The impact of digital interventions on help-seeking behaviour for mental health problems: a systematic literature review

A Brief Workplace Training Program to Support Help-Seeking for Mental Ill-Health: Protocol for the Helipad Cluster Randomized Controlled Trial

BACKGROUND

Most people with mental health problems do not seek help, with delays of even decades in seeking professional help. Lack of engagement with professional mental health services can lead to poor outcomes and functional impairment. However, few effective interventions have been identified to improve help-seeking in adults, and those that exist are not widely implemented to deliver public health impact. Co-designing interventions with people with lived experience of mental ill-health and other relevant stakeholders is critical to increase the likelihood of uptake and engagement with these programs.

OBJECTIVE

This study aims to (1) test the effectiveness of a co-designed help-seeking program on increasing professional help-seeking intentions in employees in a workplace setting; (2) determine whether the program reduces mental illness stigma and improves help-seeking intentions and behavior, mental health literacy, mental health symptoms, and work and activity functioning relative to the control condition; (3) explore factors that facilitate broader implementation of the co-designed program; and (4) explore the cost-effectiveness of the co-designed program compared to the control condition over 6 months.

METHODS

A 2-arm cluster randomized controlled trial will be conducted (target sample: N=900 from 30 to 36 workplaces, with n=25 to 35 participants per workplace). The trial will compare the relative effectiveness of an enhanced interactive program (intervention condition) with a standard psychoeducation-alone program (active control condition) on the primary outcome of professional help-seeking intentions as measured by the General Help-Seeking Questionnaire. Secondary outcomes include the impact on mental illness stigma; mental health literacy; help-seeking attitudes and behavior; work and activity functioning; quality of life; and symptoms of mental ill-health including depression, anxiety, and general psychological distress.

RESULTS

Facilitators of and risks to the trial are identified and addressed in this protocol. Recruitment of workplaces is scheduled to commence in the first quarter of 2024.

CONCLUSIONS

If effective, the program has the potential to be ready for rapid dissemination throughout Australia, with the potential to increase appropriate and efficient service use across the spectrum of evidence-based services.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12623000270617p; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=385376.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/55529.

Experiences of a digital health intervention for young people exposed to technology assisted sexual abuse: a qualitative study

BACKGROUND

There is growing evidence that Technology Assisted Sexual Abuse (TASA) represents a serious problem for large numbers of children. To date, there are very few evidence-based interventions available to young people (YP) after they have been exposed to this form of abuse, and access to support services remains a challenge. Digital tools such as smartphones have the potential to increase access to mental health support and may provide an opportunity for YP to both manage their distress and reduce the possibility of further victimization. The current study explores the acceptability of a digital health intervention (DHI; the i-Minds app) which is a theory-driven, co-produced, mentalization-based DHI designed for YP aged 12-18 who have experienced TASA.

METHODS

Semi-structured interviews were conducted with 15 YP recruited through Child and Adolescent Mental Health Services, a Sexual Assault Referral Centre and an e-therapy provider who had access to the i-Minds app as part of a feasibility clinical trial. Interviews focused on the acceptability and usability of i-Minds and were coded to themes based on the Acceptability of Healthcare Interventions framework.

RESULTS

All participants found the i-Minds app acceptable. Many aspects of the app were seen as enjoyable and useful in helping YP understand their abuse, manage feelings, and change behavior. The app was seen as usable and easy to navigate, but for some participants the level of text was problematic and aspects of the content was, at times, emotionally distressing at times.

CONCLUSIONS

The i-Minds app is useful in the management of TASA and helping change some risk-related vulnerabilities. The app was designed, developed and evaluated with YP who had experienced TASA and this may account for the high levels of acceptability seen.

TRIAL REGISTRATION

The trial was registered on the ISRCTN registry on the 12/04/2022 as i-Minds: a digital intervention for young people exposed to online sexual abuse (ISRCTN43130832).

The Effect of Digital Mental Health Literacy Interventions on Mental Health: Systematic Review and Meta-Analysis

BACKGROUND

Accelerated by technological advancements and the recent global pandemic, there is burgeoning interest in digital mental health literacy (DMHL) interventions that can positively affect mental health. However, existing work remains inconclusive regarding the effectiveness of DMHL interventions.

OBJECTIVE

This systematic review and meta-analysis investigated the components and modes of DMHL interventions, their moderating factors, and their long-term impacts on mental health literacy and mental health.

METHODS

We used a random-effects model to conduct meta-analyses and meta-regressions on moderating effects of DMHL interventions on mental health.

