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Trial registered on ANZCTR

Registration number

ACTRN12622000676718

Ethics application status

Approved

Date submitted

27/04/2022

Date registered

10/05/2022

Date last updated

27/08/2024

Date data sharing statement initially provided

10/05/2022

Date results provided

27/08/2024

Type of registration

Prospectively registered

Titles & IDs

Public title

Computer's human-likeness and its effects on willingness to disclose health-relevant information in healthy adults

Scientific title

Computer's human-likeness and its effects on willingness to disclose health-relevant information in healthy adults

Secondary ID [1]

None

Universal Trial Number (UTN)

Trial acronym

Linked study record

Health condition

Health condition(s) or problem(s) studied:

Self-disclosure of health behaviours

Self-disclosure of emotional events

Condition category

Condition code

Mental Health

Studies of normal psychology, cognitive function and behaviour

Intervention/exposure

Study type

Interventional

Description of intervention(s) / exposure

This study will be a randomized controlled trial with three experimental conditions (intervention arms).
Arm 1: A brief clinical assessment delivered by an online questionnaire
Arm 2: A brief clinical assessment delivered by a chatbot interviewer (reading and typing interface)
Arm 3: A brief clinical assessment delivered by a digital human (DH) interviewer

A community sample of adults (18-35 years old) will be block-randomized by gender to any of the three experimental conditions in a 1:1:1 ratio. For each condition, participants will complete a semi-structured clinical assessment automatically delivered by their allocated technology. The assessment comprises 24 health-relevant items collecting personal information about health behaviours and recent emotional events. The assessment is estimated to last for about 25 minutes.
The information below first describes the clinical assessment procedure and then the three types of digital assessment methods.
The clinical assessment
The semi-structured clinical assessment will be automatically delivered by a digital human (DH), a chatbot, or an online questionnaire. The assessment is broadly structured into three phases, namely, the initial rapport-building phase, the main assessment phase, and the closure. The initial phase is estimated to last for 5 minutes, involving the digital human (DH) or chatbot introducing themselves, initiating the conversation (e.g., “where are you from?”), and providing the scope for the interview (i.e., collecting some health-relevant information including health behaviours and recent emotional events). For the online questionnaire, the initial section will introduce the assessment scope and include the same rapport-building questions as in the chatbot or digital human condition. The main assessment phase is estimated to last for 15 minutes, comprising a list of close-ended questions asking for a wide range of health behaviours, and a few open-ended questions concerning experiences of recent emotional events. Finally, the closure phase is estimated to last for 5 minutes. The digital human (DH) or chatbot interviewer would remind the participants of the closure, deliver a generalized appreciation message (e.g., “I really appreciate your trust in sharing your personal experiences with me”), and close the interviewer with some uplifting questions (e.g., “Tell me about three things that you feel grateful to have in your life?). The interview questions will be delivered in a fixed sequence with some general receptive feedback provided in response to the answers. Participants can skip answering any questions by saying “I do not want to answer”.
All of the assessment items are either selected from well-established mental health and addiction screening questionnaire, validated lifestyle surveys for adults, or previous studies that have examined adults' self-disclosure of health information to conversational agents. The health behaviours items asking for habits of exercise, sugary drinks consumption, vegetables eating are selected from a lifestyle survey for university students (Silliman et al., 2004). The items asking for substance uses (tobacco, alcohol, cannabis) are selected from the well-established Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) suitable to be used for adults in New Zealand (Humeniuk, 2006). The item asking for intoxication over the past month is adapted from Schuetzler et al.’ study (2015) which examined users’ socially desirable responding to health-relevant and other personal information. All the sexual risk behaviour items are taken from the validated Sexual Risk Behaviour Scale (Fino et al., 2021) for university students. Lastly, the item asking for drunk driving practice is taken from a health risk behaviour study by Laska and colleagues (2009). For the emotional experiences items, three items asking about perceived loneliness are selected from the well-established UCLA Loneliness Scale (Russel, 1996). In addition, the open-ended items asking about recent emotional events are adapted from a study by Lucas et al. (2014), which examined the effect of human presence on self-disclosure behaviours during semi-structured interviews.

The digital human interviewer
The digital human (DH) interviewer is developed by Soul Machines Ltd (Auckland, New Zealand). The digital human interviewer is modelled to be a young adult female of mixed ethnicities. The digital human (DH) interviewer is autonomously animated and present on a website accessible to users from their personal computers and tablets. The digital human (DH) interviewer will be programmed to deliver the semi-structured interview questions in a fixed sequence and provide some general receptive feedback to users. As the digital human (DH) interviewer speaks, she will engage in human-like facial and body gestures, including displaying facial expressions, maintaining eye gaze, and moving her head and shoulders.
Participants will be informed that the digital human (DH) interviewer continuously collect speech and video data in order to communicate (e.g., to hear speech and make eye contact). These data will not be recorded, stored or analysed by the researchers. Soul Machines’ digital humans (DH) engage in data collection process compliant with the European Union General Data Protection Regulation (GDPR) (Soul Machines, 2021).

