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Multiphase Optimization Trial of Incentives for Veterans to Encourage Walking

Multiphase Optimization Trial of Incentives for Veterans to Encourage Walking

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT04518943
Enrollment
102
Registered
2020-08-19
Start date
2022-03-17
Completion date
2024-07-31
Last updated
2025-04-30

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Body Mass Index, Depression, Exercise, Hypertension, Motivation, Sedentary Behavior, Walking

Keywords

sedentary behavior, walking, motivation

Brief summary

Regular physical activity (PA) is essential to healthy aging. Unfortunately, only 5% of US adults meet guideline of 150 minutes of moderate exercise; Veterans and non-Veterans have similar levels of PA. A patient incentive program for PA may help. Behavioral economics suggests that the chronic inability to start and maintain a PA routine may be the result of present bias, which is a tendency to value immediate rewards over rewards in the future. With present bias, it is always better to exercise tomorrow because the immediate gratification of watching television or surfing the internet is a more powerful motivator than the intangible and delayed benefit of future health. Patient incentives may overcome present bias by moving the rewards for exercise forward in time. Recent randomized trials suggest that incentives for PA can be effective, but substantial gaps in knowledge prevent the implementation of a PA incentive program in Veterans Affairs (VA). First, incentive designs vary considerably. They vary by the size of the incentive, the type of incentive (cash or non-financial), the probability of earning an incentive (an assured payment for effort or a lottery-based incentive), or whether the incentive is earned after the effort is given (a gain-framed incentive) or awarded up-front and lost if the effort is not given (a loss-framed incentive). The optimal combination of these components for a Veteran population is unknown. Second, the evidence about the effective components of incentives comes from studies conducted in populations that were overwhelmingly female; often employees at large companies, with high levels of education and income. VA users, in contrast, are mostly male and lower income, and most are not employed. This is important because the investigators have theoretical reasons to believe that the effects of components of incentives are likely to vary by income and gender. Finally, few studies have managed to design an incentive such that the physical activity was maintained after the incentive was removed. Indeed, a common theme in incentivizing health behavior change is the difficulty in sustaining behavior change once the incentives are removed.

Detailed description

The investigators propose to fill the research gaps through a Multiphase Optimization Strategy (MOST) trial of incentives for walking. A MOST trial is ideally suited for situations in which a proposed intervention has many potential intervention components. A MOST trial consists of three phases. A screening phase trial is used to efficiently identify-through a factorial designed randomized trial-the effective components of a complex intervention like incentives. A refining phase trial tests the optimal dose (size or duration) of the incentives. A confirmatory phase trial tests the optimal components and dose against a usual care control. The goal of the proposed study is to conduct the screening phase trial in 128 Veterans to identify the optimal components of incentives for increasing walking among physically inactive Veterans. All Veterans in this phase will be given various components of incentives for increasing average steps per day to 7,000 steps over a 12-week habit-building period, and then maintaining the increase through a 12-week habit maintenance period. The specific aims are: Aim 1: Conduct a 24 factorial designed screening-phase trial of incentives for increasing average steps per day to 7,000 steps over 12 weeks among physically inactive Veterans. Every patient in the trial will be given a Fitbit Inspire activity monitor and assigned to a group that receives different components of incentives. The investigators will test four different incentive factors: 1) lottery vs. loss framed incentives, 2) financial vs. non-financial incentives, 3) a pre-commitment postcard reminder of a Veteran's stated intrinsic reason for commitment to PA vs. no pre-commitment postcard, and 4) a request for PA advice from a Veteran on staying active vs. no request. The first factor has never been tested in a population like the VA. Factors 2-4 are designed specifically to sustain the effects of incentives after the incentive is removed. Factor 4 is a novel hypothesis that has never been tested outside of educational research: specifically, that asking for advice from a Veteran is more motivating than giving advice to them, even if that Veteran is struggling with low physical activity themselves. The primary outcome is change in steps per week from baseline to week 24. Aim 2. Conduct cost analyses and qualitative interviews. The cost of administering each component and qualitative assessments of the acceptability of each component to trial participants will inform the decision of which components to retain for the subsequent refining and confirmatory phase trials. Aim 3. Convene an expert panel to choose components for the next phases of the MOST trial. The panel will weigh each component in terms of its effect on step counts (Aim 1), administrative costs and participant-reported qualitative assessments (Aim 2), and the strength of the theoretical basis for the component's effect on physical activity.

Interventions

BEHAVIORALWalking

Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.

Sponsors

VA Office of Research and Development
Lead SponsorFED

Study design

Allocation
RANDOMIZED
Intervention model
FACTORIAL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE (Investigator)

Masking description

Data will be masked to the principal investigator.

Intervention model description

The MOST trial design involves optimizing an intervention before it is tested against a usual control. It is optimized in that the various components of the intervention are tested against one another in a screening phase to assess which components add value to the intervention, and then only the valuable components are included in the final intervention. It is especially useful when an intervention has many potential combinations of components, as is the case with incentives. In a traditional trial, researchers would use theory or evidence from prior studies to choose an incentive design and test this package against usual care. Even if this package outperforms usual care in a randomized trial, the investigators cannot tell whether it was optimal. Some components could have provided no benefit or delivered in too small a dose to be effective. Other components could have been detrimental to subjects. There is no way to know if the components are all tested as a single package.

Eligibility

Sex/Gender
ALL
Age
50 Years to 69 Years
Healthy volunteers
No

Inclusion criteria

* Veteran that receives healthcare at VA Puget Sound Health Care System * Age 50-69 * Diagnosis of hypertension, depression or a BMI between 25-40. * Physically inactive according to self-report. . * 2,000-5,000 steps per day during the screening week * Have and be able to use a smart phone.

