QuitBet Phase II: A Digital Social Game That Pays You to Stop Smoking: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for QuitBet Phase II: A Digital Social Game That Pays You to Stop Smoking (NCT05075213), a randomized na study. The primary outcome is Number of Participants Who Reported no Cigarette Smoking During the Past 7 Days.

2. Clinical Question

The statistical contrast is between two versions of a digital contingency-management program, rather than between a program and no intervention. It therefore addresses whether adding social game elements is associated with different reported smoking outcomes compared with a deposit-contract version without those elements. It does not isolate every component of the programs, and the posted endpoint alone cannot establish how any participant's smoking changed over time.

The question is clinically relevant as a behavioral outcome, but the registry endpoint is specifically a self-reported answer to whether the participant smoked even a puff during the past 7 days. It is a time-limited measure of reported smoking status, not a measure of continuous abstinence across the follow-up period. The record does not report a biomarker-confirmed primary endpoint or a longer continuous-abstinence definition.

3. Trial Design

The registry describes a randomized, parallel-group trial with no masking. It is marked completed, and its phase field is recorded as NA. The title calls the study “QuitBet Phase II,” but the registry phase field does not assign a phase. The listed enrollment is 245. The registry dates are 2021-11-01 for study start and 2024-07-27 for primary completion.

Participants were assigned to QuitBet or QuitBet-NS (no Social Elements). QuitBet is described as a smartphone game lasting 28 days, with a participant-funded deposit contract, integrated carbon monoxide testing, a social feed, and a leaderboard. The comparator is described as retaining the deposit contract and carbon monoxide testing without the social elements. Some arm-description text is truncated, so additional operational details are not fully available in the registry record.

The brief summary describes N = 550 participants planned for assignment and says that participants would be offered a second game, with up to 8 weeks of total game duration. It also describes surveys after each game and at later follow-up points. The enrollment field, however, reports 245, and the posted outcome denominators vary by endpoint. How the difference between the planned number in the brief summary and the enrollment field relates to recruitment or the final analysis is not reported in the registry record.

The primary outcome was assessed 6 months after the start of the first game. The posted arm counts for that outcome are 30.0 among n = 109.0 in QuitBet and 35.0 among n = 136.0 in QuitBet-NS. Randomization details, such as the method used to generate the allocation sequence or whether allocation was stratified, are not reported in the registry record. Neither are the original design assumptions or a description of the analysis population.

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.

Overall Study

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 Number of Participants Who Reported no Cigarette Smoking During the Past 7 Days, measured over 6 months after the start date of their first QuitBet game.

The primary endpoint counts participants who answered “no” to the survey question asking whether they had smoked a cigarette, even just a puff, during the past 7 days. It was assessed at 6 months after the start of the participant’s first QuitBet game. The outcome is therefore a self-reported, short-window measure of smoking status at a follow-up time point.

This endpoint is useful because it directly asks about the behavior the programs aim to change and yields a clear participant count in each randomized arm. A 7-day report can describe recent smoking, but it does not establish that a participant remained abstinent throughout the months before the survey. Nor does it distinguish different patterns of smoking outside that reporting window. Those distinctions depend on the endpoint definition and the available measurements.

The description identifies a survey response as the primary measure. Although the intervention descriptions refer to integrated carbon monoxide testing, the primary endpoint is not described as a biochemical test or as a combined self-report and test outcome. The registry record does not report rules for handling a missing survey response, confirmation procedures, or an endpoint adjudication process.

As a count outcome, the result is interpreted alongside the number of participants contributing to each arm’s posted outcome. The comparison is presented as a risk difference, which expresses the difference in the probability of reporting no smoking between the groups. It is a contrast between randomized arms, not a count of all smoking behavior over the full follow-up period.

7. Results Posted in the Registry

• QuitBet-NS (no Social Elements): 25.7% (35/136) versus 27.5% (30/109) on control; risk difference -1.8 percentage points (95% CI -12.9 to 9.4), risk ratio 0.94 (0.62–1.42); unadjusted two-proportion z-test p = 0.753.

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.

Number of Participants Who Choose to Play a Second Game

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • QuitBet-NS (no Social Elements): 25.0% (34/136) versus 17.4% (19/109) on control; risk difference 7.6 percentage points (95% CI -2.6 to 17.8), risk ratio 1.43 (0.87–2.37); unadjusted two-proportion z-test p = 0.153.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between QuitBet-NS (no Social Elements) and the control. The interval gives the range of effects that remain compatible with the data.

