iKinnect2.0 App for Suicide Risk in Youth

Recruiting at 1 trial location

Overseen By Allison Ruork, PhD

Age: Any Age

Sex: Any

Trial Phase: Academic

Sponsor: Evidence-Based Practice Institute, Seattle, WA

Disqualifiers: Non-English speakers, No smartphone, others

Compensation: Varies

Number of Visits: 4

240 Participants Needed

Trial Summary

What is the purpose of this trial?

This study is a 16-week intent-to-treat randomized controlled trial (RCT) with 120 suicidal juvenile justice (JJ)-involved transition-age (TA) youth (age 15-21 years) and a primary caregiver (dyads). Dyads will be randomly assigned to iKinnect2.0 (n=60 dyads) or Life360 (control app) plus an electronic suicide resources brochure (n=60 dyads). This design will test iKinnect2.0's new features for suicide prevention against TA youth awareness of and access to high-quality suicide prevention resources, while simultaneously testing features relating to conduct problems and parent management against parents knowing the TA youth's whereabouts in real-time and controlling for dyad member engagement in technology (Life360). Participants will be assessed at baseline, 4, 8 and 16 weeks. Primary youth-reported outcomes relating to suicide risk include: Suicidal behaviors (ideation, planning, attempts), non-suicidal self-injurious behaviors, self-efficacy in coping with distress, and use of imminent distress coping strategies (behavioral skills, use of crisis stabilization plan). Youth will also report on their criminal behavior. Primary caregiver-reported outcome variables relating to youth suicide include: Self-efficacy in applying family-based suicide-prevention strategies and reported use of those strategies; caregivers will also report on their own functioning (efficacy/confidence in parenting skills, life stress), TA youth functioning (internalizing and externalizing symptoms), parental management behaviors (expectation clarity, parental monitoring, discipline effectiveness/consistency, use of rewards), and parent-youth relationship quality (communication, conflict, support). App satisfaction and use of technology outcomes (i.e., degree of app usage, features used) will be examined and reported descriptively.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your healthcare provider.

What data supports the effectiveness of the iKinnect2.0 treatment for suicide risk in youth?

Research shows that smartphone apps for mental health, like safety planning and self-monitoring apps, are considered usable and acceptable by patients with suicidal thoughts. These apps can help manage suicidal ideation by providing support and strategies, suggesting that similar apps like iKinnect2.0 could be effective in helping youth at risk. ^{1 2 3 4 5}

How is the iKinnect2.0 treatment different from other treatments for suicide risk in youth?

The iKinnect2.0 treatment is unique because it uses a mobile app to monitor and assess suicide risk in real-time, allowing for immediate intervention and support, unlike traditional methods that rely on periodic assessments. ^{6 7 8 9 10}

Research Team

Cindy Schaeffer, PhD

Principal Investigator

University of Maryland

Linda A. Dimeff, PhD

Principal Investigator

Evidence-Based Practice Institute

Eligibility Criteria

This trial is for English-speaking youth aged 15-21 at high risk of suicide, with a history of self-harm or attempts, who use a smartphone with data and live with their primary caregiver. Caregivers must also participate, be fluent in English, and have a smartphone. Youth should be involved in juvenile justice services.

Inclusion Criteria

I live with my parent or guardian almost every day of the week.

I am a young person at risk of suicide and willing to talk about it with my parent.

PARENT/GUARDIAN: Fluent in English

Exclusion Criteria

To ensure consistency of planned and actual enrollment, particularly of Black youth, researchers will begin to exclude youth of certain races once pre-established recruitment milestones for a specific racial group is achieved.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Pilot Testing

Usability and acceptability testing of iKinnect2.0 features with target end-users and stakeholders

8 weeks

Randomized Controlled Trial

Participants are randomly assigned to iKinnect2.0 or Life360 control app for a 16-week trial to test the efficacy of iKinnect2.0 in reducing suicidal behaviors and improving coping strategies

16 weeks

Assessments at baseline, 4, 8, and 16 weeks

Follow-up

Participants are monitored for safety and effectiveness after the trial

4 weeks

Treatment Details

Interventions

• Active Control App
• iKinnect2.0

Trial Overview The study compares iKinnect2.0, an app designed to help prevent suicide among young people involved in the juvenile justice system, against Life360 (a control app) plus electronic resources on suicide prevention over a period of 16 weeks.

