Strengths-Based Behavioral mHealth App for Parents of Adolescents With Type 1 Diabetes-Pilot Study: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Strengths-Based Behavioral mHealth App for Parents of Adolescents With Type 1 Diabetes-Pilot Study (NCT02877680), a randomized na study. The primary outcome is Acceptability - Number of Participants That Felt The Intervention Was Well-Received.

2. Clinical Question

3. Trial Design

The registry describes a randomized, parallel-group pilot study of a behavioral mHealth intervention for families of adolescents with type 1 diabetes. The listed enrollment is 82. The study is marked completed, with a start date of 2017-07-31 and a primary completion date of 2019-05-30. The phase is listed as NA. The sponsor is Baylor College of Medicine.

In the intervention condition, parents were offered the T1Doing Well smartphone app. The app was described as supporting parent tracking of adolescents’ strength behaviors related to diabetes, with regular feedback and training on recognizing and reinforcing positive behaviors. The comparator was usual diabetes care and study-related data collection without app use during the study period. The comparator description says the app would be offered after follow-up data collection for participants to try and comment on.

The registry lists masking as none and the trial model as parallel. It does not report allocation concealment, the randomization method, or whether any procedures were used to reduce assessment or coding bias. It also does not report the original sample-size justification, a planned allocation ratio, or whether an interim analysis was planned. These details therefore cannot be used to judge how the original design was selected or implemented.

The primary outcome was assessed at the follow-up timepoint described as 3-4 months after baseline. The registry also lists secondary measures at that same timepoint, including blood glucose monitoring frequency, HbA1c, and parent- or adolescent-reported aspects of family impact and communication. The posted sample sizes vary across outcomes, so the participants contributing data to one outcome should not be assumed to be the same as those contributing to another. The available registry information identifies the design and outcome timing, but does not provide a full account of recruitment, follow-up completion, or analysis populations.

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: Of 84 families consented, 2 families did not complete baseline questionnaires and were not randomized (1 withdrew due to time demands, 1 moved to another care institution). Of the remaining 82 families, 2 completed baseline questionnaires but were never randomized because they never completed a study visit, resulting in a randomized sample of n=80.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Out of 108 families successfully contacted and confirmed eligible, 8 families declined due to time demands, 6 were disinterested in research, and 2 felt the study was not relevant for their child's age. 8 families were unable to be contacted for a decision after giving study information. 84 families consented (78% consent rate).

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.

Population: Of 84 families (parent-adolescent dyads) consented, n=4 were never randomized (reasons stated in Participant Flow section), resulting in baseline data from 80 families.

6. Primary Endpoint

The registry lists the primary outcome as Acceptability - Number of Participants That Felt The Intervention Was Well-Received, measured over 3-4 months after baseline (follow-up timepoint).

The primary endpoint counts participants whose interview responses were classified as positive, which the study team interpreted as the intervention being well-received. The endpoint was based on verbal responses from qualitative follow-up interviews with parents and adolescents. The team coded feedback as positive, negative, or neutral, and a positive classification counted toward the posted acceptability result. The registry says the endpoint was collected 3-4 months after baseline.

Acceptability is useful in a pilot because it describes how participants responded to an intervention and can help characterize whether it was received positively by those reached for follow-up. It is distinct from a clinical outcome: a positive response does not establish that the app changed diabetes-management behavior, glycemic control, or family functioning. Those topics were represented separately among the posted secondary outcomes.

The registry reports 35 positive responses among 46 parents and 38 among 47 adolescents. It also states that the reported sample size for this outcome represents the number of people the study team was able to reach and from whom follow-up interview data were obtained. Those denominators therefore describe interview availability for the endpoint, rather than an assertion that every person enrolled contributed an interview. The record does not report further details of the interview questions, coding rules, coder agreement, or whether coding was performed without knowledge of study assignment.

7. Results Posted in the Registry

• Intervention - Adolescent: 80.9% (38/47) versus 76.1% (35/46) on control; risk difference 4.8 percentage points (95% CI -11.9 to 21.5), risk ratio 1.06 (0.86–1.32); unadjusted two-proportion z-test p = 0.576.

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.

Adherence to Diabetes Regimen (Objective) - Blood Glucose Monitoring Frequency

• 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:
  • Intervention: mean 2.60 versus 3.10 on control; difference -0.50 (95% CI -1.35 to 0.35), standardized effect d = -0.30; z-test p = 0.251.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Intervention and the control. The interval gives the range of effects that remain compatible with the data.

