Using an AI-based Voice Assistant to Manage Insulin in Diabetes: a Randomized-Control Trial
Phase: NA
Conditions: Medication Adherence
Sponsor: Stanford University

 
Using an AI-based Voice Assistant to Manage 
Insulin in Diabetes: a Randomized-Control Trial 
NCT05081011 
June 20, 2022 
1. 
PURPOSE OF THE STUDY 
a. 
Brief Summary 
The purpose of this study is twofold: 
(1) To evaluate the feasibility of a digital voice assistant to help patients manage insulin-
dependent diabetes between routine clinic visits. Specifically, the custom app that we will 
develop will help patients log their blood sugars and medication use, as well as help them 
adjust their insulin dose based on a protocol that their physician prescribes. 
 
(2) To evaluate the effects of such an intervention on specific medical outcomes, such as 
medication adherence, blood sugar control and time to optimal insulin dose, as well as 
patient self-efficacy measures. 
 
The digital voice assistant used will be an Amazon Echo Dot and it will use a custom 
application (app) to help patients manage their diabetes at home. 
b. 
Objectives 
We hope to learn whether a digital voice assistant can be reliably used by patients at 
home between clinic visits to improve management of insulin-dependent diabetes. We 
hope that this improved management will translate into improved medical outcomes. In 
the future, the findings of this study could serve as a first step towards developing an 
integrated outpatient solution that leverages voice assistant technology in the patient's 
home to improve management of multiple chronic medical conditions. 
c. 
Rationale for Research in Humans 
The purpose of this study is to test the efficacy of a digital voice assistant in individuals 
with insulin-dependent diabetes. 

June 20, 2022 
 
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2. 
STUDY PROCEDURES 
a. 
Procedures 
Screening: 
Our team will recruit patients with uncontrolled type 2 diabetes who are being started on 
insulin or who are already on insulin and having active adjustments to their dose. To 
identify patients that may be eligible for recruitment, our team will use STARR's cohort 
discovery tool to create a report of patients at four Stanford Internal Medicine clinic sites 
with type 2 diabetes and a recent hemoglobin A1c greater than 8%. Our team will then 
contact the primary care providers of these patients to ask them if they think their patients 
would benefit from inclusion in our study. The four Stanford Internal Medicine clinic 
sites are Stanford Internal Medicine East, Stanford Internal Medicine Los Altos, Stanford 
Internal Medicine Portola Valley and Stanford Internal Medicine Santa Clara.  
Recruitment process: When an eligible candidate is identified per the screening process 
above, the research team will contact the patient's primary care physician and inform 
them of this study. If the patient's primary care physician agrees that the patient would 
benefit from inclusion in this study, they will reach out to the patient and describe the 
trial to them. If the patient agrees to be contacted by the research team, their care team 
member will share their information with a member of the research team via secure 
message and a member of the team will reach out via the participant's preferred method 
of communication (phone, email, etc). Due to the COVID- 19 pandemic and the fact that 
diabetes is a risk factor for the virus, all participants will undergo the informed consent 
process remotely. The research team will make contact with the participant via the 
mechanism shared with them by the treating team, introduce the project and ask if the 
participant would like to undergo the informed consent process via an online REDCap 
survey (screenshots attached) or via a paper consent form sent in the mail. The research 
team member will then send the Informed Consent Form (ICF) to the participant via 
either a URL by email or a paper ICF by mail. The research team member will set up a 
time to discuss the ICF with the participant either by phone or video chat and will 
describe the study to the participant, review the risks and benefits outlined in the study 
consent form and address any questions from the participant before asking them for their 
consent to participate in the study. After consent is obtained, participants will complete 
an intake survey ("Participant Information Survey") and will be randomized to either the 
intervention or control group. All participant information obtained during enrollment will 
be stored securely in a HIPAA-compliant and PHIsafe Stanford Medicine Box folder.  
 
Device setup: 
After consent is obtained, the participant's treating physician will communicate their 
desired insulin protocol with the research team. The information communicated will 
include the following: 
-Starting insulin prescription 
-Goal fasting blood sugar 
-Titration parameters (e.g. Increase starting dose by 2 units every 3 days if not at goal 
fasting blood sugar) 
-Situations in which they would want their patient to contact their provider or seek 
immediate medical attention and call 911/report to an ER. 
 

