Table 1 Investigated characteristics of user documented food consumption data related to scientific relevance and extracted information (n = 176)

Dietary assessment method

The dietary assessment method used by the app for collecting food consumption data

Food diary (166), No information (8), Incidental food logging (2)

Food consumption inputs^a

The type of food consumption data inputs supported

Generic input (91), Custom input (74), Labeled or packaged food products (44), Barcodes (scanned) (39), Water (30), Food images (21), Recipes (20), Restaurant dishes (19), Nutrient/Energy input (19), Diet plans (9), Voice input (4), Food log reminder (2), No information (2)

Precompiled food database

Whether the food consumption logging is supported by selecting foods from precompiled databases

Yes (93), No (83)

Food database compilation

The official food database the apps use for calculating nutrition and energy estimations

USDA (7)

User compiled databases^a

The type of user compiled databases the app generates for logging references

Favorite eaten foods (29), Recently eaten foods input (15), Frequently eaten foods (14)

Nutrient/Energy estimation^a

The unit or level of detail nutrient and energy consumption is estimated

Calorie (94), Macronutrients (78), Carbohydrates (49), Protein (49),

Food score (26), Micronutrients (25), No information (20)

Portion size

Whether the app collect portion size estimations

Yes (96), No information (57), No (23)

Method portion size^a

The methods that was used to collect portion size estimations

Standard serving sizes (59), Weight estimation (26), Volume estimation (9), Manual energy/nutrient input (5), Custom serving sizes (4)

Location

Whether the app collects information about where the consumptions took place

No (162), Yes (14)

Occasion

Whether the app collects information about the occasion or event of the consumptions

No (175), Yes (1)

Contextual data^a

Data parameters the app collects about users other than food intake data

Motivation (107): Nutrition goals (59), Diet plans (38), Weight goals (32), Food preferences (29), Fitness goals (10), Fitness plan (10), Emotions (9), Health goals (7), Hydration goals (7), Stress level (5), Muscle building goals (3), Sleep goal (3),

Health (108): Body weight (76), BMI (22), Medications (11), Symptoms (12), Body composition (11), Body measurements (9), Body image (8), Blood sugar (8), Blood pressure (8), Heart rate (7), BMR (7), Cholesterol (4), Physical fitness (4), Oxygen saturation (2)

Physical activity (90): Exercise (59), Activity type (29), Steps (19), Activity level (14), Sleep (13)

Uncategorized (34): Posts (27), Notes (22), Comments (6), Lifelogging data (3)

Interventional influences type^a

The type of interventional influences the app contains that might have an direct influence on the recorded food intake behavior

Reminders/Notifications (54), Advices (53), Social support (23), Connected users (21), Coaching (19), Challenges (17), Personal feedback (14), Rewards (6), Encouragements (6), Allowance badge (4)

Sensors type^a

The type of own external devices the app supports (exclusive devices of third party partner apps or health and fitness sensors)

Pedometer (4), Heart rate monitor (3), Accelerometer (3)

Third party health and fitness trackers^a

The third party health and fitness trackers the app connects to

Fitbit (19), UP® – Smart Coach for Health (10), Health Mate - Steps tracker & Life coach (10), Misfit (6), Garmin Connect™ Mobile (4), Record by Under Armour, connects with UA HealthBox (2), Samsung Gear (1)

Aggregators^a

The third party data aggregators the app connects to

HealthKit (31), GoogleFit (17), Healthgraph (5), S Health (5), Human Api (3), Validic (2), Fitnesssyncer (2), HealthVault (1)