Glycemic Control - HbA1c

• 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:
  • Intervention: mean 8.70 versus 8.40 on control; difference 0.30 (95% CI -0.45 to 1.05), standardized effect d = 0.19; z-test p = 0.432.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Intervention and the control. The interval gives the range of effects that remain compatible with the data.

Diabetes Family Impact - Diabetes Family Impact Scale (DFIS), Parent-report

• 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:
  • Intervention: mean 20.70 versus 19.10 on control; difference 1.60 (95% CI -6.05 to 9.25), standardized effect d = 0.10; z-test p = 0.682.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Intervention and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes broaden the scope beyond acceptability to include objective diabetes-management measures and family-reported impacts. The posted continuous comparisons do not form a consistent pattern in direction: some intervention-group means are lower than usual-care means, while others are higher. This mixed pattern does not supply a coherent signal that can be interpreted as a uniform change across diabetes management and family outcomes.

The posted confidence intervals for the secondary comparisons include no difference, and the estimates are based on outcome-specific numbers of observations that vary. The registry record does not report adjusted analyses, a hierarchy among secondary outcomes, or an account of how the available outcomes were selected for emphasis. Consequently, the secondary estimates are best treated as exploratory descriptions rather than confirmatory tests.

These measures are related to the study’s broader goal of supporting family teamwork and diabetes management, but they answer different questions from the primary acceptability endpoint. Positive interview feedback describes reception of the app among people reached for follow-up; it does not imply improvement in blood glucose monitoring, HbA1c, family impact, or communication. Conversely, an uncertain secondary contrast does not negate the descriptive evidence that many interviewed participants gave positive feedback.

Unadjusted secondary results carry less weight because several outcomes are considered without a registry-reported multiplicity procedure or a documented analysis plan. With repeated comparisons, chance variation can produce apparently notable results even if there is no underlying effect. The record’s reported analyses are empty for the listed secondary results, so the posted recalculations do not establish that the outcomes were analyzed according to a prespecified strategy. Taken together, these secondary data help describe what was measured and the uncertainty of the observed contrasts, but they should not be used to claim an established clinical effect.

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

• Adherence to Diabetes Regimen (Objective) - Blood Glucose Monitoring Frequency
• Glycemic Control - HbA1c
• Diabetes Family Impact - Diabetes Family Impact Scale (DFIS), Parent-report
• Family Impact - Pediatric Quality of Life Impact Module (Peds QL-FI), Parent-report
• Family Communication - Helping for Health Inventory (HHI), Parent-report
• Family Communication - Helping for Health Inventory (HHI), Adolescent-report
• Diabetes Family Conflict Scale-Revised (DFCS), Parent-report
• Diabetes Family Conflict Scale-Revised (DFCS), Adolescent-report

9. Statistical Interpretation

10. Statistical Methods Explained

What does a risk difference describe?

In general, a risk difference subtracts the event proportion in one group from the event proportion in another. Here the event is a positive classification of interview feedback, and the posted contrast compares adolescents with parents. A value above zero indicates a higher positive-response proportion in the adolescent group according to that contrast; a value below zero indicates a higher proportion in the parent group. The estimate is 0.048. The scale is a difference in proportions, not a count of additional people and not a relative change.

The registry also includes relative measures for this comparison: a risk ratio of 1.063 and an odds ratio of 1.327. In general, a risk ratio compares event proportions as a ratio, while an odds ratio compares event odds. These are distinct ways of expressing a contrast, and neither should be mistaken for a treatment effect against usual care in this primary result. Their confidence intervals are wide: the posted risk-ratio interval is 0.858 to 1.316, and the odds-ratio interval is 0.491 to 3.583. These summaries convey substantial uncertainty around the relative contrasts.

What does the unadjusted p-value say?

In general, a p-value describes how compatible observed data are with a specified null model, under the assumptions of the calculation. It is not the probability that the null hypothesis is true, and it does not measure the size or importance of an effect. The primary comparison has an unadjusted p-value of 0.576. The label unadjusted means the value is not presented as corrected for multiple comparisons. It should be read together with the estimate and its confidence interval, rather than as a standalone decision about the intervention.

The secondary continuous comparisons also have unadjusted p-values. The registry does not report a multiplicity adjustment or a prespecified hierarchy for interpreting secondary outcomes. Repeated testing across several outcomes can make isolated small p-values less persuasive, so a collection of exploratory results needs more cautious interpretation than a single prespecified test with a documented analysis plan.

How are continuous outcomes compared?

