Fatigue Investigation Using Digital Outcomes (FIDO)

August 20, 2026 updated by: Liliana Barrios

Fatigue Investigation Using Digital Outcomes (FIDO) - An Exploratory Project

This study investigates objective methods to measure and monitor fatigue in patients with post-COVID-19 condition (long COVID) and multiple sclerosis (MS), and includes a group of healthy volunteers for comparison. Fatigue is a common and debilitating symptom of both conditions, but current assessment methods rely mainly on questionnaires, which can be limited by subjectivity and recall bias. The study uses a smartphone application (the FIDO app) together with wearable devices to continuously collect data on fatigue and its fluctuations during daily life, in order to evaluate the potential of digital, objective measures compared to standard assessments.

Participation lasts approximately eight weeks and includes two in-person clinic visits along with a continuous data collection phase. At the initial visit, participants complete baseline assessments and questionnaires and are introduced to the study's digital tools. During the following weeks, participants wear two wearable devices continuously (24 hours a day) and use the FIDO app to complete short daily tasks (approximately 7 minutes every two days) and brief questionnaires (approximately 5 minutes per day). All participants collect a stool sample at home at weeks 4 and 8 for gut microbiome analysis, and complete the Fatigue Scale for Motor and Cognitive Functions at week 4. At the final study visit, participants repeat a subset of the baseline assessments, provide feedback on the digital technology used, and return the study devices.

Study Overview

Status

Status

Indicates the current recruitment status or the expanded access status.
Recruiting

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

Detailed Description

FIDO is an exploratory, multicenter, observational study evaluating whether fatigue in neurological and post-infectious conditions can be characterized using digital outcomes derived from wearable sensors, smartphone-based performance tests, and self-report collected in free-living conditions.

Fatigue is among the most disabling symptoms reported in multiple sclerosis (MS) and post-COVID-19 condition (long COVID), yet it is assessed almost exclusively through questionnaires administered at single time points in clinic. Such instruments capture neither day-to-day fluctuation nor the exertion-dependent component of fatigue that patients describe. FIDO combines continuous physiological and behavioral monitoring with repeated at-home cognitive and motor testing to examine whether these signals track fatigue as it is experienced in daily life.

The primary focus of the study is feasibility: whether participants in these populations can sustain the required device wear and application-based tasks over an eight-week monitoring period, and how completeness and timeliness of data vary within and between participants. Secondary objectives address the relationship between digital measures and established clinical and self-reported assessments of fatigue, fatigability, autonomic function, and cognition. Further exploratory objectives examine sleep, post-exertional malaise, menstrual cycle phase, gut microbiome composition, machine learning models for estimating and predicting fatigue, the effect of reduced data quantity and resolution on derived measures, and participants' experience and perceived usability of the study tools.

DESIGN AND SETTING

Prospective observational cohort study with three parallel groups and no allocation or intervention. Each participant takes part for approximately eight weeks, comprising two on-site visits and a continuous home-based data collection phase between them.

STUDY PROCEDURES

Visit 1 (baseline). After written informed consent and assignment of a pseudonymized study identifier, participants complete a battery of questionnaires and clinical assessments. Instrumentation includes a 5-lead ECG Holter, an upper-arm blood pressure monitor, an ActiGraph LEAP armband, and a smartwatch. Autonomic function is assessed using a subset of an Ewing-based test battery: heart rate response to paced deep breathing at 0.1 Hz for 10 minutes; the 30:15 ratio on active standing; blood pressure and heart rate response to postural change at 1, 3, and 5 minutes standing; and blood pressure response to a sustained handgrip test at 30% of maximal voluntary contraction for 5 minutes, with blood pressure measured at 1, 3, and 5 minutes. After a two minutes break, blood pressure is measured again. Cognitive, memory, and dexterity assessments comprise the Symbol Digit Modalities Test, the Brief Visuospatial Memory Test-Revised, and the Nine-Hole Peg Test. Participants also perform two administrations each of the smartphone-based cognitive fatigability test (cFAST) and a 30-second finger tapping task, separated by rest periods and preceded by a fatigue visual analogue scale, with the order of the two smartphone tasks counterbalanced. Participants receive the study application, wearable devices and chargers, stool collection kits, printed questionnaires, and device manuals.

