Technology LiteracyNCT06689319
Factors Linked to AI Literacy in University Students
An observational study of Technology Literacy and Reading Habits, run by Nagihan Acet at 1 site in Turkey (Türkiye).
Trial summary
This study investigates the relationships between artificial intelligence (AI) literacy and factors such as academic achievement, reading habits, smartphone addiction, and internet addiction among university students. Read more about this study
- Who is it for?
- Ages 18 to 35 years
- Conditions the study is for
- Technology Literacy, Reading Habits, Smartphone Addiction
- What is being tested?
- Assessment of Artificial Intelligence Literacy
- Type of treatment
- Lifestyle or therapy programme
- How many people
- 184 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 15 Nov 2024to 15 Apr 2025
- 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 35 years
- All sexes
- No healthy volunteers
- Adult
Study population: This study will involve a selected population of university students aged 18-35, recruited from Atılım University. The participants, both male and female, will be screened based on specific inclusion and exclusion criteria, such as literacy, willingness to participate, and ability to cooperate. The sample includes individuals with varied academic backgrounds relevant to the study objectives.
Who may be able to join
- Being between 18-35 years of age.
- Willingness to participate after receiving detailed information about the study's purpose and methodology.
Who cannot join
- Missing responses in questionnaires.
- Illiteracy.
- Inability to cooperate.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: The Relationship of Artificial Intelligence Literacy With Academic Achievement, Reading Habits, Smartphone Addiction, and Internet Addiction Among University Students
This study investigates the relationships between artificial intelligence (AI) literacy and factors such as academic achievement, reading habits, smartphone addiction, and internet addiction among university students. As AI technologies become increasingly integrated into daily life, AI literacy-necessary for understanding and evaluating AI-is emerging as a critical skill. While factors like academic success and regular reading habits may enhance AI literacy, behaviors like smartphone and internet addiction may have an adverse effect by promoting superficial information access over deeper critical engagement. This prospective, observational, and cross-sectional study will assess AI literacy using the Artificial Intelligence Literacy Scale and analyze its association with academic and behavioral factors. The study will be conducted among participants aged 18-35 in the Physiotherapy and Rehabilitation Department Laboratory at Atılım University. Data will be evaluated using descriptive statistics, correlation analyses (Pearson or Spearman, depending on distribution), and significance testing. The results may highlight the impact of academic and behavioral factors on AI literacy, offering insights for educational strategies aimed at fostering critical AI competencies.
Read the detailed description
Artificial Intelligence (AI), a transformative force within information technology, is a subfield of computer science that involves creating intelligent machines and software that act and respond similarly to humans. With the introduction of ChatGPT, an OpenAI product released in November 2022, the concept of artificial intelligence has gained further popularity. Historically, a significant milestone for AI was the Turing Test, introduced by Alan Turing in 1950 to measure a machine's ability to exhibit human-like behaviors. Following this, the development of expert systems in the 1960s-70s, neural networks in the 1980s, machine learning and data mining in the 1990s, and deep learning in the 2000s each marked pivotal points in the AI timeline . Within the realm of computing, AI is often described as a "man-made homo sapiens" species . AI systems possess foundational skills such as learning, reasoning, self-improvement through experiential learning, language comprehension, and problem-solving, and are programmed as simulations of human intelligence. AI and its applications are utilized to address complex issues across diverse fields-including science, healthcare, education, engineering, business, defense, entertainment, and advertising-by means of expert systems.
The rapid integration of AI technologies into daily life has made it essential for individuals to acquire knowledge and skills related to these technologies. AI literacy represents an understanding and awareness of core artificial intelligence concepts. In this context, AI literacy is a fundamental competency that enables individuals to understand, utilize, and critically evaluate AI technologies, recognizing both their benefits and limitations. Having AI literacy can help individuals understand and manage AI technologies, offering an opportunity to become more informed and capable individuals. Therefore, it has become essential for everyone today to possess and enhance their AI literacy.
Factors such as reading habits and levels of academic achievement may positively influence the development of AI literacy. Individuals who have regular reading habits typically develop critical thinking and in-depth analysis skills, which facilitate understanding and critically evaluating AI technologies. Similarly, individuals with high academic performance are often experienced in accessing and applying knowledge, making them more adaptable to the foundational skills required for gaining AI literacy.
