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| ID | Type | Description | Link |
|---|---|---|---|
| 3P30MH062246-22S3 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| AiCure | INDUSTRY |
| RTI International | OTHER |
| National Institute of Mental Health (NIMH) | NIH |
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Young adults have a disproportionately high rate of HIV infection, high rates of attrition at all stages of the HIV care continuum, an increased risk of antiretroviral therapy (ART) nonadherence and virologic failure, and a high probability of disease progression and transmission. Tracking and monitoring objective measures of ART adherence in real time is critical to strategies to support adherence and improve clinical outcomes. However, adherence monitoring often relies on self-reported and retrospective data or requires extra effort from providers to understand adherence patterns, making it difficult for providers to accurately determine how to support their patients in real time. In the proposed interventional study, the investigators aim to pilot test an automated directly observed therapy intervention paired with conditional economic incentives to improve ART adherence among youth living with HIV (YLWH) (18-29 years-old) who have an unsuppressed HIV viral load.
Aim 1: Conduct a pilot study to assess feasibility and acceptability of the use of automated directly observed therapy with conditional economic incentives (aDOT-CEI) among YLWH (aged 18-29; N= 30) at AIDS Healthcare Foundation (AHF) clinics in California and Florida. Primary outcomes will be feasibility and acceptability, assessed using predefined feasibility metrics and acceptability surveys at three months.
Aim 2: Explore experiences of YLWH and staff/providers with the aDOT-CEI intervention and implementation facilitators and barriers. The investigators will conduct in-depth qualitative interviews with a sample of YLWH from Aim 1 and staff/providers purposively selected from participating AHF clinics to explore intervention experiences, potential influences on ART adherence, individual-level and clinic-level barriers and facilitators to intervention implementation, and suggested refinements for a future efficacy trial.
The investigators hypothesize that the aDOT-CEI intervention to improve ART adherence among YLWH will have high feasibility and acceptability.
Automated directly observed therapy (aDOT) is an innovative technology that uses artificial intelligence (AI) with computer vision and deep learning algorithms to track and support adherence through a smartphone. Additionally, aDOT provides a seamless and convenient platform for providing Conditional Economic Incentives (CEIs) because it monitors real-time adherence to automatically determine who can receive incentives. For the design and development of the mobile health app, the investigators have partnered with AiCure to use an existing HIPAA-compliant mobile health app. The investigators will invite YLWH from AIDS Healthcare Foundation (AHF) sites in CA and FL to form the study Youth Advisory Panel (YAP) and seek their input on the AiCure app. The investigators will work with AiCure to implement any required changes to the app that have emerged from formative research. The app will then be piloted with YLWH (aged 18-29; N= 30) who will use the platform for a period of 3 months (Aim 1).
The app will record video of the participant taking their HIV medication in order to monitor the participant's medication adherence. Participants will complete online surveys at baseline and 3 months. The investigators will have monthly check-ins with participants which the investigators will assess app use and help increase study engagement. The investigators will measure feasibility and acceptability through app paradata (i.e., app use information) and self-report in surveys (baseline, 3 months). And the investigators will use adherence-related medical record data from AHF to compare against adherence monitored by the AiCure app.
Following completion of the pilot, the investigators will conduct in-depth interviews (IDIs) with YLWH and staff /providers purposively selected from participating AHF clinics (Aim 2). Interviews will explore intervention experiences, potential influences on current and long-term ART adherence, unaddressed adherence barriers and the potential benefit of features (e.g., reminders), individual-level and clinic-level barriers and facilitator to intervention implementation, assess ease of use of aDOT-CEI, likes and dislikes, and suggested modifications for a future efficacy trial.
