No one accelerometer-based physical activity data collection protocol can fit all research questions.

BMC Med Res Methodol

Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, Division of Physiotherapy, Alfred Nobels Allé 23, 141 83, Huddinge, Sweden.

Published: June 2020

Background: Measuring physical activity and sedentary behavior accurately remains a challenge. When describing the uncertainty of mean values or when making group comparisons, minimising Standard Error of the Mean (SEM) is important. The sample size and the number of repeated observations within each subject influence the size of the SEM. In this study we have investigated how different combinations of sample sizes and repeated observations influence the magnitude of the SEM.

Methods: A convenience sample were asked to wear an accelerometer for 28 consecutive days. Based on the within and between subject variances the SEM for the different combinations of sample sizes and number of monitored days was calculated.

Results: Fifty subjects (67% women, mean ± SD age 41 ± 19 years) were included. The analyses showed, independent of which intensity level of physical activity or how measurement protocol was designed, that the largest reductions in SEM was seen as the sample size were increased. The same magnitude in reductions to SEM was not seen for increasing the number of repeated measurement days within each subject.

Conclusion: The most effective way of reducing the SEM is to have a large sample size rather than a long observation period within each individual. Even though the importance of reducing the SEM to increase the power of detecting differences between groups is well-known it is seldom considered when developing appropriate protocols for accelerometer based research. Therefore the results presented herein serves to highlight this fact and have the potential to stimulate debate and challenge current best practice recommendations of accelerometer based physical activity research.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7271555PMC
http://dx.doi.org/10.1186/s12874-020-01026-7DOI Listing

Publication Analysis

Top Keywords

physical activity
16
sample size
12
sem sample
8
number repeated
8
repeated observations
8
combinations sample
8
sample sizes
8
reductions sem
8
reducing sem
8
accelerometer based
8

Similar Publications

Aerobic exercise prevents renal osteodystrophy via irisin-activated osteoblasts.

JCI Insight

January 2025

Department of Nephrology, Blood Purification Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.

Renal osteodystrophy is commonly seen in patients with chronic kidney disease (CKD) due to disrupted mineral homeostasis. Given the impaired renal function in these patients, common anti-resorptive agents, including bisphosphonates, must be used with caution or even contraindicated. Therefore, an alternative therapy without renal burden to combat renal osteodystrophy is urgently needed.

View Article and Find Full Text PDF

Background: We aimed to characterize factors associated with the under-studied complication of cognitive decline in aging people with long-duration type 1 diabetes (T1D).

Methods: Joslin "Medalists" (n = 222; T1D ≥ 50 years) underwent cognitive testing. Medalists (n = 52) and age-matched non-diabetic controls (n = 20) underwent neuro- and retinal imaging.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!