STEP AND GO: A Study of Technology-based Exercise Promotion: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for STEP AND GO: A Study of Technology-based Exercise Promotion (NCT01953224), a randomized na study. The primary outcome is Change in Physical Activity From Baseline to 12 Weeks.

2. Clinical Question

3. Trial Design

STEP AND GO was a randomized, single-masked, parallel trial sponsored by The University of Texas Medical Branch, Galveston. The registry lists enrollment of 40, phase as NA, and status as completed. The recorded start was 2014-09 and primary completion was 2016-11. The record does not report who was masked, how the random sequence was generated, how allocation was concealed, or the original statistical design details.

The randomized groups were a game-based intervention and a wait-list control. The intervention was intended to use a mobile game to encourage adults to increase physical activity. The intervention arm description includes a smartphone, cellular data service, a loaded game application, song credit, headphones and an armband. Its final sentence is incomplete, so further details about participant instructions or intervention delivery are not reported in the registry record. The control description says participants would receive no intervention until the final assessment, then receive the full intervention.

The primary outcome covers change from baseline to 12 weeks. Its stated unit is minutes per day, based on physical activity measured over a 7 day period. The record reports the primary change outcome for 18 participants in each arm. Four participants’ baseline data were judged invalid under standard accelerometer cleaning and preparation protocols, described as requiring 4 days of 10+ hours of wear time. How this data-quality finding affected the analysis population is not specified. The trial enrollment figure and outcome-specific counts should therefore be read as different record entries, not as interchangeable descriptions of the analyzed population.

The registry also names secondary outcomes for fitness, body fat percentage, blood pressure, weight and motivation. Numerical results are posted for fitness, body fat percentage and weight; numerical results for blood pressure and motivation are not reported in the registry record. The record does not describe a prespecified primary analysis model, an analysis population, or a plan for handling multiple outcomes.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Before assignment: Pre-pilot results (N = 10) are not reported here, as outcomes are not comparable to those from the main trial. Specifically, the pre-pilot tested assignment of mobile phones to individuals as a delivery method for the intervention, which was found to be not feasible. Conclusions from the pre-pilot about intervention content would be biased.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Four participants' baseline data were found to be invalid based on standard protocols for accelerometer data cleaning/preparation (i.e., 4 days of 10+ hours wear time)

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

6. Primary Endpoint

The registry lists the primary outcome as Change in Physical Activity From Baseline to 12 Weeks, measured over Baseline to 12 weeks.

The primary endpoint is change in physical activity from baseline to 12 weeks. Activity was measured over a 7 day period and expressed as minutes per day. The registry describes the group summaries as means with standard deviations. This endpoint turns activity-monitor observations into a continuous measure that can be compared between the randomized groups. A mean summarizes the group’s average recorded change, while the standard deviation describes how dispersed individual changes were around that average.

Physical activity is directly connected to the intervention’s stated purpose: testing a mobile game intended to encourage adults to become more active. The measure therefore addresses the behavioral outcome the intervention was designed to influence, rather than a clinical condition or health event. A 7 day measurement period is the record’s stated basis for the daily activity measure; the record does not give further details about the monitoring schedule or how individual days contributed to each participant’s result.

The registry states that baseline values were carried forward for participants lost to follow-up. It also reports that 4 participants’ baseline data were invalid under the specified accelerometer criteria. Those details matter because the endpoint is a change score: its interpretation depends on which measurements were available and how missing or invalid readings were handled. The record does not fully describe those decisions or identify a specific analysis population. The outcome should consequently be interpreted as the posted summary of measured change, with these data-handling limits in mind.

7. Results Posted in the Registry

• Game-based Intervention: mean 8.82 versus 2.32 on control; difference 6.50 (95% CI -5.40 to 18.40), standardized effect d = 0.36; z-test p = 0.284.

8. Secondary Efficacy Endpoints

The registry also posts results for secondary outcomes. For each outcome below, the endpoint type, the test applied to the posted summary statistics, the result and its interpretation are given in turn. Secondary endpoints are usually not adjusted for multiple testing, so they support rather than confirm the primary finding.