RESULTS

Using 144 interventions with 206 effect sizes, we found a moderate effect of DMHL interventions in enhancing distal mental health outcomes (standardized mean difference=0.42, 95% CI -0.10 to 0.73; P<.001) and a large effect in increasing proximal mental health literacy outcomes (standardized mean difference=0.65, 95% CI 0.59-0.74; P<.001). Uptake of DMHL interventions was comparable with that of control conditions, and uptake of DMHL interventions did not moderate the effects on both proximal mental health literacy outcomes and distal mental health outcomes. DMHL interventions were as effective as face-to-face interventions and did not differ by platform type or dosage. DMHL plus interventions (DMHL psychoeducation coupled with other active treatment) produced large effects in bolstering mental health, were more effective than DMHL only interventions (self-help DMHL psychoeducation), and were comparable with non-DMHL interventions (treatment as usual). DMHL interventions demonstrated positive effects on mental health that were sustained over follow-up assessments and were most effective in enhancing the mental health of emerging and older adults.

CONCLUSIONS

For theory building, our review and meta-analysis found that DMHL interventions are as effective as face-to-face interventions. DMHL interventions confer optimal effects on mental health when DMHL psychoeducation is combined with informal, nonprofessional active treatment components such as skills training and peer support, which demonstrate comparable effectiveness with that of treatment as usual (client-professional interactions and therapies). These effects, which did not differ by platform type or dosage, were sustained over time. Additionally, most DMHL interventions are found in Western cultural contexts, especially in high-income countries (Global North) such as Australia, the United States, and the United Kingdom, and limited research is conducted in low-income countries in Asia and in South American and African countries. Most of the DMHL studies did not report information on the racial or ethnic makeup of the samples. Future work on DMHL interventions that target racial or ethnic minority groups, particularly the design, adoption, and evaluation of the effects of culturally adaptive DMHL interventions on uptake and mental health functioning, is needed. Such evidence can drive the adoption and implementation of DMHL interventions at scale, which represents a key foundation for practice-changing impact in the provision of mental health resources for individuals and the community.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42023363995; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023363995.

Effects of a stigma reduction intervention on help-seeking behaviors in university students: A 2019-2021 randomized controlled trial

INTRODUCTION

Stigma is one of the most frequently identified help-seeking barriers, but there is a lack of research on the effects of stigma reduction interventions on actual mental health help-seeking behaviors during crucial academic years. This research explores the effects of stigma on university students' mental health care help-seeking behaviors before and during the pandemic.

METHODS

A randomized control trial spanned from 2019 to 2021 at the University of Porto, along five evaluation moments, with students into one control group and two intervention groups-the interventions aimed to reduce depression stigma.

RESULTS

Among the 702 participants (mean age 18.87, 59.4 % female), the intervention groups significantly increased help-seeking behaviors. In 2020, the intervention groups, having reduced stigma, continued to demonstrate to be more prompt to seek mental health help. In 2021, 22 months, the effects of the intervention on help-seeking were no longer significant; however, participants in the intervention groups showed less severe symptomatology.

CONCLUSION

Stigma reduction interventions have a pronounced effect on enhancing help-seeking behaviors among university students, even during times of crisis. This study advocates for prioritizing stigma reduction in academic settings, highlighting its value in promoting mental health access during crucial academic and life challenges.

Development and codesign of flourish: A digital suicide prevention intervention for LGBTQ+ youth who have experienced online victimization

Background

LGBTQ+ youth experience disproportionately high rates of online victimization (OV), referring to harmful remarks, images, or behaviors in online settings, which is associated with suicidal risk. Current services have gaps in supporting LGBTQ+ youth facing OV events. To address these gaps, this study aims to develop Flourish, a digital suicide prevention intervention for LGBTQ+ youth who have experienced OV.

Methods

Qualitative interviews were conducted with 20 LGBTQ+ youth with past-year history of OV and lifetime history of suicidality, 11 of their parents, and 10 LGBTQ+-serving professionals. Subsequently, an iterative codesign process was conducted with 22 youth through individual and group design sessions, followed by usability testing. Data were recorded and transcribed. Qualitative interviews were analyzed using a qualitative description approach, and data from design sessions and usability testing were analyzed using rapid qualitative techniques.

Results

Interviews with youth, parents, and professionals suggested preferences for Flourish to be a partially automated, text message intervention leveraging web-based content that is a safe space for LGBTQ+ youth to seek support for OV through education, coping skills, and help-seeking resources. School and mental health services professionals considered the potential for implementing Flourish within youth services settings. Usability testing, assessed through the System Usability Scale, yielded an average rating of 91, indicating excellent perceived usability.