Eight-week remote monitoring. Participants wear an ActiGraph LEAP on the upper arm and a smartwatch on the wrist continuously, removing them only for showering and swimming, and synchronize data regularly. Participants with MS receive a Garmin smartwatch and participants with post-COVID-19 condition an Apple Watch; healthy controls receive either model. The application prompts cognitive and motor fatigue visual analogue scales four times daily at random times, morning and evening sleep surveys, and the cFAST and tapping tasks every second day (approximately seven minutes combined). Stress, food, activity, and symptom logging are available on a self-reported basis. The Fatigue Severity Scale is completed weekly by the MS and healthy control groups; it is not administered in the post-COVID-19 group, as the instrument was developed for MS and does not adequately capture fatigue in this population. A printed Fatigue Scale for Motor and Cognitive Functions is completed at week 4. Stool samples for gut microbiome analysis are collected at weeks 4 and 8 and returned by prepaid post.

Visit 2 (final visit). Participants return to their site while continuing to wear the sensors and repeat a subset of the baseline questionnaires, assessments, and smartphone-based tasks. Usability and acceptability questionnaires (UEQ and MARS), completed at home beforehand, are returned, and participants give open verbal feedback during a semi-structured interview, which is audio-recorded. All study devices and chargers are returned at the end of the visit.

Study Type

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Observational

Enrollment (Estimated)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
122

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact

The name and contact information for the person who can answer enrollment questions for a clinical study. Each location where the study is being conducted may also have a specific contact, who may be better able to answer those questions.
  • Name: Liliana Barrios, Dr. sc. ETH Zürich
  • Phone Number: +41774141174
  • Email: fido@dsi.uzh.ch

Study Locations

  • Switzerland
    • Basel
      • Basel, Basel, Switzerland, 4058
        • Recruiting
        • Neurozentrum Basel Messeturm
        • Contact:
        • Principal Investigator:
          • Ilijas Jelcic, Dr. med.
    • Canton of Zurich
      • Zurich, Canton of Zurich, Switzerland, 8001
        • Recruiting
        • Bellevue Medical Group - Neurozentrum Sternen / Neurozentrum Bellevue
        • Contact:
        • Principal Investigator:
          • Marc Hilty
      • Zurich, Canton of Zurich, Switzerland, 8001
        • Recruiting
        • University of Zurich
        • Contact:
        • Sub-Investigator:
          • Marcelo Stegmann, PhD candidate
      • Zurich, Canton of Zurich, Switzerland, 8006
        • Recruiting
        • NeuroPraxis Zürich GmbH
        • Contact:
        • Principal Investigator:
          • Sebastian Finkener, Dr. med.
      • Zurich, Canton of Zurich, Switzerland, 8008
        • Recruiting
        • Bellevue Medical Group - Neurozentrum Seefeld
        • Contact:
        • Principal Investigator:
          • Marc Hilty, Dr. med.
      • Zurich, Canton of Zurich, Switzerland, 8008
        • Recruiting
        • Klinik Lengg AG
        • Contact:
        • Principal Investigator:
          • Bartosz Bujan, Dr. med.

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

Multiple sclerosis participants will be recruited through Bellevue Medical Group (BMG) and NeuroPraxis Zürich during routine clinical visits. External patients may also participate if they have a diagnosis from a recognized medical institution.

Long COVID participants will be recruited from Klinik Lengg during routine clinical visits. External patients may also participate if they have a diagnosis from a recognized medical institution.

Healthy controls will be recruited from the general community in Zurich.

Recruitment across all groups will use physical and digital study flyers; the Long Covid Switzerland association will additionally support flyer distribution. Interested individuals may contact the UZH study team by email or phone to learn about study aims and eligibility; those willing and eligible will be directed to a study site.

Description

Inclusion Criteria Multiple Sclerosis or Long COVID

  • Confirmed diagnosis of MS (by neurologist) or Long COVID (by medical doctor).
  • Smartphone ownership.
  • Provide written informed consent.
  • Fluent in German or English.

Inclusion Criteria Healthy:

  • Smartphone ownership.
  • Provide written informed consent.
  • Fluent in German or English.

Exclusion Criteria:

  • Concomitant medication affecting fatigue or ANS such as antidepressants prescribed or adjusted 1 month before the initial visit.
  • Unable to provide informed consent.
  • Unwilling or unable to comply with the study protocol.
  • Pregnancy or lactation.
  • For controls: Diagnosis of any chronic disorders or diseases that may affect fatigue or ANS.
  • For MS: Additional comorbidities that may affect fatigue or ANS such as autoimmune disease and cardiovascular disease.
  • For long COVID: Additional comorbidities diagnosed prior to the first acute COVID-19 infection that may affect fatigue or ANS such as autoimmune disease and cardiovascular disease.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort

Group / Cohort

A group or subgroup of participants in an observational study that is assessed for biomedical or health outcomes.
Multiple Sclerosis

Participants with a clinically confirmed diagnosis of multiple sclerosis (MS). This group is monitored using two wearable devices and the FIDO smartphone application over two months.

