Table 6 Summary of mobile and wearable sensor data changes for PwMS and healthy controls
From: Modeling multiple sclerosis using mobile and wearable sensor data
Domain | Data stream | Hypothesis for PwMS and healthy controls | Frequency |
|---|---|---|---|
Physiological | Heart Rate (HR) | Increased fatigue is associated with increased HR and reduced HRV33. | Continuous |
Heart Rate Variability (HRV) | Continuous | ||
Blood Pulse Wave (BPW) | Continuous | ||
Blood Perfusion (BP) | Continuous | ||
Skin Temperature (ST) | Healthy adults have lower skin temperature than people with mild cognitive impairments36. | Continuous | |
Behavioral | Physical Activity | PwMS have problems with balance and feeling dizzy, which can have knock-on effects on their walking. PwMS are less physically active than healthy controls35. | Continuous |
Steps | People’s activity influences MS symptoms (e.g., fatigue), which in turn impact the MS disability level54. | Continuous | |
Phone locks/unlocks | These features could reflect smartphone usage, which might inform the ability to concentrate and the extend of sedentary behavior. | Event-based | |
Motor performance | Tapping Task | Decline in performance during a tapping task, or fatigability, has been previously shown as a promising objective marker of fatigue3. | Daily |
Sleep routine | Sleep Duration | Sleep disturbances are significantly higher in PwMS than in the general population. They may affect women with MS more than men63. | Daily |
Patient | Age | Age is positively related to disease severity53 and functioning of PwMS. | Once |
information | Gender | Females are more prone to MS than males52. | Once |