Work-related stressors common to agriculture have been associated with adverse mental health outcomes among adult farmers and ranchers. However, the mental health status of agricultural youth is unknown, despite farm and ranch youth being exposed to the same occupational hazards as their adult counterparts. The objective of this study was to estimate the prevalence of symptoms of depression and anxiety among farm adults and their adolescent child and examine the correlation between symptoms of mental health conditions and financial indicators described in the Family Stress Model (FSM).Farm families were recruited to participate in online surveys by mail, email, and social media. One adolescent and at least one adult from each family were invited to complete on online survey. Where available, validated instruments were used to collect mental health, stress, family dynamics, and household financial variables. Descriptive statistics were used to describe sample demographics and prevalence of symptoms of depression and anxiety. Pearson correlations describe associations between variables within the Family Stress Model.N = 122) completed the online survey. The mean age of farm parents was 41.4 years (SD = 4.4) and the mean age of farm adolescents was 15.4 (1.2). A majority of farm parents and farm adolescents were male, 58.2% and 70.5%, respectively. The sample was primarily white, non-Hispanic. In this sample of farm parents and adolescents alike, 60% met the criteria for at least mild depression, based on the Patient Health Questionnaire-9 (PHQ-9) and Patient Health Questionnaire-A (PHQ-A). Similarly, among adolescents, 45.1% met the criteria for Generalized Anxiety Disorder (GAD), as did 54.9% of adults. As a measure of economic hardship, per capita income by itself showed relatively low correlations, even with other economic measures . Parent depressed mood was in turn highly associated with adolescent depression (r = 0.83), social anxiety (r = 0.54), and generalized anxiety (r = 0.69).Farm families (The results show a strong association between parent and adolescent mental health and parental depressed mood and debt. There is not a clear association between economic stress and mental health in this sample, but further work is needed to be done at a population level. Preliminary results are promising for application of the full Family Stress Model as we continue to accrue farm families into the study cohort. In the United States, sectors of the agricultural industry have been experiencing economic downturn in the last decade . Among fThe mental health of agricultural producers and workers has become a national priority. Cooperative Extension Services, United States Department of Agriculture (USDA), state departments of agriculture, non-profits, and health and safety professionals have organized efforts to address the growing concern. Research has estimated the prevalence of mental health conditions among the population and sub-populations , 6, 8, 9Agriculture is a unique occupational industry. A majority of agricultural work occurs on private, independently owned and operated farms and ranches, which often also serve as a residence for children . As suchAgricultural work contributes to injuries and illnesses among youth. There is a growing number of epidemiologic studies of injuries to farm children which describe risk factors, medical outcomes, and injury agents , 13. As The main objective of this five-year project is to examine the relationships between work-related stress, family disruption, and mental health of farm adolescents and their farm parents by examining associations and relationships within the Family Stress Model (FSM). In this analysis, we describe the study population and identify the prevalence of positive screens for common mental health conditions among farm adolescents and adults and look for correlations between variables of the Family Stress Model. Data for this analysis was collected in summer, 2021.The Family Stress Model suggests specific processes through which economic hardship is expected to impact adolescent development by way of family disruptions, relationships, and parenting. The FSM was originally conceptualized by Conger et al., who tested the adequacy of the model on a sample of Midwest, US, farm, and rural families experiencing economic hardship during what would come to be known as the 1980's Farm Crisis  were selected because they are among the most agriculturally productive in the U.S., have a high proportion of primary producers that report farming as their full-time operation, and a high proportion of primary operators that reside on the farm they operate . HoweverTwo primary strategies were employed to recruit farm families into the study.A list of addresses of 1,000 agricultural producers in seven Midwestern states with at least one adolescent in the household was purchased from Farm Market iD (FMiD). FMiD maintains a list of farm owners and operators based on the same sources as the U.S. Department of Agriculture's (USDA) databases and estimates 95% coverage of farm owners and operators. FMiD overlayed USDA agricultural census variables with consumer variables to identify primary producers who work on a farm full-time, live on the same farm they operate, and have at least one adolescent (aged 13–17) in their household .Recruitment and survey materials were mailed to the 1,000 agricultural producers using a modified Dillman approach to encourage survey response . DillmanRecruitment also occurred online. The project team partnered with Extension, commodity groups, and farm organizations to disseminate information about the study via email, newsletters, and social media. Emails, newsletters, and social media posts directed individuals to an online study blog that described the objectives of the study, provided IRB information, directions for participating, and a link to the online surveys. Two, duplicate online surveys, with unique web addresses, were created to distinguish participants by recruitment method.The project team worked with the Research Electronic Data Capture (REDCap) administrators at the University of Illinois Urbana-Champaign to develop a series of consent forms and assent forms in addition to the online surveys, to ensure protection of participants. REDCap is a secure, web-based application designed to support data capture for research studies, providing:  an intuiA QR code and link on the study blog, provided on recruitment materials , led to an adult/parent consent form. The consent form collected name, email address, and signature of an adult/parent (P1). The form also consented an adolescent in the household to participate and collected the name and email address of the consented adolescent. Finally, the form inquired about a second adult in the household and asked for the name and email of the second adult (P2), if applicable. After consenting themselves, consenting the adolescent in the house, and providing the contact information for a second adult  the adult proceeded to the P1 online survey. REDCap auto emailed the adolescent a link to the assent form, which had to be completed by the adolescent prior to unlocking the adolescent survey. Similarly, REDCap auto-emailed the second adult in the household a link to their consent form and ultimately their online survey, the P2 survey. REDCap linked family consent forms, assent forms, and surveys. All survey participants were emailed an electronic Amazon gift card valued at $10.Online surveys were used to collect information about farm economics, family dynamics, and mental health from farm parents and adolescents. Here, we report on all survey sections; however, not all are described in the results or discussion as sample size precluded statistical modeling of FSM constructs.The Farm Stress and Mental Health Adult Survey was completed by P1 and P2, if applicable. The adult survey took a median of 19 min to complete and could be completed on a smartphone, tablet, or computer. The adult survey included the following sections and questions and/or instruments.Adults were asked whether they identify as a  primary Adults responded to the following questions: state of residency, age, gender, ethnicity, race, education, material status, number of children had, age range of children, number of children in the household, military status, employment status, number of years farming, primary farm commodity, secondary farm commodity, annual farm/ranch net sales, and residential environment.Adults responded to the following questions about their health: diagnosis of chronic health conditions , weight, height, and average number of hours of sleep each night.a = 0.81), sensitivity (89%), and specificity (82%) (not being able to stop or control worrying). Response options were; not at all, several days, over half the days, nearly every day. Response options were assigned a point-value; not at all = 0, several days = 1, over half the days = 2, nearly every day = 3. The response option point-values for each of the seven unique anxiety-related symptoms were totaled for an individual's total score. The possible score range is 0–21. Scores of 5–9 indicate mild anxiety, 10–14 moderate anxiety, and 15–21 severe anxiety  was employed to identify self-reported symptoms of anxiety among adults. This seven-item questionnaire has been widely used in population-based survey studies  and amonty (82%) . Partici anxiety .a = 0.91), sensitivity (88%), and specificity (89%) . Participants responded to the nine statements that inquire about the frequency of depressive symptoms as experienced in the past 2 weeks . Response options are; not at all, several days, over half the days, nearly every day. Each response option is assigned a point-value; not at all = 0, several days = 1, over half the days = 2, nearly every day = 3. The response option point-values for each of the seven unique depression-related symptoms are totaled for an individual's total PHQ-9 score. The possible score range is 0–27. Cut off points are: 0–4 is considered normal range or full remission; 5–9 is considered minimal depressive symptoms; 10–14 is considered major depression, mild severity; 15–19 is considered major depression, moderate severity; and 20 or higher is considered major depression, severe severity  identified self-reported symptoms of depression. The nine-item questionnaire has been used to estimate the prevalence of depression among the general U.S. population  and farmseverity .The Coronavirus Anxiety Scale (CAS) was used to identify probable cases of dysfunctional anxiety associated with the COVID-19 crisis. The five-item scale has demonstrated solid reliability and validity . Adults The Behavioral Affect Rating Scale (BARS) was used to assess relationship warmth and hostility among adult partners . Each adthere is a special person who is around when I am in need) on a seven-point Likert scale (very strongly disagree to very strongly agree). Each response was assigned a point value for a cumulative score. Three factor group  scores were calculated by summing the responses to each of the four items within each group with higher scores indicating more perceived social support  measured farm parents perceived social support  from thr support .Parenting involvement and control were assessed using a modified version of the Steinberg instrument , a 15-itAdult respondents completed three subscales adopted from the economic pressure measures developed by Conger et al. : 1) mak mak1) maFarm Stress Survey subscale scores were calculated by determining the mean response to the items within each subscale. Permissible subscale scores range from 1 to 5 give higher scores indicating higher perceived concern.A modified Farm Stress Survey was used to identify sources of farm stress among