This interventional pilot study will assess the feasibility and acceptability of aDOT-CEI and will provide preliminary data to inform an R01 to test the efficacy of aDOT-CEI in addressing disproportionately low viral suppression among YLWH.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Intervention | Experimental | All YLWH who choose to enroll in the study will receive access to AiCure, the mobile health application. The participants will be asked to use the app for 3 months, during which the investigators will assess the feasibility and acceptability of AiCure. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| aDOT-CEI | Other | For the design and development of the mobile health app, we have partnered with AiCure to use an existing HIPAA-compliant mobile health app. The app will record video of the participant taking their HIV medication in order to monitor the participant's medication adherence and track incentives for this behavior in real-time. |
| Measure | Description | Time Frame |
|---|---|---|
| Feasibility: Rate of Participant Retention | Rate of Participant Retention will be calculated as the percent of those who were retained in the study and completed the final survey. | 3 Months |
| Feasibility: Mean Logins Per Week | Mobile app data from the AiCure Application (paradata) will be used to measure the number of participant logins per week and thereby calculate mean logins per participant per week. | 3 months |
| Feasibility: Mean Number of Seconds in App Per Day | Mobile app data from the AiCure Application (Paradata) will be used to measure the number of seconds each participant spends in that app each day, thereby calculating the mean number of seconds in app per day per participant. | 3 months |
| Feasibility: Mean Percent Doses a Participant May Have "Falsified" Med-taking | The AiCure mobile application platform uses an artificial intelligence or AI platform to recognize dosing patterns recorded by users that do not correspond to what has been defined as 'normal' dosing by the AI. The AI accordingly flags any video recording featuring abnormal dosing for review by the AiCure Video Review team. Upon confirmation of abnormal dosing, the AiCure Video Review team notifies the research coordinator in the form of a "Red Alert". Each of these "Red Alerts" message will be counted as an event of intentional nonadherence, and the mean number of times a participant may have "falsified" medication taking will be calculated and compared against the number of 'normal' doses. | 3 months |
| Acceptability: System Usability Scale (SUS) >68, Considered Above Average and Acceptable | The acceptability of the AiCure Mobile application will be through the System Usability Scale (SUS). The intervention was considered acceptable if ≥80% had a SUS score >68, which is considered above average acceptability. |
| Measure | Description | Time Frame |
|---|---|---|
| Mean Percent Adherence Over the Study Period (Excluding Those Lost to Follow up) | Adherence data will be abstracted from the automated directly observed therapy platform within the AiCure mobile health application. Percent adherence will be calculated as the percentage of days over the study period that participants recorded taking their medication within the app. | 3 months |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Parya Saberi, PharmD | UCSF School of Medicine, Division of Prevention Science | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| UCSF Center for AIDS Prevention Studies | San Francisco | California | 94158 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38134037 | Derived | Stoner MCD, Ming K, Wagner D, Smith L, Patani H, Sukhija-Cohen A, Johnson MO, Napierala S, Neilands TB, Saberi P. Youth Ending the HIV Epidemic (YEHE): Protocol for a pilot of an automated directly observed therapy intervention with conditional economic incentives among young adults with HIV. PLoS One. 2023 Dec 22;18(12):e0289919. doi: 10.1371/journal.pone.0289919. eCollection 2023. |
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| ID | Title | Description |
|---|---|---|
| FG000 | Intervention | All YLWH who choose to enroll in the study will receive access to AiCure, the mobile health application. The participants will be asked to use the app for 3 months, during which the investigators will assess the feasibility and acceptability of AiCure. aDOT-CEI: For the design and development of the mobile health app, we have partnered with AiCure to use an existing HIPAA-compliant mobile health app. The app will record video of the participant taking their HIV medication in order to monitor the participant's medication adherence and track incentives for this behavior in real-time. |
| Title | Milestones | Reasons Not Completed | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
|
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| ID | Title | Description |
|---|---|---|