However, behaviors like internet addiction and smartphone addiction, while facilitating access to AI technologies, may have an adverse effect on AI literacy. Internet addiction reinforces a habit of accessing information rapidly and superficially, which can reduce critical thinking and focus. Likewise, smartphone addiction, due to its provision of constant and superficial access to information, may diminish interest in the deep thinking processes required for AI literacy. Therefore, internet and smartphone addiction could act as barriers in the processes requiring deep thought, analysis, and accumulation of knowledge essential for AI literacy.
To our knowledge, there is no comprehensive study that examines AI literacy among university students in relation to academic achievement, reading habits, smartphone addiction, and internet addiction from a multifaceted perspective.
The aim of this study is to reveal the relationships between university students' AI literacy and their levels of academic achievement, reading habits, internet addiction, and smartphone addiction.
- Conditions
- Technology Literacy, Reading Habits, Smartphone Addiction, Internet Addiction, Academic Acheivement
- Being tested
- Assessment of Artificial Intelligence Literacy
- Keywords
- artificial intelligence, reading habits, smartphone addiction, internet addiction
- Medical subject headings
- Internet Addiction Disorder
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Assessment of Artificial Intelligence LiteracyBehavioural
The Artificial Intelligence Literacy Scale will be used to determine the level of AI literacy.. The scale is a 12-item instrument designed to measure individuals' knowledge and skills in AI awareness, usage, evaluation, and ethical considerations. Items are rated on a Likert scale from 1 to 7 (1: Strongly Disagree, 7: Strongly Agree), with some items reverse-coded (items 2, 5, and 11). The minimum possible score on the scale is 12, and the maximum score is 84; a higher score indicates a higher level of AI literacy. The Turkish version of the scale will be used in this study.
| Study group | Role | What they receive |
|---|---|---|
| The group to be evaluated in terms of AI literacy | Not described Receives: Behavioral: Assessment of Artificial Intelligence Literacy |
What is being measured
The primary outcome is the main question the study is designed to answer.
Assessment of reading habits
Assessment of reading habits The Self-Report Habit Index will be used to assess reading habits . The Reading Habits Questionnaire is a 12-item instrument designed to assess individuals' reading habits, covering dimensions such as reading frequency, duration, preferred materials (books, magazines, online content, etc.), reading purpose, and reading environment. The questionnaire allows participants to respond on a 5-point Likert scale (1: Never, 5: Always). The total score obtained is used to interpret an individual's reading habits: low scores indicate infrequent reading, moderate scores represent regular but not intensive reading habits, and high scores reflect frequent reading of diverse materials. This assessment helps determine the level of an individual's reading habits and identify areas for potential improvement.
Measured: Day 1
Assessment of smartphone addiction
Smartphone addiction will be assessed using the Smartphone Addiction Scale - Short Form. This is a 10-item scale used to evaluate individuals' smartphone usage habits.Each item is scored from 1 (Strongly Disagree) to 6 (Strongly Agree), with a minimum total score of 10 and a maximum of 60. Higher scores indicate a greater risk of addiction and provide a quick assessment.
Measured: Day 1
Assessment of internet addiction
Internet addiction will be assessed using the Internet Addiction Scale - Short Form, an instrument designed to evaluate individuals' internet usage habits. Originally developed by Young (1998), the scale has been adapted as a short form consisting of 6 items for a quick assessment of internet addiction [14]. The Turkish version will be used [15]. Each item is rated from 1 (Never) to 5 (Always), with a total score ranging from 6 to 30. Higher scores indicate an increased risk of internet addiction.
Measured: Day 1
Assessment of academic achievement
The level of academic achievement will be assessed based on the cumulative grade point average (GPA) from the previous semester. This measure provides an objective indicator of students' overall academic performance, capturing their sustained efforts and intellectual engagement in coursework.
Measured: Day 1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Nov 2024
- Study start15 Nov 2024
- Primary completion15 Mar 2025
- Study completion15 Apr 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (14)Papers the sponsor has linked to this study.
- Other trials for Technology LiteracyStudies 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
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 184 participants (actual)
Sponsor and investigators
- Lead sponsor
- Nagihan Acet
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Nagihan Acet, Atılım University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06689319
- Sponsor's protocol ID
- Atılım University_5
- Status last verified
- Nov 2025
- First submitted
- 13 Nov 2024
- Last updated
- 25 Nov 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Kutlu, M., et al., Turkish adaptation of Young's Internet Addiction Test-Short Form: A reliability and validity study on university students and adolescents/Young Internet Bagimliligi Testi Kisa Formunun Turkce uyarlamasi: Universite ogrencileri ve ergenlerde gecerlilik ve guvenilirlik calismasi. Anadolu Psikiyatri Dergisi, 2016. 17(S1): p. 69-77.
