DIFFERENCES BETWEEN REGULAR HEALTH-APP USAGE:

Research question:
Among regular health-app users, are there differences between predictors: gender & age?

BACKGROUND INFORMATION

• eHealth--influence of any technology on health issues/behaviors
• Active video games- require physical activity to participate
• mHealth (mobile health)- influence of mobile technology on health issues/behaviors

This project stems from an interest in

• exercise adherence (ways to promote long-term)
• mHealth and health technology impact on behavior change

strategies

established behavior-change techniques

• prompts for target health behaviors
• feedback on target health behavior
• regular tracking/managing/updating of health behavior

In 2015, a report was released by the IMS Institute for Healthcare Informatics.

identified 26,864 (out of 165,000+) most widely used mobile health apps.

DATASET

• Pew Research Center's Internet & American Life Project (2012 Health questionnaire)
• Over 3,000 adults surveyed
• 1,800 landline phone
• 1,200 cell phone

Q26: Thinking about the health indicator you pay most attention to...how to keep track of changes? Do you use?...

DO YOU USE... (up to 3 responses)

• Paper (notebook/journal)
• Computer program (spreadsheet)
• Website/online tool
• App or other tool on phone or mobile device
• Medical device (glucose meter)
• Or do you just keep track in your head?

Response #4 indicated mobile health app user

Health-app users

vs. OTHER TRACKING METHODS

Q27: How often do you update your records or notes about this health indicator? Do you do this on a regular basis, or only when something changes?

RESPONSE OPTIONS

• Regular basis
• Only when something comes or changes
• Don't know

Response #1 indicated regular tracker (using a health app)

Hypothesis: there will be a difference between gender & age among health app users who track health behaviors regularly.

Null Hypothesis: there will be no differences between gender & age among health app users who track health behaviors regularly.

variables

• X1=Gender (0=male; 1=female)
• X2=Age
• Y=REGULARlTY of HEALTH APP USAGE
• (0=non-regular; 1=regular)

METHODS

Data Analysis

Limitations

...for consideration

• Self-report biases through survey.
• How many health apps were purchased? free? (Could influence motivation)
• More current data collected for new dataset.
• Explore cross interaction between app usage: race by gender and age. (In JMP: race*gender; race*age; race*gender*age)

QUESTIONS?

THANK YOU