Medicine X Presentation HARTS Lab 2012


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Stanford Medicine X 2012 Presentation: "Preliminary Effects of Three Different Motivational Frames in Promoting Physical Activity Using Smartphones"
By Abby King; Erik Hekler, MS, PhD; Lauren Grieco, PhD; Sandra Winter, PhD; Jylana Sheats, PhD, MPH; Matt Buman, PhD; Banny Banerjee; Jesse Cirimele; Thomas N. Robinson, MD, MPH; Beth Mezias; Frank Chen, MS

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  • 15 minutes for presentation
  • the MILES project, which stands for Mobile Interventions for Lifestyle Exercise at Stanford. The study is an NIH-funded challenge grant.
  • 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. A key design element for all of the 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.
  • SOCIAL APP: up to 6 figures per team; we can populate it with confederates while awaiting addition of other participants; More active your team is, more points you get as a team, based on an algorithm from the built-in accelerometer; can look at your history in terms of how active you’ve been; message board for anyone assigned to social app, but not a big hit with this age group; Q&A page for all apps in terms of how the apps work). (mSMILES you get a screen-shot of the highest level that bird has got to todate; can see how your bird looks for every past day; ‘jack-pot locations=flies to Paris, Amsterdam, Brussels; you get a notification that MILES got to a city that day and he whistles); (mTRACK: get grapical history/bar charts for the previous week; tips come up in response to participant pressing the question mark; tips for increasing PA and reducing sitting time). Here are the three “glance-able” displays for the applications. For the Analytic 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 utilized a bird “avatar” as the method of tracking 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. Control app: Calorific (GOOGLE) – Food-tracking app; based on Len Epstein’s “Traffic Light” tracking of foods you should eat frequently (green), in moderation (yellow), or in a reduced amount (red). Uses Self-Monitoring and goal setting around Food items.
  • Health-enhancing PA (MVPA)
  • Outcome measure: adapted Harvard Food Frequency Questionnaire (Walt Willett); looked at servings per week of specific types of food categories.
  • Whiches conundrum
  • Whiches conundrum
  • Medicine X Presentation HARTS Lab 2012