June 20, 2022 
 
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This information will be communicated with the research team via either a custom Epic 
order set currently in development or a secure online portal developed by the research 
team. The provider will only have to provide the patient's unique study ID in order to 
enter this information in the portal. This ID will be securely emailed to the provider upon 
enrollment of the patient. The provider will be asked to confirm their prescription inputs 
via the secure online portal to reduce the probability of erroneous data insertion. The 
provider will also have to confirm the ID of the patient to prevent the provider from 
erroneously placing a prescription for the wrong patient. No identifying information will 
be collected by the portal. For participants in the intervention group, this information will 
be programmed into the digital voice assistant application. For participants in the control 
group, this information will be recorded for future data analysis. 
 
Intervention group: 
Due to the COVID-19 pandemic and the fact that diabetes is a risk factor for the virus, we 
will mail devices to participants in the intervention group. We will also provide them 
with the option to set up a phone call or video chat with a member of the research team to 
help them with device set up. 
 
Device setup will include the following steps: 
-Open Alexa Echo Dot package and plug in to power 
-Downloading the "Alexa" smartphone application 
-Through the Alexa smartphone application, enabling our custom 
application (called an "Alexa 
Skill"). 
-Logging in to our custom application with a unique study ID that is 
linked to the participant's insulin protocol. 
 
A detailed document called "Alexa Setup Instructions" will be provided to subjects that 
go into granular detail on how to set up the device as well as its functionalities 
(screenshots attached). 
 
The entire set up process should take about 5-10 minutes. 
 
Control groups: 
For participants in the standard of care control group, we will email them a copy of a 
blood sugar and medication administration log for them to fill out electronically through 
REDCap or print and fill out ("Blood Sugar Log"). If a participant doesn't have access to 
a printer, we will mail them a copy of the log. 
 
As an additional control, a subset of patients receiving the voice assistant device will also 
be given a blood sugar and medication administration log and will be asked to use both 
the voice assistant and the blood sugar and medication administration log for 2 of the 8 
weeks they are participating in the trial ("Blood Sugar Log - 2 weeks only") 
 
 
 
 

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Data collection: 
 
Data will be collected daily from participants in the intervention and control groups of the 
study. Participants in the intervention group will be able to interact with our custom app 
by speaking with their Echo Dot. Participants will be instructed to "check in" with the 
app once daily at which point the app will collect information about recently checked 
blood sugars and recent administration of insulin. Data collected by the device will be 
read back to the patient for confirmation. At the end of the check in, the app will remind 
patients about the details of their insulin regimen. Based on the patient's self-reported 
data, this reminder might include a prompt to change the dose of their insulin. All 
reminders and prompts will be strictly based on the physician-prescribed insulin protocol 
at the beginning of the study, and prompts will include a summarization of the rationale 
behind the recommendation (e.g. "Your fasting blood sugars are still above goal. Based 
on your prescribed treatment plan, you should increase your dose of Lantus to 17 units."). 
No automated decisions will be made by the voice assistant. The voice assistant will only 
be assisting the patient with instructions that patients are normally given in writing by 
their provider. 
 
An instruction manual will be provided to the patient with details on how to interact with 
the device ("Alexa Setup Instructions"). The only data collected from the patient will be 
patient-reported blood sugar data and medication administration occurrences. The audio 
data collected by the device is encrypted and sent to the Amazon cloud for processing 
into relevant text. The text data is supplied to our application for analysis and storage. 
Our application will not receive or store any audio data. No protected health information 
will be collected by our app. All data will be de-identified and stored in the Amazon Web 
Services cloud. All data will be linked to a participant's unique study ID. A document 
linking a participant's unique ID to their name and medical record number will be stored 
on the secure PHI-safe Stanford Medicine Box server. 
 
Patients in the control group will use their provided log, labeled with their unique study 
ID, to record their daily blood glucose checks and insulin adherence. This is part of the 
standard of care in patients with insulin-dependent diabetes. At the end of the trial period, 
we will collect this log from patients for data analysis. Participants can choose to securely 
email or upload this log to the research team through REDCap, or if they prefer, they will 
be provided with a pre-paid envelope to mail the log free of cost. 
 
