Hello and welcome. I'm Dr. Amy Cohen, Program Director for SMI Advisor and a clinical psychologist. I'm pleased that you're joining us for today's SMI Advisor webinar, Integrating Equity and Diversity in Digital Mental Health Interventions for Depression. SMI Advisor, also known as the Clinical Support System for Serious Mental Illness, is an APA and SAMHSA initiative devoted to helping clinicians implement evidence-based care for those living with serious mental illness. Working with experts from across the SMI clinician community, our interdisciplinary effort has been designed to help you get the answers you need to care for your patients. And now I'd like to introduce you to the faculty for today's webinar, Dr. Adrienne Aguilera. Dr. Aguilera is an Associate Professor at the School of Social Welfare at the University of California, Berkeley. His current research interests focus on utilizing digital health and mobile technologies to improve health and mental health care of low-income and ethnic minority populations, with a focus on Latino and Spanish speaking populations. He's also interested in understanding how culture and socioeconomic status influence mental health and mental health treatment. A clinical psychologist, Dr. Aguilera has tested the impact of an automated text messaging intervention to improve engagement with depression treatment in a public sector setting. He continues to work on that project and has also developed a personalized physical activity intervention for patients with depression and diabetes. Dr. Aguilera, thank you for leading today's webinar. Thank you, Dr. Cohen, for having me. I'm looking forward to sharing some of my work. So I don't have any disclosures for today's talk. So the learning objectives for today are going to be to summarize the need to target digital interventions for diverse populations, to specifically explain how a text messaging intervention can be utilized to improve engagement in depression treatment, and to describe how mobile interventions for depression can be culturally tailored. To start with, I want to place the context of the fact that currently there are over 5 billion people who own mobile phones worldwide. So the total population of the world's around 7 billion or so. So the large majority of people, probably close to every adult these days, has access to a mobile phone. And with that comes a lot of power connectedness. Increasingly, smartphones are being accessed by more and more people. As a lot of you know, particularly after the pandemic, digital is becoming more and more and more. However, one of the things that I often think about is who are these digital tools, smartphones, mobile devices designed for? So in addition to mobile phones, we've seen the proliferation of wearable devices to manage one's health. So you can imagine a smartwatch could help people become more physically active, which is generally helpful to health and mental health. However, who are these tools designed for? As you can see by these ads, they tend to be targeted at least initially towards people who are already fit, who have access to travel. So upper levels of SES, not necessarily for folks from lower income backgrounds, who I would argue have a higher need for improving health by and large, if we look at public health impact. When we look at who apps are targeted for, by and large, the large majority are in English, which makes sense because so many have been developed in the US, but we don't make up the entire population of the world. So the number of apps available are disproportionately available in English compared to other languages. And when we look at broadly in terms of digital health apps, what they're targeting, we do see that 28% of apps based on a 2017 survey are targeted towards mental health and behavioral disorders. Next would be diabetes and other health problems. So we do see that there is a focus on mental health, on improving mental health and targeting this chronic illness, if you will, that needs to be managed on a regular basis. When we look at some of the most popular apps available, generally, as you can see, they tend to be related to physical activity, such as Runkeeper, Fitbit, and there's a stress management app. So Headspace would be one of those, smoking cessation apps, alcohol moderation apps. So these are some examples of some of the more popular apps available at the moment. And we can see kind of with other categories, these are some of the apps that are most widely used and most widely rated clinically. So what does digital health entail? So digital health entails a variety of things and the number of things that are subsumed under digital health are increasing by the day as new technologies come online. So what I'll focus on primarily is text messaging, but also mobile apps. I mentioned wearable devices earlier. Now, more and more, we know that telemedicine is becoming the norm also within digital health. So digital health is a lot of things. And ultimately, it is the integration of digital technology into the delivery of healthcare. What does the mental health technology landscape look like? As you saw earlier, there are a lot of apps targeted towards mental health. And here are some examples. So there's a whole group of private companies targeting computerized CBT, cognitive behavioral therapy, various aspects of CBT. So some of these are more mindfulness focused, others are more cognitive and behavioral focused. Some are more kind of standalone type interventions versus others combine working with a clinician of some sort. Telepsychiatry is more kind of what we're seeing today, a way to connect with a provider. There are also provider tools that can be used that don't have as much consumer facing technology to them. Consumer tools, hardware would be kind of more of the wearable devices and such. And then some artificial intelligence