Опубликована: Ноя. 28, 2024
Язык: Английский
Опубликована: Ноя. 28, 2024
Язык: Английский
JMIR Human Factors, Год журнала: 2024, Номер 11, С. e59908 - e59908
Опубликована: Ноя. 11, 2024
Artificial intelligence (AI)-based chatbots have emerged as potential tools to assist individuals in reducing anxiety and supporting well-being.
Язык: Английский
Процитировано
1Опубликована: Авг. 16, 2024
Background: Blended mobile health (mHealth) interventions – combining self-guided and human support components could play a major role in preventing non-communicable diseases (NCDs) common mental disorders (CMDs). This protocol paper describes sequential, multiple assignment, randomised trial aimed at (i) evaluating the effectiveness cost-effectiveness of LvL UP, an mHealth lifestyle intervention for prevention NCDs CMDs, (ii) establishing optimal blended approach UP that balances effective personalised with scalability.Methods: is 6-month holistic targeting physical activity, diet, emotional regulation. In this trial, young middle-aged Singaporean adults risk developing or CMDs will be randomly allocated to one two initial conditions (‘LvL UP’ ‘comparison’). After 4 weeks, participants categorised as non-responders from group re-randomised into second-stage conditions: continuing (LvL UP) additional motivational interviewing (MI) sessions by trained coaches + adaptive MI). The primary outcome well-being (via Warwick-Edinburgh Mental Wellbeing Scale). Secondary outcomes include anthropometric measurements, resting blood pressure, metabolic profile, status, behaviours (physical diet), work productivity, healthcare utilisation. Outcomes measured baseline, 6 months (post-intervention), 12 (follow-up).Discussion: addition proposed study design contribute increasing evidence on how introduce maximise their while remaining scalable.Trial registration: Pilot was prospectively registered ClinicalTrials.gov (NCT06360029) 7 April 2024.
Язык: Английский
Процитировано
0Опубликована: Сен. 2, 2024
Язык: Английский
Процитировано
0JMIR Mental Health, Год журнала: 2024, Номер 12, С. e51022 - e51022
Опубликована: Окт. 3, 2024
Fully automated digital interventions delivered via smartphone apps have proven efficacious for a wide variety of mental health outcomes. An important aspect is that they are accessible at low cost, thereby increasing their potential public impact and reducing disparities. However, major challenge to successful implementation the phenomenon users dropping out early. The purpose this study was pinpoint factors influencing early dropout in sample self-selected virtual agent (VA)-based behavioral intervention managing insomnia, named KANOPEE, which freely available France. From January 2021 December 2022, 9657 individuals, aged 18 years or older, who downloaded completed KANOPEE screening interview had either subclinical clinical insomnia symptoms, 4295 (44.5%) dropped (ie, did not return app continue filling subsequent assessments). primary outcome binary variable: having after completing assessment (early dropout) all treatment phases (n=551). Multivariable logistic regression analysis used identify predictors among set sociodemographic, clinical, sleep diary variables, users' perceptions program, collected during interview. mean age 47.95 (SD 15.21) years. Of those treatment, 65.1% (3153/4846) were women 34.9% (1693/4846) men. Younger (adjusted odds ratio [AOR] 0.98, 95% CI 0.97-0.99), lower education level (compared middle school; high school: AOR 0.56, 0.35-0.90; bachelor's degree: 0.35, 0.23-0.52; master's degree higher: 0.22-0.55), poorer nocturnal (sleep efficiency: 0.64, 0.42-0.96; number awakenings: 1.13, 1.04-1.23), more severe depression symptoms (AOR 1.12, 1.04-1.21) significant out. When measures included model, perceived benevolence credibility VA decreased 0.91, 0.85-0.97). As traditional face-to-face cognitive therapy presence plays an role dropout. This variable represents target address increase engagement with fully management programs. Furthermore, our results support contention can provide relevant user stimulation will eventually pay terms engagement.
