@Article{info:doi/10.2196/69333,
author="Azadi, Ali
and Garc{\'i}a-Pe{\~n}alvo, Jos{\'e} Francisco",
title="Optimizing Clinical Decision Support System Functionality by Leveraging Specific Human-Computer Interaction Elements: Insights From a Systematic Review",
journal="JMIR Hum Factors",
year="2025",
month="May",
day="6",
volume="12",
pages="e69333",
keywords="human-computer interaction",
keywords="clinical decision support system",
keywords="usability",
keywords="user-centered design",
keywords="artificial intelligence",
abstract="Background: Clinical decision support systems (CDSSs) play a pivotal role in health care by enhancing clinical decision-making processes. These systems represent a significant advancement in medical information systems. However, optimizing their effectiveness requires accounting for various human-computer interaction (HCI) elements that influence their functionality and user acceptance. Objective: This study aimed to identify and categorize key HCI elements that impact CDSS performance to enhance system usability, adaptability, and decision-making accuracy. Methods: We conducted a systematic literature review, identifying 923 studies from the databases PubMed, Scopus, and Web of Science. Papers were screened and selected based on predefined inclusion criteria. A rigorous quality assessment process was applied to ensure the relevance and reliability of the included studies. Ultimately, of the 923 papers identified, 43 (4.7\%) that specifically addressed HCI elements applicable to CDSS environments were included in the final analysis. Data extraction and synthesis were performed to answer the research questions regarding HCI elements. Results: A total of 12 distinct HCI elements were identified, each with the potential to influence CDSS functionality. These elements align with the International Organization for Standardization (ISO) 9241-11 framework, which defines usability in terms of effectiveness, efficiency, and satisfaction. ``User satisfaction,'' ``flexibility,'' and ``individuality'' enhance satisfaction by improving system adaptability and user acceptance. ``Visibility,'' ``explainability,'' and ``user control'' strengthen effectiveness by supporting decision-making and error prevention. ``Ease of use'' improves efficiency by streamlining interactions and reducing cognitive load. Some elements influence effectiveness and efficiency, such as ``data entry,'' which ensures structured inputs for decision accuracy while optimizing workflows. Likewise, ``alerts'' provide timely information for effective decision-making and, simultaneously, are designed to avoid overwhelming users and maintain system efficiency. ``Simplification'' and ``mental effort'' also optimize workflows and reduce complexity. Furthermore, ``interface'' impacts effectiveness and efficiency by supporting accurate decision-making and streamlining user interaction. This categorization, aligned with ISO 9241-11, underscores the context and task dependency of usability, highlighting that HCI elements must be adapted to different user needs and environments for effective clinical decision-making. Conclusions: This study addresses a critical gap in CDSS research by offering a comprehensive framework of HCI elements tailored to the CDSS environment. Incorporating these elements into system design can improve user satisfaction, reduce data errors, and enhance the accuracy of medical decisions. The findings lay the groundwork for future research, offering practical guidelines for developing more reliable and efficient CDSS systems in medical informatics fields. ",
doi="10.2196/69333",
url="https://humanfactors.jmir.org/2025/1/e69333"
}


@Article{info:doi/10.2196/60151,
author="Kubben, Pieter",
title="Invasive Brain-Computer Interfaces: A Critical Assessment of Current Developments and Future Prospects",
journal="JMIR Neurotech",
year="2024",
month="Jul",
day="19",
volume="3",
pages="e60151",
keywords="brain computer interfacing",
keywords="neurotechnology",
keywords="brain-computer",
keywords="interfacing",
keywords="interface",
keywords="interfaces",
keywords="invasive",
keywords="human-machine",
keywords="human-computer",
keywords="BCI",
keywords="BCIs",
keywords="brain-computer interface",
keywords="neuroscience",
keywords="technology",
keywords="digital health",
keywords="brain",
keywords="machine learning",
keywords="artificial intelligence",
keywords="AI",
keywords="ethics",
keywords="innovation",
keywords="policy",
keywords="mHealth",
keywords="mobile health",
doi="10.2196/60151",
url="https://neuro.jmir.org/2024/1/e60151"
}