Conclusions

Flourish has potential to be an acceptable intervention to support LGBTQ+ youth following OV. Future steps will include testing the feasibility and efficacy of Flourish and further examining Flourish's potential for implementation within services for LGBTQ+ youth.

User-Centered Development of Bolster, an mHealth Intervention for Early Psychosis Caregivers: Needs Assessment, Prototyping, and Field Trial

BACKGROUND

Caregivers play a critical role in the treatment and recovery of youth and young adults at risk for psychosis. Caregivers often report feeling isolated, overwhelmed, and lacking in resources. Mobile health (mHealth) has the potential to provide scalable, accessible, and in-the-moment support to caregivers. To date, few if any mHealth resources have been developed specifically for this population.

OBJECTIVE

The aim of this study was to conduct user-centered design and testing of an mHealth intervention to support early psychosis caregivers.

METHODS

We conducted a multiphase user-centered development process to develop the Bolster mobile app. In phase 1, a total of 21 caregivers were recruited to participate in a qualitative needs assessment and respond to an initial prototype of the Bolster platform. Content analysis was used to identify key needs and design objectives, which guided the development of the Bolster mobile app. In phase 2, a total of 11 caregivers were recruited to participate in a 1-week field trial wherein they provided qualitative and quantitative feedback regarding the usability and acceptability of Bolster; in addition, they provided baseline and posttest assessments of the measures of distress, illness appraisals, and family communication.

RESULTS

In phase 1, participants identified psychoeducation, communication coaching, a guide to seeking services, and support for coping as areas to address. Live prototype interaction sessions led to multiple design objectives, including ensuring that messages from the platform were actionable and tailored to the caregiver experience, delivering messages in multiple modalities (eg, video and text), and eliminating a messaging-style interface. These conclusions were used to develop the final version of Bolster tested in the field trial. In phase 2, of the 11 caregivers, 10 (91%) reported that they would use Bolster if they had access to it and would recommend it to another caregiver. They also reported marked changes in their appraisals of illness (Cohen d=0.55-0.68), distress (Cohen d=1.77), and expressed emotion (Cohen d=0.52).

CONCLUSIONS

To our knowledge, this study is the first to design an mHealth intervention specifically for early psychosis caregivers. Preliminary data suggest that Bolster is usable, acceptable, and promising to improve key targets and outcomes. A future fully powered clinical trial will help determine whether mHealth can reduce caregiver burdens and increase engagement in services among individuals affected by psychosis.

Differences in Help-Seeking Behavior among University Students during the COVID-19 Pandemic Depending on Mental Health Status: Results from a Cross-Sectional Survey

BACKGROUND

Current evidence suggests that a significant proportion of university students are affected by mental disorders and suicidal ideation. Despite this, a treatment gap exists. Therefore, the present study assessed students' knowledge and past use of on- and off-campus mental health services and help-seeking intentions. Furthermore, resilience was investigated as a potential barrier to help-seeking behavior.

METHODS

Data were collected between April and May 2022 from N = 5510 students from Saxony, Germany. To compare dependent variables, subgroups were computed according to students' mental health status. Variables were assessed using standardized questionnaires. Chi-square tests were used for comparisons between groups. Multiple regression models were used to investigate the influence of resilience on help-seeking behavior.

RESULTS

Between 34 and 38% (depending on the subgroup) of participants indicated that they were not aware of their universities' psychosocial counseling services. Furthermore, between 17 and 19% of participants indicated that they were not willing to seek help from professional mental health services. Finally, the previously found negative effect of resilience on help-seeking behavior was confirmed.

CONCLUSION

The results showed a lack of awareness regarding universities' mental health services and a treatment gap among university students. Universities and healthcare providers need to educate students about mental health services and how to access them. Further research is needed to elucidate the differential impact of resilience on mental health and help-seeking.

The Impact of a Messenger-Based Psychosocial Chat Counseling Service on Further Help-Seeking Among Children and Young Adults: Longitudinal Study

BACKGROUND

Mental crises have high prevalences in adolescence. Early interventions appear to be highly important to diminish the risk of the deterioration, recurrence, or chronification of symptoms. In recent years, various providers have started offering live chat support in psychological crises. The messenger-based psychological counseling service krisenchat aims to support young people in crises and, if necessary, provide a recommendation for a referral to the health care system or to seek further help from a trusted adult person.

OBJECTIVE

This study aimed to investigate the impact of using the counseling service of krisenchat on the further help-seeking behavior of young people, and to identify associated factors of further help-seeking.

