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WalkIT Arizona was a 2×2 factorial trial examining the effects of goal type (adaptive versus static) and reinforcement type (immediate versus delayed) to increase moderate to vigorous physical activity (MVPA) among insufficiently active adults. The 12-month intervention combined mobile health (mHealth) technology with behavioral strategies to test scalable population-health approaches to increasing MVPA. Self-reported physical activity provided domain-specific information to help contextualize the intervention effects.
The aim of this study was to report on the secondary outcomes of self-reported walking for transportation and leisure over the course of the 12-month WalkIT intervention.
A total of 512 participants aged 19 to 60 years (n=330 [64.5%] women; n=425 [83%] Caucasian/white, n=96 [18.8%] Hispanic/Latinx) were randomized into interventions based on type of goals and reinforcements. The International Physical Activity Questionnaire-long form assessed walking for transportation and leisure at baseline, and at 6 months and 12 months of the intervention. Negative binomial hurdle models were used to examine the effects of goal and reinforcement type on (1) odds of reporting any (versus no) walking/week and (2) total reported minutes of walking/week, adjusted for neighborhood walkability and socioeconomic status. Separate analyses were conducted for transportation and leisure walking, using complete cases and multiple imputation.
All intervention groups reported increased walking at 12 months relative to baseline. Effects of the intervention differed by domain: a significant three-way goal by reinforcement by time interaction was observed for total minutes of leisure walking/week, whereas time was the only significant factor that contributed to transportation walking. A sensitivity analysis indicated minimal differences between complete case analysis and multiple imputation.
This study is the first to report differential effects of adaptive versus static goals for self-reported walking by domain. Results support the premise that individual-level PA interventions are domain- and context-specific and may be helpful in guiding further intervention refinement.
Preregistered at clinicaltrials.gov: (NCT02717663) https://clinicaltrials.gov/ct2/show/NCT02717663
RR2-10.1016/j.cct.2019.05.001
Few adults meet the recommended physical activity (PA) guidelines, despite evidence of a strong dose-response relationship between PA and a range of health benefits, including decreased mortality [
Mobile health (mHealth) technology provides a platform for increasing intervention reach and quickly tailoring content in response to an individual’s behavior and preferences, but it requires evidence-based and scalable interventions to improve population health. Evidence suggests that behavioral strategies (eg, goal setting, financial reinforcement, feedback on performance) tend to be more effective than cognitive strategies (eg, education, motivation enhancement, self-belief) to increase PA in adults, yet no single strategy has consistently outperformed the rest [
The WalkIT Arizona trial was designed to address this gap in the literature by studying the effects of an mHealth intervention that combined two evidence-based behavioral strategies—goal setting and positive reinforcement through the use of financial incentives—on objectively measured PA [
We hypothesized that there would be significant main effects of goal type and reinforcement timing consistent with previous studies: those with adaptive goals would report more PA than those with static goals [
WalkIT Arizona was a 2×2 factorial randomized trial evaluating the effects of goal setting (adaptive versus static goals) combined with financial incentives (immediate versus delayed reinforcement) to increase moderate-to-vigorous PA (MVPA) among insufficiently active adults. Participant selection was balanced across geographic information system–measured neighborhood walkability (high/low) and socioeconomic status (high/low) at the census block group level, with recruitment balanced across calendar months to adjust for seasonal effects. These design factors were important to the broader study testing multilevel interactions between individual-level intervention components and neighborhood design factors for PA maintenance during the follow-up period. The study was powered to detect a 2.1 minute/day difference in main effects and 4.2 minute/day difference in interaction effects between groups using accelerometer-measured MVPA, with a sample size of 120 participants per group. Participants completed a 12-month intervention followed by a 12-month observational follow-up period. Self-reported data were collected at baseline and at 6, 12, 18, and 24 months. Analyses presented here were conducted following completion of the 12-month intervention. This study was approved by the local institutional review board; further study details are published elsewhere [
Insufficiently active adults aged 19 to 60 years (N=512) were randomized for participation between May 2016 and May 2018. Participants were screened online and via phone interview prior to attending an office visit. Inactive status was verified following a 10-day baseline period in which participants were asked to wear a wrist-worn accelerometer during their normal activities. Baseline was extended beyond the scheduled 10 days for some participants due to issues with the accelerometer, problems with the mobile app, nonadherence to accelerometer wear protocol, or illness. Participants were told they would receive one of four different PA interventions. Baseline participant characteristics are displayed in
Baseline participant characteristics.
