Table 1 Data abstraction themes and items
Study characteristics |
|---|
1. Academic discipline of authors |
鈥僡. Public health |
鈥僢. Computer Science (CS)/HCI/HF/Informatics |
鈥僣. GIS/Geographical Sciences |
鈥僤. Multidisciplinary (combination of public health and CS/HCI/HF/Informatics or GIS/Geographical Sciences) |
2. Year of publication |
鈥僡. 2000鈥�2005 |
鈥僢. 2006鈥�2010 |
鈥僣. 2011鈥�2015 |
鈥僤. 2016鈥�2020 |
鈥僥. 2021鈥�2023 |
3. Type of publication |
鈥僡. Peer-reviewed article |
鈥僢. Conference Proceeding |
鈥僣. Other |
4. Publication venue |
鈥僡. Public health |
鈥僢. Computer Science (CS) |
鈥僣. HCI/HF |
鈥僤. Informatics |
鈥僥. GIS/Geographical Sciences |
鈥僨. Other |
5. Study location |
鈥僡. North America |
鈥僢. Central America |
鈥僣. South America |
鈥僤. Europe, Asia |
鈥僥. Africa |
鈥僨. Oceania |
鈥僩. Global |
Population Health Characteristics |
1. Population health topic area |
鈥僡. Infectious disease |
鈥僢. Non-communicable disease |
鈥僣. Public health data and indicators |
鈥僤. Maternal, newborn, child, and family health |
鈥僥. Vaccines and drugs |
鈥僨. Injury |
鈥僩. Mental health and substance abuse |
鈥僪. Nutrition |
鈥僫. Other |
2. Tool type |
鈥僡. Health surveillance |
鈥僢. Program evaluation |
鈥僣. Predictive modeling |
鈥僤. Other |
3. Population health end-user |
鈥僡. Program planners |
鈥僢. Policy makers |
鈥僣. Epidemiologists |
鈥僤. Community health workers |
鈥僥. Academia |
鈥僨. Government |
鈥僩. Public health professionals not otherwise specified (NOS) |
鈥僪. Multidisciplinary roles (multiple intended user groups) |
鈥僫. Health care practitioners |
鈥僯. Other |
4. Setting |
鈥僡. Local public health |
鈥僢. Regional public health |
鈥僣. Federal public health |
鈥僤. Multiple levels of public health |
鈥僥. Community health organizations |
鈥僨. Health care organizations/hospitals |
鈥僩. Other |
Human Factors Characteristics |
1. Point in design lifecycle HF methods used |
鈥僡. Requirements gathering and analysis |
鈥僢. Design and prototyping |
鈥僣. Testing and evaluation |
鈥僤. Post-deployment evaluation |
2. Sample size (number of participants) |
3. Human factors study methods |
鈥僡. Questionnaires |
鈥僢. Interviews |
鈥僣. Focus-groups |
鈥僤. Delphi discussions |
鈥僥. A/B testing |
鈥僨. Experiments |
鈥僩. Usability testing |
鈥僪. Heuristic evaluations |
鈥僫. Task analysis |
鈥僯. Log data for user interactions |
鈥僰. Observations |
鈥僱. Workshops |
鈥僲. Informal feedback |
鈥僴. Other |
4. Direct performance measures collected in studies using A/B testing, Experiments, and User Testing |
鈥僫. Task completion time |
鈥僫i. Accuracy/Task success |
鈥僫ii. Efficiency |
鈥僫v. Number of clicks |
鈥僾. Other log data measures |
鈥僾i. Mental workload |