What web-based information is available for people with Parkinson’s disease interested in aquatic physiotherapy? A social listening study

Terrens, Aan Fleur; Soh, Sze‐Ee; Morgan, Prue

doi:10.1186/s12883-022-02669-3

Cited by 8 publications

(8 citation statements)

References 29 publications

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“…There is a challenge in searching for information on technology. A 2022 review evaluated the type and quality of information available on the web for aquatic physiotherapy targeted to people with Parkinson diseases [ 59 ]. The authors used a commercial social listening service Awario that searches social media platforms (eg, Twitter and Instagram) and the web for investigator-selected keywords [ 59 ].…”

Section: Discussionmentioning

confidence: 99%

“…A 2022 review evaluated the type and quality of information available on the web for aquatic physiotherapy targeted to people with Parkinson diseases [ 59 ]. The authors used a commercial social listening service Awario that searches social media platforms (eg, Twitter and Instagram) and the web for investigator-selected keywords [ 59 ]. The strategy used here was a form of snowball sampling where systematic reviews served as the source, and the web was searched for any devices named in these reviews.…”

Section: Discussionmentioning

confidence: 99%

“…Auditory + Heel2Toe: sensor worn over the shoe that provides real-time auditory feedback on making "good steps" in which the heel strikes first [58] Headphones, IMU sensor, and smartphone app Neurological conditions Auditory + MEDRhythms: 2 wearable sensors attached to each shoe that provide rhythmic auditory feedback based on gait parameters to improve gait [59,60] Smartphone, IMU sensor, and docking station…”

Section: Parkinson Disease and Older Adultsmentioning

confidence: 99%

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Evidence for the Efficacy of Commercially Available Wearable Biofeedback Gait Devices: Consumer-Centered Review

Mate¹,

Abou-Sharkh²,

Mansoubi³

et al. 2023

JMIR Rehabil Assist Technol

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Background The number of wearable technological devices or sensors that are commercially available for gait training is increasing. These devices can fill a gap by extending therapy outside the clinical setting. This was shown to be important during the COVID-19 pandemic when people could not access one-on-one treatment. These devices vary widely in terms of mechanisms of therapeutic effect, as well as targeted gait parameters, availability, and strength of the evidence supporting the claims. Objective This study aimed to create an inventory of devices targeting improvement in gait pattern and walking behavior and identify the strength of the evidence underlying the claims of effectiveness for devices that are commercially available to the public. Methods As there is no systematic or reproducible way to identify gait training technologies available to the public, we used a pragmatic, iterative approach using both the gray and published literature. Four approaches were used: simple words, including some suggested by laypersons; devices endorsed by condition-specific organizations or charities; impairment-specific search terms; and systematic reviews. A findable list of technological devices targeting walking was extracted separately by 3 authors. For each device identified, the evidence for efficacy was extracted from material displayed on the websites, and full-text articles were obtained from the scientific databases PubMed, Ovid MEDLINE, Scopus, or Google Scholar. Additional information on the target population, mechanism of feedback, evidence for efficacy or effectiveness, and commercial availability was obtained from the published material or websites. A level of evidence was assigned to each study involving the device using the Oxford Centre for Evidence-Based Medicine classification. We also proposed reporting guidelines for the clinical appraisal of devices targeting movement and mobility. Results The search strategy for this consumer-centered review yielded 17 biofeedback devices that claim to target gait quality improvement through various sensory feedback mechanisms. Of these 17 devices, 11 (65%) are commercially available, and 6 (35%) are at various stages of research and development. Of the 11 commercially available devices, 4 (36%) had findable evidence for efficacy potential supporting the claims. Most of these devices were targeted to people living with Parkinson disease. The reporting of key information about the devices was inconsistent; in addition, there was no summary of research findings in layperson’s language. Conclusions The amount of information that is currently available to the general public to help them make an informed choice is insufficient, and, at times, the information presented is misleading. The evidence supporting the effectiveness does not cover all aspects of technology uptake. Commercially available technologies help to provide continuity of therapy outside the clinical setting, but there is a need to demonstrate effectiveness to support claims made by the technologies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Parkinson Disease and Older Adultsmentioning

confidence: 99%

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Evidence for the Efficacy of Commercially Available Wearable Biofeedback Gait Devices: Consumer-Centered Review

Mate¹,

Abou-Sharkh²,

Mansoubi³

et al. 2023

JMIR Rehabil Assist Technol

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“…The QAMAI tool was developed using the Modi ed DISCERN (mDISCERN) instrument as its foundation [7,8]. The QAMAI tool stems from a methodology that has been well-validated and extensively applied for evaluating the quality of health information across various platforms, including websites [9], social networks [10], and other multimedia channels [11]. Through a panel of experts' guidelines, this tool underwent validation for its construct validity, internal consistency, and reliability using 40 LLM responses on colorectal surgery [12].…”