For the posted secondary outcomes, the registry presents a mean in each group and a mean difference for the intervention relative to usual care. In general, the mean summarizes the average measured value, while the mean difference expresses the contrast in the outcome’s original units. A positive difference means the intervention group’s mean is higher under the posted direction of comparison; a negative difference means it is lower. The reported standard deviations describe variability among observations within each group, not uncertainty in the estimated mean difference.

The confidence intervals for these mean differences are calculated from posted summary statistics and are unadjusted recalculations. They are not presented as the result of a more detailed adjusted model. Such summary-statistic comparisons cannot account for covariates or other design features unless those are explicitly incorporated, and the registry does not report adjusted estimates for these outcomes. The results should therefore be treated as simple group contrasts, not as evidence that potential baseline differences were controlled.

Why can variability affect interpretation?

In general, greater variability makes a group mean harder to estimate precisely, particularly when the number of observations is limited. The standard deviations differ across outcomes and groups, and the outcome sample sizes also vary. The reported confidence intervals reflect uncertainty based on the posted summary information, but they cannot supply details about the distribution of individual observations, unusual values, or departures from model assumptions. The registry record does not report diagnostic checks or an alternative analysis for non-normal data.

For a count or proportion outcome, the amount of information depends on both the number of observations and the distribution of responses. The primary endpoint is a count of positive classifications, rather than a continuous measure. Its comparison is consequently expressed as a difference in proportions, with corresponding relative summaries, rather than as a difference in means.

What can the recalculations establish?

In general, recalculation from group summaries can make comparisons easier to interpret, but it does not replace the study team’s full analysis if that analysis includes participant-level data, covariate adjustment, or features not contained in the posted summaries. In this record, the discrepancy indicator is false, so there is no noted conflict requiring a sponsor analysis to take precedence. The posted comparisons should still be understood as simple unadjusted recalculations, not as a substitute for an unreported statistical analysis plan.

The registry lists 19 outcome measures and 15 posted secondary outcomes, while the analysis count is 0 and the secondary reported-analyses lists are empty. This distinction signals that outcome information is present but that the registry does not enumerate formal analysis records for these outcomes. It does not establish why analyses were not listed or whether additional analyses were conducted outside the registry record.

11. Confidence Intervals and Precision

A confidence interval describes a range of estimates compatible with the observed data under the calculation’s assumptions. In general, a wider interval indicates less precision, while a narrower interval indicates greater precision. An interval that includes zero for a difference does not establish that the groups are identical; it indicates that the data are compatible with a range that includes no difference as well as differences in either direction.

For the primary comparison, the estimated risk difference is 0.048, and its confidence interval runs from -0.119 to 0.214. This interval crosses zero and includes both a negative and a positive contrast. Thus, the point estimate alone gives an incomplete account: the estimate favors a higher positive-response proportion among adolescents, but the interval allows uncertainty about the direction and size of the group contrast. The relative estimates are also imprecise, as reflected in their wide intervals.

The posted continuous-outcome intervals likewise include zero: the blood glucose monitoring frequency mean difference is -0.5, with an interval from -1.353 to 0.353; the HbA1c mean difference is 0.3, with an interval from -0.448 to 1.048; and the parent-reported diabetes family impact mean difference is 1.6, with an interval from -6.054 to 9.254. These ranges are broad relative to their point estimates, so the observed summaries do not tightly determine the size or direction of group differences.

Precision is limited in part by the number of observations contributing to each outcome, which varies across the posted results. The primary endpoint also depends on follow-up interviews, and its denominator reflects the people reached for interview data. The registry does not report a missing-data model or additional sensitivity analyses that would quantify how unavailable follow-up information might affect the estimates. Accordingly, confidence intervals describe statistical uncertainty under the stated calculations, not all sources of uncertainty in the study.

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 figures are planning illustrations based on the observed effect, not the original design assumptions for this pilot. The registry’s stated planning assumptions are a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation. The planned effect to detect is set at the stated fraction of the observed effect. For the outcomes in these planning displays, figures refer to participants per arm, not total events. The exception specified for time-to-event outcomes does not apply to the posted outcomes.

The 80% power column gives the illustrative number of participants per arm for a future study to have an 80% chance of detecting the specified effect under the planning assumptions, if that effect and the assumptions are correct. The 90% power column gives the corresponding planning figure for a higher chance of detecting that effect. These are design calculations, not results from participants in this pilot. The registry does not report the original sample-size justification or its original design assumptions.

Rows that plan for 75% or 50% of the observed effect use smaller target effects than the row planning for 100% of the observed effect. A smaller effect is harder to distinguish from random variation, so it requires more participants per arm under the same assumptions. The figures are sensitive to the observed effect used as their starting point; an uncertain pilot estimate may not be a reliable forecast of the effect in a future study.