adults. This 40-item instrument measures seven dimensions of stress  . ParticiThe Farm Stress and Mental Health Adolescent Survey was completed by the farm adolescent. The adolescent survey took a median of 19 min to complete and could be completed on a smartphone, tablet, or computer. The adolescent survey included the following sections and questions and/or instruments.Adolescents were asked whether they reside, at least 50% of the time, on a farm, ranch, or agricultural operation. Adolescents were also asked their age, with the following response options to choose from: 13, 14, 15, 16, 17, 18, other. In order to proceed to the remainder of the survey, adolescents must have indicated they reside on a farm, ranch, or agricultural operation at least 50% of the time and indicated they were between the ages of 13 and 18. If an adolescent indicated they were not between the ages of 13 and 18 and/or did not reside on a farm or ranch or agricultural operation at least 50% of the time they were directed to the end of the survey.Adolescents responded to questions about age, school grade, race, state of residency, farm work status, hours per week involved in farm work during the summer and school year, farm injury experience.Adolescents responded to the following questions about their health: self-rated physical and mental health, diagnosis of chronic health conditions , weight (reported in pounds), height (recorded in feet and inches), average number of hours of sleep each night.poor appetite or overeating, feeling tired or having little energy). Response options are assigned a point-value; not at all = 0, several days = 1, over half the days = 2, nearly every day = 3. The response option point-values for each of the seven symptoms were totaled for an individual's total PHQ-A score. The possible score range is 0–27. Cut off points are: 0–4 is considered normal range or full remission; 5–9 is considered minimal depressive symptoms; 10–14 is considered major depression, mild severity; 15–19 is considered major depression, moderate severity; and 20 or higher is considered major depression, severe severity  identified self-reported symptoms of depression among adolescents. The PHQ-A is a modified PHQ-9 appropriate for adolescents and has demonstrated satisfactory sensitivity, specificity, diagnostic agreement, and overall diagnostic accuracy, compared with the clinical interview . Particiseverity .The Screen for Anxiety Related Disorders (SCARED) was used to screen farm adolescents for childhood anxiety disorders including general anxiety disorder, separation anxiety disorder, panic disorder, social anxiety, and school phobia. The SCARED consists of 41 items related to the 5 factors  that parallel the DSM-IV classification of anxiety disorders . AdolescThe Adolescent Stress Questionnaire (ASQ) evaluated sources of stressors among adolescents. This is a 27-item inventory reflecting five dimensions of stress . Adolescents indicated how much stress each item has caused in the past 12 months on a Likert scale . Dimension scores were calculated by summing the affirmed response to each item within each dimension . The ASQAdolescents self-reported risk-taking activities related to substance use, sexual activity, intentional/unintentional injury. Adolescents self-reported how often they participated in the following activities in the past 30 days and the past 12 months: number of times rode in a car or other vehicle driven by someone who had been drinking alcohol, seatbelt use when riding in a car driven by someone else, how many days they text or e-mail while driving a car or other vehicle, days carried a weapon, days carried a weapon on school property, days carried a gun, days skipped school, and number of times in a physical fight. Adolescents responded to whether they considered suicide, made a plan to attempt suicide, or had attempted suicide in the past 6 months . Adolescmuch more they experience the emotion compared to before the pandemic on a five-point scale .The COVID-19 Adolescent Symptom and Psychological Experience (CASPE) was used to identify farm youths' experience with COVID-19 . AdolescSurveys for analysis were first limited to those where appropriate consents were registered and surveys were completed for one adolescent paired with at least one adult/parent participant. Various procedures were used to try to ensure that surveys for analysis were limited to eligible participants who seriously attempted to complete the instruments. It was apparent early that online recruitment had been subject to multiple cyber-attacks, including software-based, automated completion of surveys . This was obvious for several reasons. First, within hours of posting the survey link publicly online, there were over 520 responses, an unlikely influx. Additionally, there were series of up to 20 consecutive surveys submitted with identical entries, including identical start and stop times. As soon as the research team was aware of the issue, within 2 h of the original online post, the REDCap administrator added an attention check question to all surveys. The question asked participants to select the color red from a list of colors listed below.After data collection was complete, a series of recommended checks were employed to identify and exclude bot-generated responses from analysis . A numbeFirst, duplicate forms were removed from the dataset. As mentioned previously, series of up to 20 consecutive surveys were submitted with identical entries, including identical start and stop times. All duplicate time entries were reviewed, and all duplicate and suspect survey series were excluded. Duplicate surveys were further identified by electronic identification of duplicate parent or adolescent e-mail addresses, and through manual review of suspicious follow-up e-mail solicitations to the investigators.From there, the research team employed a combination of electronic and manual processing to create a series of 11 flags to help identify suspicious survey respondents. Five flags were related to discrepancies between parent/adult and adolescent surveys and included (1) different state residency for adolescent and adult; (2) less than 15-year difference between adult and adolescent ages; (3) age of adolescent outside the range reported for youngest and oldest child; (4) inconsistent race ; (5) Latino adolescent with both P1 and P2 not Latino. Two flags were related to improbable or incorrect responses on the surveys and included (6) failure to select “RED” on security question where instructed simply to “Pick RED” as the response; and (7) height outside CDC norms of 0.05–99.95%. Embedding attention check questions, requesting a specific response from survey participants, is a moderately effective strategy for detecting bot responses and identify risk of lower data quality, however, in time, bots can learn the correct answer to the attention check questions .Additionally, based on review of completion times (median 19 min), surveys with unusually short times were also believed to be invalid or incomplete and flagged . SurveysThe last three flags were based on responses to open text responses and included  incohereIn the final analysis cohort of 122 families (one adult parent and one adolescent), relatively few response items were missing. For example, individual items in the PHQ-9 were missed for at most 2/122 (1.6%) for the adult (P1) and for at most 3/122 (2.5%) of adolescents, while for both adult and adolescent five of the nine individual items showed no missing responses. To allow for some missed items, summary scores for each instrument were calculated whenever over half of items were completed, and the mean of completed items was used to scale the summary score to the same range as possible with no missing items.Descriptive summaries were created to characterize the analysis cohort using standard descriptive statistics. Cronbach's alpha was calculated as a measure of internal consistency for the items in a summary score. Pearson correlations were calculated to evaluate the strength of associations among potential FSM model indicators. In this analysis, we present demographic characteristics, correlations between FSM model indicators, and mental health outcomes.® version 9.4 (SAS Institute Inc.) statistical software.Analyses were completed using SASAll procedures performed and materials used in this study, involving human participants, were approved by the University of Illinois Urbana-Champaign Institutional Review Board.A total of 2,463 potential farm-family survey submissions were recorded. After flagging responses as described above and limiting analysis to observations with two or less flags, the final sample consisted of 122 family participants. For this analysis, families include responses from one adult parent (P1) and one adolescent. Among the 122 family participants, only 25 observations (20%) included responses from a second adult (P2). Therefore, responses from the second adult are not included in this analysis.Among adults, 81.9% self-rated their physical health as excellent or good and 85.1% self-rated their mental health as excellent or good. The most common chronic health conditions diagnosed were arthritis (16.4%), anxiety (16.4%), and hypertension (20.5%).th to 12th grade, with nearly three-quarters between 9th and 11th grade. Over half (53.7%) indicated they participate in farm/ranch work, working an average of 14.0 (SD = 10.3) hours a week during the school year and 19.7 (SD = 16.2) hours during school vacations (summer). A majority work on farm/ranch that is operated by their parents.As shown in Among farm adolescents, 93.3% self-rated their physical health as excellent or good whereas 82.6% self-rated their mental health as excellent or good. The most common chronic health condition diagnoses were anxiety (18.9%), depression (6.6%), and asthma (4.1%).Among farm parents, 60.1% met the criteria for at least mild depression based on the PHQ-9 screening instrument . Almost r = 0.11 with negative financial events, r = 0.20 with financial needs, r = 0.17 with financial situation, and r = 0.27 with debt). The economic measure most associated with parent depressed mood was debt (r = 0.67). Parent depressed mood was in turn highly associated with adolescent depression (r = 0.83), social anxiety (r = 0.54), and generalized anxiety (r = 0.69).Correlations among proposed indicators for the FSM are shown in Farm families play a critical role in ensuring a safe and adequate food supply. According to the United States Department of Agriculture's 2017 Agricultural Census, over 85% of all farms and just over 60% of land are family held, meaning owned and operated by individual or family units , 46. WhiIn this sample of 122 farm families, we observed a high prevalence of farm adults that were experiencing at least mild symptoms of anxiety and depression. Among adults, nearly a quarter met the criteria for mild depression and moderate depression and 11.5% met the criteria for moderately severe depression. While the PHQ-9 was used to screen for depression, and not diagnose, the observed distribution of respondents experiencing symptoms of at least moderate depression exceeds that of the general population. According to results from the National Health and Nutrition Examination Survey (NHANES), 8.5% of adults had depressive symptoms in 2019, characterized as a score of 10 or greater on the PHQ-9 in 2019. This statistic increased to 27.8% during the COVID-19 pandemic (March–April 2020) . It is iNearly a third of adult participants in our sample met the criteria for mild Generalized Anxiety Disorder, and 18.9% and 4.9% met the criteria for moderate and severe GAD, respectively. Again, the GAD-7 was used to screen for GAD and not diagnose. Among the general population, in 