| BG000 | Intervention | All YLWH who choose to enroll in the study will receive access to AiCure, the mobile health application. The participants will be asked to use the app for 3 months, during which the investigators will assess the feasibility and acceptability of AiCure. aDOT-CEI: For the design and development of the mobile health app, we have partnered with AiCure to use an existing HIPAA-compliant mobile health app. The app will record video of the participant taking their HIV medication in order to monitor the participant's medication adherence and track incentives for this behavior in real-time. |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Median |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Feasibility: Rate of Participant Retention | Rate of Participant Retention will be calculated as the percent of those who were retained in the study and completed the final survey. | Posted | Count of Participants | Participants | 3 Months |
|
duration of the study (3 months)
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Intervention | All YLWH who choose to enroll in the study will receive access to AiCure, the mobile health application. The participants will be asked to use the app for 3 months, during which the investigators will assess the feasibility and acceptability of AiCure. aDOT-CEI: For the design and development of the mobile health app, we have partnered with AiCure to use an existing HIPAA-compliant mobile health app. The app will record video of the participant taking their HIV medication in order to monitor the participant's medication adherence and track incentives for this behavior in real-time. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Dr. Parya Saberi | UCSF | 415-502-1000 | 17171 | parya.saberi@ucsf.edu |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Aug 1, 2023 | May 23, 2024 | Prot_SAP_000.pdf |
| ICF | No | No | Yes | Informed Consent Form | Dec 19, 2022 | May 23, 2024 | ICF_001.pdf |
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| ID | Term |
|---|---|
| D000163 | Acquired Immunodeficiency Syndrome |
| ID | Term |
|---|---|
| D015658 | HIV Infections |
| D000086982 | Blood-Borne Infections |
| D003141 | Communicable Diseases |
| D007239 | Infections |
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Not provided
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|
|
| 3 months |
| Acceptability: Client Satisfaction Questionnaire (CSQ-8) Score of ≥17, Considered Above Average and Acceptable | Client satisfaction will be measured using a Client Satisfaction Questionnaire (CSQ-8). The CSQ-8 is an 8-item scale with higher values indicating higher satisfaction. Acceptability was set as a cutoff of 80% having a score of ≥17, which is the score cutoff considered above average and acceptable for this measure. | 3 months |
| Acceptability: Likelihood of Recommending the Study to a Friend (Extremely, Very) | Recommend study to a friend. 7-point Likert Scale of how likely participants are to recommend the study to a friend. The intervention was considered acceptable if ≥80% reported likely or very likely to recommend. | 3 months. |
| Acceptability: Satisfaction With the App+Incentives (Mostly, Very) | Client satisfaction with the app+incentives. 7-point Likert Scale values from 1-7 with lower values corresponding to least satisfaction and higher values corresponding to greater satisfaction. | 3 months |
| Median Self-Reported Adherence Score at Study Exit | The preliminary effect on ART adherence will be measured through self-report using a 3-item scale that has been previously validated. Questions ask about frequency of missed medications in the last 30 days, adherence frequency in the last 30 days, and adherence rating in the last 30 days. Reponses to the 3 questions were transformed to a 0-100 scale, with higher scores indicating better adherence. A summary score was calculated as the mean of the 3 individual items. | 3 months |
| Monitoring of Behavior: Number of Seconds in App | Number of seconds in app collected using mobile app data (paradata) from AiCure platform. | 3 months |
| Monitoring of Behavior: Ease of Use | How easy/difficult was it to use your personal phone; use adherence monitoring; receive incentives? Measured using a 7-point Likert Scale ranging from easy to difficult. Higher values correspond to greater ease of use, lower values correspond to greater difficulty. | 3 months |
| Monitoring of Behavior: Frequency of App Related Issues | Did you ever have trouble accessing app, using adherence monitoring, receiving reminders, receiving incentives, or finding a private place? 7-point Likert of frequency ranging from often to never with high value corresponding to app related issues occurring with greater frequency and lower value corresponding to app related issues issues occurring with lesser frequency. | 3 months |
| years |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Ethnicity (NIH/OMB) | Count of Participants | Participants |
|
| Race (NIH/OMB) | Count of Participants | Participants |
|
| Region of Enrollment | Number | participants |
|
| Financial Situation | When asked about their financial situation, the number of participants who reported they cannot get by on the money they have or can barely get by on the money they have. | Count of Participants | Participants |
|
| Units | Counts |
|---|---|
| Participants |
|
|
| Primary | Feasibility: Mean Logins Per Week | Mobile app data from the AiCure Application (paradata) will be used to measure the number of participant logins per week and thereby calculate mean logins per participant per week. | Not Posted | 3 months | Participants |
