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Smartphone apps talk given at the International Conference for Behavioral medicine
- 1. Presenting on behalf of: Abby C. King, PhD Stanford Prevention Research Center Stanford University Eric B. Hekler, PhD School of Nutrition and Health Promotion Arizona State University
- 2. Collaborators: Abby King Tom Robinson Matt Buman Lauren Grieco Frank Chen Jesse Cirimele Beth Mezias Banny Banerjee Martin Alonso
- 3. Health promotion interventions Evidence-based Cost-effective Tailored Easy to disseminate Promote maintenance
- 5. Introduction Mobile Interventions for Lifestyle Exercise and Eating at Stanford (MILES) NHLBI-funded Challenge Grant (10/09 – 08/12) PI- King, 1RC1HL099340-01 Status: Ran wave 1 with 36 older adults; iterated on design and almost complete with second wave of data collection for final sample of 80.
- 6. Purpose Develop theoretically meaningful smartphone apps for midlife & older adults Physical activity & sedentary behavior Passively assess PA & SB Provide just-in-time feedback for behavior change
- 7. Activity Algorithm Validation N=15, Men & Women, Mean Age=55 12 laboratory-based activities 3-4 min each Hip- and pocket-worn Android phones Compared to Actigraph & Zephyr Bioharness Hekler, Buman, et al, 2010, November
- 8. Validation Results Comparison of Phone to Actigraph "Counts" Minute-level "counts" 1000 Phone AUC m/s3 800 600 y = 0.09x + 55.1 400 R² = 0.83 200 0 0 2000 4000 6000 8000 10000 12000 Actigraph "counts" Hekler, Buman, et al, 2010, November
- 9. The “Apps” Control: mTrack mSmiles mConnect Calofiric King, Hekler, et al. April, 2012, Hekler, et al. 2012, Hekler et al. 2011
- 10. Components study arms mTrack mSmiles mConnect Calorific Push component X X X X Pull component X X X X "Glance-able" display X X X X Passive activity assessment X X X X Real-time feedback X X X X Self-monitoring X X X X “Help” tab X X X X King, Hekler, et al. April, 2012, Hekler, et al. 2012, Hekler et al. 2011
- 11. Components study arms mTrack mSmiles mConnect Calorific Push component X X X X Pull component X X X X "Glance-able" display X X X X Passive activity assessment X X X X Real-time feedback X X X X Self-monitoring X X X X “Help” tab X X X X Goal-setting X X Feedback about goals X X Problem-solving X X Reinforcement X X X Variable reinforcement schedule X X Attachment X "Play" X "Jack pot" random reinforcement X Social norm comparison X Competition/collaboration X King, Hekler, et al. April, 2012, Hekler, et al. 2012, Hekler et al. 2011
- 12. MILES Study Design Pre- Baseline Feedback Follow up study Week1 Week2 Week8 Visit1 Visit2, check in Visit3 mTrack (Cognitive App, n=20) Randomize mSmiles (Affect App, n=20) mConnect (Social App, n=20) Diet Tracker Control App (n=20) Assess: Assess: Activity Assessment, Continuous Moderators Ecological Momentary Assessment, Daily Acceptability Self-report Self-report PA, Sed Beh Real-time use of phone features PA, Sed Beh King, Hekler, et al. April, 2012, Hekler, et al. 2012, Hekler et al. 2011
- 13. Preliminary Activity Results (n = 30 inactive, smartphone-naive adults ages > 45 yrs) 2-mos Daily Increases in MVPA vs. Control (Calorific) 20 MVPA Net Increase Minutes/Day - Smartphone Accelerometer P < .01 15 P < .01 10 5 P = .39 ??? ??? ??? King, Hekler, Grieco, Winter, Buman, et al., Ann Behav Med, 2012 (abstract)
- 14. Preliminary Activity Results (n = 30 inactive, smartphone-naive adults ages > 45 yrs) 2-mos Daily Increases in MVPA vs. Control (Calorific) 20 MVPA Net Increase Minutes/Day - Smartphone Accelerometer P < .01 15 P < .01 10 5 P = .39 Cognitive Affect Social King, Hekler, Grieco, Winter, Buman, et al., Ann Behav Med, 2012 (abstract) Which App for WHOM?
- 15. Preliminary Eating Results 12 10 ∆Consumption servings/wk 8 6 4 2 0 -2 -4 -6 Food-tracking App Average of Activity Hekler, King, et al. April, 2012 (N=30) Apps
- 16. Conclusions & Next Steps Game dynamics/operant conditioning and social comparison appear more influential than goal- setting and feedback May be due to specificity of data Redesigned apps, running a second wave now Exploring the use of other research methods for testing (e.g., Multiphase Optimization Strategy, Linda Collins et al., 2010).
