Optimizing Self-management COPD Treatment Through the American Lung Association Helpline: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Optimizing Self-management COPD Treatment Through the American Lung Association Helpline (NCT05106257), a randomized na study. The primary outcome is Health-related Quality of Life.

2. Clinical Question

The study asked whether different components of COPD self-management support could be evaluated through a factorial approach. The registry describes behavioral components involving the length of self-management education, ground-based walking training, inhaler training, and caregiver support. The primary clinical question concerned health-related quality of life: how did CRQ dyspnea subscale scores change from baseline to 90-day follow-up across the reported factor groups?

The study description emphasizes feasibility of using an engineering-inspired design to identify potentially useful treatment components. The registry results report group summaries, but do not report a sponsor-led statistical analysis of the primary outcome. The posted comparisons are simple, unadjusted recalculations from summary statistics. They should be read as exploratory descriptions rather than as a definitive component-by-component answer to the clinical question.

3. Trial Design

This was a randomized, unmasked, factorial study sponsored by Rush University Medical Center. The registry lists 46 participants as enrolled and the study status as completed. The recorded start date is 2021-11-05, and the primary completion date is 2023-06-30. The phase is listed as not applicable.

The behavioral components were short or long self-management education, ground-based walking training, inhaler training, and caregiver support. The registry describes 16 conditions formed by combinations of those component levels. The primary outcome record notes that its groups are recombinations of the participant-flow groups so that results can be reported by factor. Consequently, the outcome groups are not simply 16 independent treatment arms; they aggregate participants according to a particular component level.

The primary measure was change from baseline at 90-day follow-up in the CRQ dyspnea subscale. Results are reported as means and standard deviations. Several secondary measures are also listed: COPD symptom burden, self-management behaviors, and hospitalization. The first two are continuous change scores at 90-day follow-up; hospitalization is reported as incidence at that follow-up.

Randomization procedures, the allocation ratio, the exact factorial analysis model, and whether any interactions between components were planned are not reported in the registry record. The registry also does not report the original sample-size justification, the intended analysis population, or any interim-analysis plan. Although the study is completed, these omissions limit what can be concluded about how closely the posted results reflect the original design and analysis intentions.

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.

Only the first 6 of 16 groups are shown.

Overall Study

Analysis population for the primary outcome, as defined in the registry: Please note that the Arms/ Groups in the Outcome Measures are a recombination of the Participant Flow Arms/ Groups to report results by factor.

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.

Only the first 6 of 17 groups are shown.

6. Primary Endpoint

The registry lists the primary outcome as Health-related Quality of Life, measured over Change from baseline at 90-day follow-up.

The primary endpoint is change from baseline at 90-day follow-up in the dyspnea subscale of the Chronic Respiratory Disease Questionnaire (CRQ). The registry reports this measure as a mean score on a scale with a possible range of 1-7; higher scores indicate better health-related quality of life. The outcome groups are reported by recombined factor levels, including education length, walking training, inhaler training, and caregiver support.

Using a change score focuses the result on how the measure differs from each participant’s baseline rather than only on the follow-up score. In general, a patient-reported measure such as the CRQ can capture participants’ reported experience of respiratory symptoms and their effect on quality of life. The dyspnea subscale specifically concerns breathlessness, making it relevant to a study focused on COPD self-management.

Interpretation still depends on details not reported in the registry record. The record does not state how missing baseline or follow-up scores were handled, whether a particular difference was considered important to participants, or whether the primary comparison was prespecified as a main effect or another contrast. The reported means and standard deviations therefore describe the posted endpoint, but do not alone establish the clinical importance of a difference.

7. Results Posted in the Registry

• Self-management Education (Long): mean 0.70 versus 0.60 on control; difference 0.10 (95% CI -0.77 to 0.97), standardized effect d = 0.07; z-test p = 0.821.
• Ground-based Walking Training (on): mean 1.30 versus 0.60 on control; difference 0.70 (95% CI -0.19 to 1.59), standardized effect d = 0.47; z-test p = 0.122.
• Ground-based Walking Training (Off): mean 0.10 versus 0.60 on control; difference -0.50 (95% CI -1.29 to 0.29), standardized effect d = -0.37; z-test p = 0.215.
• Inhaler Training (on): mean 0.30 versus 0.60 on control; difference -0.30 (95% CI -1.21 to 0.61), standardized effect d = -0.19; z-test p = 0.516.

