Literature DB >> 33427680

Clinical Advice by Voice Assistants on Postpartum Depression: Cross-Sectional Investigation Using Apple Siri, Amazon Alexa, Google Assistant, and Microsoft Cortana.

Samuel Yang1,2, Jennifer Lee2,3, Emre Sezgin4, Jeffrey Bridge3,4, Simon Lin3,4.   

Abstract

BACKGROUND: A voice assistant (VA) is inanimate audio-interfaced software augmented with artificial intelligence, capable of 2-way dialogue, and increasingly used to access health care advice. Postpartum depression (PPD) is a common perinatal mood disorder with an annual estimated cost of $14.2 billion. Only a small percentage of PPD patients seek care due to lack of screening and insufficient knowledge of the disease, and this is, therefore, a prime candidate for a VA-based digital health intervention.
OBJECTIVE: In order to understand the capability of VAs, our aim was to assess VA responses to PPD questions in terms of accuracy, verbal response, and clinically appropriate advice given.
METHODS: This cross-sectional study examined four VAs (Apple Siri, Amazon Alexa, Google Assistant, and Microsoft Cortana) installed on two mobile devices in early 2020. We posed 14 questions to each VA that were retrieved from the American College of Obstetricians and Gynecologists (ACOG) patient-focused Frequently Asked Questions (FAQ) on PPD. We scored the VA responses according to accuracy of speech recognition, presence of a verbal response, and clinically appropriate advice in accordance with ACOG FAQ, which were assessed by two board-certified physicians.
RESULTS: Accurate recognition of the query ranged from 79% to 100%. Verbal response ranged from 36% to 79%. If no verbal response was given, queries were treated like a web search between 33% and 89% of the time. Clinically appropriate advice given by VA ranged from 14% to 29%. We compared the category proportions using the Fisher exact test. No single VA statistically outperformed other VAs in the three performance categories. Additional observations showed that two VAs (Google Assistant and Microsoft Cortana) included advertisements in their responses.
CONCLUSIONS: While the best performing VA gave clinically appropriate advice to 29% of the PPD questions, all four VAs taken together achieved 64% clinically appropriate advice. All four VAs performed well in accurately recognizing a PPD query, but no VA achieved even a 30% threshold for providing clinically appropriate PPD information. Technology companies and clinical organizations should partner to improve guidance, screen patients for mental health disorders, and educate patients on potential treatment. ©Samuel Yang, Jennifer Lee, Emre Sezgin, Jeffrey Bridge, Simon Lin. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 11.01.2021.

Entities:  

Keywords:  conversational agent; mental health; mobile health; postpartum depression; virtual assistant; voice assistant

Year:  2021        PMID: 33427680      PMCID: PMC7834933          DOI: 10.2196/24045

Source DB:  PubMed          Journal:  JMIR Mhealth Uhealth        ISSN: 2291-5222            Impact factor:   4.773


  21 in total

1.  ACOG Committee Opinion No. 757: Screening for Perinatal Depression.

Authors: 
Journal:  Obstet Gynecol       Date:  2018-11       Impact factor: 7.661

2.  Interventions to Prevent Perinatal Depression: US Preventive Services Task Force Recommendation Statement.

Authors:  Susan J Curry; Alex H Krist; Douglas K Owens; Michael J Barry; Aaron B Caughey; Karina W Davidson; Chyke A Doubeni; John W Epling; David C Grossman; Alex R Kemper; Martha Kubik; C Seth Landefeld; Carol M Mangione; Michael Silverstein; Melissa A Simon; Chien-Wen Tseng; John B Wong
Journal:  JAMA       Date:  2019-02-12       Impact factor: 56.272

3.  A general introduction to adjustment for multiple comparisons.

Authors:  Shi-Yi Chen; Zhe Feng; Xiaolian Yi
Journal:  J Thorac Dis       Date:  2017-06       Impact factor: 2.895

Review 4.  Annual Research Review: Digital health interventions for children and young people with mental health problems - a systematic and meta-review.