Characteristics | Total |
Adaptive goal + immediate reinforcement |
Static goal + immediate reinforcement |
Adaptive goal + delayed reinforcement |
Static goal + delayed reinforcement |
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Age, mean (SD) | 45.5 (9.1) | 45.6 (9.5) | 46.0 (8.9) | 46.7 (8.6) | 43.5 (9.3) | |
BMI, mean (SD) | 33.9 (7.1) | 33.7 (7.3) | 33.8 (7.3) | 33.6 (7.0) | 34.5 (7.6) | |
Female, n (%) | 330 (64.5) | 82 (64.1) | 80 (62.5) | 81 (63.3) | 87 (68.0) | |
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White | 425 (84.0) | 108 (84.4) | 106 (82.8) | 105 (82.0) | 106 (82.8) |
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Black | 31 (6.1) | 5 (3.9) | 9 (7.0) | 9 (7.0) | 8 (6.3) |
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American Indian or Alaskan Native | 14 (2.7) | 4 (3.1) | 3 (2.3) | 2 (1.6) | 5 (3.9) |
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Asian | 12 (2.3) | 4 (3.1) | 3 (2.3) | 3 (2.3) | 2 (1.6) |
Native Hawaiian or other Pacific Islander | 7 (1.4) | 3 (2.3) | 1 (0.8) | 2 (1.6) | 1 (0.8) | |
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Hispanic or Latinx | 96 (18.8) | 22 (17.2) | 26 (20.3) | 24 (18.8) | 24 (18.8) |
Prefer not to answer | 32 (6.3) | 5 (3.9) | 8 (6.3) | 10 (7.8) | 9 (7.0) | |
Current tobacco smoker, n (%) | 26 (5.1) | 3 (2.4) | 10 (7.8) | 5 (3.9) | 8 (6.3) | |
Current e-smoker, n (%) | 10 (2.0) | 2 (1.6) | 3 (2.4) | 1 (0.8) | 4 (3.2) | |
Married/living with partner, n (%) | 346 (67.6) | 82 (64.1) | 85 (66.4) | 93 (72.7) | 86 (67.2) | |
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Single family house | 392 (76.6) | 94 (73.4) | 93 (72.7) | 104 (81.3) | 101 (78.9) |
Apartment | 66 (12.9) | 16 (12.5) | 18 (14.1) | 15 (11.7) | 17 (13.3) | |
Years at current residence, mean (SD) | 7.3 (7.4) | 7.5 (7.9) | 7.3 (7.5) | 8.2 (7.0) | 6.4 (7.1) | |
Children residing in household, n (%) | 251 (49.0) | 61 (47.7) | 61 (47.5) | 64 (50.0) | 65 (50.8) | |
Number of children in household, mean (SD) | 1.0 (1.2) | 1.0 (1.2) | 1.0 (1.3) | 1.0 (1.3) | 1.0 (1.1) | |
Household income, median | $60,000-$79,999 | $80,000-$99,999 | $60,000-$79,999 | $60,000-$79,999 | $80,000-$99,999 | |
Education, median | College graduate | College graduate | College graduate | College graduate | College graduate | |
Employed full time, n (%) | 390 (76.2) | 98 (76.6) | 97 (75.8) | 94 (73.4) | 101 (78.9) | |
Distance from home to work (meters), median | 16,316 | 15,368 | 16,718 | 15,597 | 16,926 |
aRace/ethnicity cumulative is greater than 100%, as participants were asked to select all that applied.