Exclusion criteria

* MOVE participation in the past 4 months * Blind * \<2,000 steps per day * Inability to walk 20 minutes without stopping (self-report). * Eating disorder. * Dementia/ cognitive impairment * Metastatic cancer, end state renal disease, hospice, palliative care, heart failure, undergoing chemotherapy, radiation or hemodialysis, have had or are on the list for an organ transplant. * Implanted cardiovascular device such as defibrillator or ventricular device * Active psychosis/mania/behavioral flag * Pregnant women * Homeless or housing insecure * Has a paid caregiver that provides \>50% of daily living activities, lives in a nursing home, assisted living facility or group home. * Individuals that exhibit threatening, violent or inappropriate behavior during the screening phone call. * Foot Ulcer

Design outcomes

Primary

MeasureTime frameDescription
Change in Average Steps Per Day From Baseline Week to Week 12baseline to week 12The change in average steps per day from the baseline week to week 12.
Change in Average Steps Per Day From Baseline Week to Week 24baseline to week 24The change in average steps per day from the baseline week to week 24

Secondary

MeasureTime frameDescription
Self-efficacyMeasured at baseline, week 12 and 24Measured using the Exercise Self-Efficacy Scale at baseline, week 12 and week 24 (McAuley E 1993). This scale measures self-efficacy on nine different measures from not confident (0 rating) to very confident (10 rating).The range is 0 to 10. A higher rating indicates that the respondent is more confident they will be able to overcome barriers to physical activity.
Intrinsic/Extrinsic MotivationMeasured at baseline, week 12 and 24Measured using the Motivation for Physical Activity Measurement (MPAM) at baseline, week 12 and week 24 (Frederick CM 1993). This scale measures reasons and motivations for participating in physical activity having respondents indicate why they exercise. The scale ranges from 1=very true for me, 2= somewhat true for me, 3=neither true nor untrue for me, 4=somewhat untrue for me, 5= very untrue for me. The minimum score is 1 and the maximum is 5. Higher scores means more intrinsic motivation to exercise.
Mental HealthMeasured at baseline, week 12 and 24Measured using the PHQ-8 depression scale at baseline, week 12 and week 24. This assessment measures depressive symptoms over the past 2 weeks. Respondents indicate how bothered they were by the following problems on a scale from 0 (not at all) to 3 (nearly every day). The minimum and maximum score are zero and 24, respectively. A lower score indicates fewer depressive symptoms.

Countries

United States

Participant flow

Participants by arm

ArmCount
F1M1P1R1
Financial Reward, mixed lottery, pre-commitment postcard reminders, request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
6
N1M1P1R1
Non-financial reward, mixed lottery, pre-commitment postcard reminders, request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
6
N1M1P1R0
Non-financial reward, mixed lottery, pre-commitment postcard reminders, no request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
7
N1M1P0R0
Non-financial reward, mixed lottery, no pre-commitment postcard reminders, no request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
6
N1M1P0R1
Non-financial reward, mixed lottery, no pre-commitment postcard reminders, request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
6
N1L1P1R1
Non-financial reward, loss incentive, pre-commitment postcard reminders, request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
7
N1L1P1R0
Non-financial reward, loss incentive, pre-commitment postcard reminders, no request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
6
N1L1P0R0
Non-financial reward, loss incentive, no pre-commitment postcard reminders, no request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
7
N1L1P0R1
Non-financial reward, loss incentive, no pre-commitment postcard reminders, request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
6
F1M1P1R0
Financial Reward, mixed lottery, pre-commitment postcard reminders, no request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
6
F1M1P0R0
Financial Reward, mixed lottery, no pre-commitment postcard reminders, no request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
6
F1M1P0R1
Financial Reward, mixed lottery, no pre-commitment postcard reminders, request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
7
F1L1P1R1
Financial Reward, loss incentive, pre-commitment postcard reminders, request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
7
F1L1P1R0
Financial Reward, loss incentive, pre-commitment postcard reminders, no request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
7
F1L1P0R0
Financial Reward, loss incentive, no pre-commitment postcard reminders, no request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
6
F1L1P0R1
Financial Reward, loss incentive, no pre-commitment postcard reminders, request physical activity advice Walking: Inactive Veterans will be encouraged to increase their step count to 7,000 steps per day by the end of the 12-week intervention period. Weekly the step goal will increase 15% if they were successful in reaching their goal the previous week.
6
Total102

Withdrawals & dropouts

PeriodReasonFG000FG001FG002FG003FG004FG005FG006FG007FG008FG009FG010FG011FG012FG013FG014FG015
Consent Through BaselineWalked too many steps at baseline3145334741314544

