Functional Epigenetic Optimization Through Biofrequency-Guided Nutrition: A Randomized Controlled Trial
Phase: NA
Conditions: Nutrition Habits, Biomarkers / Hair, Epigenetic
Sponsor: Universidad de Almeria

Date to submit: 08-07-2025 
 
Functional Epigenetic Optimization through Biofrequency-Guided Nutrition: Protocol for a 
Randomized Controlled Trial 
 
Abstract 
Background: Biomarker-based nutrition has demonstrated superior efficacy compared to 
population-level approaches in improving health outcomes. Biofrequency analysis, a 
non-invasive technique based on the detection of electromagnetic vibrational patterns in hair 
follicles, offers a promising avenue for rapid functional assessment of nutritional status and 
epigenetic signals without reliance on blood or urine sampling. Objective: First, to evaluate the 
effect of a 90-day biofrequency-guided nutritional intervention on functional epigenetic status, as 
reflected in changes in the optimization report generated by the S-Drive system; second, to assess 
changes in adherence to the Mediterranean diet, anthropometric indicators, movement behaviors, 
and psychological well-being. Methods: In this randomized controlled trial, 154 adults from the 
Grupo Alcaraz Sport Association (Region of Murcia, Spain) will be allocated to an experimental 
group receiving individualized lifestyle recommendations based on S-Drive biofrequency 
analysis and to a control group with no intervention. Participants in the experimental arm will 
apply tailored dietary guidance over 90 days. Conclusion: This study will generate foundational 
evidence on the utility of biofrequency technology for precision nutrition. If positive, the findings 
may inform scalable, low-risk strategies for personalized dietary interventions in preventive and 
community health settings. 
Keywords: Biofrequency analysis, biomarker-based nutrition, dietary habits, mediterranean diet, 
nutritional biomarkers, nutritional intervention, young athletes 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

1. Introduction 
Biomarker-based nutrition has emerged as a key strategy in the preventive and therapeutic 
management of non-communicable chronic diseases (NCDs), as it enables interventions guided 
by measurable indicators of nutritional, metabolic, and epigenetic status [1–4].  In contrast to 
population-level approaches, nutritional interventions informed by pathophysiological, 
behavioral, and molecular parameters have demonstrated greater efficacy in modulating risk 
biomarkers and improving clinical outcomes [5–8]. In a global context marked by high prevalence 
of obesity, type 2 diabetes, and hypertension, diseases with strong dietary and environmental 
components, it is imperative to develop tools that enable efficient collection of nutritional and 
metabolic data through a lens of precision and accessibility [9–12]. 
From a biochemical and functional perspective, nutritional status can be understood as 
the dynamic equilibrium between nutrient bioavailability and tissue requirements, shaped by 
multiple factors including dietary intake, intestinal absorption capacity, endocrine-metabolic 
regulation, and the epigenetic environment [4,13]. Characterizing this status, essential for clinical 
intervention, has historically relied on invasive analyses (blood, urine, or stool samples) and self-
reported dietary records, both of which present significant limitations in terms of cost, 
acceptability, accuracy, and longitudinal traceability [2,3,14]. In this context, the integration of 
non-invasive technologies capable of indirectly capturing biophysical data offers a 
complementary alternative for preliminary nutritional profiling. 
Among these emerging technologies, biofrequency stands out. It is based on the detection 
of electromagnetic frequencies emitted by cellular and tissue structures according to their 
molecular composition and functional state [15–18]. The underlying biological premise holds that 
molecular structures exhibit specific vibrational patterns that can be amplified, digitized, and 
interpreted through bioinformatic modeling algorithms [19,20]. In the nutritional field, these 
signals allow for inferences regarding functional imbalances related to micronutrient metabolism, 
enzymatic activity, oxidative stress, xenobiotic load, and other determinants of baseline 
physiological status [21,22]. Although its clinical validation remains in early stages, its 
applicability in primary, community, or preventive care contexts is promising due to its 
portability, speed, and low invasiveness [23,24]. 
The S Drive device uses hair strands with the follicular bulb intact, collected from the 
occipital region of the scalp, as the biological matrix. This structure contains a representative set 
of accumulated epigenetic and metabolic data from follicular growth [25]. Spectral digitization is 
carried out using an electromagnetic wave emitter-receiver coil, which captures the vibrational 
spectrum of the sample in a short timeframe (~3 minutes) [25]. The signal is then processed 
through a computational algorithm, generating a report with indirect biomarker estimates of 
essential micronutrient status, fatty acid profile, oxidative load, electromagnetic field (EMF/ELF) 
exposure, and putative food sensitivity [25]. The use of the follicular bulb is justified by its high 
mitotic rate and its functional integration into the cutaneous neuroendocrine axis, acting as a 
dynamic sensor of both internal and external environments [26]. 
From an epigenetic perspective, hair strand analysis enables the identification of post-
translational DNA modifications and alterations in histone methylation or acetylation patterns 
associated with nutritional or environmental exposures [27–29]. Although indirect, this level of 
resolution allows for functional inferences regarding gene expression regulated by diet and its 
influence on physiological homeostasis [30]. In this regard, epigenetics provides a robust 
conceptual framework for understanding the bidirectional relationship between dietary factors 

