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Improving Educational Achievement through Better Sleep Habits: The Effect of Technology-Based Behavioral Interventions

Last registered on August 14, 2018

Pre-Trial

Trial Information

General Information

Title
Improving Educational Achievement through Better Sleep Habits: The Effect of Technology-Based Behavioral Interventions
RCT ID
AEARCTR-0003235
Initial registration date
August 14, 2018
Last updated
August 14, 2018, 10:03 PM EDT

Locations

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Primary Investigator

Affiliation
UCSD

Other Primary Investigator(s)

PI Affiliation
Carnegie Mellon University
PI Affiliation
University of Pittsburgh

Additional Trial Information

Status
In development
Start date
2019-01-01
End date
2022-06-30
Secondary IDs
Abstract
The proposed study investigates how technology-assisted behavioral interventions can help individuals improve their sleep habits in order to improve educational outcomes. In prior work, we find that incentives for meeting sleep goals increase sleep and also find suggestive evidence that the incentives improve academic performance. Building on these findings, we will test the impact of the following interventions among undergraduates: (1) Technology only and (2) Technology and Incentives. The Technology intervention aims to lower the costs of shifting and sustaining habits, including the costs of tracking sleep and remembering to go to bed on time. Through wearable technology (Fitbits) and a custom smartphone app, we will provide participants with reminders to go to bed and immediate feedback about sleep duration. The Technology and Incentives intervention aims to develop habits building on cue/reward models of habit formation. The Technology intervention will provide the cue to go to bed on time and sleep adequately; we will combine this with an associated reward (either Financial or Non-Financial) provided immediately each morning for meeting sleep goals. We will measure the impact of the interventions on sleep habits and academic performance.
External Link(s)

Registration Citation

Citation
, , Silvia Saccardo and Sally Sadoff. 2018. "Improving Educational Achievement through Better Sleep Habits: The Effect of Technology-Based Behavioral Interventions ." AEA RCT Registry. August 14. https://doi.org/10.1257/rct.3235-2.0
Former Citation
, et al. 2018. "Improving Educational Achievement through Better Sleep Habits: The Effect of Technology-Based Behavioral Interventions ." AEA RCT Registry. August 14. https://www.socialscienceregistry.org/trials/3235/history/42287
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2019-01-01
Intervention End Date
2021-06-30

Primary Outcomes

Primary Outcomes (end points)
We will measure the impact of the interventions on sleep habits and academic performance.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Physical and mental health
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
we will test the impact of the following interventions among 3,000 undergraduates: (1) Technology only and (2) Technology and Incentives. The Technology intervention aims to lower the costs of shifting and sustaining habits, including the costs of tracking sleep and remembering to go to bed on time. Through wearable technology (Fitbits) and a custom smartphone app, we will provide participants with reminders to go to bed and immediate feedback about sleep duration. The Technology and Incentives intervention aims to develop habits building on cue/reward models of habit formation. The Technology intervention will provide the cue to go to bed on time and sleep adequately; we will combine this with an associated reward (either Financial or Non-Financial) provided immediately each morning for meeting sleep goals.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
3,000 participants
Sample size: planned number of observations
3,000 participants
Sample size (or number of clusters) by treatment arms
600-900 per treatment arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
UC San Diego Human Research Protections Program
IRB Approval Date
2018-07-10
IRB Approval Number
180008