Baseline characteristics

CharacteristicN1M1P1R1N1M1P1R0N1M1P0R0N1M1P0R1N1L1P1R1N1L1P1R0N1L1P0R0N1L1P0R1F1M1P1R1F1M1P1R0F1M1P0R0F1M1P0R1F1L1P1R1F1L1P1R0F1L1P0R0F1L1P0R1Total
Age, Categorical
<=18 years
0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants
Age, Categorical
>=65 years
2 Participants3 Participants2 Participants0 Participants2 Participants1 Participants4 Participants1 Participants1 Participants2 Participants2 Participants2 Participants0 Participants2 Participants1 Participants1 Participants26 Participants
Age, Categorical
Between 18 and 65 years
4 Participants4 Participants4 Participants6 Participants5 Participants5 Participants3 Participants5 Participants5 Participants4 Participants4 Participants5 Participants7 Participants5 Participants5 Participants5 Participants76 Participants
Age, Continuous60.7 years
STANDARD_DEVIATION 5.1
62.3 years
STANDARD_DEVIATION 6.3
60.2 years
STANDARD_DEVIATION 7.8
60.3 years
STANDARD_DEVIATION 4.9
58.3 years
STANDARD_DEVIATION 6.5
58.3 years
STANDARD_DEVIATION 7.3
65.5 years
STANDARD_DEVIATION 2.6
58.0 years
STANDARD_DEVIATION 6
60.8 years
STANDARD_DEVIATION 6.3
61.3 years
STANDARD_DEVIATION 6.5
59.0 years
STANDARD_DEVIATION 7.5
59.7 years
STANDARD_DEVIATION 7.1
61.9 years
STANDARD_DEVIATION 2.9
60.7 years
STANDARD_DEVIATION 3.8
58.3 years
STANDARD_DEVIATION 7.4
61.3 years
STANDARD_DEVIATION 3.9
60.4 years
STANDARD_DEVIATION 5.8
Baseline steps4175 steps/day
STANDARD_DEVIATION 690
4429 steps/day
STANDARD_DEVIATION 2092
8209 steps/day
STANDARD_DEVIATION 4586
5744 steps/day
STANDARD_DEVIATION 3117
6740 steps/day
STANDARD_DEVIATION 4100
6913 steps/day
STANDARD_DEVIATION 3045
7373 steps/day
STANDARD_DEVIATION 1410
6195 steps/day
STANDARD_DEVIATION 2370
5358 steps/day
STANDARD_DEVIATION 3185
3456 steps/day
STANDARD_DEVIATION 999
5193 steps/day
STANDARD_DEVIATION 1683
4654 steps/day
STANDARD_DEVIATION 1579
6291 steps/day
STANDARD_DEVIATION 3544
6257 steps/day
STANDARD_DEVIATION 2652
6313 steps/day
STANDARD_DEVIATION 3544
7835 steps/day
STANDARD_DEVIATION 5401
5890 steps/day
STANDARD_DEVIATION 3034
Race (NIH/OMB)
American Indian or Alaska Native
0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants1 Participants0 Participants0 Participants0 Participants1 Participants0 Participants0 Participants0 Participants0 Participants2 Participants
Race (NIH/OMB)
Asian
0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants1 Participants0 Participants0 Participants0 Participants0 Participants1 Participants0 Participants0 Participants2 Participants
Race (NIH/OMB)
Black or African American
0 Participants0 Participants2 Participants0 Participants0 Participants1 Participants2 Participants0 Participants0 Participants0 Participants0 Participants1 Participants2 Participants0 Participants1 Participants1 Participants10 Participants
Race (NIH/OMB)
More than one race
1 Participants0 Participants1 Participants0 Participants0 Participants1 Participants1 Participants1 Participants0 Participants0 Participants2 Participants1 Participants0 Participants0 Participants0 Participants0 Participants8 Participants
Race (NIH/OMB)
Native Hawaiian or Other Pacific Islander
1 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants1 Participants
Race (NIH/OMB)
Unknown or Not Reported
0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants0 Participants1 Participants0 Participants0 Participants1 Participants1 Participants0 Participants0 Participants3 Participants
Race (NIH/OMB)
White
4 Participants7 Participants3 Participants6 Participants7 Participants4 Participants4 Participants4 Participants5 Participants5 Participants4 Participants4 Participants4 Participants5 Participants5 Participants5 Participants76 Participants
Region of Enrollment
United States
6 participants7 participants6 participants6 participants7 participants6 participants7 participants6 participants6 participants6 participants6 participants7 participants7 participants7 participants6 participants6 participants102 participants
Sex: Female, Male
Female
2 Participants0 Participants1 Participants0 Participants2 Participants1 Participants3 Participants1 Participants2 Participants2 Participants2 Participants1 Participants3 Participants1 Participants0 Participants2 Participants23 Participants
Sex: Female, Male
Male
4 Participants7 Participants5 Participants6 Participants5 Participants5 Participants4 Participants5 Participants4 Participants4 Participants4 Participants6 Participants4 Participants6 Participants6 Participants4 Participants79 Participants

Adverse events

Event typeEG000
affected / at risk
EG001
affected / at risk
EG002
affected / at risk
EG003
affected / at risk
EG004
affected / at risk
EG005
affected / at risk
EG006
affected / at risk
EG007
affected / at risk
EG008
affected / at risk
EG009
affected / at risk
EG010
affected / at risk
EG011
affected / at risk
EG012
affected / at risk
EG013
affected / at risk
EG014
affected / at risk
EG015
affected / at risk
deaths
Total, all-cause mortality
0 / 60 / 60 / 70 / 60 / 60 / 70 / 60 / 70 / 60 / 60 / 60 / 70 / 70 / 70 / 60 / 6
other
Total, other adverse events
0 / 60 / 60 / 70 / 60 / 60 / 70 / 60 / 70 / 60 / 60 / 60 / 70 / 70 / 70 / 60 / 6
serious
Total, serious adverse events
0 / 60 / 60 / 70 / 60 / 60 / 70 / 60 / 70 / 60 / 60 / 60 / 70 / 70 / 70 / 60 / 6

Outcome results

Primary

Change in Average Steps Per Day From Baseline Week to Week 12

The change in average steps per day from the baseline week to week 12.

Time frame: baseline to week 12

Population: The participants in the N1L1P0R0 group were not assessed because they either walked too many or to few steps at the baseline period

