Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review

Journal of Medical Internet Research, Journal Year: 2022, Volume and Issue: 24(5), P. e35371 - e35371

Published: April 9, 2022

Mobile health (mHealth) apps show vast potential in supporting patients and care systems with the increasing prevalence economic costs of noncommunicable diseases (NCDs) worldwide. However, despite availability evidence-based mHealth apps, a substantial proportion users do not adhere to them as intended may consequently receive treatment. Therefore, understanding factors that act barriers or facilitators adherence is fundamental concern preventing intervention dropouts effectiveness digital interventions.

Language: Английский

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The Development and Use of Chatbots in Public Health: Scoping Review

JMIR Human Factors, Journal Year: 2022, Volume and Issue: 9(4), P. e35882 - e35882

Published: Aug. 2, 2022

Background Chatbots are computer programs that present a conversation-like interface through which people can access information and services. The COVID-19 pandemic has driven substantial increase in the use of chatbots to support complement traditional health care systems. However, despite uptake their use, evidence development deployment public remains limited. Recent reviews have focused on during conversational agents more generally. This paper complements this research addresses gap literature by assessing breadth scope for across domain health. Objective scoping review had 3 main objectives: (1) identify application domains there is most chatbots; (2) types being deployed these domains; (3) ascertain methods methodologies evaluated applications. explored implications future light analysis use. Methods Following PRISMA-ScR (Preferred Reporting Items Systematic Reviews Meta-Analyses Extension Scoping Reviews) guidelines reviews, relevant studies were identified searches conducted MEDLINE, PubMed, Scopus, Cochrane Central Register Controlled Trials, IEEE Xplore, ACM Digital Library, Open Grey databases from mid-June August 2021. Studies included if they used or purpose prevention intervention showed demonstrable impact. Results Of 1506 identified, 32 review. results show interest past few years, shortly before pandemic. Half (16/32, 50%) applied mental COVID-19. suggest promise chatbots, especially easily automated repetitive tasks, but overall, efficacy all limited at present. Conclusions More needed fully understand effectiveness using Concerns with clinical, legal, ethical aspects well founded given speed been adopted practice. Future should address concerns expertise best practices specific health, including greater focus user experience.

Language: Английский

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Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review

Journal of Medical Internet Research, Journal Year: 2024, Volume and Issue: 26, P. e56930 - e56930

Published: April 12, 2024

Background Chatbots, or conversational agents, have emerged as significant tools in health care, driven by advancements artificial intelligence and digital technology. These programs are designed to simulate human conversations, addressing various care needs. However, no comprehensive synthesis of chatbots’ roles, users, benefits, limitations is available inform future research application the field. Objective This review aims describe characteristics, focusing on their diverse roles pathway, user groups, limitations. Methods A rapid published literature from 2017 2023 was performed with a search strategy developed collaboration sciences librarian implemented MEDLINE Embase databases. Primary studies reporting chatbot benefits were included. Two reviewers dual-screened results. Extracted data subjected content analysis. Results The categorized into 2 themes: delivery remote services, including patient support, management, education, skills building, behavior promotion, provision administrative assistance providers. User groups spanned across patients chronic conditions well cancer; individuals focused lifestyle improvements; demographic such women, families, older adults. Professionals students also alongside seeking mental behavioral change, educational enhancement. chatbots classified improvement quality efficiency cost-effectiveness delivery. identified encompassed ethical challenges, medicolegal safety concerns, technical difficulties, experience issues, societal economic impacts. Conclusions Health offer wide spectrum applications, potentially impacting aspects care. While they promising for improving quality, integration system must be approached consideration ensure optimal, safe, equitable use.

Language: Английский

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Review of artificial intelligence‐based question‐answering systems in healthcare

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2023, Volume and Issue: 13(2)

Published: Jan. 10, 2023

Abstract Use of conversational agents, like chatbots, avatars, and robots is increasing worldwide. Yet, their effectiveness in health care largely unknown. The aim this advanced review was to assess the use agents various fields care. A literature search, analysis, synthesis were conducted February 2022 PubMed CINAHL. included evidence analyzed narratively by employing principles thematic analysis. We reviewed articles on artificial intelligence‐based question‐answering systems Most identified report its effectiveness; less known about use. outlined study findings explored directions future research, provide evidence‐based knowledge systems. This article categorized under: Fundamental Concepts Data Knowledge > Human Centricity User Interaction Application Areas Health Care Technologies Artificial Intelligence

Language: Английский

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Evaluation of chatbot-delivered interventions for self-management of depression: Content analysis

Journal of Affective Disorders, Journal Year: 2022, Volume and Issue: 319, P. 598 - 607

Published: Sept. 20, 2022

Language: Английский

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Harnessing big data for tailored health communication: A systematic review of impact and techniques

International Journal of Biology and Pharmacy Research Updates, Journal Year: 2024, Volume and Issue: 3(2), P. 01 - 010

Published: April 13, 2024

In recent years, the convergence of healthcare and big data analytics has opened new avenues for tailored health communication, enabling personalized interventions improving outcomes. This systematic review investigates impact techniques harnessing communication. The synthesizes findings from diverse studies spanning sectors, including public campaigns, clinical interventions, patient engagement initiatives. It examines effectiveness communication strategies in addressing various challenges, such as chronic diseases, infectious outbreaks, mental disorders. Key highlight significant positive on behavior change, treatment adherence, empowerment. Big enable segmentation populations based socio-demographic, behavioral, characteristics, facilitating delivery targeted messages to individual preferences needs. Personalization enhances engagement, fosters trust, motivates individuals adopt healthier lifestyles adhere medical recommendations. Furthermore, explores technologies employed Machine learning algorithms, natural language processing, predictive modeling are leveraged analyze vast datasets, predict outcomes, tailor real-time. Mobile applications, social media platforms, wearable devices serve channels delivering collecting real-time data. However, also identifies challenges limitations, privacy concerns, security risks, digital divide. Ethical considerations regarding collection, consent, transparency paramount ensuring responsible use underscores transformative potential By leveraging advanced technology, stakeholders can deliver that resonate with individuals, ultimately driving change outcomes a population scale.

