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Core concepts

This page defines the concepts used across product, technical and operational discussions.

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User

A user is the owner of product data. The user record represents identity inside Moveat and is independent from any external channel. A user may authenticate through local credentials or Google OAuth. A user may later link channels such as WhatsApp or Telegram.

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Profile

A profile stores the user’s physical and preference data:

• Birth date.
• Sex.
• Height.
• Current weight snapshot.
• Timezone.
• Locale.
• Unit system.

Profile data is required for calorie target calculation and display conversion.

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Goal

A goal describes what the user wants to achieve:

• Fat loss.
• Muscle gain.
• Maintenance.

Goals also include activity level, training frequency and target weight.

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Nutrition settings

Nutrition settings define how daily calorie targets are determined.

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Meal entry

A meal entry is a user event that represents food consumption. It can be created from web input or from Agent interpretation. A meal entry may contain multiple items, each with estimated calories and macros.

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Daily nutrition summary

A daily summary is a rollup of meal entries for a user and local day. It exists to make dashboards and progress reads cheap. The summary should be derived from meal entries and must not become a substitute for canonical meal logs.

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Weight log

A weight log records the user’s body weight at a point in time. Platform stores the canonical value in kilograms and returns display values according to the user’s unit system.

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Workout session

A workout session records training performed by the user. It can contain exercises and sets with reps, load and completion details.

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Channel

A channel is a way to contact or receive messages from a user, such as WhatsApp or Telegram. Channels are intentionally separate from identity. This allows a user to have multiple contact methods without polluting the core user table.

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Coaching context

Coaching context is structured information about the user that helps future recommendations feel personalized. It may include motivation, adherence patterns, tone preferences, limitations and behavioral notes. This data supports better guidance but should not be mixed into basic account/session responses.