The chatbot interviewer
The chatbot interviewer is programmed using IBM Watson and then deployed to a personal website (Google Site). The chatbot interviewer will be displayed as a static humanoid character image on the web and accessed on a website from a computer. The chatbot interviewer will be programmed to deliver the semi-structured interview questions in a fixed sequence and provide some general receptive feedback.
Participants will be informed that their responses to the assessment will be collected by the IBM Watson. IBM engages in data collection process compliant with the European Union General Data Protection Regulation (GDPR).

The online questionnaire task
The online questionnaire will be created using Qualtrics. The online questionnaire will include the same assessment questions as in the digital human DH and chatbot conditions, and ask these questions in the exact same sequence as in the other conditions. The questionnaire will not provide any feedback to users’ inputs until to the point of submission (i.e., Your responses have been recorded. Thanks for completing this assessment.).

The intervention will take place on a desktop computer in a private clinic room at the University of Auckland Clinical Research Center. The total research session lasts for about 45 minutes. Participants will first be instructed to interact with their allocated technology by the researcher (a master's student) for about 10 minutes, including a demonstration of how to use the digital human or chatbot. Participants then will be left independently to complete a clinical assessment with their allocated technology. The researcher will be available (the master's student) in another room to provide help if needed. Participants' responses to the assessment will be recorded by the allocated technology. In addition, audio recordings will be taken for participants allocated to the digital human (DH) group (to double check the accuracy of their recorded verbal responses). Following the completion of the assessment, participants will spend about 10 minutes completing a follow-up online questionnaire on their impression of their allocated digital assessment method and their perceived sensitivty of each assessment item.

Intervention code [1]

Behaviour

Intervention code [2]

Treatment: Devices

Comparator / control treatment

Active control group (the online questionnaire group is the active control group)

Control group

Active

Outcomes

Primary outcome [1]

Participants' responses to the study-specific semi-structured clinical assessment will be extracted to measure the presence of socially desirable responses. The presence of socially desirable responses will be measured by proportions of socially desirable responses to groups of sensitive and non-sensitive close-ended health items of the study-specific semi-structured clinical assessment, with socially desirable responding defined as the tendency to over-report socially favorable information and under-report socially unfavorable information. The 18 close-ended health items will be categorized into two groups of sensitive or non-sensitive health items. The scores of all sensitive and non-sensitive close-ended items will be aggregated respectively, with a higher score indicating a greater extent to self-report socially unfavourable information, and a lower score indicating a greater extent to self-report socially favourable information. Consistent with prior research (e.g., Schuetzler et al., 2018), we take more frequent engagements with health-protective behaviors including eating vegetables and doing exercises as socially favorable. We take more frequent engagements with health risk behaviors including drinking sugared beverages, using tobacco products, drinking alcohol, using drugs, engaging with sexual risk behaviors, engaging with unsafe driving practices, as socially unfavorable. In addition, we associate a lower level of loneliness (for the three closed-ended loneliness items) and no existing diagnosis of sexually transmitted infections (for the item asking for any diagnosis of sexually transmitted infections) with a socially favorable image.

Timepoint [1]

During the research session when participants complete the assessment

Primary outcome [2]

Participants' responses to the study-specific semi-structured clinical assessment will be extracted to measure participants' willingness to provide answers or not to two groups of sensitive or non-sensitive assessment item. The 24 assessment items will be categorized into two groups of sensitive or non-sensitive health items. Each participant will be scored by counting the number of sensitive and non-sensitive items for which they choose not to not provide an answer respectively. A unit of score represents one declination of answering an assessment item.

Timepoint [2]

During the research session when participants complete the assessment

Primary outcome [3]

Participants' responses to the study-specific semi-structured clinical assessment will be extracted to measure the amount of disclosure. The amount of self-disclosure will be indicated by the total word counts of the participant's responses to the groups of sensitive or non-sensitive open-ended items respectively (these items ask for positive or negative emotional events). The 6 open-ended assessment items will be categorized into two groups of sensitive or non-sensitive items.

Timepoint [3]

During the research session when participants complete the assessment

Secondary outcome [1]

Perceived anthropomorphism will be measured by the 5-item anthropomorphism scale (Powers & Kiesler, 2006).