At the initial visit this cohort completes demographic questions and the following assessments: the Fatigue Scale for Motor and Cognitive Functions (FSMC), the Hospital Anxiety and Depression Scale (HADS), COMPASS-31, and the Multiple Sclerosis Impact Scale (MSIS-29). The MSIS-29 is administered to this cohort only.
Long COVID

Participants with a clinically confirmed diagnosis of post-COVID-19 condition (Long COVID). This group is monitored using two wearable devices and the FIDO smartphone application over two months.

At the initial visit this cohort completes demographic questions and the following assessments: the Fatigue Scale for Motor and Cognitive Functions (FSMC), the Munich Berlin Symptom Questionnaire (MBSQ), the Bell Disability Scale, the Hospital Anxiety and Depression Scale (HADS), and COMPASS-31. The MBSQ and Bell Disability Scale are administered to this cohort only, and characterise symptom severity and functional impairment.
Healthy controls
This group is monitored using two wearable devices and the FIDO smartphone application over two months. At the initial visit this cohort completes demographic questions and the following assessments: the Fatigue Scale for Motor and Cognitive Functions (FSMC), the Hospital Anxiety and Depression Scale (HADS), and COMPASS-31.

What is the study measuring?

Primary Outcome Measures

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
Outcome Measure
Measure Description
Time Frame
Percentage of Expected Smartwatch Wear Time Recorded Over Two Months
Time Frame: 2 months
Passive compliance with continuous smartwatch data collection, as a measure of feasibility of long-term fatigue monitoring. Participants wear a smartwatch continuously for two months: a Garmin smartwatch (multiple sclerosis group), an Apple Watch (post-COVID-19 condition group), or either device (healthy controls). Expected wear time is the full duration of each participant's individual enrolment (approximately 60 days), so the denominator is calculated per participant rather than as a fixed period; charging periods are not excluded, so values are not expected to reach 100%. Compliance is calculated per participant as recorded wear time divided by expected wear time, multiplied by 100. Values range from 0% to 100%, with higher values indicating greater compliance. Compliance is also compared by group, disease characteristics and demographic factors, and baseline characteristics associated with lower compliance are described.
2 months
Percentage of Expected Armband Activity Monitor Wear Time Recorded Over Two Months
Time Frame: 2 months
Passive compliance with continuous armband data collection, as a measure of feasibility of long-term fatigue monitoring. Participants wear an armband activity monitor (ActiGraph LEAP) continuously for two months. All participants wear the same device, so compliance can be compared between groups independently of device type. Expected wear time is the full duration of each participant's individual enrolment (approximately 60 days), so the denominator is calculated per participant rather than as a fixed period; charging periods are not excluded, so values are not expected to reach 100%. Compliance is calculated per participant as recorded wear time divided by expected wear time, multiplied by 100. Values range from 0% to 100%, with higher values indicating greater compliance. Compliance is also compared by group, disease characteristics and demographic factors, and baseline characteristics associated with lower compliance are described.
2 months
Percentage of Scheduled Study Application Questionnaires Completed Over Two Months
Time Frame: 2 months
Active compliance with self-reported data collection, as a measure of feasibility of long-term fatigue monitoring. Participants complete short questionnaires in the study smartphone application (FIDO app) over two months, comprising cognitive and motor fatigue visual analogue scales prompted four times daily at random times, morning and evening sleep surveys daily, and the Fatigue Severity Scale weekly in the multiple sclerosis and healthy control groups. The number scheduled follows each participant's individual enrolment (approximately 60 days). Compliance is calculated per participant as the number of questionnaires completed divided by the number scheduled, multiplied by 100. Values range from 0% to 100%, with higher values indicating greater compliance. Compliance is reported per questionnaire type, compared by group, disease characteristics and demographic factors, and baseline characteristics associated with lower compliance are described. Response latency is also evaluated.
2 months
Percentage of Scheduled Study Application Performance Tasks Completed Over Two Months
Time Frame: 2 months
Active compliance with task-based data collection, as a measure of feasibility of long-term fatigue monitoring. Participants complete two brief performance tasks in the FIDO app approximately every second day over two months: a cognitive fatigability test (cFAST) and a 30-second finger tapping task, together taking approximately seven minutes. The number scheduled follows each participant's individual enrolment (approximately 60 days). Compliance is calculated per participant as the number of sessions completed divided by the number scheduled, multiplied by 100. A session counts as completed if the participant finishes the full task protocol. Values range from 0% to 100%, with higher values indicating greater compliance. Compliance is reported per task, compared by group, disease characteristics and demographic factors, and baseline characteristics associated with lower compliance are described. Response latency is also evaluated.
2 months

