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

Depression Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05477420
• Other study ID #: SBRE- 20-275
• Status: Recruiting
• Phase: N/A
• Start date: March 20, 2022
• Est. completion date: March 30, 2024

Study information

• Verified date: August 2023
• Source: Chinese University of Hong Kong
• Contact: Kelly Chan
• Phone: +852 95706418
• Email: kellychan[@]cuhk.edu.hk
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The goals of this study is as follow: 1. to understand the acceptability/perception of seeking E-mental health service versus other options for depression in Hong Kong, 2. 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 3. to examine whether treatment preferences shift after receiving a clients' decision aids about psychotherapy in digital and in in-person format.

Description:

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 1. to understand the acceptability/perception of seeking E-mental health service versus other options for depression in Hong Kong, 2. 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 3. to examine whether treatment preferences shift after receiving a clients' decision aids about psychotherapy in digital and in in-person format.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 200
• Est. completion date: March 30, 2024
• Est. primary completion date: March 30, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Depression

Intervention

Other:
• 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.

Locations

Country	Name	City	State
Hong Kong	Department of Psychology	Hong Kong

Sponsors (1)

Lead Sponsor	Collaborator
Chinese University of Hong Kong

Country where clinical trial is conducted

Hong Kong, 

References & Publications (55)

Andersson G, Titov N, Dear BF, Rozental A, Carlbring P. Internet-delivered psychological treatments: from innovation to implementation. World Psychiatry. 2019 Feb;18(1):20-28. doi: 10.1002/wps.20610. — View Citation

Andrade LH, Alonso J, Mneimneh Z, Wells JE, Al-Hamzawi A, Borges G, Bromet E, Bruffaerts R, de Girolamo G, de Graaf R, Florescu S, Gureje O, Hinkov HR, Hu C, Huang Y, Hwang I, Jin R, Karam EG, Kovess-Masfety V, Levinson D, Matschinger H, O'Neill S, Posada-Villa J, Sagar R, Sampson NA, Sasu C, Stein DJ, Takeshima T, Viana MC, Xavier M, Kessler RC. Barriers to mental health treatment: results from the WHO World Mental Health surveys. Psychol Med. 2014 Apr;44(6):1303-17. doi: 10.1017/S0033291713001943. Epub 2013 Aug 9. — View Citation

Angermeyer MC, Matschinger H, Riedel-Heller SG. Whom to ask for help in case of a mental disorder? Preferences of the lay public. Soc Psychiatry Psychiatr Epidemiol. 1999 Apr;34(4):202-10. doi: 10.1007/s001270050134. — View Citation

Axelsson E, Andersson E, Ljotsson B, Hedman-Lagerlof E. Cost-effectiveness and long-term follow-up of three forms of minimal-contact cognitive behaviour therapy for severe health anxiety: Results from a randomised controlled trial. Behav Res Ther. 2018 Aug;107:95-105. doi: 10.1016/j.brat.2018.06.002. Epub 2018 Jun 15. — View Citation

Baumeister H, Nowoczin L, Lin J, Seifferth H, Seufert J, Laubner K, Ebert DD. Impact of an acceptance facilitating intervention on diabetes patients' acceptance of Internet-based interventions for depression: a randomized controlled trial. Diabetes Res Clin Pract. 2014 Jul;105(1):30-9. doi: 10.1016/j.diabres.2014.04.031. Epub 2014 May 10. — View Citation

Berry LL, Beckham D, Dettman A, Mead R. Toward a strategy of patient-centered access to primary care. Mayo Clin Proc. 2014 Oct;89(10):1406-15. doi: 10.1016/j.mayocp.2014.06.011. Epub 2014 Sep 4. — View Citation

Carlbring P, Andersson G, Cuijpers P, Riper H, Hedman-Lagerlof E. Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: an updated systematic review and meta-analysis. Cogn Behav Ther. 2018 Jan;47(1):1-18. doi: 10.1080/16506073.2017.1401115. Epub 2017 Dec 7. — View Citation