ArmMeasureValue (MEAN)Dispersion
F1M1P1R1Change in Average Steps Per Day From Baseline Week to Week 124224 steps/dayStandard Deviation 719
N1M1P1R1Change in Average Steps Per Day From Baseline Week to Week 124786 steps/dayStandard Deviation 6843
N1M1P1R0Change in Average Steps Per Day From Baseline Week to Week 123654 steps/dayStandard Deviation 3272
N1M1P0R0Change in Average Steps Per Day From Baseline Week to Week 122626 steps/dayStandard Deviation 0
N1M1P0R1Change in Average Steps Per Day From Baseline Week to Week 123740 steps/dayStandard Deviation 2248
N1L1P1R1Change in Average Steps Per Day From Baseline Week to Week 124493 steps/dayStandard Deviation 6036
N1L1P1R0Change in Average Steps Per Day From Baseline Week to Week 123680 steps/dayStandard Deviation 96
N1L1P0R1Change in Average Steps Per Day From Baseline Week to Week 124577 steps/dayStandard Deviation 97
F1M1P1R0Change in Average Steps Per Day From Baseline Week to Week 121110 steps/dayStandard Deviation 1390
F1M1P0R0Change in Average Steps Per Day From Baseline Week to Week 122176 steps/dayStandard Deviation 1467
F1M1P0R1Change in Average Steps Per Day From Baseline Week to Week 123216 steps/dayStandard Deviation 3366
F1L1P1R1Change in Average Steps Per Day From Baseline Week to Week 127002 steps/dayStandard Deviation 456
F1L1P1R0Change in Average Steps Per Day From Baseline Week to Week 128158 steps/dayStandard Deviation 4016
F1L1P0R0Change in Average Steps Per Day From Baseline Week to Week 123599 steps/dayStandard Deviation 226
F1L1P0R1Change in Average Steps Per Day From Baseline Week to Week 122728 steps/dayStandard Deviation 0
Comparison: This is the results of financial vs non-financial reward factor at week 12. It compares change in steps from baseline to week 12 between subjects randomized to receive financial rewards compared to those who received nonfinancial rewards. A linear mixed-effect model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignment, linear week with a spline at week 12, and interactions between factors and weeks.p-value: 0.41890% CI: [-1924, 655]linear mixed model
Comparison: This is the results of lottery vs loss-framed reward factor at week 12. It compares change in steps from baseline to week 12 between subjects randomized to receive financial rewards compared to those who received nonfinancial rewards. A linear mixed-effect model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignments, linear week with a spline at week 12, and interactions between factors and weeks.p-value: 0.03390% CI: [-3000, -385]Mixed Models Analysis
Comparison: This is the results of precommitment (PC) vs no PC factor at week 12. It compares change in steps from baseline to week 12 between subjects randomized to receive a request for PC compared to those who received no request. A linear mixed-effect model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignments, linear week with a spline at week 12, and interactions between factors and weeks.p-value: 0.24890% CI: [-347, 1988]Mixed Models Analysis
Comparison: This is the results of advice vs no advice factor at week 12. It compares change in steps from baseline to week 12 between subjects randomized to receive a request for advice to other subjects compared to those who received no request. A linear mixed-effect model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignments, linear week with a spline at week 12, and interactions between factors and weeks.p-value: 0.12590% CI: [82, 2323]Mixed Models Analysis
Primary

Change in Average Steps Per Day From Baseline Week to Week 24

The change in average steps per day from the baseline week to week 24

Time frame: baseline to week 24

Population: The participants in the N1L1P0R0 group were not assessed because they either walked too many or to few steps at the baseline period

ArmMeasureValue (MEAN)Dispersion
F1M1P1R1Change in Average Steps Per Day From Baseline Week to Week 245237 steps/dayStandard Deviation 0
N1M1P1R1Change in Average Steps Per Day From Baseline Week to Week 244631 steps/dayStandard Deviation 3513
N1M1P1R0Change in Average Steps Per Day From Baseline Week to Week 244074 steps/dayStandard Deviation 2361
N1M1P0R0Change in Average Steps Per Day From Baseline Week to Week 243804 steps/dayStandard Deviation 0
N1M1P0R1Change in Average Steps Per Day From Baseline Week to Week 246414 steps/dayStandard Deviation 0
N1L1P1R1Change in Average Steps Per Day From Baseline Week to Week 243844 steps/dayStandard Deviation 4122
N1L1P1R0Change in Average Steps Per Day From Baseline Week to Week 242487 steps/dayStandard Deviation 3072
N1L1P0R1Change in Average Steps Per Day From Baseline Week to Week 246531 steps/dayStandard Deviation 2683
F1M1P1R0Change in Average Steps Per Day From Baseline Week to Week 24614 steps/dayStandard Deviation 928
F1M1P0R0Change in Average Steps Per Day From Baseline Week to Week 243165 steps/dayStandard Deviation 2961
F1M1P0R1Change in Average Steps Per Day From Baseline Week to Week 241312 steps/dayStandard Deviation 3845
F1L1P1R1Change in Average Steps Per Day From Baseline Week to Week 245939 steps/dayStandard Deviation 0
F1L1P1R0Change in Average Steps Per Day From Baseline Week to Week 2410675 steps/day
F1L1P0R0Change in Average Steps Per Day From Baseline Week to Week 244382 steps/day
F1L1P0R1Change in Average Steps Per Day From Baseline Week to Week 243406 steps/day
Comparison: This is the results of financial vs non-financial reward factor at week 24. It compares change in steps from baseline to week 24 between subjects randomized to receive financial rewards compared to those who received nonfinancial rewards. A linear mixed-effect model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignments, linear week with a spline at week 12, and interactions between factors and weeks.p-value: 0.97290% CI: [-1685, 1616]Mixed Models Analysis
Comparison: This is the results of lottery vs loss-framed reward factor at week 24. It compares change in steps from baseline to week 24 between subjects randomized to receive lottery-based rewards compared to those who received loss-based rewards. A linear mixed-effect model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignments, linear week with a spline at week 12, and interactions between factors and weeks.p-value: 0.01990% CI: [-4134, -722]Mixed Models Analysis
Comparison: This is the results of precommitment (PC) vs no PC factor at week 24. It compares change in steps from baseline to week 24 between subjects randomized to receive a request for PC compared to those who received no request. A linear mixed-effect model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignments, linear week with a spline at week 12, and interactions between factors and weeks.p-value: 0.62790% CI: [-999, 1836]Mixed Models Analysis
Comparison: This is the results of advice vs no advice factor at week 24. It compares change in steps from baseline to week 24 between subjects randomized to receive a request for advice to other subjects compared to those who received no request. A linear mixed-effect model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignments, linear week with a spline at week 12, and interactions between factors and weeks.p-value: 0.84290% CI: [-1417, 1807]Mixed Models Analysis
Secondary

Intrinsic/Extrinsic Motivation

Measured using the Motivation for Physical Activity Measurement (MPAM) at baseline, week 12 and week 24 (Frederick CM 1993). This scale measures reasons and motivations for participating in physical activity having respondents indicate why they exercise. The scale ranges from 1=very true for me, 2= somewhat true for me, 3=neither true nor untrue for me, 4=somewhat untrue for me, 5= very untrue for me. The minimum score is 1 and the maximum is 5. Higher scores means more intrinsic motivation to exercise.

