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Editorial
The athlete monitoring cycle: a practical guide to interpreting and applying training monitoring data
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AbstractFullPdf
Jun 20179457342251
Jul 2017346426401580
Aug 2017580758511434
Sep 201785988624852
Oct 2017512551621256
Nov 201718891912687
Dec 2017588592351
Jan 2018700696472
Feb 2018418379344
Mar 2018469457158
Apr 2018375376139
May 2018331332126
Jun 2018280281110
Jul 201812301228256
Aug 2018364364133
Sep 2018357357104
Oct 2018469469184
Nov 2018448448181
Dec 2018303303121
Jan 2019329314127
Feb 2019297273201
Mar 2019291250207
Apr 2019255225164
May 2019448414221
Jun 2019346310298
Jul 2019218214177
Aug 2019214214111
Sep 2019382382164
Oct 2019377377141
Nov 2019338337112
Dec 2019351350124
Jan 2020330329140
Feb 2020326325251
Mar 2020357357158
Apr 2020417415209
May 2020520519233
Jun 2020338326198
Jul 2020362319145
Aug 2020264222147
Sep 2020405358206
Oct 2020356286233
Nov 2020305254171
Dec 2020471416195
Jan 2021492448204
Feb 2021366361207
Mar 2021520518208
Apr 2021437437230
May 2021404402154
Jun 2021507505197
Jul 2021261261111
Aug 2021266266105
Sep 2021271269104
Oct 2021352345158
Nov 2021363365188
Dec 2021385374127
Jan 2022634646182
Feb 2022375388183
Mar 2022299314139
Apr 2022298314110
May 2022349372141
Jun 2022262273112
Jul 202219519979
Aug 202218819673
Sep 202220321598
Oct 2022315323122
Nov 2022256261134
Dec 2022311315105
Jan 2023356370118
Feb 2023266272103
Mar 2023307309131
Apr 2023261267138
May 2023279278147
Jun 2023236238125
Jul 202316316192
Aug 2023178182104
Sep 2023269276180
Oct 2023289292135
Nov 2023268273117
Dec 2023280283135
Jan 2024324340362
Feb 2024250255273
Mar 2024260265119
Total612645102718822