and phenotypic regulation, independent of genotypic change [31–33]. The combined approach of 
biofrequency and hair epigenetics represents a preliminary tool for metabolic risk stratification, 
identification of specific nutritional needs, and the design of more effective dietary interventions 
[34]. 
The implementation of emerging technologies in nutrition should not be assessed solely 
from a biomedical standpoint but must also take into account their psychosocial impact. Factors 
such as perceived autonomy, comprehension of the information received, and trust in the 
technological tool directly affect therapeutic adherence, especially among individuals without a 
diagnosed chronic condition [35,36]. In this sense, the delivery of personalized reports generated 
from biofrequency analysis may enhance motivation for change, improve self-efficacy, and boost 
perceived subjective well-being. Evaluating these aspects is crucial to determine the feasibility of 
implementing such technologies in public health programs and population-level clinical 
interventions. Exploring user experience with tools like this can provide valuable insights into 
their acceptability, perceived usefulness, and transformative potential for lifestyle habits. 
Within this framework, the present study will have a twofold aim: (1) to evaluate the 
effect of a 90-day biofrequency-guided nutritional intervention on functional epigenetic status, as 
reflected in changes in the optimization report generated by the S-Drive system; (2) to assess 
changes in adherence to the Mediterranean diet, anthropometric indicators, movement behaviors, 
and psychological well-being. In a clinical environment where speed, operational efficiency, and 
technological accessibility are key factors, this technology may facilitate nutritional decision-
making and enhance the therapeutic relationship. Accordingly, this study situates itself at the 
intersection of biomedical innovation and biomarker-based nutrition, with potential implications 
for the development of more effective public health strategies. 
 
2. Materials and Methods 
2.1 Study Design and Population 
2.1.1 
Study Type 
This study will follow a randomized controlled trial design aimed at evaluating the impact of 
nutritional intervention on personal satisfaction and perceived quality of life. It will be registered 
in ClinicalTrials.gov. The protocol includes two assessment points: a baseline measurement at the 
start of the intervention (Day 0) and a second measurement after a follow-up period (Day 90), 
allowing for the analysis of within-subject changes associated with the intervention. 
 
2.1.2 
Study Population and Sampling Design 
The research will be conducted with Spanish adults who are members of the Grupo Alcaraz Sport 
Association in the Region of Murcia, Spain. The target population consists of adults aged ≥ 18 
residing in the Region of Murcia, Spain, who meet the predefined inclusion criteria: (1) no 
diagnosed chronic illnesses and (2) provision of written informed consent. These criteria are 
essential to ensure both the ethical validity of the procedure and the clinical homogeneity of the 
sample. To control for potential confounding effects related to sex and to ensure group 
equivalence, stratified random sampling by sex will be employed. This approach reduces within-
group variance and increases the statistical power of the analysis [37]. 
The sample size was calculated based on a parallel group randomized experimental 
design with two evaluation points (pre-intervention: Day 0; post-intervention: Day 90). The 
formula used compares means between two independent groups using a two-tailed Student’s t-

test, assuming a moderate effect size (d = 0.5), a significance level of α = 0.05, and statistical power 
(1–β) of 80%, according to Cohen’s criteria [38]. Under these assumptions, a minimum of 64 
participants per group (n = 128 in total) was estimated. Considering the longitudinal nature of 
the study and the risk of attrition due to dropout, withdrawal, or incomplete data, a 20% 
adjustment was applied to the initial sample size, in line with recommendations for studies with 
follow-ups ≥30 days [39]. Consequently, the final sample size was set at 77 participants per group 
(n = 154), ensuring the required statistical power and maintaining the inferential robustness of 
the analyses, even in the presence of non-differential attrition [40].  
 
2.1.3 
Ethical Approval 
The study protocol has been reviewed and approved by the Human Research Ethics Committee 
(CIH) of the University of Almería (Approval Code: UALBIO2025/010). All procedures will be 
conducted in accordance with the ethical principles set forth in the Declaration of Helsinki and 
in compliance with current Spanish legislation on biomedical research. Prior to enrollment, all 
participants will provide written informed consent, which will outline the study objectives, 
procedures, and associated rights. Participation will be voluntary and may be withdrawn at any 
time without any consequence. Personal data will be treated confidentially and anonymized in 
accordance with the General Data Protection Regulation (GDPR). No significant risks are 
anticipated, as the interventions are non-invasive. 
 
