents are asked whether each of the items has ever stopped, delayed, or discouraged them for receiving or continuing care for their mental health problems. It has a four-point response scale ranging from 0 (not at all) to 3 (a lot). The higher the score, the stronger the barrier. | at baseline and upon completion of intervention, around half an hour later from baseline | 
| Financial Barriers | To define financial barriers to health care, the following questions will be asked(Rahimi, Spertus, Reid, Bernheim, &Krumholz, 2007): In the past year, have you avoided obtaining (1) health care services/ (2) doctor's subscribed medication because of cost? Avoidance of health care services due to cost will be answered on a 5-point Likert scale ranging from never to always. | at baseline and upon completion of intervention, around half an hour later from baseline | 
| Perceived Financial Well-being | Consumer Financial Protection Bureau Financial Well-Being Scale. The CFPB Financial Well-Being Scale is a 5-item self-report instrument which reflecst a respondent's subjective sense of their financial situation. It has a five-point response scale ranging from 0 (Does not describe me at all) to 4 (Describes me completely). The lower the score, the better the perceived subjective well-being. | at baseline and upon completion of intervention, around half an hour later from baseline | 
| Conformity to masculine norm | Masculine norm will be measured by the short form of the Conformity to Masculine Norms Inventory (CMNI-30)- emotional control (3 items) and self-reliance (3 items) subscales. Items will be rated on a six-point Likert scale ranged from 0 (strongly disagree) to 5 (strongly agree). Sample items include I tend to share my feelings and It bothers me when I have to ask for help. The higher the score, the higher the conformity. | at baseline at baseline and upon completion of intervention, around half an hour later from baseline | 
| Disclosure | The Disclosure Expectations Scale. DES is an 8-item measure of one's expected consequences of disclosing distressing information to therapists (e.g., If you were dealing with an emotional problem, how beneficial for yourself would it be to self-disclose personal information about the problem to a therapist?). Items will be rated on a five-point Likert scale ranged from 1 (Not at all) to 5 (very). | at baseline and upon completion of intervention, around half an hour later from baseline | 
| Depression Stigma | Stigma towards depression will be measured using the Depression Stigma Scale (DSS). DSS consists of 18 items with a measure of the respondent's personal attitudes towards depression and a measure of the respondent's beliefs about the stigmatizing attitudes of others. Each subscale includes nine items using a 5-point Likert scale, which ranges from 4 points (strongly agree) to 0 points (strongly disagree). The choice of strongly agree or agree for each item indicates the presence of personal or perceived stigma. The higher the score, the higher the depression stigma. | at baseline and upon completion of intervention, around half an hour later from baseline | 
| E-mental health Service Awareness | Participants would be asked questions on whether they have heard about; have they previously tried; and are they currently using e-mental health services. Items would be in binary responses with 0 = never heard/ tried/using and 1 = have heard/ tried/ currently using. | at baseline and upon completion of intervention, around half an hour later from baseline | 
| Evaluation of importance of mental health treatment attributes (Rank) | Participants will be asked to think of mental health treatments in general and rank what they would consider important if they were to seek help right now, from 1 Most important to 6 The least important. The attributes are as follow: (1) Could effectively help with my mental health issuel; (2) Credible; (3) Appealing; (4) Low/No Cost; (5) Could protect my right of privacy and personal information; (6) Is accessible without/with short waiting time; (7) Could motivate me to finish the treatment; (8) Can be accessed at a convenient time; (9) No/low transportation cost; (10) Personalization with reference to my need; (11) Provides feedback; (12) Without side-effect; (13) Could provide real-time support when I am in need; (14) To help me keep track of my mental health status; (15) Can be accessed anonymously | at baseline and upon completion of intervention, around half an hour later from baseline | 
| Evaluation of importance of mental health treatment attributes (Rate) | Participants will be asked to think of mental health treatments in general and rate what they would consider important if they were to seek help right now. . Items will be rated on a seven-point Likert scale ranged from 0 not at all important) to 5 (very important). The attributes are as follow: (1) Could effectively help with my mental health issuel; (2) Credible; (3) Appealing; (4) Low/No Cost; (5) Could protect my right of privacy and personal information; (6) Is accessible without/with short waiting time; (7) Could motivate me to finish the treatment; (8) Can be accessed at a convenient time; (9) No/low transportation cost; (10) Personalization with reference to my need; (11) Provides feedback; (12) Without side-effect; (13) Could provide real-time support when I am in need; (14) To help me keep track of my mental health status; (15) Can be accessed anonymously | at baseline and upon completion of intervention, around half an hour later from baseline | 
| Likelihood of use of service | Participants would be asked the following question, To what extent would you consider the following management options for symptoms of depression such as having depressed mood and loss of interest during past 2 weeks?. Scale ranging from 1 (very unlikely) to 5 (very likely). | at baseline and upon completion of intervention, around half an hour later from baseline | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Depression, Online psychotherapies, psychotherapies, Decision aid