First considerations on Wakamola questionnaires and HORUS sensorisation

Wakamola is a chatbot designed to gather information about health habits. In its current status, the chatbot is available on Telegram messaging platform (Android, IOs and Web). The chat is composed by a set of questionnaires and a few feedback/gamification options to encourage the engagement as well as promote healthier habits. A live demo can be accessed in the following link:

Current questionnaires


The Personal section includes 20 questions. The clinicians define these questions according to the pilot, the current list is centred in a breast cancer study.

  1. Are you a participant in the pilot? (Pilot specific)
  2. What code is assigned to you in the pilot? (Pilot specific)
  3. How much do you weigh? (in kg)
  4. How tall are you? (in cms)
  5. On average, how many hours do you sleep?
  6. On average, how many cigarettes do you consume daily? (0 if you are not a smoker)
  7. Have you ever received a diagnosis of hypertension or taken medication for it?
  8. Have you ever been diagnosed with diabetes or taken medication for that?
  9. Have you ever received a diagnosis of high cholesterol or taken medication for it?
  10. Have you ever received a diagnosis of cardiovascular disease or taken medication for it?
  11. What gender do you identify with?
  12. How old are you?
  13. What is your level of education?
  14. What is your marital status?
  15. How many people are at home?
  16. Are you a student?
  17. Are you a worker?
  18. Do you follow any kind of diet?
  19. Have you changed your diet in the past year?
  20. Can you tell me your zip code?
  21. Have you taken antibiotics in the last week? (Pilot specific)


At the diet section we ask for the frequency of consumption for a series of food items. The questionnaire has two section, the first is for daily consumption while the second if for weekly consumption. Questions in the Diet section are adapted from the “Short questionnaire on frequency of dietary intake” [1]. In total, 51 questions regarding food types (items) and consumption frequencies are included. Diet question responses (items) are scored based on the “Spanish diet quality according to the healthy eating index,” with items’ scores from 1 to 10 (the higher the score of the item, the less healthy its consumption) [2].

Daily consumption

  1. Milk 🍶
  2. Yogurt
  3. Breakfast cereals
  4. Biscuits without filling or covering 🍘
  5. Bread in sandwich or with meals 🍞
  6. Wholemeal bread
  7. Olive oil
  8. Other oils: sunflower, soybean, etc.
  9. Butter

Weekly consumption

  1. Chocolate: bar, chocolates, bars or others 🍫
  2. Biscuits with chocolate or cream, with filling 🍪
  3. Cupcakes, sponge cake… 🍰
  4. Ensaimada, donut, croissant… 🍩
  5. Salad: lettuce, tomato, escalora, gazpacho… 🍅
  6. Green beans, chard or spinach 🥦
  7. Garnish vegetables such as eggplant, mushrooms… or vegetable creams 🍆
  8. Potatoes (not fried) 🥔
  9. Legume: lentils, chickpeas, beans, soybeans 🍲
  10. White rice, paella 🍚
  11. Pasta: noodles, macaroni, spaghetti 🍝
  12. Noodle or rice soups 🍵
  13. Eggs 🥚
  14. Chicken or turkey 🍗
  15. Pork, lamb, steak… 🐖
  16. Minced meat, sausage, hamburger, veal, sausages 🍔
  17. White fish: hake, grouper… 🎣
  18. Blue fish: Sardines, tuna, salmon… :Fish:
  19. Seafood: mussels, prawns, prawns, squid 🍤
  20. Croquettes, empanadillas, pizza 🍕
  21. French fries
  22. Serrano ham, cold meat (salchichon, chorizo, salami, fuet…) 🍗
  23. Cooked ham type york
  24. White or fresh cheese or low in calories
  25. Other cheeses: cured, semi-cured, creamy
  26. Citrus fruit: orange, mandarin: 🍊 🍋
  27. Other fruits: apple, pear, peach, banana… 🍐 🍎 🍌
  28. Canned or syrupy fruit
  29. Natural fruit juices 🍹
  30. Commercial fruit juices
  31. Nuts: peanuts, hazelnuts, almonds… 🌰 🌰 🌰
  32. Dairy desserts: custard, flan, cottage cheese 🍮
  33. Cream or chocolate cakes 🍰
  34. Bags of snacks type (snack) chips
  35. Confectionery: gummy jellies, candy: 🍭 🍬
  36. Ice creams 🍨
  37. Low-calorie drinks Coca-Cola Light or Zero…
  38. Sugared drinks such as Coca-Cola, Fanta or other soft drinks
  39. Wine, Sangria 🍷
  40. Beer without alcohol 🍻
  41. Beer 🍻
  42. Distilled drinks: whiskey, gin, cognac… 🍸
  43. Fermented foods: Kimchi, Kombucha, Kefir, Sauerkraut
  44. Food supplements
  45. Probiotics: insulin, artichokes, oats, honey, soya…
  46. Vitamins 💊


Physical activity

In the Activity section with 7 questions, the short form of the International Physical Activity Questionnaire (IPAQ) [3] has been applied to define the chatbot’s questions and scoring. This IPAQ version is recommended, especially when the object of investigation is population monitoring

  1. During the past 7 days, how many days did you engage in vigorous physical activities such as lifting heavy objects, digging, aerobics 🏃, or fast cycling 🚴 for more than 10 minutes?
  2. How many minutes did you spend in one of those days on those vigorous activities? 🕠
  3. During the last 7 days, how many days did you do moderate physical activities for more than 10 minutes such as carrying light objects, pedaling a bicycle 🚴 at a regular pace, or playing tennis doubles 🎾?
  4. How many minutes did you dedicate in one of those days to those moderate activities?
  5. During the last 7 days, how many days did you walk at work, on transfers, or for recreation at least 10 continuous minutes? 🏃
  6. On one of those days, how many minutes do you normally walk?
  7. During the last 7 days, how many hours in total do you usually sit?