Secondary Outcome Measures

Secondary Outcome Measures

In a clinical study's protocol, a planned outcome measure that is not as important as the primary outcome measure for evaluating the effect of an intervention but is still of interest. Most clinical studies have more than one secondary outcome measure.
Outcome Measure
Measure Description
Time Frame
Discriminative Accuracy of Cognitive Fatigability Test and Finger Tapping Test Performance for Participant Group, Stratified by Fatigue Level
Time Frame: 2 months
Participants complete a cognitive fatigability test (cFAST) and a finger tapping test at the study visits and approximately every second day over two months. Performance measures are derived from each test during analysis and are not pre-specified. They are evaluated for their ability to discriminate patients from healthy controls, reported as the area under the receiver operating characteristic curve, from 0.5 (chance) to 1.0 (perfect discrimination), per test. Results are stratified by self-reported fatigue level on the corresponding subscale of the Fatigue Scale for Motor and Cognitive Functions (cognitive subscale for cFAST, motor subscale for tapping; scores 10 to 50, higher indicating greater fatigue). Baseline factors expected to influence this endpoint are age and disease status.
2 months
Reliability and Within-Participant Variability of Cognitive Fatigability Test and Finger Tapping Test Performance
Time Frame: 2 months
Reliability of smartphone-based performance tests for repeated long-term monitoring. Performance measures are derived during analysis and are not pre-specified. Each test is administered twice in immediate succession, in counterbalanced order, at each of the two study visits, when fatigue is expected to remain stable, and repeated approximately every second day over two months, when fatigue is expected to fluctuate. Agreement across repeated administrations is reported as the intraclass correlation coefficient, from 0 to 1, higher values indicating greater consistency. The immediate-repeat estimate reflects measurement reliability; the two-month estimate reflects the proportion of variance attributable to stable differences between participants. Whether within-participant variation tracks concurrent fatigue is addressed by the corresponding association outcome. Results are reported per test. Baseline factors expected to influence this endpoint are age and disease status.
2 months
Agreement Between Cognitive Fatigability Test and Finger Tapping Test Performance and Established Fatigue and Functional Assessments
Time Frame: 2 months
Agreement between smartphone-based performance tests and established fatigue and functional assessments. Performance measures are derived during analysis and are not pre-specified. A cognitive or motor fatigue visual analogue scale (0 to 10) is completed immediately before every test session. Performance is additionally compared with the fatigue and functional assessments specified in the protocol, including the Fatigue Severity Scale weekly in the multiple sclerosis and healthy control groups, the Fatigue Scale for Motor and Cognitive Functions at the study visits and at four weeks, the Symbol Digit Modalities Test, the Brief Visuospatial Memory Test-Revised at the initial visit, handgrip strength and the Nine-Hole Peg Test. Associations are reported as correlation coefficients from -1 through 0 to +1, per test pairing. Baseline factors expected to influence this endpoint are age and disease status.
2 months
Association Between Digital Measurements Derived From Wearable Data and the Fatigue Scale for Motor and Cognitive Functions (FSMC)
Time Frame: 2 months
Association between digital measurements derived from continuously recorded wearable data and the Fatigue Scale for Motor and Cognitive Functions (FSMC). Candidate measurements are derived during analysis from raw sensor signals recorded by an armband monitor and consumer smartwatch, including photoplethysmography, inertial, barometric and temperature signals; their scales are not pre-specified. Associations are reported as correlation coefficients from -1 through 0 to +1, separately for each group.
2 months
Discriminative Accuracy of Armband-Derived Digital Measurements for Distinguishing Patients From Healthy Controls, Stratified by Fatigue Level
Time Frame: 2 months
Ability of digital measurements derived from continuously recorded armband data to distinguish patients from healthy controls. Candidate measurements are derived during analysis from raw sensor signals recorded by the armband activity monitor (ActiGraph LEAP); their scales are not pre-specified. Only armband-derived measurements are used, because all participants wear the same armband device, whereas the smartwatch differs between groups and would confound comparison between them. Discriminative accuracy for distinguishing patients from healthy controls is reported as the area under the receiver operating characteristic curve, from 0.5 (chance) to 1.0 (perfect discrimination), per derived measurement. Results are stratified by self-reported fatigue level on the Fatigue Scale for Motor and Cognitive Functions (total 20 to 100, higher indicating greater fatigue).
2 months
Performance of Machine Learning Models Trained on Digital Measurements to Estimate Fatigue and Disease Characteristics
Time Frame: 2 months
