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PreNUDGE is an Austrian research project designed to strengthen citizens' personal responsibility for their health. The project aims to increase the number of healthy life years through the digitalization of self-reported health data.

The core concept is to create a modular platform that collects and structures health data from various sources. Qualified health apps serve as the interface between citizens and the platform. The project combines nudging strategies with evidence-based health promotion to subtly motivate people to live healthier lives.

PreNUDGE focuses on the prevention of four diseases: diabetes, colorectal cancer, depression, and COPD, targeting specific groups such as children, adolescents, and working adults. The FHIR Implementation Guide (IG) is called “PreNUDGE”, which is the agreed English spelling, while the project’s German name is “PräNUDGE.”

For more, see Background.

PreNUDGE FHIR® IG for Data Provider / Data from Apps

This Implementation Guide (IG) explains how application providers can use the PreNUDGE FHIR API to deliver health indicators.

We focus on narrow standardization of the following PreNUDGE measurements:

• Physical Activity: Daily activity as Steps per day (from a EHIS PAQ questionnaire, quantity questionnaire and from a wearable device as an observation)
• Alcohol consumption as number of drinks (from a questionnaire and from an observation)
• Quality of life (from a questionnaire with a calculated score as an observation)
• Smoking: Current smoking/tobacco use status (from a questionnaire and from an observation IPS style)
• Sleep duration and quality
  • Duration - from questionnaire or as an observation
  • Quality - question 16 from the WHOQOL-BRE questionnaire or same question 16 from a single quesion questionnaire or as an observation
• Sociodemographic Data: Highest completed education (ISCED level) (from a questionnaire and from an observation)
• For Demo Purposes: Blood glucose in mg/dL (from a questionnaire and from a device as an observation)

For viewing the full questionnaires use tools like lhcforms.

Each questionnaire variant maps one-way to its corresponding observation variant. The mappings can be found at StructureMaps and can be executed with MaLaC-HD. These transformations will be performed on the server side.

Additional PreNUDGE measurements, also narrow standardized, will be specified analogously to the ones mentioned above, based on feedback from the informative ballot. The following are to be specified:

• Physical Activity: Minutes of moderate physical activity (per week), Minutes of physical activity (per week) (from a questionnaire and from a wearable device as an observation)
• Physical Activity: Number of muscle strengthening exercise sessions (per week) (from a questionnaire and from a wearable device as an observation)
• Physical Activity: Sitting hours (per day) (from a questionnaire from a wearable device as an observation)
• Nutrition: Portions of fruit and vegetables (per day) (from a questionnaire)
• Nutrition: Consumption frequency of sugary and salty foods (per week) (from a questionnaire)
• Psychosocial Factors: Self reported emotional burden (from a questionnaire)
• Psychosocial Factors: Self reported stress (from a questionnaire with a calculated score as an observation)
• Anthropometry: Body Mass Index (kg/m²) (from a questionnaire and from a wearable device as an observation)
• Workability (score per category) (from a questionnaire with a calculated score as an observation)

The following sociodemographic data are provided as patient demographic data, preferably from ID Austria. They are not collected using PreNUDGE questionnaires and are not represented as observations.

• Date of birth / age: The date of birth is represented using the mandatory Patient.birthDate element in the AT APS Patient profile. Age is derived from Patient.birthDate at the relevant point in time and is not stored as a separate PreNUDGE observation. The corresponding ID Austria attribute is birthdate (urn:oid:1.2.40.0.10.2.1.1.55).
• Gender: Administrative gender is represented using the mandatory Patient.gender element in the AT APS Patient profile. The corresponding ID Austria attribute is gender (urn:eidgvat:attributes.gender).

Besides these narrow standardized measurements, broad standardized measurements called other quantities observations and other not quantities observations are also supported. Please be aware that such broad standardized measurements do not have a corresponding questionnaire.

Observation values and missing data

PreNUDGE Observations SHOULD contain value[x] when a clinically or analytically meaningful value can be derived. If no such value can be derived, value[x] SHALL be absent and dataAbsentReason SHALL be provided.

This applies especially to observations derived from questionnaires. The original QuestionnaireResponse remains the source record for the submitted answer, including answers such as "unknown" or "not stated". The derived Observation represents the clinically or analytically usable result.

If neither value[x] nor dataAbsentReason is present, the Observation is incomplete and does not conform to the PreNUDGE data quality expectation.