**Findable**

Assign persistent IDs, provide rich metadata, register in a searchable
resource,...

**Accessible**

Retrievable by their ID using a standard protocol, metadata remain accessible
even if data aren’t...

**Interoperable**

Use formal, broadly applicable languages, use standard vocabularies, qualified
references...

#  Reusable

Rich, accurate metadata, clear licences, provenance, use of community
standards...

According to principles outlined in
_www.force11.org/group/fairgroup/fairprinciples_

The structure of the plan is as generated by the online tool DMP Online
_https://dmponline.dcc.ac.uk/_ and the contents of the sections were drafted
according to the guidance offered by DMP Online.

#  2\. ADMINISTRATIVE DETAILS

Project Name: POWER2DM - Predictive model-based decision support for diabetes
patient empowerment

Project Identifier: POWER2DM

Grant Title: NUMBER — 689444 — POWER2DM

Principal Investigator / Researcher: Albert A. de Graaf (Coordinator)

Description: Data Management Plan for POWER2DM Health and Observations of
Daily Life data used for self-management by diabetes patients

Funder: European Commission (Horizon 2020) Call Topic: PHC 28 – 2015: Self-
management of health and disease and decision support systems based on
predictive computer modelling used by the patient him or herself

#  3\. DATA SETS

A wide range of patients with diabetes might benefit from support through
POWER2DM. Two different patient populations with altered glucose metabolism
are targeted in POWER2DM: T1DM and T2DM in primary/secondary & tertiary care.
The Self Management Support System (SMSS) developed in POWER2DM will be tested
in a pragmatic RCT: the **POWER2DM Evaluation Campaign** , to establish the
accuracy and utility of the POWER2DM DSS and APIs and evaluate effectiveness
in a real-world setting. We will use three different centres who are
specialized on (some or all of) these entities: The Reina Sofia University
Hospital in Spain (T1DM), the Leiden University Medical Centre and Primary

Care Research Network in the Netherlands (T1&2DM), and the Institut für
Diabetes “Gerhardt Katsch“ in Karlsburg, Germany (T1&2 DM). Study
characteristics are as follows:

_Study design and Operation:_ The protocol for the POWER2DM Evaluation
Campaign will be pragmatic randomised trial with 9 months follow-up of
individual patients. Patients will be randomised to either Power2DM support
(active arm) or usual care (control arm). Patients in the Power2DM
intervention the first 2 weeks patient will follow an established protocol in
order to monitor any problems in using the whole system. There will be
evaluation moments at baseline, after 3, 6 and 9 months.

_Endpoints:_ Primary outcome: %Hba1c levels before and after the intervention
between the two arms

(active versus control). Secondary outcomes: Generic Quality of Life (SF-36);
Patient utilities (EQ5D-L);

Disease Specific Quality of Life (DSQLS); costs (CostQ); self-management
outcome (heiQ: Health

Education Impact Questionnaire, Summary of Diabetes Self Care Activities,
Diabetes Management Self-efficacy); lifestyle and physical activity and other
process outcomes of the POWER2DM modules and services for patients and care
providers: reliability, usability, acceptance and actual usage.

_Sample Size:_ Variable: The level of Hba1c%. Minimum detectable difference:
0.35% (Standard

Deviation 1.0%). For an alpha error of 0.05 and a power of 80%, the minimum
sample size needed is

129 subjects per group. **The POWER2DM RCT will include 140 type 1 DM and 140
type 2 DM subjects, 280 patients in total** , allowing us to face a loss to
follow of 8.5%. In pre-specified subgroup analyses of patients with T1DM and
T2DM we are able to detect a difference of 0.5% with a sample size of 63
subjects per treatment strategy per DM subtype (N=70 with 10% loss to follow-
up).

_Statistical analysis:_ The primary outcome will be analysed using the Stata
13 xtmixed command for multi-level linear regression, adjusting for clusters
at GP-level, repeated measurements within a patient values (StataCorp, College
Station, Tx, USA). Strategy by time interactions will be assessed to detect
differences between the groups at particular time points. In addition,
strategy by time by DM type will be assessed to detect differences in effects
between the two DM subtypes.