@Article{info:doi/10.2196/42901,
author="Zhang, Ying
and Liu, Xiaoyu
and Qiao, Xiaofeng
and Fan, Yubo",
title="Characteristics and Emerging Trends in Research on Rehabilitation Robots from 2001 to 2020: Bibliometric Study",
journal="J Med Internet Res",
year="2023",
month="May",
day="31",
volume="25",
pages="e42901",
keywords="rehabilitation robot",
keywords="bibliometric analysis",
keywords="interdisciplinary research",
keywords="co-occurrence analysis",
keywords="co-citation analysis",
keywords="rehabilitation",
abstract="Background: The past 2 decades have seen rapid development in the use of robots for rehabilitation. Research on rehabilitation robots involves interdisciplinary activities, making it a great challenge to obtain comprehensive insights in this research field. Objective: We performed a bibliometric study to understand the characteristics of research on rehabilitation robots and emerging trends in this field in the last 2 decades. Methods: Reports on the topic of rehabilitation robots published from January 1, 2001, to December 31, 2020, were retrieved from the Web of Science Core Collection on July 28, 2022. Document types were limited to ``article'' and ``meeting'' (excluding the ``review'' type), to ensure that our analysis of the evolution over time of this research had high validity. We used CiteSpace to conduct a co-occurrence and co-citation analysis and to visualize the characteristics of this research field and emerging trends. Landmark publications were identified using metrics such as betweenness centrality and burst strength. Results: Through data retrieval, cleaning, and deduplication, we retrieved 9287 publications and 110,619 references cited in these publications that were on the topic of rehabilitation robots and were published between 2001 and 2020. Results of the Mann-Kendall test indicated that the numbers of both publications (P<.001; St=175.0) and citations (P<.001; St=188.0) related to rehabilitation robots exhibited a significantly increasing yearly trend. The co-occurrence results revealed 120 categories connected with research on rehabilitation robots; we used these categories to determine research relationships. The co-citation results identified 169 co-citation clusters characterizing this research field and emerging trends in it. The most prominent label was ``soft robotic technology'' (the burst strength was 79.07), which has become a topic of great interest in rehabilitative recovery for both the upper and lower limbs. Additionally, task-oriented upper-limb training, control strategies for robot-assisted lower limb rehabilitation, and power in exoskeleton robots were topics of great interest in current research. Conclusions: Our work provides insights into research on rehabilitation robots, including its characteristics and emerging trends during the last 2 decades, providing a comprehensive understanding of this research field. ",
doi="10.2196/42901",
url="https://www.jmir.org/2023/1/e42901",
url="http://www.ncbi.nlm.nih.gov/pubmed/37256670"
}


@Article{info:doi/10.2196/41122,
author="Kubben, Pieter",
title="JMIR Neurotechnology: Connecting Clinical Neuroscience and (Information) Technology",
journal="JMIR Neurotech",
year="2022",
month="Aug",
day="11",
volume="1",
number="1",
pages="e41122",
keywords="neurotechnology",
keywords="neurological disorders",
keywords="treatment tools",
keywords="chronic neurological disease",
keywords="information technology",
doi="10.2196/41122",
url="https://neuro.jmir.org/2022/1/e41122"
}


