APP Elaborates Data Input From the Patient With Foot UlcerMYFOOT-DNCT05829811
Tele-homecare Service for Diabetic Foot Patients (Risk 0, Risk 1 and Risk 2 Level): Testing and Validation of Dedicated APPs and Artificial Intelligence Solutions
An observational study of APP Elaborates Data Input From the Patient With Foot Ulcer, run by Faculty Hospital AGEL Skalica.
Trial summary
MY FOOT project aims at filling the gap in mobile applications by providing evidence to both involved stakeholders, that is the remote assistance from the hospital and the patient, who is directly involved in their own care strategy. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- APP Elaborates Data Input From the Patient With Foot Ulcer
- What is being tested?
- Lifestyle APP and Artificial Intelligence solutions validation
- Type of treatment
- Medical device
- How many people
- 100 participants
- Where
- No sites listed yet
- Study dates
- 1 Sep 2023to 1 Sep 2026
- How long will I take part?
- 12 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 to 80 years
- All sexes
- Adult, Older adult
Study population: Recruitment will be performed from the ranks of guests of the Special Hospital Merkur, who come to the program from all over the Republic of Serbia. That is why Merkur is in contact with colleagues from all health centers throughout Serbia, which have diabetes counseling centers. From 5 to 8 patients with diabetes will be invited weekly to recruit the required number of participants in the pilot. AGEL: Diabetic foot patients are distributed in different health centres of the Serbian cluster. Four diabetic outpatient units will refer patients by their responsible physicians according to inclusion and exclusion criteria. There are already more than 800 patients follow up in home environment.
Who may be able to join
- The sample will include all the people accommodated in Special hospital Merkur, who sign the informed consent. Patients aged between 18 and 80 years. Patients diagnosed with Diabetes Mellitus (more than 5 years from diagnosis). Participant has adequate circulation to the affected extremity(ies), as demonstrated by at least ONE of the following within 60 days prior to enrolment/randomization: a) Dorsum transcutaneous oxygen test (TcPO2) of study leg(s) with results ≥40mmHg, OR, b) Ankle-Brachial Index (ABI) of study leg(s) with results of ≥ 0.7 and ≤ 1.3, OR; C) Toe-Brachial Index (TBI) of study extremity(ies) with results of ≥ 0.5.
Who cannot join
- People who do not give their consent to participate in the study, who do not have a mobile phone, or live in an area not covered by a mobile signal and the Internet. Participant who is pregnant, breast feeding or planning to become pregnant. Participant having multiple foot ulcers, an amputation of the forefoot or an amputation at a more proximal location of the foot. Participant having a cancer disease or life expectancy less than six months as assessed by the investigator or undergoing cancer treatment; Participant having a severe foot infection; Active infection, undrained abscess, or critical colonization of the wound(s) with bacteria in the judgment of the investigator; Participant with Hgb A1c > 12 percent within 3 months prior to randomization; Participant with a known history of poor compliance with medical treatments.
About this study
MY FOOT project aims at filling the gap in mobile applications by providing evidence to both involved stakeholders, that is the remote assistance from the hospital and the patient, who is directly involved in their own care strategy.
In particular, the application has to motivate patients and engage them in their self-care. Interaction with the mobile phone application is in the following terms:
APP elaborates data input from the patient in terms of own feeling of health status, symptoms revealed along the day, events eventually occurred, photos of the foot, including ulcer zoom (if any), APP reports back about increase / decrease in the Risk Level graph through time, maps the ulcer evolution or healing based on photos elaboration, using adequate graphs reporting time in the main axis, whilst reminds personal goals to enact care on a regular basis on the basis of the current conditions, eventually alerts the patient to contact clinicians for a visual inspection at a hospital.
Read the detailed description
Without effective self-care, people with diabetic foot ulcers (DFUs) are at risk of prolonged healing times, hospitalization, amputation, and reduced quality of life. Despite these consequences, adherence to DFU self-care remains low.
As already pointed out in the preceding paragraphs, patient education in the prevention of diabetic foot ulcers found has been recognised as providing positive short-term effects on knowledge about care of the foot, delaying foot ulceration and amputation, especially in high-risk patients. New strategies are needed to engage people in the self-care of their DFUs.
Modern information technology may assist in attracting patients' interest if any direct benefit is promptly perceived by the patient who uses it . It is also of utmost importance that health professionals, especially those who work with diabetic patients on a daily basis, are aware of such practices and then be able to convince patients they merit the best care possible to avoid any further degradation of the pathology.