technology. So this gives you a sense of what's out there. So it is exciting. There is a lot out there and there's a lot of development. And we're likely going to see more actual implementation given the move towards remote care as a result of the pandemic. And so here's a good way to think about some of the interventions that are available. So my colleague and mentor, Dr. Ricardo Munoz came up with this taxonomy to think of interventions. So type one intervention would be your standard face-to-face intervention. Type two would be a face-to-face intervention with some sort of technology adjunct. What I'll be talking about today will be more along the lines of the type two. Type three would be kind of a guided self-help intervention. So this would be like an app or an online program for depression or anxiety or some other problem. And then type four would be totally automated. So the totally automated interventions wouldn't include any sort of help, any sort of guidance from any peer or support or anything like that. Another concept that I want to make sure to discuss a bit is this idea of supportive accountability. And this is really important because often we focus on the technology and what technology has to offer for mental health interventions. But what we've learned through many years of studying various technologies as they relate to digital mental health interventions is that the human support that is received during an intervention really influences whether individuals ultimately utilize the intervention and thus benefit from the intervention. So it's not typically enough to simply give somebody a digital health app, let's say, and have them use it. We know that if we use that approach, only about 15% to 20% of people will ultimately utilize, let's say, a CBT app all the way from start to finish. So that's not a very high number. In order to increase that, we need to provide some human support. And the reason for that is it increases bond between a human and also some accountability. And the accountability, when you know you're going to check in with somebody, you have more motivation to, let's say, complete your session on your app or online, etc. So this is a really important concept to think about, and it'll be relevant to what I'll be talking about today. So we know that there, as I mentioned, there are kind of a lot of companies in this area, and there are also a lot of apps. And looking at the apps as a consumer can be very intimidating, because if you type depression in your app store, you'll get a wide variety of apps. And most consumers will likely have a difficult time thinking about which apps to download, right, which apps are reputable, which ones are actually going to help, which are evidence-based, etc. One tool that is very helpful, and it's one of many, I do know APA has a similar tool, but CyberGuide is a tool that rates apps based on evidence, user experience, as well as privacy. And it's continually being developed, and it's a good tool to direct patients to, or even for clinicians, to start to narrow down some of the applications that have more evidence and can be, we can refer with a little bit more confidence. Okay, so hopefully it was helpful to give you a sense of what the digital mental health universe looks like from a very kind of broad perspective. And now I want to talk a little bit about digital literacy, digital health disparities, which is an area that I'm particularly interested in, maybe one of the reasons you were interested in attending this talk. So when we look at technology adoption, we can see, I mentioned smartphones, and there is high levels of smartphone use and ownership across ethnic groups in the US. And that's what makes smartphones an exciting tool for delivering interventions. However, now that we're using, let's say, desktops for telemedicine, things like that, we do see that there is still a large disparity there. So if people are going to be, if, let's say, Black patients or Latinx patients are going to be accessing telemedicine, it's more likely going to be through a smartphone than through a desktop or a laptop computer. Another challenge is the lower rates of home broadband access. And those are challenging because they limit the types of interventions that can be delivered. So if you don't have access to home broadband, it might be more difficult to connect to a telemedicine visit, to a video visit. You might be able to do that on your smartphone. However, there might be limited data plans as well. So as the internet becomes more and more important to deliver interventions, it's going to become much more important to provide access to things like home broadband and smartphone data plans as a way to provide, to increase access. So next, I'm going to go through some key data, some key findings around underserved populations and digital mental health. First, the thing that's important to point out and why I think we need to attend to this issue is that racial and ethnic minorities in the U.S. are less likely to receive mental health services. These are standard mental health services, face-to-face mental health services. And when they do, they're more likely to receive lower quality of care. If that is true, then my argument would be that we need to do more to develop innovative interventions for racial and ethnic minorities, given this disparity. Even when given access to home broadband, given access to digital technologies like a smartphone app for depression, let's say. Diverse patients have been shown to face challenges completing even core functions of these apps. So, Urmi Malasarkar and colleagues did a study where they gave patients an application and asked them to do things like input their mood on a daily basis or something like that. They found that there are real challenges in some of these basic functionalities. As Steven Shuler, myself, and