Change in Physical Fitness From Baseline to 12 Weeks

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard deviations.
• Result:
  • Game-based Intervention: mean -0.01 versus 0.15 on control; difference -0.16 (95% CI -1.24 to 0.92), standardized effect d = -0.11; z-test p = 0.771.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Game-based Intervention and the control. The interval gives the range of effects that remain compatible with the data.

Change in Body Fat Percentage From Baseline to 12 Weeks

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard deviations.
• Result:
  • Game-based Intervention: mean -0.41 versus -0.20 on control; difference -0.21 (95% CI -0.94 to 0.52), standardized effect d = -0.18; z-test p = 0.572.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Game-based Intervention and the control. The interval gives the range of effects that remain compatible with the data.

Change in Weight From Baseline to 12 Weeks

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard deviations.
• Result:
  • Game-based Intervention: mean 0.02 versus -0.18 on control; difference 0.20 (95% CI -1.19 to 1.59), standardized effect d = 0.09; z-test p = 0.777.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Game-based Intervention and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes broadened measurement beyond daily physical activity to physical fitness, body fat percentage, blood pressure, weight and motivation. Numerical arm summaries and unadjusted comparisons are posted for fitness, body fat percentage and weight. Numerical results for blood pressure and motivation are not reported in the registry record. The available secondary results do not form a consistent pattern of precise differences: their estimated contrasts vary in direction, and the posted intervals for the numerical comparisons include zero.

The secondary outcomes relate to the primary question by assessing other physical or motivational measures over the same baseline-to-12-week period. They may help describe whether the primary activity measure appears alongside changes in related outcomes, but they do not replace it. The primary outcome directly measures the behavior targeted by the intervention; the secondary measures address other dimensions. The registry does not report a prespecified hierarchy or a model combining these outcomes.

Unadjusted secondary results carry less weight because multiple outcomes create multiple opportunities for an apparently notable result to arise by chance. When several hypotheses are tested without accounting for that multiplicity, the usual interpretation of each p-value can overstate the strength of evidence across the collection of outcomes. The registry record does not report a multiplicity adjustment or a plan for interpreting the family of secondary outcomes. Their estimates are therefore best treated as exploratory descriptions rather than independent confirmation of the primary result.

These findings should not be collapsed into a claim that all secondary measures agreed with, or contradicted, the primary estimate. The posted values are incomplete for some named outcomes, and the available comparisons remain uncertain. Taken together, they supply context about the outcomes measured, but the record does not establish a broad or consistent pattern of effects across all domains.

Secondary outcomes registered for this trial, for reference (only those above were recalculated):

• Change in Physical Fitness From Baseline to 12 Weeks
• Change in Body Fat Percentage From Baseline to 12 Weeks
• Change in Blood Pressure From Baseline to 12 Weeks
• Change in Weight From Baseline to 12 Weeks
• Change in Motivation From Baseline to 12 Weeks

9. Statistical Interpretation

10. Statistical Methods Explained

What does a mean change represent?

A mean is the arithmetic average of participants’ recorded changes. For this endpoint, each change concerns physical activity measured in minutes per day over the baseline-to-12-week period. The arm mean gives a compact description of the group, but it does not show the full range of individual responses. Participants can have changes quite different from the mean, and a group average can conceal that variation.

The registry pairs each mean with a standard deviation. A standard deviation describes the spread of observations around the mean in the same measurement units. Larger spread means individual changes are more dispersed. It is not the same as the uncertainty in the mean or in the difference between group means. The standard error, in contrast, describes the estimated sampling uncertainty of a statistic. The record lists standard errors for the two arm means as 4.535 and 4.038; those quantities are not individual-level variability measures.

How should the group contrast be read?

The mean difference is the intervention-arm mean minus the control-arm mean. The reported point estimate is 6.5 minutes per day. A positive value means the intervention arm had the higher observed mean change; it does not, by itself, show that assignment to the intervention caused that difference. Randomization is intended to support causal comparisons, but interpretation also depends on follow-up, outcome measurement and analysis choices. The registry record does not describe enough of the analysis plan to determine whether the posted contrast was adjusted for any other variables.