Timepoint [1]

In the follow-up questionnaire immediately following the completion of the assessment

Eligibility

Key inclusion criteria

The participants will be adults between 18 to 35 years old with English fluency (i.e., can speak, read, and write in fluent English).

Minimum age

18 Years

Maximum age

35 Years

Sex

Both males and females

Can healthy volunteers participate?

Yes

Key exclusion criteria

Participants will be excluded from the study if they have hearing difficulties or vision loss (As these participants may need special assistance with using the computer or hearing the researcher. Due to the limited resource, this study is not equipped to provide such assistance).

Study design

Purpose of the study

Treatment

Allocation to intervention

Randomised controlled trial

Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)

Block-randomisation of participants by gender was conducted by a member of the research team who was not involved in data collection. Allocations were concealed from the researcher involved in data collection in opaque envelopes. The researcher remained blinded to the participant's allocation until opening the envelope immediately prior to starting the instructions of using the technology for the participant.

Methods used to generate the sequence in which subjects will be randomised (sequence generation)

A randomisation table was generated using Research Randomizer software that block-randomised participants by gender. This randomisation was performed by a member of the research team who did not interact with participants.

Masking / blinding

Blinded (masking used)

Who is / are masked / blinded?

The people administering the treatment/s

Intervention assignment

Parallel

Other design features

Phase

Not Applicable

Type of endpoint/s

Efficacy

Statistical methods / analysis

Lucas et al. (2014) compared the effects of framing virtual humans as automatic versus teleoperated by real humans on users’ self-reported fear of judgment, impression management pressure, displays of sadness facial expressions, and self-reported willingness to disclose. In addition, Schuetzler et al. (2018) compared the effects of chatbot’s embodiment and responsiveness on people’s self-disclosure of health behaviours. They found that a responsive ,unembodied chatbot induced users’ socially desirable responding to a relatively sensitive health item (asking for intoxication over the past month) with a small to medium effect sizes with Cohen’f close to 0.25. Although these studies differ with our study in terms of technology designs (hence the manipulation of humanlike cues) and measurements, they provide some information in estimating a possible effect size of “perceived human presence” on self-disclosure behaviours. Hence, based on these studies, we choose f=0.25 as the basis of our power calculation. Using G*Power, we would need 159 participants at minimum in our sample to detect an effect size of Cohen's f= 0.25, with 80% power and alpha threshold of .05 for an analysis of variance (ANOVA). Hence, we would attempt to recruit at least 159 participants for this study.

The transcripts will be entered into LIWC2015 for automatic computation of the word counts of the participants’ responses to each open-ended assessment question. Afterward, the quantitative data will be entered into SPSS 27 for further analyses. Two multivarite analyses of variance (MANOVA) will be conducted first to compare overall differences in the three primay outcomes (i.e., socially desriable responses, willingness to provide answers, and disclosure amounts) for the sensitive and non-sensitive assessment items respectively across the three experimental conditions. To test the first hypothesis (H1), two ANOVA tests will be performed to analyse the differences in users’ responses to sensitive and non-sensitive close-ended health information items across the three conditions (i.e., DH, chatbot and questionnaire). A post-hoc test will be conducted following any significant F test result. To test the second hypothesis (H2), two ANOVA tests will be performed to detect any between-group differences in the total number of declined responses to sensitive and non-sensitive assessment items. To test the third hypothesis (H3), two ANOVA tests will be performed to detect between-group differences in the total word counts of users’ responses to the sensitive and non-sensitive open-ended items. An additional ANOVA test will be performed to analyse between-group groups in the total word counts of users' responses to those non-sensitive items that ask for positive emotional events. To test the fourth hypothesis (H4), a mediation analysis will be employed to test whether and how much the covariation (if any) between the groups and a series of dependent variables could be attributed to the perceived anthropomorphism. Furthermore, on an exploratory basis, descriptive statistics will be generated and examined to detect any trends of ethnic differences in users’ self-disclosure of health information within and between each experimental condition. To be more specific, within the digital human or chatbot condition, the mean of each dependent variable across ethnic groups will be compared to examine any trends indicating self-disclosure of different ethnic group members to the digital human, chatbot or online questionnaire may be different. In addition, the mean of each dependent variable for the same ethnic group will be further compared across conditions. This is to be done to look for any trends indicating differences in self-disclosure to the digital human, chatbot and online questionnaire within the same ethnic group.