Section: Methodsmentioning

confidence: 99%

Can AI answer my questions? Using Artificial Intelligence to help provide information for patients with a stoma

Lim,

Lirios,

Sakalkale

et al. 2024

Preprint

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Background Stomas present significant lifestyle and psychological challenges for patients, requiring comprehensive education and support. Current educational methods have limitations in offering relevant information to the patient, highlighting a potential role for Artificial Intelligence (AI). This study examined the utility of AI in enhancing stoma therapy management following colorectal surgery. Material and Methods We compared the efficacy of four prominent Large Language Models (LLM)—OpenAI's ChatGPT-3.5 and ChatGPT-4.0, Google's Gemini, and Bing's CoPilot—against a series of metrics to evaluate their suitability as supplementary clinical tools. Through qualitative and quantitative analyses, including readability scores (Flesch-Kincaid, Flesch-Reading Ease, and Coleman-Liau index) and reliability assessments (Likert scale, DISCERN score and QAMAI tool), the study aimed to assess the appropriateness of LLM-generated advice for patients managing stomas. Results There are varying degrees of readability and reliability across the evaluated models, with CoPilot and ChatGPT-4 demonstrating superior performance in several key metrics such as readability and comprehensiveness. However, the study underscores the infant stage of LLM technology in clinical applications. All responses required high school to college level education to comprehend comfortably. While the LLMs addressed users’ questions directly, the absence of incorporating patient-specific factors such as past medical history generated broad and generic responses rather than offering tailored advice. Conclusion The complexity of individual patient conditions can challenge AI systems. The use of LLMs in clinical settings holds promise for improving patient education and stoma management support, but requires careful consideration of the models' capabilities and the context of their use.

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“…The QAMAI tool has been developed based on the Modified DISCERN (mDISCERN) instrument [28,29]. The mDISCERN is a well-validated and widely used tool for assessing the quality of health information conveyed by websites [30], social networks [31], YouTube and other multimedia platforms [32]. However, the use of mDISCERN for evaluating information provided by artificial intelligence is not possible as the tool takes into account certain human characteristics such as board certification and the reputation of the content creator, which cannot be applied to artificial intelligence.…”

Section: Quality Analysis Of Medical Artificial Intelligence Tool Dev...mentioning

confidence: 99%

Validation of the QAMAI tool to assess the quality of health information provided by AI

Vaira,

Lechien,

Abbate

et al. 2024

Preprint

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Objective: To propose and validate the Quality Assessment of Medical Artificial Intelligence (QAMAI), a tool specifically designed to assess the quality of health information provided by AI platforms. Study design: observational and valuative study Setting. 27 surgeons from 25 academic centers worldwide. Methods: The QAMAI tool has been developed by a panel of experts following guidelines for the development of new questionnaires. A total of 30 responses from ChatGPT4, addressing patient queries, theoretical questions, and clinical head and neck surgery scenarios were assessed. Construct validity, internal consistency, inter-rater and test-retest reliability were assessed to validate the tool. Results: The validation was conducted on the basis of 792 assessments for the 30 responses given by ChatGPT4. The results of the exploratory factor analysis revealed a unidimensional structure of the QAMAI with a single factor comprising all the items that explained 51.1% of the variance with factor loadings ranging from 0.449 to 0.856. Overall internal consistency was high (Cronbach's alpha=0.837). The Interclass Correlation Coefficient was 0.983 (95%CI 0.973-0.991; F(29,542)=68.3; p<0.001), indicating excellent reliability. Test-retest reliability analysis revealed a moderate-to-strong correlation with a Pearson coefficient of 0.876 (95%CI 0.859-0.891; p<0.001) Conclusions: The QAMAI tool demonstrated significant reliability and validity in assessing the quality of health information provided by AI platforms. Such a tool might become particularly important/useful for physicians as patients increasingly seek medical information on AI platforms.

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What web-based information is available for people with Parkinson’s disease interested in aquatic physiotherapy? A social listening study

Cited by 8 publications

References 29 publications

Evidence for the Efficacy of Commercially Available Wearable Biofeedback Gait Devices: Consumer-Centered Review

Evidence for the Efficacy of Commercially Available Wearable Biofeedback Gait Devices: Consumer-Centered Review

Can AI answer my questions? Using Artificial Intelligence to help provide information for patients with a stoma

Validation of the QAMAI tool to assess the quality of health information provided by AI

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