The posted comparison is a simple unadjusted recalculation from the arm-level summary statistics. It is a contrast of the displayed group means, not a substitute for an unreported sponsor analysis or a model using participant-level data. The record does not report a specific statistical test or adjusted model. No discrepancy between the comparison and the posted arm summaries is flagged.

What does an interval tell the reader?

An interval expresses uncertainty around an estimate. Its endpoints identify a range of values that are compatible with the data and the method’s assumptions, at a confidence level determined by the method. The primary interval extends from -5.401 to 18.4 minutes per day. Since zero lies within it, a null mean difference remains compatible with the result. The interval also allows positive and negative differences, so the direction is not resolved with precision. Its confidence level and construction method are not reported in the registry record.

An interval should not be read as a range containing a fixed proportion of individual participant outcomes. Nor does it assign a probability to the true value after the data have been observed. It describes uncertainty in an estimate under a statistical procedure. A wide interval signals that the data do not pin down the group contrast closely, though the meaning of a given width also depends on the outcome’s units and the scientific context.

What does the p-value contribute?

The unadjusted p-value is 0.284. In general, a p-value is calculated under a null hypothesis and a statistical model. It measures how often the model would generate a result at least as extreme as the observed one if the null were true. It does not give the probability that the null is correct, and it does not show whether a difference is important. A large p-value can occur when the true difference is small, when data are variable, or when the estimate is imprecise. A small p-value would not, on its own, establish that an effect is large or clinically meaningful.

Because the registry does not report a named test or full analysis specification, the p-value should be treated as the posted unadjusted comparison rather than as evidence about a fully documented prespecified model. It should be considered with the point estimate and interval, not as a standalone verdict.

Why can over-dispersion matter?

For a continuous outcome, over-dispersion is not usually described in exactly the same way as it is for counts, but the underlying concern is general: observed variability may be larger than a model anticipates. When variation is high relative to the group difference, the estimate is less precise unless there is adequate information to estimate that contrast. Here, the standard deviations are much larger than the point difference in the same units. That pattern helps explain why the interval is broad. It does not prove that a particular model assumption was violated, and the record does not report a model diagnostic assessment.

For count outcomes, general statistical methods may need to allow for over-dispersion because the variance can exceed what a simpler count model assumes. That issue is not the form of the primary endpoint: physical activity change is posted as a continuous measure summarized by means and standard deviations. No count-model result is reported for this outcome.

How do missing and invalid observations affect interpretation?

The registry says baseline values were carried forward for participants lost to follow-up. This is a single-value imputation: the last available baseline value is used in place of a later value for a missing follow-up observation. For a change outcome, that convention effectively assigns no change to such an observation. As a general matter, carrying values forward can produce biased estimates when the unobserved later outcome would differ from baseline, and the bias may go in either direction. The record does not provide the number lost, reasons for loss, or sensitivity analyses under alternative assumptions.

Separately, 4 participants’ baseline accelerometer data were judged invalid under the stated standard criteria of 4 days of 10+ hours wear time. The record does not say whether those observations were excluded from every endpoint, whether other data remained usable, or how the final analysis population was defined. The reported primary arm counts are 18 per arm, while the safety record uses 20 participants at risk per arm. Those are outcome-specific record entries; the reason for their difference is not reported.

These methods explain why the numerical results should be read as estimates with uncertainty, not as exact descriptions of an underlying effect. The record supports describing the arm summaries, the unadjusted contrast and its interval. It does not support reconstructing unreported modeling decisions, missing-data sensitivity checks, or an analysis population definition.

11. Confidence Intervals and Precision

The primary estimated difference is 6.5 minutes per day, with an interval from -5.401 to 18.4 minutes per day. The interval is wide enough to include a negative difference, zero, and a positive difference larger than the point estimate. Thus, the direction and size of the underlying mean contrast remain uncertain. The interval’s confidence level and calculation method are not reported in the registry record.

