Erratum to: Adaptive goal setting and financial incentives: a 2 × 2 factorial randomized controlled trial to increase adults’ physical activity

Adams, Marc A.; Hurley, Jane C.; Todd, Michael; Bhuiyan, Nishat; Jarrett, Catherine L.; Tucker, Wesley J.; Hollingshead, Kevin; Angadi, Siddhartha S.

doi:10.1186/s12889-017-4231-x

Cited by 7 publications

(11 citation statements)

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“…They all provided data on BCTs used in DBCIs to change SB. Studies population were all sedentary subjects, 5 of them healthy adults ( 24 , 30 , 32 , 34 , 35 ), and 13 of them with some pathological diagnosis [overweight/obese ( 19 , 25 ), type 2 diabetes mellitus ( 23 , 26 , 36 ), heart disease ( 20 , 28 , 29 , 31 ), cancer ( 21 , 33 ), depression ( 22 ), and osteoarthritis ( 27 ) ( Table 2 )]. These studies included 2,298 individuals who were followed up for 5 weeks−3 years.…”

Section: Resultsmentioning

confidence: 99%

“…Among these groups of BCTs, the most frequently used was goal setting ( n = 10) as part of the goals and planning cluster ( Table 3 ). More specifically, it was used in five studies involving healthy adults aimed at promoting PA ( 19 , 29 , 30 , 34 , 35 ) and other five studies involving patients suffering from cardiac-related disease ( 28 , 31 ), cancer ( 33 ), diabetes ( 36 ), or musculoskeletal impairments. ( 27 ).…”

Section: Discussionmentioning

confidence: 99%

“…The second most used BCT was self-monitoring of behavior ( n = 9) as part of the feedback and monitoring cluster. However, only two out of nine studies included healthy adults ( 19 , 35 ). In light of these findings, goal setting is more frequently used in healthy populations than self-monitoring of behavior.…”

Section: Discussionmentioning

confidence: 99%

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Behavior Change Techniques and the Effects Associated With Digital Behavior Change Interventions in Sedentary Behavior in the Clinical Population: A Systematic Review

Martín-Martín

Roldán-Jiménez

De-Torres

et al. 2021

Front. Digit. Health

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Background: Sedentary behavior (SB) negatively impact health and is highly prevalent in the population. Digital behavior change interventions (DBCIs) have been developed to modify behaviors such as SB by technologies. However, it is unknown which behavior change techniques (BCTs) are most frequently employed in SB as well as the effect associated with DBCIs in this field. The aim of this systematic review was: (a) to evaluate the BCT most frequently employed in digital health including all technologies available and interventions aimed at increasing physical activity (PA), reducing sedentary time, and improving adherence to exercise in the clinical population, and (b) to review the effect associated with DBCIs in this field.Methods: The database used was Medline, as well as Scopus, Scielo, and Google Scholar. For the search strategy, we considered versions of behavior/behavioral, mHealth/eHealth/telemedicine/serious game/gamification. The terms related to PA and SB were included, the criteria for inclusion were randomized clinical trials (RCTs), adults, intervention based on digital media, and outcome variable lifestyle modification; a last 5 years filter was included. Michie's Taxonomy was used to identify BCTs. The study was registered under the number PROSPERO CRD42019138681.Results: Eighteen RCTs were included in the present systematic review, 5 of them healthy adults, and 13 of them with some illness. Studies included 2298 sedentary individuals who were followed up for 5 weeks−3 years. The most used BCTs were goal setting, problem solving, review outcomes/goals, feedback on behavior and outcomes of behavior, self-monitoring of behavior, social support, information about health consequences, and behavior practice/rehearsal. The effect associated with DBCIs showed improvements, among several related to PA and physiologic self-reported and anthropometric outcomes.Conclusion: The BCTs most used in digital health to change outcomes related to SB were goals and planning, feedback and monitoring, social support, natural consequences, repetition, and substitution. Besides these findings, DBCIs are influenced by several factors like the type of intervention, patients' preferences and values, or the number of BCTs employed. More research is needed to determine with precision which DBCIs or BCTs are the most effective to reduce SB in the clinical population.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Behavior Change Techniques and the Effects Associated With Digital Behavior Change Interventions in Sedentary Behavior in the Clinical Population: A Systematic Review

Martín-Martín

Roldán-Jiménez

De-Torres

et al. 2021

Front. Digit. Health

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“…One way timely financial incentives might work, according to behavioral economics, is by leveraging people’s predictable tendency to act in favor of their immediate self-interest, a principal referred to as “present bias” [ 10 ]. In the case of physical activity, the likelihood that someone will be more physically active should increase if a financial incentive is at stake—and the more immediate the incentive, the stronger the nudge, according to this theoretical perspective [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, at the same time research continues to examine conditions under which incentives drive sustained, long-term changes, efforts to increase efficiency, and thus scalability, of incentive interventions are also needed. The incentive magnitude typically used to promote physical activity in RCT settings (ie, US $1-US $2 per day) [ 15 , 19 , 22 , 23 , 27 , 28 ] may be simply too high for third-party payers and real-world implementation.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Carrot Rewards App, a Population-Level Incentive-Based Intervention Promoting Step Counts Across Two Canadian Provinces: Quasi-Experimental Study

