Wearable DevicesWearable WristNCT07334925
Wearable Devices for Early Detection of Postoperative Infection
An observational study of Wearable Devices, run by Benha University at 1 site in Egypt.
Trial summary
This systematic review aims to evaluate the efficacy, accuracy, and clinical applicability of wearable infection detection wristbands in postoperative patients across ophthalmology, orthopaedic surgery, and general surgery. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Wearable Devices
- What is being tested?
- White Blood Cell (WBC) and C-reactive Protein (CRP)
- How many people
- 1,284 participants
- Where
- 1 siteEgypt
- Study dates
- 1 Jul 2025to 27 Oct 2025
- How long will I take part?
- 48 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population will consist of adult patients (≥18 years) undergoing elective or semi-elective surgical procedures in ophthalmology, orthopedics, and general surgery departments at participating tertiary hospitals. Eligible procedures include surgeries associated with moderate to high postoperative infection risk, such as cataract surgery, corneal transplantation, total joint replacement, and abdominal surgery. Participants must be clinically stable at enrollment and capable of providing informed consent. Both male and female patients will be included without restriction based on race or ethnicity. This population reflects typical postoperative clinical settings in which early infection detection using wearable technology may improve outcomes and reduce morbidity.
Who may be able to join
- Adults aged 18 years or older.
- Patients undergoing ophthalmologic, orthopedic, or general surgical procedures.
- Postoperative patients monitored using a wearable infection detection device or biosensor capable of continuous or intermittent assessment of inflammatory biomarkers, including:
- White blood cell (WBC) count and/or
- C-reactive protein (CRP) levels.
- Wearable devices may incorporate artificial intelligence or machine-learning algorithms for infection prediction.
- Patients receiving standard postoperative care, including conventional laboratory testing and/or clinical monitoring, for comparison.
- Ability to provide written informed consent.
Who cannot join
- Patients aged <18 years.
- Non-human studies (animal or in-vitro).
- Use of wearable devices that monitor only physiological parameters (e.g., temperature, heart rate, oxygen saturation) without inflammatory biomarker assessment (WBC or CRP).
- Patients who are hemodynamically unstable at the time of enrollment.
- Inability or unwillingness to provide informed consent.
- Duplicate enrollment or participation in another interventional study that may interfere with outcomes.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: A Systematic Review of Wearable Infection Detection Wristbands for Postoperative Patients: Evaluating the Efficacy of WBC and CRP Monitoring and AI-Assisted Early Detection
This systematic review aims to evaluate the efficacy, accuracy, and clinical applicability of wearable infection detection wristbands in postoperative patients across ophthalmology, orthopaedic surgery, and general surgery. The review focuses on devices capable of monitoring inflammatory biomarkers-particularly white blood cell (WBC) counts and C-reactive protein (CRP)-and examines the added value of artificial intelligence (AI) algorithms for early infection detection.
The study synthesizes available evidence on clinical outcomes, predictive accuracy, usability, and feasibility of biosensor-based infection surveillance in postoperative care. It is expected to provide an evidence-based framework for integrating wearable biosensors into perioperative management protocols and to guide future multicenter clinical validation studies.
Read the detailed description
Postoperative infection remains one of the most common and serious complications following surgical procedures. Early detection of infection is critical for optimizing outcomes and reducing morbidity. Conventional laboratory monitoring using intermittent WBC and CRP testing is invasive and time-dependent, often delaying timely clinical intervention.
Recent advances in wearable biosensor technology have enabled continuous, non-invasive monitoring of physiological and biochemical parameters. Several wearable platforms are now capable of detecting early inflammatory changes through electrochemical or optical sensing, with CRP being the most validated biomarker. Integration of AI algorithms further enhances predictive performance by analyzing complex data patterns and providing early alerts to clinicians.
This systematic review adheres to PRISMA 2020 guidelines and aims to consolidate available clinical and experimental evidence on wearable biosensors capable of postoperative infection detection, emphasizing WBC and CRP monitoring wristbands and AI-assisted analysis. By synthesizing data from ophthalmology, orthopaedics, and general surgery, the review will assess diagnostic accuracy, clinical outcomes, and feasibility of these technologies in diverse healthcare contexts.
