Mobile App for Prenatal Care to Reduce Visits and Improve Satisfaction Study: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Mobile App for Prenatal Care to Reduce Visits and Improve Satisfaction Study (NCT02914301), a non-randomized na study. The primary outcome is Total Visit Number.

2. Clinical Question

The central statistical question is whether the recorded number of visits differs between the two study arms. The registry record identifies this as a prospective, quasi-randomized trial, while its allocation field specifies NON RANDOMIZED. That distinction matters: the comparison is not a randomized estimate, and differences between groups could reflect factors other than app access. The registry itself flags that the arms are not a like-for-like control comparison.

The study description frames the app as a way to provide tailored prenatal educational content and facilitate checks of vital signs and weight between visits. Those proposed functions are rationale, not evidence that the app changed care or outcomes. The primary endpoint was selected after discussions with obstetricians about the relative importance of a reduced visit schedule versus usual care, with patient safety also noted in that rationale. The record does not report the observed visit counts, an effect estimate, or uncertainty around a group comparison, so the clinical question cannot be answered from the registered results.

3. Trial Design

The registry identifies this as a multicenter prospective quasi-randomized trial. Its allocation is recorded as non-randomized, its masking as none, and its model as parallel. The record lists the study as completed, with enrollment of 118. The registered dates are 2015-07 for study start and 2016-03 for primary completion.

The parallel structure means that the record describes separate groups receiving different care approaches rather than a crossover in which the same participant receives both approaches. One arm is named Babyscripts Prenatal App; the other is named Placebo. However, the control-arm description says clinicians would discuss management options and scheduling in their usual fashion, and that patients would not receive the app. This does not describe an app-based placebo experience. The naming and the actual care descriptions should therefore be read separately.

For the app arm, the description says a research assistant would help with app download and setup of a connected weight scale and blood pressure cuff, and would collect baseline demographic and clinical data at enrollment. The control description refers to usual clinician practice and consent for study data collection. The record does not report enough detail to establish whether all other aspects of care were standardized between arms.

Non-random allocation limits the strength of a causal interpretation. In general, randomization is used to balance measured and unmeasured prognostic characteristics across groups on average. Without random allocation, group differences in baseline characteristics, clinician practice, or other influences can contribute to any observed outcome contrast. The registry specifically cautions that the arms are not a like-for-like control comparison. The allocation procedure and any adjustment strategy are not reported in the registry record.

The record lists total visit number as the primary outcome, measured from the first trimester up to postpartum, between 6 and 9 months. Gestational Weight Gain is listed as a secondary outcome. It does not report numerical results for either outcome in the information summarized for this analysis.

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 Total Visit Number, measured over From first trimester up to postpartum, between 6 and 9 months.

The primary endpoint is total visit number, measured in visits. The registry says it would be assessed by reviewing the electronic health record, over the period from the first trimester up to postpartum, between 6 and 9 months. It also states that total visit number was selected after discussions with obstetricians because the advantages and disadvantages of a reduced visit schedule versus usual care were considered important, with patient safety noted as a consideration.

As a count of visits, this endpoint captures the amount of in-person care recorded over the specified period. In general, a group mean can describe the average number of visits, while a standard deviation describes how spread out individual counts are around that mean. The registry classifies the endpoint as a continuous measure summarized by a mean and standard deviation. This is the stated summary approach, not a numerical finding: the registry record does not report the arm-specific means, standard deviations, or a between-arm effect estimate.

The endpoint does not by itself describe the appropriateness or quality of each visit, the clinical reasons for visits, or whether a change in visit frequency was safe. Nor does a total count distinguish scheduled visits from other encounters unless the outcome definition specifies that distinction; additional detail on the exact counting rules is not reported in the registry record. The app’s proposed functions and the stated objective of reducing visits are not substitutes for observed endpoint data. Without reported outcome estimates and uncertainty, the registered endpoint definition alone cannot establish whether visit number differed between the groups.