WalkIT Arizona intervention components have been described in detail elsewhere [
Participants allocated to the static goal group were asked to accumulate 30 minutes or more of MVPA daily throughout the 1-year intervention phase (eg, “Goal for 4/1 is 30 minutes”). A static goal of 30 minutes daily on at least 5 days per week aligns with current PA guidelines to obtain 150 minutes/week. Participants allocated to the adaptive goal group were assigned a goal daily based on a previously tested percentile-rank algorithm [
Participants allocated to the delayed, noncontingent reinforcement group received escalating financial reinforcement on a 60-day schedule (ie, US $15 in month 2, US $30 in month 4, US $50 in month 6, US $75 in month 8, and US $95 in month 10) for participating and syncing their accelerometer. Participants allocated to the immediate reinforcement group earned points for meeting PA goals as described elsewhere [
Self-reported PA was assessed using sections 2, 4, and 5 of the International Physical Activity Questionnaire (IPAQ)-long form. The IPAQ was part of a larger battery of self-reported measures given at baseline, 6 months, and 12 months. Total self-reported minutes of walking per week were calculated separately for transportation and leisure domains; total self-reported minutes of biking per week were collected for transportation only using IPAQ scoring guidelines. The IPAQ has demonstrated comparable reliability and validity with other self-reported measures of PA [
Demographic information was collected via self-report at screening and baseline. Categories for assessing gender, race, and ethnicity were based on National Institutes of Health guidelines. Participants also reported their date of birth, education, marital status, residence type (single family house, apartment), number of adults and children in the household, and years residing at the current address.
A generalized linear mixed model approach was used to examine intervention effects across treatment groups. Model selection was guided by distributional properties of outcome data. The outcome was total minutes of self-reported PA: time spent walking in the last week was computed separately for transportation and leisure, while time spent biking was limited to the transportation domain. All data distributions were positively skewed, with a relatively large number of zero values at each time point. We determined that hurdle models provided a better conceptual fit than zero-inflation models, as zero values could only result from remaining inactive during the intervention. Hurdle models contain two parts: a binary logit model, or hurdle, which estimated the likelihood of participants reporting 0 minutes/week of activity, and a truncated count regression model, which estimated the total number of reported minutes of activity in the last week (for those reporting values greater than 0). All count models were truncated at zero and used a negative binomial distribution to address overdispersion of data. Separate analyses were used to examine PA by activity type (walking versus biking) and domain (transportation versus leisure).
Negative binomial hurdle (NBH) models provided a nuanced examination of differences across intervention groups by activity type and domain. NBH models tested main effects and interactions among intervention parameters (goal type, reinforcement timing, time) with a random intercept allowed to vary by participant. Models 1 and 2 examined two-way interactions and included the third intervention parameter (ie, goal type or reinforcement timing) as a covariate. Model 3 examined a three-way goal by reinforcement by time interaction but had less power due to the additional interaction term. Time was specified as an ordered factor to allow for examination of linear and quadratic trends over the course of the intervention, as intervention components were assumed to have nonlinear effects at the individual level. All models were adjusted for census block-level socioeconomic status and neighborhood walkability since these factors were part of the broader research design. Predictor variables were kept consistent for hurdle and count models. All models were estimated using the generalized linear mixed models using template model builder (glmmTMB) package in R [
As glmmTMB models are estimated using only complete cases, we conducted a sensitivity analysis to examine the impact of missing data. Original analyses were compared to models estimated using (1) baseline values carried forward and (2) multiple imputation. Multiple imputation was performed using the multivariate imputation by chained equations (MICE) package [
Participant flow is depicted in
Participant flow.
Split violin plots showing distribution of self-reported walking at baseline (BL), 6 months (6M), and 12 months (12M). Horizontal lines indicate 25th, 50th, and 75th percentiles computed from density estimates.
