Is a Picture Worth a Thousand Words: A Participatory Approach to Codesign Descriptive and Pictorial Social Norm Nudges and a Randomised Controlled Trial (RCT) to Examine Their Effectiveness in Improving Mental Health Help-seeking Intention
Phase: NA
Conditions: Mental Health Help-seeking
Sponsor: The Hong Kong Polytechnic University

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Nudges to Improve Mental Health Help-seeking 
 
 
 
 
 
 
 
 
 
 
 
Is a Picture Worth a Thousand Words? A Participatory Approach to Codesign 
Descriptive and Pictorial Social Norm Nudges and a Randomised Controlled 
Trial (RCT) to Examine Their Effectiveness in Improving Mental Health Help-
seeking Intention 
 
 
Tianyin Liu, Ph.D 
Department of Applied Social Sciences 
The Hong Kong Polytechnic University 
 
 
Document date: September 1st, 2024 

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Nudges to Improve Mental Health Help-seeking 
 
 
Background 
The need to increase mental health help-seeking in older people 
Although older people tend to have lower stress reactivity and better emotional regulation in 
response to the crisis over younger adults [1], the coronavirus disease 2019 (COVID-19) is 
unprecedented in its scale and magnitude, and older adults have been disproportionally affected both 
physically and mentally [2]. On the one hand, older people are at higher risk of infection, more 
severe symptoms, and mortality [3], and stringent infection prevention and control measures such as 
social distancing are necessary to keep them safe. On the other hand, social isolation resulting from 
limited social interaction and reduced physical activities may impact older people’s mental health in 
the longer term [4]. In Hong Kong, 446 people aged 60 or above committed suicide in 2021, the 
highest number in the older age group since 1973 [5]. 
Prevention and early intervention of mental health problems can reduce the suffering of 
affected individuals and the burden of carers [6], and prevent the escalation of mild conditions into 
more severe, chronic mental health diseases and suicide [7, 8]. However, help-seeking is frequently 
delayed or omitted in the older adult population, resulting in a longer duration of untreated 
symptoms, poor health outcomes, and consequent higher healthcare expenditure [9]. In Hong Kong, 
mental health service utilisation studies have consistently shown low service utilisation. A territorial 
representative survey estimated that less than 30% of the people with common mental disorders 
would seek mental health services for their mental health problems in a year [10]. Several plausible 
explanations exist for the low help-seeking of formal mental health services, including low mental 
health literacy, unfavourable attitudes towards formal help-seeking, and mental health-related public 
and personal stigma [11-13]. 
 
Theory of planned behaviour  
Help-seeking for mental health problems is a dynamic process: it begins with the awareness of 
problems that may require external help, followed by expression of symptoms and need for support, 
then the identification of accessible help sources, and finally, the willingness and action to seek help 
[14]. This conceptualisation of help-seeking supports a logical process proposed by the theory of 
planned behaviour (TPB) (Figure 1a) [15], which uses attitude (the answer to the question: “Do I 
want to do that?”), subjective norm (“Do other people want me to do that?”), and perceived 
behavioural control (“Do I have the necessary ability to do that?”) as antecedents of intention and 

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subsequent behaviour. More recent development of TPB further differentiates two types of 
subjective norms: descriptive norms (“What do others really do?”) and injunctive social norms 
(“What others think I should do?”) [16].  
The existing literature has highlighted the utility of TPB in understanding various health 
behaviours. A meta-analysis of 237 independent prospective studies found that the TPB accounted 
for 19.3% of the variability in health behaviour, with intention being the most robust predictor [17]. 
A more recent systematic review and meta-analysis of TPB found that subjective norms were the 
main predictor of food safety behavioural intention [18]. In the context of mental health help-
seeking and Chinese society specifically, it was found that attitude, subjective norm, perceived 
behavioural control, and perceived barriers significantly predicted help-seeking intention; in 
addition, subjective norm indirectly predicted intention to seek help from mental health 
professionals through influencing attitude and perceived behavioural control [19]. This study 
highlighted the direct and indirect pathways of the subjective norm in influencing help-seeking 
intention among Chinese (see Figure 1b, further revised by differentiating descriptive and social 
norm), which differs from the Western literature, where the subjective norm is generally considered 
a very weak predictor of behavioural intention and behaviour [20]. This difference may be explained 
by the collectivist Chinese culture, which reinforces the role of subjective norms in help-seeking 
intention; or it could be attributed to the narrow conceptualisation of subjective norm in earlier 
studies, where the descriptive norm and social norm were lumped together. However, no study has 
been devoted to investigating TPB’s utility in understanding mental health help-seeking in older 
Chinese, who social norms might heavily influence since they are brought up in a more collectivist 
culture than younger Chinese. 
 
