Reading the Numbers Before Collecting Our Own: Data Collection for a Nap Study and What a Published Nap Trial's Results Actually Show
[Student Name]
University of Phoenix
NSG/456: Research Outcomes Management for the Practicing Nurse
Week 4 Assignment
[Instructor Name]
[Date]
The unit and the planned study are a composite written for a model paper.
The study compares one unit that protects a half-hour nap window on nights with a similar unit that does not, using the quasi-experimental design chosen in Week 3. This paper plans how the data would be collected and summarized, then practices interpretation on the numbers reported by two published studies, because a nurse who cannot read other researchers' results cannot judge her own.
Collecting the Data
Sleepiness. Consenting nurses on both units would rate their sleepiness at 0600 on each study shift using the Karolinska Sleepiness Scale, a nine-point scale from 1, extremely alert, to 9, very sleepy, fighting sleep. Åkerstedt and Gillberg (1990) developed the scale and showed that its ratings track objective signs of sleepiness in people who are awake and working, which makes it suitable for a busy unit where no one can stop for a longer test. Ratings would be written on a coded card and dropped in a locked box, so no manager sees them.
Nap details. On the intervention unit, each nurse who naps would record the start and end time, whether she slept and her Karolinska rating before the nap. These records show whether the intervention actually happened, which matters because in the two-hospital project reported by Geiger-Brown et al. (2016), most units never got naps started.
Errors and near misses. The patient safety office would provide a monthly count of medication errors and near misses reported by night-shift staff on each unit, with identifiers removed, as planned in Week 2.
Context. Each unit would record its night census, staffing and any shift on which coverage for a nap was refused.
Summarizing the Data
Karolinska ratings are ordinal, so each unit's ratings would be summarized with the median and interquartile range for the baseline and intervention periods, and compared with a nonparametric test. Error counts would be expressed as rates per 1,000 patient days, since census changes over time, and compared before and after on each unit. Descriptive statistics for the nap log, such as the percentage of shifts on which a nap was taken and the median nap length, would describe how fully the intervention was delivered.
Interpreting a Published Trial
Smith-Coggins et al. (2006) assigned 49 emergency department physicians and nurses at random either to no nap or to a chance to sleep for up to 40 minutes at 0300 during three consecutive night shifts. Polysomnography showed that 90% of the nap group slept, for an average of 24.8 minutes. At 0730, several results favored the nap group.
Fatigue scores on the Profile of Mood States were 7.4 in the nap group and 10.43 without a nap, a mean difference of 3.03, with a 95% confidence interval from 1.11 to 4.95 and p less than .05. The confidence interval does not include zero, so the data are consistent with a real reduction in fatigue, somewhere between about one and five points. That is a clear result.
Performance lapses on the vigilance test averaged 3.13 with a nap and 4.12 without, a difference of 0.99, reported with p less than .03 but with a confidence interval from minus 0.1 to 2.08. Here the numbers need care: an interval that crosses zero is consistent with no difference, yet the p value suggests significance, so a careful reader treats this result as suggestive rather than settled. The difference could come from rounding or from different tests, but the reader should not quote the lapse result as firmly as the fatigue result.
Intravenous insertion time on a simulator was 66.4 seconds with a nap and 86.48 seconds without, with p equal to .10, which is not statistically significant. A 20-second difference might matter in practice, but the study was too small to confirm it.
Finally, the trial reported one result against the nap: immediately after waking, at 0400, the nap group did worse on a memory task. That is sleep inertia, and it is why the Week 2 protocol included a recovery period before medication administration.
Interpreting a Descriptive Nursing Project
Geiger-Brown et al. (2016) offered 30-minute naps on six units in two hospitals; one unit fully implemented them. On that unit, nurses took 153 naps. High sleepiness was present at the start of 44% of naps, 43% of naps produced light sleep and 14% deep sleep, and nurses rated helpfulness at 7.3 on a scale of 1 to 10. These are descriptive statistics from one unit with no comparison group, so they show that napping was feasible and welcome, not that it reduced sleepiness compared with no nap. They are nonetheless useful for my study: they suggest that nurses will often be very sleepy before the nap and that deep sleep, which carries more sleep inertia, is uncommon in a 30-minute window.
Missing Data and Honest Reporting
Real data will have gaps. Nurses will forget to rate their sleepiness on busy nights, and some will stop taking part. Missing ratings are not random: the nights when a nurse is too busy to fill in a card are probably the nights she is most tired. The analysis plan would therefore report how many ratings were missing on each unit in each period, compare the characteristics of complete and incomplete shifts, and avoid quietly dropping incomplete nurses from the analysis. If more than a fifth of expected ratings were missing on either unit, the report would say so prominently, because the results would describe the nurses who kept rating rather than all the nurses on the unit.
Statistical and Clinical Significance
A result can be statistically significant and still too small to matter, or clinically important and still statistically uncertain. For my study, a one-point drop on the nine-point Karolinska scale would be worth attention only if it moved nurses from the sleepy half of the scale into the alert half. I would set that threshold before collecting data, so the results cannot be read to fit a hope.
Conclusion
The planned study would collect coded sleepiness ratings, nap logs, unit-level error rates and context data, summarized with statistics that fit each measure. Reading the published nap studies closely showed a clear effect on fatigue, a less certain effect on vigilance and a real cost in sleep inertia. Week 5 will plan how the findings would be shared.
References
Åkerstedt, T., & Gillberg, M. (1990). Subjective and objective sleepiness in the active individual. International Journal of Neuroscience, 52(1-2), 29-37. https://doi.org/10.3109/00207459008994241
Geiger-Brown, J., Sagherian, K., Zhu, S., Wieroniey, M. A., Blair, L., Warren, J., Hinds, P. S., & Szeles, R. (2016). Napping on the night shift: A two-hospital implementation project. American Journal of Nursing, 116(5), 26-33. https://doi.org/10.1097/01.NAJ.0000482953.88608.80
Smith-Coggins, R., Howard, S. K., Mac, D. T., Wang, C., Kwan, S., Rosekind, M. R., Sowb, Y., Balise, R., Levis, J., & Gaba, D. M. (2006). Improving alertness and performance in emergency department physicians and nurses: The use of planned naps. Annals of Emergency Medicine, 48(5), 596-604. https://doi.org/10.1016/j.annemergmed.2006.02.005
How this NSG 456 Week 4 example is structured
The NSG/456 description names collecting, summarizing and interpreting data as core topics. This paper first plans the study's own data collection, with instruments, timing and the summary statistics each measure would produce, then practices interpretation on published results, because reading other studies' numbers correctly is the skill a practicing nurse uses most. Students search this week as NSG 456 Week 4, NSG456 Wk 4 or NSG/456 Wk 4; all three are the same assignment.
NSG/456 Week 4 questions, answered
What does NSG/456 Week 4 usually ask for?
The course description lists collecting, summarizing and interpreting data as core topics. Many sections ask students to describe how data for their question would be collected and to interpret statistics from a published study.
What does a confidence interval tell a nurse?
The range of values within which the true effect probably lies. If a 95% confidence interval for a difference includes zero, the data are consistent with no difference at all.
Is a statistically significant result always important?
No. Statistical significance says a difference is unlikely to be due to chance; clinical significance asks whether the difference is large enough to matter to patients or nurses.
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