Grant: Operating Grant: SickKids-CIHR-IHDCYH New Investigator Grants
University of Victoria (British Columbia)
$0.00
Using digital technologies to understand and reduce intentional self-harm in youth
EF 2018-19
Résumé
Intentional self-harm is a leading cause of injury and mortality among Canadians aged 12-25, resulting in
nearly 2,500 hospital admissions and 500 deaths each year. Most studies use follow-up periods of six
months or longer to identify predictors of self-harm. Clinical decision-making, in contrast, requires
mental health professionals to judge a young person’s risk over the coming hours, days, and weeks to
make care recommendations. To address this gap and improve care, we need to identify near-term
predictors of self-harm thoughts or behaviours.
The recent proliferation of personal digital technologies such as smartphones and wearables offers
unprecedented opportunity to understand health behaviours in daily life. Sensitive accelerometers,
electrodermal and heart rate sensors, and imbedded utilization and health metrics can provide momentby-
moment information regarding the contexts of youths’ behaviours. This project will harness these
technologies to: (a) test and refine current models of self-harm with a focus on identifying near-term
predictors of self-harm thoughts and behaviours, and (b) develop risk-detection algorithms via machinelearning
to evaluate the potential utility of digital self-monitoring for youth who are at-risk of self-harm.
Because self-harm is an important concern in adolescents and young adults, I will recruit two community
samples: Sample 1 will be 70 adolescents (aged 12-17) and their caregivers; Sample 2 will be 70 young
adults (aged 18-25). Inclusion criteria will require past-month self-harm thoughts or behaviours, and
ongoing mental health care. Youth will complete five brief surveys per day, and wear a wristband sensor
to assess physical activity, sleep quality, and physiological arousal. Caregivers in Sample 1 will respond to
a single daily survey regarding their child’s mood and behaviour. Analyses will combine multilevel
modelling and machine-learning to test self-harm theories and create algorithms to classify self-harm
risk. Knowledge translation activities will share new knowledge and tools with youth, caregivers, and
healthcare professionals.
Programme
Operating Grant: SickKids-CIHR-IHDCYH New Investigator Grants
Bénéficiaire
Emplacement
British Columbia, Canada
Type
Grant
Source
CIHR Funding Decisions Database
Tel que publié
Id
395742
Name
Turner Brianna J
Region
British Columbia
Country
Canada
Orgtype
Institution Paid; Research Institution
Partnername
Hospital for Sick Children Foundation
Prcname2
Special Cases
Projecttitle
Using digital technologies to understand and reduce intentional self-harm in youth
Programtype2
Operating Grants
Orgnameinp2
University of Victoria (British Columbia)
Cihramount2
$0
Cihrequipment2
$0
Programname2
Operating Grant: SickKids-CIHR-IHDCYH New Investigator Grants
Keyworddelim
N/A
Abstract
Intentional self-harm is a leading cause of injury and mortality among Canadians aged 12-25, resulting in
nearly 2,500 hospital admissions and 500 deaths each year. Most studies use follow-up periods of six
months or longer to identify predictors of self-harm. Clinical decision-making, in contrast, requires
mental health professionals to judge a young person’s risk over the coming hours, days, and weeks to
make care recommendations. To address this gap and improve care, we need to identify near-term
predictors of self-harm thoughts or behaviours.
The recent proliferation of personal digital technologies such as smartphones and wearables offers
unprecedented opportunity to understand health behaviours in daily life. Sensitive accelerometers,
electrodermal and heart rate sensors, and imbedded utilization and health metrics can provide momentby-
moment information regarding the contexts of youths’ behaviours. This project will harness these
technologies to: (a) test and refine current models of self-harm with a focus on identifying near-term
predictors of self-harm thoughts and behaviours, and (b) develop risk-detection algorithms via machinelearning
to evaluate the potential utility of digital self-monitoring for youth who are at-risk of self-harm.
Because self-harm is an important concern in adolescents and young adults, I will recruit two community
samples: Sample 1 will be 70 adolescents (aged 12-17) and their caregivers; Sample 2 will be 70 young
adults (aged 18-25). Inclusion criteria will require past-month self-harm thoughts or behaviours, and
ongoing mental health care. Youth will complete five brief surveys per day, and wear a wristband sensor
to assess physical activity, sleep quality, and physiological arousal. Caregivers in Sample 1 will respond to
a single daily survey regarding their child’s mood and behaviour. Analyses will combine multilevel
modelling and machine-learning to test self-harm theories and create algorithms to classify self-harm
risk. Knowledge translation activities will share new knowledge and tools with youth, caregivers, and
healthcare professionals.
Competitiondate
201804
Approvedterm2
3 yrs 0 mth
Pinamesdelim
Turner, Brianna J
Approvedterm
3 ans 0 mois