Nine studies of nighttime sleep aids and ICU delirium sit in rows of design, sample, analysis, effect with interval and limitations in this NU504 Unit 7 evidence table, with writer-computed effects flagged. Searches like "nu 504 unit 7 assignment example", "nu504 unit 7 sample" and "nu504 unit 7 example" land here.
Evidence Table: Overnight Earplugs, Eye Masks and Sleep Bundles for Delirium and Sleep in Adult ICU Patients, Nine Studies Grouped by Design
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Purdue University Global
NU504: Scientific and Analytic Approaches to Advanced Evidence-Based Practice
Unit 7 Assignment
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Effects marked "computed" were calculated by the writer from counts or percentages the article reports.
Randomized Trials
1. Van Rompaey et al. (2012). Sample: 136 alert adults, Belgian university ICU; sedated patients excluded. Intervention: foam earplugs 2200 to 0600, up to five nights, against no earplugs. Measure: NEECHAM Confusion Scale three times daily; five-question sleep perception. Analysis: survival analysis and Cox regression with stepwise covariates. Effect: delirium or mild confusion, HR 0.47 (95% CI, 0.27-0.82); delirium alone, computed relative risk about 0.97, 0.49 to 1.94, from reported percentages of 19 and 20 percent. Limitations, author-stated: single center, short stays. Writer-found: combined outcome; delirium alone unchanged; longer observation in the earplug group. Supports: association with less early mild confusion; no support for less delirium.
2. Demoule et al. (2017). Sample: 64 adults, French ICU. Intervention: earplugs and eye mask from inclusion to discharge. Measure: polysomnography on the first night. Analysis: nonparametric comparisons of medians. Effect: N3 proportion 21 against 11 percent, p = 0.09, no interval reported; prolonged awakenings 21 against 31, p = 0.02. Limitations, author-stated: 30 percent did not wear earplugs all night. Writer-found: many secondary outcomes; per-protocol comparison; small sample. Supports: description of tolerability; weak association with fewer long awakenings.
3. Cardiac surgical ICU trial (Hu, Jiang, Hegadoren, & Zhang, 2015). Sample: 50 randomized, 45 analyzed, after cardiac surgery. Intervention: earplugs, eye masks and 30 minutes of relaxing music. Measure: Richards-Campbell Sleep Questionnaire; urinary melatonin and cortisol. Analysis: between-group comparisons. Effect: better perceived depth of sleep, falling asleep and overall quality, p < 0.05, no interval reported; no hormonal differences. Limitations, author-stated: timing of surgery and medication may mask hormonal effects. Writer-found: five lost after randomization; three components combined; no delirium outcome. Supports: association with better perceived sleep after cardiac surgery.
Before-and-After Studies
4. Patel et al. (2014). Sample: 167 before and 171 after, medical and surgical ICU. Intervention: noise and light reduction bundle at night, compliance above 90 percent. Measure: CAM-ICU; sleep efficiency. Analysis: chi-square and regression. Effect: delirium 33 against 14 percent; computed relative risk 0.43, 0.28 to 0.65. Limitations, writer-found: historical control; other practice changes over time; no blinding. Supports: association.
5. Kamdar et al. (2013). Sample: 300 medical ICU patients, 122 baseline and 178 intervention. Intervention: multicomponent sleep bundle with daily checklists. Measure: delirium or coma; Richards-Campbell questionnaire. Analysis: multivariable regression. Effect: delirium or coma, odds ratio 0.46, 0.23 to 0.89; perceived sleep quality not significantly improved. Limitations, writer-found: odds ratio for a common outcome overstates relative change; delirium combined with coma. Supports: association.
6. Tonna et al. (2021). Sample: 646 admissions, 332 baseline and 314 intervention, two surgical ICUs. Intervention: bundle including reduced noise and light, earplugs and eye masks, and daytime elements, with a daily checklist. Measure: CAM-ICU twice daily; Richards-Campbell questionnaire. Analysis: staggered pre-post, adjusted. Effect: proportion of ICU days with delirium 15 against 20 percent; adjusted decrease 4.9 percentage points, 0.5 to 9.2. Perceived sleep quality unchanged. Limitations, writer-found: bundle cannot separate the earplugs' contribution. Supports: association.
7. Jones and Dawson (2012). Sample: 50 before and 50 after, general critical care unit. Intervention: eye masks and earplugs offered. Measure: Likert ratings of sleep quality and quantity; open questions. Analysis: descriptive. Effect: not reported as an estimate; noise named as the main barrier to sleep by about half of each group. Limitations, writer-found: no delirium outcome; descriptive only. Supports: description.
Systematic Reviews
8. Litton et al. (2016). Nine studies, 1,455 participants; five with delirium outcomes, 832 participants. Effect: pooled relative risk of delirium 0.59, 0.44 to 0.78, with I-squared 39 percent. Limitations, author-stated: all studies at high risk of bias; cointerventions. Writer-found: randomized and before-and-after studies pooled together; includes the combined outcome from row 1. Supports: association, at low certainty.
9. Cochrane review (Hu, Jiang, Chen, et al., 2015). Review of 30 trials testing sleep promotion without drugs (1,569 patients). Effect: for earplugs or eye masks or both, delirium relative risk 0.55, 0.38 to 0.80, from two trials with 177 participants; total sleep time 2.19 hours longer, 0.41 to 3.96, from two trials. Limitations, author-stated: evidence of low or very low quality. Supports: association, at low certainty.
