About The COTIDIANA Dataset is a holistic, multimodal, and multidimensional dataset that captures three dimensions in which patients are frequently impacted by Rheumatic and Musculoskeletal Diseases (RMDs), namely, (a) mobility and physical activity, due to joint stiffness, fatigue, or pain; (b) finger dexterity, due to finger joint stiffness or pain; or (c) mental health (anxiety/depression level), due to the functional impairments or pain. We release this dataset to facilitate research in rheumatology, while contributing to the characterisation of RMD patients using smartphone-based sensor and log data. We gathered smartphone and self-reported data from 31 patients with RMDs and 28 age-matched controls, including (i) inertial sensors, (ii) keyboard metrics, (iii) communication logs, and (iv) reference tests/scales. We provide both raw and (pre-)processed dataset versions, to enable researchers or developers to use their own methods or benefit from the computed variables. Additional materials containing (a) illustrations, (b) visualization charts, and (c) variable descriptions can be consulted through this link. Citing When using this dataset, please cite P. Matias, R. Araújo, R. Graça, A. R. Henriques, D. Belo, M. Valada, N. N. Lotfi, E. Frazão Mateus, H. Radner, A. M. Rodrigues, P. Studenic, F. Nunes (2024) COTIDIANA Dataset – Smartphone-Collected Data on the Mobility, Finger Dexterity, and Mental Health of People With Rheumatic and Musculoskeletal Diseases, in IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 11, pp. 6538-6547, DOI: 10.1109/JBHI.2024.3456069. Data structure The data is organised by participant and includes: Inertial Sensor Data, retrieved from accelerometer, gyroscope, and magnetometer sensors collected during three distinct walking exercises (Timed Up and Go, Daily Living Activity, and Simple Walk); Keyboard Dynamic Metrics, collecting 38 raw variables related with the keyboard typing performance while writing 10 sentences (e.g., number of errors, words-per-minute); Communication Logs, e.g., with weekly averages of number of calls and SMS sent or received; Validated Clinical Questionnaires, such as general Health (EQ-5D-5L), Multidimensional Health Assessment Questionnaire (MDHAQ), Hospital Anxiety and Depression Scale (HADS); Validated Functional Tests, including time to perform the Timed Up and Go (TUG) and Moberg Pick-Up Test (fine motor skills); Characterization Questionnaire, containing sociodemographic and clinical information. cotidiana_dataset├── info│ ├── codebook.xlsx│ ├── missings_report.csv├── processed│ ├── com_calls│ │ └── features.csv│ ├── com_sms│ │ └── features.csv│ ├── full│ │ └── cotidiana_dataset.csv│ ├── hd_kst│ │ └── features.csv│ ├── hd_mpu│ │ └── features.csv│ ├── mob_dla│ │ └── features.csv│ ├── mob_sw│ │ └── features.csv│ ├── mob_tug│ │ └── features.csv│ ├── quest│ └── features.csv├── raw│ ├── com_calls│ │ └── p[0-58]│ │ └── calls_log.csv│ ├── com_sms│ │ └── p[0-58]│ │ └── sms_log.csv│ ├── hd_kst│ │ └── p[0-58]│ │ ├── imu│ │ │ ├── Accelerometer_s[0-9].csv│ │ │ ├── Gyroscope_s[0-9].csv│ │ │ └── Magnetometer_s[0-9].csv│ │ └── keyboard│ │ └── kb_metrics.csv│ ├── hd_mpu│ │ └── p[0-58]│ │ └── mpu_time.csv│ ├── mob_dla│ │ └── p[0-58]│ │ ├── bag│ │ │ ├── Accelerometer.csv│ │ │ ├── Gyroscope.csv│ │ │ ├── Magnetometer.csv│ │ │ └── Annotation.csv│ │ └── pocket│ │ ├── Accelerometer.csv│ │ ├── Gyroscope.csv│ │ ├── Magnetometer.csv│ │ └── Annotation.csv│ ├── mob_sw│ │ └── p[0-58]│ │ ├── ann│ │ │ └── walk_ann.csv│ │ ├── bag│ │ │ ├── Accelerometer.csv│ │ │ ├── Gyroscope.csv│ │ │ ├── Magnetometer.csv│ │ │ └── Annotation.csv│ │ └── pocket│ │ ├── Accelerometer.csv│ │ ├── Gyroscope.csv│ │ ├── Magnetometer.csv│ │ └── Annotation.csv│ ├── mob_tug│ │ └── p[0-58]│ │ ├── bag│ │ │ ├── Accelerometer.csv│ │ │ ├── Gyroscope.csv│ │ │ ├── Magnetometer.csv│ │ │ └── Annotation.csv│ │ └── pocket│ │ ├── Accelerometer.csv│ │ ├── Gyroscope.csv│ │ ├── Magnetometer.csv│ │ └── Annotation.csv│ ├── quest│ └── features.csv