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Reducing the impact of intensive care unit mattress compressibility during CPR: a simulation-based study Lin, Yiqun; Wan, Brandi; Belanger, Claudia; Hecker, Kent; Gilfoyle, Elaine; Davidson, Jennifer; Cheng, Adam Nov 16, 2017

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RESEARCH Open AccessReducing the impact of intensive care unitmattress compressibility during CPR: asimulation-based studyYiqun Lin1* , Brandi Wan2, Claudia Belanger3, Kent Hecker4, Elaine Gilfoyle5, Jennifer Davidson6 and Adam Cheng6AbstractBackground: The depth of chest compression (CC) during cardiac arrest is associated with patient survival andgood neurological outcomes. Previous studies showed that mattress compression can alter the amount of CCsgiven with adequate depth. We aim to quantify the amount of mattress compressibility on two types of ICUmattresses and explore the effect of memory foam mattress use and a backboard on mattress compression depthand effect of feedback source on effective compression depth.Methods: The study utilizes a cross-sectional self-control study design. Participants working in the pediatric intensivecare unit (PICU) performed 1 min of CC on a manikin in each of the following four conditions: (i) typical ICU mattress;(ii) typical ICU mattress with a CPR backboard; (iii) memory foam ICU mattress; and (iv) memory foam ICUmattress with a CPR backboard, using two different sources of real-time feedback: (a) external accelerometer sensordevice measuring total compression depth and (b) internal light sensor measuring effective compression depth only.CPR quality was concurrently measured by these two devices. The differences of the two measures (mattresscompression depth) were summarized and compared using multilevel linear regression models. Effective compressiondepths with different sources of feedback were compared with a multilevel linear regression model.Results: The mean mattress compression depth varied from 24.6 to 47.7 mm, with percentage of depletion from 31.2to 47.5%. Both use of memory foam mattress (mean difference, MD 11.7 mm, 95%CI 4.8–18.5 mm) and use ofbackboard (MD 11.6 mm, 95% CI 9.0–14.3 mm) significantly minimized the mattress compressibility. Use of internallight sensor as source of feedback improved effective CC depth by 7–14 mm, compared with external accelerometersensor.Conclusion: Use of a memory foam mattress and CPR backboard minimizes mattress compressibility, but depletion ofcompression depth is still substantial. A feedback device measuring sternum-to-spine displacement can significantlyimprove effective compression depth on a mattress.Trial registration: Not applicable. This is a mannequin-based simulation research.Keywords: Cardiopulmonary resuscitation, Quality, Resuscitation, Chest compressions, Mattress* Correspondence: jeffylin@hotmail.com1KidSIM-ASPIRE Simulation Research Program, Alberta Children’s Hospital,University of Calgary, 2888 Shaganappi Trail NW, Calgary, AB T3B 6A8,CanadaFull list of author information is available at the end of the article© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Lin et al. Advances in Simulation  (2017) 2:22 DOI 10.1186/s41077-017-0057-yBackgroundEach year, approximately 209,000 people are treated for in-hospital cardiac arrest (IHCA) in the USA, with a survivalrate of 22.7% in adults and 36.8% in children in 2014 [1].Cardiopulmonary resuscitation (CPR) is a critically import-ant treatment for cardiac arrest. High-quality CPR saveslives and directly influences patient outcomes [2–5]. Chestcompression (CC) depth is highly associated with patientsurvival and good neurological outcomes [4, 6, 7]. However,the quality of CPR provided in both simulated [8] and real[9, 10] cardiac arrest events is often inadequate.Providing CPR on a mattress further compromises CCdepth compliance during IHCA. Previous studies investi-gating mattress deflection on intensive care unit (ICU)mattresses in both mannequin-based [11, 12] and realpatient [13] studies show that mattress compressionoccurs during CPR and can reduce the proportion ofCCs given with adequate depth. Once mattress compres-sion is adjusted for, the proportion of CCs with appro-priate depth decreases drastically [13, 14]. Furthermore,the total vertical hand movement is significantly largerthan sternum-to-spine compression