Our interventions focus on individual, interpersonal, and institutional levels to reduce barriers, and our team learns about and is guided by awareness of community and policy level factors that affect outcomes. We have expanded our ALACRITY 1.0 transdisciplinary team by adding investigators with expertise in Technology, Implementation Science and Qualitative Analysis, Health Inequities and Diversity, Equity, and Inclusion (DEI), Primary Care, Systems Evaluation, and Public Health perspectives.
PATH-Pain
Dimitris N. Kiosses, Ph.D.
Cary Reid, M.D.
Lisa Ravdin, Ph.D.
Hybrid Type I Trial of a primary care collaborative program for older and middle-aged adults with chronic pain and depression.
The Signature Project aims to evaluate the effectiveness of PATH-Pain RCP (PATH-Pain Remote Collaborative Program), a novel primary-care based collaborative care program designed to reduce depression and pain-related disability in adults (≥ 50 years old) with chronic pain and depression. The proposed study utilizes the Center’s infrastructure and transdisciplinary team and will advance the ALACRITY 2.0 mission
PATH-Pain RCP is a manualized program which includes: a) identification of eligible participants for the program based on an EPIC algorithm and confirmation by their primary care physician; b) education about depression, pain, and pain-related disability; and c) the administration of the PATH-Pain intervention [i.e., 8 weekly remote individual sessions during the first 2 months and 3 remote monthly booster sessions in the next 3 months]. PATH-Pain RCP: 1) was developed by a multidisciplinary team with stakeholders’ input (i.e., patients, family members, primary care staff); 2) is implemented remotely by clinical specialists embedded in primary care practices; and 3) carefully involves the primary care physicians in achieving treatment goals. PATH-Pain RCP participants also use personalized mhealth apps to practice PATH-Pain RCP skills between sessions. These features are intended to increase its effectiveness, enhance its scalability, and promote sustained fidelity.
This is an effectiveness-implementation Hybrid Type I Trial of PATH-Pain RCP vs. UC. We will randomize 302 older adults (≥ 50 years old) with chronic pain and depression in 8 primary care practices serving racially and ethnically diverse populations. Assessments will be conducted at Baseline, 5, 9, and 21 weeks.
Primary Hypotheses: End of Acute Treatment Effect: PATH-Pain RCP participants will have clinically significant improvement in depression and in pain-related disability vs. UC participants over 9 weeks. Secondary Hypotheses: 1. Maintained Effect: PATH-Pain RCP participants will have clinically significant improvement in depression and in pain-related disability vs. UC participants from baseline to 21 weeks. 2. Target Engagement: PATH-Pain RCP participants will have significant reductions in expressive suppression (an aspect of emotion regulation) than UC participants over 9 weeks. Implementation Aim: To identify organizational, patient, and provider-level barriers and facilitators to implementing PATH-Pain RCP in primary care practice, guided by the Consolidated Framework for Implementation Research (CFIR) 2.0.
PATH-Pain RCP is a low-risk and potentially high yield collaborative program in primary care. If PATH-Pain RCP improves outcomes, implementation findings will support a fully powered Hybrid Type II study and inform a future adaptation of PATH-Pain RCP as standard of care in primary care settings.
FORCE (Facing Our Response to Crime Everyday)
Jo Anne Sirey, Ph.D.
Isabel Rollandi, Ph.D.
Bringing a brief therapy to reduce depression among crime victims in New York City.
Crimes against older adults (age>60) are on the increase, with robbery, assault and hate crime rates rising in New York City from 2023 to 2024. Over 50% of violent crimes against older adults are committed by strangers. Adult victims of violent crime report depression and anxiety, PTSD, and significant distress, with older adults reporting moderate to severe acute distress after the incident. Three months after a crime, 65% of victims felt that the crime affected their lives. There is a lack of post-crime interventions for older adult victims of crime.
This project will test FORCE (Facing Our Response to Crime Everyday), a virtually delivered (video or telephone) brief (9 sessions) behavioral therapy to address depression among crime victims by increasing engagement in pleasurable activities, especially social interactions, and goal setting.