2.2 Recruitment, Allocation and Baseline Assessment 
Participants will be recruited through informational campaigns conducted at the Grupo Alcaraz 
Sport Association. Each potential participant will undergo an individual interview to provide 
detailed information about the study’s objectives and procedures, assess eligibility based on 
predefined inclusion criteria, and confirm their availability for full participation. Written 
informed consent, either in physical or electronic format, will be obtained from all participants 
prior to initiating any study-related activity. 
Upon enrollment, each participant will be assigned a unique identification code to ensure the 
confidentiality of personal data. Only the principal investigator will have access to participants’ 
contact information, which will be used exclusively for follow-up, communication of study 
instructions, and delivery of individual results. During this initial meeting, the biological sample 
collection (comprising four strands of hair) will be scheduled to take place at designated time 
slots on the DZ Procenter clinic. Participants will then be randomly assigned to one of two study 
groups (control or experimental) using a computerized randomization process.  
Each participant will complete a comprehensive assessment at two points: at baseline (pre-
intervention) and at the conclusion of the study (post-intervention). These assessments will 
involve a structured questionnaire incorporating validated batteries and measurement tools 
designed to collect data on sociodemographic characteristics, anthropometric parameters, 
lifestyle habits, and psychological variables. Detailed descriptions of the instruments and 
outcome measures used are provided in the section “Instruments and Outcome Measures.” 
 
2.3 Collection and Analysis of Biological Samples 
This study will employ a longitudinal experimental design with two time points for data 
collection: baseline (Day 0) and post-intervention (Day 90). The collection of biological samples 
will take place in a multidisciplinary room. Biological data will be obtained through the collection 

and in situ analysis of hair samples, specifically from the occipital region of each participant’s 
scalp. The sampling procedure will be conducted using sterile tweezers and will not require any 
invasive technique. Immediately after extraction, the samples will be analyzed on-site using a 
non-destructive method and subsequently discarded, thereby eliminating the need for storage or 
transportation. 
The analytical process will be conducted using the S-Drive device (Cell Wellbeing, 
Hamburg, Germany). This technology incorporates a central spectral coil that functions via 
resonance frequency analysis. The device will capture the vibrational signature of the hair 
sample, which will then be digitized and transmitted to proprietary software. The system will 
process this information using a set of algorithms designed to identify frequency-based patterns 
related to various physiological systems. The analysis will generate a reading of biological 
markers associated with epigenetic processes, reflecting the resonance intensity of elements such 
as vitamins, amino acids, fatty acids, and other micronutrients. It is important to note that the 
system does not extract genetic information or quantify the presence of these compounds; 
instead, it interprets the intensity values of their frequency patterns based on resonance. 
The generation of the epigenetic profile will consist of two main components: (1) a 
computational model responsible for interpreting the biofrequency data and (2) an integrated 
reference database derived from biological, nutritional, and botanical samples. The digitized data 
will be transmitted to secure servers located in Germany, where the algorithm will generate a 
comprehensive optimization map. This map will reflect systemic interactions and challenges 
within the body, based on the frequency intensity values of the analyzed biomarkers. The 
resulting information will be synthesized into individualized reports that provide lifestyle 
optimization strategies focused on dietary, environmental, and nutritional factors. 
Both the S-Drive device and its associated analytical algorithm are proprietary 
technologies owned by Cell Wellbeing and are subject to confidentiality agreements. At the time 
of this study, technology has not yet been scientifically validated. Therefore, the aim of the present 
research will be to evaluate the potential impact of the personalized feedback provided by the 
system on health-related behavior modification. Additionally, the study will explore the 
feasibility of integrating this tool into professional health and wellness practice, with the long-
term objective of contributing to its empirical validation. 
The S-Drive is certified by multiple regulatory agencies, ensuring compliance with 
international safety and performance standards. These include recognition by the U.S. Food and 
Drug Administration (FDA) under the guidance document General Wellness: Policy for Low Risk 
Devices (FDA 1300013, UCM429674); CE certification under Directive 2014/30/EU and RoHS 
Directive 2011/65/EU, in compliance with standards EN 61326-1:2006 (EMC Requirements), EN 
60950-1:2006+A12:2011, and EN 5502/55011/55024; ETL certification by Intertek, confirming 
compliance with UL standard 60950-1 and CSA standard c22.2 No. 60950-1 (ETL Component 
Recognition No. 5000055); and China Compulsory Certification (CCC) for electrical conformity. 
These certifications support the safety profile of the device within its intended scope of 
application. 
 