Data from participants will be recorded in the POWER2DM Personal Data Store.
Appropriate privacy and data security measures will be put in place acoording
to pertinent regulations. Personal data will be stored with an anonymised
identifier. The keys that will enable to link data to an individual person
will be stored in a secured fashion on separate servers. For the Open Research
data pilot, part of the contents of the Personal Data Store will be made
available for research purposes and transferred to a Data repository according
to cinformed onsent provided by study participants.

#  4\. Data set description

POWER2DM will collect basic diabetes related data, clinical measurements,
patient data (Quality of Life-QoL questionnaires, self-management profile),
daily nutritional intake, exercise level, sleep quality, glucose measurements,
vital signs (pulse, temperature), medication intakes, etc. The provisional
list of measured parameters subdivided in 4 categories is as follows:

Table 1. Provisional list of measures in POWER2DM Evaluation Campaign dataset:

**Comment [Ad1]:** Description of the

data that will be generated or collected, its origin (in case it is
collected), nature and scale and to whom it could be useful, and whether it
underpins a scientific publication. Information on the existence (or not) of
similar data and the possibilities for integration and reuse.

Questions to consider:

● What data will you create?

Guidance: Give a brief description of the data that will

be created, noting its content and coverage

**Comment [Ad2]:** We may wish to add a

5th category containing model predictions, and a 6  th  category containing
use

characteristics of the various devices and

POWER2DM features  
  
<table>  
<tr>  
<th>

Measure Category and Name (# items)

</th>  
<th>

Code

</th> </tr>  
<tr>  
<td>

**Lifestyle and Daily Monitoring**

</td>  
<td>

**LDM**

</td> </tr>  
<tr>  
<td>

Blood Glucose Level

</td>  
<td>

1

</td> </tr>  
<tr>  
<td>

Dietary Intake

</td>  
<td>

2

</td> </tr>  
<tr>  
<td>

Activity Tracker

</td>  
<td>

3

</td> </tr>  
<tr>  
<td>

Sleep Tracker

</td>  
<td>

4

</td> </tr>  
<tr>  
<td>

Sleep Quality VAS

</td>  
<td>

5

</td> </tr>  
<tr>  
<td>

Relaxation/Stress

</td>  
<td>

6

</td> </tr>  
<tr>  
<td>

Stress VAS (1)

</td>  
<td>

7

</td> </tr>  
<tr>  
<td>

Emotional VAS (1)

</td>  
<td>

8

</td> </tr>  
<tr>  
<td>

Diabetes Medication Treatment (Type/ Dosage/Frequency)

</td>  
<td>

9

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**Questionnaires (# items)**

</td>  
<td>

**Q**

</td> </tr>  
<tr>  
<td>

WHO-5 (5)

</td>  
<td>

1

</td> </tr>  
<tr>  
<td>

PHQ-9 (9)

</td>  
<td>

2

</td> </tr>  
<tr>  
<td>

GAD-7 (7)

</td>  
<td>

3

</td> </tr>  
<tr>  
<td>

PSS (10)

</td>  
<td>

4

</td> </tr>  
<tr>  
<td>

PAID (20)

</td>  
<td>

5

</td> </tr>  
<tr>  
<td>

DSMQ-R (20)

</td>  
<td>

6

</td> </tr>  
<tr>  
<td>

ADDQoL (28)

</td>  
<td>

7

</td> </tr>  
<tr>  
<td>

HFS (27)*

</td>  
<td>

8

</td> </tr>  
<tr>  
<td>

DEPS-R (14)*

</td>  
<td>

9

</td> </tr>  
<tr>  
<td>

FCQ (15)*

</td>  
<td>

10

</td> </tr>  
<tr>  
<td>

D-FISQ (21)*

</td>  
<td>

6

</td> </tr>  
<tr>  
<td>

Gut Health (?)