@Article{info:doi/10.2196/25462,
author="Yeo, Shi Pei
and Nguyen, Ngoc Tu
and Ng, Ern Mary Pei
and Choo, Munn Robin Wai
and Yap, Kiat Philip Lin
and Ng, Pin Tze
and Wee, Liang Shiou",
title="Evaluation of the Implementation and Effectiveness of Community-Based Brain-Computer Interface Cognitive Group Training in Healthy Community-Dwelling Older Adults: Randomized Controlled Implementation Trial",
journal="JMIR Form Res",
year="2021",
month="Apr",
day="27",
volume="5",
number="4",
pages="e25462",
keywords="group-based computerized cognitive training",
keywords="cognition",
keywords="gait",
keywords="community program implementation",
keywords="healthy older adults",
keywords="cognitive",
keywords="community program",
keywords="cognitive training",
keywords="elderly",
keywords="aging",
abstract="Background: Cognitive training can improve cognition in healthy older adults. Objective: The objectives are to evaluate the implementation of community-based computerized cognitive training (CCT) and its effectiveness on cognition, gait, and balance in healthy older adults. Methods: A single-blind randomized controlled trial with baseline and follow-up assessments was conducted at two community centers in Singapore. Healthy community-dwelling adults aged 55 years and older participated in a 10-week CCT program with 2-hour instructor-led group classes twice a week. Participants used a mobile app to play games targeting attention, memory, decision making, visuospatial abilities, and cognitive flexibility. Implementation was assessed at the participant, provider, and community level (eg, reach, implementation, and facilitators and barriers). Effectiveness measures were the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Color Trails Test 2 (CTT-2), Berg Balance Scale, and GAITRite walkway measures (single and dual task gait speed, dual task cost, and single and dual task gait variability index [GVI]). Results: A total of 94 healthy community-dwelling adults participated in the CCT program (mean age 68.8 [SD 6.3] years). Implementation measures revealed high reach (125/155, 80.6\%) and moderate adherence but poor penetration of sedentary older adults (43/125, 34.4\%). The effectiveness data were based on intention-to-treat (ITT) and per-protocol (PP) analysis. In the ITT analysis, single task GVI increased (b=2.32, P=.02, 95\% CI [0.30 to 4.35]) and RBANS list recognition subtest deteriorated (b=--0.57, P=.01, 95\% CI [--1.00 to --0.14]) in both groups. In the PP analysis, time taken to complete CTT-2 (b=--13.5, P=.01, 95\% CI [--23.95 to --3.14]; Cohen d effect size = 0.285) was faster in the intervention group. Single task gait speed was not statistically significantly maintained in the intervention group (b=5.38, P=.06, 95\% CI [--0.30 to 11.36]) and declined in the control group (Cohen d effect size = 0.414). PP analyses also showed interaction terms for RBANS list recall subtest (b=--0.36, P=.08, 95\% CI [--0.75 to 0.04]) and visuospatial domain (b=0.46, P=.08, 95\% CI [--0.05 to 0.96]) that were not statistically significant. Conclusions: CCT can be implemented in community settings to improve attention and executive function among healthy older adults. Findings help to identify suitable healthy aging programs that can be implemented on a larger scale within communities. Trial Registration: ClinicalTrials.gov NCT04439591; https://clinicaltrials.gov/ct2/show/NCT04439591 ",
doi="10.2196/25462",
url="https://formative.jmir.org/2021/4/e25462",
url="http://www.ncbi.nlm.nih.gov/pubmed/33904819"
}


@Article{info:doi/10.2196/20819,
author="Daud{\'e}n Roquet, Claudia
and Sas, Corina",
title="A Mindfulness-Based Brain-Computer Interface to Augment Mandala Coloring for Depression: Protocol for a Single-Case Experimental Design",
journal="JMIR Res Protoc",
year="2021",
month="Jan",
day="18",
volume="10",
number="1",
pages="e20819",
keywords="brain-computer interface",
keywords="mental well-being",
keywords="depression",
keywords="mindfulness",
keywords="mandala coloring",
abstract="Background: The regular practice of mindfulness has been shown to provide benefits for mental well-being and prevent depression relapse. Technology-mediated interventions can facilitate the uptake and sustained practice of mindfulness, yet the evaluation of interactive systems, such as brain-computer interfaces, has been little explored. Objective: The objective of this paper is to present an interactive mindfulness-based technology to improve mental well-being in people who have experienced depression. The system, Anima, is a brain-computer interface that augments mandala coloring by providing a generative color palette based on the unfolding mindfulness states during the practice. In addition, this paper outlines a multiple-baseline, single-case experimental design methodology to evaluate training effectiveness. Methods: Adult participants who have experienced depression in the past, have finished treatment within the last year, and can provide informed consent will be able to be recruited. The Anima system, consisting of 2 tablets and a nonintrusive mental activity headband, will be delivered to participants to use during the study. Measures include state and trait mindfulness, depression symptoms, mental well-being, and user experience, and these measures will be taken throughout the baseline, intervention, and monitoring phases. The data collection will take place in the form of a questionnaire before and after each mandala-coloring session and a semistructured interview every 2 weeks. Trial results will be analyzed using structured visual analysis, supplemented with statistical analysis appropriate to single-case methodology. Results: Study results will offer new insights into the deployment and evaluation of novel interactive brain-computer interfaces for mindfulness training in the context of mental health. Moreover, findings will validate the effectiveness of this training protocol to improve the mental well-being of people who have had depression. Participants will be recruited locally through the National Health Service. Conclusions: Evidence will assist in the design and evaluation of brain-computer interfaces and mindfulness technologies for mental well-being and the necessary services to support people who have experienced depression. International Registered Report Identifier (IRRID): PRR1-10.2196/20819 ",
doi="10.2196/20819",
url="http://www.researchprotocols.org/2021/1/e20819/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33459604"
}