7. Why No Re-analysis Is Shown

8. Statistical Interpretation

9. Statistical Methods Explained

The registry classifies total visit number as a continuous outcome summarized by a mean and standard deviation. Although visit number is a count, the registered outcome specification uses this summary. In general, the mean describes the average observed value, and the standard deviation describes variability among individual observations. Neither quantity alone shows how certain a comparison between groups is. For that, an analysis would typically report an estimate of the group contrast and an uncertainty measure, such as a confidence interval. Those numerical results are not reported in the registry record.

What does a mean comparison describe?

In general, a mean comparison summarizes the difference between group averages on the outcome’s original scale. For this endpoint, that scale is visits. Such a contrast is easy to interpret as an average change in visit count between groups, but it does not show the full distribution of individual counts. Two groups could have similar averages but different variability or different patterns in the tails. The registry specifies a mean and standard deviation, but it does not report the observed values or a model used to compare them.

Why might count outcomes need special consideration?

General statistical guidance is that count outcomes can have features that matter for model choice. Counts cannot take negative values and can be unevenly distributed. Some count data also show over-dispersion, meaning that variability is greater than a chosen model would expect from its mean. When over-dispersion is present, a model that assumes less variability may produce uncertainty estimates that are too narrow. This is a general consideration only: the registry record does not report a distributional assessment, a model for count data, or whether over-dispersion was evaluated. It would be inappropriate to infer which analysis was used.

What does non-random allocation change?

In general, random allocation helps make the groups comparable in expectation at the point of assignment. Non-random allocation does not provide that same protection. Differences in baseline health, care-seeking, clinician practice, or other characteristics could be associated with both group membership and visit number. Statistical adjustment can sometimes address measured differences, but it cannot automatically remove confounding from unmeasured factors. The registry records allocation as non-randomized and warns that the arms are not a like-for-like control comparison. The allocation process, baseline balance, and any covariate adjustment are not reported in the registry record.

What can a confidence interval add?

In general, a confidence interval communicates the uncertainty around an estimated effect under the assumptions of the analysis. A narrow interval indicates greater precision than a wide interval, while the interval’s location indicates which effects remain compatible with the estimate and its method. An interval is not a guarantee that the true effect lies within its limits, and its interpretation depends on the analysis assumptions. No confidence interval for the primary endpoint is reported in the registry record. Statistical significance, clinical importance, and precision are distinct considerations; the record does not supply the numerical results needed to assess any of them.

How should planning illustrations be understood?

The record describes planning illustrations based on the observed effect, using a two-sided significance level of 0.05, equal allocation between arms, and a normal approximation. It describes effects to detect as stated fractions of the observed effect. In general, sample-size calculations estimate the number of participants needed under specified assumptions to reach a chosen power for a target effect. They do not show that a study actually achieved that power, and they do not establish the observed effect. These figures are planning illustrations, not observed power. The record does not report the numerical illustration results in the information summarized for this analysis, nor does it report the trial’s own sample-size justification. The illustrations should not be confused with evidence that the enrolled study could detect a particular effect.

In general, the assumptions in a planning calculation matter: the target effect, variability, allocation, significance level, and analysis model all influence the estimate. Here the stated calculation assumptions include equal allocation and a normal approximation, but actual assignment was non-randomized. This contrast between an illustration’s assumptions and the registered allocation is a reason to read planning calculations as hypothetical guidance, not as a description of the trial’s analysis or design. The registry does not report whether the planning illustration assumptions correspond to the observed groups.

Finally, analysis choices must be judged alongside the outcome definition, missing-data handling, and population included in analysis. Those details can affect both the estimate and its uncertainty. The registry record does not report a statistical analysis plan, the final analysis population, or the handling of missing primary endpoint data. In the absence of those details and numerical outcome results, the statistical methods used to produce a primary comparison cannot be reconstructed from the registry record.

10. Confidence Intervals for Each Arm

No two-arm comparison is shown for this trial, but each arm's mean can be given with a t-based 95% confidence interval computed from the posted mean, spread and number of participants.

