{"id":"https://openalex.org/W3020345984","doi":"https://doi.org/10.1145/3368958","title":"Toward Assessing and Recommending Combinations of Behaviors for Improving Health and Well-Being","display_name":"Toward Assessing and Recommending Combinations of Behaviors for Improving Health and Well-Being","publication_year":2020,"publication_date":"2020-01-31","ids":{"openalex":"https://openalex.org/W3020345984","doi":"https://doi.org/10.1145/3368958","mag":"3020345984"},"language":"en","primary_location":{"id":"doi:10.1145/3368958","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3368958","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3368958","source":{"id":"https://openalex.org/S4210174653","display_name":"ACM Transactions on Computing for Healthcare","issn_l":"2637-8051","issn":["2637-8051","2691-1957"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Computing for Healthcare","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3368958","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069540104","display_name":"Ehimwenma Nosakhare","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ehimwenma Nosakhare","raw_affiliation_strings":["Massachusetts Institute of Technology, Cambridge, MA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, Cambridge, MA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087366916","display_name":"Rosalind W. Picard","orcid":"https://orcid.org/0000-0002-5661-0022"},"institutions":[{"id":"https://openalex.org/I4210142372","display_name":"Human Media","ror":"https://ror.org/04072nk43","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210142372"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rosalind Picard","raw_affiliation_strings":["MIT Media Lab, Cambridge, MA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIT Media Lab, Cambridge, MA","institution_ids":["https://openalex.org/I4210142372"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.8199,"has_fulltext":true,"cited_by_count":50,"citation_normalized_percentile":{"value":0.9743876,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"1","issue":"1","first_page":"1","last_page":"29"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13283","display_name":"Mental Health Research Topics","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13283","display_name":"Mental Health Research Topics","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10235","display_name":"Health disparities and outcomes","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/3306","display_name":"Health"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10475","display_name":"Psychological Well-being and Life Satisfaction","score":0.9848999977111816,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social 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/mood","display_name":"Mood","score":0.668596088886261},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.6682153940200806},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5619086623191833},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.48707082867622375},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4751324951648712},{"id":"https://openalex.org/keywords/latent-class-model","display_name":"Latent class model","score":0.46812665462493896},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4645419418811798},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44671863317489624},{"id":"https://openalex.org/keywords/cognitive-psychology","display_name":"Cognitive psychology","score":0.4368012845516205},{"id":"https://openalex.org/keywords/well-being","display_name":"Well-being","score":0.4348151683807373},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.38567739725112915},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3727870583534241},{"id":"https://openalex.org/keywords/applied-psychology","display_name":"Applied psychology","score":0.35064196586608887},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.247847318649292}],"concepts":[{"id":"https://openalex.org/C2780733359","wikidata":"https://www.wikidata.org/wiki/Q331769","display_name":"Mood","level":2,"score":0.668596088886261},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.6682153940200806},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5619086623191833},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.48707082867622375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4751324951648712},{"id":"https://openalex.org/C70727504","wikidata":"https://www.wikidata.org/wiki/Q1806878","display_name":"Latent class model","level":2,"score":0.46812665462493896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4645419418811798},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44671863317489624},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.4368012845516205},{"id":"https://openalex.org/C2776420229","wikidata":"https://www.wikidata.org/wiki/Q7981051","display_name":"Well-being","level":2,"score":0.4348151683807373},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.38567739725112915},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3727870583534241},{"id":"https://openalex.org/C75630572","wikidata":"https://www.wikidata.org/wiki/Q538904","display_name":"Applied psychology","level":1,"score":0.35064196586608887},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.247847318649292},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3368958","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3368958","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3368958","source":{"id":"https://openalex.org/S4210174653","display_name":"ACM