@Article{info:doi/10.2196/16973,
author="Nelson, C. Elizabeth
and Sools, M. Anneke
and Vollenbroek-Hutten, R. Miriam M.
and Verhagen, Tibert
and Noordzij, L. Matthijs",
title="Embodiment of Wearable Technology: Qualitative Longitudinal Study",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Nov",
day="3",
volume="8",
number="11",
pages="e16973",
keywords="wearability",
keywords="implantable wearable",
keywords="body extension",
keywords="smart prosthesis",
keywords="implantable devices",
keywords="technology dependence",
keywords="cognitive prosthesis",
keywords="phenomenology",
keywords="embodied self-discrepancy",
keywords="technology addiction",
keywords="longitudinal qualitative design",
abstract="Background: Current technology innovations, such as wearables, have caused surprising reactions and feelings of deep connection to devices. Some researchers are calling mobile and wearable technologies cognitive prostheses, which are intrinsically connected to individuals as if they are part of the body, similar to a physical prosthesis. Additionally, while several studies have been performed on the phenomenology of receiving and wearing a physical prosthesis, it is unknown whether similar subjective experiences arise with technology. Objective: In one of the first qualitative studies to track wearables in a longitudinal investigation, we explore whether a wearable can be embodied similar to a physical prosthesis. We hoped to gain insights and compare the phases of embodiment (ie, initial adjustment to the prosthesis) and the psychological responses (ie, accept the prosthesis as part of their body) between wearables and limb prostheses. This approach allowed us to find out whether this pattern was part of a cyclical (ie, period of different usage intensity) or asymptotic (ie, abandonment of the technology) pattern. Methods: We adapted a limb prosthesis methodological framework to be applied to wearables and conducted semistructured interviews over a span of several months to assess if, how, and to what extent individuals come to embody wearables similar to prosthetic devices. Twelve individuals wore fitness trackers for 9 months, during which time interviews were conducted in the following three phases: after 3 months, after 6 months, and at the end of the study after 9 months. A deductive thematic analysis based on Murray's work was combined with an inductive approach in which new themes were discovered. Results: Overall, the individuals experienced technology embodiment similar to limb embodiment in terms of adjustment, wearability, awareness, and body extension. Furthermore, we discovered two additional themes of engagement/reengagement and comparison to another device or person. Interestingly, many participants experienced a rarely reported phenomenon in longitudinal studies where the feedback from the device was counterintuitive to their own beliefs. This created a blurring of self-perception and a dilemma of ``whom'' to believe, the machine or one's self. Conclusions: There are many similarities between the embodiment of a limb prosthesis and a wearable. The large overlap between limb and wearable embodiment would suggest that insights from physical prostheses can be applied to wearables and vice versa. This is especially interesting as we are seeing the traditionally ``dumb'' body prosthesis becoming smarter and thus a natural merging of technology and body. Future longitudinal studies could focus on the dilemma people might experience of whether to believe the information of the device over their own thoughts and feelings. These studies might take into account constructs, such as technology reliance, autonomy, and levels of self-awareness. ",
doi="10.2196/16973",
url="https://mhealth.jmir.org/2020/11/e16973",
url="http://www.ncbi.nlm.nih.gov/pubmed/33141093"
}