Mobile health apps hold great promise for people with diabetes, but few apps seek to engage people in their DFU self-care , , .
Schäfer et al. examined the risk factors of developing diabetic foot ulcers and amputation among patients with diabetes. They concluded that prediction and on-time treatment of diabetic foot ulcers (DFU) are of great importance for improving and maintaining patients' quality of life and avoiding the consequent socio-economic burden of amputation.
Machine learning is a part of the field of artificial intelligence, which can automatically learn models from data and better inform clinical decision-making. Xie et al. developed an accurate and explainable prediction model to estimate the risk of in-hospital amputation in 618 hospitalized patients with DFU. They concluded that machine learning model provided accurate estimates of the amputation rate in patients with DFU during hospitalisation. Besides, the model could inform individualised analyses of the patients' risk factors.
The severe complications associated with diabetes include diabetic ketoacidosis, nonketotic hypersmolar coma, cardiovascular disease, stroke, chronic renal failure, retinal damage and foot ulcers. There is a huge increase in the number of patients with diabetes globally and it is considered a major health problem worldwide. Early diagnosis of diabetes is helpful for treatment and reduces the chance of severe complications associated with it. Machine learning algorithms (such as ANN, SVM, Naive Bayes, PLS-DA and deep learning) and data mining techniques are used for detecting interesting patterns for diagnosing and treatment of disease. Current computational methods for diabetes diagnosis have some limitations and are not tested on different datasets or people from different countries which limits the practical use of prediction methods .
A system deploying artificial intelligence and machine learning, developed by a startup based in Vrnjacka banja (Serbia), can help predict the development of complications in the feet of diabetic patients.
MY FOOT project aims at filling the gap in mobile applications by providing evidence to both involved stakeholders, that is the remote assistance from the hospital and the patient, who is directly involved in their own care strategy.
In particular, the application has to motivate patients and engage them in their self-care. Interaction with the mobile phone application is in the following terms:
APP elaborates data input from the patient in terms of own feeling of health status, symptoms revealed along the day, events eventually occurred, photos of the foot, including ulcer zoom (if any), APP reports back about increase / decrease in the Risk Level graph through time, maps the ulcer evolution or healing based on photos elaboration, using adequate graphs reporting time in the main axis, whilst reminds personal goals to enact care on a regular basis on the basis of the current conditions, eventually alerts the patient to contact clinicians for a visual inspection at a hospital.
- Being tested
- Lifestyle APP and Artificial Intelligence solutions validation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Lifestyle APP and Artificial Intelligence solutions validationDevice
The lifestyle APP will take into account all the psychological aspect linked to the disease, including the social isolation. Lifestyle APP is not a measurement technology but it is a preventive measures against diabetes evolution that provide insights and advices daily, by the collection of structured data of parameters, essential to feed the AI for Risk analysis module. The involvement of Physicians expert in this discipline will evaluate the effectiveness of the psychosocial education.
What is being measured
The primary outcome is the main question the study is designed to answer.
Detection of vascular disorders
The diabetes care is implemented by highly qualified health care professionals and continuously supervised by the leading experts from the university hospitals in Serbia. Moreover, the professors of endocrinology, neurology, vascular surgeon and diabetology from the medical universities of Belgrade and Kragujevac and AGEL supervise all those activities
Measured: 12 months
Detection of neurological disorders
The diabetes care is implemented by highly qualified health care professionals and continuously supervised by the leading experts from the university hospitals in Serbia. Moreover, the professors of endocrinology, neurology, vascular surgeon and diabetology from the medical universities of Belgrade and Kragujevac and AGEL supervise all those activities
Measured: 12 months
Secondary outcomes (2)
Analysing of potential risk factors contributed into Risk 0 and 1
- fill the gap of mobile applications in providing evidence to both involved stakeholders, that is the remote assistance from the hospital and the patient, who is directly involved in own care strategy
Measured: 12 months
Analysing of potential precipitating factors for DFU
- fill the gap of mobile applications in providing evidence to both involved stakeholders, that is the remote assistance from the hospital and the patient, who is directly involved in own care strategy
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Apr 2023
- Study start1 Sep 2023 (estimated)
- Primary completion1 Sep 2025 (estimated)
- Study completion1 Sep 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for APP Elaborates Data Input From the Patient With Foot UlcerStudies of the same condition, open and under way.
