% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/scores_nt_fitbit.R
\name{compute_fitbit_activity_week}
\alias{compute_fitbit_activity_week}
\title{Generates weekly activity summaries}
\usage{
compute_fitbit_activity_week(df, filter_expr = qc_600min & qc_steps)
}
\arguments{
\item{df}{tbl. Daily-level Fitbit activity dataset (standard or extended),
typically \code{fitbit_ss_activity_day} or \code{fitbit_ss_ext_activity_day}.}

\item{filter_expr}{expression. Logical filtering condition applied to the
input dataset before summarization (e.g., QC thresholds).
which retains only days with at least 600 valid minutes and passing step
count quality checks.}
}
\value{
tbl. Weekly activity dataset containing:
\itemize{
\item Week identifiers (\code{wk}, \code{wk_type})
\item Day counts (\code{n_day}, \code{n_wkdy}, \code{n_wknd})
\item Activity summary metrics (steps, METs, intensity minutes, heart rate)
\item Weekly quality control flag (\code{qc_wk})
}
}
\description{
Generates weekly activity summaries from daily Fitbit activity data.

This wrapper function applies a standardized weekly aggregation pipeline
using \code{compute_fitbit_weekly_summary()} and the \code{summarize_activity()}
function to compute weekly activity metrics at the participant-session level.

Only days passing minimum quality control thresholds (\code{qc_600min} and
\code{qc_steps}) are included in the weekly aggregation.
}
\details{
This function standardizes weekly activity computation across datasets by
enforcing consistent filtering, aggregation, and output structure.
}
\note{
\itemize{
\item \code{df}: fitbit_ss_activity_day or fitbit_ss_ext_activity_day
(daily Fitbit activity dataset with QC flags)
}
}
