BACKGROUND: Variation in graphic format can substantially influence interpretation of data. Despite a large body of literature on the optimal design of graphs, little attention has been paid to the format of charts for health monitoring. AIMS: This study assessed the effect of aspect ratio (x:y ratio) and interconnecting lines on visual identification of change in biological data, such as during asthma exacerbations. METHODS: Eighty volunteers viewed 72 sets of six consecutive blocks of unidentified biological data, recording if each block of data was increasing, decreasing, or the same as the previous block. Three chart aspect ratios were examined (A, 5.2:1; B, 3.0:1; C, 1.1:1), with or without lines between data points. Datasets from lung function monitoring by asthma patients included a mild/moderate/severe fall ('exacerbation') or no exacerbation. False negative (missing true exacerbations) and false positive (identifying non-existent exacerbations) responses were calculated. RESULTS: 84% of exacerbation blocks were correctly identified. There was a significant interaction between exacerbation severity and aspect ratio (p=0.0048). The most compressed chart (C) had the fewest false negative responses. Moderate falls were missed in 24%, 12%, and 5% of trials on charts A, B, and C, respectively (C vs A: adjusted relative risk 0.19 (95% CI 0.12 to 0.30)). False positive responses were infrequent (A, 2.5%; B, 3.8%; C, 8.3%), increasing slightly if data points were joined with lines (4.3% vs 5.1%, p=0.004) . CONCLUSIONS: Compressed charts can improve the visual detection of change in biological data by up to 80%. The aspect ratio of charts should be standardised to facilitate clinical pattern recognition.
BACKGROUND: Variation in graphic format can substantially influence interpretation of data. Despite a large body of literature on the optimal design of graphs, little attention has been paid to the format of charts for health monitoring. AIMS: This study assessed the effect of aspect ratio (x:y ratio) and interconnecting lines on visual identification of change in biological data, such as during asthma exacerbations. METHODS: Eighty volunteers viewed 72 sets of six consecutive blocks of unidentified biological data, recording if each block of data was increasing, decreasing, or the same as the previous block. Three chart aspect ratios were examined (A, 5.2:1; B, 3.0:1; C, 1.1:1), with or without lines between data points. Datasets from lung function monitoring by asthmapatients included a mild/moderate/severe fall ('exacerbation') or no exacerbation. False negative (missing true exacerbations) and false positive (identifying non-existent exacerbations) responses were calculated. RESULTS: 84% of exacerbation blocks were correctly identified. There was a significant interaction between exacerbation severity and aspect ratio (p=0.0048). The most compressed chart (C) had the fewest false negative responses. Moderate falls were missed in 24%, 12%, and 5% of trials on charts A, B, and C, respectively (C vs A: adjusted relative risk 0.19 (95% CI 0.12 to 0.30)). False positive responses were infrequent (A, 2.5%; B, 3.8%; C, 8.3%), increasing slightly if data points were joined with lines (4.3% vs 5.1%, p=0.004) . CONCLUSIONS: Compressed charts can improve the visual detection of change in biological data by up to 80%. The aspect ratio of charts should be standardised to facilitate clinical pattern recognition.
Authors: Cindy Thamrin; Urs Frey; David A Kaminsky; Helen K Reddel; Andrew J E Seely; Béla Suki; Peter J Sterk Journal: Am J Respir Crit Care Med Date: 2016-11-01 Impact factor: 21.405
Authors: Helen K Reddel; Eric D Bateman; Allan Becker; Louis-Philippe Boulet; Alvaro A Cruz; Jeffrey M Drazen; Tari Haahtela; Suzanne S Hurd; Hiromasa Inoue; Johan C de Jongste; Robert F Lemanske; Mark L Levy; Paul M O'Byrne; Pierluigi Paggiaro; Soren E Pedersen; Emilio Pizzichini; Manuel Soto-Quiroz; Stanley J Szefler; Gary W K Wong; J Mark FitzGerald Journal: Eur Respir J Date: 2015-07-23 Impact factor: 16.671