Cigarettes Per Day During the Past 7 Days

• 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:
  • QuitBet-NS (no Social Elements): mean 3.25 versus 4.15 on control; difference -0.90 (95% CI -3.17 to 1.37), standardized effect d = -0.14; z-test p = 0.436.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between QuitBet-NS (no Social Elements) and the control. The interval gives the range of effects that remain compatible with the data.

Number of Participants Who Used Smoking Cessation Medication

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • QuitBet-NS (no Social Elements): 32.8% (44/134) versus 32.1% (35/109) on control; risk difference 0.7 percentage points (95% CI -11.1 to 12.6), risk ratio 1.02 (0.71–1.47); unadjusted two-proportion z-test p = 0.904.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between QuitBet-NS (no Social Elements) and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes cover different parts of the study’s rationale: whether participants chose a second game, how many cigarettes they reported smoking during the past 7 days, and whether they used smoking-cessation medication. Collectively, they provide context about engagement, smoking behavior, and use of another cessation resource. They do not measure the same construct as the primary endpoint, which asks whether any smoking was reported during a 7-day window at follow-up.

The numerical directions are mixed rather than forming a single consistent pattern across all three outcomes. The secondary estimates should be considered alongside their confidence intervals and outcome-specific denominators, not interpreted as a unified measure of the program’s effect. None replaces the primary comparison, and none by itself explains why the primary outcome was or was not reported at follow-up.

All three secondary comparisons are posted with unadjusted p-values. The registry record does not report a multiplicity adjustment, a testing hierarchy, or which secondary outcome, if any, was intended to support a confirmatory claim. When several outcomes are examined, the chance of at least one apparently unusual result can increase if each test is considered separately. In general, adjustment or a prespecified hierarchy can address that issue; neither is documented in the registry record.

For that reason, the secondary results carry less confirmatory weight than a prespecified primary outcome, and their unadjusted statistics should be treated as exploratory context. This does not make them uninformative: they can help describe engagement and related behaviors and can inform future questions. But they do not establish a coherent benefit or harm across outcomes, and the registry summaries do not report an analysis that combines them or tests a formal mediation pathway.

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

• Number of Participants Who Choose to Play a Second Game
• Cigarettes Per Day During the Past 7 Days
• Number of Participants Who Used Smoking Cessation Medication

9. Statistical Interpretation

10. Statistical Methods Explained

The main posted comparison is a difference in risks for a binary outcome. Each participant either reported no cigarette smoking during the past 7 days or did not. The arm counts and denominators are used to estimate the probability of that response in each group; the risk difference expresses one group’s estimated probability minus the other’s. In this comparison, the direction is QuitBet-NS relative to QuitBet. A negative estimate therefore points toward a lower observed probability in the no-social arm, while a positive estimate would point toward a higher one. The direction of an estimate is not, by itself, evidence that the difference is reliable.

What does a risk difference describe?

In general, a risk difference is an absolute contrast on the probability scale. It is useful because it describes how far apart the estimated outcome probabilities are, rather than expressing the contrast as a relative change. Its interpretation depends on the outcome definition and the comparison direction. For this trial, the event is reporting no smoking in the past 7 days, not smoking during the full follow-up period. The registry also reports relative measures for the primary comparison, but any such measure remains a comparison of these two arms and does not replace the endpoint definition.

How should the p-value be read?

In general, a p-value describes how unusual a test statistic at least as far from its null value as the observed statistic would be, under the assumptions of a specified model and a null hypothesis. It is not the probability that the null hypothesis is true, and it does not measure the probability that the result will replicate. The primary unadjusted p-value is 0.753. It does not establish that the arms are the same; it indicates that the posted comparison does not provide strong evidence against the no-difference null under the unadjusted test. The registry record does not describe the specific test or any model details beyond the posted comparison.

“Unadjusted” matters because the posted p-value is not described as accounting for multiple outcomes or for covariates. The registry record does not report an adjusted primary analysis, covariate adjustment, or a multiplicity procedure. The posted estimates should therefore be read as the recorded unadjusted comparisons, not as estimates adjusted for participant characteristics or a family of statistical tests.

What does a confidence interval add?

