CA20104 – Ne wo k on e idence-based
physical ac i i y in old age (PhysAgeNe )
Deli e able D2.4
Demons a o (p o o ype) o he eposi o y including eal da a
om echnology-assis ed PA in e en ions
Con ibu o s
Wo king G oup 2
Ca l-Philipp Jansen (WG2 co-leade ), Tiia Kekäläinen (WG2 leade ), Sal a o e
Tedesco (WG2 membe ), O gesa Qipo (WG2 membe ), I an Bau mans (WG2
membe ), E ja Po egijs (WG2 membe , o me WG2 leade )
Table o Con en s
1. In oduc ion ................................................................................................................................................................................. 3
2. T6: C ea e a s uc u e o a iables composing he open eposi o y ................................................... 4
2.1. Objec i e ................................................................................................................................................................................. 4
2.2. App oach ........................................................................................................................................................................... 4
3. T7: Find sui able pla o m om exis ing open eposi o ies ........................................................................ 9
3.1. Objec i e ................................................................................................................................................................................. 9
3.2. E alua ion C i e ia ...................................................................................................................................................... 9
3.3. Jus i ica ion o Selec ion ...................................................................................................................................... 9
4. T8: P epa e ac ual da a deli e y wi h small p ojec s o echnology-assis ed physical ac i i y
in e en ions in olde people .......................................................................................................................................... 11
4.1. Objec i e ........................................................................................................................................................................... 11
4.2. Implemen a ion ........................................................................................................................................................... 11
5. T9: Da a-deli e y in open eposi o y comple ed ................................................................................................ 11
6. Re e ences ............................................................................................................................................................................ 12
1. INTRODUCTION
The p o ision o da a o scien i ic esea ch is gaining impo ance – bu also in complexi y. In
he ield o heal h esea ch in pa icula , e ec i e da a sha ing is essen ial o p omo e
collabo a ion, inc ease e iciency, and d i e inno a ion. This deli e able ocuses on he p ac ical
implemen a ion o a eposi o y designed o suppo he s uc u ed and legally complian
p o ision o esea ch da a. The goal is o c ea e a ounda ion ha enables:
• s onge c oss-p ojec collabo a ion,
• mo e comp ehensi e analysis o la ge da ase s, and
• imp o ed pa ien ou comes h ough da a-d i en inno a ion.
This deli e able epo s on he ollowing asks ha a e lis ed unde D2.4:
T6: C ea e a s uc u e o a iables composing he open eposi o y;
T7: Find sui able pla o m om exis ing open eposi o ies;
T8: P epa e ac ual da a deli e y wi h small p ojec s o echnology-assis ed physical ac i i y
in e en ions in olde people;
T9: Da a-deli e y in open eposi o y comple ed.
2. T6: CREATE A STRUCTURE FOR VARIABLES COMPOSING THE
OPEN REPOSITORY
2.1. Objec i e
The goal o his ask is o de ine a minimal da ase s uc u e o agg ega ed da a epo ing in
s udies on echnology-assis ed physical ac i i y (PA) in e en ions in olde adul s.
2.2. App oach
Re iew da a ex ac ion shee s om he sys ema ic e iews lis ed. These e iews include
di e se endpoin s like neu omuscula bioma ke s, physical pe o mance, in lamma o y
ma ke s, and beha iou al ac o s. F om his, de ine a s anda dised a iable lis ac oss s udies.
This s uc u e is based on a syn hesis o he pa ame e s ypically epo ed in he sys ema ic
e iews e e enced, aligned wi h FAIR da a p inciples (Findable, Accessible, In e ope able,
Reusable), and ailo ed o u u e me a-analyses and e idence syn hesis.