METHODS

This longitudinal study analyzed anonymous data from 247 individuals who used krisenchat between October 2021 and March 2022, and received a recommendation for further help-seeking. An online survey directly after the chat assessed the perceived helpfulness of the chat and well-being after the chat. After 4 weeks, further help-seeking, facilitators and barriers to help-seeking, and self-efficacy were assessed in an online follow-up survey.

RESULTS

The most frequently recommended services or persons to seek further help from included a psychotherapist or social psychiatric service (75/225, 33.3%), a school psychologist or school social worker (52/225, 23.1%), and the user's parents (45/225, 20.0%). Of the 247 users, 120 (48.6%) indicated that they contacted the recommended service or person, and of these, 87 (72.5%) stated that they already had an appointment (or talk) with the respective service or person or that an appointment (or talk) was scheduled. The most frequently reported facilitators for further help-seeking were mental health literacy (54/120, 45.0%), improvement of self-efficacy (55/120, 45.8%), and symptom recognition (40/120, 33.3%). In users not displaying further help-seeking behavior, the most frequent barriers included stigmatization (60/127, 47.2%), lack of mental health literacy (59/127, 46.5%), need for self-reliance and autonomy (53/127, 41.7%), and negative family beliefs regarding help services (53/127, 41.7%). Subgroup comparisons indicated significantly higher levels of self-efficacy in users displaying further help-seeking behavior than in those not displaying further help-seeking behavior. Both subgroups did not differ in gender, age, recommended service or person, chat topics, perceived helpfulness, and well-being.

CONCLUSIONS

The findings of this study indicate that children and young adults receiving counseling on krisenchat benefit in terms of seeking further help. Further help-seeking seems to be associated with higher levels of self-efficacy.

TRIAL REGISTRATION

Deutsches Register Klinischer Studien DRKS00026671; https://tinyurl.com/4fm5xe68.

Evaluation of an online suicide prevention program to improve suicide literacy and to reduce suicide stigma: A mixed methods study

Low-threshold e-health approaches in prevention to reduce suicide stigma are scarce. We developed an online program containing video reports on lived experience of suicide and evidence-based information on suicidality. We evaluated the program by a mixed methods design. We examined pre-post-changes of program completers (n = 268) in suicide literacy, suicide stigma (self and perceived), and self-efficacy expectation of being able to seek support in psychologically difficult situations using linear mixed models. To examine reported changes and helpful program elements 12-26 weeks after program completion, we content analyzed transcripts of telephone interviews (n = 16). Program completers showed more suicide literacy (Cohen's d = .74; p < .001), higher self-efficacy expectations to seek support (d = .09; p < .01), lower self-stigma (subscales glorification/normalization: d = -.13, p = .04; isolation/depression: d = -.14; p = .04; stigma: d = -.10; p = .07; n = 168) compared to baseline. We found no significant differences in perceived suicide stigma. We identified lived experience reports, the possibility of sharing own narrative on stigma and suicidality, and information on support as helpful elements. The current online program can increase suicide literacy and self-efficacy expectations to seek support and reduce self-stigma. We recommend a larger randomized controlled trial with longer follow-up to confirm these findings.

Shifts in patterns of help-seeking during the COVID-19 pandemic: The case of Hong Kong's younger generation

RATIONALE

Help-seeking can convert an individual's bonding social capital into social support, which has been shown to buffer the impact of psychological distress. The younger generation (individuals aged 15-35 years) have been the least likely to actively seek help despite facing a rising burden of mental health problems. COVID-19 pandemic restrictions may have altered their help-seeking behaviors, but the extent of such shift remains little understood, particularly in Asian contexts.

OBJECTIVE

To understand how the younger generation's patterns of help-seeking (activation of different combinations of support sources) have shifted in pandemic times, who have experienced the shift, and what explanatory factors are involved.

METHODS

Data were obtained from two waves (2019, 2020) of online survey responses by 438 community-dwelling younger generation people in Hong Kong, recruited through the authors' affiliated institutions and territory-wide community outreach organizations. Latent class analysis was conducted on participants' self-reported help-seeking behaviors in each survey wave. Constituents' characteristics in each latent class were examined, and between-wave changes in individuals' class membership were identified. Logistic regressions identified explanatory factors that significantly explained the changes.

RESULTS

Three consistent patterns of help-seeking were identified in both survey waves. A major shift was observed for individuals with poorer mental health histories who faced moderate distress. They relied on their family, friends, and partner pre-pandemic, but no longer activated these supports during the pandemic. Posting status updates on social media, along with various communication habits and sociodemographic factors that differed by age group, were associated with this shift.