R code and output

arm <- c("Babyscripts Prenatal App", "Placebo")
n   <- c(45, 51)
m   <- c(7.9, 10.2)
sp  <- c(1.8, 1.8)
sd  <- sp      # posted standard deviations
for (i in seq_along(n)) {
  se <- sd[i] / sqrt(n[i]); h <- qt(0.975, n[i] - 1) * se
  cat(sprintf("CARM|%s|%d|%.3f|%.3f|%.3f|%.3f\n", arm[i], n[i], m[i], sd[i], m[i] - h, m[i] + h))
}

CARM  Babyscripts Prenatal App  45  7.900  1.800  7.359  8.441
CARM  Placebo  51  10.200  1.800  9.694  10.706

11. Multiplicity, Missing Data and Analysis Populations

The registry lists total visit number as the primary outcome and Gestational Weight Gain as a secondary outcome. It also records two outcome measures and one analysis. These administrative counts do not establish how many statistical tests were conducted, which outcomes were included in the reported analysis, or whether any multiplicity adjustment was planned. The testing hierarchy and handling of multiple comparisons are not reported in the registry record.

In general, testing several outcomes or several contrasts can increase the chance of a false-positive finding if each is interpreted as a separate confirmatory test. A prespecified primary outcome can help clarify which question is central, while adjustment or other prespecified strategies may address multiplicity. The registry identifies a primary endpoint, but the statistical plan for distinguishing confirmatory from exploratory results is not reported. No numerical outcome results or p-values are available in the record summarized for this analysis, so multiplicity cannot be evaluated from observed findings.

Missing-data methods are also not reported in the registry record. The outcome was to be measured through electronic health record review, but the record does not specify how incomplete records, missing visits, or participants without a complete measurement period would be handled. In general, missingness can affect an estimate when availability of outcome data is related to participant characteristics or outcomes. The amount and pattern of missing primary endpoint data, and any sensitivity analyses, are not reported.

The analysis population is not described. The record does not state whether the primary analysis included all enrolled participants, only those with available endpoint data, or another defined group. It does not report protocol deviations or exclusions from the analysis. These omissions limit assessment of how the registered population relates to the population included in any analysis. A completed-study status and enrollment figure do not, by themselves, establish the analysis denominator or completeness of follow-up.

12. 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 adverse-event record lists the app arm with 45 at risk, zero serious adverse events, zero other adverse events, and zero deaths. The Placebo arm is listed with 51 at risk, zero serious adverse events, zero other adverse events, and zero deaths. No serious adverse-event terms are listed.

These are the safety counts recorded in the registry, not proof that no adverse events occurred outside the record’s definitions or observation process. The record does not describe how events were collected, the duration or completeness of safety follow-up, or whether the listed at-risk counts represent all participants enrolled in each arm. The safety denominators should therefore be read as the counts attached to the adverse-event entries, not as group sizes inferred from the overall enrollment.

In general, an observed count of zero means that no events of that specified type were recorded in the reported data; it does not establish that the underlying risk is zero. Sparse or absent events can also leave substantial uncertainty about the frequency of uncommon outcomes. No further event-level details or safety estimates are reported in the registry record. The study’s stated aim of reducing visits safely cannot be established by these counts alone, especially without numerical primary outcome results and fuller safety methods.

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

The registry record establishes that a prospective, multicenter study compared a prenatal care app approach with a usual-care approach and identified total visit number as its primary endpoint. It also records that the study was completed and that enrollment was 118. The record does not provide the primary outcome estimates needed to determine how many visits were observed in either arm or how the group averages compared.

Accordingly, the posted information does not establish whether total visit number was lower, higher, or similar in the app arm. It also does not establish the precision of a possible difference or whether a difference would be clinically important. The stated objective of reducing visits is a research aim, not a reported result. The same distinction applies to the proposed app functions, including educational content and checks of weight and vital signs between visits: they describe the intervention rationale rather than measured effects.

Even if a numerical group contrast were available, the non-randomized allocation would limit causal interpretation. Differences between groups might arise from factors other than the intervention, and the registry cautions that the arms are not a like-for-like control comparison. The record does not report an adjustment strategy or enough information to assess baseline comparability. Therefore, an unadjusted difference, if reported elsewhere, would not by itself show that app access caused the difference.

The safety entries list no serious adverse events, other adverse events, or deaths in either recorded safety group. Those entries are limited to the safety information in the registry and do not establish that the approach is safe in all settings or for all participants. The record does not report the listed satisfaction objective as a numerical outcome, and Gestational Weight Gain is identified as secondary without a numerical result in the information summarized for this analysis.