Young, K.S., Internet addiction test. Center for on-line addictions, 2009.
Noyan, C.O., et al., Validity and reliability of the Turkish version of the Smartphone Addiction Scale-Short version among university students/Akilli Telefon Bagimliligi Olceginin Kisa Formunun universite ogrencilerinde Turkce gecerlilik ve guvenilirlik calismasi. Anadolu Psikiyatri Dergisi, 2015. 16(S1): p. 73-82.
Kwon M, Kim DJ, Cho H, Yang S. The smartphone addiction scale: development and validation of a short version for adolescents. PLoS One. 2013 Dec 31;8(12):e83558. doi: 10.1371/journal.pone.0083558. eCollection 2013.
PubMed 24391787Verplanken, B. and S. Orbell, Reflections on past behavior: a self-report index of habit strength 1. Journal of applied social psychology, 2003. 33(6): p. 1313-1330.
Çelebi, C., et al., Artificial intelligence literacy: An adaptation study. Instructional Technology and Lifelong Learning, 2023. 4(2): p. 291-306.
Wang, B., P.-L.P. Rau, and T. Yuan, Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behaviour & information technology, 2023. 42(9): p. 1324-1337.
Kong, S.-C., W.M.-Y. Cheung, and G. Zhang, Evaluating an artificial intelligence literacy programme for developing university students' conceptual understanding, literacy, empowerment and ethical awareness. Educational Technology & Society, 2023. 26(1): p. 16-30.
Laupichler, M.C., et al., Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 2022. 3: p. 100101.
Copeland, B.J. and D. Proudfoot, Artificial intelligence: History, foundations, and philosophical issues, in Philosophy of psychology and cognitive science. 2007, Elsevier. p. 429-482.
Haenlein, M. and A. Kaplan, A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review, 2019. 61(4): p. 5-14.
Turing, A.M., Computing machinery and intelligence. 2009: Springer.
Muggleton, S., Alan Turing and the development of Artificial Intelligence. AI communications, 2014. 27(1): p. 3-10.
Kamble, R. and D. Shah, Applications of artificial intelligence in human life. International Journal of Research-Granthaalayah, 2018. 6(6): p. 178-188.
Study at a glance
From the registry record, last updated by the sponsor on 25 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Relationship of Artificial Intelligence Literacy With Academic Achievement, Reading Habits, Smartphone Addiction, and Internet Addiction Among University Students
- ClinicalTrials.gov ID
- NCT06689319
- Acronym / sponsor ID
- Atılım University_5
- Conditions
- Technology Literacy, Reading Habits, Smartphone Addiction, Internet Addiction, Academic Acheivement
- Keywords
- artificial intelligence, reading habits, smartphone addiction, internet addiction
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 184 participants (actual)
- Lead sponsor
- Nagihan AcetUniversity, hospital or other
- Study start
- 15 Nov 2024 (actual)
- Primary completion
- 15 Mar 2025 (actual)
- Study completion
- 15 Apr 2025 (actual)
- First posted
- 14 Nov 2024 (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 study investigates the relationships between artificial intelligence (AI) literacy and factors such as academic achievement, reading habits, smartphone addiction, and internet addiction among university students. As AI technologies become increasingly integrated into daily life, AI literacy-necessary for understanding and evaluating AI-is emerging as a critical skill. While factors like academic success and regular reading habits may enhance AI literacy, behaviors like smartphone and internet addiction may have an adverse effect by promoting superficial information access over deeper critical engagement. This prospective, observational, and cross-sectional study will assess AI literacy using the Artificial Intelligence Literacy Scale and analyze its association with academic and behavioral factors. The study will be conducted among participants aged 18-35 in the Physiotherapy and Rehabilitation Department Laboratory at Atılım University. Data will be evaluated using descriptive statistics, correlation analyses (Pearson or Spearman, depending on distribution), and significance testing. The results may highlight the impact of academic and behavioral factors on AI literacy, offering insights for educational strategies aimed at fostering critical AI competencies.