Cavanagh K, Shapiro DA, Van Den Berg S, Swain S, Barkham M, Proudfoot J. The acceptability of computer-aided cognitive behavioural therapy: a pragmatic study. Cogn Behav Ther. 2009;38(4):235-46. doi: 10.1080/16506070802561256. — View Citation

Chien I, Enrique A, Palacios J, Regan T, Keegan D, Carter D, Tschiatschek S, Nori A, Thieme A, Richards D, Doherty G, Belgrave D. A Machine Learning Approach to Understanding Patterns of Engagement With Internet-Delivered Mental Health Interventions. JAMA Netw Open. 2020 Jul 1;3(7):e2010791. doi: 10.1001/jamanetworkopen.2020.10791. — View Citation

Clement S, Brohan E, Jeffery D, Henderson C, Hatch SL, Thornicroft G. Development and psychometric properties the Barriers to Access to Care Evaluation scale (BACE) related to people with mental ill health. BMC Psychiatry. 2012 Jun 20;12:36. doi: 10.1186/1471-244X-12-36. — View Citation

Clement S, Schauman O, Graham T, Maggioni F, Evans-Lacko S, Bezborodovs N, Morgan C, Rusch N, Brown JS, Thornicroft G. What is the impact of mental health-related stigma on help-seeking? A systematic review of quantitative and qualitative studies. Psychol Med. 2015 Jan;45(1):11-27. doi: 10.1017/S0033291714000129. Epub 2014 Feb 26. — View Citation

Coulter A, Stilwell D, Kryworuchko J, Mullen PD, Ng CJ, van der Weijden T. A systematic development process for patient decision aids. BMC Med Inform Decis Mak. 2013;13 Suppl 2(Suppl 2):S2. doi: 10.1186/1472-6947-13-S2-S2. Epub 2013 Nov 29. — View Citation

Cuijpers P, Clignet F, van Meijel B, van Straten A, Li J, Andersson G. Psychological treatment of depression in inpatients: a systematic review and meta-analysis. Clin Psychol Rev. 2011 Apr;31(3):353-60. doi: 10.1016/j.cpr.2011.01.002. Epub 2011 Jan 16. — View Citation

De las Cuevas C, Penate W, de Rivera L. Psychiatric patients' preferences and experiences in clinical decision-making: examining concordance and correlates of patients' preferences. Patient Educ Couns. 2014 Aug;96(2):222-8. doi: 10.1016/j.pec.2014.05.009. Epub 2014 May 22. — View Citation

Dorow M, Lobner M, Pabst A, Stein J, Riedel-Heller SG. Preferences for Depression Treatment Including Internet-Based Interventions: Results From a Large Sample of Primary Care Patients. Front Psychiatry. 2018 May 17;9:181. doi: 10.3389/fpsyt.2018.00181. eCollection 2018. — View Citation

Gelhorn HL, Sexton CC, Classi PM. Patient preferences for treatment of major depressive disorder and the impact on health outcomes: a systematic review. Prim Care Companion CNS Disord. 2011;13(5):PCC.11r01161. doi: 10.4088/PCC.11r01161. — View Citation

Givens JL, Houston TK, Van Voorhees BW, Ford DE, Cooper LA. Ethnicity and preferences for depression treatment. Gen Hosp Psychiatry. 2007 May-Jun;29(3):182-91. doi: 10.1016/j.genhosppsych.2006.11.002. — View Citation

Gultzow T, Smit ES, Hudales R, Dirksen CD, Hoving C. Smoker profiles and their influence on smokers' intention to use a digital decision aid aimed at the uptake of evidence-based smoking cessation tools: An explorative study. Digit Health. 2020 Dec 29;6:2055207620980241. doi: 10.1177/2055207620980241. eCollection 2020 Jan-Dec. — View Citation

