Search results

• 2024

  Deciphering seasonal depression variations and interplays between weather changes, physical activity, and depression severity in real-world settings: Learnings from RADAR-MDD longitudinal mobile health study

  Zhang, Y., Folarin, A. A., Ranjan, Y., Cummins, N., Rashid, Z., Conde, P., Stewart, C., Sun, S., Vairavan, S., Matcham, F., Oetzmann, C., Siddi, S., Lamers, F., Simblett, S., Wykes, T., Mohr, D. C., Haro, J. M., Penninx, B. W. J. H., Narayan, V. A. & Hotopf, M. & 3 others, Dobson, R. J. B., Pratap, A. & consortium, R.-C., 17 Apr 2024.

  Research output: Working paper/Preprint › Preprint

  File
  98 Downloads (Pure)
• 2023

  Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing

  Zhang, Y., Folarin, A. A., Sun, S., Cummins, N., Ranjan, Y., Rashid, Z., Stewart, C., Conde, P., Sankesara, H., Laiou, P., Matcham, F., White, K. M., Oetzmann, C., Lamers, F., Siddi, S., Simblett, S., Vairavan, S., Myin-Germeys, I., Mohr, D. C. & Wykes, T. & 7 others, Haro, J. M., Annas, P., Penninx, B. W., Narayan, V. A., Hotopf, M., Dobson, R. J. & consortium, R.-C., 5 Dec 2023.

  Research output: Working paper/Preprint › Preprint

  File
  70 Downloads (Pure)
• 2022

  Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings

  Zhang, Y., Folarin, A. A., Sun, S., Cummins, N., Vairavan, S., Qian, L., Ranjan, Y., Rashid, Z., Conde, P., Stewart, C., Laiou, P., Sankesara, H., Matcham, F., White, K. M., Oetzmann, C., Ivan, A., Lamers, F., Siddi, S., Simblett, S. & Rintala, A. & 10 others, Mohr, D. C., Myin-Germeys, I., Wykes, T., Haro, J. M., Penninx, B. W., Narayan, V. A., Annas, P., Hotopf, M., Dobson, R. J. & consortium, R.-C., 29 Jan 2022.

  Research output: Working paper/Preprint › Preprint

  File

  • Characteristics 100%
  • Daily Life 100%
  • Dataset 66%
  • Wearable Device 50%
  • Wearable Sensor 33%
  88 Downloads (Pure)
• 2021

  Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study

  Zhang, Y., Folarin, A. A., Sun, S., Cummins, N., Ranjan, Y., Rashid, Z., Conde, P., Stewart, C., Laiou, P., Matcham, F., Oetzmann, C., Lamers, F., Siddi, S., Simblett, S., Rintala, A., Mohr, D. C., Myin-Germeys, I., Wykes, T., Haro, J. M. & Pennix, B. W. & 4 others, Narayan, V. A., Annas, P., Hotopf, M. & Dobson, R. J., 26 Apr 2021.

  Research output: Working paper/Preprint › Preprint

  File

  • Bluetooth 100%
  • Depressive Symptom 100%
  • Bluetooth Device 100%
  • Bayesian 50%
  • Linear Regression Model 50%
  58 Downloads (Pure)