2.4 Nutritional Intervention 
Participants assigned to the experimental group will receive a standardized 90-day 
nutritional intervention based on the individualized S-Drive optimization report. This report is 
derived from biofrequency analysis of hair follicle samples and synthesizes potential 

physiological imbalances into tailored lifestyle and dietary recommendations. The same report 
will also serve as the study’s primary outcome, enabling assessment of functional changes from 
baseline to follow-up (see Section 2.5). 
The intervention comprises two main components: a personalized dietary plan and a 
health education module. Participants will receive structured materials including: (1) detailed 
dietary guidelines; (2) a monthly sample meal plan; (3) a shopping list; (4) selected recipes, and 
(5) visual preparation aids. 
To support adherence, a daily food intake log will be provided. The educational module 
will address key health concepts such as stress physiology, epigenetic mechanisms, chronic 
disease prevention, and mindful eating. Content will be delivered through printed materials and 
two initial group video sessions. Periodic email check-ins will be conducted to monitor 
adherence, reinforce key messages, and provide tailored feedback. 
Participants in the control group will be instructed to maintain their habitual dietary 
habits and will not receive any intervention materials or follow-up support. This design ensures 
that all participants in the experimental group receive a consistent and evidence-informed 
intervention, while allowing valid comparisons with the control group. 
 
2.4.1 
Specific Dietary Guidelines 
Participants will be instructed to prioritize hydration, consuming at least 2 liters of water daily, 
supplemented with herbal infusions (e.g., green tea) or homemade vegetable juices (e.g., celery 
and apple) [41]. Fruit intake will be limited to 1–4 pieces per day, consumed outside main meals 
and avoided in the evening, with an emphasis on single-variety servings [42]. Vegetables will be 
consumed abundantly, preferably raw or steamed, with the goal of achieving a diet composed of 
60% raw and natural foods [43]. 
Dairy consumption will be restricted to organic goat or sheep yogurt and kefir, 2–3 times 
per week [44,45]. Whole grains such as buckwheat, brown rice, millet, amaranth, and quinoa will 
be encouraged, while gluten-containing grains like spelt, rye, and oats will be limited [46]. 
Legumes, including lentils, chickpeas, and adzuki beans, will be recommended as primary plant-
based protein sources [47]. 
Animal proteins will be carefully selected: turkey, chicken, and rabbit will be permitted 
up to twice weekly, while fish will be included 2–3 times per week, avoiding large species and 
prioritizing wild-caught varieties [48,49]. Organic eggs will be recommended up to four times 
weekly, prepared scrambled, soft-boiled, or poached to preserve nutritional integrity [50]. 
Healthy fats will be sourced from avocado, raw nuts, and seeds, while extra virgin olive 
oil will be recommended for raw use [51]. Meals will be seasoned with spices such as turmeric, 
ginger, and rosemary, added after cooking to maximize bioactive properties [52]. Specialized 
foods like sprouts, seaweed, and mushrooms will be incorporated for their micronutrient density. 
 
2.4.2 
Meal Composition and Lifestyle 
Meals will follow a 3:1 vegetable-to-protein or carbohydrate ratio, avoiding combinations of 
dense proteins with dense carbohydrates [42,43,49]. Participants will be encouraged to dine early 
and engage in light activity afterward [53]. Breakfasts will focus on vegetables and fruit 
smoothies, with no more than one fruit per serving [42]. Cooking methods will prioritize nutrient 
preservation, favoring steaming and low-temperature techniques over frying or high-heat 
roasting [54]. 

Lifestyle recommendations will include daily sun exposure (30 minutes in the morning) 
and physical activity emphasizing both muscular strengthening and mental well-being [53,55]. 
Strict avoidance of tobacco, alcohol, caffeine, processed foods, refined sugars, and artificial 
sweeteners will be mandated [56,57]. Red meat, processed meats (with the exception of high-
quality Iberian ham), and large or farmed fish with high mercury content will be excluded [49]. 
This standardized approach will ensure that all participants receive the same optimized 
dietary framework, designed to improve most biomarkers under study while remaining feasible 
for a diverse group. The intervention will balance scientific rigor with practical applicability, 
eliminating the need for individualized adjustments and promoting measurable health outcomes. 
 
2.4.3 
Post-Intervention Assessment 
At the conclusion of the 90-day period, biological sample collection (hair samples) and self-
administered questionnaire procedures (see “Instruments and Outcome Measures” section) will 
be repeated for both groups following the same standardized protocol. The follow-up assessment 
will aim to determine observed changes in the experimental group compared to the control 
group. 
 
2.5 Instruments and Measures 
At baseline (Day 0) and at follow-up (Day 90), participants will complete a comprehensive self-
administered questionnaire comprising multiple modules. These modules have been specifically 
designed to collect detailed data on sociodemographic characteristics, self-reported 
anthropometric measures, lifestyle-related behaviors (including physical activity, dietary 
patterns, and substance use), and psychological outcomes (encompassing depressive symptoms, 
anxiety, stress, and disordered eating behaviors). Validated instruments and structured items will 
be utilized within each module to ensure the methodological rigor and reliability of the data 
collection process across the two assessment time points. 
 
