Influence of dairy farms’ characteristics and technological level on attitude towards augmented reality

Abstract

The implementation of Decision Support System and data visualization technologies, as Augmented Reality (AR), offers potential advantages in herd management by enabling real-time access to critical animal data, enhancing decision-making, and providing training opportunities for farmers. However, investigating the possible spread of technologies in livestock farming is fundamental to understand which factors could influence the adoption of specific technologies by farmers. For this reason, this study aimed to investigate factors influencing farmers’ attitudes towards the use of AR technologies, such as Smart Glasses for AR, in livestock farms. The research involved 18 dairy farms with different technological adoption level where the main discrimination technology was the Automatic Milking System (AMS). The study revealed that farms that use AMS generally had greater technological implementation and were more familiar with advanced livestock technologies. While both AMS farmers and Conventional Milking Parlor (CMP) farmers had positive attitudes toward AR use, CMP farmers perceived greater potential benefits because of their limited access to on-site animal data. The study concludes that augmented reality has the potential to enhance the efficiency of livestock farming data use, especially on CMP farms, by offering an innovative method to visualize animal data. While the results align with existing literature, future studies should consider geographical constraints, sample size, and temporal and cultural variations. Finally, it is also recommended to explore the interoperability of AR with existing precision livestock farming technologies and to address the main barriers to its adoption, such as cost and training.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-38898-6.

Introduction

Recent technological advancements, as smart sensors, digital technologies, and Internet of Things (IoT), allows farmers to gather information on various parameters, such as animal behavior, health status, feed intake, environmental conditions, and production performance^1,2. This scenario poses the basis for the conceptualization of the Precision Livestock Farming (PLF) approach, that has been defined as the use of real-time monitoring technologies to manage individual animals^3,4. Particularly, in dairy farms, Automatic Milking System (AMS) revolutionized the approach to animal farming. In fact, farms equipped with AMSs have access to a greater volume of data, which often requires advanced skills to be managed and effectively used⁵ while their interpretation is a key step for the correct use of PLF technologies⁶. This aspect constitutes one of the main challenges for the successful spreading of PLF⁷. On one hand, the use of mathematical modelling techniques or machine learning technologies could reduce the impact of this problem⁸, nevertheless, livestock operators and farmers cannot be excluded from the decision-making process, as their expert knowledge is another key factor for the correct management of the herd⁹. In this scenario, Decision support system plays a fundamental role to connect technological tool and farming operator, supporting their decision with an efficient and comprehensive visualization of farm data. To improve the efficiency of Decision support systems it is fundamental to increase the interoperability between PLF tools, increasing communication efficiency between different sensors and technology¹⁰. In this scenario, Augmented Reality (AR) represent an interesting solution for improving the visualization of data directly on field and during farm management operation, offering valuable insights and facilitating better management practices. This technology consists of overlays of virtual information (e.g., text, images, 3D models) onto the real-world environment¹¹, and with a more advanced system, an interactive and immersive experience can be provided¹². Typically, AR systems utilize mobile devices such as smartphones and tablets to visualize digital elements, but wearable devices, such as Smart Glasses (SG) could be the best choice for implementing AR for professional purposes¹³. While in other sectors, such as medicine, assembly, and construction, AR is considered an almost established technology^14–16, in agricultural and related environments, it is still considered an emerging technology¹⁷. The implementation of AR in livestock farming could offer several potential benefits. First, AR systems could facilitate the digital transition of livestock farms, providing operators with immediate access to valuable information and improving herd management, allowing precise data-driven decision-making. Second, AR could enhance the training and education of farmers and workers by providing interactive tutorials, virtual demonstrations, and real-time guidance¹⁸. However, the implementation of AR in livestock environments also poses certain challenges. The technology must be user friendly, comfortable to wear for extended periods, and robust enough to withstand the harsh conditions of agricultural operations¹⁹. Additionally, the integration of AR with existing PLF systems and farm management software is crucial to ensure seamless data exchange and interoperability. Data privacy and security are also important concerns, as the use of AR involves collecting and processing sensitive information about livestock and farm operations. Addressing these challenges requires interdisciplinary collaboration among researchers, engineers, farmers, and policymakers⁵. Also, to assess the feasibility and acceptance of AR in livestock farming, the perceptions and attitudes of dairy farmers toward the use of AR systems need to be evaluated, as well as the farm activities that could benefit the implementation of this technology. A recent study investigated the farmers intention to use of AR devices, which resulted mainly influenced by farmers attitude toward the use of these types of tools, as well as by compatibility with existing technologies of the farms²⁰. Moreover, the usage of digital technologies in livestock farms varies in relation to the farm size and specialization as well as husbandry system²¹. In fact, for the actual implementation of a new tool or system, an important aspect to consider is the level of technology effectively deployed in the farm and the attitudes that farmers have toward innovative technologies²². For this reason, the objective of the study was to assess how farmers’ attitude towards the potential use of AR technologies and SG for AR are influenced by farms’ features, such as management system, technological level and operators’ characteristics.

