Factors influencing the use of natural health products, in particular for concentration and cognition in Germany

BACKGROUND

Natural health products (NHP) are an important part of the healthcare system. They are mainly non-prescription and sold over the counter, which requires active decision making by the consumer. Within the framework of the Complementary and Alternative Healthcare Model, this study aims to identify factors that influence NHP usage, in particular related to concentration and cognition (CC), a topic that concerns all ages and social classes within the population.

METHODS

Data were collected by means of a representative online survey (n = 1,707) in Germany in April 2022. Three user groups were defined: NHPCC users, who used NHP for CC (12 month prevalence); nCC-NHP users, who used NHP but not for CC indications (12 month prevalence); and past NHP users, who have used NHP but not within the previous 12 months. Independent influencing variables were categorized into predisposing, enabling, need, and health service use factors. Data were analyzed with descriptive statistics, inferential statistics, and binary logistic regression models to compare NHPCC users to nCC-NHP users (model 1) and to past NHP users (model 2).

RESULTS

A higher share of NHPCC and nCC-NHP users compared to past NHP users were women, self-medicated with NHP, and used information about NHP provided by health professionals or on product. Their openness-to-change value orientation was more pronounced than of past users. Compared to nCC-NHP and past NHP users, the probability of being an NHPCC user increased if an individual had more difficulties in daily attention and memory performance, made use of health professionals and literature to seek information about NHP, and used NHP for health support and illness prevention. Additionally, a female gender, NHP self-medication, and having higher values of self-transcendence were significant indicators for NHPCC usage compared to past NHP usage.

CONCLUSION

NHP manufacturers, health professionals, and policymakers should be aware of the factors that lead to NHP consumption decisions and consider them in the development and optimization of healthcare strategies as well as in the marketing and communication strategies of companies producing NHP, in particular for CC. The current study can contribute to characterizing the target groups and to defining the aims and communication channels of such campaigns.

Usage of Natural Health Products (NHPs) for respiratory diseases: user characteristics and NHP-Consumption behavior during the Covid-19 pandemic in Germany

BACKGROUND

Respiratory diseases (RD) can challenge healthcare systems around the globe. Natural health products (NHPs) are popular complementary and alternative medicine options for health issues concerning non-fatal RD. Little is known about the characteristics of the users of RD-NHPs and about their NHP consumption behavior during the Covid-19 pandemic in Germany.

METHODS

A representative online survey was conducted in Germany in 2022. 1707 participants were classified based on having used NHPs for RD within the previous 12 months, having used NHPs but not for RD within the previous 12 months and not having used NHPs. Data were analyzed using descriptive and inferential statistical methods as well as a multinomial logistic regression model.

RESULTS

Users of RD-NHPs within the previous 12 months were more likely to be employed and to consult pharmacists more often for non-fatal health issues than individuals who did not take RD-NHPs. RD-NHP users were more likely to suffer from a Covid-19 infection and to have children living in the same household than other NHP users. Compared to non-NHP users, RD-NHP users were more likely to be female, highly educated and have stronger openness-to-change value orientations. Vaccination-related behavior was no indicator of RD-NHP usage. Most RD-NHP users took NHPs in self-medication. Few reported informing their practitioner about their self-medication. Drugstores were the most visited supply source for NHPs during the pandemic, followed by pharmacies. Common information sources regarding NHPs were the products themselves and pharmacists.

CONCLUSION

This study emphasized the important role of NHPs as a popular prevention and treatment option for RD. RD-NHPs were more likely used by individuals who were employed, who suffered from a RD and who consult pharmacists for non-fatal health issues. The importance of product information and pharmacies as information sources should be considered to make communication strategies about safe self-medication options with RD-NHPs more effective, which could help to reduce the burden of health facilities regarding non-fatal RD. To improve and develop future pandemic-control strategies, health professionals and policy makers should consider NHP usage behavior and provide critical information about chances and risks of self-medicated NHP consumption.

Improved weed segmentation in UAV imagery of sorghum fields with a combined deblurring segmentation model

BACKGROUND

Efficient and site-specific weed management is a critical step in many agricultural tasks. Image captures from drones and modern machine learning based computer vision methods can be used to assess weed infestation in agricultural fields more efficiently. However, the image quality of the captures can be affected by several factors, including motion blur. Image captures can be blurred because the drone moves during the image capturing process, e.g. due to wind pressure or camera settings. These influences complicate the annotation of training and test samples and can also lead to reduced predictive power in segmentation and classification tasks.

RESULTS

In this study, we propose DeBlurWeedSeg, a combined deblurring and segmentation model for weed and crop segmentation in motion blurred images. For this purpose, we first collected a new dataset of matching sharp and naturally blurred image pairs of real sorghum and weed plants from drone images of the same agricultural field. The data was used to train and evaluate the performance of DeBlurWeedSeg on both sharp and blurred images of a hold-out test-set. We show that DeBlurWeedSeg outperforms a standard segmentation model that does not include an integrated deblurring step, with a relative improvement of [Formula: see text] in terms of the Sørensen-Dice coefficient.

CONCLUSION

Our combined deblurring and segmentation model DeBlurWeedSeg is able to accurately segment weeds from sorghum and background, in both sharp as well as motion blurred drone captures. This has high practical implications, as lower error rates in weed and crop segmentation could lead to better weed control, e.g. when using robots for mechanical weed removal.