depth when CPR isperformed on a mattress [12]. The additional motionand increased workload causes provider fatigue, whichpotentially impacts quality of CPR during the manage-ment of IHCA.The use of a backboard underneath the patient duringresuscitation has been shown to partly attenuate thecompression effect of the mattress. Backboard use re-duces the additional vertical hand movement of CPRproviders and mattress compression depth [12], resultingin improved CC depth and percentage of CC with ad-equate depth [12, 15–19]. Other techniques that increasethe rigidity of the mattress, such as the use of a mattresscompression cover and a vacuum pump, have alsoshown to improve the effective compression depth [20].Mattress compressibility has primarily been discussed asit relates to development of pressure sores in critically illpatients [21]. For this reason, it has been felt thatcompressible mattresses were potentially preferable forcritically ill patients. It is vital, therefore, that we under-stand the relative risks of a very compressible mattresson CPR quality so the relative risks and benefits withrespect to pressure sores and CPR quality can be deter-mined. Some new ICU mattresses have memory foam toreduce overall compressibility. The effect of memoryfoam on CC depth has not been described.Although the use of real-time feedback has beenshown to improve quality of CPR in both training andclinical environments [8, 22], the use of single force anddeflection sensor fails to adjust mattress compressibilityand may overestimate compression depth when CPR isperformed on a mattress. This may compromise effectivecompression depth [11, 13]. No prior studies haveexamined the impact of feedback source on CC depthwhen provided in different mattress contexts.In this study, we aim to (i) quantify the amount ofdepletion when CPR is performed on two differentICU mattresses, with or without use of backboard; (ii)explore factors (i.e., mattress type, use of backboard)associated with mattress compression depth; and (iii)explore the effect of feedback sources on effectivecompression depth by pediatric intensive care unit(PICU) healthcare providers.MethodsWe conducted a cross-sectional simulation-based studyin the KidSIM Simulation Center at Alberta Children’sHospital, an academic tertiary care healthcare facility inCalgary, Canada. Institutional review board approval wassecured from the University of Calgary Conjoint HealthResearch Ethics Board and informed consent wasobtained from all participants. We report our study inaccordance with reporting guidelines for simulation-based research [23].ParticipantsHealthcare professionals from Alberta Children’s HospitalPICU were recruited to participate in the study. Inclusioncriteria included (a) PICU healthcare providers (nurse,nurse practitioner, attending physician, respiratory therap-ist, resident, and fellow) and (b) receipt of Basic LifeSupport (BLS), Pediatric Advanced Life Support (PALS),and/or Advanced Cardiac Life Support (ACLS) certifica-tion within the past 2 years. Participants were excluded ifthey were unable to perform CC due to physical and/ormedical reasons.Outcome measuresWe used Laerdal Resusci Anne QCPR™ mannequin andthe Laerdal CPR Meter™ to concurrently collect data forCC depth. The Laerdal Resusci Anne QCPR™ manne-quin (internal device) measures the absolute sternum-to-spine displacement of the mannequin with a light sensorlocated inside, representing actual CC depth provided tothe mannequin (i.e., effective compression depth). TheLaerdal CPR Meter™ (external device) is an accelerom-eter sensor-based device placed on the chest of the man-nequin during CC, which measures the total verticalhand movement delivered by the healthcare providersduring CPR. The total vertical movement is the sum ofeffective compression depth and the amount of mattresscompressibility (i.e., total compression depth; see Fig. 1).The primary outcome measure is the amount of mat-tress compression that occurred during CPR (i.e., mat-tress compression depth), calculated by the difference inmeasured CC depth between the mannequin’s internaldevice and the external device. The secondary outcomeLin et al. Advances in Simulation  (2017) 2:22 Page 2 of 8is the effective compression depth measured by the in-ternal device.Study proceduresAll participants completed a demographic survey upon re-cruitment