Dementia is a progressive condition that affects cognitive and functional abilities. There is a need for reliable and continuous health monitoring of People Living with Dementia (PLWD) to improve their quality of life and support their independent living. Healthcare services often focus on addressing and treating already established health conditions that affect PLWD. Managing these conditions continuously can inform better decision-making earlier for higher-quality care management for PLWD. The Technology Integrated Health Management (TIHM) project developed a new digital platform to routinely collect longitudinal observational and measurement data within the home and apply machine learning and analytical models for the detection and prediction of adverse health events affecting the well-being of PLWD. This work describes the TIHM dataset collected during the second phase (i.e., feasibility study) of the TIHM project. The data was collected from homes of 56 PLWD and associated with events and clinical observations (daily activity, physiological monitoring, and labels for health-related conditions). The study recorded an average of 50 days of data per participant, totalling 2803 days. We have provided raw data and guidelines on how to access, visualise, manipulate and predict health-related events within the dataset, available on the Github repository. The Jupyter Notebooks have been developed using Python 3.9. The dataset is provided for research and patient benefit purposes. Please acknowledge the Surrey and Borders Partnership NHS Foundation Trust in any publication or use of this dataset.

This project was supported by a grant from the Office of Life Sciences at the Department of Health UK and NHS England, grant number (TS/N009894/1). The dataset is provided for research and cannot be used for commercial purposes. Please acknowledge the Surrey and Borders Partnership NHS Foundation Trust in any publication or use of this dataset.