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.

COPD Symptom Burden

• 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:
  • Self-management Education (Long): mean -3.10 versus -1.90 on control; difference -1.20 (95% CI -4.75 to 2.35), standardized effect d = -0.22; z-test p = 0.508.
  • Ground-based Walking Training (on): mean -3.90 versus -1.90 on control; difference -2.00 (95% CI -5.66 to 1.66), standardized effect d = -0.36; z-test p = 0.285.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Self-management Education (Long) and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Ground-based Walking Training (on) and the control. The interval gives the range of effects that remain compatible with the data.

Self-management Behaviors

• 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:
  • Self-management Education (Long): mean 12.70 versus 3.60 on control; difference 9.10 (95% CI -3.00 to 21.20), standardized effect d = 0.52; z-test p = 0.140.
  • Ground-based Walking Training (on): mean 10.90 versus 3.60 on control; difference 7.30 (95% CI -5.83 to 20.43), standardized effect d = 0.41; z-test p = 0.276.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Self-management Education (Long) and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Ground-based Walking Training (on) and the control. The interval gives the range of effects that remain compatible with the data.

Hospitalization

• 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:
  • Self-management Education (Long): 8.3% (2/24) versus 9.1% (2/22) on control; risk difference -0.8 percentage points (95% CI -17.1 to 15.6), risk ratio 0.92 (0.14–5.96); unadjusted two-proportion z-test p = 0.927.
  • Ground-based Walking Training (on): 13.6% (3/22) versus 9.1% (2/22) on control; risk difference 4.5 percentage points (95% CI -14.2 to 23.3), risk ratio 1.50 (0.28–8.12); unadjusted two-proportion z-test p = 0.635.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Self-management Education (Long) and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Ground-based Walking Training (on) and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes broaden the study’s focus beyond health-related quality of life to COPD symptom burden, self-management behaviors, and hospitalization. Across the posted group summaries, the continuous outcomes show varied average changes, while the hospitalization counts are sparse. This mixture of directions and magnitudes is not unusual in a small exploratory study, but it does not establish a consistent pattern across outcomes.

The secondary measures are related to the primary question but do not answer it in the same way. Symptom burden and self-management behaviors describe other aspects of participants’ experience or behavior, while hospitalization is a clinical event count. These results can offer context for the CRQ dyspnea findings, but they should not be combined into a single overall conclusion. The registry does not report an integrated analysis linking secondary outcomes to the primary endpoint.

The record lists three secondary outcomes and reports no sponsor-led analyses for them. Some simple unadjusted comparisons are available for the continuous outcomes and hospitalization, but they do not appear as a complete confirmatory analysis. The secondary results therefore carry less weight than a prespecified primary analysis would: they are exploratory, involve multiple outcomes and comparisons, and have limited precision. Unadjusted testing also leaves the possibility of chance findings across repeated comparisons.

In general, secondary outcomes are most informative when they help assess consistency, suggest questions for future study, or clarify the range of outcomes measured. Here, the registry summaries describe what was recorded at 90-day follow-up, but the missing analysis details and small group sizes limit stronger interpretation. The secondary pattern neither confirms nor overturns the primary result; it adds descriptive information while leaving the component effects uncertain.

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

• COPD Symptom Burden
• Self-management Behaviors
• Hospitalization

9. Statistical Interpretation

10. Statistical Methods Explained

The registry reports a randomized factorial design. In general, a factorial design evaluates more than one intervention component within one study, allowing investigators to examine component effects and potentially interactions among components. The registry lists four component dimensions and 16 combinations. It does not report the planned factorial model, whether interaction effects were prespecified, or how the analysis would account for the combinations.

What do the mean and standard deviation describe?

The primary endpoint is continuous, so each group is summarized by a mean change score and a standard deviation. The mean describes the average observed change in that group. The standard deviation describes how spread out individual change scores are around the mean; it is not the uncertainty of the mean itself. For example, the short-education group has a mean change of 0.6 and a standard deviation of 1.5, based on 22 participants. A mean should therefore be read alongside the spread and the group size.