Evaluation of machine learning models developed during the project to estimate fatigue and disease characteristics from digital measurements derived from wearable and smartphone data. Models are newly developed for the study's research objectives; no existing models are validated and none is intended for clinical use. Model families and features are not pre-specified. For classification tasks, performance is reported as the area under the receiver operating characteristic curve, from 0.5 (chance) to 1.0 (perfect discrimination). For regression tasks, performance is reported as prediction error against self-reported fatigue scores, with lower error indicating better performance. Performance is estimated using cross-validation with participant-level splits; for personalised models, splits are made within participants over time. Results are reported separately for each group.
2 months
Ability of Wearable-Derived Measures to Detect Autonomic Dysfunction Assessed Against Clinical Autonomic Testing
Time Frame: 2 months
Whether continuously recorded wearable data can detect autonomic dysfunction. At both study visits participants undergo a standardised autonomic test battery based on Ewing's protocol, recorded using a 5-lead ECG Holter and blood pressure monitor, and complete COMPASS-31 (weighted total 0 to 100, higher indicating greater symptom burden). Metrics derived from continuous photoplethysmography and other armband and smartwatch signals, which are not pre-specified, are compared with these measures and reported as measures of association, from -1 through 0 to +1. Discriminative accuracy for identifying participants classified as having autonomic dysfunction on the battery is reported as the area under the receiver operating characteristic curve, from 0.5 (chance) to 1.0 (perfect discrimination). Autonomic comparisons are reported here rather than under the clinical assessment agreement outcome. Results are reported separately for each group, within which the smartwatch is the same device.
2 months
Association Between Sleep Measures, Sleep Questionnaires and Self-Reported Fatigue
Time Frame: 2 months
Relationship between sleep and self-reported fatigue. Sleep is assessed subjectively through morning and evening surveys completed daily in the FIDO app, and objectively through measures derived from continuous armband and smartwatch recording, including sleep duration, efficiency, timing and estimated sleep stages; these are derived during analysis and not pre-specified, and stages are estimated rather than measured directly. Both are related to fatigue on the cognitive and motor visual analogue scales (0 to 10, higher indicating greater fatigue) and the fatigue questionnaires collected during the study; the interval between sleep and the fatigue reports it is related to is not pre-specified. They are also compared with each other. Relationships are reported as measures of association, from -1 through 0 to +1, and agreement between sleep measures as the mean difference between methods. Results are reported separately for each group.
2 months
Association Between Digital Measurements and Lifestyle Reporting and Self-Reported State Fatigue
Time Frame: 2 months
Relationship between continuously recorded digital measurements and self-reported state fatigue. Participants rate cognitive and motor fatigue on a visual analogue scale (0 to 10, higher indicating greater fatigue) four times daily at random times. Digital measurements derived from armband and smartwatch recording, together with lifestyle data logged by participants in the FIDO app at their own initiative, including diet, activity and symptoms, are related to these ratings; the interval between a measurement and the ratings it is related to is not pre-specified, so that measurements preceding a fatigue rating can be examined as possible precursors. Sleep measures and smartphone-based test performance are reported under their own outcomes. Relationships are reported as measures of association, from -1 through 0 to +1, separately for each group.
2 months
Association Between Exertion and Subsequent Post-Exertional Malaise and Symptom Severity in Participants With Post-COVID-19 Condition
Time Frame: 2 months
Assessment of post-exertional malaise, defined as worsening of symptoms following exertion, in participants with post-COVID-19 condition. Participants log symptoms in the FIDO app at their own initiative, rating severity as low, moderate or high. Measures of exertion and physiological response are derived during analysis from continuous armband and smartwatch recording, including activity, photoplethysmography-derived metrics, blood pressure and skin temperature, and from self-logged activity. These are related to subsequently reported symptom occurrence and severity and to fatigue visual analogue scale ratings (0 to 10, higher indicating greater fatigue), and are considered relative to each participant's usual level; neither the threshold defining exertion nor the interval before subsequent reports is pre-specified. Time since infection, medications and sleep quality are considered as modifying factors. Relationships are reported as measures of association, from -1 through 0 to +1.
2 months
Performance of Machine Learning Models Trained to Predict Self-Reported State Fatigue Fluctuations
Time Frame: 2 months
Evaluation of machine learning models developed during the project to predict short-term fluctuations in self-reported state fatigue from digital measurements and lifestyle reporting. Models may use measurements recorded before the fatigue rating being predicted, to assess whether objective signals precede fatigue episodes. Models are newly developed for the study's research objectives; no existing models are validated and none is intended for clinical use. Model families and features are not pre-specified. For classification of high versus low fatigue states, performance is reported as the area under the receiver operating characteristic curve, from 0.5 (chance) to 1.0 (perfect discrimination). For continuous prediction of fatigue ratings (0 to 10), performance is reported as prediction error, with lower error indicating better performance. Cross-validation uses participant-level splits. Results are reported separately for each group.