These values are not observed power. Observed power is not an appropriate way to reinterpret the completed results as a probability that the study succeeded. The planning figures instead show how proposed design size changes when the target effect is reduced or when the desired power is raised, while holding the stated calculation assumptions fixed. They should be read as illustrations rather than as a recommendation or as evidence that any particular future sample size is sufficient.

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 = 0.761; treatment proportion p₁ = 0.809; average p̄ = 0.785.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.5813 = 1.1393.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.5803 = 0.4884.
4. n = (1.1393 + 0.4884)² / (0.0476)² = 1,167.3, rounded up to 1,168 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 <- 0.760869565217391                          # control proportion
p1 <- 0.808510638297872                          # 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(2877680)
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 = 1,168 (80% power), 1,563 (90% power)
 75% of observed effect: n per arm = 2,118 (80% power), 2,835 (90% power)
 50% of observed effect: n per arm = 4,857 (80% power), 6,502 (90% power)
power.prop.test: 1167.3 
Simulated power at n = 1,168 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 record does not report a statistical analysis plan specifying a primary testing procedure, an ordering of secondary outcomes, or a method for controlling multiplicity. The listed secondary outcomes cover several domains, including diabetes management, glycemic control, family impact, and communication. When many outcomes are examined, the chance of finding an apparently notable result by chance can increase. Without a stated adjustment or hierarchy, the unadjusted secondary comparisons should be interpreted as exploratory rather than as a coordinated set of confirmatory tests.

The record lists 19 outcome measures and 15 posted secondary outcomes. It reports 0 analyses, and the secondary entries have no reported analyses listed. The registry does not explain this difference or describe whether additional analyses were performed elsewhere. It is therefore not possible to determine from the registry record whether the posted outcomes represent all measured outcomes, all planned analyses, or all analyses conducted.

Missing-data handling is also not reported in the registry record. For the primary endpoint, the record explicitly defines the sample size as the number of people the team could reach and from whom it obtained follow-up interview data. This makes the interview denominator a measure of available follow-up responses, but the reasons that other participants did not contribute interview data are not stated. The registry does not report imputation, weighting, sensitivity analyses, or comparisons between participants with and without follow-up data.

The secondary outcome denominators vary. Without a reported account of missingness, it is not possible to know whether differences in availability reflect outcome-specific measurement, incomplete follow-up, or other reasons. The registry record also does not specify analysis populations such as intention-to-treat or per-protocol populations, nor does it report how participants were handled if they did not use the app as intended. These omissions limit assessment of the robustness and generalizability of the posted comparisons; they do not by themselves establish that the analyses were biased.

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.

The registry reports no serious adverse events, other adverse events, or deaths in either listed group. For the intervention group, 55 were at risk for serious adverse events and 55 for other adverse events; the corresponding event counts were 0 and 0, with 0 deaths. For usual care, 25 were at risk for each adverse-event category; the reported counts were 0 and 0, with 0 deaths. No serious adverse event terms are listed.

These figures describe the adverse-event information entered in the registry, using the stated at-risk denominators. They do not establish that no event of any kind occurred outside the recorded categories or observation period. The record does not provide definitions of adverse events, severity grading details, or further information about how events were collected and assessed. The zero counts should therefore be read as the posted safety summary, not as proof that an intervention is free of risk.

The at-risk counts differ from the outcome-specific sample sizes reported elsewhere, and the record does not explain the relationship between these denominators. Safety counts should not be combined with efficacy or acceptability results, nor should the difference in at-risk counts be interpreted as a comparison of risk without further details about follow-up and event ascertainment.

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

The primary result supports a limited descriptive conclusion: among the parents and adolescents reached for follow-up interviews, the registry reports positive feedback classifications in both groups. The comparison between the parent and adolescent respondent groups has a positive point estimate for the adolescent group, but its confidence interval includes zero and extends in both directions. It therefore does not precisely establish a difference in acceptability between those respondent types.

The primary endpoint is not an outcome comparison between app and usual care. It asks whether feedback about the intervention was coded positively among interview respondents, while the posted comparison contrasts parents with adolescents. It cannot establish that the app improved diabetes management, glycemic control, family communication, or family impact. Those were separate secondary measures, and their posted estimates are uncertain and unadjusted.

The randomized, parallel-group design is relevant to comparisons of intervention and usual care, but the main acceptability result as posted is not itself a randomized treatment-versus-control contrast. The registry information does not give enough detail about recruitment, follow-up, allocation procedures, or missing-data handling to determine how representative the interview respondents were or how robust the estimates are to unavailable data.