Self-reported leisure walking, transportation walking, and transportation biking (minutes/week).
Self-reported physical activity | Total |
Adaptive goal + immediate reinforcement |
Static goal + immediate reinforcement |
Adaptive goal + delayed reinforcement |
Static goal + delayed reinforcement |
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Baseline |
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Meana (SD) | 45.3 (77.0) | 41.1 (93.5) | 36.6 (61.7) | 37.9 (87.6) | 53.3 (115.0) |
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Reported >0 minb, n (%)c | 296 (57.8) | 73 (57.0) | 75 (58.6) | 70 (54.7) | 78 (60.9) | |
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6 months |
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Mean (SD) | 97.3 (128.2) | 78.7 (155.2) | 102.6 (196.2) | 80.1 (170.6) | 80.1 (100.8) | |
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Reported >0 min, n (%) | 331 (76.6) | 80 (20.0) | 79 (71.2) | 91 (80.5) | 81 (75.0) | |
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12 months |
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Mean (SD) | 89.8 (110.3) | 78.0 (113.0) | 65.7 (87.9) | 58.5 (92.4) | 58.0 (78.7) | |
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Reported >0 min, n (%) | 329 (78.7) | 76 (79.2) | 79 (76.0) | 95 (83.3) | 79 (76.0) | |
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Baseline |
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Mean (SD) | 42.2 (91.4) | 40.2 (51.5) | 38.0 (48.9) | 39.4 (62.1) | 63.4 (120.6) | |
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Reported >0 min, n (%) | 297 (58.0) | 76 (59.4) | 73 (57.0) | 67 (52.3) | 81 (63.3) | |
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6 months |
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Mean (SD) | 85.5 (160.0) | 93.2 (113.5) | 117.6 (155.7) | 92.9 (126.2) | 84.9 (110.0) | |
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Reported >0 min, n (%) | 305 (70.0) | 67 (66.3) | 80 (71.4) | 77 (67.0) | 81 (75.0) | |
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12 months |
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Mean (SD) | 64.6 (93.4) | 83.8 (95.6) | 102.2 (133.1) | 86.8 (102.2) | 86.4 (106.9) | |
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Reported >0 min, n (%) | 296 (70.6) | 67 (69.8) | 74 (71.2) | 78 (67.8) | 77 (74.0) |
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Baseline |
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Mean (SD) | 4.6 (25.8) | 9.3 (43.1) | 4.9 (23.2) | 2.9 (11.1) | 1.0 (9.4) | |
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Reported >0 min, n (%) | 33 (6.4) | 12 (9.1) | 9 (6.9) | 9 (7.0) | 3 (2.3) | |
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6 months |
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Mean (SD) | 5.4 (29.2) | 5.5 (22.8) | 4.2 (22.9) | 4.5 (18.6) | 7.7 (45.5) | |
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Reported >0 min, n (%) | 33 (7.5) | 11 (10.6) | 6 (5.3) | 10 (8.7) | 6 (5.6) | |
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12 months |
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Mean (SD) | 5.6 (25.7) | 3.9 (17.0) | 2.6 (10.8) | 9.9 (35.8) | 5.4 (29.5) | |
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Reported >0 min, n (%) | 34 (8.0) | 6 (6.1) | 9 (8.5) | 13 (11.3) | 6 (5.8) |
aCalculated means include respondents reporting no physical activity (ie, 0 minutes).
bmin: minutes.
cPercentages are based on total number of participant responses received for each time point.
The subheadings below indicate the interaction specified in NBH models, with other remaining parameters entered as covariates. As sensitivity analysis revealed little impact of missing data, the results discussed below are for complete cases; model parameters using multiple imputation are presented in
The overall proportion of participants reporting any (versus no) leisure walking increased from 57.8% (296/512) at baseline to 78.7% (329/418) at 12 months, as shown in
NBH model results displaying odds and risk ratios are shown in
Reinforcement by time interaction in negative binomial count model 2 for leisure walking at baseline (BL), 6 months (6M), and 12 months (12M).
