Sleep and Appetite as Risk Markers for Suicidality in a Cross-National Cohort of Suicidal Patients

2 research findings from this AFSP-funded study.

Research finding · August 2024

Digital safety plans can be an effective tool for people with suicidal thoughts and behaviors

A safety plan is a suicide prevention tool for helping someone navigate suicidal feelings and urges. Clinicians work with their clients to personalize the plan and provide detailed strategies to manage a crisis. Prior research has shown that safety plans help reduce the risk of suicide. Typically, safety plans are completed in paper format, but recent studies have shown that digitizing the tool in the form of an app may be a promising way of enhancing its availability and customization.

To test this, Drs. Phillipe Courtet and Enrique Baca-Garcia recruited 105 individuals with suicidal thoughts and behaviors and installed a digital safety plan, SmartCrisis 2.0, on their smartphones. Participants rated the app's usefulness at 7.4 out of 10, its ease of use at 8.9, and their likelihood of recommending it at 8.6. Overall satisfaction with the app was 9.6. The most frequently used feature of the app was identifying "warning signs" of a crisis, particularly feelings of sadness or loneliness. The next most used feature was "internal coping strategies," with exercise being the most common. These results show high participation and satisfaction rates, suggesting that this app-based safety plan is a feasible and well-received intervention, supporting its potential implementation in clinical practice.

Citation: Porras-Segovia, A., De Granda-Beltrán, A. M., Gallardo, C., Abascal-Peiró, S., Barrigón, M. L., Artés-Rodríguez, A., López-Castroman, J., Courtet, P., & Baca-García, E. (2024). Smartphone-based safety plan for suicidal crisis: The SmartCrisis 2.0 pilot study. Journal of psychiatric research, 169, 284–291. https://doi.org/10.1016/j.jpsychires.2023.11.039

This finding comes from an AFSP-funded study — see the grant below ↓

Research finding · April 2024

Using real-time smartphone monitoring may help predict suicide risk

Advancements in technology offer the field of suicide prevention many promising opportunities when it comes to predicting when risk will occur and intervening when it does. One way these opportunities are manifesting is with real-time smartphone monitoring. By collecting data from willing participants, researchers are given the chance to construct predictive models based on patterns in behavior and changes in these patterns over time. These changes are considered critical periods, and their relationship with suicide-risk events has been in need of further study.

Aside from studying sleep and appetite, Dr. Enrique Baca-Garcia and his colleagues recruited 225 patients with a history of suicidal thoughts and behaviors and passively tracked data from their smartphones (e.g., distance walked, steps taken, time spent at home, and app usage) for six months to test the predictive capacities of real-time smartphone monitoring. Using an algorithm that constructed daily activity profiles for participants according to the data, Dr. Baca-Garcia was able to detect changes in behavior patterns of the participants. By the end of the study period, 18 (8%) of participants had attempted suicide and 14 (6.2%) presented to the emergency department for psychiatric care. After assessing the data, Dr. Baca-Garcia found that the behavioral changes detected by the algorithm (such as those mentioned above, like steps taken and app usage) predicted increased risk for suicidal ideation and behavior in a time frame of one week, indicating strong accuracy. Early detection of risk is a critical feature of intervening to save lives.

Citation: Barrigon, M. L., Romero-Medrano, L., Moreno-Muñoz, P., Porras-Segovia, A., Lopez-Castroman, J., Courtet, P., Artés-Rodríguez, A., & Baca-Garcia, E. (2023). One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study. Journal of medical Internet research, 25, e43719. https://doi.org/10.2196/43719

This finding comes from an AFSP-funded study — see the grant below ↓

The grant behind this work · 2017 Linked Standard Research Grant