a couple of colleagues wrote recently, if marginalized and underserved populations are not considered during the development and evaluation of digital mental health interventions, these technologies might serve to further entrench rather than overcome existing disparities. This is something that we must be attuned to as we start to proliferate these digital technologies. We also know that folks from Latinx backgrounds are more likely to be smartphone-dependent. So, again, this is related to a previous point where whatever interventions are delivered are likely going to be delivered on a smartphone. They're also more likely than other groups to interact via text messaging or mobile messaging platforms such as WhatsApp. Again, these can be potential opportunities for delivering interventions. In a survey of primary care Latinx patients, 86% stated an interest in utilizing a health app and 40% expressed motivation to use apps daily. So, there is a desire to engage. The challenge is whether developers are meeting this desire with interventions that match patient needs. We see similar rates of smartphone ownership from African-Americans and also more likely to be smartphone-dependent, but we consistently see a very, very low rate of African-American recruitment in mobile health intervention studies. And the challenge with that, particularly as we move towards utilizing big data and personalizing interventions around machine learning algorithms and artificial intelligence, these algorithms are based on data from users. If users are not adequately diverse, then when a user from a diverse background tries to engage in the intervention, it may not be as effective for them. So, at a recent digital health equity workshop organized by the National Academy of Sciences, Engineering, and Medicine, some key points came out from this workshop. And one that I think is worth reiterating is that technology is really about people. So, addressing disparities goes back to the foundation of placing people first and not technology first. So, as much as we can get enamored by the technology, we have to remember that it's about improving the lives of people and improving care, which oftentimes is based on relationships. And that is an aspect, there is a curative aspect to that as well. We need to have community engagement in designing, evaluating, and adapting interventions. I would argue that it's better to design from the ground up than to try to adapt, which often happens with these types of interventions. Reducing disparities also requires us to think about beyond technology, to think about health in all policies. And looking at major determinants of health within systems, which we have all been made aware of recently with the recent focus on systemic racism. And we know that those experiences play key factors in health and mental health. And the more we recognize that, the more we can integrate those realities into our interventions as well. And one of the last findings from the workshop was that these technologies have a unique potential to reduce disparities because of their extensive use. I would argue that we need to capitalize on that possibility and that potential. So, some colleagues were looking at the factors associated with use of interventions, of digital interventions. And what we see is that some of the factors that were most related to use were race. So, whites were more included and related to using digital interventions than others. And so, it's important to think about how demographic factors are integrated and how they are even studied. So, one of the important things to think about with regards to this chart is that many people are not even assessing digital literacy or English language preferences or income, etc. Okay. So, first I talked a little bit about digital mental health broadly, then about digital equity. Now I'm going to talk to you more specifically about the work that I've been doing, which is integrating technology into CBT for depression in the public sector. And so, why focus on this issue? One is because we know that attendance to cognitive behavioral therapy, to frankly any psychotherapy or even medication management, predicts improvement overall. We also know that within CBT, 23% of people drop out after one session. And only 23% of the people that we measured completed their homework when completed in a standard paper and pencil method. Lastly, 50% of people finish less than eight, or in our case, half of their sessions. So, there's a need to increase the engagement of these interventions that we know have evidence behind them. Here you can see what CBT group attendance looked like in our clinic when we just measured it with treatment as usual, with a standard intervention being delivered. This is a 16 session intervention. So, ideally we would see a large spike at number 16, but instead we see a large spike at around one or two sessions. Many of you see clients are probably not surprised by this, but there's a large drop-off after people come in for one or two sessions. So, a lot of people will come in and for whatever reason, either maybe it's not a good fit for them, or maybe their depression is symptoms are too high and they're not able to engage. There's a variety of reasons, but if they're not going to attend throughout, they're not going to get the full dosage of treatment. So, attrition is a broad problem generally. In psychotherapy interventions, attrition is estimated anywhere from 20 to 60%. And we do know that higher attrition is seen among low-income and I think minority patients. And also that there is higher attrition for technology-based interventions. These are all challenges that we need to face. So, the goal of these studies that I'll be talking about shortly is to leverage text messaging as a way to improve treatment quality and intervention in CBT for depression. Why