2019, 3.4%, and 2.7% of adults experienced moderate or severe symptoms of anxiety, respectively . Among fAmong farm adolescents in our study, 22.1% met the criteria for moderate depression, 11.5% met the criteria for moderately severe depression, and 0.8% met the criteria for severe depression, based on the PHQ-A. In the U.S. it is estimated 4.1 million adolescents (aged 12–17), or 17.0%, experience at least one major depressive episode per year . Among tStatistics from national public health surveys suggest a mental health crisis in the U.S. . ConversAmong our sample of farm adults 34.7% and 50.4% self-rated their mental health as excellent and good, respectively. Similarly, among farm adolescents, 35.5% and 47.1% self-rated their mental health as excellent and good, respectively. Single item, self-rated mental health (SRMH) measures to assess mental health are increasingly popular in epidemiological surveys, because they reduce respondent burden and simplify analyses . Some anN = 122) precluded the research team from further statistical modeling in line with the Family Stress Model, however, this is an area for future analysis in future years.Results from this study show relatively low correlation between per capita income and negative financial events, financial needs, financial situation, and debt. Importantly, parental depressed mood was significantly correlated with all economic indicators, income, and debt. In addition, and perhaps one of the most important result of this analysis, the correlation between parental depressed mood and adolescent depressed mood was high (0.83). These results are very consistent with previous applications of the Family Stress Model and suggest there is a relationship between emotional distress and financial and economic situations among both farm parents and farm adolescents. This relationship has been documented in previous research of non-farm families , 59. Then = 18) which encouraged the research team to consider online recruitment efforts, including using social media. Online recruitment resulted in a compromised survey link and thousands of questionable survey responses. While the research team employed evidence-based best practices for identifying illegitimate responses (While preliminary results contribute to the body of knowledge around farm family mental health, there are limitations to consider. The small sample size precluded full statistical modeling of the Family Stress Model as demonstrated in previous applications of the theory , 21–23. esponses , 45, thiesponses . Parentsesponses . Here, aTo our knowledge, this is the first published study to examine the correlation between farm parent and adolescent mental health since the Conger et al., initial study of farm and rural families in Iowa . The resAdolescent mental health is a public health priority, and increased attention on agricultural producer mental health has led to a needed influx of research, resources, and services to meet the unique need of the occupation. This research contributes important information about the prevalence of symptoms of anxiety and depression among a sample of farm parents and adolescents and correlations within families for consideration of resource and service development. Importantly, farm parent and adolescent mental health, specifically, symptoms of depression and anxiety are highly correlated. Additional research is required to further understand these relationships and the risk and protective factors for adolescent and adult mental health. Additionally, mental health resource and services should consider the family and influence of farm economics on farm family mental health.The raw data supporting the conclusions of this article will be made available by the authors, upon request, when the funding cycle is completed.The studies involving human participants were reviewed and approved by University of Illinois Urbana-Champaign Institutional Review Board. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.JR and RB conceptualized the study. JR collected data. RB conducted data analysis. All authors contributed to the article and approved the submitted version.


In Thailand, The National Science and Technology Development Agency developed ThaiSook, a behavior-tracking app, to promote healthy lifestyles. The Faculty of Medicine, Prince of Songkla University (MED PSU)×ThaiSook Healthier Challenge encouraged employees to use the app over a 28-day period . Until recently, no previous studies have examined the association of generations and group sizes with mobile health  app use. Understanding these relationships can inform the design of effective mHealth interventions and facilitate targeted interventions.This study aimed to (1) compare the overall app usage and logging function across different generations and group sizes and (2) describe the demographic characteristics of the participants of the MED PSU×ThaiSook Healthier Challenge.U and Kruskal-Wallis tests for continuous variables) were conducted over sex, generations, initial BMI, and group size. Finally, multiple logistic regression models were used to examine the relationship between the independent variables used by the ThaiSook app and consistent users who had used the app for at least 80% of the participation period.We conducted a secondary data analysis of the data from the ThaiSook prospective cohort study. Data were collected through the app and comprised demographic characteristics  and behaviors . The outcomes consisted of users who used the app for at least 80% of the participation period (≥23 days). Bivariate tests , belonged to a medium-sized group of 6-10 members , and belonged to generation Y . Multivariate analysis revealed that the overall app usage was 2.09 times higher in women than in men . The older generations used all logging functions more frequently than did generation Y . The use of all logging functions was higher among participants belonging to larger groups than among those belonging to smaller groups . Water logging was most used , followed by food, sleep, and workout logging.P<.05). Older generations and larger groups engaged with the app more consistently than younger generations and smaller groups and individuals.The MED PSU×ThaiSook Healthier Challenge participants were mostly females from generation Y and medium-sized groups. Water logging was most frequently used, followed by fruit and vegetable logging. The results indicate that generation and group size were significantly associated with consistent and daily usage ( Globally, mobile health  technology has grown rapidly in recent years. The global mHealth app market size was expected to grow at a compound annual growth rate of 25.3% in 2021-2022. mHealth technology mainly targets healthy individuals for daily use and fitness activities. However, another target group comprises patients with noncommunicable diseases who use mHealth apps for health monitoring -8, mHealFactors influencing the frequent use of mHealth tracking apps include sex ,16, educThaiSook, the mHealth app developed by the National Science and Technology Development Agency (NSTDA), aimed to reduce health-risk behaviors and promote a healthy lifestyle. Participants used the app to improve their desirable health behaviors through a web-based competition model with expert coaching, team building, and group competition to win rewards. Predictors of premature death were recorded, namely weight, vegetable and fruit consumption, exercise, blood pressure, blood lipid levels, blood sugar levels, and smoking. These 7 parameters were based on Folsom’s publication, “American Heart Association’s Life’s Simple 7: Avoiding Heart Failure and Preserving Cardiac Structure and Function.” According to Life's Simple 7, the aforementioned 7 predictors highly associated with premature death ,26 were To date, no research has explored the association between generations, group sizes, and the use of mHealth apps. Therefore, further research is warranted to comprehensively comprehend the intricate interplay between these factors. Gaining insights into these dynamics can inform the development of effective mHealth interventions and facilitate targeted approaches. In 2022, a collaboration between the NSTDA and the Faculty of Medicine of the Prince of Songkla University (MED PSU) led to the MED PSU×ThaiSook Healthier Challenge. This initiative encouraged employees to use the app over a 28-day period, presenting an opportunity to address this knowledge gap through research. Consequently, this study aimed to (1) compare the overall usage of the app and the logging function across different generations and group sizes and (2) describe the demographic characteristics of the participants in the MED PSU×ThaiSook Healthier Challenge.Hypothesis 1 (H1): A person’s generation positively associates with mHealth usage and logging function.Hypothesis 2 (H2): Group size positively associates with mHealth usage and logging function.This study was a secondary analysis of data from the NSTDA’s prospective cohort study , which in turn was extracted from the ThaiSook database.The MED PSU×ThaiSook Healthier Challenge, a collaboration between NSTDA, who developed the ThaiSook app, and the Faculty of Medicine of the Prince of Songkla University (PSU), encouraged employees to use the app over a 28-day period. The campaign aimed to promote employees’ healthy behaviors in the Faculty of Medicine.One week before the kickoff event, communication channels such as circular letters, internal messaging platforms, bulletin boards, and web-based newsletters were used to raise employee awareness about the campaign. The Faculty of Medicine’s website also featured a dedicated section providing detailed information about the campaign, its goals, and how to participate.The kickoff event for the mHealth tracking campaign, named “MED PSU×ThaiSook Healthier Challenge,” took place on July 6, 2022, from 9 AM to 12 PM. The event was held in the multipurpose area of the Faculty of Medicine. The event was also live-streamed and recorded through the faculty’s Facebook account. To set the tone for the campaign and inspire employees, ThaiSook’s developer from NSTDA (DS) and 4 physicians from the faculty  delivered the keynote address. The developer guided the employees through the app’s features, functionality, and data input process. This demonstration showed how to track activities, monitor progress, and use the app’s resources. To ensure that employees were proficient in using ThaiSook, interactive training workshops were conducted. These sessions were led by the developer and trained facilitators who were knowledgeable about the app’s features and functionality. During the training sessions, employees were provided with their smartphones preloaded with ThaiSook. This hands-on approach allowed participants to navigate the app, explore its features, and practice data input and tracking.The MED PSU×ThaiSook Healthier Challenge was held for 28 days  at the Faculty of Medicine (PSU). Of the 6112 employees, 827 participants (a response rate of 13.5%) were divided into 164 teams of different sizes and took part in the challenge. They were allowed to form teams independently, with no limit on the number of group members; thus, even 1-person teams were included. All the data were self-reported (see details in the “Data Collection” section). The participants submitted their date of birth and self-recorded their weight, height, and health behavior findings. Furthermore, they were encouraged to track their health parameters regularly using ThaiSook. The app provided real-time personalized insights based on the tracked data. Real-time progression reports were generated and shared with participants in the group.To meet inclusion criteria, employees  had to be employees of the Faculty