Dataset Information

Social listening - revealing Parkinson's disease over day and night.

ABSTRACT:

Background

Nocturnal symptoms in Parkinson's disease are often treated after management of daytime manifestations. In order to better understand the unmet needs of nocturnal symptoms management, we analyzed the characteristics and burden of nocturnal symptoms from patients' perspectives and explored their changes over time. Overall symptoms (occurring at day or night) were collected to compare whether the unmet needs related to nocturnal symptoms and to overall symptoms are different.

Methods

We used a Social Listening big-data technique to analyze large amounts of Parkinson's disease symptoms in dialogues available from social media platforms in 2016 to 2018. These symptoms were classified as either overall symptoms or nocturnal symptoms. We used share of voice (SOV) of symptoms as a proportion of total dialogues per year to reflect the characteristics of symptoms. Negative sentiment score of symptoms was analyzed to find out their related burden.

Results

We found the SOV for overall motor symptoms was 79% and had not increased between 2016 and 2018 (79%, p =?0.5). The SOV for non-motor symptoms was 69% and had grown by 7% in 2018 (p ConclusionsThe growing SOV and the greater negative sentiment of nocturnal symptoms suggest management of nocturnal symptoms is an unmet need of patients. A greater emphasis on detecting and treating nocturnal symptoms with 24-h care is encouraged.

SUBMITTER: Zhang H

PROVIDER: S-EPMC7780378 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Social listening - revealing Parkinson's disease over day and night.

Zhang Hui H Meng Fanwen F Li Xingyu X Ning Yali Y Cai Meng M

BMC neurology 20210104 1

<h4>Background</h4>Nocturnal symptoms in Parkinson's disease are often treated after management of daytime manifestations. In order to better understand the unmet needs of nocturnal symptoms management, we analyzed the characteristics and burden of nocturnal symptoms from patients' perspectives and explored their changes over time. Overall symptoms (occurring at day or night) were collected to compare whether the unmet needs related to nocturnal symptoms and to overall symptoms are different.<h4 ...[more]

PMID: 33397315

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Project description:IntroductionSocial media listening (SML) is an approach to assess patient experience in different indications. This is the first study to report the results of using SML to understand patients' experiences of living with dry eye disease (DED).MethodsPublicly available, English-language social media content between December 2016 and August 2017 was searched employing pre-defined criteria using Social Studio®, an online aggregator-tool for posts from social media channels. Using natural language processing (NLP), posts were indexed using patient lexicon and disease-related keywords to derive a set of patient posts. NLP was used to identify relevance, followed by further manual evaluation and analysis to generate patient insights.ResultsIn all, 2279 possible patient records were identified following NLP, which were filtered for relevance to disease area by analysts, resulting in a total of 1192 posts which formed the basis of this study. Of these, 77% (n = 915) were from the USA. Symptoms, causes, diagnosis and treatments were the most commonly discussed themes. Most common symptoms mentioned were eye dryness (138/901), pain (114/901) and blurry vision (110/901). Pharmaceutical drugs (prescription and over-the-counter; 55%; 764/1393), followed by medical devices (20%; 280/1393), were mentioned as major options for managing symptoms. Of the pharmaceutical drugs, eye drops (33%; 158/476) and artificial tears (10%; 49/476) were the most common over-the-counter options reported, and Restasis® (22%; 103/476) and Xiidra® (6%; 27/476) were the most common prescription drugs. Patients voiced a significant impact of DED on their daily activities (4%; 9/224), work (23%; 51/224) and driving (12%; 26/224). Lack of DED specialists, standard diagnostic procedures, effective treatment options and need to increase awareness of DED among patients were identified as the key unmet needs.ConclusionsInsights revealed using SML strengthen our understanding about patient experiences and their unmet needs in DED. This study illustrates that an SML approach contributed effectively in generating patient insights, which can be utilised to inform early drug development process, market access strategies and stakeholder discussions.FundingNovartis Pharma AG, Basel, Switzerland. Plain language summary available for this article.

Dataset Information

Social listening - revealing Parkinson's disease over day and night.

Background

Methods

Results

Publications

Social listening - revealing Parkinson's disease over day and night.

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