Hanson K, Webb TL, Sheeran P, Turpin G. Attitudes and Preferences towards Self-help Treatments for Depression in Comparison to Psychotherapy and Antidepressant Medication. Behav Cogn Psychother. 2016 Mar;44(2):129-39. doi: 10.1017/S1352465815000041. Epub 2015 Feb 20. — View Citation

Harter M, Klesse C, Bermejo I, Bschor T, Gensichen J, Harfst T, Hautzinger M, Kolada C, Kopp I, Kuhner C, Lelgemann M, Matzat J, Meyerrose B, Mundt C, Niebling W, Ollenschlager G, Richter R, Schauenburg H, Schulz H, Weinbrenner S, Schneider F, Berger M. [Evidence-based therapy of depression: S3 guidelines on unipolar depression]. Nervenarzt. 2010 Sep;81(9):1049-68. doi: 10.1007/s00115-010-3084-7. German. — View Citation

Hockenberry JM, Joski P, Yarbrough C, Druss BG. Trends in Treatment and Spending for Patients Receiving Outpatient Treatment of Depression in the United States, 1998-2015. JAMA Psychiatry. 2019 Aug 1;76(8):810-817. doi: 10.1001/jamapsychiatry.2019.0633. Erratum In: JAMA Psychiatry. 2019 May 29;: — View Citation

Joseph-Williams N, Elwyn G, Edwards A. Knowledge is not power for patients: a systematic review and thematic synthesis of patient-reported barriers and facilitators to shared decision making. Patient Educ Couns. 2014 Mar;94(3):291-309. doi: 10.1016/j.pec.2013.10.031. Epub 2013 Nov 9. — View Citation

Josephine K, Josefine L, Philipp D, David E, Harald B. Internet- and mobile-based depression interventions for people with diagnosed depression: A systematic review and meta-analysis. J Affect Disord. 2017 Dec 1;223:28-40. doi: 10.1016/j.jad.2017.07.021. Epub 2017 Jul 10. — View Citation

Kaltenthaler E, Sutcliffe P, Parry G, Beverley C, Rees A, Ferriter M. The acceptability to patients of computerized cognitive behaviour therapy for depression: a systematic review. Psychol Med. 2008 Nov;38(11):1521-30. doi: 10.1017/S0033291707002607. Epub 2008 Jan 21. — View Citation

Kenwright M, Marks I, Gega L, Mataix-Cols D. Computer-aided self-help for phobia/panic via internet at home: a pilot study. Br J Psychiatry. 2004 May;184:448-9. doi: 10.1192/bjp.184.5.448. Erratum In: Br J Psychiatry. 2004 Aug;185(2):178. Marks, Isaac M [corrected to Marks, Isaac]. — View Citation

Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001 Sep;16(9):606-13. doi: 10.1046/j.1525-1497.2001.016009606.x. — View Citation

Loh A, Kremer N, Giersdorf N, Jahn H, Hanselmann S, Bermejo I, Harter M. [Information and participation interests of patients with depression in clinical decision making in primary care]. Z Arztl Fortbild Qualitatssich. 2004 Mar;98(2):101-7. German. — View Citation

Mahoney A, Li I, Haskelberg H, Millard M, Newby JM. The uptake and effectiveness of online cognitive behaviour therapy for symptoms of anxiety and depression during COVID-19. J Affect Disord. 2021 Sep 1;292:197-203. doi: 10.1016/j.jad.2021.05.116. Epub 2021 Jun 4. — View Citation

March S, Day J, Ritchie G, Rowe A, Gough J, Hall T, Yuen CYJ, Donovan CL, Ireland M. Attitudes Toward e-Mental Health Services in a Community Sample of Adults: Online Survey. J Med Internet Res. 2018 Feb 19;20(2):e59. doi: 10.2196/jmir.9109. — View Citation

McCall HC, Sison AP, Burnett JL, Beahm JD, Hadjistavropoulos HD. Exploring Perceptions of Internet-Delivered Cognitive Behaviour Therapy among Public Safety Personnel: Informing Dissemination Efforts. Int J Environ Res Public Health. 2020 Aug 19;17(17):6026. doi: 10.3390/ijerph17176026. — View Citation