Notes on the Table
Effects were standardized to relative risk where counts allowed, so rows can be scanned for direction and precision on one scale; odds ratios and hazard ratios that could not be converted are labeled as such. The analysis entries matter because a Cox model, a logistic regression and a simple comparison of proportions produce numbers that mean different things. Blank effects are marked not reported rather than left empty.
References
Demoule, A., Carreira, S., Lavault, S., Pallanca, O., Morawiec, E., Mayaux, J., Arnulf, I., & Similowski, T. (2017). Impact of earplugs and eye mask on sleep in critically ill patients: A prospective randomized study. Critical Care, 21(1), 284. https://doi.org/10.1186/s13054-017-1865-0
Hu, R.-F., Jiang, X.-Y., Chen, J., Zeng, Z., Chen, X. Y., Li, Y., Huining, X., Evans, D. J., & Wang, S. (2015). Non-pharmacological interventions for sleep promotion in the intensive care unit. Cochrane Database of Systematic Reviews, 2015(10), CD008808. https://doi.org/10.1002/14651858.CD008808.pub2
Hu, R.-F., Jiang, X.-Y., Hegadoren, K. M., & Zhang, Y.-H. (2015). Effects of earplugs and eye masks combined with relaxing music on sleep, melatonin and cortisol levels in ICU patients: A randomized controlled trial. Critical Care, 19(1), 115. https://doi.org/10.1186/s13054-015-0855-3
Jones, C., & Dawson, D. (2012). Eye masks and earplugs improve patient's perception of sleep. Nursing in Critical Care, 17(5), 247-254. https://doi.org/10.1111/j.1478-5153.2012.00501.x
Kamdar, B. B., King, L. M., Collop, N. A., Sakamuri, S., Colantuoni, E., Neufeld, K. J., Bienvenu, O. J., Rowden, A. M., Touradji, P., Brower, R. G., & Needham, D. M. (2013). The effect of a quality improvement intervention on perceived sleep quality and cognition in a medical ICU. Critical Care Medicine, 41(3), 800-809. https://doi.org/10.1097/CCM.0b013e3182746442
Litton, E., Carnegie, V., Elliott, R., & Webb, S. A. R. (2016). The efficacy of earplugs as a sleep hygiene strategy for reducing delirium in the ICU: A systematic review and meta-analysis. Critical Care Medicine, 44(5), 992-999. https://doi.org/10.1097/CCM.0000000000001557
Patel, J., Baldwin, J., Bunting, P., & Laha, S. (2014). The effect of a multicomponent multidisciplinary bundle of interventions on sleep and delirium in medical and surgical intensive care patients. Anaesthesia, 69(6), 540-549. https://doi.org/10.1111/anae.12638
Tonna, J. E., Dalton, A., Presson, A. P., Zhang, C., Colantuoni, E., Lander, K., Howard, S., Beynon, J., & Kamdar, B. B. (2021). The effect of a quality improvement intervention on sleep and delirium in critically ill patients in a surgical ICU. Chest, 160(3), 899-908. https://doi.org/10.1016/j.chest.2021.03.030
Van Rompaey, B., Elseviers, M. M., Van Drom, W., Fromont, V., & Jorens, P. G. (2012). The effect of earplugs during the night on the onset of delirium and sleep perception: A randomized controlled trial in intensive care patients. Critical Care, 16(3), R73. https://doi.org/10.1186/cc11330
How this NU504 Unit 7 example is structured
Grouping by design keeps comparisons fair before any synthesis begins, with randomized trials first, then before-and-after studies, then the two reviews. Each row records the analysis used, because a Cox model, a logistic regression and a comparison of proportions produce numbers that mean different things. Effects are standardized to relative risk where the published counts allow, and those the writer computed are marked, including the before-and-after study's relative risk of 0.43 and an approximate figure for delirium alone in the earplug trial; hazard and odds ratios that could not be converted keep their labels. Limitations are split into author-stated and writer-found, and the last entry in each row states the claim it supports: cause, association or description.
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Attach the studies, or the table template and citation list from your course, together with the Unit 7 instructions and rubric. Every row returns with its analysis named, effects on a common scale where the counts allow, and limitations split between what authors admitted and what appraisal found. Free first custom sample, 24-48h. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
NU504 Unit 7 questions, answered
Can I calculate an effect size that the study did not report?
Often, if the paper gives the raw counts or means and standard deviations. A relative risk, risk difference or mean difference with its interval can be computed from those figures with standard formulas or a free online calculator. Mark every computed value clearly and say how it was obtained, so a reader never mistakes it for a number the authors published.
Why include the analysis method in an evidence table?
Because the same outcome can be summarized in ways that mean different things. A crude comparison, a regression adjusted for severity and a time-to-event model can give different estimates from one dataset. Knowing which was used tells a reader whether confounding was addressed and how to compare the row with others. In an analytic course it is frequently a required column.
Should the systematic review get its own row?
Yes, with care. A review's pooled estimate usually includes some of the same trials already in the table, so counting both as separate support double-counts evidence. The row should note which included trials overlap and report the pooled estimate with its confidence limits and a heterogeneity statistic such as I-squared. Many writers place reviews in a separate block for exactly this reason.