2 months
Association Between Digital Measurements Derived From Wearable Data and the Fatigue Severity Scale (FSS)
Time Frame: 2 months
Association between digital measurements derived from continuously recorded wearable data and the Fatigue Severity Scale (FSS). Candidate measurements are derived during analysis from raw sensor signals recorded by an armband monitor and a consumer smartwatch, including photoplethysmography, inertial, barometric and temperature signals; their scales are not pre-specified. The FSS is administered only to participants in the multiple sclerosis and healthy control groups; associations are reported for these groups as correlation coefficients from -1 through 0 to +1.
2 months
Effect of Data Amount, Granularity and Missing Data on Digital Measurements of Fatigue and Disease Characteristics
Time Frame: 2 months
Post hoc analysis of how the amount, resolution and completeness of recorded data affect the digital biomarker outcomes for fatigue and disease characteristics. The recorded dataset is resampled at reduced temporal resolutions, with increasing proportions of data removed, and truncated to shorter recording durations, simulating lower-specification devices, less frequent synchronisation and shorter wear periods. Digital measurements derived from these reduced datasets are compared with those derived from the full dataset, and agreement is reported as correlation coefficients from -1 through 0 to +1. For truncation, the outcome is additionally the number of days after which a measurement no longer changes by more than a predefined margin. Machine learning models trained on the reduced datasets are compared with those trained on the full dataset using the same metrics. Results are reported separately for each group, and inform the design of data collection in future studies.
2 months
Association Between Menstrual Cycle Phase and Self-Reported Fatigue and Digital Measurements
Time Frame: 2 months
Assessment of whether menstrual cycle phase is associated with fatigue in participants who menstruate. The dates of the most recent menstrual period are recorded at the initial visit and cycle dates covering the recording period by questionnaire at the final visit; phase is estimated from these as follicular or luteal, with continuous wearable measurements used to corroborate self-reported phase. Hormonal contraceptive use is recorded at baseline and accounted for, as it may suppress or alter cyclical variation; perimenopausal and postmenopausal participants are considered separately; and cycle regularity may be affected by the conditions studied, which is taken into account when interpreting phase estimates. Phase is related to fatigue on the visual analogue scale (0 to 10) and the Fatigue Severity Scale (score 1 to 7), higher values indicating greater fatigue, and to digital measurements. Associations are reported as measures of association, from -1 through 0 to +1.
2 months
Gut Microbiome Composition and Differences Between Participant Groups
Time Frame: 4 weeks and 8 weeks
Characterisation of gut microbiome composition and its variation between clinical groups. Participants collect stool samples at four and eight weeks after the initial visit, which are analysed for microbial composition, summarised as within-sample (alpha) diversity, between-sample (beta) diversity and relative abundance of bacterial taxa. Differences in these measures are examined between participants with multiple sclerosis, participants with post-COVID-19 condition and healthy controls. Participant characteristics that may alter composition are recorded and reported, including medication use such as antibiotics, dietary and lifestyle changes recorded through the diary function of the FIDO app, body mass index, time since disease onset or acute SARS-CoV-2 infection, gastrointestinal symptoms and comorbidities. Values are averaged across the two sampling points to give a more stable estimate; change between them is also examined.
4 weeks and 8 weeks
Perceived Usability of the FIDO App Measured by the User Experience Questionnaire and Mobile Application Rating Scale
Time Frame: 2 months
Perceived usability and quality of the FIDO app. Before the final visit participants complete the User Experience Questionnaire, yielding six scale scores from -3 to +3 where higher values indicate a more positive experience, and the Mobile Application Rating Scale, yielding four objective quality subscales and a subjective quality subscale, scored 1 to 5 where higher values indicate better quality. The subjective quality subscale covers willingness to recommend the app, expected frequency of future use and overall rating. Associations between usability scores and compliance with scheduled questionnaires and performance tasks are reported as correlation coefficients from -1 through 0 to +1. Results are reported separately for each group.
2 months
Association Between Gut Microbiome Composition and Fatigue, Autonomic Function and App-Logged Diet and Symptoms
Time Frame: 2 months