Materials and methods

Ethics approval

All the procedures of this research were carried out in accordance with relevant guidelines and regulations of Italy and EU legislations (D.L. 26/2014 and Council Directive 2010/63/EU). Based on previous research protocols the Azienda Regionale della salute (ARES Sardegna) ethical committee reported that, when no healthcare investigators interact with the procedures of the study, the adopted protocol does not require an opinion from the ethics committee. Moreover, informed consent was obtained from all the human participating subjects for publication of identifying information and images in an online open-access publication.

Study area and farms involved

The research took place in Arborea, located in Sardinia, Italy (39°46’21.94"N, 08°34’52.64"E), within a lowland region close to the sea that is used for dairy farming with intensive management practices. A total of 18 farms were involved in the study (Fig. 1) as found in similar studies^13,23. The selection of farms was made on a voluntary basis, and participants were divided into two groups based on the milking system adopted, with nine farms equipped with AMSs and nine farms using Conventional Milking Parlors (CMPs).

Fig. 1.

Map of the study area with the locations of the participant farms grouped by milking system type (AMS = Automatic Milking System; CMP = Conventional Milking System). The map was generated using QGIS 2.28.4-Firenze (https://qgis.org) by the authors.

Data collection and questionnaire structure

After a brief presentation of the research objectives, the data were collected via a semi structured survey available in the Supplementary Materials S1. The questionnaire was administered through face-to-face individual interviews to the manager of each farm involved in the study. The survey consisted of four sections with a total of 51 questions, including open-ended and multiple-choice questions, providing both quantitative and qualitative data. The first section focused on gathering general data about the farm and operators (e.g., sex, age, livestock heads, year of foundation); the second section of the survey focused on the milking system, machinery, software, and related variables (e.g., type and functionality of the cooling system, type of feeding system); and the third section investigated the relationship between the farmer and the technology (e.g., source of information, frequency of technology renewal and farmer’s approach to adopting new technology). Finally, the fourth section addressed the use of SG for AR in livestock farming. Specifically, this section was composed of 11 statements, and participants were asked to rate their level of agreement with each statement on a five-point Likert scale (from “strongly disagree” to “strongly agree”). Fourth section’s statements were divided into three subsections: “Attitude toward using” (ATT; statements 40 to 43), “Intention to use” (ITU; statements 44 to 46), and “Perceived Benefit” (PB; statements 47 to 50). The questionnaire’s statements used were adapted from established models in the technology acceptance studies, investigating the acceptance of precision agricultural systems²⁴. In particular, were used items from the Technology Acceptance Model (TAM) as applied in studies assessing user intentions to adopt AR and SG^20,25. The specific statements used in this study were selected to quantify farmers attitude toward AR technology use and their intention in adopting SG devices for livestock management as well as the benefit derived from the implementation of this technology.

Before the fourth section of the questionnaire compiling, a hands-on demonstration of Microsoft HoloLens 2 (Microsoft, USA) equipped with a specific application designed for livestock data management was performed to show the features and potential implementation of AR in livestock farming.

Data analysis

Descriptive statistics (arithmetic average and standard deviation) were calculated for each statement of the questionnaire. The Kruskal‒Wallis test was used to compare results between the two types of livestock farms because of nonparametric data trends. Finally, Pearson’s correlation analysis was performed to investigate the correlation between each statement of the survey. RStudio (version 2022.07.2 build 576) was used to perform statistical analysis. Qualitative data, obtained from open-ended questions, were analyzed individually to identify common themes and insights form the farmers, which complemented the quantitative findings.