Sociodemographic Data 
Participants will provide self-reported information regarding their sex and age. Data will also be 
gathered on their current engagement in educational pursuits, employment status, participation 
in remunerated activities, and their specific occupation. Additionally, participants will be asked 
to indicate the primary reason for their most recent visit to primary care services. Educational 
attainment will be categorized as follows: (a) incomplete primary education (<6 years), (b) 
completed primary education (6 years), (c) incomplete secondary education (<4 years), (d) 
completed secondary education (4 years), (e) incomplete higher education (<2 years of 
baccalaureate studies), and (f) completed higher education (2 years of baccalaureate studies or a 
bachelor's degree). 
 
Primary Outcome: Functional Epigenetic Optimization 
unctional status will be assessed using the S-Drive optimization report, which analyzes resonance 
frequency patterns from hair follicle samples through proprietary algorithms. The report 
classifies each participant’s physiological functioning into 12 core domains, grouped into four 
thematic categories: 
- 
Micronutrient-related indicators: vitamins, minerals, amino acids, fatty acids, 
antioxidants. 

- 
Systemic physiological indicators: immune system, gut health, circulatory function. 
- 
Epigenetic and environmental stressors: exposure to electromagnetic fields (EMF), 
extremely low-frequency (ELF) radiation, toxins, food additives. 
- 
Dietary guidance: food choices, hydration, and supplementation recommendations. 
 
Each domain is classified into one of four qualitative levels: “not flagged” (no action required), 
“to be considered”, “advisable”, or “priority”, indicating increasing levels of physiological 
imbalance or urgency for nutritional support. 
For analytical purposes, these levels will be transformed into ordinal values (Not flagged 
= 0, To be considered = 1, Advisable = 2, Priority = 3). Two derived scores will be used: (1) the total 
number of “priority” domains per participant, and (2) a cumulative optimization score summing 
the ordinal values across all domains (range: 0–36). These variables will be analyzed to assess 
within-subject changes and between-group differences from baseline to follow-up. 
 
Secondary Outcomes 
Anthropometric Data 
Anthropometric data, including current body weight (in kilograms) and height (in centimeters), 
will be self-reported by participants. These measurements will be used to compute body mass 
index (BMI), which is calculated by dividing weight in kilograms by the square of height in meters 
(kg/m²). BMI categories will be defined in accordance with World Health Organization (WHO) 
standards: BMI < 18.5 (“underweight”), 18.5 ≤ BMI < 24.9 (“normal weight”), 25 ≤ BMI < 29.9 
(“overweight”), 30 ≤ BMI < 34.9 (“obesity class I”), 35 ≤ BMI < 39.9 (“obesity class II”), and BMI ≥ 
40 (“obesity class III”) [58]. Although self-reported measurements may be susceptible to bias, 
previous research indicates that self-reported BMI estimates are generally valid in adult 
populations [59]. 
 
Lifestyle Data 
24-Hour Movement Behaviors 
Physical activity across work/school, transportation, and leisure domains will be assessed using 
the validated Spanish version of the Physical Activity Scale 2.1 (PAS-2.1S) [60]. This instrument 
comprises nine items: six items evaluate daily activities (sleep duration on weekdays and 
weekends, sedentary behavior, active physical activity, leisure time, and commuting time), and 
three items assess weekly physical activity. To assess sleep duration, participants will report 
average hours of sleep on weekdays and weekends. Sedentary behaviors will be captured 
through items spent commuting (by car or public transportation) and screen-based or reading 
activities during leisure time. 
The weekly activity subscale will collect data on light (e.g., walking, light cleaning), 
moderate (e.g., gardening, moderate sports), and vigorous (e.g., running, football) leisure-time 
physical activities. Activity intensity levels will be expressed in Metabolic Equivalent of Task 
(METs), based on the Physical Activity Compendium [61]. MET values include sleep (0.9), sitting 
work (1.5), standing/walking work (2.0), heavy lifting or stair climbing (5.0), commuting while 
seated (1.5), leisure-time sitting (1.0), light activity (3.0), moderate activity (5.0), and vigorous 
activity (6.0). To compute total weekly METs, daily activities (work and transportation) will be 
multiplied by five (days) and sleep and leisure activities by seven (days). Missing daily activity 

hours (if reported as <24 hours) will be multiplied by two to account for the discrepancy. METs 
from weekly activities will be summed up to obtain total energy expenditure. 
 