Relationship between gut microbiome composition, fatigue and autonomic function. Stool samples collected at four and eight weeks are analysed for microbial composition, summarised as diversity and relative abundance of bacterial taxa, averaged across the two sampling points. These measures are related to fatigue on the Fatigue Scale for Motor and Cognitive Functions (total 20 to 100) and the Fatigue Severity Scale in the multiple sclerosis and healthy control groups (1 to 7), higher values indicating greater fatigue; to autonomic function assessed by COMPASS-31 and a standardised autonomic test battery; to digital measurements derived from wearable data; and to diet and symptom entries logged in the FIDO app. Participant characteristics that may influence composition are reported alongside the associations rather than adjusted for. Associations are reported as measures of association, from -1 through 0 to +1, separately for each group.
2 months
Change in Lifestyle Metrics From the Two Months Before Study Participation to the Two-Month Data Collection Period
Time Frame: Two months before enrollment and two months during data collection
Assessment of whether participation in the study, which involves no intervention, was associated with a change in participants' everyday behaviour. Lifestyle metrics recorded by participants' own devices before enrolment, including step count and other activity and sleep measures, are extracted retrospectively from Apple Health for the two months preceding the initial visit and compared with the corresponding metrics recorded during the two-month data collection period. Change is reported as the difference between the two periods for each metric, with positive values indicating an increase during the study. This analysis is possible only for participants who use an Apple device and who consent to sharing historical data, and results are therefore reported for that subgroup and separately for each group.
Two months before enrollment and two months during data collection
Participant Experience of Long-Term Digital Monitoring Assessed by Semi-Structured Feedback Interviews
Time Frame: 2 months
Participant experience of using the FIDO app and wearable devices over two months. At the final visit participants complete a semi-structured interview in German or English, which is audio recorded and transcribed. Transcripts are analysed thematically, covering intention to continue using the app and devices beyond the study, moments of disengagement, barriers to sustained use, sources of motivation, perceived fit of the study design to individual needs, and experience of the wearable devices. Participants also report whether they owned wearable devices before the study and which device placement they preferred, upper arm or wrist; these are reported as counts and proportions. Themes are reported with frequency across participants and illustrative anonymised quotations, examined for differences between the multiple sclerosis, post-COVID-19 condition and healthy control groups, and compared with usability scores and compliance data.
2 months
Association Between Digital Measurements Derived From Wearable Data and the Munich Berlin Symptom Questionnaire (MBSQ)
Time Frame: 2 months
Association between digital measurements derived from continuously recorded wearable data and the Munich Berlin Symptom Questionnaire (MBSQ). Candidate measurements are derived during analysis from raw sensor signals recorded by an armband monitor and consumer smartwatch, including photoplethysmography, inertial, barometric and temperature signals; their scales are not pre-specified. The MBSQ is administered only to participants in the Post-COVID-19 group; associations are reported for this group as correlation coefficients from -1 through 0 to +1.
2 months
Association Between Digital Measurements Derived From Wearable Data and the Bell Disability Scale
Time Frame: 2 months
Association between digital measurements derived from continuously recorded wearable data and the Bell Disability Scale. Candidate measurements are derived during analysis from raw sensor signals recorded by an armband monitor and consumer smartwatch, including photoplethysmography, inertial, barometric and temperature signals; their scales are not pre-specified. The Bell Disability Scale is administered only to participants in the Post-COVID-19 group; associations are reported for this group as correlation coefficients from -1 through 0 to +1.
2 months
Association Between Digital Measurements Derived From Wearable and Smartphone Data and the Symbol Digit Modalities Test (SDMT)
Time Frame: 2 months
Association between digital measurements derived from continuously recorded wearable and smartphone data and the Symbol Digit Modalities Test (SDMT). Candidate measurements are derived during analysis from raw sensor signals recorded by an armband monitor and consumer smartwatch, including photoplethysmography, inertial, barometric and temperature signals, and from smartphone-based measurements collected through the study application. Their scales are not pre-specified. Associations are reported as correlation coefficients from -1 through 0 to +1, separately for each group.
2 months
Association Between Digital Measurements Derived From Wearable and Smartphone Data and the Brief Visuospatial Memory Test-Revised (BVMT-R)
Time Frame: 2 months
Association between digital measurements derived from continuously recorded wearable data and from smartphone-based assessments and the Brief Visuospatial Memory Test-Revised (BVMT-R). Candidate measurements are derived during analysis from raw sensor signals recorded by an armband monitor and consumer smartwatch, including photoplethysmography, inertial, barometric and temperature signals, and from smartphone-based active tasks and questionnaires administered through the study application; their scales are not pre-specified. Associations are reported as correlation coefficients from -1 through 0 to +1, separately for each group.
2 months