Transactions on Computing for Healthcare","issn_l":"2637-8051","issn":["2637-8051","2691-1957"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Computing for Healthcare","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3368958","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3368958","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3368958","source":{"id":"https://openalex.org/S4210174653","display_name":"ACM Transactions on Computing for Healthcare","issn_l":"2637-8051","issn":["2637-8051","2691-1957"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Computing for Healthcare","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G226754772","display_name":"Multi-scale modeling of sleep behaviors in social networks","funder_award_id":"5r01gm105018-05","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G7968272860","display_name":null,"funder_award_id":"R01GM105018","funder_id":"https://openalex.org/F4320337373","funder_display_name":"Center for Information Technology"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320332195","display_name":"Samsung","ror":"https://ror.org/04w3jy968"},{"id":"https://openalex.org/F4320337373","display_name":"Center for Information Technology","ror":"https://ror.org/03jh5a977"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3020345984.pdf","grobid_xml":"https://content.openalex.org/works/W3020345984.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1536014355","https://openalex.org/W1579406079","https://openalex.org/W1880262756","https://openalex.org/W1971460256","https://openalex.org/W2024212131","https://openalex.org/W2037646636","https://openalex.org/W2046446443","https://openalex.org/W2046498081","https://openalex.org/W2074440385","https://openalex.org/W2099336098","https://openalex.org/W2102892488","https://openalex.org/W2105752956","https://openalex.org/W2111046157","https://openalex.org/W2163922914","https://openalex.org/W2164513234","https://openalex.org/W2169753097","https://openalex.org/W2267637137","https://openalex.org/W2346599834","https://openalex.org/W2408722882","https://openalex.org/W2586937454","https://openalex.org/W2609406514","https://openalex.org/W2779582454","https://openalex.org/W2802769465","https://openalex.org/W2914398499","https://openalex.org/W2952113171","https://openalex.org/W6714307276"],"related_works":["https://openalex.org/W2769501189","https://openalex.org/W4315588616","https://openalex.org/W4312773271","https://openalex.org/W2888805565","https://openalex.org/W2962686197","https://openalex.org/W3005513013","https://openalex.org/W2207653751","https://openalex.org/W2611137333","https://openalex.org/W4389543811","https://openalex.org/W4291700620"],"abstract_inverted_index":{"Multiple":[0],"behaviors":[1,44,89,178,216],"typically":[2],"work":[3,197],"together":[4],"to":[5,11,38,65,75,102,115,121,131,191,206],"influence":[6],"health,":[7],"making":[8],"it":[9],"hard":[10],"understand":[12],"how":[13,114,211],"one":[14],"behavior":[15],"might":[16],"compensate":[17],"for":[18],"another.":[19],"Rich":[20],"multi-modal":[21,68],"datasets":[22],"from":[23,47,146],"mobile":[24],"sensors":[25],"and":[26,50,56,94,96,110,126,137,169,183,194,221],"advances":[27],"in":[28,72,187],"machine":[29],"learning":[30],"are":[31,159],"today":[32],"enabling":[33],"new":[34,200],"kinds":[35],"of":[36,43,53,79,88,144,149,162,166,176,210,213],"associations":[37],"be":[39],"made":[40],"between":[41],"combinations":[42,87,212],"objectively":[45],"assessed":[46],"daily":[48,163],"life":[49],"self-reported":[51,164],"levels":[52,165],"stress,":[54,192],"mood,":[55,193],"health.":[57,195],"In":[58],"this":[59],"article,":[60],"we":[61,127,153],"present":[62,179],"a":[63,147,199],"framework":[64],"(1)":[66],"map":[67],"messy":[69],"data":[70,145,204],"collected":[71],"the":[73,123,133,139,174],"\u201cwild\u201d":[74],"meaningful":[76],"feature":[77],"representations":[78],"health-related":[80],"behaviors,":[81,125],"(2)":[82],"uncover":[83,132,184],"latent":[84,118,134,157],"patterns":[85,101,158,175],"comprising":[86],"that":[90,106,155],"best":[91],"predict":[92],"health":[93,109,220],"well-being,":[95],"(3)":[97],"use":[98,116],"these":[99,156],"learned":[100],"make":[103],"evidence-based":[104],"recommendations":[105],"may":[107,217],"improve":[108],"well-being.":[111,222],"We":[112,172],"show":[113],"supervised":[117],"Dirichlet":[119],"allocation":[120],"model":[122,140],"observed":[124],"apply":[128],"variational":[129],"inference":[130],"patterns.":[135],"Implementing":[136],"evaluating":[138],"on":[141,180],"5,397":[142],"days":[143,182],"group":[148],"244":[150],"college":[151],"students,":[152],"find":[154],"indeed":[160],"predictive":[161],"stressed-calm,":[167],"sad-happy,":[168],"sick-healthy":[170],"states.":[171],"investigate":[173],"modifiable":[177,214],"different":[181],"several":[185],"ways":[186],"which":[188],"they":[189],"relate":[190],"This":[196],"contributes":[198],"method":[201],"using":[202],"objective":[203],"analysis":[205],"help":[207],"advance":[208],"understanding":[209],"human":[215,219],"promote":[218]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":24},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":3}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}