Detailed description
Artificial Intelligence (AI), a transformative force within information technology, is a subfield of computer science that involves creating intelligent machines and software that act and respond similarly to humans. With the introduction of ChatGPT, an OpenAI product released in November 2022, the concept of artificial intelligence has gained further popularity. Historically, a significant milestone for AI was the Turing Test, introduced by Alan Turing in 1950 to measure a machine's ability to exhibit human-like behaviors. Following this, the development of expert systems in the 1960s-70s, neural networks in the 1980s, machine learning and data mining in the 1990s, and deep learning in the 2000s each marked pivotal points in the AI timeline . Within the realm of computing, AI is often described as a "man-made homo sapiens" species . AI systems possess foundational skills such as learning, reasoning, self-improvement through experiential learning, language comprehension, and problem-solving, and are programmed as simulations of human intelligence. AI and its applications are utilized to address complex issues across diverse fields-including science, healthcare, education, engineering, business, defense, entertainment, and advertising-by means of expert systems.
The rapid integration of AI technologies into daily life has made it essential for individuals to acquire knowledge and skills related to these technologies. AI literacy represents an understanding and awareness of core artificial intelligence concepts. In this context, AI literacy is a fundamental competency that enables individuals to understand, utilize, and critically evaluate AI technologies, recognizing both their benefits and limitations. Having AI literacy can help individuals understand and manage AI technologies, offering an opportunity to become more informed and capable individuals. Therefore, it has become essential for everyone today to possess and enhance their AI literacy.
Factors such as reading habits and levels of academic achievement may positively influence the development of AI literacy. Individuals who have regular reading habits typically develop critical thinking and in-depth analysis skills, which facilitate understanding and critically evaluating AI technologies. Similarly, individuals with high academic performance are often experienced in accessing and applying knowledge, making them more adaptable to the foundational skills required for gaining AI literacy.
However, behaviors like internet addiction and smartphone addiction, while facilitating access to AI technologies, may have an adverse effect on AI literacy. Internet addiction reinforces a habit of accessing information rapidly and superficially, which can reduce critical thinking and focus. Likewise, smartphone addiction, due to its provision of constant and superficial access to information, may diminish interest in the deep thinking processes required for AI literacy. Therefore, internet and smartphone addiction could act as barriers in the processes requiring deep thought, analysis, and accumulation of knowledge essential for AI literacy.
To our knowledge, there is no comprehensive study that examines AI literacy among university students in relation to academic achievement, reading habits, smartphone addiction, and internet addiction from a multifaceted perspective.
The aim of this study is to reveal the relationships between university students' AI literacy and their levels of academic achievement, reading habits, internet addiction, and smartphone addiction.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| The group to be evaluated in terms of AI literacy | – | Behavioral: Assessment of Artificial Intelligence Literacy |
Who may qualify
- Ages
- 18 to 35 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
- Study population
- This study will involve a selected population of university students aged 18-35, recruited from Atılım University. The participants, both male and female, will be screened based on specific inclusion and exclusion criteria, such as literacy, willingness to participate, and ability to cooperate. The sample includes individuals with varied academic backgrounds relevant to the study objectives.
Key inclusion criteria (2 listed)
- Being between 18-35 years of age.
- Willingness to participate after receiving detailed information about the study's purpose and methodology.
Key exclusion criteria (3 listed)
- Missing responses in questionnaires.
- Illiteracy.
- Inability to cooperate.
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 |
|---|---|---|
| Assessment of Artificial Intelligence LiteracyThe Artificial Intelligence Literacy Scale will be used to determine the level of AI literacy.. The scale is a 12-item instrument designed to measure individuals' knowledge and skills in AI awareness, usage, evaluation, and ethical considerations. Items are rated on a Likert scale from 1 to 7 (1: Strongly Disagree, 7: Strongly Agree), with some items reverse-coded (items 2, 5, and 11). The minimum possible score on the scale is 12, and the maximum score is 84; a higher score indicates a higher level of AI literacy. The Turkish version of the scale will be used in this study. | Behavioural | The group to be evaluated in terms of AI literacy |
Primary outcome
Assessment of reading habits
Time frame: Day 1
Assessment of reading habits The Self-Report Habit Index will be used to assess reading habits . The Reading Habits Questionnaire is a 12-item instrument designed to assess individuals' reading habits, covering dimensions such as reading frequency, duration, preferred materials (books, magazines, online content, etc.), reading purpose, and reading environment. The questionnaire allows participants to respond on a 5-point Likert scale (1: Never, 5: Always). The total score obtained is used to interpret an individual's reading habits: low scores indicate infrequent reading, moderate scores represent regular but not intensive reading habits, and high scores reflect frequent reading of diverse materials. This assessment helps determine the level of an individual's reading habits and identify areas for potential improvement.