into the study. Participants performed CC on aLaerdal Resusci Anne QCPR™ mannequin (weight = 8.5 kg)placed on an ICU bed (Hill-Rom 1000™ medical surgicalbed) in each of the following four conditions: (i) on a typicalICU mattress (Advance 1000™ foam) only, (ii) on a typicalICU mattress with a CPR backboard, (iii) on a memoryfoam ICU mattress (Hill-Rom Accumax Quantum™ VPCMattress) only, and (iv) on a memory foam ICU mattresswith a CPR backboard. Participants were asked to performhigh-quality CPR with a compression depth of 5–6 cm, at arate of 100–120 per minute, with full chest recoil betweencompressions, in compliance with 2015 CPR guidelines[24–26]. Participants repeated each scenario twice and re-ceived real-time visual feedback for CPR quality providedby one of two different sources: (a) Laerdal CPR Meter™(external device: accelerometer sensor) and (b) LaerdalSkillreporter™ (internal device: light sensor), in a randomorder generated by an online random number generator(8 min of chest compression in total). Between two 1-minchest compression sessions, each participant had opportun-ity to rest and rehydrate for 3–5 min to avoid fatigue.We standardized the simulation-based research environ-ment to reduce risk of bias [27]. We utilized the ICU mat-tresses with same size (213.4 cm × 90.2 cm × 15.2 cm) foreach participant. The CPR backboard (hard plastic;53.5 cm × 38 cm× 1 cm) used in conditions (ii) and (iv)was placed in a longitudinal orientation with the long axisof the backboard stretching down the length of the mat-tress. In all four conditions, the ICU mattress was placedon a bed at a fixed height of 75 cm. Participants providedcompressions on a 23-cm high stepstool to optimize CCdepth [28]. The study was conducted in a quiet room withno external stimuli. There were no observers in the roomother than one or two research assistants. Participantswere allowed to see the summary of their CPR perform-ance at the end of each 2-min cycle.Sample sizeSample size estimation assumed that the main effectsof memory foam mattress and backboard on mattresscompression depth would comprise two comparisons.Allowing for Bonferroni adjustment, a significancelevel of 0.025 and power of 0.8 were used. We usedindividual chest compression as the unit of measurewith each participant providing at least 100 compressionsin each session. Assuming a standard deviation of 10 mmin compression depth, a total of 16 participants would de-tect a difference of 10 mm in mattress compression depth,even with a high correlation (ρ = 0.8) between repeatedmeasures. The sample size allows us to detect a differenceof 7 mm in effective compression depth with a signifi-cance level of 0.05 and power of 0.8.Statistical analysisAll analyses were conducted with R software (version3.3.2, available at www.r-project.org) with “lme4” pack-age [29]. To quantify the amount of mattress compress-ibility, we used data when internal device was used as asource of feedback (measuring effective CC depth). Wesummarized compression depth collected by an externaldevice and an internal device as well as their differenceswith descriptive statistics (mean and standard deviation).Percentage of depletion was calculated (differencedivided by total compression depth) in all four scenarios.To evaluate the effect of memory foam mattress use andbackboard use on mattress compression depth, we usethe same data to conduct mixed effect linear regressionanalyses. To evaluate the effect of feedback source oneffective compression depth, we conducted a mixedFig. 1 Description of study setting. Legend external device—measuring total displacement (effective compression depth+mattress compressiondepth); internal light sensor—measuring sternum-to-spine displacement of the mannequin (effective compression depth); mattress compressiondepth = total compression depth—effective compression depthLin et al. Advances in Simulation  (2017) 2:22 Page 3 of 8effect linear regression analysis with random interceptand slopes, adjusting for use of backboard and types ofmattress, with all data collected.ResultsStudy populationData from total of 12,101 individual compressionswere analyzed. Compressions provided by 16 partici-pants were included in the analysis, each of whomperformed CPR for all four study conditions with twodifferent sources of feedback. Three males (19%) and13 females (81%) participated, including 3 physicians(19%), 10 nurses (62%), and 3 respiratory therapists(19%). All participants had Basic Life Support (BLS)and Pediatric Advanced Life Support (PALS) certifica-tion within the past 2 years and were actively in-volved in simulation-based training and/or research.Amount of chest compression depletionWhen spine-to-sternal displacement was used asource of feedback, all healthcare providers performedguideline compliant chest compression (mean effectivecompression depth 51–54 mm). The mattress com-pression depth was 47.7 ± 18.7 mm (47.7%) [mean ±SD (percentage of depletion)] with typical mattressonly, 34.8 ± 11.7 mm (40.3%) when the typical mat-tress was used with a CPR backboard, 34.7 ± 5.4 mm(39.2%) when a memory foam mattress was used, and24.6 ± 5.5 mm (31.1%) when a memory foam mattresswas used with a CPR backboard (see Fig. 2).Effect of foam mattress and use of backboard ondepleted depthUsing a memory foam mattress decreased the mattresscompression depth by 11.7 (95% CI 4.8–18.5) mm. Theeffect of a memory foam mattress was more pronouncedwithout use of CPR backboard (13.3, 95% CI 6.4–20.2 mm) compared to with CPR backboard use (10.0,95% CI 3.2–16.9 mm).Use of a CPR backboard decreased the depleted com-pression depth by 11.6 (95% CI 9.0–14.3) mm. The effectof backboard use was greater with a typical mattress(13.3, 95% CI 10.6–15.9 mm) compared to memoryfoam mattress (10.0, 95% CI 7.4–12.7 mm) (see Table 1).Effect of different sources of feedback on effectivecompression depthWhen an anterior device was used as the source of feed-back, participants failed to conduct guideline compliantCPR in all four scenarios (mean CC depth 37.9–46.2 mm).When sternal-to-spine displacement was used to guideCPR quality, participants conducted guideline-complianteffective CPR (mean CC depth 51.3–54.3 mm) in all fourscenarios. The estimated effect of using an internal deviceon effective compression depth was 14.3 (95% CI 12.0–16.5) mm for a typical PICU mattress only, 8.7 (95% CI6.5–11.0) mm on a typical PICU mattress with a CPRbackboard, 13.0 (95% CI 10.7–15.3) mm on a memoryfoam mattress only, and 7.5 (95% CI 5.2–9.8) mm on amemory foam mattress with a CPR backboard (seeTable 2).DiscussionThis is the first study to demonstrate the effect of mem-ory foam mattress on mattress compressibility duringCPR and reinforce the limitation of single accelerometersensor for feedback on chest compression depth, to ourknowledge. We demonstrated that providing CC on anICU mattress leads to a significant amount of mattresscompressibility. The combined use of memory foammattress and a CPR backboard resulted in the leastamount of mattress compression depth. Using devices tomeasure sternum-to-spine displacement as a source forreal-time feedback improved effective compression depth,compared with single anterior accelerometer sensor.Previous studies have demonstrated the importance ofhigh-quality CPR on survival and neurological outcomeof patients with cardiac arrest. In adult out-of-hospitalcardiac arrest victims, even a slight increase in CC depth(5 mm) was associated with increased survival tohospital discharge (adjusted odds ratio, OR 1.29) and fa-vorable neurologic outcomes (adjusted OR 1.30) [4]. Inpediatric cardiac arrest, guideline-compliant compres-sion depth (CC depth > 51 mm) was associated with im-proved 24-h survival [7]. However, most healthcareFig. 2 Mean chest compression depth in four study conditions.Legend: *percentage represents proportion of mattress compressiondepth over total compression depth. CC number of compressionLin et al. Advances in Simulation  (2017) 2:22 Page 4 of 8providers fail to provide guideline-compliant CPR duringreal [3, 4, 7, 9, 10] or simulated [8, 30] resuscitationevents.Mattress compressibility further compromises the CCdepth of healthcare providers managing cardiac arrest.Noordergraaf et al. reported that the total vertical handmovement is larger when CPR was conducted on mat-tress [12], which further increased workload and fatiguesof compression providers. Mattress compressibility hasbeen shown to decrease the effective CC depth andpercentage of compression meeting guideline inCPR-certified healthcare providers [31]. Previousmannequin-based studies have demonstrated the mat-tress compressibility ranged from 4 mm (ED stretchermattress) to 13 mm (ICU mattress) depending