</td>  
<td>

11

</td> </tr>  
<tr>  
<td>

ASQ (3)

</td>  
<td>

12

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**Clinical/Lab Tests**

</td>  
<td>

**CLT**

</td> </tr>  
<tr>  
<td>

HbA1c

</td>  
<td>

1

</td> </tr>  
<tr>  
<td>

Fasting Glucose

</td>  
<td>

2

</td> </tr>  
<tr>  
<td>

Fasting insulin

</td>  
<td>

3

</td> </tr>  
<tr>  
<td>

Insulin sensitivity (%HOMA-2 S): based on fasting glucose/insulin

</td>  
<td>

4

</td> </tr>  
<tr>  
<td>

Beta cell function (%HOMA-2-B): based on fasting glucose/insulin

</td>  
<td>

5

</td> </tr>  
<tr>  
<td>

Inflammation (mg/l hs-CRP)

</td>  
<td>

6

</td> </tr>  
<tr>  
<td>

Tissue damage (TC, HDL-C, LDL-C,TG, liver damage blood markers, kidney damage
markers, neuropathy markers, smoking status)

</td>  
<td>

7

</td> </tr>  
<tr>  
<td>

Non-estrefied fatty acids

</td>  
<td>

8

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**PatientCharacteristics**

</td>  
<td>

**PC**

</td> </tr>  
<tr>  
<td>

Anamnese: Age/ Gender/Height/Type Diabetes /MedicalHistory (Time since
diagnosis and Complications)/AS4

</td>  
<td>

1

</td> </tr> </table>  
<table>  
<tr>  
<th>

Weight

</th>  
<th>

2

</th> </tr>  
<tr>  
<td>

BMI (calculated from Weight and Height)

</td>  
<td>

3

</td> </tr>  
<tr>  
<td>

Waist

</td>  
<td>

4

</td> </tr>  
<tr>  
<td>

Blood pressure

</td>  
<td>

5

</td> </tr> </table>

Note: *indicates that this measure will only be used if a patient engages in
an associated selfmanagement task (e.g. only insulin users will be asked about
anxiety related to using insulin) or they indicate associated problems in
other questionnaires (e.g. DEPS-R will be administered if the patient
indicates issues regarding eating)

**5\. Data Capture methods:**

The data of the different categories in Table 1 will be captured as follows:

  * How will the data be created? 

  

The following data sources are planned:

  * PC category: measured by medical professional 

  

  * CLT category: measured in Clinical Chemistry lab 

  

  * Q category: patient self-evaluation 

  

  * LDM category: data will be registered by devices and sensors used by the patients, or entered by the patient in a mobile or web application ● What standards or methodologies will you use? 

  

specification.

**Comment [Ad5]:**

This is a

suggestion

. It

will result in a large set of files

(

~30 per

patient)

We do not at present envision different versions of the dataset since

only unprocessed data are

collected

.

**Comment [Ad6]:**

open for discussion.

We may want to store some processed

data as well e.g.

weekly aggregates of

p

hysical activity , calorie intake,sleep,

stress, etc.

The following standards will be used:

  * PC category: medical professional standards 

  

  * CLT category: Clinical Chemistry lab methods/standards 

  

  * Q category: established questionnaire methods/standards/scoring methodology; 

  

  * LDM category: standards and methods acoording to specifications of devices and sensors used ● How will you structure and name your folders and files? 

  

Folders will be named according to pilot site name / measure category/measure
code (cf. Table 1).

File names will be named according to the (anonymous) subject identifier. The
naming will be

Subject_Site_Category_Code. Each entry in a file specifies a value plus the
associated date/time

**Comment [Ad3]:** We still need to

decide: 3separate local labs in the 3 pilot regions or a single central lab?

**Comment [Ad4]:** We need to decide

whether filling out of questionnaires will be supervised or not

  * How will you ensure that different versions of a dataset are easily identifiable? 

  

**6\. Metadata:**

  * How will you capture / create the metadata? 

  

We plan to capture the metadata in a “readme” text file. The strict file
naming convention should

further allow to uniquely identify which data is contained in each file.

  * Can any of this information be created automatically? 

  

The “readme” text file will be created by hand. The data file names will be
created automatically.

  * What metadata standards will you use and why? 

  

POWER2DM will create a dataset of a diverse nature, not matching any of the
disciplinary metadata standards for Biology, Earth Science, Physical Science,
Social Science & Humanities, and General research Data offered by DCC on their
website  _http://www.dcc.ac.uk/resources/metadata-standards_ .