The change scores are measured on the CRQ dyspnea subscale, for which higher scores indicate better health-related quality of life. The outcome is a change from baseline, not a follow-up score alone. A positive mean change describes an increase from baseline on this scale; it does not by itself show that the change was important to participants.

How should the comparisons be understood?

The posted continuous-outcome comparisons are mean differences, with confidence intervals and unadjusted p values. These calculations are simple, unadjusted recalculations from the posted group summaries. They are not a replacement for a prespecified analysis using participant-level data. The reference group identified in the registry is short self-management education. Because results are recombined by factor, the component-level group summaries can include participants assigned to different combinations of the other components. A pairwise comparison against the short-education group is not automatically the same as a factorial main-effect estimate.

In general, a confidence interval describes a range of estimates compatible with the observed data under the assumptions of the calculation. If the interval includes zero for a mean difference, the data are compatible with no difference as well as with differences in either direction. A p value describes how inconsistent the observed data are with a specified null hypothesis under the calculation’s assumptions; it is not the probability that the null hypothesis is true. The registry record does not state the confidence level used for the posted intervals.

The posted p values are unadjusted. In general, when many comparisons are examined, the chance of at least one apparently unusual result can rise. Whether and how to control that issue depends on the prespecified analysis plan and the intended interpretation of the comparisons. A multiplicity plan is not reported in the registry record. The p values should consequently not be treated as confirmatory tests of all component effects.

What can the secondary summaries add?

COPD symptom burden and self-management behaviors are continuous measures reported as change from baseline at 90-day follow-up, using group means and standard deviations. Hospitalization is reported as a count of participants at that follow-up. The registry lists these as secondary outcomes, but no sponsor-reported analyses are listed for them. The comparisons accompanying some of the posted summaries are likewise unadjusted recalculations. They should be interpreted as exploratory descriptions, not as a complete analysis of secondary outcomes.

In general, a binary outcome such as hospitalization has fewer possible values per participant than a continuous score. With small groups and relatively few events, estimates can be imprecise. This is reflected in the broad uncertainty in the posted hospitalization comparisons. The registry does not report a model that adjusts for the factorial structure or for other participant characteristics.

The registry reports standard deviations for the continuous measures but does not report the distribution of individual scores, diagnostic checks, or treatment of outliers. In general, the appropriateness of a mean-based comparison depends partly on the distribution and the analysis assumptions. The posted summary statistics cannot show whether those assumptions were checked.

The sample-size illustrations use a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation, with the target effect set to a stated fraction of the observed effect. These are planning illustrations based on the observed summaries, not the original study design. The original design assumptions and sample-size justification are not reported in the registry record.

11. Confidence Intervals and Precision

The primary comparison between long and short self-management education has an estimated mean difference of 0.1 points. Its confidence interval extends from a negative value to a positive value, so it includes zero and allows for differences in either direction. The unadjusted p value is 0.821. The interval is wide relative to the point estimate, reflecting substantial uncertainty about the size and direction of the difference.

All posted confidence intervals for the primary comparisons include zero. The walking-training-on comparison has a point estimate of 0.7 points, with an unadjusted p value of 0.122, but its interval also extends across zero. Estimates for the other component summaries likewise have intervals that include zero. The observed point estimates should therefore not be interpreted as precise estimates of component effects.

Confidence intervals are useful because they show the range of values compatible with the data under the calculation’s assumptions, rather than focusing only on whether a p value crosses a threshold. In general, a broad interval indicates that the data leave substantial uncertainty about the magnitude of an effect. The small group sizes and variability in the reported scores limit precision in these results.

The registry record does not state the confidence level used for the intervals. It also does not report a prespecified clinically important difference for the CRQ measure. As a result, the intervals can be described in relation to zero, but their clinical implications cannot be judged against a registry-defined threshold. The posted summaries are best treated as estimates with considerable uncertainty, not as precise evidence that a component has or lacks an 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 sample-size illustrations are planning scenarios based on the observed effect sizes, not the original study’s design justification. For these continuous and binary outcomes, the figures are participants per arm. The record notes that time-to-event illustrations, if present, would instead report total events; no time-to-event outcome is listed among the outcomes.

The stated assumptions are a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation. Each scenario considers detecting 100%, 75%, or 50% of the observed effect. The 80% power column gives the estimated participants per arm under those assumptions for an 80% chance of detecting the specified effect if it is truly present. The 90% power column gives the corresponding planning estimate for a 90% chance. These are design concepts, not guarantees about a future study’s result.

