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

Last registered on March 01, 2019

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
March 01, 2019, 3:02 PM EST

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 and health.
External Link(s)

Registration Citation

Citation
Giuntella, Osea, Silvia Saccardo and Sally Sadoff. 2019. "Improving Educational Achievement through Better Sleep Habits: The Effect of Technology-Based Behavioral Interventions ." AEA RCT Registry. March 01. https://doi.org/10.1257/rct.3235-2.0
Former Citation
Giuntella, Osea et al. 2019. "Improving Educational Achievement through Better Sleep Habits: The Effect of Technology-Based Behavioral Interventions ." AEA RCT Registry. March 01. https://www.socialscienceregistry.org/trials/3235/history/42287
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
We will test the impact of reminders, feedback, financial incentives and non-financial incentives.
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, academic performance and health.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will test the impact of reminders, feedback, financial incentives and non-financial incentives.
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
2100
Sample size: planned number of observations
2100
Sample size (or number of clusters) by treatment arms
Control n=600
Non-Financial Incentives: n=600
Financial Incentives: n=600
Delayed Financial Incentives: n=100
Delayed Financial Incentives, no reminders and feedback: n=100
Reminders and Feedback: n=100
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
MDES for each incentive treatment: 0.16 MDES for pooled incentive treatments: 0.14
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Pittsburgh
IRB Approval Date
2017-12-18
IRB Approval Number
PRO17090135
Analysis Plan

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