of Medicine, PSU, who participated in the ThaiSook between July 11, 2022, and August 7, 2022. The exclusion criterion was the following: participants who had not reported their birth date in the ThaiSook.Since there have been no previous studies on the association between the use of the app  across different generations and group sizes , a power analysis was conducted.P=.61, P=.58, P=.17, P=.56, and P=.59 for baby boomers, generation X, individual, medium, and large members, respectively. The X parameter π was set at 0.061; 0.360; 0.029; 0.579; and 0.079, respectively. A significance level (α) of .05 and a total sample size of 761 were applied. The power analysis yielded a power of 0.7; 1; 0.8; 1; and 0.9, respectively, for all variables. These results indicate sufficient power to detect the anticipated effect size with the given sample size and significance level for the logistic regression model, except for the baby boomer group.A post hoc power analysis was conducted to assess the achieved power in logistic regression. The analysis used odds ratios of 2.54 (95% CI 1.30-4.92); 1.96 (95% CI 1.42-2.72); 0.24 (95% CI 0.07-0.84); 2.06 (95% CI 1.47-2.88); and 2.85 (95% CI 1.58-5.16) for baby boomers, generation X, and individual, medium, and large groups, respectively. The hypothesized probability of Y=1 given X=1 was set at The data used were secondary data from the MED PSU×ThaiSook Challenge, which belonged to the NSTDA and the Faculty of Medicine of PSU. The data were recorded by the participants. All collected data were kept confidential and were only accessible to the researchers through the ThaiSook database. The data consisted of 2 parts: the demographic characteristics of the participants  and the use of the ThaiSook app ; it was classified according to the World Health Organization Asian BMI classifications into the following: underweight (<18.5 kg/m2), average (18.5-22.9 kg/m2), overweight (23-24.9 kg/m2), obese class I (25-29.9 kg/m2), and obese class II (≥30 kg/m2) [The primary independent variables were group size and generation. Group size was classified into the following 4 groups: individual (1 member per group), small (2-5 members per group), medium (6-10 members per group), and large (>10 members per group). The participants were divided into the following 4 generations based on the year of their birth: baby boomers  ; generatass I 25-.9 kg/m2,The MED PSU×ThaiSook Challenge was conducted for 28 days. The number of days the app was used serves as a measure of user participation. Participants who had used the app for at least 80% of the total participation period (not less than 23 days) were defined as “consistent users,” and those who had used the app every day were defined as “daily users.” Furthermore, we measured the daily usage frequency of the overall app and all logging functions (time and usage per day).U and Kruskal-Wallis tests were used to compare the continuous variables. Multiple logistic regression models were used to measure the magnitude of the independent variables that affected consistent users’ usage of the ThaiSook app. The initial model included potential independent variables (P<.20), namely sex, group size, generation, and initial BMI. Univariate analysis yielded several variables (P<.20). To derive the final model, a manual backward stepwise refinement process was conducted. Adjusted odds ratios (AORs) along with their corresponding 95% CIs were calculated. Statistical significance was determined at a threshold of P<.05. The data analysis was performed using R software  and several packages, including epicalc, foreign, survival, MASS, nnet, data.table, rio, and dplyr.Descriptive statistics, such as medians with IQRs, were used to express the demographic characteristics and app logging data . The statistical analysis began by conducting a Shapiro-Wilk test to assess the normality of the variables. After confirming the normality assumption, Pearson chi-square test was used to compare categorical variables, and the Mann-Whitney The clinical trial was registered according to the WHO International Clinical Trials Registry Platform (WHO-ICTRP) at the Thai Clinical Trials Registry registry ID TCTR20220611001). This study was approved by the Human Research Ethics Committee of the Faculty of Medicine of the Songkla University (REC.65-395-9-2). The HREC allows additional informed consent to be waived from the secondary analysis. All collected data from the participants were kept confidential, anonymous, and accessible only to the researchers through the ThaiSook database. No compensation for participants was provided.Overall, 827 individuals participated in this study. Among these, approximately 49.8% (412/827) were deemed consistent users, and one-third were deemed daily users . The median usage duration was 22 days , belonged to generation Y , and had a normal BMI . The majority of participants were in the medium-sized group , followed by the small-sized  and large-sized  groups. Only 24 users  participated individually (1 member per group).P<.05). The number of participation days was higher for female participants (23.5 days) than for male participants (15 days). The older generations  used the app more routinely than did the younger generations . Moreover, the study results showed that group users had a longer duration of app usage (ranging from 19 to 15 days) than did individual users (4.5 days). In contrast, the initial BMI did not significantly affect the average usage duration.Sex, generation, and group size had a statistically significant impact on the average app usage duration (P<.05). Strikingly, the older generations used the app more regularly than did the younger generations, and participants belonging to larger groups used the app more regularly.Furthermore, the study results showed that sex, generation, and group size were significantly associated with consistent and daily usage , followed by fruit and vegetable logging (2.64 times per usage day). Other logging functions were used approximately once a day .Regarding consistency in app usage, female participants were 2.09 times more likely to be consistent users compared to male participants . The younger generations used logging functions less frequently, especially for water, fruit and vegetable, and sleep logging.In terms of group size, medium (AOR 1.57-2.51) and large (AOR 2.29-4.19) groups were associated with higher use of all logging functions  as compared to small groups. Compared with small groups, individual (1-member) groups were approximately 4-5 times less likely to use the overall app and water logging.The MED PSU×ThaiSook challenge participants were primarily female, belonged to generation Y, and had a normal BMI. Most participants were in the medium-sized group, followed by small-sized and large-sized groups (in that order). Female participants used the app more frequently than male participants. Compared to individual usage, group participation was associated with higher adherence. The older generation participated more routinely than did the younger generation. In contrast, the initial BMI did not significantly affect user participation. The most regularly used logging function was water logging, followed by fruit and vegetable logging, sleep logging, and workout logging. Overall, app usage was significantly higher in women, older generations, and larger groups than in men, younger generations, and smaller groups, respectively. However, it is important to acknowledge that multiple factors influence health outcomes, including genetics, environmental factors, socioeconomic status, and health care access. While recording health data through mHealth apps can contribute to better health management, it is only 1 aspect of a comprehensive approach to health care.Our findings indicated that female participants were more likely to be consistent users than male participants. The results of this study on sex corresponded with the findings of previous studies: compared to men, women were more likely to think about well-being and friendships . In addiThe ThaiSook app was designed to be user friendly, intuitive, easy to access, and simple to navigate, catering to users of all generations. A key feature of the app is its streamlined login process, requiring users to log in only once using a 1-time password sent to their phone number. The interface for logging activities has been carefully crafted to effectively fulfill its intended purpose. In this study, app usage was higher in the older generations than in the younger generations in a dose-response relationship  as per Bradford Hill's criteria for causation in Epidemiology . SurveysThe effect that grouping has on establishing more power in an individual’s development and behavior is known as the “peer effect” . German Water logging was the most frequently used logging function, which can be explained by the fact that participants could record a maximum amount of 200 mL for each log. For example, 400 mL of drinking water were considered 2 logs of 200 mL and were counted as 2 records. In addition, water logging is easy to use compared to the other logging functions. For example, the participants did not upload details on fruit and vegetable and workout logging as frequently as they did for water logging (details in The strength of this study lies in its novelty: it is among the first to examine the association between generations, group sizes, and mHealth app use. This study is only the second to collect real-world data on mHealth app usage in an upper- to middle-income country . ThaiSooThis study has some limitations. First, the response rate for the MED PSU×ThaiSook Challenge was 13.5%. The selection bias should be acknowledged in this study. The participants might be more interested in health promotion than those who did not participate in the challenge. Second, the MED PSU×ThaiSook Challenge was a competition with team-based rewards; both the competition and the rewards drove enthusiasm in participant usage; thus, the data may be different from normal usage data. Third, the participation period may have been too short to observe long-term app usage. People may tend to use the app more consistently during the early phase. According to the Transtheoretical Model, each stage takes more than a month to eventually build a routine use of the app . Fourth,Our results suggest that group use should be given greater consideration when developing new mHealth apps. This study showed that a team-based model produces a peer effect, for example, more group members tend to create more consistent users. In addition, design and functions should focus on target users who are females, baby boomers, and belong to generation X. Additionally, mHealth apps that are free to download can benefit anyone interested in a healthy lifestyle. For future research, we recommend extending the observation period to assess long-term behavioral changes and conducting the challenge in varied settings to examine its effectiveness in different contexts.The MED PSU×ThaiSook Healthier Challenge participants were predominantly female, from generation Y, and from medium-sized organizations. The most frequently used logging function was water logging, followed by fruit and vegetable logging. According to the findings, generation and group size were substantially related to consistent and everyday usage. Surprisingly, older generations used the app more than younger generations. Furthermore, larger groups used the app with greater consistency than smaller groups and individuals. This study suggests that group use should be considered when developing new mHealth apps, as it leads to more consistent users. Targeting females, baby boomers, and generation X is important. Free downloads benefit those interested in a healthy lifestyle.


