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Feasibility, engagement, and preliminary clinical outcomes of a digital biodata-driven intervention for anxiety and depression
HypothesisThe main hypothesis is that a digital, biodata-driven, and personalized program would exhibit high user retention and engagement, followed by more effective management of their depressive and anxiety symptoms. ObjectiveThis pilot study explores the feasibility, acceptability, engagement, a...
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Published in: | Frontiers in digital health 2022-07, Vol.4, p.868970-868970 |
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creator | Tsirmpas, Charalampos Andrikopoulos, Dimitrios Fatouros, Panagiotis Eleftheriou, Georgios Anguera, Joaquin A. Kontoangelos, Konstantinos Papageorgiou, Charalabos |
description | HypothesisThe main hypothesis is that a digital, biodata-driven, and personalized program would exhibit high user retention and engagement, followed by more effective management of their depressive and anxiety symptoms. ObjectiveThis pilot study explores the feasibility, acceptability, engagement, and potential impact on depressive and anxiety and quality of life outcomes of the 16-week Feel Program. Additionally, it examines potential correlations between engagement and impact on mental health outcomes. MethodsThis single-arm study included 48 adult participants with mild or moderate depressive or anxiety symptoms who joined the 16-week Feel Program, a remote biodata-driven mental health support program created by Feel Therapeutics. The program uses a combination of evidence-based approaches and psychophysiological data. Candidates completed an online demographics and eligibility survey before enrolment. Depressive and anxiety symptoms were measured using the Patient Health Questionnaire and Generalized Anxiety Disorder Scale, respectively. The Satisfaction with Life Scale and the Life Satisfaction Questionnaire were used to assess quality of life. User feedback surveys were employed to evaluate user experience and acceptability. ResultsIn total, 31 participants completed the program with an overall retention rate of 65%. Completed participants spent 60 min in the app, completed 13 Mental Health Actions, including 5 Mental Health Exercises and 4.9 emotion logs on a weekly basis. On average, 96% of the completed participants were active and 76.8% of them were engaged with the sensor during the week. Sixty five percent of participants reported very or extremely high satisfaction, while 4 out of 5 were very likely to recommend the program to someone. Additionally, 93.5% of participants presented a decrease in at least one of the depressive or anxiety symptoms, with 51.6 and 45% of participants showing clinically significant improvement, respectively. Finally, our findings suggest increased symptom improvement for participants with higher engagement throughout the program. ConclusionsThe findings suggest that the Feel Program may be feasible, acceptable, and valuable for adults with mild or moderate depressive and/or anxiety symptoms. However, controlled trials with bigger sample size, inclusion of a control group, and more diverse participant profiles are required in order to provide further evidence of clinical efficacy. |
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ObjectiveThis pilot study explores the feasibility, acceptability, engagement, and potential impact on depressive and anxiety and quality of life outcomes of the 16-week Feel Program. Additionally, it examines potential correlations between engagement and impact on mental health outcomes. MethodsThis single-arm study included 48 adult participants with mild or moderate depressive or anxiety symptoms who joined the 16-week Feel Program, a remote biodata-driven mental health support program created by Feel Therapeutics. The program uses a combination of evidence-based approaches and psychophysiological data. Candidates completed an online demographics and eligibility survey before enrolment. Depressive and anxiety symptoms were measured using the Patient Health Questionnaire and Generalized Anxiety Disorder Scale, respectively. The Satisfaction with Life Scale and the Life Satisfaction Questionnaire were used to assess quality of life. User feedback surveys were employed to evaluate user experience and acceptability. ResultsIn total, 31 participants completed the program with an overall retention rate of 65%. Completed participants spent 60 min in the app, completed 13 Mental Health Actions, including 5 Mental Health Exercises and 4.9 emotion logs on a weekly basis. On average, 96% of the completed participants were active and 76.8% of them were engaged with the sensor during the week. Sixty five percent of participants reported very or extremely high satisfaction, while 4 out of 5 were very likely to recommend the program to someone. Additionally, 93.5% of participants presented a decrease in at least one of the depressive or anxiety symptoms, with 51.6 and 45% of participants showing clinically significant improvement, respectively. Finally, our findings suggest increased symptom improvement for participants with higher engagement throughout the program. ConclusionsThe findings suggest that the Feel Program may be feasible, acceptable, and valuable for adults with mild or moderate depressive and/or anxiety symptoms. However, controlled trials with bigger sample size, inclusion of a control group, and more diverse participant profiles are required in order to provide further evidence of clinical efficacy.