text messages? Well, text messages are widely accessible, they're cost-efficient, they're easy to use. I'm guessing virtually everybody on this webinar probably uses them. They have high engagement rates. So, most text messages that people receive get looked at, at least briefly, get acknowledged in some way. And this is often different than, let's say, receiving a notification on your phone that can be turned off or ignored. It's harder to do that with text messages. Lastly, people tend to report a greater sense of personal connection when getting a text message. Even when it's known to be coming from an automated source, people report that they feel cared for. If they know the program or if there's some human behind it. And you can imagine that you might have that experience when you get a message from somebody that you love, somebody that you care about, you're more likely to respond to them and feel that connection via text. So, I want to describe the setting of the intervention a bit. So, as I mentioned, this is Group Cognitive Behavioral Therapy for Depression at a public hospital in San Francisco. And the majority of our patients, where we did most of the quantitative work, was Spanish-speaking patients. The model of treatment is a weekly meeting for 16 weeks, consisting of four modules focused on four weeks of thoughts, four weeks of activities, four weeks of people and interpersonal interactions, and four weeks of health. There was rolling admission, so people could enter at various points of the intervention, and the group continued on for, as a matter of fact, years while the study was going on. The design is a non-randomized pragmatic trial, meaning that the control arm received standard CBT and the intervention arm received group CBT plus the automated text messaging. We weren't able to randomize in part because of preferences to the time and day of the week of each intervention and making sure that the sizes of each group remained similar. The intervention was delivered by myself and another social worker or a psychologist. And here are the patient characteristics in the study where we compared the intervention engagement. Some key takeaways are to look at the average age. So, the average age is 52 years old. Oftentimes, when we think about technology-based interventions, or particularly texting, our brain often goes to a younger population. We saw this as a challenge, but also an opportunity. Folks that are older than what we might think of as regular users of texting may have a harder time engaging, potentially. But one of the positives that we saw is that they also reported that because they received less text messages overall, the messages that we sent were relatively more valuable. 68% of our participants had less than a high school education. So again, try to paint a picture of folks who are not highly educated, maybe older than what you might think of as a typical high level texter. But the goal is to reach an underserved population to create an innovative intervention from the ground up, in a sense. Most people own smartphones and most people use text messaging. If they did not use text messaging prior, we offered to teach patients how to text message, how to respond to messages, at least in basic ways. So, the way that homework is typically done in this intervention is exemplified by this sheet here. So, we ask patients to indicate the day of the week and then indicate their average mood throughout the day overall. And then to indicate some other report of either mood, activity, interpersonal interactions, etc. to start to help them get a sense of the connection between mood and, in this case, healthy activities. And the problem is, as I mentioned earlier, only 23% of people were completing this form. So, what we did is turn this into a texting intervention that consisted of mood monitoring text messages as kind of taking this and making it into a text format, theme-based text messages, which are related to the theme of that week. So, reinforcing what was covered in session that week. We sent medication reminders for people who opted into that. And these were general medication reminders for any medication they might be taking. It could be psychotropic or it could be for high blood pressure, whatever they wanted, but it was a general reminder. And then we send group attendance reminders. Here are examples of the mood monitoring text messages. So, we asked people to send a number and what they were either doing or thinking in that moment. And these were sent randomly throughout the day to capture different times of the day and also feel a little bit more natural. We use the variable ratio schedule to provide feedback to the messages that were sent. And we did that because we know that this type of learning schedule increases response rates. So, for example, if somebody responded with an eight, we might say automatically, it looks like you're feeling good. Think about what you're doing and thinking to keep your mood high. If it was a six, which we would categorize as a medium or mid-level mood, what could you think of right now to feel just a bit better? What thought would help you feel better now? These are examples of the theme-based text messages. One is based on thoughts, the second, activities, and the third, interpersonal interactions. And these were sent at the end of the day. So, you notice there's some were simply tips and some were kind of questions or thought questions for patients to think about. And there's an example of a medication reminder. And then this is an example of a group reminder sent the day before session. Okay. So, once we received responses, this is an example of what the data looked like. So, I'd like you to point your attention to the pink line. So, in this case, somebody responded. We can see that mood range from a six to a nine throughout the week. And what