The study is described as a pilot, and the outcome findings are preliminary. The registry record does not report the intervention’s original sample-size justification, a full analysis plan, or detailed results for every listed outcome measure. The sample-size illustrations based on observed effects are planning aids and do not demonstrate that the study had a particular probability of detecting an effect.

Thus, the results describe participant reception and offer exploratory information about selected outcomes at the stated follow-up. They do not establish that the app is effective or ineffective, and they do not determine what would happen in a larger or different population. Their clearest statistical contribution is to show the observed acceptability counts and the substantial uncertainty around the group comparison, alongside the limitations of the available outcome and analysis reporting.

17. Why This Trial Matters Statistically

This trial illustrates why a pilot’s acceptability endpoint should be distinguished from clinical effectiveness. An app may be received positively by people who engage with interviews, while evidence about behavior, glycemic control, or family outcomes remains uncertain. Treating those questions separately prevents favorable feedback from being overstated as a clinical effect.

It also shows the importance of reporting the respondent and analysis populations clearly. The primary endpoint denominator is defined by successful follow-up contact and interview data, while secondary denominators vary by measure. Such differences affect how results can be compared and how confidently they can be generalized. Without information about unavailable data, a reader cannot tell whether those who contributed outcomes resemble those who did not.

Another statistical issue is the difference between an estimate and the information it contains. The primary risk-difference estimate is positive, but its confidence interval spans zero and meaningful differences in either direction. The direction of a point estimate should not be treated as a settled finding when the interval is broad. This principle applies to the posted continuous secondary comparisons as well.

The registry also demonstrates why secondary results require context. Multiple outcomes address related but distinct domains, and the record does not describe a multiplicity strategy or a prespecified hierarchy. Unadjusted exploratory comparisons can help guide future questions, but they should not be treated as conclusive findings. Finally, planning calculations based on observed effects can illustrate how future study size changes with the target effect, while remaining distinct from the original design rationale and from observed power.

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.

Several limitations specific to this record constrain interpretation. The primary outcome relies on qualitative interview responses that were classified by the study team as positive, negative, or neutral. The registry does not report the interview instrument, coding manual, coder agreement, or whether coders were masked to assignment. Readers therefore cannot assess the reproducibility of the positive classification from the registry information.

The primary endpoint is based on participants reached for follow-up and interviewed, rather than an explicitly described analysis population covering all randomized participants. The record does not report why some people were not reached, how many declined interviews, or whether follow-up respondents differed from nonrespondents. The secondary outcome denominators also vary, with no registry-reported missing-data approach or sensitivity analysis.

The intervention description gives its broad purpose and components but not the extent of app engagement, fidelity, or exposure. The record also does not give detailed eligibility criteria, recruitment methods, participant characteristics, or care-setting information. Those omissions limit assessment of how well the study population and implementation resemble other families or settings.

The statistical reporting is incomplete for evaluating confirmatory claims: the registry does not report the original sample-size justification, a full statistical analysis plan, a multiplicity procedure, or analysis populations. The secondary analyses are not listed as reported analyses. The recalculated summary comparisons are unadjusted and cannot address covariate imbalance or other participant-level features not represented in the posted summaries.

Finally, the study is a pilot with an acceptability-focused primary endpoint and imprecise comparisons. Its posted findings are useful for describing the recorded responses and identifying uncertainty, but do not settle the effect of the app on clinical or family outcomes. These limitations reflect what is and is not documented in the registry record, rather than evidence that any particular unreported procedure was or was not conducted.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Strengths-Based Behavioral mHealth App for Parents of Adolescents With Type 1 Diabetes-Pilot Study — NCT02877680.
• Registry record last updated 2020-09-24; status last verified 2020-09.
• Results first posted 2020-06-09.
• 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, parallel-group pilot study of the T1Doing Well behavioral smartphone app for parents of adolescents with type 1 diabetes. The primary endpoint counted positive interview feedback at follow-up, classified as indicating that the intervention was well-received. The record reports 35 positive responses among 46 parents and 38 among 47 adolescents; the unadjusted comparison between respondent groups is imprecise.

Posted secondary results cover selected measures of blood glucose monitoring frequency, HbA1c, and parent-reported diabetes family impact. Their unadjusted comparisons are uncertain, and the registry does not report a multiplicity procedure, missing-data method, or full analysis plan. The posted safety summary records zero serious adverse events, other adverse events, and deaths in both listed groups. Overall, the registry offers descriptive pilot findings, not conclusive evidence about clinical effectiveness.