Bounce Back Now: A Low-Cost Intervention to Facilitate Post-Disaster Recovery

Disaster survivors need freely accessible resources to self-monitor their emotional recovery and help them address mental health needs they may develop after a disaster. The investigators will evaluate a novel, scalable, and highly sustainable smartphone/web-based intervention that is designed to address post-disaster PTSD and depressed mood. The intervention, Bounce Back Now, will be tested in a study with 5,000 disaster survivors and will be accessible via any internet-connected device (e.g., laptop, tablet, smartphone).

Globally, an average of 390 disasters, 100,000 related deaths, 260 million disaster victims, and $140 billion in economic damages were recorded per year between 2002-2012. The U.S. is one of the top 5 countries-with China, Philippines, India, and Indonesia-most frequently affected by disasters. The adverse mental health effects of disasters are extraordinary and costly. Up to 38% of distressed survivors presenting to shelters and assistance centers have stress-related and adjustment disorders. Most survivors who develop disaster-related mental health problems do not receive services. The 2013 report of the International Federation of Red Cross and Red Crescent Societies calls for technology-based solutions to heighten access to disaster assistance resources. With US cellphone and smartphone ownership now topping 90% and 60%, respectively, opportunities are tremendous to create cost-efficient, scalable solutions that increase capacity to address mental health needs in disaster-affected communities. The investigators propose highly innovative, time-sensitive research to examine the effectiveness of a widely accessible, technology-based disaster mental health intervention.

Mental health recovery trajectories are highly variable in disaster settings. 7,14 Most survivors recover without intervention and may need nothing more than a brief symptom tracking resource (i.e., watchful waiting). Some develop mental health needs (e.g., post-traumatic stress, depression) for which a brief self-help intervention may aid recovery. Some have chronic mental health problems (pre-existing or disaster related) that necessitate formal treatment. Others (e.g., serious mental illness) may require immediate assistance. The proposed intervention, Bounce Back Now (BBN), addresses each level of need using a web/smartphone-based approach that builds on the investigator's prior work. BBN consists of 3 key components: (1) a symptom and activity-tracking component that we piloted in emergency department settings, (2) a self-help intervention that was found to be efficacious in a recent RCT with families affected by a tornado outbreak, and (3) a resource component that principally connects survivors to the SAMHSA-administered Disaster Distress Helpline (DDH) when immediate assistance is needed (e.g., serious mental illness) or when local mental health treatment referrals are needed or preferred.

This study will leverage the mental health workforce to recruit 5,000 disaster survivors via partnerships with the American Red Cross (ARC) and Office of the Assistant Secretary for Preparedness and Response (ASPR). These partnerships allow cost-efficient testing of interventions, recruitment of individuals at high risk for developing disaster-related mental health problems, and collection of data at the level of triage to aid in prediction of mental health needs and coordination of care. Participants will be randomly assigned to receive BBN vs. enhanced usual care (i.e., EUC; usual care plus comprehensive resource list). BBN will be optimized for smartphones but accessible from any device with an internet connection.

Eligibility Criteria:
Inclusion Criteria:

1. individuals must be aged 18 years and older who were directly or indirectly affected by the disaster.
2. individual must be at least 18 years of age
3. have access to an internet-accessible device or computer, and
4. be English-speaking (i.e., all of the intervention content will be developed in the English language; it is cost-prohibitive to develop the system in multiple language in the testing phase)