Measuring quality of life in mental health: Are we asking the right questions?

Measuring quality-adjusted-life years using generic preference-based quality of life measures is common practice when evaluating health interventions. However, there are concerns that measures in common use, such as the EQ-5D and SF-6D, focus overly on physical health and therefore may not be appropriate for measuring quality of life for people with mental health problems. The aim of this research was to identify the domains of quality of life that are important to people with mental health problems in order to assess the content validity of these generic measures. Qualitative semi-structured interviews were conducted with 19 people, recruited from UK mental health services, with a broad range of mental health problems at varying levels of severity. This complemented a previous systematic review and thematic synthesis of qualitative studies on the same topic. Seven domains important to quality of life for people with mental health problems were identified: well-being and ill-being; relationships and a sense of belonging; activity; self-perception; autonomy, hope and hopelessness; and physical health. These were consistent with the systematic review, with the addition of physical health as a domain, and revealed a differing emphasis on the positive and negative aspects of quality of life according to the severity of the mental health problems. We conclude that the content of existing generic preference-based measures of health do not cover this domain space well. Additionally, because people may experience substantial improvements in their quality of life without registering on the positive end of a quality of life scale, it is important that the full spectrum of negative through to positive aspects of each domain are included in any quality of life measure

Dataset of manually measured QT intervals in the electrocardiogram

BACKGROUND: The QT interval and the QT dispersion are currently a subject of considerable interest. Cardiac repolarization delay is known to favor the development of arrhythmias. The QT dispersion, defined as the difference between the longest and the shortest QT intervals or as the standard deviation of the QT duration in the 12-lead ECG is assumed to be reliable predictor of cardiovascular mortality. The seventh annual PhysioNet/Computers in Cardiology Challenge, 2006 addresses a question of high clinical interest: Can the QT interval be measured by fully automated methods with accuracy acceptable for clinical evaluations? METHOD: The PTB Diagnostic ECG Database was given to 4 cardiologists and 1 biomedical engineer for manual marking of QRS onsets and T-wave ends in 458 recordings. Each recording consisted of one selected beat in lead II, chosen visually to have minimum baseline shift, noise, and artifact. In cases where no T wave could be observed or its amplitude was very small, the referees were instructed to mark a 'group-T-wave end' taking into consideration leads with better manifested T wave. A modified Delphi approach was used, which included up to three rounds of measurements to obtain results closer to the median. RESULTS: A total amount of 2*5*548 Q-onsets and T-wave ends were manually marked during round 1. To obtain closer to the median results, 8.58 % of Q-onsets and 3.21 % of the T-wave ends had to be reviewed during round 2, and 1.50 % Q-onsets and 1.17 % T-wave ends in round 3. The mean and standard deviation of the differences between the values of the referees and the median after round 3 were 2.43 ± 0.96 ms for the Q-onset, and 7.43 ± 3.44 ms for the T-wave end. CONCLUSION: A fully accessible, on the Internet, dataset of manually measured Q-onsets and T-wave ends was created and presented in additional file: 1 (Table 4) with this article. Thus, an available standard can be used for the development of automated methods for the detection of Q-onsets, T-wave ends and for QT interval measurements