</description><identifier>ISSN: 2673-253X</identifier><identifier>EISSN: 2673-253X</identifier><identifier>DOI: 10.3389/fdgth.2022.868970</identifier><identifier>PMID: 35958737</identifier><language>eng</language><publisher>Frontiers Media S.A</publisher><subject>data-driven therapeutics ; Digital Health ; emotion detection ; generalized anxiety disorder ; major depressive disorder ; psychophysiological data</subject><ispartof>Frontiers in digital health, 2022-07, Vol.4, p.868970-868970</ispartof><rights>Copyright © 2022 Tsirmpas, Andrikopoulos, Fatouros, Eleftheriou, Anguera, Kontoangelos and Papageorgiou. 2022 Tsirmpas, Andrikopoulos, Fatouros, Eleftheriou, Anguera, Kontoangelos and Papageorgiou</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c442t-13354622269381c7512706e69d7d59a3c2881a4ffbaefc7252c35608fc7362003</citedby><cites>FETCH-LOGICAL-c442t-13354622269381c7512706e69d7d59a3c2881a4ffbaefc7252c35608fc7362003</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9359094/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9359094/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27923,27924,53790,53792</link.rule.ids></links><search><creatorcontrib>Tsirmpas, Charalampos</creatorcontrib><creatorcontrib>Andrikopoulos, Dimitrios</creatorcontrib><creatorcontrib>Fatouros, Panagiotis</creatorcontrib><creatorcontrib>Eleftheriou, Georgios</creatorcontrib><creatorcontrib>Anguera, Joaquin A.</creatorcontrib><creatorcontrib>Kontoangelos, Konstantinos</creatorcontrib><creatorcontrib>Papageorgiou, Charalabos</creatorcontrib><title>Feasibility, engagement, and preliminary clinical outcomes of a digital biodata-driven intervention for anxiety and depression</title><title>Frontiers in digital health</title><description>HypothesisThe main hypothesis is that a digital, biodata-driven, and personalized program would exhibit high user retention and engagement, followed by more effective management of their depressive and anxiety symptoms. ObjectiveThis pilot study explores the feasibility, acceptability, engagement, and potential impact on depressive and anxiety and quality of life outcomes of the 16-week Feel Program. Additionally, it examines potential correlations between engagement and impact on mental health outcomes. MethodsThis single-arm study included 48 adult participants with mild or moderate depressive or anxiety symptoms who joined the 16-week Feel Program, a remote biodata-driven mental health support program created by Feel Therapeutics. The program uses a combination of evidence-based approaches and psychophysiological data. Candidates completed an online demographics and eligibility survey before enrolment. Depressive and anxiety symptoms were measured using the Patient Health Questionnaire and Generalized Anxiety Disorder Scale, respectively. The Satisfaction with Life Scale and the Life Satisfaction Questionnaire were used to assess quality of life. User feedback surveys were employed to evaluate user experience and acceptability. ResultsIn total, 31 participants completed the program with an overall retention rate of 65%. Completed participants spent 60 min in the app, completed 13 Mental Health Actions, including 5 Mental Health Exercises and 4.9 emotion logs on a weekly basis. On average, 96% of the completed participants were active and 76.8% of them were engaged with the sensor during the week. Sixty five percent of participants reported very or extremely high satisfaction, while 4 out of 5 were very likely to recommend the program to someone. Additionally, 93.5% of participants presented a decrease in at least one of the depressive or anxiety symptoms, with 51.6 and 45% of participants showing clinically significant improvement, respectively. Finally, our findings suggest increased symptom improvement for participants with higher engagement throughout the program. ConclusionsThe findings suggest that the Feel Program may be feasible, acceptable, and valuable for adults with mild or moderate depressive and/or anxiety symptoms. However, controlled trials with bigger sample size, inclusion of a control group, and more diverse participant profiles are required in order to provide further evidence of clinical efficacy.</description><subject>data-driven therapeutics</subject><subject>Digital Health</subject><subject>emotion detection</subject><subject>generalized anxiety disorder</subject><subject>major depressive disorder</subject><subject>psychophysiological data</subject><issn>2673-253X</issn><issn>2673-253X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNpVkstqHDEQRZuQEBvHH5Cdllm4J3q0XptAMHFiMHiTQHZCLVW3ZbqliaQxmY2_PfKMCfaqLlXFKW5xu-4jwRvGlP48-bnebSimdKOE0hK_6U6pkKynnP1--0KfdOel3GOMKSeUYv6-O2FccyWZPO0er8CWMIYl1P0FgjjbGVaI9QLZ6NE2wxLWEG3eI7eEGJxdUNpVl1YoKE3IIh_mUFt3DMnbanufwwNEFGKF3EQNKaIp5Yb7G6DuD1gPDVxKG33o3k12KXD-XM-6X1fffl7-6G9uv19ffr3p3TDQ2hPG-CAopUIzRZxsRiQWILSXnmvLHFWK2GGaRguTk5RTx7jAqmkmKMbsrLs-cn2y92abw9osmWSDOTRSno3NNbgFjCSeNBADPuKBCDVqJUBxPjDtFLaisb4cWdvduIJ3zWS2yyvo60kMd2ZOD0a3t2M9NMCnZ0BOf3ZQqllDcbAsNkLaFdO8USLbWd5WyXHV5VRKhun_GYLNUwzMIQbmKQbmGAP2D2vZp0k</recordid><startdate>20220722</startdate><enddate>20220722</enddate><creator>Tsirmpas, Charalampos</creator><creator>Andrikopoulos, Dimitrios</creator><creator>Fatouros, Panagiotis</creator><creator>Eleftheriou, Georgios</creator><creator>Anguera, Joaquin A.