{"id":1004,"date":"2025-11-13T16:53:29","date_gmt":"2025-11-13T16:53:29","guid":{"rendered":"https:\/\/igormatias.com\/?p=1004"},"modified":"2026-02-02T12:57:32","modified_gmt":"2026-02-02T12:57:32","slug":"model-twin-randomization-motr-for-estimating-the-recurring-individual-treatment-effect","status":"publish","type":"post","link":"https:\/\/igormatias.com\/pt\/2025\/11\/13\/model-twin-randomization-motr-for-estimating-the-recurring-individual-treatment-effect\/","title":{"rendered":"Model-Twin Randomization (MoTR) for Estimating the Recurring Individual Treatment Effect"},"content":{"rendered":"

Abstract: Temporally dense single-person \u201csmall data\u201d have become widely available thanks to mobile apps and wearable sensors. Many caregivers and self-trackers want to use these data to help a specific person change their behavior to achieve desired health outcomes. Ideally, this involves discerning possible causes from correlations using that person’s own observational time series data. In this paper, we estimate within-individual average treatment effects of physical activity on sleep duration. We introduce the model-twin randomization (MoTR; \u201cmotor\u201d) method for analyzing an individual’s intensive longitudinal data. Formally, MoTR is an application of the g-formula (i.e., standardization, back-door adjustment) under serial interference. It estimates stable recurring individual treatment effects, as is done in n-of-1 trials and single case experimental designs. We compare our approach to standard methods (with possible confounding) to show how to use causal inference to make better personalized recommendations for health behavior change, and analyze up to almost eight years of the authors’ own Fitbit steps and sleep data.<\/span><\/p>\n

Eric J. Daza1<\/sup>, Igor Matias2<\/sup>, Logan Schneiderl3<\/sup><\/p>\n

1<\/sup>Stats-of-1, Evidation, USA, 2<\/sup>University of Geneva, Geneva, Switzerland, 3<\/sup>Stanford Medicine, Alphabet, USA<\/p>\n

In Statistics in Medicine, Volume 44, November 2025.<\/span><\/p>\n

Article: here<\/a><\/p>\n\n\n

<\/p>","protected":false},"excerpt":{"rendered":"

Abstract: Ego depletion refers to the idea that self-control and decision-making abilities become depleted or diminished after engaging in prolonged or demanding cognitive tasks. When conducting research with humans, states such as stress are usually asked directly to the participants by using ecological momentary assessment (EMA). However, as those answers are self-reported, they are prone to bias due to the lack of user commitment or effort to assess the stress levels the best they can. This paper investigates the relationship between ego depletion and stress level reporting, specifically focusing on the bias in self-reported stress levels. The hypothesis tested in this study suggests that higher levels of ego depletion lead to greater bias in self-reported stress levels. Data collected by Berrocal et al. using EMA were analyzed to examine this hypothesis. The dataset included self-reporting stress levels by individuals being assessed and stress level reports from their peers. Data was collected using a mobile app, incorporating passive smartphone usage data alongside EMA responses. The hypothesis was tested and studied employing artificial intelligence algorithms. The results partially confirmed the initial hypothesis, revealing that lower ego depletion was associated with reduced bias in stress level reporting. Notably, when analyzing data from working days, the morning period demonstrated the least bias compared to the afternoon. These findings suggest that individuals have higher self-capacity and willingness to provide accurate stress level assessments during the morning hours. Challenges encountered in the research included limitations related to the holidays considered and potential confounders such as flexible time schedules or post-lunch sleepiness. This paper releases its data publicly, allowing for further examination and replication of the findings. Future research is encouraged to expand upon these conclusions.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[12],"tags":[],"class_list":["post-1004","post","type-post","status-publish","format-standard","hentry","category-journal"],"gutentor_comment":0,"yoast_head":"\nModel-Twin Randomization (MoTR) for Estimating the Recurring Individual Treatment Effect<\/title>\n<meta name=\"description\" content=\"Abstract: Ego depletion refers to the idea that self-control and decision-making abilities become depleted or...\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/igormatias.com\/pt\/2025\/11\/13\/model-twin-randomization-motr-for-estimating-the-recurring-individual-treatment-effect\/\" \/>\n<meta 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