The appropriate reading is therefore descriptive and bounded: the registry specifies a question, intervention, comparator, design, and outcome, but does not report enough outcome data to answer the central comparison. No conclusion about benefit, lack of benefit, or safety can be drawn from the endpoint definition or study objective alone.

14. Why This Trial Matters Statistically

This trial raises a useful statistical question because it measures care utilization rather than a laboratory value or a single clinical event. Total visit number is directly interpretable as an average count, but its meaning depends on what encounters were counted and on the period of observation. The registry gives the general measurement period and says the electronic health record would be reviewed, while the exact counting rules are not reported in the registry record.

The study also illustrates why the design should shape interpretation. A non-randomized comparison may be vulnerable to differences in patient characteristics and clinical practice between groups. That concern is especially relevant when the outcome is a care-use measure, because visit frequency may reflect clinical need, access, clinician decisions, and follow-up practices in addition to the intervention. The registry explicitly warns that the arms are not a like-for-like control comparison.

Statistically, the outcome is specified as a mean with standard deviation, although visits are counted. General methods for count outcomes can require attention to distribution and variability, including possible over-dispersion. The registry record does not report which analytical model was used or whether these features were assessed. This makes transparent reporting of the estimate, uncertainty, outcome distribution, and analysis approach especially important.

The record states that total visit number was prioritized after discussion with obstetricians, while Gestational Weight Gain was listed as a secondary outcome. That helps identify the intended primary question. Yet the absence of numerical estimates and analysis details means readers cannot assess effect size, precision, missingness, or the relationship between the planned question and reported findings. The statistical importance of the case lies in how clearly it shows that a completed trial record and a defined endpoint are not substitutes for interpretable outcome reporting.

15. 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.

A key limitation is that numerical results for the primary outcome are not reported in the registry record summarized for this analysis. Without arm-specific means, standard deviations, an effect estimate, or its uncertainty, the primary comparison cannot be evaluated. Numerical results for the listed secondary outcome are also not reported in the information.

The non-randomized allocation is an additional limitation. The registry cautions that the arms are not a like-for-like control comparison, and the allocation procedure is not reported. The record also does not report baseline characteristics by arm or an adjustment method. These gaps prevent assessment of how well the groups were comparable or whether an analysis addressed measured differences.

The arm naming may cause confusion: one group is called Placebo, but its description specifies usual clinician practice and no access to the app. The record does not describe a matching app-based placebo. The app-arm description includes setup of a connected scale and blood pressure cuff, so the intervention includes more than app content alone as described. The record does not report whether all participants used the equipment or how adherence was assessed.

The outcome definition is limited in detail. It names total visit number and a broad period extending from the first trimester up to postpartum, between 6 and 9 months, but does not report exact counting rules or how missing electronic health record data would be handled. The analysis population, multiplicity approach, statistical model, and sensitivity analyses are not reported in the registry record.

Finally, the safety entries contain zero recorded adverse events and deaths in each listed group, but the event collection procedures and follow-up completeness are not reported. These safety counts do not resolve the stated safety question or establish the completeness of adverse-event ascertainment.

16. Related Tutorials

17. Related Statistical Calculators

18. Sources

• ClinicalTrials.gov: Mobile App for Prenatal Care to Reduce Visits and Improve Satisfaction Study — NCT02914301.
• Registry record last updated 2022-12-16; status last verified 2022-11.
• Results first posted 2022-12-16.
• 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.

19. Record Summary

The registry record describes a completed, multicenter prospective quasi-randomized trial with non-randomized allocation, no masking, and a parallel model. Enrollment is recorded as 118. The intervention is the Babyscripts Prenatal App, compared with an arm named Placebo whose description specifies usual clinician practice without app access.

Total visit number, measured through electronic health record review from the first trimester up to postpartum, between 6 and 9 months, is the primary endpoint. Gestational Weight Gain is listed as a secondary outcome. The record describes the trial’s aims and measurement approach but does not report numerical results for the primary outcome in the information summarized for this analysis. It also cautions that the arms are not a like-for-like control comparison. Safety entries list zero serious adverse events, zero other adverse events, and zero deaths in each recorded group.