In general, a confidence interval shows the range of estimates reasonably compatible with the data under the method’s assumptions. A wider interval indicates less precision than a narrower interval. For a risk difference, zero represents no difference on that scale. The primary interval extends across zero, so it includes both a negative and a positive difference. This uncertainty is consistent with the unadjusted p-value; neither statistic says that the true difference is exactly zero.

For the continuous secondary outcome, the registry reports means and standard deviations for cigarettes per day during the past 7 days, along with a mean difference. QuitBet has a posted mean of 4.15 and standard deviation of 6.63, based on n = 51.0. QuitBet-NS has a posted mean of 3.25 and standard deviation of 5.93, based on n = 74.0. The mean difference is -0.9, with a confidence interval from -3.166 to 1.366 and an unadjusted p-value of 0.436. In general, a mean difference compares average values on the measured scale. The standard deviations describe variation among observed values, not uncertainty in the difference itself; the interval describes uncertainty in the estimated contrast.

What is not specified?

The registry record does not report a detailed statistical analysis plan, the particular test used for each comparison, a missing-data strategy, covariate adjustments, or analysis-population definitions. It also does not report whether an interim analysis was planned or conducted, or the original assumptions used to design the trial. The registry’s planning illustration is a separate calculation based on observed effects and should not be mistaken for the trial’s own design justification.

In general, binary outcomes can be analyzed with methods that model the probability of an event, while continuous outcomes can be analyzed by comparing means or by modeling the measured values. Different methods can make different assumptions, particularly about how observations are distributed and how missing data arise. The posted summaries do not provide enough detail to determine which model specifications or sensitivity checks were used. The interpretation should therefore remain tied to the estimates and unadjusted comparisons actually recorded.

11. Confidence Intervals and Precision

Precision concerns how narrowly the data locate an estimated effect, not whether the effect is important. The primary risk-difference confidence interval extends from negative to positive values and includes zero. It is broad enough to leave uncertainty about both the direction and size of the difference between the social and no-social versions. The unadjusted p-value of 0.753 is consistent with that uncertainty, but it does not show that the underlying effects are identical.

For a binary outcome, the information available to estimate the difference depends in part on the event counts and the number of observations contributing to the comparison. The posted primary outcome includes 30.0 reports of no smoking in QuitBet and 35.0 in QuitBet-NS, with denominators of n = 109.0 and n = 136.0, respectively. Those counts do not yield a highly precise estimate of a modest between-arm difference. The confidence interval is more informative about that uncertainty than a conclusion based only on the observed counts or p-value.

The cigarettes-per-day comparison is also imprecise: its mean-difference interval runs from -3.166 to 1.366. Since the interval includes zero, the posted data do not clearly distinguish a lower mean in one arm from a lower mean in the other. The standard deviations are substantial relative to the mean difference, indicating variation among the reported values; the registry record does not give a separate measure of precision for each arm mean.

Confidence intervals are not guarantees that the true value lies within the printed range. In general, they describe uncertainty under a statistical method and its assumptions. The registry record does not provide enough analysis detail to assess all such assumptions, including handling of missing follow-up data. The displayed precision should therefore be read as conditional on the recorded comparison and the method behind it, which is not fully described.

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 illustrations based on the observed effect, not the trial’s original sample-size justification. For these outcomes, the figures are participants per arm rather than total events. The registry’s planning 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 the stated fraction of the observed effect. The trial’s own original design assumptions are not reported in the registry record.

The 80% power column describes the number of participants per arm that a hypothetical study would require, under those planning assumptions, to have an 80% chance of detecting the specified effect if that effect were the true one. The 90% power column uses the same idea with a higher probability of detection, and therefore generally calls for more participants. These are planning calculations for a future study, not a description of the power achieved by the completed trial. They are not observed power.

Each planning row uses a different fraction of the observed effect: 100%, 75%, or 50%. A smaller effect is harder to distinguish from random variation, so the calculated number of participants per arm increases as the assumed effect becomes smaller. The illustrations should be read as conditional comparisons across effect assumptions, not as a recommendation for a particular study size. They also do not account for design or analysis features that are not included in the stated assumptions, such as departures from equal allocation or losses to follow-up.

The displayed planning numbers vary substantially across the outcomes because the observed effects and outcome variability differ. Some estimates are especially small relative to their uncertainty, which can yield very large planning requirements when a future study is sized to detect that observed effect. These calculations do not establish that the observed estimate is the true effect; planning on an unstable estimate can be misleading. A future design would need its own justified target effect and assumptions, neither of which is specified for the original trial in the registry record.