Results

Farm characterization

All the participants’ farms were intensive husbandry system, with lactating cows housed always indoors. Farmers were all male with an age ranging from 22 to 64 years, with a mean age of 47.72±13 years. There was a difference in age between the AMS farm owners and CMP farm owners, where the former result younger with 44.22±11 years, instead of 51.22±14 years. The total number of cows (AMS: 403.44±255; CMP: 285.33±105) and the number of lactating cows (AMS: 201.67±147; CMP: 160.00±54) were similar for both types of milking systems. In accordance with previous findings^26,27, the milk yield per cow was statistically greater for AMS farms, with an average of 11849.91±1710 kg/year, than for CMP farms, with 9547.41±1420 kg/year. In terms of education, the majority of the farmers (72.5%) had at least a high school diploma (89% for AMS farms and 56% for CMP farms). Overall, the survey did not highlight relevant differences between AMS farms and CMP farms in terms of their general structure (Table 1).

Table 1.

Summary of the characteristics of participants’ farms. The average values and standard deviation for the different groups were reported. N = 18.

Items	AMS	CMP	Total
Number of farms	9	9	18
Age of the farmer (years)	44.22±11.49a	51.22±14.85a	47.7±13.38
Employees (N)	4.33±3.24a	4.00±1.58a	4.16±2.4
Heads in lactation (N)	201.66±147.77a	160.00±54.50a	180.83±110.15
Total cows (N)	403.44±255.27a	285.33±105.64a	344.39±199.02
Milk yield per cow (kg/year)	11849.91±1710.83a	9547.41±1420.65b	10698.66±1931.45
High school diploma (%)	89	56	72.5

Abbreviations: AMS = Automatic Milking System; CMP = Conventional Milking Parlor.

^a-b Different letters indicate significant differences in a row (P < 0.01).

With respect to the type of technology used on the farms (Table 2), most farms adopted a self-propelled mixer wagon for Total Mixed Ration (TMR) preparation and distribution. However, the automated feeding system was utilized by the 22% of the farmers with AMSs.

Table 2.

Summary of the technologies adopted by farms with the proportion of farms per each group which use the specific technology (items column). N = 18.

Items	Percentage of use (%)
	AMS	CMP	Total
Feeding
Self-propelled TMR mixer	100	100	100
Automated feeding system	22	0	11
Automatic feed pusher	33	11	22
Calf feeding
Fixed calf ration mixer	11	56	33
Milk delivering system	11	11	11
Automatic calf feeder system	44	11	27.5
Other	33	22	27.5
Comfort
THI-based System	89	56	77.5
Fans in feeding area	100	89	94.5
Mist cooling system in feeding area	100	89	94.5
Fans in cubicle	100	89	94.5
Manure scraper system	100	100	100
Manure management robot	0	0	0
Cow brush	56	67	61.5
Identification systems and sensors
Collars	89	0	44.5
Pedometers	11	22	16.5
GPS auricular tags	0	0	0
Rumen bolus	0	22	11
Thermal sensors	0	0	0
Energy supply
Photovoltaics	22	22	22

AMS = Automatic Milking System; CMP = Conventional Milking Parlor.

The automated feed pushing system was not widely used and was present in 33% of the AMS farms and 11% of the CMP farms. Another interesting aspect was calf feeding management, where automatic calf feeder systems were preferred in 44% of the AMS farms, whereas fixed ratio mixers were the most common technology in 56% of the CMP farms. With respect to the operating mode of the cooling system, the results revealed that in 89% of the AMS farms, the system was activated on the basis of the values of the Temperature Humidity Index (THI) and not on the basis solely of air temperature values, whereas 56% of the CMP farms had THI-based operation. Concerning the equipment installed for cow comfort and hygiene (fans, water sprinklers, scrapers, and brushes), both types of farms achieved similar results. A notable difference was observed in Electronic Identification systems. Specifically, all the AMS farms were required to have an Electronic Identification system for the operation of the milking robot (89% collars and 11% pedometers), whereas fewer than 50% of the CMP farms had one (22% pedometers and 22% rumen boluses).

The results revealed that Lely (Lely Industries, N.V., Maassluis, the Netherlands) and De Laval (DeLaval International AB, Tumba, Sweden) were the most common companies of AMS (44% for each) adopted by the participants’ farms, whereas only one farm used a milking station produced by TDM (Total Dairy Management, Nutriservice s.r.l., Model Merlyn, San Paolo, BS, Italy) milking robot. The number of robots ranged from 1 to 8, with 44 to 71 cows per robot, which falls into the considered optimal interval of animals/robots, which ranges from 55 to 65 cows per AMS²⁸. The average milking rate per cow ranged from 2.5 to 3.4 milking/d per cow. Additional systems were found on the AMSs considered, where the most common systems resulted in the analysis of milk (67%) and the disinfection of hooves (33%; Table 3). All the farmers who did not have any additional systems in the AMS stated that if they could invest, they would add the milk analysis system, confirming how this feature is perceived to be the most useful among participants.