Precision is influenced by the amount of information and the variability of the outcome. The primary outcome counts are 18 in each arm, and the arm standard deviations are 19.24 and 17.13 minutes per day. The observed spread is substantial relative to the estimated contrast, so the group means do not determine the difference closely. This is consistent with the broad interval and the unadjusted p-value of 0.284. The p-value does not replace the interval: it gives a different summary of the comparison under a null model, while the interval makes the range of compatible contrasts visible.

The interval should not be interpreted as a range for individual changes, and its crossing zero does not prove equivalence. Equivalence would require a prespecified margin and a design and analysis suited to that question; such a margin or analysis is not reported in the registry record. Nor does the interval show the probability that the true effect lies within its endpoints. It communicates uncertainty under the method used to calculate it.

Missing follow-up values were handled by carrying baseline values forward, and the record identifies 4 invalid baseline accelerometer data records. Without fuller information about these observations and the interval method, precision cannot be assessed independently of those choices. The result is best characterized as a positive point estimate with substantial uncertainty, rather than as a precise estimate of the intervention’s effect.

12. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The planning figures are participants per arm, not totals across both arms and not total events. They are an illustration based on the observed effect, not the trial’s original design calculation. The stated assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is set to the stated fraction of the observed effect. The original trial’s own sample-size assumptions are not reported in the registry record.

The 80% power column describes the number of participants per arm that the planning calculation estimates would give an 80% chance of detecting the specified effect under those assumptions, if that effect and model assumptions were correct. The 90% power column gives the corresponding planning target for a higher chance of detection. These are design targets, not guarantees about what a future study will find. The figures are not observed power, and they do not describe the probability that the intervention is effective.

Each planning row uses a different fraction of the observed primary effect: 100%, 75% or 50%. Smaller effects require more participants to distinguish them from sampling variability under the same assumptions. This explains why the planning counts rise as the assumed target effect gets smaller. The observed effect in a small study can be unstable, so planning directly from it may not be a reliable basis for a future design. No allowance for attrition or other design considerations is stated in the planning assumptions.

The sample-size curve is intended to show the relationship between effect size and the corresponding planning requirement. Read it as an assumption-based illustration, not as evidence that the completed trial had a particular power or that a future study will achieve the displayed performance. The registry’s enrollment figure is 40, while the planning estimates are much larger; those figures answer different questions and should not be conflated.

13. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, Δ is the difference in means to detect and σ is the common standard deviation.

Worked calculation for this trial

1. Observed difference Δ = 6.500; pooled standard deviation σ = 18.216.
2. n = 2 × (1.96 + 0.842)² × 18.216² / 6.500² = 123.3, rounded up to 124 participants per arm for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
delta <- 6.5                      # observed difference in means
sigma <- 18.2155771250872                     # pooled standard deviation
n_per_arm <- function(power, frac = 1) {
  ceiling(round(2 * (z_a + qnorm(power))^2 * sigma^2 / (delta * frac)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (t-based, so very slightly larger)
cat("power.t.test:", round(power.t.test(delta = delta, sd = sigma, power = 0.80)$n, 1), "\n")

# Simulation: exact two-sample t-test using simulated means and variances
set.seed(1953224)
n    <- n_per_arm(0.80)
reps <- 20000
m_diff <- rnorm(reps, delta, sigma * sqrt(2 / n))             # difference in sample means
s2     <- sigma^2 * rchisq(reps, 2 * (n - 1)) / (2 * (n - 1))   # pooled sample variance
tstat  <- m_diff / sqrt(s2 * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(tstat) > qt(0.975, 2 * (n - 1))), reps))

Output from running the code

100% of observed effect: n per arm = 124 (80% power), 166 (90% power)
 75% of observed effect: n per arm = 220 (80% power), 294 (90% power)
 50% of observed effect: n per arm = 494 (80% power), 661 (90% power)
power.t.test: 124.2 
Simulated power at n = 124 per arm: 0.797 (20000 replicates)

The simulation draws many sets of group means and variances at the planned sample size and counts how often the two-sample t-test rejects the null. It should land close to the target power; at very small required sample sizes it can fall below it, because the normal approximation is rough there.