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, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.275; treatment proportion p₁ = 0.257; average p̄ = 0.266.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.6251 = 1.2252.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6250 = 0.5260.
4. n = (1.2252 + 0.5260)² / (-0.0179)² = 9,596.3, rounded up to 9,597 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
p0 <- 0.275229357798165                          # control proportion
p1 <- 0.257352941176471                          # treatment proportion
n_per_arm <- function(power, frac = 1) {
  q1 <- p0 + (p1 - p0) * frac               # smaller effect = fraction of the observed difference
  pbar <- (p0 + q1) / 2
  z_b <- qnorm(power)
  ceiling(round((z_a * sqrt(2 * pbar * (1 - pbar)) + z_b * sqrt(p0 * (1 - p0) + q1 * (1 - q1)))^2 / (q1 - p0)^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 (unrounded n per arm)
cat("power.prop.test:", round(power.prop.test(p1 = p0, p2 = p1, power = 0.80)$n, 1), "\n")

# Simulation: how often does the z-test reject at the 80%-power sample size?
set.seed(5075213)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 9,597 (80% power), 12,847 (90% power)
 75% of observed effect: n per arm = 17,152 (80% power), 22,961 (90% power)
 50% of observed effect: n per arm = 38,796 (80% power), 51,936 (90% power)
power.prop.test: 9596.3 
Simulated power at n = 9,597 per arm: 0.802 (20000 replicates)

The simulation draws many pairs of event counts at the planned sample size and counts how often the two-proportion z-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 identifies a primary outcome and three secondary outcomes, but it does not report how statistical testing across these outcomes was planned. No multiplicity adjustment, hierarchical testing sequence, or allocation of a family-wise error rate is reported in the registry record. The posted p-values are unadjusted. As a result, the primary comparison can be read as the designated main result, while the secondary comparisons require additional caution because multiple outcomes are available for interpretation.

Missing data are also not described in sufficient detail to assess their impact. The denominators differ across posted outcomes: the primary outcome uses n = 109.0 and n = 136.0, while the cigarettes-per-day outcome uses n = 51.0 and n = 74.0, and the medication outcome uses n = 109.0 and n = 134. The registry record does not explain why these denominators differ, how missing responses were handled, whether participants were excluded for particular reasons, or whether any imputation or sensitivity analysis was used. The counts alone do not establish the reason for missing or unavailable values.

The analysis populations are not reported. In particular, the registry record does not state whether results follow an intention-to-treat approach, whether eligibility for each outcome required a completed survey, or how participants who did not complete follow-up were classified. These choices can affect estimates, especially when outcomes rely on self-report and follow-up responses are unavailable for some randomized participants.

The registry lists no reported analyses, and does not provide a detailed statistical analysis plan, covariate adjustments, interim-analysis information, or endpoint-specific model specifications. These omissions limit evaluation of prespecification and robustness. They do not invalidate the posted summaries, but they mean the comparisons should be interpreted as the statistics recorded, without assuming undocumented procedures.

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 serious adverse event terms

Serious events with the highest arm-level rate: hospitalization (0.7%).

The registry reports serious adverse events for the two arms. QuitBet has 0 serious adverse events among 109 participants at risk, and QuitBet-NS has 1 among 136 at risk. The listed serious-event term is hospitalization, reported as 0.7%. Deaths are reported as 0 in both arms.

The record reports 0 other adverse events in each arm. These figures are counts recorded in the registry; they do not, by themselves, establish whether an event was caused by a study intervention. A serious event occurring in one arm is not enough to show a difference in safety between programs, particularly when the total number of events is small.

In general, safety counts require context about event definitions, collection procedures, follow-up duration, and the completeness of reporting. The registry record does not provide detailed causal assessments or further event narratives. The figures should therefore be read as the serious and other adverse events posted for the trial, not as a comprehensive comparison of all possible harms or a basis for individual medical decisions.

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

The primary result is a randomized comparison of two program versions at one follow-up point. In the posted data, 30.0 participants in QuitBet and 35.0 in QuitBet-NS reported no cigarette smoking during the past 7 days. The estimated risk difference is slightly negative for QuitBet-NS relative to QuitBet, its confidence interval includes zero, and the unadjusted p-value is 0.753. The evidence does not precisely establish a difference between the groups, nor does it demonstrate equivalence.