Table 3.

Characteristics and implementation of automatic milking system (AMS) farms.

Items	AMS brand
	TDM	Lely				Delaval
Farm	1	2	3	4	5	6	7	8	9
Number of AMS	3	1	2	2	5	1	8	2	3
Feet disinfection	-	-	+	-	+	-	-	-	+
BCS calculator	-	-	-	-	-	-	-	-	+
Weighting system	-	+	-	+	-	-	-	-	-
Milk analysis system	+	+	+	+	+	-	-	-	+
N° of cows per AMS station	64	59	71	63	60	44	62	47	55
Milking per cow per day	2.8	3.4	2.6	2.8	2.8	2.9	2.5	2.5	3

- Means that the technology is not present.

+ Indicates that the technology is present.

Most of the CMP farms (89%) had a herringbone milking parlor and milked twice a day. One interesting observation was the widespread use of various technologies on CMP farms. Specifically, all farms implemented automatic cluster removers, which are important systems for avoiding overmilking and preserving animal welfare; 89% of the farms used a variable-frequency drive, which reduces energy consumption and enhances the energy efficiency of the milking process; finally, the results revealed that 66% of the farms used milk recorder jars to obtain the daily individual milk yield, which aided in improving herd management (Table 4).

Table 4.

Characteristics and technologies implemented by conventional milking parlor (CMP) farms.

Items	Parlor design
	Parallel	Herringbone
Farm	10	11	12	13	14	15	16	17	18
Electronic identification system	-	-	-	-	-	-	-	-	-
Automatic cluster remover	+	+	+	+	+	+	+	+	+
Milk recorder jar	+	-	+	-	+	+	+	+	-
Milk meter	-	-	-	-	-	-	-	-	-
Variable frequency drive	+	+	-	+	+	+	+	+	+
Automatic gate	+	-	-	+	-	-	-	-	-
Milking per day	2	2	2	3	2	2	2	2	2

- Means that the technology is not present.

+ Indicates that the technology is present.

Finally, while the AMS operator asserted that the disparity in technology adoption stems from a higher level of confidence in advanced technologies, all operators identified “cost” as the primary limiting factor, followed by “lack of time to dedicate to technology” and “difficulties in learning how to use it”.

Farmers attitude towards augmented reality and the use of data

In Fig. 2 are reported the results of the fourth section of the questionnaire regarding the farmers’ attitude towards using SG for AR in livestock farming. Both AMS and CMP farms rated SG positively, with average overall scores of 4.27±0.63 and 4.52±0.65 out of 5, respectively (Fig. 2a). In addition, considering the attitude’s categories only the PB category resulted in a significant difference between the two types of farms studied (P < 0.01), with average scores of 3.89±0.09 for AMS and 4.53±0.21 for CMP (Fig. 2a). Despite no statistical differences were found for ITU and ATT, ATT obtained the highest average scores between the categories with a score of 4.60±0.68 out of 5. This was due to statement 40 (“Good Idea”; Supplementary material S1), statement 42 (“Advantageous”; Supplementary material S1) scores which were very close to 5 (4.78±0.42 and 4.72±0.46 respectively; Fig. 2b). For the ITU specific statements 44 (“Like to use”; Supplementary material S1) obtained the highest average score (4.61±0.60) without differences between AMS and CMP farms (Fig. 2c).

Fig. 2.

Average score of the fourth section of the proposed questionnaire, regarding augmented reality and Smart Glasses (SG) in dairy farms, summarized by category (a) and divided into singular statements for each category: Attitude toward using (b), Intention to use (c), and Perceived benefit (d). (AMS = Automated Milking System, CMP = Conventional Milking Parlor). (*) indicates significant differences at P < 0.05. (a) ATT= “Attitude toward using SG”; ITU= “Intention to use SG”; PB= “Perceived Benefit of SG use”. (b) GI= “Good Idea; SX= “Is sensate”; ADV= “advantageous”; ENJ= “enjoyable”. (c) LK= “Like to use”; ITT= “Intent”; PLN= “Plan”. d) PRF= “Profit”; DMP= “Decision making process”; PRC= “Price”; CST= “Cost reduction”.