onmattress types [13]. In our study, we found the mattresscompression depth was as high as 24.6 mm, with 31.1%of CC depth depleted by mattress compressibility, evenin the best-case scenario. This means providers need topush approximately one-third deeper than the guideline-recommended depth, to ensure CC provided isguideline-compliant. CPR providers should be aware thatproviding chest compressions on mattresses feels differ-ent from those taught during a conventional BLS course,where compressions are typically done on the floor orhard surfaces. CPR training should allow healthcareproviders to practice chest compressions with properreal-time feedback on the mattress that patients are typ-ically placed on in their relevant clinical unit.Given the importance of high-quality CPR, it is criticalto identify strategies to reduce mattress compressibilityduring resuscitation events. In our study, we found theuse of a memory foam mattress significantly improvesmattress compressibility by 11.7 mm, which couldpotentially result in a significant difference in patientoutcomes. Previous research on techniques that increasethe rigidity of the mattress have shown similaroutcomes. Oh et al. reported an innovative mattress withseveral tubes inserted for deflation improved CC effi-ciency (proportion of effective depth over total depth)from 42 to 81% [32]. Other methods like mattress com-pression covers and vacuum pumps also significantly de-creased mattress compression depth and increased theefficiency of chest compression [20]. We also found thatthe application of a CPR backboard reduces mattresscompressibility by a significant depth (11.6 mm) in ourstudy. Our results are consistent with previous studiesthat demonstrate reduction in vertical hand movementof CPR providers and mattress compressibility [12] withuse of a CPR backboard. CPR backboard use does notnecessarily guarantee effective compression depth. Someprevious studies showed that the utilization of a CPRboard increased CC depth and resulted in guideline-compliant depth [16, 18], while other studies reportedCC depth well below guideline [14, 33], since the effect-ive compression depth was influenced by numerousother factors, such as timing and frequency of training[34–36] and the use of feedback [8, 22, 37, 38].Table 1 Effect of backboard use and memory foam on mattress compression depthMattress compression depth (mm) Mean (95% confidence interval)No backboard Backboard Backboard benefit Backboard meanbenefit main effectTypical mattress 47.9 (40.5, 55.3) 34.6 (28.6, 40.7) 13.3 (10.6, 15.9) 11.6 (9.0, 14.3)Memory foam 34.6 (28.6, 40.7) 24.6 (22.5, 26.7) 10.0 (7.4, 12.7)Memory foam benefit 13.3 (6.4, 20.2) 10.0 (3.2, 16.9) NA NAMemory foam mean benefit main effect 11.7 (4.8, 18.5) NA NAPresented are the mean adjusted values from a mixed effects linear model. Fixed effect: mattress type, use of CPR backboard and interaction between mattresstype and use of CPR backboard. Random effect: intercept, mattress type, and use of CPR backboard. Goodness of fit: marginal R2 = 0.33, conventional R2 = 0.82NA not applicableTable 2 Effect of source of feedback on effective compression depthEffective compression depth (mm) Mean (95% confidence interval)Source of feedbackAnterior sensor Internal measure DifferenceTypical mattress only 37.8 (34.1, 41.5) 52.1 (50.1, 54.1) 14.3 (12.0, 16.5)Typical mattress + backboard 42.9 (39.5, 46.3) 51.7 (49.8, 53.5) 8.7 (6.5, 11.0)Memory foam mattress only 41.2 (37.9, 44.4) 54.2 (52.3, 56.1) 13.0 (10.7, 15.3)Memory foam mattress + backboard 46.3 (43.2, 49.3) 53.7 (52.0, 55.5) 7.5 (5.2, 9.8)Presented are the mean adjusted values from a mixed effects linear model. Fixed effect: mattress type, CPR backboard use, feedback source, interaction betweenfeedback source and mattress type, and interaction between feedback source and CPR backboard use. Random effect: intercept, mattress type, CPR backboarduse, and feedback source. Goodness of fit: marginal R2 = 0.43, conventional R2 = 0.80Lin et al. Advances in Simulation  (2017) 2:22 Page 5 of 8The use of real-time feedback devices has played animportant role in improving CPR quality [8, 22, 37, 38].Our study indicates that the source of feedback may in-fluence the quality of CPR delivered. The use of singleanterior force sensor results in misinterpretation of CCdepth when CPR is performed on the mattress. Nishisakiet