{"id":"https://openalex.org/W4319586375","doi":"https://doi.org/10.1109/dsaa54385.2022.10032390","title":"Expect the gap: A recommender approach to estimate the absenteeism of self-monitoring mHealth app users","display_name":"Expect the gap: A recommender approach to estimate the absenteeism of self-monitoring mHealth app users","publication_year":2022,"publication_date":"2022-10-13","ids":{"openalex":"https://openalex.org/W4319586375","doi":"https://doi.org/10.1109/dsaa54385.2022.10032390"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa54385.2022.10032390","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/dsaa54385.2022.10032390","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032307445","display_name":"Miro Schleicher","orcid":"https://orcid.org/0000-0002-5987-5325"},"institutions":[{"id":"https://openalex.org/I95793202","display_name":"Otto-von-Guericke-Universit\u00e4t Magdeburg","ror":"https://ror.org/00ggpsq73","country_code":"DE","type":"education","lineage":["https://openalex.org/I95793202"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Miro Schleicher","raw_affiliation_strings":["Otto-von-Guericke-University Magdeburg,Knowledge Management & Discovery Lab,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Otto-von-Guericke-University Magdeburg,Knowledge Management & Discovery Lab,Germany","institution_ids":["https://openalex.org/I95793202"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068305831","display_name":"Ruediger Pryss","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099379","display_name":"Zimmer Biomet (Germany)","ror":"https://ror.org/016731f38","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210099379","https://openalex.org/I4210115238"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ruediger Pryss","raw_affiliation_strings":["University of Würzburg,Institute of Clinical Epidemiology and Biometry,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Würzburg,Institute of Clinical Epidemiology and Biometry,Germany","institution_ids":["https://openalex.org/I4210099379"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019200935","display_name":"Johannes Schobel","orcid":"https://orcid.org/0000-0002-6874-9478"},"institutions":[{"id":"https://openalex.org/I4210124377","display_name":"Neu-Ulm University of Applied Sciences","ror":"https://ror.org/03ggzay52","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210124377"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Johannes Schobel","raw_affiliation_strings":["Neu-Ulm University of Applied Sciences,Institute DigiHealth,Germany","Institute DigiHealth, Neu-Ulm University of Applied Sciences, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Neu-Ulm University of Applied Sciences,Institute DigiHealth,Germany","institution_ids":["https://openalex.org/I4210124377"]},{"raw_affiliation_string":"Institute DigiHealth, Neu-Ulm University of Applied Sciences, Germany","institution_ids":["https://openalex.org/I4210124377"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065508660","display_name":"Winfried Schlee","orcid":"https://orcid.org/0000-0001-7942-1788"},"institutions":[{"id":"https://openalex.org/I60668342","display_name":"University of Regensburg","ror":"https://ror.org/01eezs655","country_code":"DE","type":"education","lineage":["https://openalex.org/I60668342"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Winfried Schlee","raw_affiliation_strings":["University of Regensburg,Department of Psychiatry and Psychotherapy,Germany","Department of Psychiatry and Psychotherapy, University of Regensburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Regensburg,Department of Psychiatry and Psychotherapy,Germany","institution_ids":["https://openalex.org/I60668342"]},{"raw_affiliation_string":"Department of Psychiatry and Psychotherapy, University of Regensburg, Germany","institution_ids":["https://openalex.org/I60668342"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083456364","display_name":"Myra Spiliopoulou","orcid":"https://orcid.org/0000-0002-1828-5759"},"institutions":[{"id":"https://openalex.org/I95793202","display_name":"Otto-von-Guericke-Universit\u00e4t Magdeburg","ror":"https://ror.org/00ggpsq73","country_code":"DE","type":"education","lineage":["https://openalex.org/I95793202"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Myra Spiliopoulou","raw_affiliation_strings":["Otto-von-Guericke-University Magdeburg,Knowledge Management & Discovery Lab,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Otto-von-Guericke-University Magdeburg,Knowledge Management & Discovery Lab,Germany","institution_ids":["https://openalex.org/I95793202"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.3985,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.92430704,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"7","issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11446","display_name":"Mobile Health and mHealth Applications","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/3600","display_name":"General Health Professions"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11446","display_name":"Mobile Health and mHealth Applications","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/3600","display_name":"General Health Professions"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.9868999719619751,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11519","display_name":"Digital Mental Health Interventions","score":0.9843000173568726,"subfield":{"id":"https://openalex.org/subfields/3202","display_name":"Applied Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/duration","display_name":"Duration (music)","score":0.7723668813705444},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7489489912986755},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6825354695320129},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5243585109710693},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.48705151677131653},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4766139090061188},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.447905033826828},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4429619312286377},{"id":"https://openalex.org/keywords/mhealth","display_name":"mHealth","score":0.4383406937122345},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.4166969656944275},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39578762650489807},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3665018379688263},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33106082677841187}],"concepts":[{"id":"https://openalex.org/C112758219","wikidata":"https://www.wikidata.org/wiki/Q16038819","display_name":"Duration (music)","level":2,"score":0.7723668813705444},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7489489912986755},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6825354695320129},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5243585109710693},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.48705151677131653},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4766139090061188},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.447905033826828},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4429619312286377},{"id":"https://openalex.org/C2779363104","wikidata":"https://www.wikidata.org/wiki/Q17069079","display_name":"mHealth","level":3,"score":0.4383406937122345},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.4166969656944275},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39578762650489807},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3665018379688263},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33106082677841187},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C27415008","wikidata":"https://www.wikidata.org/wiki/Q7256382","display_name":"Psychological intervention","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa54385.2022.10032390","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/dsaa54385.2022.10032390","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W387672264","https://openalex.org/W1540089290","https://openalex.org/W1994389483","https://openalex.org/W2031201873","https://openalex.org/W2034322181","https://openalex.org/W2080320419","https://openalex.org/W2552480641","https://openalex.org/W2554658613","https://openalex.org/W2563010184","https://openalex.org/W2916286829","https://openalex.org/W2949624543","https://openalex.org/W3011400958","https://openalex.org/W3036366020","https://openalex.org/W3047010778","https://openalex.org/W3107209321","https://openalex.org/W3117542045","https://openalex.org/W3155015460","https://openalex.org/W3200416022","https://openalex.org/W4285283518","https://openalex.org/W4312585626","https://openalex.org/W6729542563","https://openalex.org/W6775047284"],"related_works":["https://openalex.org/W1484355083","https://openalex.org/W2772628444","https://openalex.org/W4220714703","https://openalex.org/W2735929803","https://openalex.org/W2170391450","https://openalex.org/W2098758514","https://openalex.org/W3008845055","https://openalex.org/W2041004656","https://openalex.org/W4376854386","https://openalex.org/W1966742602"],"abstract_inverted_index":{"Adherence":[0],"and":[1,64,76,132,148,208,225,252,267,276,282],"phases":[2],"of":[3,8,19,54,77,96,112,143,192,199,202,213,230,249,264,303],"non-adherence":[4],"in":[5,58],"the":[6,24,31,51,70,94,97,122,170,175,184,190,196,200,205,210,214,222,243,247,261,265,301],"usage":[7],"mHealth":[9],"apps":[10],"for":[11,158,183,283,296],"self-monitoring":[12],"generate":[13],"time":[14,72,129,244],"series":[15,73,130,245],"characterized":[16],"by":[17],"gaps":[18,75,87],"varying":[20],"duration.":[21],"These":[22,152],"reduce":[23],"knowledge":[25],"that":[26,46,84,189,288],"can":[27,154,237],"be":[28,156,238],"gained":[29],"from":[30],"data":[32,266],"because":[33],"important":[34],"observations":[35],"are":[36,80,106,119,133,140],"not":[37,179],"captured.":[38],"In":[39,216,272],"this":[40,68,217,273],"paper,":[41],"an":[42],"approach":[43,290],"is":[44,91,165],"presented":[45],"allows":[47],"experts":[48,297],"to":[49,60,168,240,246,260,269,298,305],"estimate":[50,169],"possible":[52,167],"duration":[53,95,172],"a":[55,109,219,226,292,306],"user\u2019s":[56],"absence":[57],"order":[59],"build":[61],"on":[62,195,204,209],"it":[63],"take":[65],"appropriate":[66,197],"action.For":[67],"purpose,":[69],"users\u2019":[71],"(with":[74],"different":[78],"lengths)":[79],"decomposed":[81],"into":[82,108],"sequences":[83],"have":[85],"no":[86],"anymore.":[88],"Each":[89],"sequence":[90,177],"associated":[92],"with":[93,115,135,161],"subsequent":[98],"gap.":[99],"Using":[100],"unsupervised":[101,128],"binning,":[102],"these":[103],"gap":[104,117,149,171,228],"values":[105],"sorted":[107],"predefined":[110],"number":[111,201],"categories.":[113],"Sequences":[114],"similar":[116],"durations":[118],"thus":[120],"assigned":[121,134],"same":[123,176],"category":[124,150],"labels,":[125],"grouped":[126],"using":[127],"clustering":[131,236],"cluster":[136,146],"labels.":[137],"Thus":[138],"triplets":[139,153],"derived":[141],"consisting":[142],"user":[144],"ID,":[145],"label":[147],"label.":[151],"then":[155],"used":[157,239],"collaborative":[159,250],"filtering":[160,251],"matrix":[162,255,284],"factorization.":[163],"It":[164],"now":[166],"even":[173],"if":[174],"has":[178],"yet":[180],"been":[181],"observed":[182],"app":[185],"user.The":[186],"results":[187,281],"show":[188],"quality":[191],"binning":[193],"depends":[194],"choice":[198],"categories,":[203],"technique":[206],"used,":[207],"maximum":[211,227],"length":[212,229],"gaps.":[215],"example,":[218],"5-star":[220],"rating,":[221],"Fischer-Jenks":[223],"algorithm":[224],"30":[231],"days.":[232],"We":[233],"demonstrate":[234],"how":[235,253],"gradually":[241],"adapt":[242],"conditions":[248],"complex":[254,262],"factorization":[256,285],"models":[257],"respond":[258],"better":[259,270,299],"structures":[263],"lead":[268],"results.":[271],"work,":[274],"K-Medoids":[275],"Agglomerative":[277],"Clustering":[278],"obtained":[279],"applicable":[280],"SVD++.We":[286],"believe":[287],"our":[289],"provides":[291],"valuable":[293],"back-end":[294],"tool":[295],"assess":[300],"adherence":[302],"users":[304],"selfmonitoring":[307],"app.":[308]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}