Article Figures & Data
Figures
- Figure 1.
Flowchart of practice recruitment and selection of spirometry tests for the analysis.
- Figure 2.
Rate of adequate spirometry tests in intervention and usual practice groups during 12 months of follow-up: primary (panel A) and secondary (panel B) outcomes.
Tables
Characteristic Intervention Control PValuea FEV1 = forced expiratory volume in 1 second; FVC = forced vital capacity. a From Student t test for independent samples. b One baseline questionnaire was not returned in the usual practice group (after 2 reminders). c From Pearson χ2 test. d From prebronchodilator spirometry test. e Predicted values from the European Community for Steel and Coal (ECSC), 1993. Practices Number of practices 9 10b Practice type, n (%) Solo 2 (22) 2 (22) Duo 1 (11) 3 (33) Group 5 (56) 2 (22) Multidisciplinary health care center 1 (11) 2 (22) Physicians per practice, mean (SD), n 3.2 (1.6) 2.3 (1.7) .582 Practice size, patients, mean (SD), n 6,521 (3,428) 4,754 (3,012) .263 Spirometry test operators per practice, mean (SD), n 2.0 (0.9) 2.3 (0.9) .461 Spirometry experience, mean (SD), y 5.6 (3.0) 5.0 (3.0) .673 Spirometry tests per month, mean (SD), n 13.7 (15.0) 16.6 (10.6) .644 Tests Spirometry tests, n 490 645 Unique patients, n 457 615 Age, mean (SD), y 53.3 (16.1) 53.9 (17.9) .550 Male, n (%) 245 (50) 303 (47) .421c FEV1, mean (SD), Ld 2.56 (0.97) 2.55 (0.98) .829 FEV1, as % of predicted value, mean (SD)e 84.5 (23.4) 84.0 (20.8) .719 - Table 2.
Reasons for Lack of Acceptability of Forced Expiratory Blows in the Intervention and Control Groups
Reason Period 1 Period 2 Period 3 Period 4 Period 5 All Periods a From multilevel logistic regression analysis. e-Learning + feedback, blow (test), n 306 (102) 330 (113) 261 (87) 273 (91) 291 (97) 1470 (490) Usual practice, blow (test), n 348 (116) 456 (152) 348 (116) 405 (135) 378 (126) 1935 (645) Blows with poor start e-Learning + feedback, n (%) 21 (20.6) 23 (20.4) 19 (21.8) 16 (17.6) 16 (16.5) 95 (19.4) Usual practice, n (%) 27 (23.3) 33 (21.7) 28 (24.1) 37 (27.4) 25 (19.8) 150 (23.3) P valuea 0.857 0.931 0.933 0.095 0.664 0.474 Blows with artifacts during exhalation e-Learning + feedback, n (%) 11 (10.8) 23 (20.4) 18 (20.7) 17 (18.7) 14 (14.4) 83 (16.9) Usual practice, n (%) 11 (9.5) 17 (11.2) 13 (11.2) 23 (17.0) 14 (11.1) 78 (12.1) P valuea 0.644 0.026 0.214 0.987 0.757 0.311 Blows with unsatisfactory exhalation Abrupt end e-Learning + feedback, n (%) 6 (5.9) 15 (13.3) 10 (11.5) 5 (5.5) 5 (5.2) 41 (8.4) Usual practice, n (%) 12 (10.3) 9 (5.9) 3 (2.6) 9 (6.7) 5 (4.0) 38 (5.9) P valuea 0.190 0.410 0.082 0.710 0.727 0.702 Duration <6 sec or no plateau in volume-time curve e-Learning + feedback, n (%) 35 (34.3) 38 (33.6) 30 (34.5) 29 (31.9) 28 (28.9) 160 (32.7) Usual practice, n (%) 55 (47.5) 63 (41.4) 44 (37.9) 69 (51.1) 49 (38.9) 280 (43.4) P valuea 0.180 0.380 0.749 0.171 0.854 0.371
Supplemental Figure
Supplemental Figure 1, Panels A & B. Rate of adequate spirometry tests for the primary outcome per practice and per period for the intervention.
Files in this Data Supplement:
The Article in Brief
Effect of e-Learning and Repeated Performance Feedback on Spirometry Test Quality in Family Practice: A Cluster Trial
Tjard R. Schermer , and colleagues
Background Spirometry, a tool for measuring lung function, can be used to diagnose and monitor chronic respiratory conditions. This study investigates whether e-learning, followed by bimonthly performance feedback, improves the quality of spirometry tests in family practices.
What This Study Found In the course of 1 year, a combination of e-learning and repeated feedback had a small and late effect on the quality of spirometry tests performed by family medicine nurses.
Implications
- This intervention does not seem to compensate for a lack of rigorous training and experience in performing spirometry tests. The authors suggest exploring other models to provide family medicine practices with good-quality spirometry tests.