Time frame: Measured at baseline, week 12 and 24

Population: The participants in the N1L1P0R0 group were not assessed because they walked too many steps at the baseline period

ArmMeasureGroupValue (MEAN)Dispersion
F1M1P1R1Intrinsic/Extrinsic MotivationBaseline2.1 scores on a scaleStandard Deviation 0.7
F1M1P1R1Intrinsic/Extrinsic MotivationWeek 122.2 scores on a scaleStandard Deviation 0.3
F1M1P1R1Intrinsic/Extrinsic MotivationWeek 241.8 scores on a scaleStandard Deviation 0.5
N1M1P1R1Intrinsic/Extrinsic MotivationWeek 122.8 scores on a scaleStandard Deviation 1.2
N1M1P1R1Intrinsic/Extrinsic MotivationBaseline2.7 scores on a scaleStandard Deviation 0.6
N1M1P1R1Intrinsic/Extrinsic MotivationWeek 242.5 scores on a scaleStandard Deviation 1.2
N1M1P1R0Intrinsic/Extrinsic MotivationWeek 122.4 scores on a scaleStandard Deviation 0.6
N1M1P1R0Intrinsic/Extrinsic MotivationWeek 242.7 scores on a scaleStandard Deviation 0.5
N1M1P1R0Intrinsic/Extrinsic MotivationBaseline2.5 scores on a scaleStandard Deviation 0.3
N1M1P0R0Intrinsic/Extrinsic MotivationWeek 243.0 scores on a scaleStandard Deviation 0
N1M1P0R0Intrinsic/Extrinsic MotivationBaseline2.8 scores on a scaleStandard Deviation 0
N1M1P0R0Intrinsic/Extrinsic MotivationWeek 123.0 scores on a scaleStandard Deviation 0
N1M1P0R1Intrinsic/Extrinsic MotivationBaseline2.2 scores on a scaleStandard Deviation 0.8
N1M1P0R1Intrinsic/Extrinsic MotivationWeek 122.4 scores on a scaleStandard Deviation 0.6
N1M1P0R1Intrinsic/Extrinsic MotivationWeek 242.6 scores on a scaleStandard Deviation 0.5
N1L1P1R1Intrinsic/Extrinsic MotivationBaseline2.6 scores on a scaleStandard Deviation 0.9
N1L1P1R1Intrinsic/Extrinsic MotivationWeek 122.1 scores on a scaleStandard Deviation 1.6
N1L1P1R1Intrinsic/Extrinsic MotivationWeek 242.4 scores on a scaleStandard Deviation 1.2
N1L1P1R0Intrinsic/Extrinsic MotivationWeek 241.2 scores on a scaleStandard Deviation 0.2
N1L1P1R0Intrinsic/Extrinsic MotivationBaseline1.0 scores on a scaleStandard Deviation 0
N1L1P1R0Intrinsic/Extrinsic MotivationWeek 121.8 scores on a scaleStandard Deviation 0.1
N1L1P0R1Intrinsic/Extrinsic MotivationBaseline2.0 scores on a scaleStandard Deviation 0.2
N1L1P0R1Intrinsic/Extrinsic MotivationWeek 242.3 scores on a scaleStandard Deviation 0
N1L1P0R1Intrinsic/Extrinsic MotivationWeek 121.8 scores on a scaleStandard Deviation 0.2
F1M1P1R0Intrinsic/Extrinsic MotivationBaseline2.7 scores on a scaleStandard Deviation 0.4
F1M1P1R0Intrinsic/Extrinsic MotivationWeek 242.7 scores on a scaleStandard Deviation 0.8
F1M1P1R0Intrinsic/Extrinsic MotivationWeek 122.8 scores on a scaleStandard Deviation 0.7
F1M1P0R0Intrinsic/Extrinsic MotivationWeek 241.7 scores on a scaleStandard Deviation 0.6
F1M1P0R0Intrinsic/Extrinsic MotivationBaseline1.8 scores on a scaleStandard Deviation 0.4
F1M1P0R0Intrinsic/Extrinsic MotivationWeek 122.0 scores on a scaleStandard Deviation 0.7
F1M1P0R1Intrinsic/Extrinsic MotivationBaseline1.8 scores on a scaleStandard Deviation 0.3
F1M1P0R1Intrinsic/Extrinsic MotivationWeek 122.0 scores on a scaleStandard Deviation 0.4
F1M1P0R1Intrinsic/Extrinsic MotivationWeek 242.2 scores on a scaleStandard Deviation 0.5
F1L1P1R1Intrinsic/Extrinsic MotivationWeek 242.1 scores on a scaleStandard Deviation 0.3
F1L1P1R1Intrinsic/Extrinsic MotivationBaseline2.1 scores on a scaleStandard Deviation 0.1
F1L1P1R1Intrinsic/Extrinsic MotivationWeek 122.3 scores on a scaleStandard Deviation 0.3