Recruitment

Recruitment status

Completed

Date of first participant enrolment

Anticipated

13/06/2022

Actual

16/06/2022

Date of last participant enrolment

Anticipated

30/09/2023

Actual

26/09/2022

Date of last data collection

Anticipated

15/10/2023

Actual

30/09/2022

Sample size

Target

159

Accrual to date

Final

160

Recruitment outside Australia

Country [1]

New Zealand

State/province [1]

Auckland

Funding & Sponsors

Funding source category [1]

University

Name [1]

The University of Auckland

Address [1]

The University of Auckland
Private Bag 92019
Auckland 1142

Country [1]

New Zealand

Primary sponsor type

University

Name

The University of Auckland

Address

The University of Auckland
Private Bag 92019
Auckland 1142

Country

New Zealand

Secondary sponsor category [1]

None

Name [1]

Address [1]

Country [1]

Ethics approval

Ethics application status

Approved

Ethics committee name [1]

Auckland Health Research Ethics Committee

Ethics committee address [1]

Auckland Health Research Ethics Committee
The University of Auckland
Private Bag 92019
Auckland 1142

Ethics committee country [1]

New Zealand

Date submitted for ethics approval [1]

21/02/2022

Approval date [1]

17/05/2022

Ethics approval number [1]

AH23991

Summary

Brief summary

The current study aims to investigate whether and how increasing human-likeness of a computer would affect users’ self-disclosure of health information in a clinical assessment context. And if so, what may be the psychological process facilitating such effects. To be specific, this study will compare users’ self-disclosure behaviours to an interactive, realistic humanlike digital human interviewer, an interactive, less humanlike chatbot interviewer, and a non-interactive online questionnaire task in the context of receiving a brief assessment on health-relevant information. 
The primary hypothesis is that computer’s increasing human-likeness will have differential impacts on users’ self-disclosure of health information depending on the sensitivity and valence of the health information. In particular, a computer’s increasing human-likeness will decrease users’ willingness to disclose sensitive health information, manifested in a stronger socially desirable responding pattern and a preference for not providing an answer. On the other hand, a computer’s increasing human-likeness may increase users’ willingness to disclose non-sensitive health information, especially for those positive health information. As such, our first hypothesis is that a more humanlike digital human interviewer should elevate stronger socially desirable responses to questions asking for sensitive health information, compared with a less humanlike chatbot interviewer, then to a least humanlike online questionnaire task (H1). Our second hypothesis is that a digital human interviewer would receive a higher proportion of users’ declined responses (i.e., choose I do not want to answer) to those sensitive health information, compared with a chatbot interviewer, then an online questionnaire task (H2). Meanwhile, a digital human interviewer may increase users’ self-disclosure of non-sensitive and positive health information, compared with a chatbot interviewer, then to an online questionnaire task (H3). The secondary hypothesis is, the above effects, if any, may be mediated by variations in users’ perceived anthropomorphism of the computers (H4). That is, users will perceive a stronger sense of “human presence” when interacting with a more humanlike digital human interviewer, compared to a chatbot interviewer and an online questionnaire task. Consequently, users may modify their self-disclosure behaviours in the same direction as how the real human presence affects self-disclosure.

Trial website

Trial related presentations / publications

Public notes

The contact details of the principal investigator are as below. 
Elizabeth Broadbent 
Department of Psychological Medicine
The University of Auckland
Email: e.broadbent@auckland.ac.nz 
Phone: (09) 3737599 Ext. 86756

Contacts

Principal investigator

Name

Prof Elizabeth Broadbent (supervisor)

Address

Department of Psychological Medicine
The University of Auckland, Faculty of Medical and Health Sciences
Building 507
85 Park Road
Grafton
Auckland 1023
New Zealand.

Country

New Zealand

Phone

+64 9 3737599

Fax

e.broadbent@auckland.ac.nz

Contact person for public queries

Name

Elizabeth Broadbent (supervisor)

Address

Department of Psychological Medicine
The University of Auckland, Faculty of Medical and Health Sciences
Building 507
85 Park Road
Grafton
Auckland 1023
New Zealand.

Country

New Zealand

Phone

+64 9 3737599

Fax

e.broadbent@auckland.ac.nz

Contact person for scientific queries

Name

Elizabeth Broadbent (supervisor)

Address

Department of Psychological Medicine
The University of Auckland, Faculty of Medical and Health Sciences
Building 507
85 Park Road
Grafton
Auckland 1023
New Zealand.

Country

New Zealand

Phone

+64 9 3737599

Fax

e.broadbent@auckland.ac.nz

Data sharing statement

Will individual participant data (IPD) for this trial be available (including data dictionaries)?

No/undecided IPD sharing reason/comment

Neither ethics board approval nor informed consent from participants were obtained to share participant data publicly.

What supporting documents are/will be available?

Doc. No.	Type	Citation	Link	Email	Other Details	Attachment
15873	Other				The assessment procedure	383981-(Uploaded-31-05-2022-14-33-05)-Study-related document.docx

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