The result does not answer whether QuitBet is better than no intervention. Both arms received a digital program with a deposit contract, and the contrast is focused on the social-element version versus the no-social version. It also does not show that either program caused participants to stop smoking. The endpoint is self-reported status during the past 7 days at the follow-up survey, not verified continuous abstinence over the full period.

The secondary outcomes add information about choosing another game, reported cigarettes per day, and medication use, but they do not change the definition of the primary outcome. Their unadjusted results are not a substitute for a prespecified analysis plan or multiplicity control. Variation in denominators and the lack of reported missing-data methods further limit conclusions about how fully the posted outcomes represent all randomized participants.

More generally, a statistical estimate is not a personal prediction. The trial-level comparison does not determine what an individual participant would experience, and these results should not be used as medical advice. They describe the posted outcomes and their uncertainty within this study’s randomized comparison. The registry record does not report enough methodological detail to settle questions about sensitivity to missing data, analysis populations, or alternative modeling choices.

17. Why This Trial Matters Statistically

This trial presents a useful statistical contrast because the arms share a deposit-contract approach while differing in whether social game elements are included. The comparison therefore focuses on a design feature of a digital behavioral program rather than contrasting the program with no intervention. That distinction is important when interpreting what the randomized result can address.

The primary endpoint is a participant-level report of recent smoking, making its definition straightforward but limited in time scope. A report of no smoking during the past 7 days is not the same as continuous abstinence over the full follow-up period. Clear endpoint definitions help readers distinguish what was measured from broader claims about quitting.

The posted confidence interval and unadjusted p-value illustrate why a single point estimate is not enough. The estimated contrast is uncertain and compatible with differences in either direction. Reading uncertainty alongside the observed counts avoids treating a non-significant result as proof of no difference. The secondary outcomes similarly show why outcomes should be interpreted according to their own measurement scales and denominators.

Finally, the registry record makes visible several important reporting questions: the relationship between the brief summary’s planned N = 550 and the enrollment field’s 245, the absence of a reported statistical analysis plan, and the lack of detail on missing data and analysis populations. Those details matter because they affect how confidently readers can connect the posted summaries to the full randomized trial. They also identify areas where clearer reporting would improve statistical interpretation.

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 trial compares two related program versions, so the result does not estimate the effect of QuitBet relative to usual care or no intervention. The primary endpoint is self-reported and describes only the past 7 days at the 6-month follow-up. It does not measure continuous abstinence or establish smoking status throughout the interval since the game began.

The registry record does not report participant eligibility criteria or characteristics, so it is not possible to assess how closely the enrolled participants resemble a particular population. It also does not provide full descriptions of the interventions: portions of the arm descriptions are truncated. Details about adherence, exposure to the social features, and implementation are not fully reported in the registry record.

The registry’s brief summary describes N = 550 as planned, while the enrollment field reports 245. The record does not explain this difference. The outcome denominators also vary, and reasons for unavailable outcome data and missing-data handling are not reported. These issues limit assessment of how the posted outcome summaries relate to the full randomized population.

Finally, the registry does not report the original sample-size justification, a detailed analysis plan, analysis populations, covariate adjustment, or multiplicity handling. The posted comparisons are unadjusted, and the secondary outcomes should be treated cautiously. The planning illustrations use observed effects and are not a substitute for the trial’s design assumptions. These reporting limitations restrict the strength of conclusions that can be drawn from the posted results.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: QuitBet Phase II: A Digital Social Game That Pays You to Stop Smoking — NCT05075213.
• Registry record last updated 2026-02-27; status last verified 2026-02.
• Results first posted 2026-02-27.
• 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

The registry describes a completed randomized, unmasked, parallel-group trial of QuitBet versus QuitBet-NS (no Social Elements) for smoking cessation. The enrollment field reports 245; the brief summary separately describes N = 550 planned. The primary outcome is the count of participants reporting no cigarette smoking during the past 7 days at 6 months after the first game began.

The posted primary comparison is an unadjusted risk difference with a confidence interval that includes zero and a p-value of 0.753. The registry also posts three secondary outcomes, with unadjusted comparisons, and serious adverse-event counts of 0 in QuitBet and 1 in QuitBet-NS. Detailed analysis methods, missing-data handling, and the original sample-size justification are not reported in the registry record.