Participant Groups

Group I: iKinnect2.0 Experimental Treatment 1 Intervention

Parent-Youth dyads assigned to the iKinnect2.0 condition will be given access to the iKinnect2.0 app that has been developed for this study. Parent and youth will be asked to download the app to their phone during the baseline assessment process and asked to use it as often as they would like throughout the duration of the 16 week trial. The app is designed to be used several times throughout each day.

Group II: Attention-Control Placebo App & Supporting Materials Placebo Group 1 Intervention

Parent-Youth dyads assigned to the control condition will be asked to download the free Life360 app to their phone during the baseline assessment process and will also be given access to an online suicide resources brochure. Participants will be asked to use the control-condition app and associated materials as often as they would like throughout the duration of the 16 week trial.

Who Is Running the Clinical Trial?

Evidence-Based Practice Institute, Seattle, WA

Lead Sponsor

Trials

9

Recruited

29,800+

National Institute of Mental Health (NIMH)

Collaborator

Trials

3,007

Recruited

2,852,000+

University of Maryland

Collaborator

Trials

171

Recruited

325,000+

Findings from Research

The study evaluated the feasibility of two mental health apps, BackUp and mEMA, for treating depressed outpatients with suicidal ideation, involving 17 participants over a 3-month period.

Both apps were found to be usable and acceptable, scoring above the thresholds for usability and satisfaction, but the study did not meet the criteria for sufficient uptake, indicating that further exploration is needed for integrating these apps into routine mental healthcare.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The feasibility of using smartphone apps as treatment components for depressed suicidal outpatients. Nuij, C., van Ballegooijen, W., de Beurs, D., et al.[2023]

The pilot phase of the SmartCrisis 2.0 trial demonstrated that a smartphone-based safety plan for patients with recent suicidal behavior is feasible and well-accepted, with a participation rate of 77% and high satisfaction ratings (overall satisfaction score of 9.6 out of 10).

Patients found the safety plan useful (7.4/10) and easy to use (8.9/10), with common coping strategies including walking and exercise, indicating that the app could be a valuable tool in clinical practice for supporting mental health.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Smartphone-based safety plan for suicidal crisis: The SmartCrisis 2.0 pilot study. Porras-Segovia, A., De Granda-Beltrán, AM., Gallardo, C., et al.[2023]

The mobile app developed as part of the SERO suicide prevention program aims to reduce suicides and suicide attempts by enhancing self-management for individuals at risk, incorporating strategies like safety planning and mood tracking.

The app's design was informed by collaboration among health professionals, individuals at risk, and their families, ensuring it addresses real needs and integrates essential suicide prevention strategies effectively.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

SERO - A New Mobile App for Suicide Prevention. Meier, L., Gurtner, C., Nuessli, S., et al.[2022]

The LifeBuoy app, designed based on dialectical behavior therapy (DBT), is being tested in a randomized controlled trial with 378 young adults aged 18 to 25 to evaluate its effectiveness in reducing suicidal thoughts and improving mental well-being.

This study is significant as it is the first to assess a DBT-informed smartphone app for suicide prevention in young adults, incorporating safety measures like clinical psychologist support for participants at risk.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Mobile Health Intervention (LifeBuoy App) to Help Young People Manage Suicidal Thoughts: Protocol for a Mixed-Methods Randomized Controlled Trial. Han, J., McGillivray, L., Wong, QJ., et al.[2020]

The study involving 20 adolescents discharged from a mental health ward showed that the smartphone apps BeyondNow and BlueIce were feasible and acceptable, with most participants accessing them at least once during the six-week follow-up.

While many participants found the apps easy to use, a significant portion (47%) felt they would not ensure safety during a crisis; however, improvements in suicide resilience were noted, indicating potential benefits in managing self-harm and suicidal behaviors.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The feasibility of using smartphone apps to manage self-harm and suicidal acts in adolescents admitted to an inpatient mental health ward. Muscara, F., Ng, O., Crossley, L., et al.[2022]

A pilot study involving 60 students across eight schools demonstrated that machine learning models can effectively identify suicide risk based on language samples collected during therapy sessions, with models achieving good accuracy (AUC up to 0.78).

