Feasibility of Internet-Based Health-Related Quality of Life Data Collection in a Large Patient Cohort
Bibliographic record
Abstract
BACKGROUND: Patient registries are commonly used to track survival and medical outcomes in large cohorts. However, large-scale collection of health-related quality of life (HRQOL) data is more challenging because such data must be collected directly from patients. Internet-based HRQOL questionnaires are a potential solution, allowing home data collection with immediate storage in a central database. OBJECTIVES: Our objectives were to investigate the sociodemographic predictors of Internet use and willingness to convey HRQOL information over the Internet in a Canadian tertiary care patient population and to determine whether Internet use patterns of tertiary care patients differ from those of the general Canadian population. Additionally, we sought to identify the success of home completion of Internet-based HRQOL questionnaires, as well as factors hindering home completion. METHODS: We surveyed 644 patients at the Toronto General and St. Michael's Hospitals from November 2003 through July 2006 within a prospective, longitudinal cohort study of HRQOL in patients with lung disease or lung transplants. Using multiple logistic regression, we assessed patient age, gender, rurality, marital status, and employment or education status as potential sociodemographic predictors of having an Internet-accessible home computer, using email at least weekly, and willingness to complete a quality of life questionnaire over the Internet. Patients electing to complete questionnaires over the Internet were followed from September 2005 through March 2008 to assess completion of HRQOL questionnaires from home, identify barriers for noncompletion, and determine sociodemographic predictors for home completion. RESULTS: Of the 644 patients, the median age was 51 years, with a similar number of males and females. Most were urban Ontario residents, were unemployed, and were married or in a common-law relationship. Having an Internet-accessible home computer was reported by 79.7% (513/644) of patients and use of email at least weekly by 66.5% (414/623) of patients. A majority of patients (57.1% 368/644) were willing to complete HRQOL questionnaires over the Internet via an emailed link. Of the participating 644 patients, 368 elected to complete future questionnaires from home and, as part of a gradual roll-out of the home HRQOL questionnaire, 211 were sent emails inviting them to do so. Of the invited patients, 78% (165/211) completed at least one questionnaire from home. The most common reason for noncompletion was a lack of or an inability to find time to complete the questionnaire. No statistically significant sociodemographic predictors of Internet use were associated with completion or noncompletion of questionnaires from home. CONCLUSIONS: Home, Internet-based HRQOL assessment is feasible in tertiary care patient populations with a high predicted rate of Internet usage based on sociodemographic parameters. A large minority of patients were unwilling or unable to take part in home HRQOL assessments indicating that alternative methods of data collection are still required. However, the majority of patients electing to complete home HRQOL assessments went on to do so over the Internet.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".