HIV/AIDS, Mobile Health, Young Adults
Conditions
Brief summary
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.
Detailed description
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.
Interventions
OTHERaDOT-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.
Sponsors
AiCure
RTI International
National Institute of Mental Health (NIMH)
University of California, San Francisco
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SUPPORTIVE_CARE
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 29 Years
Healthy volunteers
No
Inclusion criteria
* 18 to 29 years of age * Have access to a smartphone * Can speak and read English * Client is at an AHF clinic in California or Florida * Client has an unsuppressed HIV viral load at least 3 months post HIV diagnosis * Client is on once daily oral antiretroviral therapy * Consent to participate in the proposed study
Exclusion criteria
* YLWH who are newly HIV diagnosed in the past 3 months * Smartphone is a Samsung galaxy s21 or iPhone 5SE (app is not supported by these phones)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Acceptability: Satisfaction With the App+Incentives (Mostly, Very) | 3 months | 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. |
| Feasibility: Mean Number of Seconds in App Per Day | 3 months | 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. |
| Feasibility: Mean Percent Doses a Participant May Have Falsified Med-taking | 3 months | 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. |
| Acceptability: System Usability Scale (SUS) >68, Considered Above Average and Acceptable | 3 months | 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. |
| Acceptability: Client Satisfaction Questionnaire (CSQ-8) Score of ≥17, Considered Above Average and Acceptable | 3 months | 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. |
| Acceptability: Likelihood of Recommending the Study to a Friend (Extremely, Very) | 3 months. | 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. |
| Feasibility: Rate of Participant Retention | 3 Months | Rate of Participant Retention will be calculated as the percent of those who were retained in the study and completed the final survey. |
| Feasibility: Mean Logins Per Week | 3 months | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Median Self-Reported Adherence Score at Study Exit | 3 months | 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. |
| Monitoring of Behavior: Number of Seconds in App | 3 months | Number of seconds in app collected using mobile app data (paradata) from AiCure platform. |
| Monitoring of Behavior: Ease of Use | 3 months | 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. |
| Monitoring of Behavior: Frequency of App Related Issues | 3 months | 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. |
| Mean Percent Adherence Over the Study Period (Excluding Those Lost to Follow up) | 3 months | 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. |
Countries
United States
Participant flow
Participants by arm
| Arm | Count |
|---|---|
| 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. | 28 |
| Total | 28 |
Withdrawals & dropouts
| Period | Reason | FG000 |
|---|---|---|
| Overall Study | Lost to Follow-up | 6 |
Baseline characteristics
| Characteristic | Intervention |
|---|---|
| Age, Continuous | 28 years |
| Ethnicity (NIH/OMB) Hispanic or Latino | 12 Participants |
| Ethnicity (NIH/OMB) Not Hispanic or Latino | 16 Participants |
| Ethnicity (NIH/OMB) Unknown or Not Reported | 0 Participants |
| Financial Situation | 15 Participants |
| Race (NIH/OMB) American Indian or Alaska Native | 0 Participants |
| Race (NIH/OMB) Asian | 0 Participants |
| Race (NIH/OMB) Black or African American | 11 Participants |
| Race (NIH/OMB) More than one race | 4 Participants |
| Race (NIH/OMB) Native Hawaiian or Other Pacific Islander | 1 Participants |
| Race (NIH/OMB) Unknown or Not Reported | 2 Participants |
| Race (NIH/OMB) White | 10 Participants |
| Region of Enrollment United States | 28 participants |
| Sex: Female, Male Female | 1 Participants |
| Sex: Female, Male Male | 27 Participants |
Adverse events
| Event type | EG000 affected / at risk |
|---|---|
| deaths Total, all-cause mortality | 0 / 28 |
| other Total, other adverse events | 0 / 28 |
| serious Total, serious adverse events | 0 / 28 |
Outcome results
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.
Time frame: 3 months
| Arm | Measure | Value (COUNT_OF_PARTICIPANTS) |
|---|---|---|
| Intervention | Acceptability: Client Satisfaction Questionnaire (CSQ-8) Score of ≥17, Considered Above Average and Acceptable | 22 Participants |
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.
Time frame: 3 months.
| Arm | Measure | Value (COUNT_OF_PARTICIPANTS) |
|---|---|---|
| Intervention | Acceptability: Likelihood of Recommending the Study to a Friend (Extremely, Very) | 16 Participants |
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.
Time frame: 3 months
| Arm | Measure | Value (COUNT_OF_PARTICIPANTS) |
|---|---|---|
| Intervention | Acceptability: Satisfaction With the App+Incentives (Mostly, Very) | 20 Participants |
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.
Time frame: 3 months
| Arm | Measure | Value (COUNT_OF_PARTICIPANTS) |
|---|---|---|
| Intervention | Acceptability: System Usability Scale (SUS) >68, Considered Above Average and Acceptable | 18 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.
Time frame: 3 months
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.
Time frame: 3 months
| Arm | Measure | Value (MEAN) |
|---|---|---|
| Intervention | Feasibility: Mean Number of Seconds in App Per Day | 28 seconds/day |
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.
Time frame: 3 months
| Arm | Measure | Value (MEAN) |
|---|---|---|
| Intervention | Feasibility: Mean Percent Doses a Participant May Have Falsified Med-taking | 2 % of doses |
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.
Time frame: 3 Months
| Arm | Measure | Value (COUNT_OF_PARTICIPANTS) |
|---|---|---|
| Intervention | Feasibility: Rate of Participant Retention | 22 Participants |
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.
Time frame: 3 months
| Arm | Measure | Value (MEAN) |
|---|---|---|
| Intervention | Mean Percent Adherence Over the Study Period (Excluding Those Lost to Follow up) | 72.7 percentage of days adherent |
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.
Time frame: 3 months
| Arm | Measure | Value (MEDIAN) |
|---|---|---|
| Intervention | Median Self-Reported Adherence Score at Study Exit | 82.7 overall adherence score |
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.
Time frame: 3 months
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.
Time frame: 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.
Time frame: 3 months
| Arm | Measure | Value (MEAN) |
|---|---|---|
| Intervention | Monitoring of Behavior: Number of Seconds in App | 28 seconds/day |