14. Multiplicity, Missing Data and Analysis Populations

The registry names multiple secondary outcomes in addition to the primary physical-activity endpoint. Numerical results are available for some, but the registry does not report a multiplicity procedure, such as an adjustment to account for testing several outcomes, nor does it describe a hierarchy that would determine which secondary results should be treated as confirmatory. Without such a plan, unadjusted secondary p-values should be interpreted cautiously. Considering several results together increases the chance that at least one may look notable by chance, even when no corresponding underlying difference exists.

The registry record lists no reported analyses in its analysis count, and the secondary outcomes have no reported analysis entries. Arm summaries and unadjusted comparisons are nevertheless posted. The record does not explain the relationship between those summary comparisons and any prespecified analysis plan. It also does not report an adjusted primary analysis, interim analyses, a formal stopping rule, or a statistical analysis plan. These omissions do not establish that such procedures were absent; their details are simply not reported in the registry record.

Missing data are relevant to the primary outcome. The description says baseline values were carried forward for participants lost to follow-up. This implies that a missing later value was represented by the baseline value, but the number lost, timing of loss and reasons are not reported. As a general statistical concern, this approach assumes no change for carried-forward observations and may not reflect the outcomes that would have been observed. The direction of any bias cannot be determined from the registry entry alone. No sensitivity analysis using alternative missing-data assumptions is reported.

The record also identifies 4 participants’ baseline accelerometer data as invalid because they did not meet the stated standard data-cleaning criteria of 4 days of 10+ hours wear time. How invalid records were treated at follow-up and whether participants were excluded from the primary analysis are not reported. The primary outcome count is 18 per arm, while safety figures use 20 at risk per arm; the record does not explain the difference. It does not identify an intention-to-treat, per-protocol or other analysis population. These gaps limit assessment of potential selection and missing-data effects.

15. Safety Results

The registry reports serious adverse events as affected participants divided by participants at risk.

Deaths

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 0% reporting threshold.

Most frequent non-serious adverse event terms

The registry lists 20 participants at risk for serious adverse events in each arm. Serious adverse events affected 0 in the game-based intervention arm and 0 in the wait-list control arm. Deaths are listed as 0 in each arm. No serious adverse-event terms are listed. These are the posted counts; the record does not provide additional detail about event definitions, monitoring procedures, severity categories or timing.

Other adverse events affected 2 participants in the intervention arm and 3 in the control arm, with 20 at risk in each. These counts should be read as descriptive registry entries, not as a formal comparison of risk. The record does not report a statistical analysis of adverse events, an interval for the between-arm difference, or details that would support assessment of event types and duration. The absence of listed serious events in this record does not establish that an intervention is free of risk. Safety interpretation is limited to the events and follow-up information recorded.

16. What the Result Does, and Does Not, Mean

The main statistical result is an estimated mean difference of 6.5 minutes per day in change in physical activity, with a broad interval extending from -5.401 to 18.4 minutes per day. The intervention arm had the higher observed mean change, but the interval includes zero and values on either side. The result therefore leaves substantial uncertainty about the direction and size of the group difference. The unadjusted p-value of 0.284 is consistent with that uncertainty; it is not proof of no effect.

The outcome is a measure of activity, not a direct measure of cardiovascular disease, obesity, cancer, diabetes or another clinical event. The record’s listed conditions describe the study context, while the primary endpoint concerns activity measured over a 7 day period. Secondary outcomes include fitness, body fat percentage, blood pressure, weight and motivation, but complete numerical results are not posted for all named measures. The available data do not establish an effect across all these domains.

The trial was randomized, which supports comparing the assigned groups, but the record does not describe the randomization procedures, masking details, analysis population or a full statistical plan. The primary description also states that baseline values were carried forward for participants lost to follow-up and notes 4 invalid baseline accelerometer records. These details make it important not to treat the posted contrast as an exact or fully adjusted causal estimate.