Conditional estimates for an NBH model testing a three-way goal by reinforcement by time interaction and nested two-way interactions for leisure walking are displayed in
Negative binomial hurdle model examining goal by reinforcement by time (model 3) for leisure walking.
|
Zero hurdle model |
Count model |
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Parametera | ORb,c (95% CI) | RRd (95% CI) | ||
Intercept | 3.45 (2.30-5.18) | <.001**** | 96.84 (81.65-114.84) | <.001**** |
Socioeconomic status block (high) | 0.82 (0.60-1.12) | .165 | 0.89 (0.77-1.02) | .131 |
Walkability block (high) | 0.94 (0.69-1.29) | .641 | 1.03 (0.90-1.18) | .637 |
Goal (adaptive) | 1.15 (0.74-1.80) | .540 | 0.84 (0.69-1.01) | .067* |
Reinforcement (immediate) | 0.88 (0.57-1.37) | .512 | 1.07 (0.88-1.30) | .580 |
Time: linear | 1.80 (1.15-2.81) | .009*** | 1.13 (0.94-1.34) | .184 |
Time: quadratic | 0.70 (0.43-1.11) | .129 | 0.92 (0.78-1.10) | .368 |
Goal by time: linear | 1.79 (0.95-3.39) | .073* | 1.09 (0.85-1.40) | .488 |
Goal by time: quadratic | 0.90 (0.46-1.79) | .771 | 1.00 (0.79-1.28) | .987 |
Reinforcement by time: linear | 1.07 (0.57-2.02) | .875 | 1.47 (1.15-1.89) | .002*** |
Reinforcement by time: quadratic | 1.14 (0.60-2.20) | .678 | 0.70 (0.55-0.90) | .005*** |
Goal by reinforcement | 1.07 (0.57-2.00) | .746 | 1.01 (0.76-1.32) | .842 |
Goal by reinforcement by time: linear | 0.69 (0.28-1.71) | .465 | 0.68 (0.48-0.97) | .033** |
Goal by reinforcement by time: quadratic | 0.80 (0.31-2.08) | .711 | 1.28 (0.90-1.81) | .156 |
aReferent groups for parameters are listed in parentheses.
bOdds ratio (OR) reflects the odds of reporting any leisure walking (versus none).
cOR, risk ratio (RR), and 95% CI are exponentiated coefficients of conditional estimates.
dRR reflects the proportional increase (values >1) or decrease (values <1) in non-zero leisure walking minutes/week associated with a one unit change in the predictor.
*
**
***
****
Goal by reinforcement by time interaction in negative binomial count model 3 for leisure walking at baseline (BL), 6 months (6M), and 12 months (12M).
The overall proportion of participants reporting any (versus no) transportation walking increased from 58.0% (297/512) at baseline to 70.6% (296/419) at 12 months, as shown in
NBH model results are shown as odds and risk ratios in
Reinforcement by time interaction in negative binomial count model 2 for transportation walking at baseline (BL), 6 months (6M), and 12 months (12M).
Conditional estimates for an NBH model testing a three-way goal by reinforcement by time interaction and nested two-way interactions for transportation walking are shown in
Negative binomial hurdle model examining goal by reinforcement by time (model 3) for transportation walking.