Finally, user satisfaction survey data will be collected at the beginning and end of the 
study from patients in the intervention and control groups. The first survey will include 
"Problem Areas in Diabetes" ("PAID") questions validated in the literature as a rapid 
screen for diabetes-related emotional distress 
(https://pubmed.ncbi.nlm.nih.gov/19841892/). This survey is labeled "Diabetes Attitudes 
(PAID) Survey" (screenshots attached). The second survey will include questions 
specifically tailored towards interrogating the patient's attitude toward and comfort with 
insulin dose adjustments ("Medication Adherence Survey"). The third and final survey 
will include questions on attitudes towards healthcare technology ("Health Technology 
Survey"). All surveys will be collected online via REDCap. 
 

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Data analysis: 
Members of the research team will review the collected blood sugar and insulin 
administration data for analysis. Specifically, we will analyze the collected blood sugar 
data to determine how often blood sugars were checked and how well-controlled they 
were. We will also analyze insulin administration data to determine how adherent 
participants were to their prescribed regimen and how effectively participants were able 
to titrate their insulin to an effective dose. We will also analyze study survey data to 
assess participant's diabetes selfefficacy and attitudes towards healthcare technology. 
 
In addition to reviewing data collected by our app and REDCap, the research team will 
also access the medical record of participants who have completed the trial to obtain 
certain lab results and medication data to help supplement any missing data from the trial. 
Specifically, the research team will obtain recent blood sugar and Hemoglobin A1c 
values, as well as the most recent insulin prescription. This will help the research team 
perform data analysis in cases where trial data are incomplete. Please note that this is 
only for participants who have been consented, enrolled and have completed the full 8 
weeks of the trial. 
 
If participants decide to withdraw from the study, they can do so at any time by 
contacting Ashwin Nayak at 650-308-8062 or aknayak@stanford.edu. After study 
completion, participants will be able to keep their Amazon Echo device for personal use. 
Their specific login ID will be disabled which will preclude them from using our custom 
application after study completion. If participants decide to withdraw from the study 
before completion, they will be asked to return their Amazon Echo device. They will be 
provided with pre-paid packaging to do so. 
 
Infection control strategies implemented to ensure the safety of the research staff/faculty 
who will come to campus and to mitigate risks to them and their families: 
1. Personnel are trained on the need to stay home and get tested if they have a new cough, 
fever, difficulty breathing, chills, muscle pain, headache, sore throat, recent loss of taste 
or smell. 
2. All research staff/faculty will follow Stanford's social distancing protocol, which 
includes: 
a. Maintaining physical distancing. 
b. Use of face coverings and eye protection. 
c. Frequent handwashing and use of hand sanitizer. 
3. Researchers and support staff returning to campus will be required to use the Health 
Check tool to report health status each day. 
4. There will be no participant-facing research staff as the informed consent and patient 
enrollment process will be conducted virtually. 
Infection control strategies implemented to ensure the safety of the research participants: 
1. All devices will be cleaned prior to being mailed to the participant. 
2. Participants will perform self-setup of their device to avoid the need for an in-person 
visit with research staff. 

June 20, 2022 
 
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b. 
Procedure Risks 
This study poses minimal risk to its participants. Patients in the control group will receive 
standard of care management of insulin-dependent diabetes. Patients in the intervention 
arm will not be subject to any risky treatments. They will be interacting with a digital tool 
that will collect information and remind them of their physician-prescribed treatment 
plan. The digital tool will not make any autonomous medical decisions or alter the 
physician's plan in any way. All data collected by the digital tool is anonymized. The 
voice assistant device will confirm the patient's self-reported data with readback to the 
subject and verbal confirmation by the subject, prior to storing the data. If a blood sugar 
reading is outside a standard safe range as determined by their physician, the voice 
assistant device will provide them the same instructions that would be provided on 
written instructions by their provider. For example, if a patient's reported blood sugar is 
less than 70, the voice assistant may notify the subject that their doctor has instructed at 
such blood sugars to consume something sweet and recheck their sugar. If a patient's 
reported blood sugar is greater than 350, the voice assistant may notify the patient that 
their doctor has instructed at such blood sugars to notify a medical professional 
immediately. If a patient's sugars are dangerously hypoglycemic or dangerously 
hyperglycemic as determined by their provider, the device will inform the patient to seek 
medical attention immediately and report to an ER/call 911 per their provider's 
instructions. 
c. 
Use of Deception in the Study 
No deception strategy will be used in this study. 
d. 
Use of Audio and Video Recordings 
Amazon Echo devices (and all similar voice assistant technologies) record audio during 
user interactions. This audio data is encrypted and sent to the Amazon cloud for 
processing into relevant text. The text data is supplied to our application for analysis and 
storage. Our application will not receive or store any audio data. All audio data collected 
by Amazon Echo devices are visible in the Alexa app for users to see and delete. 
Furthermore, Amazon explicitly states that its devices do not record audio data when 
users are not interacting with its devices. Whenever an Amazon Echo device is recording 
audio data, a special indicator light will turn on to alert the user. These details will be 
included in the informed consent process and patients will be instructed on how to delete 
audio recordings in their Amazon privacy settings if desired. 
e. 
Alternative Procedures or Courses of Treatment 
There are no other alternative procedures for collecting data from a digital voice assistant 
in a patient's home. No standard treatments will be withheld from participants. The 
alternative is not to participate in the study. 
f. 
Will it be possible to continue the more (most) appropriate therapy for the 
participant(s) after the conclusion of the study? 
Yes. 