Eating Patterns 
Adherence to the Mediterranean diet 
Adherence to the Mediterranean diet (MedDiet) will be measured using the PREvención con 
DIeta MEDiterránea (PREDIMED) questionnaire [62]. Participants will report the frequency or 
quantity of consumption of 12 core dietary components (e.g., fruits, vegetables, olive oil, nuts) 
and two related dietary habits. The 14-item tool assigns binary scores (0 or 1) to each component, 
with total scores ≥9 indicative of high adherence to the MedDiet. 
 
Drug consumption 
Tobacco smoking 
Tobacco use will be assessed with two questions. First, participants will be asked: “Have you ever 
smoked tobacco in your lifetime?”, with response options ranging from (a) never to (g) 30 times 
or more. Second, adolescents will be asked about current smoking frequency: (a) I do not smoke, 
(b) less than once a week, (c) more than once a week but not daily, and (d) every day. 
Alcohol consumption 
Alcohol consumption will be evaluated through the question: “Have you ever consumed alcohol 
in your lifetime?”, with response options: (a) never, (b) once or twice, (c) 3 to 5 times, (d) 6 to 9 
times, (e) 10 to 19 times, (f) 20 to 29 times, and (g) 30 times or more. Further, participants will 
indicate the frequency of consumption of various alcoholic beverages, with responses converted 
into days per week: : (a) never (0 points), (b) almost never (0.10 points), (c) monthly (0.25 points), 
(d) weekly (1 point), and (e) daily (7 points). Mean weekly frequency will be computed. 
Participants will be categorized into: (a) non-drinkers (never consumed any alcoholic beverage), 
(b) regular drinkers (weekly or more frequently), and (c) irregular drinkers (monthly or less 
frequently). Binge drinking will be assessed with: “Have you ever consumed enough alcohol to 
become intoxicated?”, with frequency options: (a) never, (b) once, (c) 2-3 times, (d) 4-10 times, 
and (e) more than 10 times. Excessive consumption will be defined as having become intoxicated 
once or more. 
 
Cannabis use 
Cannabis consumption will be assessed with the question: “Have you ever used cannabis in your 
lifetime?”, using response options: (a) never, (b) once or twice, (c) 3 to 5 times, (d) 6 to 9 times, (e) 
10 to 19 times, (f) 20 to 29 times, and (g) 30 times or more. 
 
Psychological Outcomes 
Depressive Symptoms  
Depressive symptoms will be measured using the Spanish version of the Beck Depression 
Inventory-II (BDI-II) [63]. This self-administered instrument consists of 21 items, each with four 
response options representing increasing severity over the preceding two weeks. Item scores 
range from 0 to 3, yielding a total score between 0 and 63. 
 
Anxiety Symptoms  

Anxiety symptoms will be evaluated using the Generalized Anxiety Disorder-7 (GAD-7) 
questionnaire, validated for primary care [64]. This instrument contains seven items [65], 
assessing symptoms such as nervousness, worry, restlessness, and irritability over the previous 
14 days. Items are rated on a four-point Likert scale: never (0 points), several days (1 point), half 
of the days (2 points), and almost every day (3 points). Total scores range from 0 to 21, with 
thresholds as follows: no anxiety (0-4 points), mild anxiety symptoms (5-9 points), moderate 
anxiety symptoms (10-14 points), and severe anxiety symptoms (15-21 points). 
 
Perceived Stress  
Perceived stress will be assessed using the Spanish version of the Perceived Stress Scale-14 (PSS-
14) [66,67]. This 14-item instrument utilizes a five-point response scale: never (0 points), almost 
never (1 point), once in a while (2 points), often (3 points), and very often (4 points). The total 
score is calculated by reversing the responses for items 4, 5, 6, 7, 9, 10, and 13 using the conversion 
scale (0=4, 1=3, 2=2, 3=1, 4=0) and then summing the scores of all 14 items. Higher scores reflect 
greater perceived stress. 
 
Disordered Eating 
Disordered eating behaviors will be evaluated using the Sick, Control, One, Fat, Food (SCOFF) 
questionnaire, a concise and validated screening instrument designed to identify individuals at 
risk for eating disorders [68]. The SCOFF consists of five items that capture core dimensions of 
disordered eating, including restrictive intake, preoccupation with weight and shape, and 
compensatory behaviors. Each item requires a binary (yes/no) response, and a score of ≥2 
affirmative answers indicate an increased risk for an eating disorder. 
 
2.6 Data Analysis 
All statistical analyses will be performed using R software (version 4.4.1; R Foundation for 
Statistical Computing, Vienna, Austria) within the RStudio environment (version 2024.9.0.375; 
Posit PBC, Boston, MA, USA). A two-tailed p-value < 0.05 will be considered statistically 
significant. 
 