|
Zero hurdle model |
Count model | ||
Parametera | ORb,c (95% CI) | RRd (95% CI) | ||
Intercept | 3.00 (1.80-4.98) | <.001*** | 81.98 (66.04-101.77) | <.001*** |
Socioeconomic status block (high) | 0.79 (0.54-1.17) | .240 | 0.70 (0.59-0.83) | <.001*** |
Walkability block (high) | 1.83 (1.23-2.71) | .003** | 1.03 (0.86-1.22) | .711 |
Goal (adaptive) | 0.65 (0.37-1.13) | .132 | 0.99 (0.77-1.26) | .912 |
Reinforcement (immediate) | 0.81 (0.46-1.43) | .417 | 1.00 (0.79-1.28) | .956 |
Time: linear | 1.67 (1.03-2.72) | .038* | 1.07 (0.89-1.29) | .450 |
Time: quadratic | 0.75 (0.45-1.25) | .270 | 0.75 (0.63-0.91) | .003** |
Goal by time: linear | 1.24 (0.64-2.41) | .526 | 1.10 (0.84-1.44) | .489 |
Goal by time: quadratic | 0.81 (0.40-1.64) | .557 | 1.12 (0.86-1.47) | .405 |
Reinforcement by time: linear | 1.13 (0.58-2.22 | .745 | 1.22 (0.93-1.59) | .120 |
Reinforcement by time: quadratic | 0.79 (0.39-1.63) | .545 | 0.95 (0.72-1.24) | .687 |
Goal by reinforcement | 1.39 (0.63-3.06) | .299 | 1.03 (0.69-1.38) | .891 |
Goal by reinforcement by time: linear | 0.67 (0.26-1.75) | .416 | 1.01 (0.69-1.49) | .881 |
Goal by reinforcement by time: quadratic | 1.72 (0.63-4.70) | .292 | 1.07 (0.73-1.57) | .735 |
aReferent groups for parameters are listed in parentheses.
bOdds ratio (OR) reflects the odds of reporting any leisure walking (versus none).
cOR, risk ratio (RR), and 95% CI are exponentiated coefficients of conditional estimates.
dRR reflects the proportional increase (values >1) or decrease (values <1) in non-zero transportation walking minutes/week associated with a one unit change in the predictor.
*
**
***
The overall proportion of participants reporting any (versus no) transportation biking increased from 6.4% (33/512) at baseline to 8.0% (34/425) at 12 months, as shown in
Goal by time interaction in negative binomial count model 1 for transportation biking at baseline (BL), 6 months (6M), and 12 months (12M).
NBH model results are shown as odds and risk ratios in
Reinforcement by time interaction in negative binomial count model 2 for transportation biking at baseline (BL), 6 months (6M), and 12 months (12M).
Conditional estimates for an NBH model testing a three-way goal by reinforcement by time interaction and nested two-way interactions for transportation biking are shown in
Goal by reinforcement by time interaction for negative binomial hurdle model 3 for transportation biking at baseline (BL), 6 months (6M), and 12 months (12M).
Goal by reinforcement by time interaction in negative binomial count model 3 for transportation biking at baseline (BL), 6 months (6M), and 12 months (12M).
Negative binomial hurdle model examining goal by reinforcement by time (model 3) for transportation biking.
|
Zero hurdle model |
Count model |
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Parametera | ORb,c (95% CI) | RRd (95% CI) | ||
Intercept | 0.016 (0.00002-0.001) | <.001**** | 53.31 (30.19-94.14) | <.001**** |
Socioeconomic status block (high) | 1.32 (0.40-4.36) | .651 | 0.74 (0.52-1.06) | .104 |
Walkability block (high) | 0.85 (0.26-2.79) | .794 | 1.30 (0.91-1.85) | .151 |
Goal (adaptive) | 3.87 (0.57-26.16) | .165 | 0.96 (0.56-1.66) | .895 |
Reinforcement (immediate) | 2.25 (0.30-16.93) | .432 | 0.82 (0.45-1.47) | .496 |
Time: linear | 10.22 (1.27-82.43) | .029** | 2.26 (1.38-3.69) | .001*** |
Time: quadratic | 0.39 (0.08-1.95) | .250 | 0.67 (0.44-1.01) | .055* |
Goal by time: linear | 0.21 (0.02-2.05) | .182 | 0.58 (0.32-1.06) | .076* |
Goal by time: quadratic | 2.68 (0.42-17.12) | .297 | 1.84 (1.05-3.23) | .034** |
Reinforcement by time: linear | 0.17 (0.02-1.85) | .147 | 0.32 (0.18-0.59) | <.001**** |
Reinforcement by time: quadratic | 5.10 (0.67-39.07) | .117 | 1.16 (0.68-1.99) | .590 |
Goal by reinforcement | 0.36 (0.03-4.69) | .436 | 1.19 (0.57-2.49) | .637 |
Goal by reinforcement by time: linear | 1.91 (0.12-29.97) | .645 | 3.79 (1.71-8.40) | .001*** |
Goal by reinforcement by time: Quadratic | 0.08 (0.01-0.99) | .049** | 0.82 (0.39-1.70) | .588 |
aReferent groups for parameters are listed in parentheses.