June 20, 2022 
 
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g. 
Study Endpoint(s) 
We will be evaluating the end points every 2 weeks for 8 weeks. The clinical end points 
we will be monitoring are blood sugars obtained through standard of care treatment. The 
study will not be terminated early for any foreseeable reason. The study will end after 
two months of data collection per patient. During the two months of data collection, the 
treating physician will be able to make changes to the patient's insulin regimen at any 
time but logging into our secure research portal. These changes will be reflected instantly 
in our Alexa application. 
3. 
BACKGROUND  
a. 
Past Experimental and/or Clinical Findings 
There is a large amount of literature showing how digital health interventions can lead to 
improved clinical outcomes. In the area of voice assistant for use in diabetes care 
management, studies have shown that interactive voice responses through voice 
recognition software have the potential to improve medication and lifestyle modification 
adherence in diabetes and improve clinical outcomes. For example, in a randomized 
control trial of adults with diabetes, patients who received automated, interactive phone-
delivered management interventions had greater decreases in hemoglobin A1c compared 
to standard of care (BMC Public Health 12 (2012), 602). Another randomized crossover 
study reported on a smartphone application that can synthesize verbal descriptions of 
meals and accurately return the corresponding insulin bolus dose (J Diabetes 10 (2018), 
600-608). A similar voice-activated device was identified in calculating insulin dosages 
in the visually impaired with diabetic retinopathy (2011 24th Canadian Conference on 
Electrical and Computer Engineering (CCECE), 2011, pp. 000904- 000907.) 
 
Ultimately, no study has yet embarked on what this study looks to investigate; however, a 
review of key leading opinions from clinical, engineering, and data security experts have 
identified smart devices, voice assistants and the Internet of medical things (IoMT) as a 
key untapped area of potential for optimizing the home health care ecosystem to reduce 
diabetic complications, including diabetic foot ulcers (Journal of diabetes science and 
technology 12 (2018), 577-586). 
b. 
Findings from Past Animal Experiments 
N/A 
4. 
DEVICES USED IN THE STUDY 
a. 
Investigational Devices (Including Commercial Devices Used Off-Label) 
Investigational Device 1 
Name: 
Echo Dot 
Description: 
Voice assistant 
Significant Risk? 
Non-significant risk 
Rationale for Non-Significant Risk 
This is a non-significant risk device because it doesn't pose a serious risk 
to health, safety or welfare of the subject. Its use is not of substantial 
importance in diagnosing, curing or treating disease. 

