Digital Biohacking Approach to Dietary Interventions: A Comprehensive Strategy for Healthy and Sustainable Weight Loss
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Data Collection
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- Food consumption data: Our team developed a web-based application (ArMOnIA, https://www.apparmonia.com, accessed on 25 January 2024) which allowed us to retrieve a comprehensive list of all the food consumed over a period of more than 1 year for each participant. This application facilitated the comprehensive tracking of all foods consumed by each participant over a period exceeding one year. Participants could input their dietary intake during the monitoring period directly into the application, with the data stored in a NoSQL database. This list includes detailed information about the macronutrient composition, calorie intake, and food category of each food item. Furthermore, the foods have been categorized into six meals for each day: the main ones (Breakfast, Lunch, and Dinner) and snacks between them.
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- Carbon footprint assessment: We conducted a thorough analysis of the carbon footprint impact associated with each food item. To calculate this impact, we utilized a classification system that corresponds to the My Emission-free Food Carbon Footprint Calculator database (https://myemissions.green/food-carbon-footprint-calculator/, accessed on 25 January 2024). Each food was assigned to the respective food class, enabling us to accurately determine its carbon footprint impact.
- Daily calorie intake and macronutrient composition were obtained from ArMOnIA, where users input their dietary information into a structured NoSQL database.
- Daily weight and Resting Metabolic Rate (RMR) were obtained from the Mi Body Composition Scale 2 [20]. Users weighed themselves each morning before breakfast, with this data accessed via an API integrated into ArMOnIA through an Amazfit Developer Account.
- Daily energy expenditure for Physical Activities (PA) was collected from MiBand 6 [21]. MiBand 6 was worn 24/7 for the duration of the study, allowing it to be recharged for one hour approximately once a week. These activities were accurately recorded and retrieved through dedicated APIs, as outlined in the previous point.
2.3. Digital Biohacking
- Initialization: The algorithm begins by copying a dataset related to dietary habits (diet_week) into another variable (bh). Additionally, empty lists are initialized to store information about replacements (indexes, switches, meals, new, calories_reduction, impact_reduction, quantity), and an empty dictionary (dictionary) is created to track correspondences between replaced and new food items.
- Data iteration: For each unique date-meal combination, the algorithm evaluates the total calorie intake. If it is below a threshold (e.g., 100 kcal), no replacement is made. Otherwise, the food item with the highest caloric intake (excluding condiments and spices) is identified for replacement. The algorithm searches for alternative foods within the same meal category, aiming to reduce the caloric intake by 100–200 kcal while considering the carbon footprint impact.
- Food replacement: Suitable alternative food options are selected randomly from a pre-defined list, ensuring they belong to the same macro-category and have similar nutritional properties but lower caloric content and environmental impact. The replacements are recorded in lists, and a DataFrame summarizing these changes is created.
- Simulation using the PMA: The PMA simulates the effects of the proposed dietary interventions by optimizing parameters to minimize RMSE in GRU models. These parameters are tailored to each participant’s metabolic profile, enabling accurate predictions of weight changes and metabolic outcomes. Techniques such as Walk-Forward Validation (WFV) and Walk-Forward Simulation (WFS) are employed to validate the model’s predictions.
- Handling missing data: To maintain data integrity, missing values are addressed using methods from previous studies, ensuring that the dataset remains robust and reliable for simulation purposes.
2.4. Validation
2.4.1. Simulations
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- Firstly, it evaluates whether there is a statistically significant difference between the mean weight changes of the two distributions. If the p-value resulting from the t-test is below the chosen significance level () we can conclude that there is a significant difference between the distributions.
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- Secondly, the t-test also allows us to investigate the directionality of the difference. By checking the sign of the t-statistic, we can determine if the simulated weight changes tend to be lower (statistically negative) or higher than the actual weight changes. A negative t-statistic indicates that, on average, the simulated weight changes are lower than the actual weight changes.
2.4.2. Real Data
2.5. Computational Requirements and Python Libraries
3. Results
3.1. Simulations
3.2. Linear Regression Analysis
4. Discussion
5. Limitations and Future Trends
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Parameter | Average Daily Variation | Decrease Percentage |
---|---|---|
Average daily intake reduction | −236.78 ± 50.65 kcal | 14.24 ± 3.1% |
Average daily carbon footprint impact reduction | 15.12 ± 1.13% |
Participant | Actual Delta Weight | Simulated Delta Weight | Delta Weight Loss 2 | p-Value 3 | t-Statistic 4 |
---|---|---|---|---|---|
0 | 0.17 ± 0.54 kg | −0.29 ± 0.64 kg | −0.48 ± 0.54 kg | −5.81 | |
1 | −0.02 ± 0.86 kg | −0.70 ± 0.56 kg | −0.68 ± 0.77 kg | −6.24 | |
2 | 0.82 ± 0.72 kg | 0.12 ± 0.92 kg | −0.70 ±1.06 kg | −4.64 | |
3 | 0.40 ± 0.44 kg | −0.12 ± 0.87 kg | −0.52 ± 1.01 kg | −3.62 |
Participant | p-Value 2 | Slope (Kg/Kcal) 3 | 4 | Pearson Coefficient 5 |
---|---|---|---|---|
0 | −0.0008 | 0.25 | −0.5 | |
1 | −0.0003 | 0.11 | −0.33 | |
2 | −0.0009 | 0.14 | −0.37 | |
3 | −0.0007 | 0.38 | −0.62 |
Generic Diets | Digital Biohacking | |
---|---|---|
Personalization | Limited | Highly Customized |
User-specific data consideration | Minimal | Comprehensive |
Incorporation of taste preferences | Limited | Extensive |
Sustainability (emission reduction) | Not addressed | Addressed |
Long-term adherence potential | Challenging | Promising |
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Abeltino, A.; Bianchetti, G.; Serantoni, C.; Riente, A.; De Spirito, M.; Maulucci, G. Digital Biohacking Approach to Dietary Interventions: A Comprehensive Strategy for Healthy and Sustainable Weight Loss. Nutrients 2024, 16, 2021. https://doi.org/10.3390/nu16132021
Abeltino A, Bianchetti G, Serantoni C, Riente A, De Spirito M, Maulucci G. Digital Biohacking Approach to Dietary Interventions: A Comprehensive Strategy for Healthy and Sustainable Weight Loss. Nutrients. 2024; 16(13):2021. https://doi.org/10.3390/nu16132021
Chicago/Turabian StyleAbeltino, Alessio, Giada Bianchetti, Cassandra Serantoni, Alessia Riente, Marco De Spirito, and Giuseppe Maulucci. 2024. "Digital Biohacking Approach to Dietary Interventions: A Comprehensive Strategy for Healthy and Sustainable Weight Loss" Nutrients 16, no. 13: 2021. https://doi.org/10.3390/nu16132021