Regarding the specific statement of PB category, the greatest differences were observed in statement 48 (“Decision making process”; Supplementary material S1) regarding the use of SG as a support system in the decision process (AMS: 3.78±1.39, CMP: 4.56±0.72), in statement 49 (“Price”; Supplementary material S1) regarding the price of SG (AMS: 4.00±0.70, CMP: 4.67±0.70), and in statement 50 (“Cost reduction”; Supplementary material S1) regarding the possible reduction in cost associated with the use of SG (AMS: 3.89±0.92, CMP: 4.67±0.70; Fig. 2 d). In these statements, related to the perceived benefit of using SG in the farm, the attitude resulted better always for the CMP farmers. These outcomes highlighted that there are contrasting attitudes towards the use of AR and SG on the two types of farms.

Considering the correlation analysis results, the overall score of farmers attitude towards the use of SG was found to be correlated with interest in articles about technology for livestock farming (r = 0.68, P < 0.01) whereas it was not correlated with the age of the farmer (r = −0.06, P < 0.01) or the level of education (r = 0.07, P < 0.01). In addition, productivity data (e.g., milk yield history and milk quality) and sanitary status (e.g., medical treatment history) were reported by all participants as key information to be visualized directly on-field.

Discussion

The study assessed how farmers’ attitudes towards the use of AR technologies and SG are influenced by farm features. Overall, the survey did not highlight relevant differences between AMS and CMP farms in terms of general structure. Nevertheless, the questionnaire underlined several disparities in terms of technological implementation and attitude towards farm technologies. In contrast to relevant literature²⁹, herd size seems not to influence technological implementation, while, as highlighted by Groher et al. 2020²¹ the type of husbandry system plays a key role in the adoption of digital technologies, with sensors and measuring devices integrated into milking parlours being more commonly used than data-processing technologies. It was, however, very interesting to note that all participants turned out to be male, confirming the gender gap in the agricultural and livestock sector. Still, discussing differences in technology adoption related to the presence of women in the livestock industry is very difficult, as this remains a controversial and not yet fully investigated topic³⁰. Moreover, even if no correlation was discovered between the age of the farmer or educational level and the use of AMSs, the adoption of automatic or semiautomatic technology was higher in farms that utilized AMSs, where operator tended to be younger. This trend partially aligns with previous research suggesting that younger farmers are more inclined towards precision and advanced tools for monitoring and managing their herds³¹. Also, is important to consider that AMS robot allows an easier integration with most of relevant PLF technologies³², especially it is strictly connected to the use of Electronic Identification. In fact, the persistent absence of this type of system in CMP farms poses a significant structural obstacle for the potential implementation of any automatic or semiautomatic technology. Finally, it is interesting to note that the majority of optional technologies for the milking systems in the farms involved in this study primarily focused on evaluating milk yield and quality, as well as the health status of the animals.

Regarding AR devices and SG the overall farmers’ attitude resulted positive, highlighting the interest of the studied husbandry systems to this digital technology. This result is in accordance with another study on the usage intention of smart glasses by agricultural stakeholders²⁰ where the overall attitude towards AR and SG were positive. However, the outcomes of this study, highlighted that there is different perspective between AMS and CMP farms for the use of AR and SG. In particular, the lower PB scores given by AMS farms may be because these farms already have different ways to access animal data on-field (e.g., smartphone applications, AMS displays), whereas on CMP farms, animal data are usually stored in the farm PC and are thus not accessible during on-field activities. Thus, the benefit derived from the implementation of SG and AR technology, for visualizing data directly on field and superimposed on the specific cow, is greatly perceived by farmers that did not have access to other digital devices linked to animal data on field. Indeed, the study highlights how CMP farmers view AR devices as a useful tool for providing information aimed at improving the decision-making process of farmers. Another reason that could have led to PB differences is the different software used. In fact, AMS farmers consider the software provided by the AMS company (e.g., DelPro, DeLaval International AB, v4.5, Tumba, Sweden; Time for Cows T4C and its update HorizonTM, Lely Industries, N.V., Maassluis, the Netherlands; and Crystal, Total Dairy Management, Nutriservice s.r.l., San Paolo, BS, Italy) to be straightforward and easy to use. By contrast, CMP farmers use general software (e.g., Dairy Comp 305, Valley Agricultural Software, Tulare, CA; Excel) or software provided by cooperatives (Ecostalla, Drop s.r.l., Arborea, OR, Italy), which are reported to be unstable, difficult to use, and time-consuming as require manual entry of data. Moreover, as confirmed by Van Hertem et al. 2017³³, simple visualization is a key component for a precise analysis of animal data. Another key finding of the study is that both types of farms, regardless of their current technological level, strongly agree that SG can serve as a valuable auxiliary system for data visualization and management, and that it could be a beneficial technology for dairy farms. Personal attitudes, particularly the personal interest in technological advancements, resulted as the most crucial factor in the adoption of new technologies. Finally, both farmers expressed interest in SG and AR devices as tools to enhance the overall management of their dairy farms, with plans to incorporate these technologies into their operations in the future.