The findings are expected to inform future research directions, highlight existing technological gaps, and propose recommendations for clinical implementation and regulatory validation.
- Conditions
- Wearable Devices
- Being tested
- White Blood Cell (WBC), C-reactive Protein (CRP)
- Keywords
- Wearable biosensors, postoperative infection, artificial intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
White Blood Cell (WBC)Diagnostic test
Detected by wearable device
C-reactive Protein (CRP)Diagnostic test
detected by wearable device
| Study group | Role | What they receive |
|---|---|---|
| Group Monitoring | Wearable infection detection wristbands / biosensors Receives: Diagnostic Test: White Blood Cell (WBC); Diagnostic Test: C-reactive Protein (CRP) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy of Wearable Devices for Detection of Postoperative Infection
Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of wearable devices for detecting postoperative infection, using standard clinical diagnosis as the reference standard.
Measured: 1 week
Time to Postoperative Infection Detection Using Wearable Devices Compared With Standard Care
Time interval between clinical onset of postoperative infection and detection by wearable devices compared with detection by standard postoperative care protocols.
Measured: 1 week
Predictive Accuracy of AI-Integrated Wearable Monitoring for Early Postoperative Infection
Improvement in early postoperative infection prediction accuracy achieved by AI-integrated wearable monitoring compared with traditional laboratory-based monitoring methods.
Measured: 1 week
Secondary outcomes (4)
Secondary outcomes
Patient recovery metrics (readmission rate, wound healing time).
Feasibility and usability of wearable devices in postoperative settings.
Device validation quality and risk of bias across included studies.
Measured: 1 week
Time to Wound Healing
Duration from surgery to clinically confirmed wound healing based on standardized postoperative assessment criteria.
Measured: 1 week
Feasibility and Usability of Wearable Devices in Postoperative Monitoring
Assessment of feasibility and usability of wearable devices in the postoperative setting, including adherence rates, device-related issues, and patient-reported usability scores.
Measured: 1 week
Methodological Quality and Risk of Bias of Wearable Device Validation
Quality of wearable device validation and risk of bias assessed using standardized evaluation tools appropriate for diagnostic accuracy studies.
Measured: 1 week
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Jan 2026
- Study start1 Jul 2025
- Primary completion26 Oct 2025
- Study completion27 Oct 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Wearable DevicesStudies of the same condition, open and under way.
- More trials run by Benha UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 1,284 participants (actual)
- Target duration
- 48 Months
Sponsor and investigators
- Lead sponsor
- Benha University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ehab Mohamed Elsayed Mohamed Saad, Benha University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07334925
- Sponsor's protocol ID
- AI-Assisted Detect Infection
- Acronym
- Wearable Wrist
- Status last verified
- Jul 2025
- First submitted
- 21 Dec 2025
- Last updated
- 12 Jan 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 12 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Systematic Review of Wearable Infection Detection Wristbands for Postoperative Patients: Evaluating the Efficacy of WBC and CRP Monitoring and AI-Assisted Early Detection
- ClinicalTrials.gov ID
- NCT07334925
- Acronym / sponsor ID
- Wearable Wrist / AI-Assisted Detect Infection
- Conditions
- Wearable Devices
- Keywords
- Wearable biosensors, postoperative infection, artificial intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,284 participants (actual)
- Lead sponsor
- Benha UniversityUniversity, hospital or other
- Study start
- 1 Jul 2025 (actual)
- Primary completion
- 26 Oct 2025 (actual)
- Study completion
- 27 Oct 2025 (actual)
- First posted
- 12 Jan 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This systematic review aims to evaluate the efficacy, accuracy, and clinical applicability of wearable infection detection wristbands in postoperative patients across ophthalmology, orthopaedic surgery, and general surgery. The review focuses on devices capable of monitoring inflammatory biomarkers-particularly white blood cell (WBC) counts and C-reactive protein (CRP)-and examines the added value of artificial intelligence (AI) algorithms for early infection detection.
The study synthesizes available evidence on clinical outcomes, predictive accuracy, usability, and feasibility of biosensor-based infection surveillance in postoperative care. It is expected to provide an evidence-based framework for integrating wearable biosensors into perioperative management protocols and to guide future multicenter clinical validation studies.