Nudge theory   
There are competing theories to TPB in explaining behavioural choices, and one of the most 
prominent theories, despite not yet being coherent, is the nudge theory (NT). According to the dual-
process theory in psychology, there are two basic modes of reasoning: reflective vs automatic [21]. 
While TPB assumes humans make logical decisions based on a slow and reflective process that 
considers explicit goals and intentions, NT rides on the fast and automatic system highly susceptible 
to environmental influences and cognitive biases [22], such as valuing the present more than the 
future, following the social norm, and attaching to the status quo. An example of a popular nudge 
that uses the status quo bias is having people registered as organ donors by default with the 

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possibility of opt-out, which leads to a higher donation rate than a system in which donors must 
actively opt-in [22].  
Although TPB and NT are conceptualised on different modes of reasoning, they converge on 
changing social norm as a pathway to foster behavioural change. Injunctive social norms, as 
previously mentioned, are answers to the question “what others think I should do”; their relevance to 
an individual can be multiplied by the motivation to comply with different referent groups, such as 
family, friends, colleagues, and society in general [16]. Nudges based on social norms (norm-
nudges) can be compelling interventions compared to traditional approaches, and they have been 
increasingly used in promoting health and mental health behaviours. For example, a meta-analysis of 
interventions aiming to promote hygiene behaviours in 11 developing countries and the most 
effective messages were based on automatic motivational mechanisms that utilised social norms 
[23]. A more recent empirical study used a brief social norm for suicide prevention among college 
students, and e-mails containing the social norm-nudge (e.g., “A recent survey found that 95.6% of 
people believe that you should get help if you are suffering from anxiety, and most people who 
suffer from anxiety and seek treatment report improvement”) were 164% more likely to lead to link-
clicking relative to those without the nudge [24].  
Conformity with local social norms has been shown as a powerful device in driving 
behavioural changes. Nevertheless, empirical studies using social norm-nudge for promoting mental 
health behaviours are scarce, even more so in the older adult population, who have a lower mental 
health help-seeking rate than other age groups [25].  
 
Picture superiority in the digital age  
People living in the modern world are becoming more visually oriented than ever, especially with 
the popularity of social media in the digital age. Visual communication has been an important topic 
in health communication research [26]. Systematic reviews suggest that pictures closely linked to 
written or spoken text have many beneficial effects over text alone, such as improving recall, 
increasing attention, enhancing understanding of complicated materials, and improving perceptions 
of disease severity and efficacy [27].  
 
Social media platforms, along with apps and other user-friendly tools, are making 
disseminating multimodal artefacts easier to an unprecedented number of people. Hong Kong’s 
older adults have more access to social media than before. According to the Census and Statistics 
Department’s Thematic Household Survey Report No. 73 released in May 2022, nearly one million 

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people aged 65 years and above own a smartphone in 2021, accounting for 73% of the total elderly 
population, up from 68.1% in 2019 and 60% in 2018 [28]. The increase in smartphone ownership 
among older people in Hong Kong lays the ground for intervention and research utilising their 
digital information access, and digital images may have the advantage over text in engaging older 
people, especially among those with lower literacy. However, little is known about the application of 
pictures in constructing social norm nudges, and no study has been done to examine the effects of 
pictures in fostering changes in mental health help-seeking in older people. 
 
Given the research gaps mentioned above, we raise four research questions in the proposed study: 
(1) What are the utilities of revised TPB in understanding the mental health help-seeking pathway in 
older Chinese? 
(2) How effective are descriptive social norm nudges in changing subjective norms and improving 
mental health help-seeking intention compared to the knowledge-based text? 
(3) How effective are pictorial social norm nudges in changing subjective norms and improving 
mental health help-seeking intention compared to the knowledge-based text? 
(4) Are pictures more effective than descriptions in changing subjective norms and improving 
mental health-seeking intention in older Chinese? 
Research plan and methodology 
Design 
This study will ride on a large-scale community-based holistic mental health support project for 
older adults at risk of or with subthreshold depression in Hong Kong [31]. We will conduct a three-
arm randomised controlled trial (RCT) to examine the effectiveness of social norm nudges over 
traditional knowledge-based text. 
 
Participants 
A total of 540 older adults aged 60 years or above will be recruited through local NGOs. This 
sample size is calculated based on the three-arm RCT design, with the social norm and help-seeking 
intention as two primary outcomes. Using G*Power, based on previous literature, assuming a 
conservative effect size (d=0.15), 80% power and 5% significance level, the minimum required 
sample size is 432 to detect a significantly small effect size in a linear mixed model with three 
groups. To account for 20% dropouts in total (at T1 and T2), we propose a sample of 432/0.8 = 540 

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people to account for the attrition  
The inclusion criteria for participants are: (1) aged 60 years or above at the time of the 
baseline assessment; (2) have no severe mental disorder or cognitive impairment; (3) have no severe 
visual impairment; (4) be able to read Chinese; and (5) have a smartphone installed with instant 
communication application that can receive text and images. Older adults with difficulty in 
communication will be excluded, and participants who show imminent suicidal risk at any stage of 
the study will be referred to appropriate services. 
 