The Comune di Sicilia goat, originating in Sicily , is in the process of being officially acknowledged as a breed. To better characterize this population, this study examined 78 goats from two locations, recording the goats’ morphological traits and measurements. Moreover, these goats were genotyped using a medium-density SNPchip and compared with goats from 15 different Italian breeds. The study found that the Comune di Sicilia goats have unique physical and genetic characteristics that distinguish them from other breeds in the same area. However, moderate variability was observed, likely influenced by how the goats were chosen by breeders. Additionally, the genomic comparison of goats with different traits led to the identification of genes possibly linked to anotia and wattle presence in goat species. To preserve this breed and avoid problems with inbreeding, a combined approach integrating phenotypic and genomic analyses is warranted.ST and runs of homozygosity, leading to the identification of potential candidate genes associated with anotia and wattle presence in goats. The Comune di Sicilia breed exhibited distinctive genomic and phenotypic features, setting it apart from other breeds in the same region. However, moderate variability, possibly influenced by selection practices, was also observed. To ensure the breed’s preservation and prevent excessive inbreeding, a comprehensive approach considering both morphology and genomic background is recommended. This study contributes valuable insights into the genetic peculiarities of the Comune di Sicilia goat, supporting its recognition as a unique and valuable breed.The Comune di Sicilia, a local goat breed from Sicily , is currently undergoing recognition as a distinct breed. This study aims to characterize the population both phenotypically and genomically to advance its recognition process. A total of 78 subjects from two locations were enrolled, and their phenotypic data, including qualitative traits and morphometric measurements of adult animals, were recorded and statistically analyzed. The goats were genotyped using the Illumina 50 k Goat SNPchip, comparing them with 473 goats from 15 Italian breeds. Population structure, phylogenetic relationships, admixture, and genomic inbreeding were analyzed. Additionally, subjects with different morphological traits were compared using F Riproduzione, Allevamento e Miglioramento degli Animali Domestici in Sicilia” . Chicoli described these goats as long- and wire-haired, with a variety of possible coat colors , a large head, a rather large size, and very developed udders in females, with a daily production of about three litres of milk.In recent years, the environment was influenced by globalization, urbanization, population growth, global warming, and climate change. Consequently, local breeds were affected as well, and biodiversity is under threat, making it evident that ensuring the sustainability of livestock breeding necessitates the surveillance and preservation of native breeds that are well adapted to the local environment ,2,3, henHistorically, studies aimed at describing livestock populations primarily relied on phenotypic and historical data. However, recent advancements in molecular tools now offer the opportunity to augment these conventional zootechnical evaluations, providing a more comprehensive and accurate characterization of animal breeds and facilitating an improved recognition process. In this context, the BIOSAVE project, “Use of phenotypic and genomic descriptors for the recovery, definition of genetic originality, and zootechnical management of Sicilian endangered local breeds”, was approved in 2021 and financed by the PSR Sicilia 2014–2020—Sub-measure 10.2b “Support for the conservation of genetic resources in agriculture and forestry”. The overall aim of this project is to ascertain the official recognition and subsequent developmental significance of Sicilian livestock breeds in relation to land, landscape, and sustainability. It also seeks to explore the role of public policies and the multifunctional approach involving research institutions in supporting these local breeds. Hence, based on the Chicoli’s first description of the “Comune di Sicilia” goat , and witThis study was performed according to the ethical principles that have their origins in the Italian Veterinarians’ Ethical Code  and the The study was conducted in western Sicily, in two farms located in Petralia Sottana and Bolognetta, two administrative areas in the province of Palermo representing different agro-ecological areas of Sicily. Petralia Sottana is part of the Madonie Park, with a warm and temperate climate, and is located at an altitude of 1039 m above sea level, specifically between latitude 37°48′0″ N and longitude 14°5′0″ E. This mountainous area is characterized by large extents for grazing with extensive arable crops. The territory of Bolognetta is mainly hilly and characterized by a warm and temperate climate; it is located between latitude 37°57′39″ N, longitude 13°27′78″ E, and altitude 348 m above sea level. In both farms, goats were reared under a semi-extensive farming system, where feeding management is based on grazing spontaneous fodder essences during the day, and during the night, shelter is provided in the stable called “mannara” (from Arabic ‘manzrah’: closed area), which in the local dialect refers to a traditional enclosure where sheep and goats are usually penned at night [A total of 78 goats (comprising 9 bucks and 69 does) exhibiting the most typical phenotypic characteristics of the Comune di Sicilia (CCS) goat population were carefully selected from the two herds. Specifically, 25 does and 6 bucks were selected from Petralia Sottana farm, and 44 does and 3 bucks from Bolognetta farm.Morphometric linear measurements were taken only on the 41 adult subjects (4 bucks and 37 does with an age comprised between 18 months and 5 years); specifically, their age was estimated through the evaluation of the dentition ,16. DataWither height (WH) was measured as the vertical distance from the top of the withers to the ground.Croup height (CrH) was measured as the vertical distance from the top of the croup to the ground.Chest height (ChH) was measured as the vertical distance from sternum to withers.Chest length (ChL) was measured as the distance between the top behind the scapular and the costal arch bounded by the last rib.Trunk length (TL) measured as the distance from the point at the top behind the scapular to the base of the tail.Croup length (CrL) was measured as the distance between the iliac tuberosity and the ischial tuberosity.Chest width (CW) it was measured as the distance between the right retro-scapular area and the left retro-scapular area.Hip breadth (HB) was taken as the distance between the two iliac tuberosities.Coxofemoral width (CxW) was taken as the distance between the two trochanteric tuberosities.Hearth girth (HG) was measured as a circumferential measure taken around the chest just behind the front legs and withers.Shin circumference (SC) was measured from the left mid-metacarpus.2 − 0.69 × HG + 14.7. According to the latter authors, heart girth measurement was determined to be the best predictor of live BW with a regression coefficient of 0.92. Measurements were recorded in the morning before the animals were released for grazing to avoid the effect of feeding and watering on the goats’ size and conformation. All measurements were performed by the same person in order to avoid inter-individual variations as reported by Sheriff et al. (2021) [Moreover, live body weight (BW) was calculated according to Natsir et al. 2010)  with the10  with . (2021) . All meaAccordingly, the following traits were recorded using a graduated stick and a measuring tape and expressed in centimeters (cm):Regarding qualitative data, coat color pattern and type, presence or absence of horn, ears, wattles, and beard were also registered for all 78 subjects according to Sponenberg et al. (1998) and Henkel et al. (2019) ,22.n = 37 female goats) obtained: mean, first quartile, median, third quartile, standard deviation, 95% confidence interval of the mean, skewness, kurtosis, and Shapiro–Wilk test to assess if data were normally distributed.Phenothypic data were analyzed with SAS software . The univariate procedure of SAS was used to determine the following descriptive statistics for the distribution of the morphometric values , horn (yes or no), and wattles (yes or no). Individual goats were included as random effect. In addition, for the ANOVA analysis, trunk length (TL), chest width (CW), and shin circumference (SC) were log-transformed and presented as back-transformed data due to their not-normal distribution. Pair-wise comparisons were performed using the least significant difference test. Statistical significance was declared at ®, San Diego, CA, USA). Experimental protocol was authorized by the Regional Department of Agriculture, Rural Development and Mediterranean Fisheries—Sicilian Region  Italy, n. G49J21006760009, prot. 0012062, 14 July 2021.The genomic analyses were performed on 78 CCS goats, consisting of 9 bucks and 69 does. This group comprised all the animals that underwent morphological evaluation as previously described, along with additional subjects selected to ensure a representative sample of the breed. Moreover, special attention was given to limit direct relatedness among the individuals in the cohort. Blood samples were collected and about 3 mL of each sample was placed in a sterile tube containing ethylenediamine tetra-acetic acid (EDTA) and stored in the refrigerator or freezer until analysis. The DNA extraction and genotyping were outsourced and performed using the Goat 