Your goal is to extract structured information from the user's input that matches the form described below. When extracting information please make sure it matches the type information exactly. Do not add any attributes that do not appear in the schema shown below.

```TypeScript

 { // Information on the clinical trial characteristics from the abstract
 conditions: string // The condition or disease being treated in the clinical trial
 drug_or_intervention: string // The drug or intervention used in the clinical trial
}
```


Please output the extracted information in JSON format. Do not output anything except for the extracted information. Do not add any clarifying information. Do not add any fields that are not in the schema. If the text contains attributes that do not appear in the schema, please ignore them. All output must be in JSON format and follow the schema specified above. Wrap the JSON in <json> tags.



Input:Novel method for measurement of fatigue in multiple sclerosis: Real-Time Digital Fatigue Score.  The study's objective was to develop a real-time measurement for fatigue and to evaluate whether it is an effective clinical trial outcome measure compared with the Fatigue Severity Scale (FSS) and the Modified Fatigue Impact Scale (MFIS). Forty-nine subjects with MS and an FSS >4 recorded Real-Time Digital Fatigue Scores (RDFSs) on a wrist-worn device four times a day over 3 weeks. Scores were scaled 0-10, with 10 representing the worst possible fatigue. FSS and MFIS were evaluated and compared with RDFSs. Mean RDFSs significantly correlated with FSS (r = 0.55, p < 0.001) and MFIS (r = 0.55, p < 0.001). RDFS captured circadian variations in fatigue, with scores increasing from mean 3.4 at 9 a.m., to 4.0 at 1 p.m., 4.5 at 5 p.m., and 5.0 at 9 p.m. When all scores over all days were included in a mixed-model analysis of circadian variation, the differences in RDFS between times were more significant than in an analysis that included only single scores of data isolated from the first day of monitoring. RDFS is a promising measure. RDFS significantly correlated with FSS and MFIS, captured real-time daily and circadian variations in fatigue, and provided multiple measurements of fatigue that provided statistical advantages over FSS and MFIS. Output:
{'conditions': 'Multiple Sclerosis', 'interventions': 'Drug: American ginseng extract HT-1001|Drug: placebo'}