1. S udy-Le el Me ada a
Va iable Name Type Desc ip ion
S udy_ID S ing Unique iden i ie (e.g. Fi s Au ho _Yea )
Yea _o _Publica ion In ege Ac ual yea o publica ion
DOI S ing Digi al Objec Iden i ie o he publica ion
Ti le S ing Full s udy i le
Au ho s S ing/Lis Names o i s and las au ho (o ull lis i needed)
Coun y Ca ego ical Coun y whe e he s udy was conduc ed
S udy_Design Ca ego ical RCT / Coho / Pilo / Feasibili y s udy / Case se ies
Rec ui men _Sou ce Ca ego ical Communi y-dwelling / Assis ed li ing / Clinical
E hical_App o al Bina y Was e hical app o al epo ed? Yes/No
Con lic _o _In e es Bina y Was a COI decla a ion p o ided?
Ad e se_e en s Bina y We e AEs epo ed? Yes/No
2. Pa icipan Cha ac e is ics
Va iable Name Type Desc ip ion
Sample_Size_To al In ege To al numbe o pa icipan s
Mean_Age In ege Mean age ± SD o pa icipan s
Female_Pe cen In ege Pe cen o sample ha is emale
Clinical_sample Bina y Clinical sample: yes o no
Clinical_diagnosis S ing P ima y diagnosis ele an o mobili y, e.g.
Pa kinson’s, MS, F ail y, …
Inclusion_C i e ia S ing Tex ual desc ip ion o coded ield (e.g., age ≥ 65,
MMSE > 24)
Exclusion_C i e ia S ing Common exclusion: cogni i e impai men ,
pacemake , se e e isual impai men
3. In e en ion De ails
Va iable Name Type Desc ip ion
Exe cise_Type Ca ego ical e.g., unning, walking, balance, esis ance aining,
powe aining, mul imodal, mul icomponen ,…
In e en ion_Type S ing e.g., Exe game, Wea able PA acke , Robo ics,
e-Coaching, Mobile App, Online T aining,…
Tech_De ice S ing De ice used o in e en ion deli e y, e.g., Table ,
Sma phone, PC, Gaming console
Tech_Spec S ing B and/model o he de ice i applicable (e.g., Fi bi
Inspi e, Nin endo Wii, Windows PC, …)
In e _Du a ion_weeks In ege To al leng h o he in e en ion in weeks
Sessions_Pe _Week In ege F equency o he sessions
Session_Leng h_Minu es In ege Du a ion o a single session
Session_In ensi y In ege In ensi y o exe cise(s)
Supe ision_Type Ca ego ical Fully supe ised / Pa ially supe ised / Remo e-
only / No supe ised
Con ol_G oup Ca ego ical Type o con ol g oup: Ac i e con ols / Inac i e
con ols / Wai ing con ols / No con ols
4. Ou come Va iables – Agg ega ed Da a
Va iable Uni s No es
P ima y_ou come S ing Name o p ima y ou come a iable
P im_ou c_bl In ege Baseline alue: Median and IQR o Mean ± SD
P im_ou c_pos In ege Pos alue: Median and IQR o Mean ± SD
Fu he (seconda y) ou come a iables om hen on can be g ouped hema ically based on
he ype o in e en ion and e iew ocus.
4a. Physical Pe o mance Me ics
The ollowing a e he mos o en used measu es o in insic capaci y. These a e no manda o y,
bu a e sugges ed o be used o compa ibili y ac oss s udies. O he measu es can be added,
o cou se.