The integration of voice collection technology into therapists' workflows shows promise for enhancing risk identification in adolescents, potentially leading to earlier interventions for those at risk of suicide or self-harm.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Feasibility Study Using a Machine Learning Suicide Risk Prediction Model Based on Open-Ended Interview Language in Adolescent Therapy Sessions. Cohen, J., Wright-Berryman, J., Rohlfs, L., et al.[2022]

The STOP-SAS is a reliable and valid web-based questionnaire designed to assess suicide risk in children and adolescents, developed through extensive collaboration and testing with 395 adolescents, 110 children, 637 parents, and 716 clinicians.

The questionnaire demonstrated excellent internal consistency across all user groups and strong correlations with the established Columbia Suicide Severity Rating Scale (C-SSRS), indicating its effectiveness in identifying suicidality.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Development and psychometric properties of the Suicidality: Treatment Occurring in Paediatrics (STOP) Suicidality Assessment Scale (STOP-SAS) in children and adolescents. Flamarique, I., Santosh, P., Zuddas, A., et al.[2022]

In a study of 32 adolescent psychiatric inpatients, researchers used daily diaries to track risk factors for suicidal ideation, demonstrating that combining multiple risk factors significantly improved the prediction of suicidal crises, with accuracy rates reaching an area under the ROC curve (AUC) of 0.80-0.91.

The findings suggest that mobile technology can effectively identify early signs of suicide-related crises, which could enhance clinical decision-making and intervention strategies for at-risk youth.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using Intensive Longitudinal Data to Identify Early Predictors of Suicide-Related Outcomes in High-Risk Adolescents: Practical and Conceptual Considerations. Czyz, EK., Yap, JRT., King, CA., et al.[2022]

Machine learning methods, including decision trees and random forests, did not improve the classification of suicide attempt history among youth in medical settings, indicating that the relationships may be primarily linear rather than complex.

Key factors associated with suicide attempts included a history of suicide planning, active and passive suicidal ideation, and nonsuicidal self-injury, highlighting the need for focused suicide risk screenings in pediatric healthcare.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using machine learning to classify suicide attempt history among youth in medical care settings. Burke, TA., Jacobucci, R., Ammerman, BA., et al.[2021]

Real-time monitoring technology offers promising opportunities to study suicidal thoughts and behaviors in youth, but researchers must be cautious and fully understand the limitations of these methods before widespread adoption.

The article outlines future directions for research, including new questions and analytical methods, while also addressing potential technical, logistical, and ethical challenges that may arise in implementing real-time monitoring for at-risk youth.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Real-Time Monitoring of Suicide Risk among Adolescents: Potential Barriers, Possible Solutions, and Future Directions. Kleiman, EM., Glenn, CR., Liu, RT.[2023]

References

1. Switzerland pubmed.ncbi.nlm.nih.gov

The feasibility of using smartphone apps as treatment components for depressed suicidal outpatients. [2023]

2. England pubmed.ncbi.nlm.nih.gov

Smartphone-based safety plan for suicidal crisis: The SmartCrisis 2.0 pilot study. [2023]

3. Netherlands pubmed.ncbi.nlm.nih.gov

SERO - A New Mobile App for Suicide Prevention. [2022]

4. Canada pubmed.ncbi.nlm.nih.gov

A Mobile Health Intervention (LifeBuoy App) to Help Young People Manage Suicidal Thoughts: Protocol for a Mixed-Methods Randomized Controlled Trial. [2020]

5. United States pubmed.ncbi.nlm.nih.gov

The feasibility of using smartphone apps to manage self-harm and suicidal acts in adolescents admitted to an inpatient mental health ward. [2022]

6. Switzerland pubmed.ncbi.nlm.nih.gov

A Feasibility Study Using a Machine Learning Suicide Risk Prediction Model Based on Open-Ended Interview Language in Adolescent Therapy Sessions. [2022]

7. England pubmed.ncbi.nlm.nih.gov

Development and psychometric properties of the Suicidality: Treatment Occurring in Paediatrics (STOP) Suicidality Assessment Scale (STOP-SAS) in children and adolescents. [2022]

8. United States pubmed.ncbi.nlm.nih.gov

Using Intensive Longitudinal Data to Identify Early Predictors of Suicide-Related Outcomes in High-Risk Adolescents: Practical and Conceptual Considerations. [2022]

9. Netherlands pubmed.ncbi.nlm.nih.gov

Using machine learning to classify suicide attempt history among youth in medical care settings. [2021]

10. England pubmed.ncbi.nlm.nih.gov

Real-Time Monitoring of Suicide Risk among Adolescents: Potential Barriers, Possible Solutions, and Future Directions. [2023]