The result does not show that the game works, nor does it show that the game does not work. It describes the recorded group summaries and an uncertain unadjusted difference under the information available in the registry. The study’s enrollment and outcome counts, outcome variability, incomplete follow-up details and lack of reported multiplicity procedures all limit how strongly the findings can be generalized. No clinical advice follows from these statistics alone.

17. Why This Trial Matters Statistically

This trial illustrates a central distinction in biostatistics: the size of an estimate and the certainty around it are separate issues. The primary point estimate is positive, but the interval also includes zero and differences in the opposite direction. Reading only the point estimate would understate uncertainty; reading only the p-value would discard information about the possible range of group differences. Interpreting the estimate together with its interval gives a fuller account of what the data can and cannot resolve.

The observed variation is also important. The standard deviations are large relative to the estimated difference, and the primary outcome counts are modest. This combination makes precise estimation difficult. The planning illustration makes the broader design principle visible: detecting smaller effects generally requires larger studies under otherwise unchanged assumptions. Those planning figures are not a retrospective measure of the trial’s power.

STEP AND GO also shows why registry interpretation must account for endpoint construction and missingness. Physical activity was measured over a 7 day period, baseline values were carried forward for participants lost to follow-up, and some baseline accelerometer data were deemed invalid under stated criteria. These are not peripheral details: they shape which observations contribute to a change score and how that score should be understood.

Finally, multiple secondary outcomes can add useful context while also increasing the need for a clear analysis plan. Without reported multiplicity handling or a stated hierarchy, secondary comparisons are less persuasive as confirmatory evidence. The record supports a careful statistical description of the posted results, while leaving important design and analysis details unresolved.

18. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

The record does not report who was masked, how randomization was conducted, how allocation was concealed, or whether outcome assessors were masked. The intervention description is incomplete, and the record does not give sufficient detail about participant adherence, exposure to the game or implementation. The original sample-size justification and the trial’s prespecified statistical plan are also not reported. These omissions restrict evaluation of potential bias and the fit between planned and posted analyses.

The primary outcome is based on accelerometer data, with 4 participants’ baseline records deemed invalid under the stated wear-time criteria. Although the record states that baseline values were carried forward for participants lost to follow-up, it does not report the number lost, reasons for loss, or sensitivity analyses. It also does not explain whether invalid baseline data led to exclusion or how their follow-up measurements were treated. The outcome-specific counts and safety at-risk counts differ, but the record does not explain why.

The available primary comparison is unadjusted, and no adjusted model or analysis population is reported. The confidence interval level and construction method are not given. The record also does not report multiplicity handling for the several secondary outcomes, and numerical results are absent for blood pressure and motivation. These gaps limit both the assessment of uncertainty and the strength of claims that can be made across outcomes.

The study lists enrollment of 40, while the primary outcome summaries include 18 participants per arm. The registry does not explain the relationship between these figures. The primary endpoint is a behavioral measure over baseline to 12 weeks, so findings should not be extended to longer-term activity or clinical outcomes not reported in the record. Generalizability beyond the study population and setting cannot be assessed from the posted details alone.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: STEP AND GO: A Study of Technology-based Exercise Promotion — NCT01953224.
• Registry record last updated 2018-08-01; status last verified 2018-04.
• Results first posted 2018-08-01.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

22. Record Summary

NCT01953224, STEP AND GO: A Study of Technology-based Exercise Promotion, was a randomized, single-masked, parallel trial sponsored by The University of Texas Medical Branch, Galveston. The record lists enrollment of 40, status completed, and a study period from 2014-09 to primary completion in 2016-11. The intervention used a mobile game to encourage physical activity and was compared with a wait-list control.

The primary outcome was change in physical activity from baseline to 12 weeks, measured in minutes per day over a 7 day period. The estimated mean difference was 6.5 minutes per day, with an interval from -5.401 to 18.4 and an unadjusted p-value of 0.284. The estimate is positive but imprecise. Baseline values were carried forward for participants lost to follow-up, and 4 baseline accelerometer records were reported invalid under the stated criteria. The registry does not report a complete analysis plan, analysis population, multiplicity procedure or interval confidence level.