Evaluating recruitment methods for selection bias: A large, experimental study of hand biomechanics
Biomechanics studies rely on non-random recruitment methods to obtain study participants. However, the use of common recruitment methods and small sample sizes may influence a given study’s generalizability due to selection bias. To improve generalizability, ecological validity, and participant conv...
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Published in | Journal of biomechanics Vol. 182; p. 112558 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
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Elsevier Ltd
01.03.2025
Elsevier Limited |
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Abstract | Biomechanics studies rely on non-random recruitment methods to obtain study participants. However, the use of common recruitment methods and small sample sizes may influence a given study’s generalizability due to selection bias. To improve generalizability, ecological validity, and participant convenience, recent biomechanics studies have moved beyond lab conditions. However, it is unknown if simply leaving the lab space and increasing sample sizes reduces the risks associated with selection bias. Previous studies relied on chart and literature reviews to identify selection bias. Herein, we build upon this work by exploring the potential for and influence of selection bias in three common recruiting methods by performing an experimental, population-level study on hand biomechanics. Hand biomechanics was assessed in the community using a portable lab setup to measure hand function, grip strength, and pinch strength. A total of 642 apparently healthy participants were recruited across 18 locations, with 426 participants selected based on complete data responses and being between the ages of 18 to 35. Sex stratified analysis was performed to see how recruiting only biomechanists, undergraduate students, or university affiliates changed population estimates of hand strength. The presence of selection bias was observed in all three test cases with both male and female biomechanists, graduate students, and non-university affiliates having significant increases in pinch and grip strengths ranging from 6.2% to 19.4% above overall population values. This study quantitively shows how simply leaving the lab and increasing subject recruitment does not eliminate the potential for selection bias in studies of hand biomechanics. |
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AbstractList | Biomechanics studies rely on non-random recruitment methods to obtain study participants. However, the use of common recruitment methods and small sample sizes may influence a given study’s generalizability due to selection bias. To improve generalizability, ecological validity, and participant convenience, recent biomechanics studies have moved beyond lab conditions. However, it is unknown if simply leaving the lab space and increasing sample sizes reduces the risks associated with selection bias. Previous studies relied on chart and literature reviews to identify selection bias. Herein, we build upon this work by exploring the potential for and influence of selection bias in three common recruiting methods by performing an experimental, population-level study on hand biomechanics. Hand biomechanics was assessed in the community using a portable lab setup to measure hand function, grip strength, and pinch strength. A total of 642 apparently healthy participants were recruited across 18 locations, with 426 participants selected based on complete data responses and being between the ages of 18 to 35. Sex stratified analysis was performed to see how recruiting only biomechanists, undergraduate students, or university affiliates changed population estimates of hand strength. The presence of selection bias was observed in all three test cases with both male and female biomechanists, graduate students, and non-university affiliates having significant increases in pinch and grip strengths ranging from 6.2% to 19.4% above overall population values. This study quantitively shows how simply leaving the lab and increasing subject recruitment does not eliminate the potential for selection bias in studies of hand biomechanics. Biomechanics studies rely on non-random recruitment methods to obtain study participants. However, the use of common recruitment methods and small sample sizes may influence a given study's generalizability due to selection bias. To improve generalizability, ecological validity, and participant convenience, recent biomechanics studies have moved beyond lab conditions. However, it is unknown if simply leaving the lab space and increasing sample sizes reduces the risks associated with selection bias. Previous studies relied on chart and literature reviews to identify selection bias. Herein, we build upon this work by exploring the potential for and influence of selection bias in three common recruiting methods by performing an experimental, population-level study on hand biomechanics. Hand biomechanics was assessed in the community using a portable lab setup to measure hand function, grip strength, and pinch strength. A total of 642 apparently healthy participants were recruited across 18 locations, with 426 participants selected based on complete data responses and being between the ages of 18 to 35. Sex stratified analysis was performed to see how recruiting only biomechanists, undergraduate students, or university affiliates changed population estimates of hand strength. The presence of selection bias was observed in all three test cases with both male and female biomechanists, graduate students, and non-university affiliates having significant increases in pinch and grip strengths ranging from 6.2% to 19.4% above overall population values. This study quantitively shows how simply leaving the lab and increasing subject recruitment does not eliminate the potential for selection bias in studies of hand biomechanics.Biomechanics studies rely on non-random recruitment methods to obtain study participants. However, the use of common recruitment methods and small sample sizes may influence a given study's generalizability due to selection bias. To improve generalizability, ecological validity, and participant convenience, recent biomechanics studies have moved beyond lab conditions. However, it is unknown if simply leaving the lab space and increasing sample sizes reduces the risks associated with selection bias. Previous studies relied on chart and literature reviews to identify selection bias. Herein, we build upon this work by exploring the potential for and influence of selection bias in three common recruiting methods by performing an experimental, population-level study on hand biomechanics. Hand biomechanics was assessed in the community using a portable lab setup to measure hand function, grip strength, and pinch strength. A total of 642 apparently healthy participants were recruited across 18 locations, with 426 participants selected based on complete data responses and being between the ages of 18 to 35. Sex stratified analysis was performed to see how recruiting only biomechanists, undergraduate students, or university affiliates changed population estimates of hand strength. The presence of selection bias was observed in all three test cases with both male and female biomechanists, graduate students, and non-university affiliates having significant increases in pinch and grip strengths ranging from 6.2% to 19.4% above overall population values. This study quantitively shows how simply leaving the lab and increasing subject recruitment does not eliminate the potential for selection bias in studies of hand biomechanics. |
ArticleNumber | 112558 |
Author | Tappan, Isaly Diaz, Maximillian T. Lindbeck, Erica M. Nichols, Jennifer A. Durai, Lavanya Kearney, Kalyn M. Harley, Joel B. |
Author_xml | – sequence: 1 givenname: Maximillian T. orcidid: 0000-0002-3972-1046 surname: Diaz fullname: Diaz, Maximillian T. organization: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States – sequence: 2 givenname: Lavanya surname: Durai fullname: Durai, Lavanya organization: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States – sequence: 3 givenname: Kalyn M. surname: Kearney fullname: Kearney, Kalyn M. organization: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States – sequence: 4 givenname: Erica M. surname: Lindbeck fullname: Lindbeck, Erica M. organization: Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL, United States – sequence: 5 givenname: Isaly surname: Tappan fullname: Tappan, Isaly organization: Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL, United States – sequence: 6 givenname: Joel B. surname: Harley fullname: Harley, Joel B. organization: Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL, United States – sequence: 7 givenname: Jennifer A. orcidid: 0000-0001-5167-9197 surname: Nichols fullname: Nichols, Jennifer A. email: jnichols@bme.ufl.edu organization: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States |
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