Miller DP Jr, Spangler JG, Case LD, Goff DC Jr, Singh S, Pignone MP. Effectiveness of a web-based colorectal cancer screening patient decision aid: a randomized controlled trial in a mixed-literacy population. Am J Prev Med. 2011 Jun;40(6):608-15. doi: 10.1016/j.amepre.2011.02.019. — View Citation

Murray CJ, Lopez AD. Measuring the global burden of disease. N Engl J Med. 2013 Aug 1;369(5):448-57. doi: 10.1056/NEJMra1201534. No abstract available. — View Citation

Musiat P, Goldstone P, Tarrier N. Understanding the acceptability of e-mental health--attitudes and expectations towards computerised self-help treatments for mental health problems. BMC Psychiatry. 2014 Apr 11;14:109. doi: 10.1186/1471-244X-14-109. — View Citation

Musiat P, Tarrier N. Collateral outcomes in e-mental health: a systematic review of the evidence for added benefits of computerized cognitive behavior therapy interventions for mental health. Psychol Med. 2014 Nov;44(15):3137-50. doi: 10.1017/S0033291714000245. Epub 2014 Feb 19. — View Citation

National Collaborating Centre for Mental Health (UK). Depression: The Treatment and Management of Depression in Adults (Updated Edition). Leicester (UK): British Psychological Society; 2010. Available from http://www.ncbi.nlm.nih.gov/books/NBK63748/ — View Citation

O'Connor AM. Validation of a decisional conflict scale. Med Decis Making. 1995 Jan-Mar;15(1):25-30. doi: 10.1177/0272989X9501500105. — View Citation

Patel SR, Bakken S. Preferences for participation in decision making among ethnically diverse patients with anxiety and depression. Community Ment Health J. 2010 Oct;46(5):466-73. doi: 10.1007/s10597-010-9323-3. Epub 2010 Jun 17. — View Citation

Perestelo-Perez L, Rivero-Santana A, Sanchez-Afonso JA, Perez-Ramos J, Castellano-Fuentes CL, Sepucha K, Serrano-Aguilar P. Effectiveness of a decision aid for patients with depression: A randomized controlled trial. Health Expect. 2017 Oct;20(5):1096-1105. doi: 10.1111/hex.12553. Epub 2017 Mar 10. — View Citation

Practice guideline for the treatment of patients with major depressive disorder (revision). American Psychiatric Association. Am J Psychiatry. 2000 Apr;157(4 Suppl):1-45. No abstract available. — View Citation

Rahimi AR, Spertus JA, Reid KJ, Bernheim SM, Krumholz HM. Financial barriers to health care and outcomes after acute myocardial infarction. JAMA. 2007 Mar 14;297(10):1063-72. doi: 10.1001/jama.297.10.1063. — View Citation

Raue PJ, Schulberg HC, Heo M, Klimstra S, Bruce ML. Patients' depression treatment preferences and initiation, adherence, and outcome: a randomized primary care study. Psychiatr Serv. 2009 Mar;60(3):337-43. doi: 10.1176/ps.2009.60.3.337. — View Citation

Renn BN, Hoeft TJ, Lee HS, Bauer AM, Arean PA. Preference for in-person psychotherapy versus digital psychotherapy options for depression: survey of adults in the U.S. NPJ Digit Med. 2019 Feb 11;2:6. doi: 10.1038/s41746-019-0077-1. eCollection 2019. — View Citation

Santucci LC, McHugh RK, Barlow DH. Direct-to-consumer marketing of evidence-based psychological interventions: introduction. Behav Ther. 2012 Jun;43(2):231-5. doi: 10.1016/j.beth.2011.07.003. Epub 2011 Jul 28. — View Citation