Assessment of smartphone addiction
Time frame: Day 1
Smartphone addiction will be assessed using the Smartphone Addiction Scale - Short Form. This is a 10-item scale used to evaluate individuals' smartphone usage habits.Each item is scored from 1 (Strongly Disagree) to 6 (Strongly Agree), with a minimum total score of 10 and a maximum of 60. Higher scores indicate a greater risk of addiction and provide a quick assessment.
Assessment of internet addiction
Time frame: Day 1
Internet addiction will be assessed using the Internet Addiction Scale - Short Form, an instrument designed to evaluate individuals' internet usage habits. Originally developed by Young (1998), the scale has been adapted as a short form consisting of 6 items for a quick assessment of internet addiction [14]. The Turkish version will be used [15]. Each item is rated from 1 (Never) to 5 (Always), with a total score ranging from 6 to 30. Higher scores indicate an increased risk of internet addiction.
Assessment of academic achievement
Time frame: Day 1
The level of academic achievement will be assessed based on the cumulative grade point average (GPA) from the previous semester. This measure provides an objective indicator of students' overall academic performance, capturing their sustained efforts and intellectual engagement in coursework.
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 25 Nov 2025.
- Results publicationKutlu, M., et al., Turkish adaptation of Young's Internet Addiction Test-Short Form: A reliability and validity study on university students and adolescents/Young Internet Bagimliligi Testi Kisa Formunun Turkce uyarlamasi: Universite ogrencileri ve ergenlerde gecerlilik ve guvenilirlik calismasi. Anadolu Psikiyatri Dergisi, 2016. 17(S1): p. 69-77.
- Results publicationYoung, K.S., Internet addiction test. Center for on-line addictions, 2009.
- Results publicationNoyan, C.O., et al., Validity and reliability of the Turkish version of the Smartphone Addiction Scale-Short version among university students/Akilli Telefon Bagimliligi Olceginin Kisa Formunun universite ogrencilerinde Turkce gecerlilik ve guvenilirlik calismasi. Anadolu Psikiyatri Dergisi, 2015. 16(S1): p. 73-82.
- Results publication: PubMed 24391787Kwon M, Kim DJ, Cho H, Yang S. The smartphone addiction scale: development and validation of a short version for adolescents. PLoS One. 2013 Dec 31;8(12):e83558. doi: 10.1371/journal.pone.0083558. eCollection 2013.
- Results publicationVerplanken, B. and S. Orbell, Reflections on past behavior: a self-report index of habit strength 1. Journal of applied social psychology, 2003. 33(6): p. 1313-1330.
- Results publicationÇelebi, C., et al., Artificial intelligence literacy: An adaptation study. Instructional Technology and Lifelong Learning, 2023. 4(2): p. 291-306.
- Results publicationWang, B., P.-L.P. Rau, and T. Yuan, Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behaviour & information technology, 2023. 42(9): p. 1324-1337.
- Results publicationKong, S.-C., W.M.-Y. Cheung, and G. Zhang, Evaluating an artificial intelligence literacy programme for developing university students' conceptual understanding, literacy, empowerment and ethical awareness. Educational Technology & Society, 2023. 26(1): p. 16-30.
- Results publicationLaupichler, M.C., et al., Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 2022. 3: p. 100101.
- Results publicationCopeland, B.J. and D. Proudfoot, Artificial intelligence: History, foundations, and philosophical issues, in Philosophy of psychology and cognitive science. 2007, Elsevier. p. 429-482.
- Results publicationHaenlein, M. and A. Kaplan, A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review, 2019. 61(4): p. 5-14.
- Results publicationTuring, A.M., Computing machinery and intelligence. 2009: Springer.
- 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 Technology Literacy and Reading Habits, run by Nagihan Acet at 1 site in Turkey (Türkiye).
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 2 inclusion and 3 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 25 Nov 2025. 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 Turkey (Türkiye). 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?
Assessment of reading habits (time frame: Day 1). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06689319, which also lists 14 linked publications.
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 25 Nov 2025.