Text readability and legibility on iPad with comparison to paper and computer screen

U radu autori istražuju čitkost teksta na iPad uređaju i djelomično ga uspoređuju s čitanjem sa zaslona računala i papira. Na temelju ranijih istraživanja, mjerene su dvije nezavisne varijable: brzina čitanja (T) i subjektivna težina čitanja (Z). U istraživanju je sudjelovalo 220 studenata starih između 18 i 48 godina podijeljeno u 11 grupa od po 20 sudionika. Ključni rezultati su: Sans serif font Gotham čitkiji je i čitljiviji na iPadu od Minion Pro serifnog fonta, iako kad su veličine fonta veće, nema statistički značajne razlike između njih; zbog manjeg broja znakova po retku, dvostupačni prijelom najteže je čitljiv; tekst prelomljen u minimalno 79 znakova po retku i više dovoljno je čitak za čitanje na iPadu; s obzirom na kvalitetu čitanja, tekstovi prikazani na iPadu mogu zamijeniti tekstove tiskane na papiru.In this paper authors examine readability and legibility of text on iPad and compare it partially to reading from computer screen and paper. Following previous research, two independent variables were measured: speed of reading (T) and subjective difficulty of reading (Z). 220 university students aged 18÷48 participated in the experiment and were divided into 11 groups of 20 participants. Key findings are as follows: Sans serif Gotham font is more readable and legible on iPad than Minion Pro serif font, although, when presented in bigger font sizes there is no significant difference between the two letter cases; two column spread of text was the hardest to read, giving fewer number of characters per row; layout of text with 79 characters per row and above should be readable and legible enough for reading texts on iPad; considering quality of reading, texts displayed on iPad can replace texts printed on paper

Additional file 1: of SurvivalGWAS_Power: a user friendly tool for power calculations in pharmacogenetic studies with “time to event” outcomes

Details of Weibull regression model. (DOCX 14 kb

Graphs of predicted dose and actual warfarin dose in the Liverpool prospective study validation cohort.

<p>Graphs of predicted dose and actual warfarin dose in the Liverpool prospective study validation cohort.</p

List of Dose Prediction Models [4]–[6], [8], [15], [36].

<p>1. Was the paper published after 2003?</p><p>2. Does the model contain more than two di_erent variables than another dose prediction regression model already selected? (Although, where relevant novelty existed similar dose prediction regression models were compared and this novelty explained.)</p><p>3. Does the model include only covariates measured in the Liverpool study?</p><p>4. As R-squared is the most frequently reported statistic to judge model performance, is the value of this statistic above 0.5? Implying that more than 50% of the variability in the MD needs of the model's derivation cohort had been explained by the model.</p><p>a. R-squared statistic from derivation dataset not reported.</p><p>b. Reason for inclusion explained in manuscript.</p><p>List of Dose Prediction Models <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114896#pone.0114896-Solomon1" target="_blank">[4]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114896#pone.0114896-Zhu1" target="_blank">[6]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114896#pone.0114896-Sconce1" target="_blank">[8]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114896#pone.0114896-Wadelius2" target="_blank">[15]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114896#pone.0114896-Anderson2" target="_blank">[36]</a>.</p

Demographic, Clinical and Pharmacogenetic Information of Patient in the Validation Cohort.

<p>Demographic, Clinical and Pharmacogenetic Information of Patient in the Validation Cohort.</p

INR-Time profiles of three patients receiving standard care.

<p>INR-Time profiles of three patients receiving standard care.</p

Graphs of predicted dose and actual warfarin dose in the EU-PACT trial control arm validation cohort.

<p>Graphs of predicted dose and actual warfarin dose in the EU-PACT trial control arm validation cohort.</p

Summary Statistics about the Performance of the Six Dosing Algorithms[4]–[6], [8], [15], [36].

<p>Summary Statistics about the Performance of the Six Dosing Algorithms<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114896#pone.0114896-Solomon1" target="_blank">[4]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114896#pone.0114896-Zhu1" target="_blank">[6]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114896#pone.0114896-Sconce1" target="_blank">[8]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114896#pone.0114896-Wadelius2" target="_blank">[15]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114896#pone.0114896-Anderson2" target="_blank">[36]</a>.</p

Additional file 1: Table S1. of Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues

Goodness-of-fit tests, model diagnostics, comparison instruments, and other model assessment tools. (DOCX 162 kb

Additional file 1 of joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes

An appendix (appendix.pdf) is available that includes details on the score vector and M-step estimators. (PDF 220 kb