2.6.1. Descriptive Analysis 
Descriptive statistics will be computed to characterize the sample in terms of sociodemographic, 
anthropometric, lifestyle, and psychological variables, stratified by sex and group assignment 
(intervention vs. control). Continuous variables will be summarized using means and standard 
deviations (or medians and interquartile ranges, when non-normally distributed). Categorical 
variables will be presented as absolute and relative frequencies. 
 
2.6.2. Inferential Analysis 
Baseline comparisons between groups will be conducted to assess initial comparability. 
Categorical variables will be analyzed using chi-square or Fisher’s exact tests; continuous 
variables using t-tests or ANOVA (or non-parametric equivalents such as Mann–Whitney U or 
Kruskal–Wallis tests, as appropriate). 
Longitudinal changes in outcomes from baseline (Day 0) to follow-up (Day 90) will be 
analyzed using repeated-measures models. Specifically: 
- 
For continuous outcomes: linear mixed-effects models will be applied. 

- 
For categorical outcomes: generalized linear mixed-effects models (GLMMs) will be 
used. 
All models will include subject-specific random effects and fixed effects for group, time, 
sex, and relevant interactions. These models will account for intra-individual correlation and 
allow estimation of time-by-group effects. 
 
2.6.3. Analysis of the Primary Outcome (S-Drive Report) 
The primary outcome (functional epigenetic optimization) will be operationalized using two 
metrics derived from the S-Drive report: 
1. Priority count: the number of physiological domains marked as “Priority” (range: 0–12). 
2. Optimization score: the sum of ordinal values assigned to each domain (Not flagged = 0, 
To be considered = 1, Advisable = 2, Priority = 3; total range: 0–36). 
These outcomes will be analyzed as follows: 
- 
Within-group changes (pre–post) will be assessed using the Wilcoxon signed-rank test 
(for paired data). 
- 
Between-group comparisons of change scores will be analyzed using the Mann–Whitney 
U test. 
- 
Additionally, ordinal or linear mixed-effects models will be employed to model 
longitudinal changes, adjusting for baseline values, sociodemographic characteristics, 
and relevant behavioral covariates. 
This analytical strategy accommodates the ordinal nature of the data and enables robust 
estimation of the intervention’s effect on the functional profile. 
 
2.6.4. Analysis of Secondary Outcomes 
Secondary continuous outcomes will be analyzed using linear mixed-effects models, while binary 
or ordinal outcomes (e.g., alcohol use, disordered eating risk, anxiety levels) will be modeled 
using GLMMs or ordinal logistic models, as appropriate. Covariates will be included to adjust 
for potential confounding. 
 
2.6.5. Missing Data 
The extent and patterns of missing data will be explored by group and sex. If missingness is <5%, 
complete-case analysis will be used. If >5% and data are assumed to be missing at random (MAR), 
multiple imputation techniques (e.g., via chained equations) will be applied to minimize potential 
bias and preserve statistical power. 
 
3. Discussion 
This protocol outlines the methodological design of a randomized controlled trial aimed at 
evaluating the functional utility, preliminary clinical efficacy, and acceptability of a biofrequency-
guided nutritional intervention in Spanish adults. In a context marked by the high prevalence of 
NCDs [69] and the limited capacity of healthcare systems to provide personalized preventive 
strategies [9], the development of non-invasive, accessible, and individually tailored tools 
acquires strategic relevance. The proposed approach lies at the intersection of technological 
innovation and precision nutrition, aligning with the emerging paradigm of personalized and 
predictive medicine. 

