Adaptive step goals and rewards: a longitudinal growth model of daily steps for a smartphone-based walking intervention

Elizabeth V. Korinek, Sayali S. Phatak, Cesar A. Martin, Mohammad T. Freigoun, Daniel Rivera, Marc Adams, Pedja Klasnja, Matthew Buman, Eric B. Hekler

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Adaptive interventions are an emerging class of behavioral interventions that allow for individualized tailoring of intervention components over time to a person’s evolving needs. The purpose of this study was to evaluate an adaptive step goal + reward intervention, grounded in Social Cognitive Theory delivered via a smartphone application (Just Walk), using a mixed modeling approach. Participants (N = 20) were overweight (mean BMI = 33.8 ± 6.82 kg/m2), sedentary adults (90% female) interested in participating in a 14-week walking intervention. All participants received a Fitbit Zip that automatically synced with Just Walk to track daily steps. Step goals and expected points were delivered through the app every morning and were designed using a pseudo-random multisine algorithm that was a function of each participant’s median baseline steps. Self-report measures were also collected each morning and evening via daily surveys administered through the app. The linear mixed effects model showed that, on average, participants significantly increased their daily steps by 2650 (t = 8.25, p < 0.01) from baseline to intervention completion. A non-linear model with a quadratic time variable indicated an inflection point for increasing steps near the midpoint of the intervention and this effect was significant (t2 = −247, t = −5.01, p < 0.001). An adaptive step goal + rewards intervention using a smartphone app appears to be a feasible approach for increasing walking behavior in overweight adults. App satisfaction was high and participants enjoyed receiving variable goals each day. Future mHealth studies should consider the use of adaptive step goals + rewards in conjunction with other intervention components for increasing physical activity.

Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
JournalJournal of Behavioral Medicine
DOIs
StateAccepted/In press - Sep 16 2017

Fingerprint

Reward
Walking
Growth
Nonlinear Dynamics
Telemedicine
Self Report
Smartphone

Keywords

  • Adaptive goals
  • mHealth
  • Personalized behavior change
  • Walking behavior

ASJC Scopus subject areas

  • Psychology(all)
  • Psychiatry and Mental health

Cite this

Adaptive step goals and rewards : a longitudinal growth model of daily steps for a smartphone-based walking intervention. / Korinek, Elizabeth V.; Phatak, Sayali S.; Martin, Cesar A.; Freigoun, Mohammad T.; Rivera, Daniel; Adams, Marc; Klasnja, Pedja; Buman, Matthew; Hekler, Eric B.

In: Journal of Behavioral Medicine, 16.09.2017, p. 1-13.

Research output: Contribution to journalArticle

Korinek, Elizabeth V. ; Phatak, Sayali S. ; Martin, Cesar A. ; Freigoun, Mohammad T. ; Rivera, Daniel ; Adams, Marc ; Klasnja, Pedja ; Buman, Matthew ; Hekler, Eric B. / Adaptive step goals and rewards : a longitudinal growth model of daily steps for a smartphone-based walking intervention. In: Journal of Behavioral Medicine. 2017 ; pp. 1-13.
@article{9686b448230c4719ab8a2e9bbe17c1d3,
title = "Adaptive step goals and rewards: a longitudinal growth model of daily steps for a smartphone-based walking intervention",
abstract = "Adaptive interventions are an emerging class of behavioral interventions that allow for individualized tailoring of intervention components over time to a person’s evolving needs. The purpose of this study was to evaluate an adaptive step goal + reward intervention, grounded in Social Cognitive Theory delivered via a smartphone application (Just Walk), using a mixed modeling approach. Participants (N = 20) were overweight (mean BMI = 33.8 ± 6.82 kg/m2), sedentary adults (90{\%} female) interested in participating in a 14-week walking intervention. All participants received a Fitbit Zip that automatically synced with Just Walk to track daily steps. Step goals and expected points were delivered through the app every morning and were designed using a pseudo-random multisine algorithm that was a function of each participant’s median baseline steps. Self-report measures were also collected each morning and evening via daily surveys administered through the app. The linear mixed effects model showed that, on average, participants significantly increased their daily steps by 2650 (t = 8.25, p < 0.01) from baseline to intervention completion. A non-linear model with a quadratic time variable indicated an inflection point for increasing steps near the midpoint of the intervention and this effect was significant (t2 = −247, t = −5.01, p < 0.001). An adaptive step goal + rewards intervention using a smartphone app appears to be a feasible approach for increasing walking behavior in overweight adults. App satisfaction was high and participants enjoyed receiving variable goals each day. Future mHealth studies should consider the use of adaptive step goals + rewards in conjunction with other intervention components for increasing physical activity.",
keywords = "Adaptive goals, mHealth, Personalized behavior change, Walking behavior",
author = "Korinek, {Elizabeth V.} and Phatak, {Sayali S.} and Martin, {Cesar A.} and Freigoun, {Mohammad T.} and Daniel Rivera and Marc Adams and Pedja Klasnja and Matthew Buman and Hekler, {Eric B.}",
year = "2017",
month = "9",
day = "16",
doi = "10.1007/s10865-017-9878-3",
language = "English (US)",
pages = "1--13",
journal = "Journal of Behavioral Medicine",
issn = "0160-7715",
publisher = "Springer New York",