Other questionnaires

Other sets of questions can be implemented in Wakamola if the evaluation team and the expert stakeholders consider they are relevant for HORUS, such as the Perceived Stress Scale (PSS), the Trait Meta-Mood Scale 24 (TMMS-24) or Rosenberg Self-Esteem Scale (RSE).


Despite Wakamola in its current state it’s not able to use the smartphone sensors, a reimplementation to a native application it’s possible. At BDSLab, we have experience using sensors to perform quality of life monitoring with the app Lalaby ( We have published our experience in two research papers [4], [5].

There is the list of sensors we have experience working with, what information is sent to the server/database and the possibilities for HORUS. It is crucial to specify that all computation to transform sensitive information into useful data for the project will occur within the device prior to its recording in the server/database.

Sensor/Source Current use Possibilities for HORUS
Location Distance covered during the day in kilometers Compute (in device) if the participant has been near a landmark of the project (Parks, Healthy neighbourhoods, other relevant places from the WP2).


Store in the database the number of times/amount of time near a landmark or a total score if landmarks are categorized.


Information is aggregated daily.

Accelerometer Quantity of Movement Measure (total or differential) activity of the participant. The measurements are performed several times a day, then aggregated and sent to the server on daily basis.


Measure adherence of the paticipant to the pilot by measuring the amount of movement between days.

Step sensor Number of steps Measure the number of daily steps performed by the participants. This measurement can also be compared to estimate the adherence.
Other apps monitoring Number of calls and use Internet (in MB) Measure (total of differential) social activity through the number of incoming and outcoming phone calls. Measure (total of differential) social activity through data usage (related to media consumption) daily.


Screen time Overall screen time of the device and screen time for the monitoring app Measure total exposure to the smartphone daily.
Light sensor Exposure to light Measure the amount of light received in Luxes (lumens/m2). Exposure to daylight can be estimated through the measure in a daily basis.


Microphone Frequency and Intensity of the sound Store frequency information about the environment: intensity and dominating frequency. Used to measure exposure to the conversations or noise.





The use of questionnaires and sensors within the same application can facilitate the evaluation of the pilots as well as its engagement. First, because having only one application where the data is requested results in only one point of friction. The use of sensorization is also fundamental for the success of the intervention, since the use of this technology enable us to record more information than a set of questionnaires at specific timepoints and does not require the user to perform any additional action, reducing even more the friction to participate.

The use of location is fundamental for the project. Without the use of location, we’ll end up with two unlinked sources of information. The mapping for each city produced at WP2 and individual level information collected at the pilots. It is crucial to take advantage of this opportunity and link these two sources using the location. Studies using these liked sources are novel and have a huge impact societal and scientifically.

Finally, the respect for the participants privacy is primordial in this study. This is why sensitive data such as the location will be transformed in useful non-identifiable data for the project. This sensitive data will computed locally in the participant’s phone and only the anonymous version of this information will be sent to the server to be added to the database. In addition, the server will completely comply with the GDPR legislation.



[1]         I. Trinidad Rodríguez, J. Fernández Ballart, G. Cucó Pastor, E. Biarnés Jordà, and V. Arija Val, “Validación de un cuestionario de frecuencia de consumo alimentario corto: Reproducibilidad y validez,” Nutricion Hospitalaria, vol. 23, no. 3, pp. 242–252, 2008.

[2]         A. I. Norte Navarro and R. Ortiz Moncada, “Calidad de la dieta española según el índice de alimentación saludable,” Nutricion Hospitalaria, vol. 26, no. 2, pp. 330–336, 2011.

[3]         S. C. Mantilla Toloza and A. Gómez-Conesa, “El Cuestionario Internacional de Actividad Física. Un instrumento adecuado en el seguimiento de la actividad física poblacional,” Revista Iberoamericana de Fisioterapia y Kinesiología, vol. 10, no. 1, pp. 48–52, 2007.

[4]         S. Asensio-Cuesta, Á. Sánchez-García, J. A. Conejero, C. Saez, A. Rivero-Rodriguez, and J. M. García-Gómez, “Smartphone Sensors for Monitoring Cancer-Related Quality of Life: App Design, EORTC QLQ-C30 Mapping and Feasibility Study in Healthy Subjects,” International Journal of Environmental Research and Public Health, vol. 16, no. 3, 2019.

[5]         S. Asensio-Cuesta et al., “Testing Lung Cancer Patients’ and Oncologists’ Experience with the Lalaby App for Monitoring the Quality of Life through Mobile Sensors and Integrated Questionnaires,” International Journal of Human–Computer Interaction, pp. 1–11, Oct. 2022.