What Do Anesthesiologists Know about p Values, Confidence Intervals, and Correlations: A Pilot Survey

Investigating Users' Attitudes Toward Automated Smartwatch Cardiac Arrest Detection: Cross-Sectional Survey Study

Background

Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality in the developed world. Timely detection of cardiac arrest and prompt activation of emergency medical services (EMS) are essential, yet challenging. Automated cardiac arrest detection using sensor signals from smartwatches has the potential to shorten the interval between cardiac arrest and activation of EMS, thereby increasing the likelihood of survival.

Objective

This cross-sectional survey study aims to investigate users' perspectives on aspects of continuous monitoring such as privacy and data protection, as well as other implications, and to collect insights into their attitudes toward the technology.

Methods

We conducted a cross-sectional web-based survey in the Netherlands among 2 groups of potential users of automated cardiac arrest technology: consumers who already own a smartwatch and patients at risk of cardiac arrest. Surveys primarily consisted of closed-ended questions with some additional open-ended questions to provide supplementary insight. The quantitative data were analyzed descriptively, and a content analysis of the open-ended questions was conducted.

Results

In the consumer group (n=1005), 90.2% (n=906; 95% CI 88.1%-91.9%) of participants expressed an interest in the technology, and 89% (n=1196; 95% CI 87.3%-90.7%) of the patient group (n=1344) showed interest. More than 75% (consumer group: n= 756; patient group: n=1004) of the participants in both groups indicated they were willing to use the technology. The main concerns raised by participants regarding the technology included privacy, data protection, reliability, and accessibility.

Conclusions

The vast majority of potential users expressed a strong interest in and positive attitude toward automated cardiac arrest detection using smartwatch technology. However, a number of concerns were identified, which should be addressed in the development and implementation process to optimize acceptance and effectiveness of the technology.

Patients' experiences of engaging with electronic Patient Reported Outcome Measures (PROMs) after the completion of radiation therapy for breast cancer: a pilot service evaluation

INTRODUCTION

Over 60 % of people who develop breast cancer will receive radiation therapy (RT) as part of their treatment. Side effects of RT may include inflammation, erythema, desquamation and fatigue. Electronic Patient Reported Outcomes Measures (ePROMs) enable patients to report side effects prior to their scheduled post-RT appointment. This pilot service evaluation aims to explore patients' perceptions regarding the value of the ePROM system, ease of its use and barriers to using the system, after breast irradiation.

METHODS

From July-November 2021, evaluation surveys were posted to 100 people who had received RT to their breast to explore their experience of using the ePROM. Ethical approval was obtained through Ulster University and the Western Health and Social Care Trust (WHSCT), Northern Ireland.

RESULTS

Fifty-two people responded to the survey, of which 27 respondents indicated that they had accessed the ePROM. Despite few participants experiencing significant side effects, the majority of participants recommended the ePROM indicating that it was an important source of support. Those who experienced significant side effects found the system to be prompt and effective. Barriers to accessing the ePROM included technical issues with the link, concerns about confidentiality and forgetting to access the link. Access to the ePROM increased with higher education levels.

CONCLUSIONS

This pilot service evaluation demonstrated that ePROMs are valued by patients and can provide rapid real-time access to support, offering individual care and reassurance. For patients with longer RT schedules (>10 fractions), the introduction of ePROMs during RT was viewed favourably by participants. All patients may benefit from the option of receiving ePROMs post-RT.

A Guide to Basic Statistics for Educational Research

INTRODUCTION

Clinician-educators often need to produce scholarship for academic promotion. While some programs exist to help with faculty development skills, few provide adequate statistical training to help educators evaluate their work.

METHODS

From January 2020 through January 2021, faculty at three academic centers attended one of five in-person or virtual seminars with dedicated statistical training for medical education interventions. These 90-minute seminars included a 45-minute PowerPoint presentation of common statistical tests used for educational interventions followed by small breakout groups to help attendees work on additional practice examples. After each seminar, surveys were distributed in person or virtually to obtain feedback.

RESULTS

Forty-three faculty attended the five seminars, with a range of surgical and nonsurgical specialties represented. Of these attendees, 38 (88%) completed session evaluations. The majority of respondents (n = 34, 90%) rated the session as extremely useful in helping them know how to use statistics in their scholarly work. Most participants agreed or strongly agreed they had adequate time to practice skills (n = 30, 79%). Self-rated confidence in using statistics was significantly higher after the session compared to before (3.00 post vs. 1.97 pre, p < .0001). Most participants (n = 32, 84%) rated the session as excellent and the small-group practice as most useful (n = 16, 42%), but many (n = 26, 69%) wanted more skills practice.

DISCUSSION

This intervention shows that dedicated training on biostatistics used in educational interventions can help clinician-educators improve self-rated confidence and knowledge in choosing statistical tests in educational scholarship.

Statistical Significance Versus Clinical Importance of Observed Effect Sizes: What Do P Values and Confidence Intervals Really Represent?

Effect size measures are used to quantify treatment effects or associations between variables. Such measures, of which >70 have been described in the literature, include unstandardized and standardized differences in means, risk differences, risk ratios, odds ratios, or correlations. While null hypothesis significance testing is the predominant approach to statistical inference on effect sizes, results of such tests are often misinterpreted, provide no information on the magnitude of the estimate, and tell us nothing about the clinically importance of an effect. Hence, researchers should not merely focus on statistical significance but should also report the observed effect size. However, all samples are to some degree affected by randomness, such that there is a certain uncertainty on how well the observed effect size represents the actual magnitude and direction of the effect in the population. Therefore, point estimates of effect sizes should be accompanied by the entire range of plausible values to quantify this uncertainty. This facilitates assessment of how large or small the observed effect could actually be in the population of interest, and hence how clinically important it could be. This tutorial reviews different effect size measures and describes how confidence intervals can be used to address not only the statistical significance but also the clinical significance of the observed effect or association. Moreover, we discuss what P values actually represent, and how they provide supplemental information about the significant versus nonsignificant dichotomy. This tutorial intentionally focuses on an intuitive explanation of concepts and interpretation of results, rather than on the underlying mathematical theory or concepts.