For example, the planning figures for 100% of the observed primary education comparison are 3,532 participants per arm for 80% power and 4,729 participants per arm for 90% power. The listed figures differ across comparisons because the observed effects differ. When the assumed effect is smaller, more participants are needed: the 75% and 50% scenarios have larger planning requirements than the 100% scenario.

These figures are not observed power. They are illustrations computed from the observed effects and the stated planning assumptions. They do not establish what sample size the investigators originally intended, and the original design assumptions are not reported in the registry record. In particular, they do not account for any analysis details that are not stated, such as factorial interactions, attrition, or multiplicity procedures.

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 = 0.100; pooled standard deviation σ = 1.500.
2. n = 2 × (1.96 + 0.842)² × 1.500² / 0.100² = 3,532.0, rounded up to 3,532 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 <- 0.1                      # observed difference in means
sigma <- 1.5                     # 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(5106257)
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 = 3,532 (80% power), 4,729 (90% power)
 75% of observed effect: n per arm = 6,280 (80% power), 8,406 (90% power)
 50% of observed effect: n per arm = 14,128 (80% power), 18,914 (90% power)
power.t.test: 3533 
Simulated power at n = 3,532 per arm: 0.801 (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 reports multiple component-level comparisons and three secondary outcomes, but a plan for handling multiplicity is not reported in the registry record. It is not stated whether the primary comparisons were intended as a family of tests, whether any were designated as primary contrasts, or whether adjustments for multiple testing were planned. The posted p values are unadjusted and should be understood accordingly.

In general, multiplicity matters when several hypotheses are tested: even when none of the underlying effects is present, a larger set of tests creates more opportunity for a small p value to occur by chance. This is not a reason to disregard exploratory results, but it is a reason to avoid treating isolated unadjusted results as definitive. The factorial structure adds another question: the registry does not state whether interactions among components were planned or how they would affect interpretation of main effects.

Handling of missing data is also not reported in the registry record. Although enrollment is 46, the numbers contributing to the secondary continuous summaries are smaller and vary by outcome and group. The record does not explain whether this reflects missing follow-up measurements, outcome-specific eligibility, or another reason. It does not report an imputation method, a sensitivity analysis, or the assumptions used to address incomplete data.

The analysis population is not described. The registry does not state whether results follow intention-to-treat, per-protocol, or another population definition, nor does it describe exclusions after randomization. The participant-level flow details needed to understand those choices are not included in the registry record.

These omissions do not change the posted descriptive statistics, but they limit assessment of the analysis process and the robustness of the inferences. A prespecified statistical analysis plan, complete participant flow, and clear reporting of missing-data and multiplicity methods would help readers distinguish confirmatory results from exploratory comparisons.

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

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: copd exacerbation/ hospitalization (31.8%).

The adverse-event summaries are reported by recombined factor groups, rather than as 16 distinct combination groups. The registry lists participants affected by serious and other adverse events, the number at risk in each reported group, and deaths. No deaths are listed in any of the reported groups. The listed serious-event counts range from 1 to 7 across the factor-level summaries; counts of participants with other adverse events range from 6 to 10.

The most frequently listed serious-event term is COPD exacerbation/ hospitalization, reported as 31.8% of serious events. This percentage describes the recorded serious-event term, not the likelihood of an event for an individual participant. Since the group summaries are recombined by factor, the same participant may contribute to summaries for more than one component dimension. The counts should not be added across these summaries to produce a study-wide total.

These figures describe what the registry reports; they do not establish that an event was caused by a particular component. The registry record does not provide a causal assessment or comparative safety analysis. As with the efficacy summaries, the small group sizes and recombined reporting call for cautious interpretation.

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

The results establish that the registry contains outcome summaries for a randomized factorial study of COPD self-management components, with change in CRQ dyspnea score as the primary endpoint. The short- and long-education groups had mean changes of 0.6 and 0.7 points, respectively. Other factor-level summaries also show observed mean changes, but the posted unadjusted comparisons have confidence intervals that include zero.

These findings do not demonstrate that a component caused a particular change. Participants were assigned to combinations of behavioral components, and the outcome summaries recombine those assignments by factor. The simple comparisons use the short-education group as the reference; they are not necessarily the same as estimated factorial main effects. The registry does not report the model needed to separate component effects or assess interactions.