al. reported that proportion of CC meeting CPRguidelines was only 31.1% with overestimation of 13 mmin CC depth when adjusting for mattress compressibility[13]. Similarly, Hellevuo et al. demonstrated that real-time feedback from single sensor overestimated the ef-fective CC depth by 10 mm when CPR was performedby practicing paramedics [31]. In our study, we foundhealthcare providers failed to achieve guideline-compliant effective compression depth in all scenarioswhen an anterior sensor was used as a source of feed-back. When using internal device, which measuressternum-to-spine displacement, the effective compres-sion depth was improved for 7–14 mm. Although thesample size estimation is primarily based on mattresscompression depth, even in the case where there wasthe smallest effect demonstrated (i.e., memory foammattress + backboard), the lower limit of the 95% confi-dence interval (5.2 mm) remained clinically significant(greater than 5 mm [4]). Since our patients will not havean internal device built inside, accelerometer is still themain technology for CPR feedback used in real patientresuscitation. However, healthcare providers should pref-erentially utilize dual sensor devices, where an additionalaccelerometer sensor put on the back of the patient ad-justs for mattress compressibility. If a single anteriorsensor device is used, healthcare providers should notpurely rely on the feedback information entirely, becausethe feedback provided is an overestimation of effectivecompression depth. Critically ill patients are at risk ofpressure sores, and the type of mattress that they lie oncan increase that risk [21]. Therefore, one must alsoconsider both CPR quality and pressure sore risk whenselecting mattresses in an ICU setting.LimitationsOur study has several limitations. First, we focused onthe mattress compressibility in a simulated environmentwith only one type of mannequin. In the real clinicalworld, the factors associated with mattress compressibil-ity might be more complicated, such as patient size/weight, size, the orientation of the backboard [19], andchest compliance. The weight of the mannequin torsoused in the study was much lower than the real patients,which could lead to potential bias in estimating mattresscompressibility. Second, we did not directly link themattress compression depth with effective compressiondepth in our study. However, we believe reducing mat-tress compressibility will result in decreased fatigue levelof CPR providers, and thus leading to improved effectivecompression depth. Third, we chose only two differenttypes of mattress used in the ICU. This limitsgeneralizability of the study. Fourth, unlike previousstudies using mechanical devices, we recruited CPR-certified healthcare providers to provide CC. This couldintroduce some variability in quality of compressions.However, we provided real-time feedback to participantsto minimize the variability of compression quality. Inaddition, this better represents the variance encounteredin real clinical encounters and thus improves thegeneralizability. Fifth, the majority of participants werefemale in this study, thus possibly influencing thegeneralizability of the conclusion. Last, but not the least,we were not able to examine chest recoil due to tech-nical limitations.ConclusionChest compression depth is significantly depleted whenCPR is performed on an ICU mattress. Mattress firmingtechnology should be considered for patients at high riskfor cardiac arrest. A CPR backboard should always be usedwhen managing cardiac arrest. When real-time feedback isused, healthcare providers should consider devices thatmeasures sternum-to-spine displacement (i.e., dual acceler-ometer sensor) to improve effective compression depth.AbbreviationsACLS: Advanced Cardiovascular Life Support; BLS: Basic Life Support; CC: Chestcompression; CPR: Cardiopulmonary resuscitation; ICU: Intensive care unit;IHCA: In-hospital cardiac arrest; MD: Mean difference; OR: Odds ratio;PALS: Pediatric Advanced Life Support; PICU: Pediatric intensive care unitAcknowledgementsNot applicableFundingThis study was funded by an infrastructure grant jointly provided by theAlberta Children’s Hospital Research Institute, the Alberta Children’s HospitalFoundation, and the Department of Pediatrics, Cumming School ofMedicine, University of Calgary. Brandi Wan received