Language: Английский

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Digital Behavior Change Interventions for the Prevention and Management of Type 2 Diabetes: Systematic Market Analysis

Journal of Medical Internet Research, Journal Year: 2021, Volume and Issue: 24(1), P. e33348 - e33348

Published: Nov. 15, 2021

Advancements in technology offer new opportunities for the prevention and management of type 2 diabetes. Venture capital companies have been investing digital diabetes that behavior change interventions (DBCIs). However, little is known about scientific evidence underpinning such or degree to which these leverage novel technology-driven automated developments as conversational agents (CAs) just-in-time adaptive intervention (JITAI) approaches.

Language: Английский

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Conversational Agents in Health Care: Scoping Review of Their Behavior Change Techniques and Underpinning Theory

Journal of Medical Internet Research, Journal Year: 2022, Volume and Issue: 24(10), P. e39243 - e39243

Published: Aug. 24, 2022

Conversational agents (CAs) are increasingly used in health care to deliver behavior change interventions. Their evaluation often includes categorizing the techniques (BCTs) using a classification system of which BCT Taxonomy v1 (BCTTv1) is one most common. Previous studies have presented descriptive summaries interventions delivered by CAs, but no in-depth study reporting use BCTs these has been published date.This review aims describe CAs and identify theories guiding their design.We searched PubMed, Embase, Cochrane's Central Register Controlled Trials, first 10 pages Google Scholar April 2021. We included primary, experimental evaluating intervention CA. coding followed BCTTv1. Two independent reviewers selected extracted data. Descriptive analysis frequent itemset mining clusters were performed.We 47 on mental (n=19, 40%), chronic disorders (n=14, 30%), lifestyle 30%) There 20/47 embodied (43%) 27/47 (57%) represented female character. Most rule based (34/47, 72%). Experimental 63 BCTs, (mean 9 BCTs; range 2-21 BCTs), while comparisons 32 2 2-17 BCTs). 4.1 "Instruction how perform behavior" 72%), 3.3 "Social support" (emotional; 27/47, 57%), 1.2 "Problem solving" (24/47, 51%). A total 12/47 (26%) informed theory, mainly Transtheoretical Model Social Cognitive Theory. Studies same theory different BCTs.There need for more explicit improved CA enhance effectiveness improve reproducibility research.

Language: Английский

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Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER): Development of a Conceptual Framework

JMIR mhealth and uhealth, Journal Year: 2022, Volume and Issue: 10(10), P. e38740 - e38740

Published: Aug. 26, 2022

Conversational agents (CAs), also known as chatbots, are computer programs that simulate human conversations by using predetermined rule-based responses or artificial intelligence algorithms. They increasingly used in health care, particularly via smartphones. There is, at present, no conceptual framework guiding the development of smartphone-based, CAs care. To fill this gap, we propose structured and tailored guidance for their design, development, evaluation, implementation.The aim study was to develop a implementation smartphone-delivered, rule-based, goal-oriented, text-based care.We followed approach Jabareen, which based on grounded theory method, framework. We performed 2 literature reviews focusing care frameworks mobile interventions. identified, named, categorized, integrated, synthesized information retrieved from then applied developing CA testing it feasibility study.The Designing, Developing, Evaluating, Implementing Smartphone-Delivered, Rule-Based Agent (DISCOVER) includes 8 iterative steps grouped into 3 stages, follows: comprising defining goal, creating an identity, assembling team, selecting delivery interface; including content building conversation flow; evaluation CA. were complemented cross-cutting considerations-user-centered design privacy security-that relevant all stages. This successfully support lifestyle changes prevent type diabetes.Drawing published evidence, DISCOVER provides step-by-step guide smartphone-delivered CAs. Further diverse areas settings variety users is needed demonstrate its validity. Future research should explore use deliver interventions, behavior change potential safety concerns.

Language: Английский

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Developing a Technical-Oriented Taxonomy to Define Archetypes of Conversational Agents in Health Care: Literature Review and Cluster Analysis

Journal of Medical Internet Research, Journal Year: 2022, Volume and Issue: 25, P. e41583 - e41583

Published: Dec. 19, 2022

The evolution of artificial intelligence and natural language processing generates new opportunities for conversational agents (CAs) that communicate interact with individuals. In the health domain, CAs became popular as they allow simulating real-life experience in a care setting, which is conversation physician. However, it still unclear technical archetypes can be distinguished. Such are required, among other things, harmonizing evaluation metrics or describing landscape CAs.The objective this work was to develop technical-oriented taxonomy characterize based on their characteristics.We developed characteristics scientific literature empirical data by applying development framework. To demonstrate applicability taxonomy, we analyzed last years review. form design CAs, applied k-means clustering method.Our comprises 18 unique dimensions corresponding 4 perspectives (setting, processing, interaction, agent appearance). Each dimension consists 2 5 characteristics. validated 173 were identified out 1671 initially retrieved publications. clustered into distinctive archetypes: text-based ad hoc supporter; multilingual, hybrid hybrid, single-language temporary advisor; and, finally, an embodied advisor, rule input output options.From cluster analysis, learned time important from perspective distinguish CA archetypes. Moreover, able identify additional distinctive, dominant relevant when evaluating health-related (eg, options complexity personality). Our reflect current characterized based, simple systems terms personality interaction. With increase research interest field, expect more complex will arise. archetype-building process should repeated after some check whether emerge.

Language: Английский

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