June 20, 2022 
 
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Investigational Device 2 
Name: 
Insulin titration algorithm 
Description: 
Insulin titration protocol algorithm 
Significant Risk?  
Non-significant risk 
Rationale for Non-Significant Risk 
PCPs routinely provide patients with instructions on how to titrate their 
insulin at home. This algorithm helps patients implement these 
instructions (which is something they would otherwise be doing with no 
assistance). We do not believe that the software is more dangerous than 
standard of care, and is hopefully safer for a few reasons: 1) The provider 
will be able to set a limit to how much and how often the algorithm will 
increase the insulin dose. 2) The provider will be able to set a maximum 
insulin dose. 3) The default insulin titration protocol that we provide 
mirrors the 2020 consensus statement from AACE (American Association 
of Clinical Endocrinologists) and ACE (American College of 
Endocrinology) guidelines (https://pro.aace.com/pdfs/diabetes/algorithm-
exec-summary.pdf), but is more conservative to have less tolerance for 
hypoglycemic events 4) In partnership with Endocrinologists and as 
Internists ourselves, we have a well thought out, professional guideline-
based, conservative hypoglycemia protocol that reduces insulin dose and 
automatically turns off the titration algorithm if needed to account for 
various clinical situations where hypoglycemia may be present. The 
protocol is also designed to account for when providers may have 
erroneously started too high an insulin dose. Please see our hypoglycemia 
protocol attached in section 16 for details. 5.) There are multiple steps for 
confirmation when reporting data to ensure correct data acquisition, 
including reminders with every single check in that a "fasting blood 
sugar" is one where one has not had anything to eat in the 8 hours prior 
b. 
IDE-Exempt Devices 
N/A 
5. 
DRUGS, BIOLOGICS, REAGENTS, OR CHEMICALS USED IN THE STUDY 
N/A 
6. 
PARTICIPANT POPULATION 
a. 
Planned Enrollment 
(i) 50 participants 
(ii) 50 participants 
(iii) Patients at Stanford Internal Medicine Clinics with a diagnosis of Type 2 Diabetes 
who are clinically indicated to initiate or increase their long acting insulin. 
b. 
Age, Gender, and Ethnic Background 
The age range of this study is 18 years of age or older. There are no conditions on the 
gender or ethnicity of the participant. Male and female participants are going to be 
enrolled and we aspire to keep an appropriate demographic distribution. 
c. 
Vulnerable Populations 
No vulnerable populations will be targeted in this study. 

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d. 
Rationale for Exclusion of Certain Populations 
Children will not be included in the study because the Internal Medicine team involved 
specialises in adult care. 
e. 
Stanford Populations 
N/A 
f. 
Healthy Volunteers 
None 
g. 
Recruitment Details 
Our team will recruit patients with type 2 diabetes who have been recently started on 
insulin (or who are having active adjustments to their insulin regimen). To identify 
patients that may be eligible for recruitment, our team will use Stanford's STARR cohort 
discovery tool and an Epic workbench report overseen by Dr. Anu Phadke, the Stanford 
Population Health Medical Director, and Jimmy Dang, the Stanford Population Health 
Quality manager. We will use these tools to create a report of patients at Stanford Internal 
Medicine and Endocrinology clinics with type 2 diabetes and a recent hemoglobin A1c 
greater than 8%. Specifically, this is done through a cohort discovery of patients who 
have seen providers who practice at the Stanford primary care and Endocrinology clinic 
sites. In addition, we will also be identifying patients through advertisements through the 
Stanford Diabetes Research Core (https://sdrc.stanford.edu/sdrc-research-
cores/dctc/home). Specifically, the recruitment director with the core, Dr. Christopher 
Gardner, will be giving us access to a listserv of Stanford-affiliated patients who have 
requested to be contacted about diabetes-related clinical trial opportunities. 
 
For potential participants identified through STARR or Epic workbench reports, our team 
will first contact the primary care providers and/or Endocrinologists of these patients 
(depending on which providers are managing or co-managing the patient's diabetes 
regimen). If the primary care providers and/or Endocrinologists agree that their patient 
might benefit from inclusion in our study, a member of the patient's care team will first 
contact the patient to notify them of the study and obtain approval for initiation of the 
research team's enrollment process. Upon approval, our research team will reach out to 
provide more information and conduct the informed consent process if the patient would 
like to participate. 
 
For potential participants identified through Dr. Christopher Gardner's listserv, if these 
participants directly respond to our listserv advertisement, our research team will reply 
back to them directly. Our team will also contact their primary care providers and/or 
endocrinologists if the potential participant decides to enroll in the trial. 
h. 
Eligibility Criteria 
i. 
Inclusion Criteria 
We will include patients who are 18 years of age or older with a diagnosis of Type 2 
Diabetes with need for initiation or increase of a long-acting insulin regimen. 