funding from AlbertaChildren’s Hospital Research Institution for a summer studentship.Availability of data and materialsThe data and materials used in the present study are available on requestfrom the corresponding author.Authors’ contributionsYL participated in the study concept and design, conducted the statisticalanalysis, and drafted the manuscript. BW, CB, and JD participated in designof study, contributed in data collection, and revised the manuscript. KHparticipated in the design of the study, contributed in data analysis andinterpretation, and revised the manuscript. EG participated in the design ofthe study and revised the manuscript. AC participated in study concept anddesign, contributed in data collection, and revised the manuscript. Allauthors read and approved the final manuscript.Ethics approval and consent to participateInstitutional ethical board approval (University of Calgary, REB16–0931) wassecured and informed consent was obtained from all participants.Consent for publicationNot applicableLin et al. Advances in Simulation  (2017) 2:22 Page 6 of 8Competing interestsThe authors declare that they have no competing interests.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in publishedmaps and institutional affiliations.Author details1KidSIM-ASPIRE Simulation Research Program, Alberta Children’s Hospital,University of Calgary, 2888 Shaganappi Trail NW, Calgary, AB T3B 6A8,Canada. 2Faculty of Nursing, University of British Columbia, T201-2211Westbrook Mall, Vancouver, BC V6T 2B5, Canada. 3Faculty of Kinesiology,Queens University, 99 University Ave, Kingston, ON K7L 3N6, Canada.4Department of Community Health Sciences, Cumming School of Medicineand Faculty of Veterinary Medicine, University of Calgary, 3330 Hospital DrNW, Calgary, AB T2N 4N1, Canada. 5Department of Pediatrics, Section ofCritical Care, Cumming School of Medicine, Alberta Children’s Hospital,University of Calgary, 2888 Shaganappi Trail NW, Calgary, AB T3B 6A8,Canada. 6Division of Emergency Medicine, Department of Pediatrics andKidSIM-ASPIRE Research Program, Alberta Children’s Hospital, 2888Shaganappi Trail NW, Calgary, AB T3B 6A8, Canada.Received: 4 September 2017 Accepted: 8 November 2017References1. 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Acta Anaesthesiol Scand. 2014;58(3):323–8.Lin et al. Advances in Simulation  (2017) 2:22 Page 7 of 832. Delvaux AB, Trombley MT, Rivet CJ, Dykla JJ, Jensen D, Smith MR, Gilbert RJ.Design and development of a cardiopulmonary resuscitation mattress. JIntensive Care Med. 2009;24(3):195–9.33. Perkins GD, Smith CM, Augre C, Allan M, Rogers H, Stephenson B, Thickett DR.Effects of a backboard, bed height, and operator position on compressiondepth during simulated resuscitation. Intensive Care Med. 2006;32(10):1632–5.34. Oermann MH, Kardong-Edgren SE, Odom-Maryon T. Effects of monthlypractice on nursing students’ CPR psychomotor skill performance.Resuscitation. 2011;82(4):447–53.35. Sutton RM, Niles D, Meaney PA, Aplenc R, French B, Abella BS, Lengetti EL,Berg RA, Helfaer MA, Nadkarni V. Low-dose, high-frequency CPR trainingimproves skill retention of in-hospital pediatric providers. Pediatrics.2011;128(1):e145–51.36. Niles D, Sutton RM, Donoghue A, Kalsi MS, Roberts K, Boyle L, Nishisaki A,Arbogast KB, Helfaer M, Nadkarni V. “Rolling refreshers”: a novel approach tomaintain CPR psychomotor skill competence. Resuscitation. 2009;80(8):909–12.37. Allan KS, Wong N, Aves T, Dorian P. The benefits of a simplified method forCPR training of medical professionals: a randomized controlled study.Resuscitation. 2013;84(8):1119–24.38. Mpotos N, Yde L, Calle P, Deschepper E, Valcke M, Peersman W,Herregods L, Monsieurs K. Retraining basic life support skills usingvideo, voice feedback or both: a randomised controlled trial.Resuscitation. 2013;84(1):72–7.•  We accept pre-submission inquiries •  Our selector tool helps you to find the most relevant journal•  We provide round the clock customer support •  Convenient online submission•  Thorough peer review•  Inclusion in PubMed and all major indexing services •  Maximum visibility for your researchSubmit your manuscript atwww.biomedcentral.com/submitSubmit your next manuscript to BioMed Central and we will help you at every step:Lin et al. Advances in Simulation  (2017) 2:22 Page 8 of 8


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