CONCLUSIONS

Shifts in the younger generation's patterns of help-seeking may be an early warning signal to invest additional resources in facilitating help-seeking among the younger generation. Findings also serve as a reminder that public health restrictions may have inadvertent mental health implications that should be considered in future scenarios.

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

BACKGROUND

A single generalizable metric that accurately predicts early dropout from digital health interventions has the potential to readily inform intervention targets and treatment augmentations that could boost retention and intervention outcomes. We recently identified a type of early dropout from digital health interventions for smoking cessation, specifically, users who logged in during the first week of the intervention and had little to no activity thereafter. These users also had a substantially lower smoking cessation rate with our iCanQuit smoking cessation app compared with users who used the app for longer periods.

OBJECTIVE

This study aimed to explore whether log-in count data, using standard statistical methods, can precisely predict whether an individual will become an iCanQuit early dropout while validating the approach using other statistical methods and randomized trial data from 3 other digital interventions for smoking cessation (combined randomized N=4529).

METHODS

Standard logistic regression models were used to predict early dropouts for individuals receiving the iCanQuit smoking cessation intervention app, the National Cancer Institute QuitGuide smoking cessation intervention app, the WebQuit.org smoking cessation intervention website, and the Smokefree.gov smoking cessation intervention website. The main predictors were the number of times a participant logged in per day during the first 7 days following randomization. The area under the curve (AUC) assessed the performance of the logistic regression models, which were compared with decision trees, support vector machine, and neural network models. We also examined whether 13 baseline variables that included a variety of demographics (eg, race and ethnicity, gender, and age) and smoking characteristics (eg, use of e-cigarettes and confidence in being smoke free) might improve this prediction.

RESULTS

The AUC for each logistic regression model using only the first 7 days of log-in count variables was 0.94 (95% CI 0.90-0.97) for iCanQuit, 0.88 (95% CI 0.83-0.93) for QuitGuide, 0.85 (95% CI 0.80-0.88) for WebQuit.org, and 0.60 (95% CI 0.54-0.66) for Smokefree.gov. Replacing logistic regression models with more complex decision trees, support vector machines, or neural network models did not significantly increase the AUC, nor did including additional baseline variables as predictors. The sensitivity and specificity were generally good, and they were excellent for iCanQuit (ie, 0.91 and 0.85, respectively, at the 0.5 classification threshold).

CONCLUSIONS

Logistic regression models using only the first 7 days of log-in count data were generally good at predicting early dropouts. These models performed well when using simple, automated, and readily available log-in count data, whereas including self-reported baseline variables did not improve the prediction. The results will inform the early identification of people at risk of early dropout from digital health interventions with the goal of intervening further by providing them with augmented treatments to increase their retention and, ultimately, their intervention outcomes.

Personalised depression forecasting using mobile sensor data and ecological momentary assessment

INTRODUCTION

Digital health interventions are an effective way to treat depression, but it is still largely unclear how patients' individual symptoms evolve dynamically during such treatments. Data-driven forecasts of depressive symptoms would allow to greatly improve the personalisation of treatments. In current forecasting approaches, models are often trained on an entire population, resulting in a general model that works overall, but does not translate well to each individual in clinically heterogeneous, real-world populations. Model fairness across patient subgroups is also frequently overlooked. Personalised models tailored to the individual patient may therefore be promising.

METHODS

We investigate different personalisation strategies using transfer learning, subgroup models, as well as subject-dependent standardisation on a newly-collected, longitudinal dataset of depression patients undergoing treatment with a digital intervention ( N = 65 patients recruited). Both passive mobile sensor data as well as ecological momentary assessments were available for modelling. We evaluated the models' ability to predict symptoms of depression (Patient Health Questionnaire-2; PHQ-2) at the end of each day, and to forecast symptoms of the next day.

RESULTS

In our experiments, we achieve a best mean-absolute-error (MAE) of 0.801 (25% improvement) for predicting PHQ-2 values at the end of the day with subject-dependent standardisation compared to a non-personalised baseline ( MAE = 1.062 ). For one day ahead-forecasting, we can improve the baseline of 1.539 by 12 % to a MAE of 1.349 using a transfer learning approach with shared common layers. In addition, personalisation leads to fairer models at group-level.

DISCUSSION

Our results suggest that personalisation using subject-dependent standardisation and transfer learning can improve predictions and forecasts, respectively, of depressive symptoms in participants of a digital depression intervention. We discuss technical and clinical limitations of this approach, avenues for future investigations, and how personalised machine learning architectures may be implemented to improve existing digital interventions for depression.