Va iable Uni s No es
Gai _Speed_P e In ege Mean ± SD [m/s] baseline alue
Gai _Speed_Pos In ege Mean ± SD [m/s] pos alue
SPPB_Sco e_P e In ege Sho Physical Pe o mance Ba e y baseline
measu emen alue [Sco e]
SPPB_Sco e_Pos In ege Sho Physical Pe o mance Ba e y pos measu emen
alue [Sco e]
Handg ip_S eng h In ege Dynamome y baseline [kg] baseline alue
TUG_Time_P e In ege Timed Up & Go Tes baseline alue [sec]
TUG_Time_Pos In ege Timed Up & Go Tes pos alue [sec]
… con inued … use impo an physical pe o mance ou comes as
necessa y
4b. Bioma ke s
Va iable Type No es
Bioma ke _use Bina y Was he e a measu emen o bioma ke s? Yes/no
Bioma ke _1 S ing Name o bioma ke 1
Bioma ke _1_P e In ege Baseline alue [uni ] o bioma ke 1
Bioma ke _1_Pos In ege Pos alue [uni ] o bioma ke 1
Bioma ke _2 S ing Name o bioma ke 1
… con inued … …
4c. Quali y o li e / Mo i a ion / Adhe ence
Inclusion o measu es o adhe ence is highly ecommended. I measu es o adhe ence,
mo i a ion, and quali y o li e a e a ailable, hese can be epo ed unde his ca ego y. Se e al
measu es o adhe ence can be epo ed (e.g., a endance, comple ion, …); he lis would ha e
o be adap ed acco dingly
Va iable Type No es
Adhe ence_Ra e_Technol In ege Pe cen age o sessions comple ed using he
echnology
Adhe ence_Ra e_Exe cise In ege Pe cen age o expec ed exe cises comple ed (i
possibly di e ing om Technology use)
D opou _Ra e_Pe cen In ege % o pa icipan s los o ollow-up
Mo i a ion_Scale_P e In ege e.g., In insic Mo i a ion In en o y
QoL_Sco e_Pos In ege SF-36 / WHOQOL-BREF, e c.
5. Quali y/Me ada a Flags (Op ional)
Repo i a ailable.
Va iable Type Desc ip ion
Risk_O _Bias_Sco e In ege /Ca ego ical e.g., ROB2 ool
GRADE_Ce ain y_Le el Ca ego ical High / Mode a e / Low
Funding_Sou ce S ing Public / P i a e / Mixed
Con lic _o _In e es _YN Bina y Yes / No
AEs_Numbe In ege Numbe o ad e se e en s
3. T7: FIND SUITABLE PLATFORM FROM EXISTING OPEN
REPOSITORIES
3.1. Objec i e
The objec i e o his ask is o iden i y a sui able pla o m om he landscape o exis ing open
da a eposi o ies ha aligns wi h he p inciples o Open Science, suppo s long- e m da a
p ese a ion, and complies wi h Eu opean legal and e hical s anda ds, no ably he Gene al
Da a P o ec ion Regula ion (GDPR). The selec ed pla o m should se e he needs o
esea che s wi hin he COST Ac ion and beyond, and enable complian and sus ainable
sha ing o esea ch da a ou pu s.
3.2. E alua ion C i e ia
In line wi h he equi emen s o COST Ac ions and b oade EU unding schemes (e.g. Ho izon
Eu ope), he ollowing c i e ia we e de ined o e alua ing sui able eposi o ies:
• GDPR compliance and da a go e nance
• Suppo o FAIR da a p inciples (Findable, Accessible, In e ope able, Reusable)
• Open access and inclusi i y
• Cos s uc u e and sus ainabili y
• Technical in as uc u e and usabili y
• Suppo o ci a ion, e sioning, and pe sis en iden i ie s (DOIs)
• Compa ibili y wi h EU unde s’ manda es and Open Science policies
Selec ed Reposi o y: Zenodo
Following a compa a i e e iew o mul iple eposi o y pla o ms (including Figsha e, D yad,
Mendeley Da a, and ins i u ional op ions), Zenodo has been iden i ied as a highly sui able
pla o m o da a sha ing wi hin he COST Ac ion amewo k.
3.3. Jus i ica ion o Selec ion
1. GDPR Compliance and Legal Reliabili y
Zenodo is hos ed by CERN and ope a ed unde he OpenAIRE ini ia i e, which ensu es ull
compliance wi h he EU Gene al Da a P o ec ion Regula ion (GDPR). This includes anspa en
da a handling policies, anonymiza ion capabili ies, and obus use consen managemen ,
making i a secu e choice o p ojec s in ol ing sensi i e o pe sonal da a.