bOdds ratio (OR) reflects the odds of reporting any leisure walking (versus none).
cOR, risk ratio (RR), and 95% CI are exponentiated coefficients of conditional estimates.
dRR reflects the proportional increase (values >1) or decrease (values <1) in non-zero transportation biking minutes/week associated with a one unit change in the predictor.
*
*
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This study reported the secondary outcomes of the WalkIT Arizona trial, a 12-month mHealth intervention combining goal setting (adaptive versus static goals) with financial reinforcement (immediate versus delayed) to increase PA among insufficiently active adults. Analyses examined differences in self-reported PA by intervention group, activity type (walking, biking), and activity domain (transportation, leisure). It is notable that only a small subset of participants (no more than 8% of the sample at any time point) reported biking, which was limited to the transportation domain. While walking was an activity readily accessible to all participants, biking required additional equipment and skills, including perceived comfort and safety while riding. Biking for transportation likely also required additional planning with regard to route, weather, and storage. For these reasons, we have focused our discussion on walking, as these findings represent a larger proportion of the study sample and are more likely to be generalizable.
All intervention groups reported greater time walking at 12 months relative to baseline for both leisure and transportation, with differences in the trajectory of walking time observed by group and domain. Closer examination of effects using NBH count models indicated differences in duration of walking time by domain: the independent effect of goal (model 1), a reinforcement by time interaction (models 2 and 3), and a three-way goal by reinforcement by time interaction (model 3) were only significant for leisure walking. Time was the only significant independent factor contributing to the count model for reported transportation walking in both complete case and multiple imputation analyses. Prior studies that have shown this effect for goal type [
Although the WalkIT Arizona intervention did not target any specific domain of activity, differential effects were observed for transportation and leisure walking, and our hypotheses regarding similar intervention effects across leisure and transportation domains were not supported. However, these findings show that adaptive goals alone were similarly effective to static goals at increasing reported leisure and transportation activities over time. Immediate reinforcement alone or combined with goal setting were more effective than delayed reinforcement at increasing leisure walking at 12 months but not transportation-related walking. It is possible that immediate financial reinforcement is a stronger intervention stimulus to promote leisure walking than delayed reinforcement, but not a strong enough stimulus to overcome barriers (eg, low walkability) to adopting transportation walking. While we adjusted models for block-level walkability, we did not account for distance between participants’ home and work, or walkability surrounding their workplace. These factors may further explain some of our findings, as this study occurred in a large, sprawling metropolitan area. These results support the premise that individual-level PA interventions are domain- and context specific and could be helpful in guiding further multilevel intervention refinement.