| Primary | Feasibility: Mean Number of Seconds in App Per Day | Mobile app data from the AiCure Application (Paradata) will be used to measure the number of seconds each participant spends in that app each day, thereby calculating the mean number of seconds in app per day per participant. | Posted | Mean | Full Range | seconds/day | 3 months |
|
|
|
| Primary | Feasibility: Mean Percent Doses a Participant May Have "Falsified" Med-taking | The AiCure mobile application platform uses an artificial intelligence or AI platform to recognize dosing patterns recorded by users that do not correspond to what has been defined as 'normal' dosing by the AI. The AI accordingly flags any video recording featuring abnormal dosing for review by the AiCure Video Review team. Upon confirmation of abnormal dosing, the AiCure Video Review team notifies the research coordinator in the form of a "Red Alert". Each of these "Red Alerts" message will be counted as an event of intentional nonadherence, and the mean number of times a participant may have "falsified" medication taking will be calculated and compared against the number of 'normal' doses. | Posted | Mean | Full Range | % of doses | 3 months |
|
|
|
| Primary | Acceptability: System Usability Scale (SUS) >68, Considered Above Average and Acceptable | The acceptability of the AiCure Mobile application will be through the System Usability Scale (SUS). The intervention was considered acceptable if ≥80% had a SUS score >68, which is considered above average acceptability. | Posted | Count of Participants | Participants | 3 months |
|
|
|
| Primary | Acceptability: Client Satisfaction Questionnaire (CSQ-8) Score of ≥17, Considered Above Average and Acceptable | Client satisfaction will be measured using a Client Satisfaction Questionnaire (CSQ-8). The CSQ-8 is an 8-item scale with higher values indicating higher satisfaction. Acceptability was set as a cutoff of 80% having a score of ≥17, which is the score cutoff considered above average and acceptable for this measure. | Posted | Count of Participants | Participants | 3 months |
|
|
|
| Primary | Acceptability: Likelihood of Recommending the Study to a Friend (Extremely, Very) | Recommend study to a friend. 7-point Likert Scale of how likely participants are to recommend the study to a friend. The intervention was considered acceptable if ≥80% reported likely or very likely to recommend. | Posted | Count of Participants | Participants | 3 months. |
|
|
|
| Primary | Acceptability: Satisfaction With the App+Incentives (Mostly, Very) | Client satisfaction with the app+incentives. 7-point Likert Scale values from 1-7 with lower values corresponding to least satisfaction and higher values corresponding to greater satisfaction. | Posted | Count of Participants | Participants | 3 months |
|
|
|
| Secondary | Mean Percent Adherence Over the Study Period (Excluding Those Lost to Follow up) | Adherence data will be abstracted from the automated directly observed therapy platform within the AiCure mobile health application. Percent adherence will be calculated as the percentage of days over the study period that participants recorded taking their medication within the app. | Posted | Mean | Full Range | percentage of days adherent | 3 months |
|
|
|
| Secondary | Median Self-Reported Adherence Score at Study Exit | The preliminary effect on ART adherence will be measured through self-report using a 3-item scale that has been previously validated. Questions ask about frequency of missed medications in the last 30 days, adherence frequency in the last 30 days, and adherence rating in the last 30 days. Reponses to the 3 questions were transformed to a 0-100 scale, with higher scores indicating better adherence. A summary score was calculated as the mean of the 3 individual items. | Posted | Median | Inter-Quartile Range | overall adherence score | 3 months |
|
|
|
| Secondary | Monitoring of Behavior: Number of Seconds in App | Number of seconds in app collected using mobile app data (paradata) from AiCure platform. | Posted | Mean | Full Range | seconds/day | 3 months |
|
|
|
| Secondary | Monitoring of Behavior: Ease of Use | How easy/difficult was it to use your personal phone; use adherence monitoring; receive incentives? Measured using a 7-point Likert Scale ranging from easy to difficult. Higher values correspond to greater ease of use, lower values correspond to greater difficulty. | Not Posted | 3 months | Participants |
| Secondary | Monitoring of Behavior: Frequency of App Related Issues | Did you ever have trouble accessing app, using adherence monitoring, receiving reminders, receiving incentives, or finding a private place? 7-point Likert of frequency ranging from often to never with high value corresponding to app related issues occurring with greater frequency and lower value corresponding to app related issues issues occurring with lesser frequency. | Not Posted | 3 months | Participants |
| 0 |
| 28 |
| 0 |
| 28 |
| 0 |
| 28 |
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| D015229 |
| Sexually Transmitted Diseases, Viral |
| D012749 | Sexually Transmitted Diseases |
| D016180 | Lentivirus Infections |
| D012192 | Retroviridae Infections |
| D012327 | RNA Virus Infections |
| D014777 | Virus Diseases |
| D012897 | Slow Virus Diseases |
| D000091662 | Genital Diseases |
| D000091642 | Urogenital Diseases |
| D007153 | Immunologic Deficiency Syndromes |
| D007154 | Immune System Diseases |