60 K SNP BeadChips on an iScan System  [PLINK software (version 1.9)  was usedPLINK 1.9 was used to perform a multidimensional scaling analysis (MDS) to visualize the genetic distances among the goat populations included in the study. In-house scripts were used for computing bootstrapped Reynolds distances among breeds  and idenIS) were calculated using PLINK 1.9. A sliding window approach was used to estimate runs of homozygosity (ROH) in all subjects using the following parameters: ROHquartile = 0.99, minNsnp = 10, maxNsnp = 30, windef = 20, interval = 5, hetallowed = 0, minKblength = 1000, density = 500, maxInternalGap = 500, and maxmiss = 2. The ROH-based inbreeding coefficient (FROH) was calculated by dividing the total length of ROH in a subject by the total length of the autosomes covered by the SNPs, as described by McQuillan et al. (2008) [To elucidate the genetic diversity of the analyzed breeds, expected heterozygosity (He), observed heterozygosity (Ho), and Wright’s fixation index (F. (2008) ,28. ThisThe genomic effective population size (Ne) trend, ranging from 13 to 983 generations ago, was estimated using the linkage disequilibrium (LD) method for all the populations using SneP software (version 1.1) .ST and ROH analyses. Specifically, the following groups were compared: (a) 39 horned vs. 39 polled goats ; (b) 74 goats with ears  vs. 4 presenting microtia ; and (c) 44 goats with wattles vs. 34 without wattles . In particular, the SNPs falling in the top 1% FST values and delta H-score  were retained and mapped on the ARS 1.2. Their associated genes were further investigated.In addition, we investigated the selection signatures associated with specific morphological features found in CCS goats, such as microtia and the presence or absence of wattles and horns using FQualitative phenotypic data were assessed in 78 goats . Most ofWattles were present in 44% of the analyzed goats. Additionally, 40% of the individuals, both male and female, were horned, in most cases presenting spiral or lyre horns. It is noteworthy that the presence of horns in goats is advantageous for self-defense and thermoregulation  and seemp > 0.05). However, the trunk length (TL), chest width (CW), and shin circumference (SC) showed a not normal distribution (p < 0.05): TL presented a left-skewed asymmetrical and slightly leptokurtic distribution; CW was right-skewed; and SC was leptokurtic.p < 0.05), and a tendency for SC , whereas there were not observed statistical differences for other morphological traits. No significant effects on the morphological measurements were instead found between animals with and without horns and with and without wattles.Comparison of quantitative and qualitative traits between the two farms are summarized in When comparing the CCS goats’ average body traits with those reported in the breed standards of the other native Sicilian goats  , some diThese results, in line with historical references of the breed , highligThe observed morphologic variability might be attributed to lack of a systematic selection program in the breed that would help for setting up specific selection criteria. Moreover, the differences between the two sampled farms might depend on the management system, genetic by environment interaction, the breed characteristic itself, or the presence of strains within the breed. The existed variation is an opportunity for sustainable improvement, conservation, and utilization work that would be designed for this breed.Following quality control and the removal of direct relatives, a total of 487 animals and 48,039 SNPs were retained and used for ROH and selection signature investigation. To ensure homogeneity across the 15 Italian breeds, a maximum of 35 animals were considered for each breed, resulting in a final dataset of 411 subjects for population structure analyses .The results of multidimensional scaling (MDS) analyses are presented in The results of the phylogenetic tree analysis based on Reynold distances  are consThe admixture analysis revealed that K = 11 was the best-fitting number of clusters, as determined by the model’s c-v value. Collectively, these findings provide strong evidence supporting the genomic originality of the CCS breed, marking a significant advancement in its recognition process. However, the evidence of the presence of subjects with a more admixed background highlights the importance of complementing phenotypical evaluations with genomic analyses to select the most suitable breeding animals and optimize their matings.o) than expected heterozygosity (He), with a FIS equal to 0.012. This is in line with what was observed for most of the other Southern Italian breeds. Genomic inbreeding (FROH) ranged from 1.5% (MES) to 15% (MAL). CCS, in particular, had a mean FROH equal to 6.0%, a value near most of the other breeds. Interestingly, more than 50% of the FROH derived from ROH > 16 MB in NIC, MON, CCS, and ASP breeds, implying recent inbreeding events [Genetic variability and ROH were investigated for all the included breeds . CCS breg events . These rThe genomic effective population size (Ne) based on LD was computed for each of the studied breeds. In ST and ROH were used to compare CCS goats with different phenotypic characteristics. For these analyses, all 78 CCSs were included. All the results related to the selection signature analyses, including the complete name of the genes, are reported in Fn = 39) and polled (n = 39), we identified 480 SNPs within the top 1% FST values (0.12–0.25), which fell within 136 genes. With regard to ROH analysis, 197 SNPs on 184 genes were in the top 1% delta H-score. Three genes—PCDHAC2, NRG2, and HBEGF—were found by both analyses. As previously mentioned, polled animals in our study exhibited normally formed genitalia, and PIS was excluded through PCR analysis. However, to the best of our knowledge, none of the genes identified by our analyses were previously associated with horn development in goats or other species. It is plausible that a breed-specific mutation is responsible for polledness in this particular breed. For example, this was the case in cattle species, where several were identified as causative factors for polledness [Comparing horned  and with anotia (n = 4), 480 SNPs and 143 genes were identified with FST analysis (top 1% = 0.44–0.87), and 189 SNPs and 178 genes with ROH analysis. Three genes, namely ATP12A, RNF17, and CENPJ, were in common. Interestingly, one of the ROH regions, located on chromosome 7, was found to be a selection signature in La Mancha goats, a breed in which the anotia trait is fixed [HSPA9, mutations of which are responsible for human Even-Plus syndrome [ST and/or ROH analyses are associated with various syndromes that frequently include microtia or similar ear malformations among their symptoms according to the Human Phenotype Ontology database [CENPJ and RNF17, both found by both the analyses, to primary autosomal recessive microcephaly 6 and Seckel syndrome; TCOF1 to Treacher Collins syndrome; EYA1 to brachio-oto-renal, branchiootic, and oto-facio-cervical syndromes; SPEN and RERE to 1p36 deletion syndrome and RERE-related neurodevelopmental syndrome; and ORC6 to Meier–Gorlin syndrome 3 and ear-patella-short stature syndrome. Moreover, GJB2 and 6 are associated to syndromic or non-syndromic deafness, whereas SUFU, which presented extremely high FST values, is considered to play a pivotal role in mammalian cochlear hair cell differentiation [In the comparison of CCS goats with ears  and without wattles (n = 34) led to the identification of 480 SNPs on 137 genes in the top 1% (0.08–0.20). Instead, 203 SNPs on 197 genes were comprised in the top 1% of delta H-score in ROH analysis. FAN1 and TRPM1 genes were retained in both methods. Among the identified genes, SLC9A9 and NEDD4 genes were previously accounted as a potential candidate gene for wattle presence in goat [NEDD4 gene  and its binding protein N4BP1 (through FST), further supporting their potential roles in the development of wattles in goats. Another study, instead, suggested possible association between the same phenotype and CSMD1 [KIF7, whose mutation causes acrocallosal syndrome in humans, often presenting preauricular skin tags [ADAMTSL3, known to influence the shape of the comb in chickens [F in goat . Interesnd CSMD1 , which ewith n =  and withchickens .Local livestock populations, such as the Comune di Sicilia goat breed, play a crucial role in the preservation of rural communities in harsh and marginal areas, ensuring income for these communities and contributing to the preservation of the territory.The investigation of the Comune di Sicilia goat revealed that, presently, this population demonstrates both genomics and phenotype distinctiveness, setting it apart from other breeds reared in the same breeding ranges, representing a step forward in its recognition process. However, it is important to acknowledge that some degree of variability exists within the population, likely influenced by breeders’ selection preferences and the relatively recent initiation of a formal selection process.To ensure the recognition and conservation of this population, a combined approach is imperative, taking into account both the morphology and appearance of the animals as well as their genomic background. This comprehensive evaluation will enable the identification and choice of the most suitable individuals, promoting the breed’s originality while preventing excessive inbreeding.Furthermore, the Comune di Sicilia breed presents intriguing phenotypic peculiarities that warrant further investigation from a genomic perspective. Such research can provide valuable insights into the development of these traits within the goat species.


