Exploring of cardiac autonomic activity with heart rate variability in long-term kratom (<i>Mitragyna speciosa</i> Korth.) users: a preliminary study

Saengmolee, Wanumaidah; Cheaha, Dania; Sa-ih, Nusaib; Kumarnsit, Ekkasit

doi:10.7717/peerj.14280

Cited by 5 publications

(5 citation statements)

References 39 publications

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“…There were six articles published during the reviewed period that enrolled and evaluated human participants [53 ▪ ,54 ▪ ,55–58]. Two US publications were based on the same research protocol (NCT04392011), enrolling a small sample of healthy human volunteers who were administered kratom-derived chemicals and other chemicals under laboratory conditions.…”

Section: Resultsmentioning

confidence: 99%

“…Each of the three Malaysian studies enrolled and compared two small cohorts of participants: one with and one without self-reported histories of kratom consumption. One study from Thailand compared heart rate variability (HRV) indices among a sample of individuals with self-reported long-term history of chewing fresh kratom leaves and among healthy controls without histories of kratom consumption [58].…”

Section: Studies Enrolling Human Participantsmentioning

confidence: 99%

“…58]. Two US publications were based on the same research protocol (NCT04392011), enrolling a small sample of healthy human volunteers who were administered kratom-derived chemicals and other chemicals under laboratory conditions.…”

mentioning

confidence: 99%

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Research and publication gaps on kratom and kratom products: a scoping review of current literature

Vicknasingam,

Karunakaran,

Chawarski

2024

Current Opinion in Psychiatry

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Purpose of review Kratom plant, products derived from the plant, and plant phytochemicals are of great interest among researchers, clinicians, and consumers. However, there is a paucity of rigorously collected scientific data on their risk/safety profile and public health impact. This scoping review discusses original research articles published between 2022 and 2023. It focuses on identifying publication gaps on topics related to epidemiology, public health, and risk/safety profiles comparing evidence collected by researchers from Southeast Asia and the West. Recent findings Our review of the Scopus database identified a total of 55 publications, including clinical case reports and case series reports, surveys, studies enrolling human participants, and publications based on large-scale national surveys or large-scale national or international health system database records. Summary Overall, there is dearth of reliable data on key epidemiological factors, including the prevalence rates, and on objective and reliable indices of the risk/safety profiles. Rigorous and systematic studies including improved epidemiological surveillance, human laboratory, and controlled clinical studies are urgently needed to advance our understanding of public health consequences of consuming kratom and kratom-derived products and to improve our understanding of their risk/safety profile and additional analytical studies to better inform development of needed regulatory oversight.

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Section: Resultsmentioning

confidence: 99%

Section: Studies Enrolling Human Participantsmentioning

confidence: 99%

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Research and publication gaps on kratom and kratom products: a scoping review of current literature

Vicknasingam,

Karunakaran,

Chawarski

2024

Current Opinion in Psychiatry

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“…These represent the normalized low-and high-frequency components of the heart rate variability. Normalizing these features to the total power has been shown to be important in reducing the effect of variation of the overall power [27].…”

Section: Feature Extractionmentioning

confidence: 99%

On the robustness of machine learning models for stress and anxiety recognition from heart activity signals

Henry¹,

Lloyd²,

Turner³

et al. 2023

Preprint

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<p>Many recent studies have addressed the detection of negative affective states such as stress and anxiety from physiological signals taken from body-worn sensors. Typically, machine learning classifiers are applied to features derived from sensor signals, and several authors have reported high accuracy results from a range of signals including cardiac, skin conductance and skin temperature. However, the issue of how robust these models are for deployment in the field is rarely addressed. In this paper, we use open data from two large experimental studies to evaluate the generalizability of models derived from cardiac signals, focusing on detection of stress and anxiety. We choose the cardiac signal since the commonly used heart-rate variability features can be derived from multiple sensor modalities, allowing us to evaluate the robustness of models within, as well as between, experimental settings. We show that consistent classification outside the original experimental setting relies on high-quality training data with minimal artefacts, and that models may often train on proxies within the noise of lower quality data. Our results also underline the importance of including a wide range of emotional states in the training data to minimize erroneous classification from unseen regions of feature space.</p>

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“…These represent the normalized low-and high-frequency components of the HRV. Normalizing these features to the total power has been shown to be important in reducing the effect of variation in the overall power [28].…”

Section: Feature Extractionmentioning

confidence: 99%

On the Robustness of Machine Learning Models for Stress and Anxiety Recognition From Heart Activity Signals

et al. 2023

View full text Add to dashboard Cite

Many recent studies have addressed the detection of negative affective states such as stress and anxiety from physiological signals taken from body-worn sensors. Typically, machine learning classifiers are applied to features derived from sensor signals, and several authors have reported high accuracy results from a range of signals including cardiac, skin conductance, and skin temperature. However, the issue of how robust these models are for deployment in the field is rarely addressed. In this article, we use open data from two large experimental studies to evaluate the generalizability of models derived from cardiac signals, focusing on detection of stress and anxiety.We choose the cardiac signal since the commonly used heart rate variability features can be derived from multiple sensor modalities, allowing us to evaluate the robustness of models within, as well as between, experimental settings. We show that consistent classification outside the original experimental setting relies on high-quality training data with minimal artifacts, and that models may often train on proxies within the noise of lower quality data. Our results also underline the importance of including a wide range of emotional states in the training data to minimize erroneous classification from unseen regions of feature space.

show abstract

Exploring of cardiac autonomic activity with heart rate variability in long-term kratom (Mitragyna speciosa Korth.) users: a preliminary study

Cited by 5 publications

References 39 publications

Research and publication gaps on kratom and kratom products: a scoping review of current literature

Research and publication gaps on kratom and kratom products: a scoping review of current literature

On the robustness of machine learning models for stress and anxiety recognition from heart activity signals

On the Robustness of Machine Learning Models for Stress and Anxiety Recognition From Heart Activity Signals

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