- 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
- Case-control
- Time perspective
- Prospective
- Enrolment
- 100 participants (estimated)
- Target duration
- 12 Months
Sponsor and investigators
- Lead sponsor
- Faculty Hospital AGEL Skalica
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05829811
- Sponsor's protocol ID
- 101095372
- Acronym
- MYFOOT-D
- Status last verified
- Apr 2023
- First submitted
- 13 Apr 2023
- Last updated
- 26 Apr 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 26 Apr 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Tele-homecare Service for Diabetic Foot Patients (Risk 0, Risk 1 and Risk 2 Level): Testing and Validation of Dedicated APPs and Artificial Intelligence Solutions
- ClinicalTrials.gov ID
- NCT05829811
- Acronym / sponsor ID
- MYFOOT-D / 101095372
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Faculty Hospital AGEL SkalicaUniversity, hospital or other
- Study start
- 1 Sep 2023 (estimated)
- Primary completion
- 1 Sep 2025 (estimated)
- Study completion
- 1 Sep 2026 (estimated)
- First posted
- 26 Apr 2023 (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.
MY FOOT project aims at filling the gap in mobile applications by providing evidence to both involved stakeholders, that is the remote assistance from the hospital and the patient, who is directly involved in their own care strategy.
In particular, the application has to motivate patients and engage them in their self-care. Interaction with the mobile phone application is in the following terms:
APP elaborates data input from the patient in terms of own feeling of health status, symptoms revealed along the day, events eventually occurred, photos of the foot, including ulcer zoom (if any), APP reports back about increase / decrease in the Risk Level graph through time, maps the ulcer evolution or healing based on photos elaboration, using adequate graphs reporting time in the main axis, whilst reminds personal goals to enact care on a regular basis on the basis of the current conditions, eventually alerts the patient to contact clinicians for a visual inspection at a hospital.
Detailed description
Without effective self-care, people with diabetic foot ulcers (DFUs) are at risk of prolonged healing times, hospitalization, amputation, and reduced quality of life. Despite these consequences, adherence to DFU self-care remains low.
As already pointed out in the preceding paragraphs, patient education in the prevention of diabetic foot ulcers found has been recognised as providing positive short-term effects on knowledge about care of the foot, delaying foot ulceration and amputation, especially in high-risk patients. New strategies are needed to engage people in the self-care of their DFUs.
Modern information technology may assist in attracting patients' interest if any direct benefit is promptly perceived by the patient who uses it . It is also of utmost importance that health professionals, especially those who work with diabetic patients on a daily basis, are aware of such practices and then be able to convince patients they merit the best care possible to avoid any further degradation of the pathology.
Mobile health apps hold great promise for people with diabetes, but few apps seek to engage people in their DFU self-care , , .
Schäfer et al. examined the risk factors of developing diabetic foot ulcers and amputation among patients with diabetes. They concluded that prediction and on-time treatment of diabetic foot ulcers (DFU) are of great importance for improving and maintaining patients' quality of life and avoiding the consequent socio-economic burden of amputation.
Machine learning is a part of the field of artificial intelligence, which can automatically learn models from data and better inform clinical decision-making. Xie et al. developed an accurate and explainable prediction model to estimate the risk of in-hospital amputation in 618 hospitalized patients with DFU. They concluded that machine learning model provided accurate estimates of the amputation rate in patients with DFU during hospitalisation. Besides, the model could inform individualised analyses of the patients' risk factors.
The severe complications associated with diabetes include diabetic ketoacidosis, nonketotic hypersmolar coma, cardiovascular disease, stroke, chronic renal failure, retinal damage and foot ulcers. There is a huge increase in the number of patients with diabetes globally and it is considered a major health problem worldwide. Early diagnosis of diabetes is helpful for treatment and reduces the chance of severe complications associated with it. Machine learning algorithms (such as ANN, SVM, Naive Bayes, PLS-DA and deep learning) and data mining techniques are used for detecting interesting patterns for diagnosing and treatment of disease. Current computational methods for diabetes diagnosis have some limitations and are not tested on different datasets or people from different countries which limits the practical use of prediction methods .
A system deploying artificial intelligence and machine learning, developed by a startup based in Vrnjacka banja (Serbia), can help predict the development of complications in the feet of diabetic patients.
MY FOOT project aims at filling the gap in mobile applications by providing evidence to both involved stakeholders, that is the remote assistance from the hospital and the patient, who is directly involved in their own care strategy.