Detailed description
Postoperative infection remains one of the most common and serious complications following surgical procedures. Early detection of infection is critical for optimizing outcomes and reducing morbidity. Conventional laboratory monitoring using intermittent WBC and CRP testing is invasive and time-dependent, often delaying timely clinical intervention.
Recent advances in wearable biosensor technology have enabled continuous, non-invasive monitoring of physiological and biochemical parameters. Several wearable platforms are now capable of detecting early inflammatory changes through electrochemical or optical sensing, with CRP being the most validated biomarker. Integration of AI algorithms further enhances predictive performance by analyzing complex data patterns and providing early alerts to clinicians.
This systematic review adheres to PRISMA 2020 guidelines and aims to consolidate available clinical and experimental evidence on wearable biosensors capable of postoperative infection detection, emphasizing WBC and CRP monitoring wristbands and AI-assisted analysis. By synthesizing data from ophthalmology, orthopaedics, and general surgery, the review will assess diagnostic accuracy, clinical outcomes, and feasibility of these technologies in diverse healthcare contexts.
The findings are expected to inform future research directions, highlight existing technological gaps, and propose recommendations for clinical implementation and regulatory validation.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Target follow-up
- 48 Months
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Group Monitoring | – | Diagnostic Test: White Blood Cell (WBC), Diagnostic Test: C-reactive Protein (CRP)Wearable infection detection wristbands / biosensors |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population will consist of adult patients (≥18 years) undergoing elective or semi-elective surgical procedures in ophthalmology, orthopedics, and general surgery departments at participating tertiary hospitals. Eligible procedures include surgeries associated with moderate to high postoperative infection risk, such as cataract surgery, corneal transplantation, total joint replacement, and abdominal surgery. Participants must be clinically stable at enrollment and capable of providing informed consent. Both male and female patients will be included without restriction based on race or ethnicity. This population reflects typical postoperative clinical settings in which early infection detection using wearable technology may improve outcomes and reduce morbidity.
Key inclusion criteria (8 listed)
- Adults aged 18 years or older.
- Patients undergoing ophthalmologic, orthopedic, or general surgical procedures.
- Postoperative patients monitored using a wearable infection detection device or biosensor capable of continuous or intermittent assessment of inflammatory biomarkers, including:
- White blood cell (WBC) count and/or
- C-reactive protein (CRP) levels.
Key exclusion criteria (6 listed)
- Patients aged <18 years.
- Non-human studies (animal or in-vitro).
- Use of wearable devices that monitor only physiological parameters (e.g., temperature, heart rate, oxygen saturation) without inflammatory biomarker assessment (WBC or CRP).
- Patients who are hemodynamically unstable at the time of enrollment.
- Inability or unwillingness to provide informed consent.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| White Blood Cell (WBC)Detected by wearable device | Diagnostic test | Group Monitoring |
| C-reactive Protein (CRP)detected by wearable device | Diagnostic test | Group Monitoring |
Primary outcome
Diagnostic Accuracy of Wearable Devices for Detection of Postoperative Infection
Time frame: 1 week
Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of wearable devices for detecting postoperative infection, using standard clinical diagnosis as the reference standard.
Time to Postoperative Infection Detection Using Wearable Devices Compared With Standard Care
Time frame: 1 week
Time interval between clinical onset of postoperative infection and detection by wearable devices compared with detection by standard postoperative care protocols.
Predictive Accuracy of AI-Integrated Wearable Monitoring for Early Postoperative Infection
Time frame: 1 week
Improvement in early postoperative infection prediction accuracy achieved by AI-integrated wearable monitoring compared with traditional laboratory-based monitoring methods.
4 secondary outcomes
Secondary outcomes
Time frame: 1 week
Time to Wound Healing
Time frame: 1 week
Feasibility and Usability of Wearable Devices in Postoperative Monitoring
Time frame: 1 week
Methodological Quality and Risk of Bias of Wearable Device Validation
Time frame: 1 week
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Jan 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Wearable Devices, run by Benha University at 1 site in Egypt.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 12 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy of Wearable Devices for Detection of Postoperative Infection (time frame: 1 week). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Jan 2026; AzTrials last refreshed its copy on 7 Nov 2026.