Mitchell

White²,

Lau

et al. 2018

JMIR Mhealth Uhealth

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BackgroundThe Carrot Rewards app was developed as part of an innovative public-private partnership to reward Canadians with loyalty points, exchangeable for retail goods, travel rewards, and groceries for engaging in healthy behaviors such as walking.ObjectiveThis study examined whether a multicomponent intervention including goal setting, graded tasks, biofeedback, and very small incentives tied to daily step goal achievement (assessed by built-in smartphone accelerometers) could increase physical activity in two Canadian provinces, British Columbia (BC) and Newfoundland and Labrador (NL).MethodsThis 12-week, quasi-experimental (single group pre-post) study included 78,882 participants; 44.39% (35,014/78,882) enrolled in the Carrot Rewards “Steps” walking program during the recruitment period (June 13–July 10, 2016). During the 2-week baseline (or “run-in”) period, we calculated participants’ mean steps per day. Thereafter, participants earned incentives in the form of loyalty points (worth Can $0.04 ) every day they reached their personalized daily step goal (ie, baseline mean+1000 steps=first daily step goal level). Participants earned additional points (Can $0.40) for meeting their step goal 10+ nonconsecutive times in a 14-day period (called a “Step Up Challenge”). Participants could earn up to Can $5.00 during the 12-week evaluation period. Upon meeting the 10-day contingency, participants could increase their daily goal by 500 steps, aiming to gradually increase the daily step number by 3000. Only participants with ≥5 valid days (days with step counts: 1000-40,000) during the baseline period were included in the analysis (n=32,229).The primary study outcome was mean steps per day (by week), analyzed using linear mixed-effects models.ResultsThe mean age of 32,229 participants with valid baseline data was 33.7 (SD 11.6) years; 66.11% (21,306/32,229) were female. The mean daily step count at baseline was 6511.22. Over half of users (16,336/32,229, 50.69%) were categorized as “physically inactive,” accumulating <5000 daily steps at baseline. Results from mixed-effects models revealed statistically significant increases in mean daily step counts when comparing baseline with each study week (P<.001). Compared with baseline, participants walked 115.70 more steps (95% CI 74.59 to 156.81; P<.001) at study week 12. BC and NL users classified as “high engagers” (app engagement above sample median; 15,511/32,229, 48.13%) walked 738.70 (95% CI 673.81 to 803.54; P<.001) and 346.00 (95% CI 239.26 to 452.74; P<.001) more steps, respectively. Physically inactive, high engagers (7022/32,229, 21.08%) averaged an increase of 1224.66 steps per day (95% CI 1160.69 to 1288.63; P<.001). Effect sizes were modest.ConclusionsProviding very small but immediate rewards for personalized daily step goal achievement as part of a multicomponent intervention increased daily step counts on a population scale, especially for physically inactive individuals and individuals who engaged more with the walking program. Positive effects in both BC...

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Adaptive Goals and Reinforcement Timing to Increase Physical Activity in Adults: A Factorial Randomized Trial

Adams

Todd

Angadi

et al. 2022

American Journal of Preventive Medicine

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Introduction: Potent lifestyle interventions to increase moderate-to-vigorous physical activity are urgently needed for population-level chronic disease prevention. This trial tested the independent and joint effects of a mobile health system automating adaptive goal setting and immediate financial reinforcement for increasing daily walking among insufficiently active adults.Study Design: Participants were randomized into a 2 (adaptive versus static goal setting) X 2 (immediate versus delayed financial incentive timing) condition factorial trial to increase walking.

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Erratum to: Adaptive goal setting and financial incentives: a 2 × 2 factorial randomized controlled trial to increase adults’ physical activity

Cited by 7 publications

References 1 publication

Behavior Change Techniques and the Effects Associated With Digital Behavior Change Interventions in Sedentary Behavior in the Clinical Population: A Systematic Review

Behavior Change Techniques and the Effects Associated With Digital Behavior Change Interventions in Sedentary Behavior in the Clinical Population: A Systematic Review

Evaluating the Carrot Rewards App, a Population-Level Incentive-Based Intervention Promoting Step Counts Across Two Canadian Provinces: Quasi-Experimental Study

Adaptive Goals and Reinforcement Timing to Increase Physical Activity in Adults: A Factorial Randomized Trial

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