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ii. 
Exclusion Criteria 
We will exclude patients who: 
- are 17 years of age or younger 
- are on insulin pumps 
- have impaired decision-making capacity 
- do not speak English 
- do not own a smartphone or have access to a WiFi network at home (due to the 
technological requirements of the device and application) 
i. 
Screening Procedures 
As stated above, our team will screen patients by creating a STARR report of patients at 
Stanford Internal Medicine clinics with type 2 diabetes and a recent hemoglobin A1c 
greater than 8%. Our team will then reach out to the primary care providers of these 
patients to identify patients who are candidates for starting long acting insulin, or who are 
already on long acting insulin but having active adjustments to their regimen. 
j. 
Participation in Multiple Protocols 
During the enrollment process, we will ask patients if they are involved in other studies. 
We don't anticipate there being conflicts but if there are co-enrollments we will look into 
complications. 
k. 
Payments to Participants 
Participants in the intervention arm will not be paid but they will be allowed to keep the 
Amazon Echo Dot after their involvement in the study concludes. At the conclusion of 
the study, participants in the control arm will also be given an Amazon Echo Dot for 
compensation. 
l. 
Costs to Participants 
None 
m. 
Planned Duration of the Study 
The probable duration of the entire study is 18 months. 
(i) One month 
(ii) Two months per participant 
(iii) Up to 15 months 
7. 
RISKS 
a. 
Potential Risks 
i. 
Investigational devices 
As stated earlier, instructions to patients to change their insulin dose will always strictly 
adhere to the physician-prescribed insulin protocol. However, if the patient inaccurately 
self-reports blood glucose data, dose adjustments based on this data can cause medical 
complications. To minimize the risk of this, the application will ask the patient to confirm 

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self-reported data, and when the instructed intervention is read back, the device will again 
summarize the patient's self-reported data and be clear that the intervention is based on 
the data provided by the patient and is as instructed by the patient's provider (e.g. "Based 
on your reported fasting blood sugar of 182 this morning, your doctor has instructed you 
to increase your insulin glargine to 17 units tonight up from 15 units yesterday."). 
Furthermore, all dose adjustments will be small, making them very unlikely to cause 
major adverse events such as hypoglycemia. 
ii. 
Investigational drugs 
None 
iii. 
Commercially available drugs, biologics, reagents or chemicals 
None 
iv. 
Procedures 
No investigational procedures will be performed. 
v. 
Radioisotopes/radiation-producing machines 
None 
vi. 
Physical well-being 
vii. 
Risks of participating in this study include the possibility of inaccurate self-reporting 
by participants leadingto improper insulin dose adjustments. As stated above, our 
application will confirm all self-reported dataprior to logging it by reading back 
reported values to patients and requiring verbal confirmation from thepatient. 
Furthermore, all dose adjustments will be small (ranging from 2-4 units based on the 
participant'sprescription). Such small dose adjustments to long-acting insulin are very 
unlikely to cause major adverseevents such as hypoglycemia.Psychological well-
being 
No risk to psychological well-being. We hope that the intervention group psychologically 
benefits from increased engagement in their health. Furthermore, our application will 
provide patients with positive messages of encouragement. 
viii. 
Economic well-being 
No risk to economic well-being. 
ix. 
Social well-being 
No risk to social well-being. 
x. 
Overall evaluation of risk 
Low 
b. 
International Research Risk Procedures 
N/A 

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c. 
Procedures to Minimize Risk 
As stated earlier, all prompts delivered by the application will be strictly based on the 
patient's physician's care plan. 
d. 
Study Conclusion 
The experiment will terminate after two months of data collection per patient. 
e. 
Data Safety Monitoring Plan (DSMC) 
i. 
Data and/or events subject to review 
N/A 
ii. 
Person(s) responsible for Data and Safety Monitoring 
N/A 
iii. 
Frequency of DSMB meetings 
N/A 
iv. 
Specific triggers or stopping rules 
N/A 
v. 
DSMB Reporting 
N/A 
vi. 
Will the Protocol Director be the only monitoring entity? (Y/N) 
Yes 
8. 
BENEFITS 
PHI will only be collected once during enrollment, which includes an entry survey on REDCap 
(screenshots attached). The least amount of information will be obtained to accomplish the 
purpose of the research. This data will be stored securely in a folder on the HIPAA-compliant 
Stanford Medicine Box. A unique study ID will be randomly generated for each patient (not 
derived from any identifiable information). This ID will be used with our application and will be 
linked with the self-reported patient data that we collect using the Amazon Echo Dot. No PHI 
will be collected or stored by our application on the Amazon Echo Dot. 
9. 
PRIVACY AND CONFIDENTIALITY 
All participant information and specimens are handled in compliance with the Health Insurance 
Portability and Accountability Act (HIPAA) and privacy policies of Stanford University, Stanford 
Health Care, and Stanford Children’s Health.