Future possibilities

Parameters that have been highlighted by farmers as useful for visualization through AR systems include feeding management (e.g., stock level and individual feed intake rate), the farm environment (e.g., humidity, temperature, and cooling system status) and reproduction (e.g., heat detection, genealogy, and pregnancy status). Additionally, the use of AR and SG for the identification and positioning of animals was reported as a fundamental feature to be developed. Individual animal identification can be performed through Radio Frequency Identification (RFID) tags, QR codes¹³, or machine learning techniques³⁴ and represents a fundamental step in the visualization of specific animal data in AR. In particular, the use of RFID tags, which are already widely used in livestock environments, could be the easiest step for animal identification for augmented reality technologies³⁵. Positioning animals on the farm could be achieved using various technologies, such as Global Navigation Satellite System devices, Ultra High Radio Frequency Identification systems, Ultra Wide Band beacons, or LoRa-based technology. These technologies could enable livestock operators to localize animals and, if connected to SG, to visualize information directly superimposed on specific animals without the need for manual identification³⁶.

Limitations of the study

The study investigated how farmers’ attitudes towards the use of AR technologies, and specifically SG for AR are influenced by farm characteristics. The selection of investigated variable, as implemented technology and farmers personal information or opinions, were based on literature research. Therefore, is possible that some relevant variables were excluded from the survey. Moreover, while the results largely align with existing literature, the geographical constraints, sample size, and the absence of female farmers employed in the study may have influenced the outcomes. Additionally, considering that the adoption of technologies and their perception can evolve over time and vary based on cultural perspectives, it would be beneficial to conduct an updated version of the study in the future, encompassing different geographical areas and time periods.

Conclusions

The present study provides insights into the current technology adoption trends of dairy farms and possible implementations of AR technology with existing PLF systems. The results highlighted no difference between farms using AMSs or CMPs in terms of general characteristics (age of the owner, number of employees, total number of cows). However, the adoption of AMSs positively influences the adoption of other automatic or semiautomatic technologies. Even if the attitude toward AR technology was found to be strongly correlated especially with the personal interest in technologies of the farmers, a greater benefit was perceived by CMP farm operators. In fact, a minor diffusion of advanced machinery for herd management also reduces the diffusion of effective and efficient tools for field visualization of animal data. In conclusion, the present study highlights how AR technology could effectively increase the efficiency of data utilization in modern livestock farming if it is properly connected to other farm technologies. Future studies will focus on the study of the interoperability of the PLF tool to create an automated and complete data gathering and analysis system that will allow AR visualization of predictions, general data, and environmental variables, which could help farmers improve individual animal management.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Pinna Daniele: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing, Data Curation, Software, Visualization, Validation, Formal analysis; Sara Gabriele: Methodology, Investigation, Writing – review & editing, Data Curation, Visualization, Validation, Formal analysis; Cresci Roberta: Investigation, Writing – original draft, Writing – review & editing, Visualization, Validation, Formal analysis; Petronella Alice: Investigation, Writing – review & editing, Visualization, Validation; Todde Giuseppe: Methodology, Writing – review & editing, Visualization, Validation, Formal analysis; Atzori Alberto Stanislao: Conceptualization, Writing – review & editing, Visualization, Validation, Supervision; Caria Maria: Conceptualization, Writing – review & editing, Visualization, Validation, Supervision, Project administration, Funding acquisition.

Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of research participants.

Declarations

Competing interests

The authors declare no competing interests.