Minimum sampling recommendations for applied ambulatory assessment

Ambulatory assessment is popular in research settings for its ability to assess real-world functioning. It is useful for estimating an individual's typical level of a behavior (individual mean), how (un)stable that behavior is (individual standard deviation), how behaviors associate with others or specific contexts (within-person correlation), and shifts in those statistics that might signal an important change in functioning (e.g., early warning signal). However, many of the methodological advances have not made the jump from the lab to clinical practice. Effective use of ambulatory assessment in applied settings to understand functioning and guide potential interventions requires development and application of psychometric standards for N = 1 assessments. We conducted a simulation study to determine how many assessments are necessary to achieve sufficiently reliable (i.e., precise and stable) estimates of an individual's mean and standard deviation on a single variable as well as the correlation between two variables. To ensure the ecological validity of the simulation conditions, we used real time series data from a large sample that included psychiatric patients and nonpatients (capturing realistic levels of autocorrelation and skewness). We found that the minimum number of assessments depends on the statistic of interest and the temporal characteristics of the variable of interest. Individual means can be estimated reliably with a reasonably small number of observations under most conditions, but adequately precise and stable individual correlations require more assessments than may be achievable in many applied settings. Implications of these results for the potential of applied ambulatory assessment in clinical practice are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).