We do not consider the development of a dedicated standard for POWER2DM
essential for the efficient dissemination of the project results. The proposed
metadata capturing, based on the documentation in the “readme” text file
together with the strict file naming convention will allow interested
researchers to re-use the data without much difficulty.

#  7\. Data sharing, repository and restrictions

The POWER2DM dataset concerns personal medical and behavioural data. Data
storage in the

POWER2DM Personal Data Store will be subject to strict privacy/security
measures dictated by the Ethics criteria that apply to the project (Ethics
Deliverables of Work package 9). In transferring data to a data repository for
sharing, special care will be taken to preserve the same standard of data
privacy/security. This will be accomplished by properly anonymising the data
and ensuring that the keys to link data to patient identity are not
transferred. As an additional privacy precaution, data may be aggregated to a
certain extent depending on requirements of the POWER2DM models (i.e. still
allowing the reproduction of the results).

As a guiding principle for sharing, study participants are considered owner of
their personal data. Therefore, participants will be asked to participate in
the Open Research Data Pilot by giving informed consent to their data being
made publicly available after proper anonymisation and aggregation mentioned
above. This consent will be asked in a second separate consent form, in
addition to the standard informed consent to have their data made available to
the project team for research purposes. While the latter is required for
participation in the study, the response to the Open Research Data Pilot
sharing consent form will not be part of the inclusion criteria.

Method for data sharing:

  * How will you make the data available to others? 

  

The data will be stored in a data repository

**Comment [Ad7]:** open for discussion

  * With whom will you share the data, and under what conditions? 

  

The data will be publicly available for any party without the requirement to
attribute the data to the

POWER2DM Consortium (Open Access, Creative Commons CC Zero License (cc-zero)
(see **Comment [Ad8]:** proposed  _http://ufal.github.io/public-license-
selector/_ )

  * Are any restrictions on data sharing required? e.g. limits on who can use the data, when and for what purpose. 

  

No restrictions on who can use the data and for what purpose apply.

  * What restrictions are needed and why? 

  

An embargo period of maximum 12 months after finalization of the project is
deemed required to

allow sufficient time for publication of the results, and for establishment of
intellectual property

  * What action will you take to overcome or minimise restrictions? 

  

Subjects participating in the study will be asked to give separate informed
consent to make their data publicly available for any purpose. Publications
and patent applications will be planned as early as possible yet realistic.

  * Where (i.e. in which repository) will the data be deposited? 

  

The data plus instruction files for usage will be deposited in the Zenodo
cost-free data repository for sharing  ( _http://www.zenodo.org_ )  . The
Zenodo procedures for long-term preservation of the data will be put in place.
The duration of data preservation is still to be decided. The data is not of a
very complex nature. The approximated end volume will depend on several
factors including the number of participants willing to take part in the Open
Research Data Pilot, and the degree of aggregation to be applied, and as a
consequence is difficult to predict at the current time.

(

patent applications

)

.

**Comment [Ad9]:**

suggestion

No associated costs will be involved with the data sharing.

**8\. Preservation Plan:**

The following applies:

  * What is the long-term preservation plan for the dataset? 

  

The dataset will be deposited in the Zenodo data repository.

  * Will additional resources be needed to prepare data for deposit or meet charges from data repositories? 

  

No. The data preparation for deposit is part of Workpackage 7 (Dissemination)
and the deposit in Zenodo is free of charge. If psossible, the physical
depositing of the data will be done already before the end of the project, but
the embargo will be in place until the end of the period required for
publications and securing of intellectual property.

  * What additional resources are needed to deliver your plan? 

  

No additional resources are needed. Since the data will be publicly available
without restrictions, we do not need to keep a supervised data release system
in place.

  * Is additional specialist expertise (or training for existing staff) required? 

  

No additional specialist expertise is required. The readme files supplied with
the deposited data will contain all the information required to use the data.

  * Do you have sufficient storage and equipment or do you need to cost in more? 

  

This does not apply since the dat will be deposited in the Zenodo repository

  * Will charges be applied by data repositories? 

  

No. Zenodo is a cost-free repository.

  * Have you costed in time and effort to prepare the data for sharing / preservation? 

  

Yes. This is part of Workpackage 7 Dissemination.