F1L1P1R0Intrinsic/Extrinsic MotivationWeek 243.7 scores on a scaleStandard Deviation 0
F1L1P1R0Intrinsic/Extrinsic MotivationWeek 122.2 scores on a scaleStandard Deviation 0.8
F1L1P1R0Intrinsic/Extrinsic MotivationBaseline1.7 scores on a scaleStandard Deviation 0.2
F1L1P0R0Intrinsic/Extrinsic MotivationBaseline1.7 scores on a scaleStandard Deviation 0.5
F1L1P0R0Intrinsic/Extrinsic MotivationWeek 122.0 scores on a scaleStandard Deviation 0
F1L1P0R0Intrinsic/Extrinsic MotivationWeek 243.7 scores on a scaleStandard Deviation 0
F1L1P0R1Intrinsic/Extrinsic MotivationWeek 242.3 scores on a scaleStandard Deviation 1.8
F1L1P0R1Intrinsic/Extrinsic MotivationBaseline2.5 scores on a scaleStandard Deviation 2.1
F1L1P0R1Intrinsic/Extrinsic MotivationWeek 122.6 scores on a scaleStandard Deviation 2.2
Comparison: This is the results of financial vs non-financial reward factor at week 12. It compares intrinsic motivation in week 12 between subjects randomized to receive financial rewards compared to those who received nonfinancial rewards. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator..p-value: 0.48190% CI: [-0.19, 0.47]Regression, Linear
Comparison: This is the results of financial vs non-financial reward factor at week 24. It compares intrinsic motivation in week 24 between subjects randomized to receive financial rewards compared to those who received nonfinancial rewards. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.38890% CI: [-0.16, 0.52]Regression, Linear
Comparison: This is the results of lottery vs loss-framed reward factor at week 12. It compares intrinsic motivation at week 12 between subjects randomized to receive lottery-based rewards compared to those who received loss-based rewards. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.92690% CI: [-0.38, 0.34]Regression, Linear
Comparison: This is the results of lottery vs loss-framed reward factor at week 24. It compares intrinsic motivation at week 24 between subjects randomized to receive lottery-based rewards compared to those who received loss-based rewards. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.44690% CI: [-0.54, 0.2]Regression, Linear
Comparison: This is the results of precommitment (PC) vs no PC factor at week 12. It compares intrinsic motivation at week 12 between subjects randomized to receive a request for PC to compared to those who received no request. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.71190% CI: [-0.27, 0.42]Regression, Linear
Comparison: This is the results of precommitment (PC) vs no PC factor at week 24. It compares efficacy at week 24 between subjects randomized to receive a request for PC to compared to those who received no request. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.34590% CI: [-0.55, 0.15]Regression, Linear
Comparison: This is the results of advice vs no-advice factor at week 12. It compares intrinsic motivation at week 12 between subjects randomized to receive a request for advice compared to those who received no request. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.92890% CI: [-0.33, 0.36]Regression, Linear
Comparison: This is the results of advice vs no-advice factor at week 24. It compares intrinsic motivation at week 24 between subjects randomized to receive a request for advice to other subjects compared to those who received no request. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.37290% CI: [-0.55, 0.16]Regression, Linear
Secondary