- 17. Thank you! Abby King king@stanford.edu Eric Hekler desiginghealth.lab.asu.edu Twitter: @ehekler ehekler@asu.edu
Hinweis der Redaktion
- We have been exploring this in the MILES project, which stands for Mobile interventions for lifestyle exercise at Stanford. As stated earlier, the study is an NIH-funded challenge grant. We are currently finalizing development of our three applications. We plan to start our pilot study in January 2011.
- To bring us back to our main goals, we need to be rapidly develop evidence-based, cost-effective, tailored, easy to disseminate interventions that promote maintenance of behavior change.
- We have been exploring this in the MILES project, which stands for Mobile interventions for lifestyle exercise at Stanford. As stated earlier, the study is an NIH-funded challenge grant. We are currently finalizing development of our three applications. We plan to start our pilot study in January 2011.
- The charge for this study is to develop theoretically meaningful smartphone apps for mid-life and older adults that will increase physical activity & decrease sedentary behavior concurrently. As I was suggesting before, a key design element for all of our applications is the passive assessment of physical activity and sedentary behaviors as this allows us to provide just-in-time feedback that can be framed with different mechanisms for behavior change.
- So that’s exactly what I took the lead on conducting and was just at the mHealth Summit last week in DC presenting. Specifically, we conducted a validation study of Android phones, the phones we are using in our intervention trial, among 15 mid-life & older adult men & women. We had our participants engage in 12 laboratory-based activities, such as walking on the treadmill, for 3-4 minutes each and then compared the values we gathered from the Android phones to the highly validated Actigraph.
- Results revealed that, in fact, the three phones were giving very similar data to the Actigraph. The above is just one of the three phones but the other phones were similar. We then utilized this regression equation to calculate appropriate cut-points for classifying sedentary behavior and moderate/vigorous intensity physical activity for real-time classification via the phones.
- First, here are the three “glance-able” displays for the applications. Although the information gathered is identical, minutes engaged in sedentary behavior and MVPA, the way we are displaying it is quite different in each app. For the cognitive app, we wanted to frame the information relative to goals as this model assumes that behavior change occurs through active goal-setting and problem-solving through an active “cognitive” process. For the “affect” app, we utilizing a bird “avatar” as the method of tracking your activity. In this app, as you are more active, the bird flies faster, is happier, and becomes more playful. The idea here is that we believe a person would map the bird’s mood, particularly as it feels happier to their own mood and thus create a link up between being more active and feeling better. Finally, for the social app, you will notice that there are multiple stick figures on the home screen. With this design, the idea here is that a person will be motivated to be more active based on the level of activity of other participants in the study via social norm motivations. These glance-able displays set up the differences between the three apps but now I’m going to show you some more specific elements in each.
- Beyond the common elements, there are also unique elements for the three active applications, as identified here. The key idea study was to parse apart different ways to frame the information about physical activity and sedentary behavior. Rather than labor through this chart, I’m going to show you images from each of the applications to help you get a sense of how they are similar and different.
- Beyond the common elements, there are also unique elements for the three active applications, as identified here. The key idea study was to parse apart different ways to frame the information about physical activity and sedentary behavior. Rather than labor through this chart, I’m going to show you images from each of the applications to help you get a sense of how they are similar and different.
- The plan for MILES is that we will conduct a small pilot study that will last 8 weeks. During these 8 weeks, we will randomize participants to receive one of four arms, what we call the cognitive, affect, or social app, or a diet tracker control app. Outside of this design, which is focused on exploring competing mechanisms of behavior change, we also plan to assess at baseline self-report behavioral measures as well as measures thought to moderate the effectiveness of each intervention. In addition, we have also built in features for gathering daily ecological momentary assessment similar to what we did in the CHART-2 trial that I described earlier. And finally, at the end of the study, we plan to assess self-report behavior again and also explore the acceptability of the applications.
- We have been exploring this in the MILES project, which stands for Mobile interventions for lifestyle exercise at Stanford. As stated earlier, the study is an NIH-funded challenge grant. We are currently finalizing development of our three applications. We plan to start our pilot study in January 2011.
- We have been exploring this in the MILES project, which stands for Mobile interventions for lifestyle exercise at Stanford. As stated earlier, the study is an NIH-funded challenge grant. We are currently finalizing development of our three applications. We plan to start our pilot study in January 2011.