CONTEXT: Automating data collection from patients can improve data quality, enhance compliance, and decrease costs in longitudinal studies. About half of all households in industrialized countries now have a home computer. OBJECTIVE: While we previously validated the ChronoRecord software for self-reporting mood on a home computer with patients who have bipolar disorder, this study further investigates whether this technology created a bias in the collected data. METHODS: During the validation study, 80 of 96 (83%) patients returned 8662 days of data (mean, 114.7 +/- 32.3 SD days). The patients' demographics were compared with those of similar longitudinal studies in which patients used paper-based data collection tools. In addition, because demographic characteristics may influence attitudes toward technology, observer-rated scores on the Hamilton Depression Rating Scale and Young Mania Rating Scale were used to group patients by severity of illness, and the self-reported mood ratings were analyzed for evidence of bias from the patients' gender, ethnicity, diagnosis, age, disability status, or years of education. Analysis was performed using the 2-way analysis of variance and general linear model. RESULTS: The patients' demographic characteristics were very similar to those of patients with bipolar disorder who participated in comparable longitudinal studies using paper-based tools. After grouping the patients by severity of illness, none of the demographic variables had a significant effect on the patients' self-reported mood using the automated tool. CONCLUSION: The use of a computer does not seem to bias sample data. As with studies using paper-based self-reporting, results from studies of patients using ChronoRecord software on a home computer to report mood can be generalized.
CONTEXT: Automating data collection from patients can improve data quality, enhance compliance, and decrease costs in longitudinal studies. About half of all households in industrialized countries now have a home computer. OBJECTIVE: While we previously validated the ChronoRecord software for self-reporting mood on a home computer with patients who have bipolar disorder, this study further investigates whether this technology created a bias in the collected data. METHODS: During the validation study, 80 of 96 (83%) patients returned 8662 days of data (mean, 114.7 +/- 32.3 SD days). The patients' demographics were compared with those of similar longitudinal studies in which patients used paper-based data collection tools. In addition, because demographic characteristics may influence attitudes toward technology, observer-rated scores on the Hamilton Depression Rating Scale and Young Mania Rating Scale were used to group patients by severity of illness, and the self-reported mood ratings were analyzed for evidence of bias from the patients' gender, ethnicity, diagnosis, age, disability status, or years of education. Analysis was performed using the 2-way analysis of variance and general linear model. RESULTS: The patients' demographic characteristics were very similar to those of patients with bipolar disorder who participated in comparable longitudinal studies using paper-based tools. After grouping the patients by severity of illness, none of the demographic variables had a significant effect on the patients' self-reported mood using the automated tool. CONCLUSION: The use of a computer does not seem to bias sample data. As with studies using paper-based self-reporting, results from studies of patients using ChronoRecord software on a home computer to report mood can be generalized.
Authors: R W Kupka; W A Nolen; L L Altshuler; K D Denicoff; M A Frye; G S Leverich; P E Keck; S L McElroy; A J Rush; T Suppes; R M Post Journal: Br J Psychiatry Suppl Date: 2001-06
Authors: S E Locke; H B Kowaloff; R G Hoff; C Safran; M A Popovsky; D J Cotton; D M Finkelstein; P L Page; W V Slack Journal: JAMA Date: 1992-09-09 Impact factor: 56.272
Authors: Maria Faurholt-Jepsen; Maj Vinberg; Mads Frost; Ellen Margrethe Christensen; Jakob Bardram; Lars Vedel Kessing Journal: BMC Psychiatry Date: 2014-11-25 Impact factor: 3.630
Authors: Han Li; Dahlia Mukherjee; Venkatesh Basappa Krishnamurthy; Caitlin Millett; Kelly A Ryan; Lijun Zhang; Erika F H Saunders; Ming Wang Journal: BMC Res Notes Date: 2019-12-04
Authors: Maria Faurholt-Jepsen; Klaus Munkholm; Mads Frost; Jakob E Bardram; Lars Vedel Kessing Journal: BMC Psychiatry Date: 2016-01-15 Impact factor: 3.630