    1. 1. Preliminary Effects of ThreeMotivational Frames in PromotingPhysical Activity via Smartphones Abby C. King, PhD On behalf of Healthy Aging Research & Technology Solutions Lab (HARTS) Stanford Prevention Research Center Stanford University School of Medicine Medicine X conference, Sept. 2012, Stanford © Stanford University
    2. 2. Study Collaborators: • Eric Hekler (behavioral science) • Lauren Grieco (physical activity; behavioral science) • Sandra Winter (behavioral science) • Jylana Sheats (nutrition; behavioral science) • Matt Buman (physical activity; behavioral science) • Banny Banerjee (design/engineering) • Jesse Cirimele (computer science) • Tom N. Robinson (medicine) • Beth Mezias (programming) • Frank Chen (computer science) • Martin Alonso (design)NHLBI-funded Challenge Grant 5RC1HL099340 Stanford Prevention Research Center
    3. 3. Healthy Aging Research & Technology Solutions Lab (HARTS) – A bit About Us• Interdisciplinary team of health, behavioral, &computer scientists & engineers• Located at Stanford Prevention Research Center, School of Medicine• Our Vision: Harness technology to advance“borderless” health promotion solutions for all Stanford Prevention Research Center
    4. 4. Ultimate Goal of Health Promotion Interventions• Evidence-based• Cost-effective• Personalized• Easy to disseminate to diverse groups (to reduce ‘digital divide’/health disparities)• Promote maintenance of healthy behaviors over time Stanford Prevention Research Center
    5. 5. Mobile Interventions for Lifestyle Exercise at Stanford (MILES) - Purpose• Develop & test theoretically based smartphone apps for inactive midlife & older adults (new to technology)• Passively assess PA & SB via built-in accelerometer• Provide just-in-time feedback for behavior change Stanford Prevention Research Center
    6. 6. Harnessing the Power of Smartphones– MILES Study Apps developed to test 3 different Motivational Frames (Android platform)Analytic: Goal-setting, feedback, problem-solvingSocial: Social comparisons, support, competition (feedback embedded in social context)Emotional (affect): Emotional attachment to an avatar, game-like feedback, play,vicarious rewards Physical Activity & Sedentary behavior Outcomes – Validated Phone-based Accelerometry (Hekler et al, Nov. 2010) Stanford Prevention Research Center
    7. 7. Behavior Change Apps (“glance-able” displays)Analytic Affect Social Control(mTrack) (mSmiles) (mConnect) (Calorific)Tips, ‘Jack-pot’ Message Food trackingAdvice rewards board Stanford Prevention Research Center
    8. 8. MILES - Methods• Screened for eligibility online or by phone• Attended study orientation• Trained in basics of smartphones• Carried phone for 1 week to collectbaseline info (built-in accelerometer)• Randomized to 1 of 4 study apps• 7 weeks of assigned app (activity measuredcontinuously; daily diary)• Completed questionnaires at baseline & 8-week post-intervention Stanford Prevention Research Center
    9. 9. MILES Participants (N for Expt. 1 = 30)• Average Age: 58 yrs (range: 45 - 80)• Women: 65%• 4 or more Yrs of College: 74%• White: 87%• Employed Full- or Part-time: 78%• < 1 hr/Week of moderate-vigorous PA (increasesHR, breathing); No Contraindications to Walking• Sitting usually > 10 hr/Day; and• Cell phone user, but not Smartphone Stanford Prevention Research Center
    10. 10. MILES – Early Results (n = 30 inactive, smartphone-naive adults ages > 45 yrs) 2-mos Daily Increases in MVPA vs. Control (Calorific) 20 P < .01 MVPA Net Daily Minutes - Smartphone Accelerometer 15 P < .01 10 5 P = .39 ??? ??? ???King, Hekler, Grieco, Winter, Buman, et al., April, 2012 (abstract)
    11. 11. MILES – Early Results (n = 30 inactive, smartphone-naive adults ages > 45 yrs) 2-mos Daily Increases in MVPA vs. Control (Calorific) 20 P < .01 MVPA Net Daily Minutes - Smartphone Accelerometer 15 P < .01 10 5 P = .39 Analytic Affect SocialKing, Hekler, Grieco, Winter, Buman et al., April, 2012 (abstract)
    12. 12. Putting 2-mo. PA increases into a health context (relative to Control) . . .• for Social App:had Net increase = more than halfway(54%) toward ultimate goal of 20-30 mins/dayMVPA• for Affect App:Net increase = more than 1/3 of way (37%)toward ultimate goal of 20-30 mins/day MVPA
    13. 13. MILES - Preliminary Eating Results Food-tracking AppHekler, King, et al. April, 2012 [abstract]N=30; Harvard FFQ Mean of Activity Apps
    14. 14. Participant Perceptions of MILES Apps (post-test questionnaire—Study 1)• 93% found apps easy to use• 73% found time spent on apps “just about right”• 75% had increased awareness of PA & sitting time• 66% found apps motivated moving more/sitting less• 23% reported hard time remembering to use app• 22% felt too little training on how to use appsWinter S, Hekler E, Grieco L, et al. April 2012 [abstract]
    15. 15. Conclusions & Next Steps• Social comparison & affect/game dynamics had greater 2-month net gains in PA than a more standard approach (goal-setting + feedback) - May be due to technical glitches with Analytic app• Evaluating accelerometer-based Sedentary behavior & self-reported behavior change• Redesigned apps, finishing a second wave now
    16. 16. Conclusions & Next Steps - continued• Exploring additional Timely Questions: - Which apps for Whom? - Sustainability of behavior change - Will they work in other (underserved) groups? (e.g., Latino adults) - & How best to combine several health behaviors to increase behavioral synergies?
    17. 17. THANK YOU!