In particular, the application has to motivate patients and engage them in their self-care. Interaction with the mobile phone application is in the following terms:
APP elaborates data input from the patient in terms of own feeling of health status, symptoms revealed along the day, events eventually occurred, photos of the foot, including ulcer zoom (if any), APP reports back about increase / decrease in the Risk Level graph through time, maps the ulcer evolution or healing based on photos elaboration, using adequate graphs reporting time in the main axis, whilst reminds personal goals to enact care on a regular basis on the basis of the current conditions, eventually alerts the patient to contact clinicians for a visual inspection at a hospital.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Target follow-up
- 12 Months
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Recruitment will be performed from the ranks of guests of the Special Hospital Merkur, who come to the program from all over the Republic of Serbia. That is why Merkur is in contact with colleagues from all health centers throughout Serbia, which have diabetes counseling centers. From 5 to 8 patients with diabetes will be invited weekly to recruit the required number of participants in the pilot. AGEL: Diabetic foot patients are distributed in different health centres of the Serbian cluster. Four diabetic outpatient units will refer patients by their responsible physicians according to inclusion and exclusion criteria. There are already more than 800 patients follow up in home environment.
Key inclusion criteria (1 listed)
- The sample will include all the people accommodated in Special hospital Merkur, who sign the informed consent. Patients aged between 18 and 80 years. Patients diagnosed with Diabetes Mellitus (more than 5 years from diagnosis). Participant has adequate circulation to the affected extremity(ies), as demonstrated by at least ONE of the following within 60 days prior to enrolment/randomization: a) Dorsum transcutaneous oxygen test (TcPO2) of study leg(s) with results ≥40mmHg, OR, b) Ankle-Brachial Index (ABI) of study leg(s) with results of ≥ 0.7 and ≤ 1.3, OR; C) Toe-Brachial Index (TBI) of study extremity(ies) with results of ≥ 0.5.
Key exclusion criteria (1 listed)
- People who do not give their consent to participate in the study, who do not have a mobile phone, or live in an area not covered by a mobile signal and the Internet. Participant who is pregnant, breast feeding or planning to become pregnant. Participant having multiple foot ulcers, an amputation of the forefoot or an amputation at a more proximal location of the foot. Participant having a cancer disease or life expectancy less than six months as assessed by the investigator or undergoing cancer treatment; Participant having a severe foot infection; Active infection, undrained abscess, or critical colonization of the wound(s) with bacteria in the judgment of the investigator; Participant with Hgb A1c > 12 percent within 3 months prior to randomization; Participant with a known history of poor compliance with medical treatments.
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 |
|---|---|---|
| Lifestyle APP and Artificial Intelligence solutions validationThe lifestyle APP will take into account all the psychological aspect linked to the disease, including the social isolation. Lifestyle APP is not a measurement technology but it is a preventive measures against diabetes evolution that provide insights and advices daily, by the collection of structured data of parameters, essential to feed the AI for Risk analysis module. The involvement of Physicians expert in this discipline will evaluate the effectiveness of the psychosocial education. | Device | – |
Primary outcome
Detection of vascular disorders
Time frame: 12 months
The diabetes care is implemented by highly qualified health care professionals and continuously supervised by the leading experts from the university hospitals in Serbia. Moreover, the professors of endocrinology, neurology, vascular surgeon and diabetology from the medical universities of Belgrade and Kragujevac and AGEL supervise all those activities
Detection of neurological disorders
Time frame: 12 months
The diabetes care is implemented by highly qualified health care professionals and continuously supervised by the leading experts from the university hospitals in Serbia. Moreover, the professors of endocrinology, neurology, vascular surgeon and diabetology from the medical universities of Belgrade and Kragujevac and AGEL supervise all those activities
2 secondary outcomes
Analysing of potential risk factors contributed into Risk 0 and 1
Time frame: 12 months
Analysing of potential precipitating factors for DFU
Time frame: 12 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Apr 2023.
- Individual participant dataSharing planned: No
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.
Questions about this trial
What is this clinical trial studying?
An observational study of APP Elaborates Data Input From the Patient With Foot Ulcer, run by Faculty Hospital AGEL Skalica.
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 1 inclusion and 1 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: status not confirmed, as of the sponsor's last update on 26 Apr 2023. Status can change between updates, so confirm with the study contact before making plans.
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?
Results are not available in the source record.
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?
Detection of vascular disorders (time frame: 12 months). The study lists 2 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 26 Apr 2023.