Procedures 
A three-arm RCT will be conducted. Participants will be recruited from local NGOs and public 
housing estates and then screened for eligibility. Those who are eligible and give consent to 
participate will be randomised into three groups by a researcher blind to the intervention design. 
Participants will receive descriptive social norm nudges (intervention group 1), pictorial social norm 
nudges (intervention group 2), or traditional mental health education information (control) for 2 
weeks consecutively through a smartphone (see Figure 2 for examples of intervention materials). 
Researchers blind to group allocation will conduct telephone interviews with participants at the 
baseline (T0), 2 weeks after the baseline (post-intervention, T1), and 12 weeks after the baseline 
(follow-up, T2).  
 
Measurements 
Primary outcomes: 
1. Subjective norms: these will be measured by 6 items adapted from the validated Chinese 
version of the TPB questionnaire (C-TPB) [10] and the difference between descriptive and 
social norms based on Ham et al.’s model [16]. Three items will measure descriptive norms 
describing what society in general does when facing mental health challenges, and the other 
three items will measure social norms describing what the significant others of the 
participant would do. Responses will be rated on a 6-point Likert scale, with higher scores 
indicating higher levels of subjective norms about mental health help-seeking.  
2. Mental health help-seeking intention: this will be measured on a 3-item subscale of C-TPB 
[10]. Responses will be rated on a 6-point Likert scale, with higher scores indicating higher 
levels of help-seeking intention.  

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Secondary outcomes: 
3. Perceived behavioural control: this will be measured on a 3-item subscale of the C-TPB. 
Responses are rated on a 6-point Likert scale, with higher scores indicating higher levels of 
behavioural control. 
4. Help-seeking attitude: measured on a 5-item subscale of C-TPB, items will be measured on a 
6-point semantic differential scale, with higher scores indicating a more positive attitude 
towards help-seeking. 
5. Perceived barriers to help-seeking: measured by a 6-item scale adapted from the Chinese 
American Psychiatric Epidemiological study (CAPES) [34], responses are rated on a 6-point 
Likert scale, with higher scores indicating more significant perceived barriers to help-
seeking. 
6. Mental health conditions 
a. Depression: the Patient Health Questionnaire (PHQ-9) will be used [35]. It is a 9-item 
instrument that incorporates depression diagnostic criteria with other leading major 
depressive symptoms and rates the frequency of the symptoms, factoring into the 
scoring severity index. PHQ-9 scores of 5-9, 10-14, 15-19, 20 and above represent 
mild, moderate, moderately severe, and severe depression, respectively  
b. Anxiety: the Generalised Anxiety Disorder 7-item scale (GAD-7) will be used [36]. It 
is a 7-item scale; responses to each item are rated on a 4-point Likert scale and range 
from 0 to 3. It taps on the most prominent diagnostic features for GAD, and scores of 
5, 10, and 15 are taken as the cut-off points for mild, moderate, and severe anxiety, 
respectively.  
c. Loneliness: the UCLA Loneliness Scale (UCLA-3) will be used [37]. It is a 3-item 
scale measuring an individual’s perceived loneliness. Each item is evaluated with 
scores ranging from 0 (never) to 3 (often), the total score is the sum of all items, and 
a higher score indicates a higher level of perceived loneliness. 
Control variables: age, gender, living status, education (years and highest attainment), work years, 

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and self-rated physical health will be collected at the baseline. 
Data analysis plan 
First, descriptive statistics and correlations will be examined among all variables, and the normality 
and distributions of the outcome variables will be checked using the Kolmogorov-Smirnov test and 
the Shapiro-Wilk test. Cronbach’s alpha will be used to assess the internal consistency of the revised 
C-TPB, PHQ-9, GAD-7, and UCLA-3.  
Second, we will use T0 data and follow a two-stage modelling procedure [38], using 
confirmatory factor analysis (CFA) to examine the adequacy of the measurement for each of the 
constructs (primary and secondary outcomes), and then perform structural equation modelling 
(SEM) to compare the fit of the revised TPB (Fig. 1b) [19] and the original model (Fig. 1a) [30]. We 
will use the maximum likelihood (ML) estimation method to examine the fit of models to their 
respective observed variance-covariance matrices, and evaluate model fit based on multiple indexes 
of goodness-of-fit, i.e., absolute fit (chi-square), the comparative fit index (CFI), the Tucker–Lewis 
index (TLI), and the root mean square error of approximation (RMSEA), as recommended [39].  
Third, we will follow the Transparent Reporting of Evaluations with Nonrandomized 
Designs (TREND) statement and perform an intention-to-treat analysis. Multiple imputation models 
for missing values will be conducted to investigate the potential effects of missing data and attrition, 
to serve as sensitivity analysis. Observations at three-time points will be nested within individuals 
and individuals nested within groups, and changes in outcome measures will be assessed by a three-
level linear mixed model.   
 
 

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