In partnership with a digital health software company, the University research team created two versions of a mobile application to help behavioral health technicians (BHT's) who work with students with autism collect data. The first version comprises a basic electronic platform for data collection. The second version has the same basic electronic platform for data collection, plus additional features designed to increase motivation to collect data and ease the burden of data collection.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
NONE
Enrollment
27
Behavioral economics features designed to increase motivation to collect data and ease of data collection on the electronic platform.
Electronic platform to collect data
University of Pennsylvania
Philadelphia, Pennsylvania, United States
The Difference in Data Collection Consistency Between Groups Across Three Weeks
Consistency was the percentage of intervals in which aides entered data per session with children during each school day, across a three week period. These metrics were captured via web analytics in partnership with our digital health company.
Time frame: Three-week trial period
The Difference in Data Collection Completion Between Groups Across Three Weeks
Consistency was the percentage of intervals in which aides entered data per session with children during each school day, across a three week period. These metrics were captured via web analytics in partnership with our digital health company.
Time frame: Three-week trial period
The Difference in Data Collection Timeliness Between Groups Across Three Weeks
Consistency was the percentage of intervals in which aides entered data per session with children during each school day, across a three week period. These metrics were captured via web analytics in partnership with our digital health company.
Time frame: Three-week trial period
Intentions for Implementors
We adapted scales to measure mechanisms affecting data collection, including intentions, using social psychology methods (Armitage \& Connor, 2001). The intentions measure had one item, which was rated from 1=strongly disagree to 7=strongly agree. Strongly agree meant stronger intentions to take data. Aides completed measures at baseline and post-trial (3 weeks). Data reported are changes over time.
Time frame: Assessed at baseline and end of 3-week trial period
System Usability Scale
We used the System Usability Scale (SUS; Brooke, 1996) to measure aides' reactions to various statements regarding the app's usability with 10 items that use a 5-point scale from (1) strongly disagree to (5) strongly agree. The SUS has high internal consistent reliability (Cronbach's alpha = .91) and demonstrated sensitivity to change (Lewis, 2018). We adapted the original questionnaire to replace "system" with "app" across all items. The SUS is calculated as a proportion score out of 100, with higher scores indicating higher usability (≥85 = Excellent; ≥71 = Good; ≥51 = Okay (Bangor et al.).
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Time frame: Three-week trial period
Attitudes for Implementors
We adapted scales to measure mechanisms affecting data collection, including attitudes, using social psychology methods (Armitage \& Connor, 2001). The attitudes measure had one item, which was rated from 1=good to 7=bad (meaning that taking data would be bad). Attitudes had 2 items. These items were averaged to create the scale score which could range from 1 to 7 for each measure. Aides completed measures at baseline and post-trial (3 weeks). Data reported are changes over time.
Time frame: Assessed at baseline and end of 3-week trial period
Perceived Norms for Implementors
We adapted scales to measure mechanisms affecting data collection, including perceived norms, using social psychology methods (Armitage \& Connor, 2001). The perceived norms measure had one item, which was rated from 1=strongly disagree to 7=strongly agree. Strongly agree meant stronger influence of norms on taking data. Aides completed measures at baseline and post-trial (3 weeks). Data reported are changes over time.
Time frame: Assessed at baseline and end of 3-week trial period
Descriptive Norms for Implementors
We adapted scales to measure mechanisms affecting data collection, including descriptive norms, using social psychology methods (Armitage \& Connor, 2001). The perceived norms measure had one item, which was rated from 1=strongly disagree to 7=strongly agree. Strongly agree meant stronger influence of norms on taking data. Aides completed measures at baseline and post-trial (3 weeks). Data reported are changes over time.
Time frame: Assessed at baseline and end of 3-week trial period
Self-Efficacy for Implementors
We adapted scales to measure mechanisms affecting data collection, including self-efficacy, using social psychology methods (Armitage \& Connor, 2001). The self-efficacy measure had one item, which was rated from 1=strongly disagree to 7=strongly agree. Strongly agree meant stronger self-efficacy about taking data. Self-Efficacy had 2 items. These items were averaged to create the scale score which could range from 1 to 7 for each measure. Aides completed measures at baseline and post-trial (3 weeks). Data reported are changes over time.
Time frame: Assessed at baseline and end of 3-week trial period