Authors:  Chris Hollis; Caroline J Falconer; Jennifer L Martin; Craig Whittington; Sarah Stockton; Cris Glazebrook; E Bethan Davies
Journal:  J Child Psychol Psychiatry       Date:  2016-12-10       Impact factor: 8.982

5.  Postnatal depression across countries and cultures: a qualitative study.

Authors:  M R Oates; J L Cox; S Neema; P Asten; N Glangeaud-Freudenthal; B Figueiredo; L L Gorman; S Hacking; E Hirst; M H Kammerer; C M Klier; G Seneviratne; M Smith; A-L Sutter-Dallay; V Valoriani; B Wickberg; K Yoshida
Journal:  Br J Psychiatry Suppl       Date:  2004-02

Review 6.  High-performance medicine: the convergence of human and artificial intelligence.

Authors:  Eric J Topol
Journal:  Nat Med       Date:  2019-01-07       Impact factor: 53.440

7.  Consumer Use of "Dr Google": A Survey on Health Information-Seeking Behaviors and Navigational Needs.

Authors:  Kenneth Lee; Kreshnik Hoti; Jeffery David Hughes; Lynne M Emmerton
Journal:  J Med Internet Res       Date:  2015-12-29       Impact factor: 5.428

8.  Responses to addiction help-seeking from Alexa, Siri, Google Assistant, Cortana, and Bixby intelligent virtual assistants.

Authors:  Alicia L Nobles; Eric C Leas; Theodore L Caputi; Shu-Hong Zhu; Steffanie A Strathdee; John W Ayers
Journal:  NPJ Digit Med       Date:  2020-01-29

9.  Interrater reliability: the kappa statistic.

Authors:  Mary L McHugh
Journal:  Biochem Med (Zagreb)       Date:  2012       Impact factor: 2.313

View more
  6 in total

1.  Comparing Older and Younger Adults Perceptions of Voice and Text-based Search for Consumer Health Information Tasks.

Authors:  Karen Bonilla; Brian Gaitan; Jamie Sanders; Noami Khenglawt; Aqueasha Martin-Hammond
Journal:  AMIA Annu Symp Proc       Date:  2022-02-21

2.  Reliability of Commercial Voice Assistants' Responses to Health-Related Questions in Noncommunicable Disease Management: Factorial Experiment Assessing Response Rate and Source of Information.

Authors:  Caterina Bérubé; Zsolt Ferenc Kovacs; Elgar Fleisch; Tobias Kowatsch
Journal:  J Med Internet Res       Date:  2021-12-20       Impact factor: 5.428

3.  Mitigating Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: Exploratory Mixed Methods Experiment.

Authors:  Timothy W Bickmore; Stefán Ólafsson; Teresa K O'Leary
Journal:  J Med Internet Res       Date:  2021-11-09       Impact factor: 5.428

4.  "Hey Siri, Help Me Take Care of My Child": A Feasibility Study With Caregivers of Children With Special Healthcare Needs Using Voice Interaction and Automatic Speech Recognition in Remote Care Management.

Authors:  Emre Sezgin; Brannon Oiler; Brandon Abbott; Garey Noritz; Yungui Huang
Journal:  Front Public Health       Date:  2022-03-03

5.  Design and Formative Evaluation of a Virtual Voice-Based Coach for Problem-solving Treatment: Observational Study.

Authors:  Corina R Ronneberg; Nancy E Wittels; Olu A Ajilore; Jun Ma; Thomas Kannampallil; Vikas Kumar; Nan Lv; Joshua M Smyth; Ben S Gerber; Emily A Kringle; Jillian A Johnson; Philip Yu; Lesley E Steinman
Journal:  JMIR Form Res       Date:  2022-08-12

6.  Evaluating Voice Assistants' Responses to COVID-19 Vaccination in Portuguese: Quality Assessment.

Authors:  Carlos Maurício Seródio Figueiredo; Tiago de Melo; Raphaela Goes
Journal:  JMIR Hum Factors       Date:  2022-03-21
  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.