Mental Health

Measured using the PHQ-8 depression scale at baseline, week 12 and week 24. This assessment measures depressive symptoms over the past 2 weeks. Respondents indicate how bothered they were by the following problems on a scale from 0 (not at all) to 3 (nearly every day). The minimum and maximum score are zero and 24, respectively. A lower score indicates fewer depressive symptoms.

Time frame: Measured at baseline, week 12 and 24

Population: The participants in the N1L1P0R0 group were not assessed because they walked too many steps at the baseline period

ArmMeasureGroupValue (MEAN)Dispersion
F1M1P1R1Mental HealthWeek 122.0 scores on a scaleStandard Deviation 1
F1M1P1R1Mental HealthBaseline4.3 scores on a scaleStandard Deviation 0.6
F1M1P1R1Mental HealthWeek 242.0 scores on a scaleStandard Deviation 0
N1M1P1R1Mental HealthBaseline6.0 scores on a scaleStandard Deviation 6.2
N1M1P1R1Mental HealthWeek 125.0 scores on a scaleStandard Deviation 6.7
N1M1P1R1Mental HealthWeek 245.3 scores on a scaleStandard Deviation 5.3
N1M1P1R0Mental HealthWeek 1215.5 scores on a scaleStandard Deviation 2.1
N1M1P1R0Mental HealthBaseline12.7 scores on a scaleStandard Deviation 4.7
N1M1P1R0Mental HealthWeek 247.5 scores on a scaleStandard Deviation 3.5
N1M1P0R0Mental HealthWeek 2411 scores on a scaleStandard Deviation 0
N1M1P0R0Mental HealthBaseline10.0 scores on a scaleStandard Deviation 0
N1M1P0R0Mental HealthWeek 1210.0 scores on a scaleStandard Deviation 0
N1M1P0R1Mental HealthBaseline8.3 scores on a scaleStandard Deviation 2.5
N1M1P0R1Mental HealthWeek 243.3 scores on a scaleStandard Deviation 2.5
N1M1P0R1Mental HealthWeek 125.8 scores on a scaleStandard Deviation 4.6
N1L1P1R1Mental HealthWeek 123.0 scores on a scaleStandard Deviation 0
N1L1P1R1Mental HealthBaseline6.0 scores on a scaleStandard Deviation 5.3
N1L1P1R1Mental HealthWeek 249.7 scores on a scaleStandard Deviation 7.4
N1L1P1R0Mental HealthWeek 247.0 scores on a scaleStandard Deviation 1.4
N1L1P1R0Mental HealthBaseline4.0 scores on a scaleStandard Deviation 5.7
N1L1P1R0Mental HealthWeek 129.0 scores on a scaleStandard Deviation 0
N1L1P0R1Mental HealthWeek 122.8 scores on a scaleStandard Deviation 2.5
N1L1P0R1Mental HealthBaseline3.0 scores on a scaleStandard Deviation 1.4
N1L1P0R1Mental HealthWeek 242.0 scores on a scaleStandard Deviation 2.8
F1M1P1R0Mental HealthBaseline4.4 scores on a scaleStandard Deviation 4.6
F1M1P1R0Mental HealthWeek 126.6 scores on a scaleStandard Deviation 5.8
F1M1P1R0Mental HealthWeek 246.0 scores on a scaleStandard Deviation 9
F1M1P0R0Mental HealthWeek 123.3 scores on a scaleStandard Deviation 2.1
F1M1P0R0Mental HealthBaseline1.0 scores on a scaleStandard Deviation 1.4
F1M1P0R0Mental HealthWeek 244.0 scores on a scaleStandard Deviation 4
F1M1P0R1Mental HealthWeek 125.3 scores on a scaleStandard Deviation 3.6
F1M1P0R1Mental HealthBaseline4.8 scores on a scaleStandard Deviation 4.4
F1M1P0R1Mental HealthWeek 247.2 scores on a scaleStandard Deviation 4.5
F1L1P1R1Mental HealthWeek 244.7 scores on a scaleStandard Deviation 6.4
F1L1P1R1Mental HealthWeek 127.0 scores on a scaleStandard Deviation 7.2
F1L1P1R1Mental HealthBaseline7.3 scores on a scaleStandard Deviation 7
F1L1P1R0Mental HealthWeek 123.0 scores on a scaleStandard Deviation 1.4
F1L1P1R0Mental HealthBaseline5.0 scores on a scaleStandard Deviation 1.4
F1L1P1R0Mental HealthWeek 245.0 scores on a scaleStandard Deviation 0
F1L1P0R0Mental HealthBaseline2.5 scores on a scaleStandard Deviation 3.5
F1L1P0R0Mental HealthWeek 240 scores on a scaleStandard Deviation 0
F1L1P0R0Mental HealthWeek 120 scores on a scaleStandard Deviation 0
F1L1P0R1Mental HealthWeek 123.5 scores on a scaleStandard Deviation 4.9
F1L1P0R1Mental HealthWeek 243.5 scores on a scaleStandard Deviation 0.7
F1L1P0R1Mental HealthBaseline3.0 scores on a scaleStandard Deviation 1.4
Comparison: This is the results of financial vs non-financial reward factor at week 12. It compares phq8 mental health scores in week 12 between subjects randomized to receive financial rewards compared to those who received nonfinancial rewards. A linear model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.76790% CI: [-1.6, 2.3]Regression, Linear
Comparison: This is the results of financial vs non-financial reward factor at week 24. It compares phq8 mental health scores in week 24 between subjects randomized to receive financial rewards compared to those who received nonfinancial rewards. A linear model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.590% CI: [-1.12, 2.69]Regression, Linear
Comparison: This is the results of lottery vs loss-framed reward factor at week 12. It compares phq8 mental health scores at week 12 between subjects randomized to receive lottery-based rewards compared to those who received loss-based rewards. A linear model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24, and interactions between factors and weeks.p-value: 0.63190% CI: [-1.43, 2.61]Regression, Linear
Comparison: This is the results of lottery vs loss-framed reward factor at week 24. It compares phq8 mental health scores at week 24 between subjects randomized to receive lottery-based rewards compared to those who received loss-based rewards. A linear model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24, and interactions between factors and weeks.p-value: 0.6290% CI: [-2.69, 1.44]Regression, Linear
Comparison: This is the results of pre-commitment (PC) vs no PC factor at week 12. It compares phq8 mental health scores at week 12 between subjects randomized to receive a request for PC to compared to those who received no request. A linear model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.88690% CI: [-1.77, 2.12]Regression, Linear
Comparison: This is the results of precommitment (PC) vs no PC factor at week 24. It compares phq8 mental health scores at week 24 between subjects randomized to receive a request for PC to compared to those who received no request. A linear model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.95-2.03% CI: [-2.03, 1.88]Regression, Linear
Comparison: This is the results of advice vs no-advice factor at week 12. It compares phq mental health scores at week 12 between subjects randomized to receive a request for advice to other subjects compared to those who received no request. A linear model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.24890% CI: [-3.51, 0.61]Regression, Linear
Comparison: This is the results of advice vs no-advice factor at week 24. It compares phq mental health scores at week 24 between subjects randomized to receive a request for advice to other subjects compared to those who received no request. A linear model was estimated with independent variables of demographics, steps and outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.31790% CI: [-3.25, 0.79]Regression, Linear
Secondary

Self-efficacy

Measured using the Exercise Self-Efficacy Scale at baseline, week 12 and week 24 (McAuley E 1993). This scale measures self-efficacy on nine different measures from not confident (0 rating) to very confident (10 rating).The range is 0 to 10. A higher rating indicates that the respondent is more confident they will be able to overcome barriers to physical activity.

Time frame: Measured at baseline, week 12 and 24

Population: The participants in the N1L1P0R0 group were not assessed because they walked too many steps at the baseline period