The sensory test indicated that consumer acceptance of the studied sensory attributes differed significantly between groups, and bread with high levels of FFP (10 and 15% FFP) was generally more poorly rated than the control (0%) and 5% FFP for most of the variables studied. Despite this, all groups received acceptable scores  from consumers. The sensory analysis concluded that there is a possible niche in the market for these improved versions of bread products.In response to the demand for healthier foods in the current market, this study aimed to develop a new bread product using a fermented food product (FFP), a plant-based product composed of soya flour, alfalfa meal, barley sprouts, and viable microorganisms that showed beneficial effects in previous studies. White bread products prepared with three different substitution levels  of FFP were evaluated for physical characteristics , color indices , quality properties , protein content, crumb digital image analysis, and sensory characteristics. The results revealed that FFP significantly affected all studied parameters, and in most cases, there was a dose–response effect. FFP supplementation affected the nutritional profile and increased the protein content ( Consumer behaviors have changed dramatically in recent decades, probably because of increased concerns about the impact of food quality on overall health. This situation encourages healthy eating habits by reducing the consumption of unhealthy products and promoting the selection of more nutritious foods . Among tDue to food producers’ increasing interest in healthier food products, considerable efforts have been made to develop improved foods. A promising approach involves enhancing the nutritional quality of the marketed products. Bread, traditionally made of wheat flour, is one of the oldest foods and is widely consumed in most cultures ,10. It iIn the last few years, multiple investigations have been conducted to improve bread and bakery products’ quality. Several strategies can be implemented, such as decreasing salt ,20 or caFrequently, changes in food formulation can cause unexpected changes in the physicochemical and sensory properties of the final product ,40, and The present study examined bread fortified with different concentrations  of fermented food product (FFP). FFP primarily comprises legumes and cereals, and also includes live microorganisms and fermentable compounds, as well as bioactive ingredients generated during the production process, collectively making it a health-promoting product. FFP was initially intended for livestock supplementation, and previous studies have shown beneficial effects in animals, including improved health status in rabbits  and dairIn this context, we attempted to develop a novel food product from bread by using FFP as a high-nutritional-value component, to serve as an alternative vehicle for providing the product to humans. The objectives of the present study were (1) to develop bread with different enrichment levels of FFP; (2) to evaluate the influence of different FFP replacement levels on the physical characteristics, color indices, quality properties, protein content and crumb structure of the final bread products; and (3) to explore whether the incorporation of FFP in the studied proportions led to products with acceptable sensory properties.5 colony-forming units (CFU)/g total bacteria, 4.6 × 107 CFU/g Lactobacilli, and 1 × 105 CFU/g yeast and fungi. The nutritional and physicochemical characteristics of FFP are summarized in FFP is derived from a plant-based food product manufactured and distributed by a Spanish company  and is commercialized for livestock supplementation (FDA Registered). Its main components are soya flour, alfalfa meal, and barley sprouts, along with a combination of lactic acid bacteria (LAB) and nonbitter beer yeast  that goes through two fermentative processes. Its microbiological composition includes 2.0 × 10n = 4 each group).We studied three bread formulations with different replacement levels of bread flour by FFP , and bread without FFP (Bread-0) was included as a control. The flour blends were prepared and tested in triplicate, for a total of 12 bread loaves. For the sensory analysis See , 12 extrThe outcome measures included loaf dimensions , mass and volume, specific volume (SV), crumb and crust color, protein content in the bread products, crumb digital image analysis, and consumer panel evaluation.Bread wheat flour , salt, sugar , active dry yeast , and water were used in each experiment. Extravirgin olive oil (EVOO)  was used as liquid shortening. All ingredients were purchased from a local grocery store and stored at room temperature (RT) until use. FFP provided by the manufacturer  was obtained from the same production batch. FFP was processed as follows before carrying out the experiments: (1) it was milled to a fine flour using a 0.5 mm sieve; (2) the resulting flour was autoclaved at 121 °C for 15 min to sterilize the product and improve preservation; (3) flour was stored in a sterile glass container at 4 °C until use. A bread machine  was used in the baking process to obtain a consistent baking method. This model included 19 automatic cooking programs, three loaf sizes , and three toast colors .Based on this experimental design, 12 bread loaves were prepared using different bread flour replacement levels. The ingredients and proportions of each blend are listed in ® bags, labeled, and stored at 4 °C or −20 °C until further analysis.When the cooking program was completed, the loaves were removed from the pan and cooled at RT. The loaf mass and volume were then determined, and the loaves were packaged in ZiplocL*), redness/greenness (a*), and yellowness/blueness (b*) of the bread crust and crumb were determined in triplicate for each bread loaf.The maximum height (to the top of the mound), length (to the greatest extent), and width of each bread loaf were measured using a regular ruler. The color attributes of bread were determined using a chromameter . For this purpose, the lightness  was determined in each group according to the Dumas method with a Rapid N Max Exceed . Nitrogen (N) was determined following the AOAC Method of Analysis 935.36, and the protein content (%) in the sample was estimated as N × 6.25. Protein values were adjusted for dry matter.3/g) as previously described [Once the loaves were cooked and cooled at RT, the mass, volume, and SV were determined. Loaf volume (mL) was determined using the mustard seed displacement method, according to the AACC 10-05.01 approved method. The SV was calculated by dividing the loaf volume by the load mass , cells area (%), number of cells; main length (mm), cell volume (mm3), cell diameter (mm), and cell wall thickness (mm). Measurements were recorded in triplicate for each bread loaf.For deeper insight into the structural changes with different percentages of FFP, digital image analysis was carried out in all the groups using the C-Cell Image Analysis System . Each bread loaf was cut transversally into 1 cm thick slices using a food slicer . The first two slices were discarded, and the third to fifth slices were used for analysis. To assess the differences between the cell structures of the different groups, we studied the following parameters: slice brightness, slice area . To evaluate the sensory attributes of bread, panelists and potential consumers at South Dakota State University  were recruited via email. A total of 42 panelists  aged 20–54 from 16 different nationalities  were recruited from among the university students and staff. All participants were declared regular bread consumers. The subjects received written information about the test and all the participants provided their informed consent to participate in the study. The study was exempt from ethical committee review.® bag for freshness. On the day of the sensory test, frozen and refrigerated samples were left at RT. Prior to the sensory test, the bread loaves were cut transversally into 1 cm slices. Slices were cut in half longitudinally, and the test sample consisted of one half-piece of each bread group, including both crumb and crust. The samples were coded with random three-digit codes, and the sequences were randomized. All groups were presented at the same time to the panelists, along with water and McIntosh apples to cleanse the palate between samples. The panelists were asked to evaluate the appearance, crumb and crust color, odor, hardness, taste, aftertaste, and overall liking of the codified samples using a questionnaire  and the samples for the sensory evaluation were refrigerated (4 °C) and kept in a plastic Ziplocionnaire . For theionnaire . Values n = 9 in each group . All data are expressed as mean ± standard deviation (SD). All statistical analyses were performed using SPSS software for Microsoft (IBM SPSS Statistics 20), and the significant level was established at α = 0.05. Analysis of variance (ANOVA) was conducted to test differences between treatments, and Tukey´s post hoc multiple comparisons were performed to determine where significant differences existed among the groups. Data from the sensory evaluation were analyzed using the Kruskal–Wallis test, and frequency analysis was performed using the chi-squared test. The Spearman correlation coefficient (ρ) was estimated to determine the linear association between some test variables with an interval scale, while Pearson correlation coefficient was used for variables with an ordinal scale. The results were interpreted according to the degree of association as very high (ρ = 0.9–1), high (ρ = 0.7–0.9), moderate (ρ = 0.5–0.7), or low (ρ = 0.2–0.5), and statistical significance was set at p < 0.05.Each group  was baked in triplicate, and each parameter was determined in triplicate, for a total of The results for each determination and the corresponding discussion are presented in the following sections.p < 0.001). A similar effect has been previously described for composite flours [The effects of different FFP substitution levels on the physical properties and color attributes of bread were analyzed . Loaf pee flours  or distie flours , and it p < 0.05). Loaf length was