</creator><creator>Kontoangelos, Konstantinos</creator><creator>Papageorgiou, Charalabos</creator><general>Frontiers Media S.A</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20220722</creationdate><title>Feasibility, engagement, and preliminary clinical outcomes of a digital biodata-driven intervention for anxiety and depression</title><author>Tsirmpas, Charalampos ; Andrikopoulos, Dimitrios ; Fatouros, Panagiotis ; Eleftheriou, Georgios ; Anguera, Joaquin A. ; Kontoangelos, Konstantinos ; Papageorgiou, Charalabos</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c442t-13354622269381c7512706e69d7d59a3c2881a4ffbaefc7252c35608fc7362003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>data-driven therapeutics</topic><topic>Digital Health</topic><topic>emotion detection</topic><topic>generalized anxiety disorder</topic><topic>major depressive disorder</topic><topic>psychophysiological data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tsirmpas, Charalampos</creatorcontrib><creatorcontrib>Andrikopoulos, Dimitrios</creatorcontrib><creatorcontrib>Fatouros, Panagiotis</creatorcontrib><creatorcontrib>Eleftheriou, Georgios</creatorcontrib><creatorcontrib>Anguera, Joaquin A.</creatorcontrib><creatorcontrib>Kontoangelos, Konstantinos</creatorcontrib><creatorcontrib>Papageorgiou, Charalabos</creatorcontrib><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Frontiers in digital health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tsirmpas, Charalampos</au><au>Andrikopoulos, Dimitrios</au><au>Fatouros, Panagiotis</au><au>Eleftheriou, Georgios</au><au>Anguera, Joaquin A.</au><au>Kontoangelos, Konstantinos</au><au>Papageorgiou, Charalabos</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Feasibility, engagement, and preliminary clinical outcomes of a digital biodata-driven intervention for anxiety and depression</atitle><jtitle>Frontiers in digital health</jtitle><date>2022-07-22</date><risdate>2022</risdate><volume>4</volume><spage>868970</spage><epage>868970</epage><pages>868970-868970</pages><issn>2673-253X</issn><eissn>2673-253X</eissn><abstract>HypothesisThe main hypothesis is that a digital, biodata-driven, and personalized program would exhibit high user retention and engagement, followed by more effective management of their depressive and anxiety symptoms. ObjectiveThis pilot study explores the feasibility, acceptability, engagement, and potential impact on depressive and anxiety and quality of life outcomes of the 16-week Feel Program. Additionally, it examines potential correlations between engagement and impact on mental health outcomes. MethodsThis single-arm study included 48 adult participants with mild or moderate depressive or anxiety symptoms who joined the 16-week Feel Program, a remote biodata-driven mental health support program created by Feel Therapeutics. The program uses a combination of evidence-based approaches and psychophysiological data. Candidates completed an online demographics and eligibility survey before enrolment. Depressive and anxiety symptoms were measured using the Patient Health Questionnaire and Generalized Anxiety Disorder Scale, respectively. The Satisfaction with Life Scale and the Life Satisfaction Questionnaire were used to assess quality of life. User feedback surveys were employed to evaluate user experience and acceptability. ResultsIn total, 31 participants completed the program with an overall retention rate of 65%. Completed participants spent 60 min in the app, completed 13 Mental Health Actions, including 5 Mental Health Exercises and 4.9 emotion logs on a weekly basis. On average, 96% of the completed participants were active and 76.8% of them were engaged with the sensor during the week. Sixty five percent of participants reported very or extremely high satisfaction, while 4 out of 5 were very likely to recommend the program to someone. Additionally, 93.5% of participants presented a decrease in at least one of the depressive or anxiety symptoms, with 51.6 and 45% of participants showing clinically significant improvement, respectively. Finally, our findings suggest increased symptom improvement for participants with higher engagement throughout the program. ConclusionsThe findings suggest that the Feel Program may be feasible, acceptable, and valuable for adults with mild or moderate depressive and/or anxiety symptoms. However, controlled trials with bigger sample size, inclusion of a control group, and more diverse participant profiles are required in order to provide further evidence of clinical efficacy.</abstract><pub>Frontiers Media S.A</pub><pmid>35958737</pmid><doi>10.3389/fdgth.2022.868970</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | data-driven therapeutics Digital Health emotion detection generalized anxiety disorder major depressive disorder psychophysiological data |
title | Feasibility, engagement, and preliminary clinical outcomes of a digital biodata-driven intervention for anxiety and depression |
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