Pharmaceutical combinations and methods for using such combinations to treat depression are disclosed. In various embodiments (he pharmaceutical combinations include combinations of omega-3 fatty acids, pharmacological sleep agents, and non-pharmacological sleep therapies, and may include other ingredients such as antidepressants. The present invention relates pharmaceutical combinations and methods for their use to treat depression.

Pharmaceutical combinations and methods for using such combinations to treat depression are disclosed. In various embodiments (he pharmaceutical combinations include combinations of omega-3 fatty acids, pharmacological sleep agents, and non-pharmacological sleep therapies, and may include other ingredients such as antidepressants. The present invention relates pharmaceutical combinations and methods for their use to treat depression.

A wearable health and lifestyle device including at least a measurement module configured to be worn by a user in at least a first wearing position, the measurement module comprising a 3-axis accelerometer unit configured to provide acceleration data and inclination data, a temperature measurement unit configured to provide temperature data, a light radiation measurement unit configured to provide light radiation data, said light radiation measurement unit comprising at least one multi-spectral sensor configured to measure wavelength bands over the range 290 nm to 1150 nm, a storage module configured to receive and store said acceleration data, said inclination data, said temperature data and said light radiation data, and an analysis module configured to analyze a data set comprising acceleration data, inclination data, temperature data and light radiation data.

A wearable health and lifestyle device including at least a measurement module configured to be worn by a user in at least a first wearing position, the measurement module comprising a 3-axis accelerometer unit configured to provide acceleration data and inclination data, a temperature measurement unit configured to provide temperature data, a light radiation measurement unit configured to provide light radiation data, said light radiation measurement unit comprising at least one multi-spectral sensor configured to measure wavelength bands over the range 290 nm to 1150 nm, a storage module configured to receive and store said acceleration data, said inclination data, said temperature data and said light radiation data, and an analysis module configured to analyze a data set comprising acceleration data, inclination data, temperature data and light radiation data.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.

A therapeutic nesting apparatus is disclosed for a treating sensory-related, neuropsychological conditions by altering neural response to reduce sensory input and improve biological, neurological, and psychological performance. The apparatus provides for a body to lie in a natural position creating a cocoon-like sensation that allows the brain to alter neural response. The nesting apparatus allows the user's head to be relaxed for open airways and minimize strain on the neck. The lower portion of the nesting apparatus may support the legs in a gently bent position to allow the feet to be on or off the ground depending on the user's preference. The sides of the nesting apparatus are designed to hold the user's arms by their side to give a hug-like sensation as well as allowing the arms to be free.

A therapeutic nesting apparatus is disclosed for a treating sensory-related, neuropsychological conditions by altering neural response to reduce sensory input and improve biological, neurological, and psychological performance. The apparatus provides for a body to lie in a natural position creating a cocoon-like sensation that allows the brain to alter neural response. The nesting apparatus allows the user's head to be relaxed for open airways and minimize strain on the neck. The lower portion of the nesting apparatus may support the legs in a gently bent position to allow the feet to be on or off the ground depending on the user's preference. The sides of the nesting apparatus are designed to hold the user's arms by their side to give a hug-like sensation as well as allowing the arms to be free.

The present disclosure, in one embodiment, is a computer-implemented method for the detection of and intervention in traumatic nightmares. In one embodiment, a user wears a watch wirelessly connected to a phone. The watch may include an accelerometer, gyroscope, and heartrate monitor. The application may monitor these sensors and intervene with haptic feedback if the application detects a traumatic nightmare. In one embodiment, the application may include a monitoring module that collects data from the watch's accelerometer, gyroscope, and heartrate sensors. The application may then estimate and record stress levels based on these sensors. The application may also include an intervention module that responds to high stress levels with haptic feedback that increases in intensity of previous efforts to intervene were unsuccessful.

The present disclosure, in one embodiment, is a computer-implemented method for the detection of and intervention in traumatic nightmares. In one embodiment, a user wears a watch wirelessly connected to a phone. The watch may include an accelerometer, gyroscope, and heartrate monitor. The application may monitor these sensors and intervene with haptic feedback if the application detects a traumatic nightmare. In one embodiment, the application may include a monitoring module that collects data from the watch's accelerometer, gyroscope, and heartrate sensors. The application may then estimate and record stress levels based on these sensors. The application may also include an intervention module that responds to high stress levels with haptic feedback that increases in intensity of previous efforts to intervene were unsuccessful.