We will compare FORCE with a community mental health (MH) referral for older crime victims of a recent crime with depression. Our aims are: 1) Finalize the FORCE Therapy Manual, Training Plan, and Build Operations Manual for day-to-day procedures; 2) Evaluate the reach, feasibility, and acceptability (client satisfaction questionnaire of FORCE; and 3) Evaluate the preliminary effectiveness of FORCE: We will test FORCE versus a referral in 60 clients in New York City. This is the first study of a partnered, community-based therapy delivered remotely to reduce depression among older crime victims. FORCE integrates wearable technology (Fitbit) to build links between activity and sleep and depressive symptoms. Crime against older adults is increasing with a significant minority of victims reporting depression and no manualized interventions tailored to this population. A partnered approach offers access to care for older crime victims, whose needs could go unserved.
Self-Guided CBT App for Older Adults
Faith Gunning, Ph.D.
Jennifer Bress, Ph.D.
Using a cognitive behavioral therapy app to improve access to depression treatment in city-dwelling middle-aged and older adults.
Depression is the most common psychiatric disorder and is associated with increased risk of physical disability, frailty, and mortality in middle-aged and older adults. Adequate treatment relieves symptoms and reduces mortality, but only 22% of individuals with major depressive disorder receive treatment. The treatment gap is even more pronounced in older adults, with financial cost, geographical distance, and reluctance to discuss problems with a clinician cited as common barriers to treatment. Digital mental health interventions have the potential to increase access to care, but existing apps often omit core therapeutic components, have substantial drop-out rates, and are not tailored specifically for an older audience.
We developed Maya, a cognitive behavioral therapy (CBT) app, to address the most significant problems with existing mental health apps. The Maya app has a highly customizable design and includes multiple strategies derived from behavioral economics to enhance app engagement. Our preliminary research with the Maya app in young adults has demonstrated efficacy on par with pharmacological trials.
This project aims to test a version of Maya optimized for a middle-aged and older adult audience, assess its user engagement and usability, evaluate its efficacy in a community setting, and test its engagement of cognitive and behavioral targets. The study will be embedded in community centers run by our partners at Commonpoint Queens. To inform future implementation efforts, qualitative interviews with participants and community center staff will include questions that query potential implementation barriers and facilitators.
This proposal seeks to increase access to evidence-based interventions, develop tailored interventions for specific developmental stages that engage validated targets, and understand intervention response across the lifespan, in diverse representative populations and contexts. The study will consist of a randomized controlled trial embedded in a community setting in which 120 participants aged 50 and older with MDD will be randomized to 6 weeks of the Maya app or a digital psychoeducation control condition. We will test: 1) app engagement, acceptability, and user satisfaction with the Maya app; 2) preliminary efficacy of the Maya app; and 3) its engagement of self-reported cognitive (rumination) and behavioral (behavioral disengagement) targets. This study addresses the critical need for increased access to high-quality depression treatment in middle-aged and older adults.
TREE Connect App
Nili Solomonov, Ph.D.
Samprit Banerjee, Ph.D.
TREE-Connect: A novel machine-learning powered App and psychotherapy for late-life depression
Late-life depression is estimated to affect nearly 30% of older adults across the world. Evidence-based psychotherapies are effective in reducing depression in late life. We developed Engage & Connect (E&C) - a remotely delivered (via zoom) neuroscience-informed psychotherapy aimed to improve brain functions by improving social connectedness and reducing isolation.
To improve the efficacy of Engage & Connect, we designed a machine-learning powered application that helps older adults make progress during therapy. This app is personalized and tailored to participant’s progress during the intervention. The App also allows older adults who cannot access mental health services to receive our high-quality evidence-based therapy.
This project will test TREE-Connect (Technology dRiven Enhancement to Engage & Connect), a novel hybrid therapy that includes 5 psychotherapy video sessions of Engage & Connect and 4 self-administered sessions with our novel App. The TREE-Connect App includes information to help participants work towards their goals, activities to complete between sessions and tools to track progress during the therapy. During therapy, participants will wear a wearable watch that will activity, sleep, and heart rate, and complete questions about their mood daily.
This project will test whether TREE-Connect helps in reducing depression and improving social connectedness among older adults.