Savelberg W, van der Weijden T, Boersma L, Smidt M, Willekens C, Moser A. Developing a patient decision aid for the treatment of women with early stage breast cancer: the struggle between simplicity and complexity. BMC Med Inform Decis Mak. 2017 Aug 1;17(1):112. doi: 10.1186/s12911-017-0505-6. — View Citation

Seidler ZE, Dawes AJ, Rice SM, Oliffe JL, Dhillon HM. The role of masculinity in men's help-seeking for depression: A systematic review. Clin Psychol Rev. 2016 Nov;49:106-118. doi: 10.1016/j.cpr.2016.09.002. Epub 2016 Sep 10. — View Citation

Sewell WC, Solleveld P, Seidman D, Dehlendorf C, Marcus JL, Krakower DS. Patient-Led Decision-Making for HIV Preexposure Prophylaxis. Curr HIV/AIDS Rep. 2021 Feb;18(1):48-56. doi: 10.1007/s11904-020-00535-w. Epub 2021 Jan 8. — View Citation

Spurgeon JA, Wright JH. Computer-assisted cognitive-behavioral therapy. Curr Psychiatry Rep. 2010 Dec;12(6):547-52. doi: 10.1007/s11920-010-0152-4. — View Citation

Stacey D, Legare F, Boland L, Lewis KB, Loiselle MC, Hoefel L, Garvelink M, O'Connor A. 20th Anniversary Ottawa Decision Support Framework: Part 3 Overview of Systematic Reviews and Updated Framework. Med Decis Making. 2020 Apr;40(3):379-398. doi: 10.1177/0272989X20911870. — View Citation

Stacey D, Legare F, Lewis K, Barry MJ, Bennett CL, Eden KB, Holmes-Rovner M, Llewellyn-Thomas H, Lyddiatt A, Thomson R, Trevena L. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2017 Apr 12;4(4):CD001431. doi: 10.1002/14651858.CD001431.pub5. — View Citation

Steidtmann D, Manber R, Arnow BA, Klein DN, Markowitz JC, Rothbaum BO, Thase ME, Kocsis JH. Patient treatment preference as a predictor of response and attrition in treatment for chronic depression. Depress Anxiety. 2012 Oct;29(10):896-905. doi: 10.1002/da.21977. Epub 2012 Jul 5. — View Citation

Swift JK, Callahan JL. The impact of client treatment preferences on outcome: a meta-analysis. J Clin Psychol. 2009 Apr;65(4):368-81. doi: 10.1002/jclp.20553. — View Citation

Taylor S, Landry CA, Paluszek MM, Fergus TA, McKay D, Asmundson GJG. Development and initial validation of the COVID Stress Scales. J Anxiety Disord. 2020 May;72:102232. doi: 10.1016/j.janxdis.2020.102232. Epub 2020 May 4. — View Citation

Wallin EE, Mattsson S, Olsson EM. The Preference for Internet-Based Psychological Interventions by Individuals Without Past or Current Use of Mental Health Treatment Delivered Online: A Survey Study With Mixed-Methods Analysis. JMIR Ment Health. 2016 Jun 14;3(2):e25. doi: 10.2196/mental.5324. — View Citation

Wills CE, Holmes-Rovner M. Preliminary validation of the Satisfaction With Decision scale with depressed primary care patients. Health Expect. 2003 Jun;6(2):149-59. doi: 10.1046/j.1369-6513.2003.00220.x. — View Citation

Witteman HO, Ndjaboue R, Vaisson G, Dansokho SC, Arnold B, Bridges JFP, Comeau S, Fagerlin A, Gavaruzzi T, Marcoux M, Pieterse A, Pignone M, Provencher T, Racine C, Regier D, Rochefort-Brihay C, Thokala P, Weernink M, White DB, Wills CE, Jansen J. Clarifying Values: An Updated and Expanded Systematic Review and Meta-Analysis. Med Decis Making. 2021 Oct;41(7):801-820. doi: 10.1177/0272989X211037946. — View Citation

* Note: There are 55 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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
Primary	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	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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