}

TY - JOUR

T1 - Adaptive step goals and rewards

T2 - a longitudinal growth model of daily steps for a smartphone-based walking intervention

AU - Korinek, Elizabeth V.

AU - Phatak, Sayali S.

AU - Martin, Cesar A.

AU - Freigoun, Mohammad T.

AU - Rivera, Daniel

AU - Adams, Marc

AU - Klasnja, Pedja

AU - Buman, Matthew

AU - Hekler, Eric B.

PY - 2017/9/16

Y1 - 2017/9/16

N2 - Adaptive interventions are an emerging class of behavioral interventions that allow for individualized tailoring of intervention components over time to a person’s evolving needs. The purpose of this study was to evaluate an adaptive step goal + reward intervention, grounded in Social Cognitive Theory delivered via a smartphone application (Just Walk), using a mixed modeling approach. Participants (N = 20) were overweight (mean BMI = 33.8 ± 6.82 kg/m2), sedentary adults (90% female) interested in participating in a 14-week walking intervention. All participants received a Fitbit Zip that automatically synced with Just Walk to track daily steps. Step goals and expected points were delivered through the app every morning and were designed using a pseudo-random multisine algorithm that was a function of each participant’s median baseline steps. Self-report measures were also collected each morning and evening via daily surveys administered through the app. The linear mixed effects model showed that, on average, participants significantly increased their daily steps by 2650 (t = 8.25, p < 0.01) from baseline to intervention completion. A non-linear model with a quadratic time variable indicated an inflection point for increasing steps near the midpoint of the intervention and this effect was significant (t2 = −247, t = −5.01, p < 0.001). An adaptive step goal + rewards intervention using a smartphone app appears to be a feasible approach for increasing walking behavior in overweight adults. App satisfaction was high and participants enjoyed receiving variable goals each day. Future mHealth studies should consider the use of adaptive step goals + rewards in conjunction with other intervention components for increasing physical activity.

AB - Adaptive interventions are an emerging class of behavioral interventions that allow for individualized tailoring of intervention components over time to a person’s evolving needs. The purpose of this study was to evaluate an adaptive step goal + reward intervention, grounded in Social Cognitive Theory delivered via a smartphone application (Just Walk), using a mixed modeling approach. Participants (N = 20) were overweight (mean BMI = 33.8 ± 6.82 kg/m2), sedentary adults (90% female) interested in participating in a 14-week walking intervention. All participants received a Fitbit Zip that automatically synced with Just Walk to track daily steps. Step goals and expected points were delivered through the app every morning and were designed using a pseudo-random multisine algorithm that was a function of each participant’s median baseline steps. Self-report measures were also collected each morning and evening via daily surveys administered through the app. The linear mixed effects model showed that, on average, participants significantly increased their daily steps by 2650 (t = 8.25, p < 0.01) from baseline to intervention completion. A non-linear model with a quadratic time variable indicated an inflection point for increasing steps near the midpoint of the intervention and this effect was significant (t2 = −247, t = −5.01, p < 0.001). An adaptive step goal + rewards intervention using a smartphone app appears to be a feasible approach for increasing walking behavior in overweight adults. App satisfaction was high and participants enjoyed receiving variable goals each day. Future mHealth studies should consider the use of adaptive step goals + rewards in conjunction with other intervention components for increasing physical activity.

KW - Adaptive goals

KW - mHealth

KW - Personalized behavior change

KW - Walking behavior

UR - http://www.scopus.com/inward/record.url?scp=85029477348&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85029477348&partnerID=8YFLogxK

U2 - 10.1007/s10865-017-9878-3

DO - 10.1007/s10865-017-9878-3

M3 - Article

C2 - 28918547

AN - SCOPUS:85029477348

SP - 1

EP - 13

JO - Journal of Behavioral Medicine

JF - Journal of Behavioral Medicine

SN - 0160-7715

ER -