The biofrequency analysis technology employed in this study is based on the detection 
and digitization of electromagnetic signals emitted by cellular structures, whose vibrational 
frequencies are associated with their molecular composition and functional state [15,16,18,20]. 
The use of the hair follicle as a biological matrix is particularly innovative, due to its high mitotic 
turnover, its role as an epigenetic sensor of the internal and external environment, and its capacity 
to store metabolic and environmental information longitudinally [26]. The underlying hypothesis 
suggests that these vibrational patterns may be indirectly correlated with micronutrient 
imbalances, oxidative stress, food sensitivities, and exposure to environmental contaminants, 
thereby enabling the development of nutritional profiles without the need for invasive or costly 
methods. 
In terms of expected outcomes, it is anticipated that participants in the experimental 
group will demonstrate greater improvement in eating patterns (in terms of adherence to the 
Mediterranean diet), a reduction in disordered eating behaviors, lower levels of stress and 
affective symptoms, and a stronger sense of control and satisfaction with their health status. High 
acceptability of the personalized report generated by biofrequency is also expected, along with a 
positive perception among participants regarding the practical applicability of the 
recommendations. Collectively, these findings would provide preliminary evidence for the 
potential of biofrequency technologies as complementary tools for metabolic risk stratification, 
the design of individualized nutritional interventions, and the promotion of healthy lifestyle 
habits in community settings. 
From a methodological perspective, the protocol is characterized by a rigorous 
experimental design, including sex-stratified randomization, two longitudinal assessment points, 
and the application of mixed-effects statistical models for data analysis. This structure will not 
only allow for the control of potential confounding variables but also facilitate the exploration of 
differentiated effects by sex, time, and group-intervention interaction. Moreover, the inclusion of 
multiple dimensions of health status, anthropometric, behavioral, and psychological, enables a 
comprehensive evaluation of the intervention’s impact, going beyond the traditional reduction of 
clinical trials to unidimensional or exclusively biochemical metrics. The use of validated 
instruments to assess adherence to the Mediterranean diet, affective symptoms, disordered eating 
behaviors, and perceived stress further strengthens the study’s internal validity. 
Nevertheless, this protocol also presents limitations inherent to its preliminary phase. 
Firstly, the S-Drive system and its biofrequency interpretation algorithm have not yet undergone 
substantial clinical validation through independent peer-reviewed studies. While the device is 
certified by regulatory agencies in terms of electromagnetic safety, its diagnostic reliability and 
predictive value still needs to be verified against standardized biomarkers. Additionally, self-
reported anthropometric and behavioral data may introduce information biases, although these 
may be partially mitigated through the use of standardized questionnaires. The 90-day duration, 
while sufficient to detect initial behavioral changes, may be insufficient to assess the sustainability 
of the intervention’s effects. Finally, the exploratory nature of the study, while necessary given 
the novelty of the tool, limits its ability to establish definitive causal relationships beyond the 
proposed hypothesis. 
Despite these limitations, this study constitutes an original and necessary contribution 
toward the integration of emerging technologies in the field of biomarker-based nutrition. Its 
user-centered design, the simultaneous consideration of epigenetic and behavioral variables, and 
its implementation in a naturalistic (non-clinical) setting make it a replicable and scalable model. 

Should positive outcomes be observed in terms of dietary adherence, well-being, or reduction of 
risk indicators, this protocol could serve as a foundation for future research combining objective 
biomarkers, metabolomic analysis, and digital tools for continuous monitoring. 
This protocol represents a structured first step toward the empirical evaluation of 
biofrequency as a supportive tool in biomarker-based nutrition interventions. Its originality lies 
in integrating non-invasive epigenetic reading tools with behavioral strategies to improve 
lifestyle habits, within a controlled experimental framework. Should the study results 
demonstrate significant clinical or psychosocial benefits, it could open new avenues for the use 
of portable bioinformatic technologies in primary care, public health programs, and the field of 
preventive wellness. Overall, the study presents a high-impact hypothesis supported by solid 
theoretical foundations, offering an opportunity to rethink current models of nutritional 
assessment through a systemic, transdisciplinary, and person-centered lens. 
 
4. Conclusions 
The present study represents an innovative step toward integrating emerging biofrequency 
technologies into the field of biomarker-based nutrition. By addressing the interaction between 
micronutrient status, epigenetic signals, and dietary behavior, this research proposes a novel 
framework for understanding and influencing health-related outcomes through non-invasive 
and individualized strategies. The potential of biofrequency analysis to identify nutritional 
imbalances and guide tailored interventions could transform current models of nutritional care, 
particularly in preventive and community health contexts. If proven effective, this approach may 
offer a scalable and accessible solution to promote healthier lifestyles, enhance subjective well-
being, and reduce the burden of nutrition-related chronic diseases. 
 
Author Contributions: Conceptualization, D.M.-S.; J.F.L.-G. validation, D.M.-S.; investigation, 
D.M.-S.; L. F. A., and M. A. C.; writing—original draft preparation, E.C.-V.; writing—review and 
editing, J.F.L.-G., F.Q.-C., D. M. S., L. F. A., and M. A. C.; visualization, J.F.L.-G.; supervision, 
D.M.-S.; project administration, D.M.-S. and J.F.L.-G. All the authors have read and agreed to the 
published version of the manuscript. 
 
Funding: This study will be funded by Cell Wellbeing. The funder of the study had no role in 
study design, data collection, data analysis, data interpretation, or writing of the report. 
 
Data availability statement: Not applicable. 
 
Acknowledgments: The authors would like to thank Cell Wellbeing for providing the 
consumables and technical resources required for the implementation of the biofrequency 
assessments. The authors also acknowledge the collaboration of DZ Procenter (Murcia) for 
facilitating the collection of biological samples and the Grupo Alcaraz Sport Association for 
facilitating the sample and expressing their sincere gratitude to all participants for their time and 
commitment to the study. 
 
Conflicts of interest: Leire Fradua Arteaga reports a commercial relationship with Cell Wellbeing 
as an external collaborator. 
 

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