unaffected by FFP, and no significant differences were observed between the groups.With respect to loaf width, only Bread-5 differed from the control (Bread-0) (L*), less red (lower a*), and yellow (lower b*) appearance with FFP inclusion . Compared with Bread-0, L*, a*, and b* decreased 25.1, 24.1, and 35.8% in Bread-15, respectively. We also found a darker (lower L*), and yellow (lower b*) appearance in the bread crumb, with decreases of 30.9 and 2.8%, respectively, in Bread-15, compared with Bread-0 (p < 0.001 in both). The a* parameter was profoundly altered by FPP and markedly increased with all the substitution levels (p < 0.001), especially in Bread-10, more than 3.5 times greater than Bread-0.Color indices were studied to determine the effect of FFP on the physical characteristics of the experimental pieces of bread and revealed that the treatments significantly influenced the crust and crumb color . In the In agreement with the data presented above , Figure p > 0.05). This was expected because the bread formula was fixed to 1.5 lb loaves. However, in terms of volume, we found differences between groups with FFP levels equal to or greater than 10%, with 18 and 32% decreases in Bread-10 and Bread-15, respectively (p < 0.001)  B.p < 0.001)  C, and sup < 0.001).The amount and type of protein in bread blench, which depends on the ingredients, also impact starch gelatinization and reduce swelling power ,60,62. TBread volume has been related to bread shelf life  and chewOn another note, previous studies investigating the compactness of bread products have reported that it can impact starch digestion and the glycemic response, insinuating that more compacted bread with reduced volume could led to a reduced glycemic impact . FFP is Considering the above, there is a potential for Bread-10 and Bread-15 to have a gentler glycemic response than control bread. Nevertheless, appropriate experiments are required to confirm this hypothesis.p < 0.001) . As expected, we found that the protein content in Bread-0 (9.4%) increased with the incorporation of FFP, reaching 10.4, 11.3 and 12.1% of protein content in Bread-5, Bread-10, and Bread-15, respectively. This indicated that even the lowest FFP supplementation significantly increased the protein content of the bread loaves (< 0.001) . A similThe protein content of the final product is influenced by the nutritional characteristics of the flour and other ingredients used during the bread-baking process. While the use of FFP (in this study), DDGS , and legAccording to the nutrition claims by the European Commission in Regulation (EC) No. 1924/2006, only food with at least 12 and 20% of the energy value by protein can be considered a “source of protein” and “high in protein”, respectively . Considep < 0.001), which is in good agreement with data from color parameters, where FFP dramatically darkened crumb and crust color .Image analysis data showed that FFP significantly affected the cellular structure of bread. A proportional decrease in slice brightness was found with increasing levels of FFP (st color . The Spep < 0.001). Indeed, we identified a very high negative correlation between the two parameters . In previous studies, however, incorporating novel ingredients, such as orange pomace [Both the cell area and the number of cells were only affected by FFP levels equal to or greater than 10% (Bread-10 and Bread-15), with a negative and positive effect, respectively (e pomace  and diste pomace  in breadp < 0.001). The incorporation of FFP had a different effect on cell diameter and wall thickness, which were significantly reduced at replacement levels of 15% (p < 0.01). No significant linear correlation was identified between cell volume and cell diameter  and wall thickness . A previous study investigated the impact of different replacement levels of breadfruit flour on bread quality parameters [Cell volume increased in Bread-5 and Bread-10, while Bread-15 remained similar to the control (Bread-0) . As shown in the digital photographs, there were also physical differences in the appearance of the bread slices within the groups. In addition to presenting a more irregular shape, bread with FFP . The control (Bread-0) was liked significantly more in all the sensory parameters than the other experimental bread groups , and obtained the best scores in general appearance and overall liking. Besides that, most scores were equal to or greater than 5 in all the parameters and groups, except for the general appearance in Bread-15 . We can conclude that adding FFP to bread affected consumer evaluation, but consumers still accepted all products. When the mean scores were calculated for all parameters, the best punctuation  was registered for both crumb and crust color in Bread-0.p < 0.001). Despite this, all groups received punctuation closer to or greater than 5, which indicated that most panelists had a neutral or positive perception. This effect is likely due to the soya present in the FFP. Soya and other ingredients, such as chickpea [p < 0.001 in both). Organoleptic properties, particularly taste and olfaction, strongly influence consumer behavior and food acceptance [In chickpea  or sorghchickpea , can sigchickpea ,71. Indeceptance . Therefop < 0.001), even at the lowest incorporation level (Bread-5). We believe this might have biased other organoleptic attributes and had an important effect on the perception of the sensory characteristics. Previous studies have indicated the impact of color on sensory evaluations, particularly in bakery products [Bread color was significantly affected by FFP incorporation, as shown in products ,43. Darkproducts , which cOn another note, other authors have concluded that pH, which is influenced by the fermentation process, significantly affects bread crumb color . Althougp < 0.001). This could be due to the higher percentage of humidity in bread loaves containing FFP. Unfortunately, the bread texture analysis was not performed.The treatment also altered hardness perception, and its hedonic evaluation decreased with increasing FFP content (p < 0.001). Curiously, Bread-10 (17.1%) was preferred over Bread-5 (9.8%), and only a small minority chose Bread-15 (2.4%).At the end of the sensory test, panelists were asked to rate their group of choice, and most of them indicated Bread-0 (70.7%), clearly surpassing the other groups (p > 0.05). Similarly, there was no significant correlation between the preferred sample and nationality ; however, we found that the individuals who preferred Bread-10 and Bread-15 were from countries whose traditional gastronomy is full of flavored and tasty food . Therefore, it is likely that bread enriched with FFP would be better accepted in specific countries or cultures.Food neophobia can dramatically alter consumers´ acceptance of food. It can be affected by demographic, cultural, and social factors ,73, as wHealth is an important motivation for consumers when choosing a functional food, and so does price . PreviouThere is abundant space for further progress in analyzing the incorporation of FFP in bread products. More experiments should address key aspects such as FFP dosage, safety, and stability of the final product, and alternative delivery systems or additional ingredients should also be considered.In the formulation, we used extra-virgin olive oil for shortening, which is the main source of dietary fat in the Mediterranean diet and is associated with many health benefits . BecauseOn another note, FFP is a good source of fiber, and its incorporation as an ingredient can change the carbohydrate digestibility, sugar content, and glycemic index ,82 in brRegarding the sensory properties of the experimental pieces, they could be fully explored through a sensory analysis by trained panelists with sensory experience in evaluating different types of bakery products, including more texture and sensory scores . It woulLastly, FFP is expected to provide many bioactive compounds in the baked products; however, the functional effects of bioactive components is definitely determined by their viability, which in turn is influenced by the matrix, food processing, storage, and digestion process . For thiThis study is the first attempt to incorporate FFP into a food matrix to develop enriched food for human consumption. Our study aimed to create a bread product with improved nutritional properties that can generate interest in regular consumers of FF products. We evaluated three different substitution levels  of wheat flour for FFP and compared them with control (Bread-0). The quantity of FFP significantly affected all the studied parameters, including physical characteristics, color indices, quality attributes, protein content, and bread structure. The results showed that incorporating FFP into bread formulation could be a valuable strategy for increasing bread protein content. However, future studies should analyze the proximate composition of the final bakery products to determine whether there are also relevant differences in dietary fiber and other macronutrients such as fats and carbohydrates. Besides, the microbiological analysis of loaves with FFP could clarify whether spores or partially activated microorganisms interfere with dough fermentation.In addition, FFP affected the sensory properties of bread, as indicated by panelists. Sensory tests revealed that the consumers perceived significant differences in the palatability and sensory attributes of the groups. Bread with different levels of FFP had worse sensory scores than the control in most of the studied characteristics, although consumers accepted all groups. On top of that, some panelists indicated their liking for some attributes in Bread-10 and Bread-15, suggesting a market niche for this type of product.In conclusion, FFP could be included as a functional ingredient in bread or related products to decrease the use of wheat flour, thus potentially increasing the nutritional and functional properties of bread products. Whether the doses  and the format of FFP (milled and autoclaved) used in this study could lead to significant health improvements in consumers remains unexplored, and could be evaluated in future trials.