STAR Duodecim eHealth Tool to Recognize Chronic Disease Risk Factors and Change Unhealthy Lifestyle Choices Among the Long-Term Unemployed: Protocol for a Mixed Methods Validation Study
Bibliographic record
Abstract
BACKGROUND: Lifestyle choices and socioeconomic status have a significant impact on the expected onset of diseases, age of death, and risk factors concerning long-term illnesses and morbidity. STAR is an online health examination tool, which gives users a report that includes an evaluation of their life expectancy and an estimated risk for developing common long-term illnesses based on questions about health, characteristics, lifestyle, and quality of life. OBJECTIVE: The goals of this study are to (1) review the capacity of STAR to recognize morbidity risks in comparison to a traditional nurse-led health examination and patient-reported health challenges; (2) evaluate the user experience and usability of STAR; and (3) assess the potential impact of STAR on the health confidence and motivation of patients to make healthier lifestyle choices. METHODS: This mixed methods validation study will consist of a quantitative part (questionnaires) and a qualitative part (phone interviews and open-ended questions from the questionnaires). The participants will include 100 long-term unemployed individuals attending a health check for the unemployed. The participants will be recruited from three Finnish public health centers in Espoo, Hämeenlinna, and Tampere. At the health centers, the participants will use STAR and attend a nurse's health check. Surveys with multiple-choice and open-ended questions will be collected from the participants, the nurse, and a study assistant. The questionnaires include questions about the participant's background and health challenges from the patient and nurse points of view, as well as questions about how well the health challenges matched the STAR report. The questionnaires also gather data about user experience, health confidence, and usability of STAR. A study assistant will fill out an observer's form containing questions about use time and possible problems encountered while using STAR. A sample of the unemployed participants will be interviewed by telephone subsequently. For the quantitative data, descriptive statistics and a reliability analysis will be performed, and mean sum scores will be computed for the study variables. Thematic analysis of the qualitative data will be performed. RESULTS: This study was approved by the Ethics Committee of the Expert Responsibility Area of Tampere University Hospital in June 2020 (ETL Code R20067). Data collection will begin in June 2021 and will take approximately 3-6 months. CONCLUSIONS: Online health examinations can improve the effectiveness of primary prevention in health care by supporting efficient evidence-based morbidity risk estimation and motivating patients to change unhealthy behaviors. A multimethod approach is used to allow for assessment of the tool's usefulness from the points of view of both professionals and patients. This study will further provide a rich understanding of how the tool can be used as part of routine health checks, and how and why the tool may or may not motivate users for making healthier lifestyle choices. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/27668.
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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 | 0.058 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".