I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Predicting Physical Exercise Adherence in Fitness Apps Usin..:
Jossa-Bastidas, Oscar
;
Zahia, Sofia
;
Fuente-Vidal, Andrea
...
International Journal of Environmental Research and Public Health. 18 (2021) 20 - p. 10769 , 2021
Link:
https://doi.org/10.3390/ijerph182010769
RT Journal T1 <Title>Predicting Physical Exercise Adherence in Fitness Apps Using a Deep Learning Approach</Title> UL https://suche.suub.uni-bremen.de/peid=cr-10.3390_ijerph182010769&Exemplar=1&LAN=DE A1 Jossa-Bastidas, Oscar A1 Zahia, Sofia A1 Fuente-Vidal, Andrea A1 Sánchez Férez, Néstor A1 Roda Noguera, Oriol A1 Montane, Joel A1 Garcia-Zapirain, Begonya PB MDPI AG YR 2021 SN 1660-4601 JF International Journal of Environmental Research and Public Health VO 18 IS 20 SP 10769 LK http://dx.doi.org/https://doi.org/10.3390/ijerph182010769 DO https://doi.org/10.3390/ijerph182010769 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