It is interesting to compare these findings with primary study outcomes that utilized accelerometer-measured MVPA. In primary analyses, a main effect of goal type was significant such that those with adaptive goals had a greater probability of initiating any MVPA bout minutes/day (versus none), whereas a main effect of reinforcement was significant such that immediate reinforcement was more successful at increasing total MVPA bout minutes/day. Interactions between goal type and reinforcement timing on MVPA bout minutes/day indicated that the group with adaptive goals combined with immediate reinforcement outperformed other groups, except for the group with static goals combined with immediate reinforcement. Analyses with self-reported data also indicated differences between hurdle and count models, although these findings were less robust. For hurdle models, a goal by time effect was observed only for leisure walking and was nonsignificant. Count models showed a main effect for goal type favoring static goals and a main effect for reinforcement timing favoring immediate reinforcement, as well as a goal by reinforcement by time interaction supporting static or adaptive goals combined with immediate reinforcement. These results were also only significant for leisure walking. There were no significant effects of intervention parameters for reported minutes of transportation walking; an observed reinforcement by time interaction was no longer significant in multiple imputation analyses.
Our registered secondary aim referred to self-reported PA as measured by the IPAQ but was not specific to walking or cycling. Although self-reported PA may be less accurate than objective data, the examination of domain-specific PA (eg, transportation versus leisure walking) provides a better conceptual alignment and allows for a more comprehensive understanding of participant behavior within a walking intervention, which may be useful in guiding intervention refinement. The WalkIT Arizona intervention maintained a broad focus on increasing ambulatory activities at a moderate intensity or greater. While there was little difference in the proportion of participants who endorsed any walking for leisure versus transportation, reported duration of walking increased significantly more for leisure walking than transportation walking at 12 months. These findings are consistent with prior studies indicating the prevalence and correlates of leisure walking differ from those of transportation walking [
The reported findings should be considered in the context of several limitations. As this study reported on secondary outcomes, the WalkIT Arizona trial was powered to detect effects using accelerometer data and not self-reported PA, which has greater variability. We elected to examine main effects of parameter by time using two-way interactions in models 1 and 2, as these models had greater power than model 3, which included a third interaction term. Lack of power may have also contributed to differences in findings between these analyses and the primary outcomes. Concerns have also been raised regarding the accuracy and sensitivity of self-reported PA, as correlations with objective measures tend to be low to moderate [
This is the first study to report differential effects of adaptive versus static goals and immediate versus delayed reinforcement for self-reported walking by domain. Despite limited power for these secondary analyses, the results support the premise that individual-level PA interventions are domain- and context-specific. This information may be helpful in guiding intervention refinement and increasing generalizability to other populations.
Multiple imputation negative binomial hurdle model examining goal x time interaction (model 1) for leisure walking.
Multiple imputation negative binomial hurdle model examining reinforcement x time interaction (model 2) for leisure walking.
Multiple imputation negative binomial hurdle model examining goal x reinforcement x time interaction (model 3) for leisure walking.
Multiple imputation negative binomial hurdle model examining goal x time Interaction (model 1) for transportation walking.
Multiple imputation negative binomial hurdle model examining reinforcement x time interaction (model 2) for transportation walking.
Multiple imputation negative binomial hurdle model examining goal x reinforcement x time interaction (model 3) for transportation walking.
Negative binomial hurdle model examining goal x time interaction (model 1) for leisure walking.
Negative binomial hurdle model examining reinforcement x time interaction (model 2) for leisure walking.
Negative binomial hurdle model examining goal x time interaction (model 1) for transportation walking.
Negative binomial hurdle model examining reinforcement x time interaction (model 2) for transportation walking.
Negative binomial hurdle model examining goal x time interaction (model 1) for transportation biking.
Negative binomial hurdle model examining reinforcement x time interaction (model 2) for transportation biking.
CONSORT-EHEALTH checklist v1.6.1.
generalized linear mixed models using template model builder
International Physical Activity Questionnaire
mobile health
moderate-to-vigorous physical activity
negative binomial hurdle
physical activity
risk ratio
This work was supported by the National Cancer Institute at the National Institutes of Health (R01CA198915). The funding agency was not involved in any aspect of this study or manuscript. The authors acknowledge the support of many undergraduate and graduate research assistants helping with data collection.
The authors also acknowledge JCH’s contributions, leading to her posthumous authorship.
None declared.