SNERSCIC contributes data to the National Spinal Cord Injury Database, which includes more than 32,000 individuals with SCI. Information is gathered from individuals, with their consent, during hospitalization and through follow up at one and five years after injury, and every five years thereafter. Data from the SCIMS National Database provides information about the course of recovery, trends in cause and severity of SCI, health service delivery and costs, treatment, and rehabilitation outcomes. The database is a rich source of data for research and analysis of outcomes.

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The proposed project will seek to identify individual, person-level trajectories to define the phenotype for those at greatest risk for declining mobility and/or increasing psychosocial distress (i.e., anxiety, depression, social isolation) as they transition home. Digital phenotyping involves the “moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices.” Smart phones allow for passive collection of accelerometry to assess level of activity, GPS coordinates to document mobility in the community, and call logs to examine social connectivity and support. These passive digital data components can be coupled with active smart phone based data collection using Patient Reported Outcome Measures (PROM) developed with advanced psychometrics and administered using precise computerized adaptive tests (CATs). This granular data provides dynamic information on individual-level mobility and psychosocial trajectories without significant burden (i.e., passive data requires no subject input). These individual trajectories can be assessed for common factors among those who demonstrate declining mobility and/or increasing psychosocial distress to derive predictive algorithms.

We will use the validated Personal Network Survey for Clinical Research (PERSNET) instrument that elaborates and quantifies the structure and composition of individual social networks using graph theory metrics. These metrics are distinct compared to other measures of social support in their ability to capture density and granular details of an individual unique social network. Our study will characterize the relationship of social network characteristics with health (medical complications post-injury), function (mobility and self-care), and social participation (satisfaction with social roles and activities and independence) among those with SCI and create a robust foundation on which we can begin to devise possible interventions. Network-based interventions have been shown to be efficacious in other widely prevalent diseases, such as addiction disorders and hypertension. The project would enable examination of social network structures for potentially at-risk subgroups by race/ethnicity, level of injury and completeness, gender, and socioeconomic status, and provide a detailed analysis of how the social environment interacts with demographic and injury-related factors to lead to health disparities in different sub-groups.

Lead Principal Investigator: Dr. Mike Boninger, UPitt

SNERSCIC Lead: Diana Pernigotti dpernigotti@gaylord.org 203-679-3563

Aim 1 – Collect descriptive data on Assistive Technology (AT) used by individuals with Spinal Cord Injury (SCI) including wheelchair make, model, failures, and repairs. This data will enable us to track the impact of insurance market changes, find differences in wheelchair failures, and present this information to wheelchair users.

Hypothesis:

The prevalence and type of wheelchair failures will vary across manufacturers.
Device satisfaction will be inversely correlated with adverse consequences.
Response in service time for repairs will vary by vendor and service region.
Users will experience additional consequences secondary to waiting for a repair to be completed.
Intensity of wheelchair use will be associated with incidence of repairs.

Aim 2 – Explore the influence of individual characteristics such as financial strain, educational quality, health literacy, geographic location, and self-efficacy on differences in AT quality and other health related outcomes for individuals with SCI.