The results also do not establish that there is no effect. The groups are small, the score variability is substantial, and the confidence intervals are broad. A result that is compatible with zero may also be compatible with differences in either direction. The record does not provide a threshold for a clinically important CRQ change, so the clinical importance of the observed estimates cannot be determined from these summaries alone.

The secondary outcomes add descriptive information on symptoms, behavior, and hospitalization but do not provide a confirmatory answer. Their analyses are not reported, and multiple unadjusted comparisons require caution. The serious adverse-event summaries report no deaths, but they do not provide a causal safety analysis.

In short, the posted results describe an exploratory study and its observed outcome summaries. They can help frame questions about how to evaluate self-management components in a larger and more fully analyzed study. They do not establish that any specific component improves health-related quality of life, nor do they rule out potentially important effects.

17. Why This Trial Matters Statistically

The study is statistically interesting because its factorial structure is intended to examine several behavioral components within a single randomized study. Such designs can be efficient for learning about individual components, but the estimates depend on appropriate modeling and clear specification of the contrasts. The registry lists 16 combinations and then reports outcomes by recombined factor levels, making it important to distinguish group summaries from component-effect estimates.

The posted primary results also illustrate why point estimates alone are not enough. The observed mean difference between education groups is 0.1 points, while the confidence interval spans values on both sides of zero. The unadjusted p value is 0.821. Together, these statistics show that the estimate is uncertain; they do not prove equivalence or absence of an effect.

With 46 participants enrolled, the study offers limited precision for comparisons across several components. The planning illustrations based on observed effects show how strongly required sample sizes can depend on the assumed effect. Smaller effects call for larger studies under the stated assumptions. Those illustrations are not the original design justification, which is not reported in the registry record.

The record is also a useful example of the difference between descriptive outcome reporting and a complete statistical analysis. Group means, standard deviations, and recalculated comparisons provide a starting point, but details about the factorial model, missing data, analysis populations, and multiplicity are necessary to support stronger inferences. Those details are not reported. The study therefore highlights both the promise of component-focused designs and the importance of transparent analysis plans and reporting.

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 small enrollment of 46 participants limits the precision of comparisons across multiple behavioral components. Group sizes for the primary summaries range from 22 to 24, and the observed standard deviations are sizeable relative to several mean differences. The resulting confidence intervals include zero and leave substantial uncertainty.

Results are reported after recombining participants by factor level. This is useful for describing outcome summaries by component, but it does not by itself account for the other factors or show whether component effects interact. The registry does not report the factorial model or the planned contrasts, so the simple pairwise comparisons should not be treated as isolated causal effects.

The registry does not report the original sample-size justification or design assumptions. Its planning illustrations are based on the observed effects and should not be mistaken for the study’s original plan. Details of randomization procedures, the analysis population, missing-data handling, and multiplicity adjustments are also absent. The record lists no sponsor-reported analyses for the outcomes.

Finally, the reported follow-up is at 90 days. Longer-term outcomes are not reported in the registry record. The clinical importance threshold for the CRQ dyspnea subscale is also not reported, and the adverse-event summaries do not provide causal attribution. These limitations restrict both the strength and scope of conclusions from the posted results.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Optimizing Self-management COPD Treatment Through the American Lung Association Helpline — NCT05106257.
• Registry record last updated 2025-02-20; status last verified 2025-01.
• Results first posted 2025-02-20.
• 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

NCT05106257 is a completed, randomized, unmasked factorial study sponsored by Rush University Medical Center. It enrolled 46 participants and examined combinations of short or long self-management education, ground-based walking training, inhaler training, and caregiver support. The primary outcome was change from baseline at 90-day follow-up in the CRQ dyspnea subscale, with higher scores indicating better health-related quality of life.

The primary education-group means were 0.6 for short education and 0.7 for long education. The posted unadjusted comparison was 0.1 points, and its confidence interval included zero. Secondary outcomes covered COPD symptom burden, self-management behaviors, and hospitalization. The registry does not report sponsor-led analyses for these outcomes or key details of the original analysis plan, missing-data methods, or multiplicity handling. Overall, the posted summaries are exploratory and imprecise.