ArmMeasureGroupValue (MEAN)Dispersion
F1M1P1R1Self-efficacyWeek 244.3 score on a scaleStandard Deviation 0.2
F1M1P1R1Self-efficacyWeek 125.9 score on a scaleStandard Deviation 1.1
F1M1P1R1Self-efficacyBaseline5.1 score on a scaleStandard Deviation 1.4
N1M1P1R1Self-efficacyBaseline5.7 score on a scaleStandard Deviation 1.5
N1M1P1R1Self-efficacyWeek 246.7 score on a scaleStandard Deviation 1.9
N1M1P1R1Self-efficacyWeek 126.4 score on a scaleStandard Deviation 2.1
N1M1P1R0Self-efficacyBaseline5.9 score on a scaleStandard Deviation 1.4
N1M1P1R0Self-efficacyWeek 246.8 score on a scaleStandard Deviation 2.4
N1M1P1R0Self-efficacyWeek 125.0 score on a scaleStandard Deviation 2.1
N1M1P0R0Self-efficacyWeek 241.3 score on a scaleStandard Deviation 0
N1M1P0R0Self-efficacyBaseline2.1 score on a scaleStandard Deviation 0
N1M1P0R0Self-efficacyWeek 122.1 score on a scaleStandard Deviation 0
N1M1P0R1Self-efficacyWeek 125.0 score on a scaleStandard Deviation 1.6
N1M1P0R1Self-efficacyWeek 245.0 score on a scaleStandard Deviation 1.8
N1M1P0R1Self-efficacyBaseline5.6 score on a scaleStandard Deviation 3.7
N1L1P1R1Self-efficacyWeek 247.1 score on a scaleStandard Deviation 2.1
N1L1P1R1Self-efficacyBaseline5.0 score on a scaleStandard Deviation 2
N1L1P1R1Self-efficacyWeek 125.6 score on a scaleStandard Deviation 1.4
N1L1P1R0Self-efficacyBaseline9.5 score on a scaleStandard Deviation 0.7
N1L1P1R0Self-efficacyWeek 125.8 score on a scaleStandard Deviation 3.1
N1L1P1R0Self-efficacyWeek 248.2 score on a scaleStandard Deviation 1.7
N1L1P0R1Self-efficacyWeek 244.9 score on a scaleStandard Deviation 1.3
N1L1P0R1Self-efficacyBaseline6.4 score on a scaleStandard Deviation 0.5
N1L1P0R1Self-efficacyWeek 126.6 score on a scaleStandard Deviation 1.8
F1M1P1R0Self-efficacyWeek 245.6 score on a scaleStandard Deviation 1.9
F1M1P1R0Self-efficacyBaseline5.6 score on a scaleStandard Deviation 2.9
F1M1P1R0Self-efficacyWeek 125.0 score on a scaleStandard Deviation 1.1
F1M1P0R0Self-efficacyWeek 245.0 score on a scaleStandard Deviation 2.8
F1M1P0R0Self-efficacyBaseline5.0 score on a scaleStandard Deviation 3.3
F1M1P0R0Self-efficacyWeek 123.2 score on a scaleStandard Deviation 1.9
F1M1P0R1Self-efficacyBaseline6.9 score on a scaleStandard Deviation 1.9
F1M1P0R1Self-efficacyWeek 245.4 score on a scaleStandard Deviation 1.5
F1M1P0R1Self-efficacyWeek 125.5 score on a scaleStandard Deviation 1
F1L1P1R1Self-efficacyWeek 126.0 score on a scaleStandard Deviation 1.3
F1L1P1R1Self-efficacyBaseline5.9 score on a scaleStandard Deviation 2.3
F1L1P1R1Self-efficacyWeek 246.0 score on a scaleStandard Deviation 0.2
F1L1P1R0Self-efficacyBaseline5.2 score on a scaleStandard Deviation 0.2
F1L1P1R0Self-efficacyWeek 124.4 score on a scaleStandard Deviation 1.3
F1L1P1R0Self-efficacyWeek 244.1 score on a scaleStandard Deviation 0
F1L1P0R0Self-efficacyWeek 125.6 score on a scaleStandard Deviation 0
F1L1P0R0Self-efficacyBaseline4.9 score on a scaleStandard Deviation 3.5
F1L1P0R0Self-efficacyWeek 244.3 score on a scaleStandard Deviation 0
F1L1P0R1Self-efficacyWeek 244.2 score on a scaleStandard Deviation 1.3
F1L1P0R1Self-efficacyBaseline6.5 score on a scaleStandard Deviation 2.6
F1L1P0R1Self-efficacyWeek 122.2 score on a scaleStandard Deviation 1
Comparison: This is the results of financial vs non-financial reward factor at week 12. It compares change in efficacy from baseline to week 12 between subjects randomized to receive financial rewards compared to those who received nonfinancial rewards. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.73990% CI: [-1.05, 0.7]Regression, Linear
Comparison: This is the results of financial vs non-financial reward factor at week 24. It compares change in efficacy from baseline to week 24 between subjects randomized to receive financial rewards compared to those who received nonfinancial rewards. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.34790% CI: [-1.42, 0.39]Mixed Models Analysis
Comparison: This is the results of lottery vs loss-framed reward factor at week 12. It compares change in efficacy from baseline to week 12 between subjects randomized to receive lottery-based rewards compared to those who received loss-based rewards. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.29390% CI: [-0.35, 1.57]Mixed Models Analysis
Comparison: This is the results of lottery vs loss-framed reward factor at week 24. It compares efficacy at week 24 between subjects randomized to receive lottery-based rewards compared to those who received loss-based rewards. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.59990% CI: [-0.67, 1.3]Mixed Models Analysis
Comparison: This is the results of precommitment (PC) vs no PC factor at week 12. It compares efficacy at week 12 between subjects randomized to receive a request for PC to other subjects compared to those who received no request. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.00790% CI: [0.58, 2.4]Regression, Linear
Comparison: This is the results of precommitment (PC) vs no PC factor at week 24. It compares efficacy at week 24 between subjects randomized to receive a request for PC compared to those who received no request. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.00290% CI: [0.86, 2.7]Regression, Linear
Comparison: This is the results of advice vs no-advice factor at week 12. It compares efficacy at week 12 between subjects randomized to receive a request for advice to other subjects compared to those who received no request. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.06890% CI: [0.1, 1.94]Regression, Linear
Comparison: This is the results of advice vs no-advice factor at week 24. It compares efficacy at week 24 between subjects randomized to receive a request for advice to other subjects compared to those who received no request. A linear model was estimated with independent variables of demographics, steps and survey-based outcomes at baseline, indicators for each of the four factor assignments, indicator for week 12 or 24 week, and interactions between factors and week indicator.p-value: 0.7490% CI: [-0.76, 1.14]Regression, Linear

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026