Computers & Education, Journal Year: 2023, Volume and Issue: 201, P. 104812 - 104812
Published: April 27, 2023
Language: Английский
Journal of Eating Disorders, Journal Year: 2022, Volume and Issue: 10(1)
Published: May 8, 2022
Abstract Advances in machine learning and digital data provide vast potential for mental health predictions. However, research using the field of eating disorders is just beginning to emerge. This paper provides a narrative review existing explores benefits, limitations, ethical considerations aid detection, prevention, treatment disorders. Current primarily uses predict disorder status from females’ responses validated surveys, social media posts, or neuroimaging often with relatively high levels accuracy. early work evidence improve current screening methods. ability these algorithms generalise other samples be used on mass scale only explored. One key benefit over traditional statistical methods simultaneously examine large numbers (100s 1000s) multimodal predictors their complex non-linear interactions, but few studies have explored this Machine also being develop chatbots psychoeducation coping skills training around body image disorders, implications intervention. The use personalise options, ecological momentary interventions, clinicians discussed. accurate, rapid, cost-effective More needed diverse participants ensure that models are unbiased, generalisable all people There important limitations utilising practice. Thus, rather than magical solution, should seen as an tool researchers, eventually clinicians, identification,
Language: Английский
Citations
53
Journal of Medical Internet Research, Journal Year: 2022, Volume and Issue: 24(4), P. e32630 - e32630
Published: April 27, 2022
Background The working alliance refers to an important relationship quality between health professionals and clients that robustly links treatment success. Recent research shows can develop affective bond with chatbots. However, few studies have investigated whether this perceived is affected by the social roles of differing closeness a chatbot impersonate allowing users choose role chatbot. Objective This study aimed at understanding how be expressed using set interpersonal cues examining these affect clients’ experiences development chatbot, depending on characteristics (ie, age gender) they freely chatbot’s role. Methods Informed theory response theory, we developed design codebook for chatbots different along continuum. Based codebook, manipulated fictitious care one four distinct common in settings—institution, expert, peer, dialogical self—and examined effects usage intentions web-based lab study. included total 251 participants, whose mean was 41.15 (SD 13.87) years; 57.0% (143/251) participants were female. Participants either randomly assigned conditions (no choice: n=202, 80.5%) or could interact personas (free n=49, 19.5%). Separate multivariate analyses variance performed analyze differences (1) within no-choice group (2) free-choice groups. Results While main effect persona insignificant (P=.87), found based participants’ demographic profiles: gender (P=.04, ηp2=0.115) (P<.001, ηp2=0.192) significant interaction (P=.01, ηp2=0.102). younger than 40 years reported higher scores interpersonally more distant expert institution chatbots; older outcomes closer peer dialogical-self option significantly benefited perceptions further (eg, bond: 5.28, SD 0.89; 4.54, 1.10; P=.003, ηp2=0.117). Conclusions Manipulating possible avenue designers tailor user-specific factors improve behavioral toward Our results also emphasize benefits letting
Language: Английский
Citations
50
International Journal of Eating Disorders, Journal Year: 2022, Volume and Issue: 55(9), P. 1229 - 1244
Published: Aug. 18, 2022
Abstract Objective A significant gap exists between those who need and receive care for eating disorders (EDs). Novel solutions are needed to encourage service use address treatment barriers. This study developed evaluated the usability of a chatbot designed pairing with online ED screening. The tool aimed promote mental health utilization by improving motivation self‐efficacy among individuals EDs. Methods prototype, Alex, was using decision trees theoretically‐informed components: psychoeducation, motivational interviewing, personalized recommendations, repeated administration. Usability testing conducted over four iterative cycles, user feedback informing refinements next iteration. Post‐testing, participants (N= 21) completed System Scale (SUS), Usefulness, Satisfaction, Ease Use Questionnaire (USE), semi‐structured interview. Results Interview detailed aspects enjoyed necessitating improvement. Feedback converged on themes: experience, qualities, content, ease use. Following refinements, users described Alex as humanlike, supportive, encouraging. Content perceived novel personally relevant. USE scores across domains were generally above average (~5 out 7), SUS indicated “good” “excellent” final iteration receiving highest score. Discussion Overall, reflected positively interactions including initial version. Refinements cycles further improved experiences. provides preliminary evidence feasibility acceptance services Public Significance Low rates have been observed following disorder Tools needed, scalable, digital options, that can be easily paired screening, improve addressing utilization.
Language: Английский
Citations
48
BMC Health Services Research, Journal Year: 2022, Volume and Issue: 22(1)
Published: July 9, 2022
Technological progress in artificial intelligence has led to the increasing popularity of virtual assistants, i.e., embodied or disembodied conversational agents that allow chatting with a technical system natural language. However, only little comprehensive research is conducted about patients' perceptions and possible applications assistant healthcare cancer patients. This aims investigate key acceptance factors value-adding use cases for patients diagnosed cancer.Qualitative interviews eight former four doctors Dutch radiotherapy institute were determine what they find most important gain insights into applications. The unified theory technology (UTAUT) was used structure inductively modified as result interviews. subsequent model triangulated via an online survey 127 respondents cancer. A structural equation relevance factors. Through multigroup analysis, differences between sample subgroups compared.The found support all UTAUT: performance expectancy, effort social influence facilitating conditions. Additionally, self-efficacy, trust, resistance change, added extension UTAUT. Former helpful receiving information logistic questions, treatment procedures, side effects, scheduling appointments. quantitative study constructs expectancy (ß = 0.399), 0.258), 0.114), trust 0.210) significantly influenced behavioral intention assistant, explaining 80% its variance. Self-efficacy 0.792) acts antecedent expectancy. Facilitating conditions change not have significant relationship user intention.Performance are leading determinants acceptance. latter dependent on patient's self-efficacy. Therefore, including during development introduction VA important. high indicates need reliable, secure service should be promoted such. Social suggests using endorsing VA.
Language: Английский
Citations
45
Computers & Education, Journal Year: 2023, Volume and Issue: 201, P. 104812 - 104812
Published: April 27, 2023
Language: Английский
Citations
40