Other Outcome Measures

Other Outcome Measures

For clinical trials, a planned measurement described in the protocol that is used to determine the effect of an intervention/treatment on participants. For observational studies, a measurement or observation that is used to describe patterns of diseases or traits, or associations with exposures, risk factors, or treatment. Types of outcome measures include primary outcome measure and secondary outcome measure.
Outcome Measure
Measure Description
Time Frame
Autonomic Function and Symptoms in Participants With Multiple Sclerosis, Post-COVID-19 Condition and Healthy Controls
Time Frame: Baseline and 8 weeks
Characterisation of autonomic function and its variation between clinical groups. At both study visits participants undergo a standardised autonomic test battery based on Ewing's protocol, comprising heart rate response to paced deep breathing at 0.1 Hz, the 30:15 ratio on active standing, blood pressure response to postural change, and blood pressure response to sustained handgrip at 30% of maximal voluntary contraction, recorded using a 5-lead ECG Holter and blood pressure monitor. Autonomic symptoms are assessed by COMPASS-31 (weighted total 0 to 100, higher indicating greater symptom burden). Values from each component of the battery are compared between participants with multiple sclerosis, participants with post-COVID-19 condition and healthy controls.
Baseline and 8 weeks
Agreement Between Blood Pressure Estimated From the Armband Activity Monitor and Cuff-Measured Blood Pressure During Clinical Autonomic Testing
Time Frame: Baseline visits
Whether blood pressure estimated from photoplethysmography by the armband activity monitor (ActiGraph LEAP), worn on the upper arm, agrees with cuff-measured blood pressure. Comparison is made only at the study visits, during the autonomic test battery, when cuff blood pressure is recorded in the supine position, at one, three and five minutes of standing, and before, during and after a five-minute sustained handgrip contraction at 30% of maximal voluntary contraction. Estimated and cuff-measured systolic and diastolic blood pressure are compared at each of these timepoints, in millimetres of mercury. Agreement is assessed using Bland-Altman analysis and reported as the mean difference between methods with 95% limits of agreement. Agreement during postural change and sustained contraction, when blood pressure is expected to vary, is examined separately.
Baseline visits
Time From Notification to Completion of Scheduled Study Application Questionnaires and Performance Tasks Over Two Months
Time Frame: 2 months
Responsiveness to scheduled prompts, as a measure of the effort required to sustain active data collection rather than whether it is completed at all. The FIDO app records the time at which each prompt is sent and the time at which the corresponding questionnaire or task is submitted; latency is the interval between these timestamps, in minutes, calculated per completed prompt, with longer intervals indicating slower responding. Prompts that are never completed are not assigned a latency and are captured by the completion rate outcomes. Latency is summarised per participant as the median and as within-participant variability over the two-month period, reported separately for each prompt type (fatigue visual analogue scale, sleep surveys, Fatigue Severity Scale, cognitive fatigability test, finger tapping task), and compared by group, disease characteristics and demographic factors.
2 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
Liliana Barrios

Collaborators

Collaborators

An organization other than the sponsor that provides support for a clinical study. This support may include activities related to funding, design, implementation, data analysis, or reporting.
University of Zurich
ETH Zurich (Switzerland)
Innosuisse - Swiss Innovation Agency
Centre Suisse d'Electronique et de Microtechnique (CSEM), Switzerland
Insel Gruppe AG, University Hospital Bern

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
May 26, 2026

Primary Completion (Estimated)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
May 1, 2028

Study Completion (Estimated)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
May 1, 2028

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
August 14, 2026

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
August 20, 2026

First Posted (Actual)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
August 24, 2026

Study Record Updates

Last Update Posted (Actual)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
August 24, 2026

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
August 20, 2026

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
August 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant numbers.
  • 2024-01878
  • 108.672 IP-LS (Other Grant/Funding Number: Innosuisse)
  • Postdoc Fellowship (Other Grant/Funding Number: Digital Society Initiative - University of Zurich)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

IPD Plan Description

A decision on whether individual participant data will be shared has not yet been made. Some participants separately and voluntarily consent to future re-use of their coded data in other research projects (including abroad), distinct from participation in this study itself. However, this consent is not tied specifically to data underlying published results, and no defined external data request process, repository, or access mechanism has yet been established. A decision may be made at a later stage of the project.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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