Язык: Английский
Процитировано
0Опубликована: Апрель 27, 2024
Язык: Английский
Процитировано
0International Journal of MS Care, Год журнала: 2024, Номер 26(Q4), С. 347 - 354
Опубликована: Дек. 9, 2024
Fatigue is common in multiple sclerosis (MS); it significantly impairs quality of life, and treatment options are limited. A randomized controlled trial Elevida, a self-guided, online German fatigue intervention, showed significant benefit. We tested an English version Elevida with people MS Australia. Participants were volunteers who self-reported at least mild (≥ 43 on the Scale for Motor Cognitive Functions scale), some mobility (Expanded Disability Status < 8), no or cognitive difficulties (≤ 32 Multiple Sclerosis Neuropsychological Questionnaire). completed 9-week program, commenting rating its acceptability. The Chalder was baseline, end-of-program, 2 months later. undertook qualitative (thematic analysis) quantitative (before/after differences, using paired t test) analyses. Thirty-eight expressed interest study; 26 eligible; 20 began study. Fifteen participants (75%) program (mean [SD]: 58.9 [10.5] years age, 67% women, 9 relapsing MS, 6 progressive MS). Over 90% completing rated acceptability as good very good, approximately 70% found helpful. Three themes identified: Positive negative comments features, incorrect assumptions content, personal experiences reflections. Significant improvement (P .01) scores from baseline to completion maintained after completion. acceptable effective MS-related fatigue. Identified will guide further development satisfy users' sense autonomy, competence, relatedness.
Язык: Английский
Процитировано
0Journal of Psychiatric Practice, Год журнала: 2024, Номер 30(6), С. 400 - 410
Опубликована: Ноя. 1, 2024
Recently, the field of psychiatry has experienced a transformative shift with integration digital tools into traditional therapeutic approaches. Digital encompasses wide spectrum applications, ranging from phenotyping, smartphone wearable devices, virtual/augmented reality, and artificial intelligence (AI). This convergence innovations potential to revolutionize mental health care, enhancing both accessibility patient outcomes. However, despite significant progress in psychiatry, its implementation presents plethora challenges ethical considerations. Critical problems that require careful investigation are raised by issues such as data privacy, divide, legal frameworks, dependability instruments. Furthermore, there risks several hazards associated psychiatric practice. A better understanding growing is needed promote development effective interventions improve accuracy diagnosis. The overarching goal this review paper provide an overview some current opportunities highlighting benefits inherent challenges. also aims at providing guidelines for future research proper clinical
Язык: Английский
Процитировано
0Journal of Medical Internet Research, Год журнала: 2024, Номер unknown
Опубликована: Ноя. 28, 2024
Escalating mental health demand exceeds existing clinical capacity, requiring scalable digital solutions. However, engagement remains challenging. Conversational agents enhance by making programs more interactive and personalized but have not been widely used. This study evaluated a program for anxiety against external comparators. The used an AI-driven conversational agent to deliver clinician-written content via machine learning, with clinician oversight user support. aimed evaluate the engagement, effectiveness, safety of this structured, evidence-based human support mild, moderate severe generalized anxiety. Statistical analyses determine whether reduced than propensity-matched waiting control was statistically non-inferior real-world face-to-face typed cognitive behavioral therapy (CBT). Prospective participants (N=299) were recruited from NHS or social media in UK given use up 9 weeks (study conducted October 2023 May 2024). Endpoints collected before, during after program, at one-month follow-up. External comparator groups generated through propensity-matching sample Talking Therapies (NHS TT) data ieso Digital Health (typed-CBT) Dorset Healthcare University Foundation Trust (DHC) (face-to-face CBT). Superiority non-inferiority compare symptom reduction (change on GAD-7 scale) group groups. included time spent per participant calculated. Participants median 6 hours over 53 days, 78% (n=232) engaged (i.e. completed 2 14 days). There large clinically meaningful symptoms (per-protocol (PP; n=169): change = -7.4, d 1.6; intention-to-treat (ITT; n=299): -5.4, d=1.1). PP effect superior (d 1.3), CBT (p <.001) typed-CBT <.001). Similarly, ITT sample, showed superiority (d=0.8) (p=.002) approaching significance (p=.06). Effects sustained Clinicians overseeing mean 1.6 (31 - 200 minutes) sessions participant. By combining AI support, achieved outcomes comparable human-delivered care while significantly reducing required 8 times relative global estimates. These findings highlight potential technology scale healthcare, address unmet need, ultimately impact quality life economic burden globally. ISRCTN id: 52546704.
Язык: Английский
Процитировано
0Опубликована: Ноя. 28, 2024
Язык: Английский
Процитировано
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