@Article{info:doi/10.2196/20979,
author="Hesam-Shariati, Negin
and Newton-John, Toby
and Singh, K. Avinash
and Tirado Cortes, A. Carlos
and Do, Nguyen Tien-Thong
and Craig, Ashley
and Middleton, W. James
and Jensen, P. Mark
and Trost, Zina
and Lin, Chin-Teng
and Gustin, M. Sylvia",
title="Evaluation of the Effectiveness of a Novel Brain-Computer Interface Neuromodulative Intervention to Relieve Neuropathic Pain Following Spinal Cord Injury: Protocol for a Single-Case Experimental Design With Multiple Baselines",
journal="JMIR Res Protoc",
year="2020",
month="Sep",
day="29",
volume="9",
number="9",
pages="e20979",
keywords="EEG neurofeedback",
keywords="neuropathic pain",
keywords="spinal cord injury",
keywords="thalamus",
keywords="serious games",
keywords="brain-machine interface",
keywords="brain-computer interface",
keywords="single-case experimental design",
abstract="Background: Neuropathic pain is a debilitating secondary condition for many individuals with spinal cord injury. Spinal cord injury neuropathic pain often is poorly responsive to existing pharmacological and nonpharmacological treatments. A growing body of evidence supports the potential for brain-computer interface systems to reduce spinal cord injury neuropathic pain via electroencephalographic neurofeedback. However, further studies are needed to provide more definitive evidence regarding the effectiveness of this intervention. Objective: The primary objective of this study is to evaluate the effectiveness of a multiday course of a brain-computer interface neuromodulative intervention in a gaming environment to provide pain relief for individuals with neuropathic pain following spinal cord injury. Methods: We have developed a novel brain-computer interface-based neuromodulative intervention for spinal cord injury neuropathic pain. Our brain-computer interface neuromodulative treatment includes an interactive gaming interface, and a neuromodulation protocol targeted to suppress theta (4-8 Hz) and high beta (20-30 Hz) frequency powers, and enhance alpha (9-12 Hz) power. We will use a single-case experimental design with multiple baselines to examine the effectiveness of our self-developed brain-computer interface neuromodulative intervention for the treatment of spinal cord injury neuropathic pain. We will recruit 3 participants with spinal cord injury neuropathic pain. Each participant will be randomly allocated to a different baseline phase (ie, 7, 10, or 14 days), which will then be followed by 20 sessions of a 30-minute brain-computer interface neuromodulative intervention over a 4-week period. The visual analog scale assessing average pain intensity will serve as the primary outcome measure. We will also assess pain interference as a secondary outcome domain. Generalization measures will assess quality of life, sleep quality, and anxiety and depressive symptoms, as well as resting-state electroencephalography and thalamic $\gamma$-aminobutyric acid concentration. Results: This study was approved by the Human Research Committees of the University of New South Wales in July 2019 and the University of Technology Sydney in January 2020. We plan to begin the trial in October 2020 and expect to publish the results by the end of 2021. Conclusions: This clinical trial using single-case experimental design methodology has been designed to evaluate the effectiveness of a novel brain-computer interface neuromodulative treatment for people with neuropathic pain after spinal cord injury. Single-case experimental designs are considered a viable alternative approach to randomized clinical trials to identify evidence-based practices in the field of technology-based health interventions when recruitment of large samples is not feasible. Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12620000556943; https://bit.ly/2RY1jRx International Registered Report Identifier (IRRID): PRR1-10.2196/20979 ",
doi="10.2196/20979",
url="http://www.researchprotocols.org/2020/9/e20979/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32990249"
}


@Article{info:doi/10.2196/16356,
author="Pisarchik, N. Alexander
and Maksimenko, A. Vladimir
and Hramov, E. Alexander",
title="From Novel Technology to Novel Applications: Comment on ``An Integrated Brain-Machine Interface Platform With Thousands of Channels'' by Elon Musk and Neuralink",
journal="J Med Internet Res",
year="2019",
month="Oct",
day="31",
volume="21",
number="10",
pages="e16356",
keywords="brain-computer interface",
keywords="brain-machine interface",
keywords="brain",
keywords="electroencephalography",
doi="10.2196/16356",
url="http://www.jmir.org/2019/10/e16356/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31674923"
}


@Article{info:doi/10.2196/16339,
author="Kirsch, F. Robert
and Ajiboye, Bolu A.
and Miller, P. Jonathan",
title="The Reconnecting the Hand and Arm with Brain (ReHAB) Commentary on ``An Integrated Brain-Machine Interface Platform With Thousands of Channels''",
journal="J Med Internet Res",
year="2019",
month="Oct",
day="31",
volume="21",
number="10",
pages="e16339",
keywords="brain computer interfacing",
keywords="intracortical recording",
keywords="neural engineering",
keywords="neurosurgery",
doi="10.2196/16339",
url="http://www.jmir.org/2019/10/e16339/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31674921"
}


@Article{info:doi/10.2196/16321,
author="Maynard, David Andrew
and Scragg, Marissa",
title="The Ethical and Responsible Development and Application of Advanced Brain Machine Interfaces",
journal="J Med Internet Res",
year="2019",
month="Oct",
day="31",
volume="21",
number="10",
pages="e16321",
keywords="brain machine interface",
keywords="ethics",
keywords="neuroethics",
keywords="bioethics",
keywords="ethical innovation",
keywords="responsible innovation",
keywords="risk",
keywords="risk innovation",
doi="10.2196/16321",
url="http://www.jmir.org/2019/10/e16321/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31674917"
}


@Article{info:doi/10.2196/16194,
author="Musk, Elon
and ",
title="An Integrated Brain-Machine Interface Platform With Thousands of Channels",
journal="J Med Internet Res",
year="2019",
month="Oct",
day="31",
volume="21",
number="10",
pages="e16194",
keywords="brain-machine interface",
keywords="sensory function",
keywords="motor function",
keywords="neurology",
doi="10.2196/16194",
url="http://www.jmir.org/2019/10/e16194/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31642810"
}