we would typically do is focus in on a couple of key points. So, here, a couple of key points that are interesting to point out is on Wednesday, this person responded, they were an eight. Estoy con una amiga. I'm with a friend. And the day before, responded with a six. Estoy en casa. I'm at home. So, we would point out here the difference between the activity of engaging in a positive social interaction and the impact that that has on one's mood versus staying at home alone and explore this theme. So, this was very helpful in providing some context around mood ratings and getting real world data as opposed to retrospective data. When we looked at differences in depression scores over time, there was some indication that the texting group did fare better over time. However, given the high levels of dropout over time, we weren't able to find any significant differences in PHQ-9 scores over time. However, what we did find, because we were interested in looking at engagement, we found that folks who received a text message attended over twice as many sessions. So, they attended six sessions instead of two and a half sessions for the folks who did not receive text messages. So, it's quite a big improvement. For people who texted, they stayed in treatment for about 13 and a half weeks out of 16 versus three weeks. And what's important to note here is this is different from the previous slide in that they didn't attend 13 and a half weeks, but there was some contact with them for quite a while longer. This means that, you know, you might imagine that somebody shows up to a couple of sessions and then maybe stops showing up, but they're still getting text messages. And when maybe they feel up to it, when they feel ready, they're able to re-engage and go back to session. Whereas folks who are not getting texts, they drop off and they kind of stay away. They don't come back as much. So, this is a way to continue some form of contact with individuals, albeit automated. So, the main implication here is that texting adjuncts can lengthen the stay in treatment. And really the most important aspect of that is that we can help more people get better. So, now that we have all this data and responses to text messages throughout the week, we wanted to think about how we can use this data to improve treatment. So, we wanted to know, can the data that we get between sessions predict future attendance? So, we wanted to see whether average response rates or delay or average mood in the week before or the day before were related to whether or not someone's going to show up to session that week. And we did find indeed that when mood was one for every point in mood that increased, or let's say if somebody's mood was one point lower than their average the day before session, they were 32% less likely to attend. That's important because if we have that data, we can then up our interventions for trying to encourage people to show up. We can send an automated message, we could send a personal message, or we could even call individuals. And here you can see the probability of attending session based on the prior day mood. As mood increases, the probability of attending session also increased, which makes sense because if you're not feeling great, it's harder to gather up the motivation to attend sessions. So, the implications for this are to identify critical points to develop micro-interventions and tailoring. We can outreach to patients, target low mood, and talk about the importance of attendance, either in an automated way or via clinician outreach. We also wanted to look at, are these mood ratings any proxy for something like the PHQ-9? And indeed, we did find that mood ratings were related to people's mood. We did find that mood ratings were related to PHQ-9 that individuals filled out that day, as well as their average mood in the past week. They were not related to their average mood in the past two weeks, which is interesting because in theory, PHQ-9 measures two-week symptoms. As a clinician, and maybe in your experience, you also may have noticed difficulty patients have sometimes thinking back two weeks. So, this is an interesting finding for a couple reasons. But generally, what we see is that the higher mood ratings people are reporting, the lower their PHQ-9 ratings and vice versa. So, why is this important? It's a relatively straightforward study, but it's suggesting that we may be able to use simple mood ratings to assess symptoms over time. And if we do that, we may be able to do remote monitoring for larger numbers of people. In scenarios such as what we find ourselves now, these types of approaches might be useful for monitoring and assessing versus having people come in in person and fill out PHQ-9s. This is a chart that shows that groups, we wanted to find, we wanted to see what types of users we had in our intervention. And we ran some analyses where we found four clusters of patients. And so, what you can see is the red cluster on the top right are folks who responded to text messages at high rates and attended sessions at a high rate. So, these are people who were much more engaged generally throughout. In the green, what you see is people who responded to text messages but didn't attend as much. And here you can see one of the key differences is an age difference. That group is younger. So, they're tech savvy, but not as engaged. They also had higher levels of symptoms overall. And then on the left, you see folks that attended at high rates but didn't respond. This is an older group typically with lower levels of lower PHQ-9 scores. And so, here age really seems to be a driving factor. And then folks who did not respond and did not attend, they have the highest PHQ-9 scores. So, this is also helpful in thinking about for whom will these types of interventions work the most. As we can see, there is an age factor in terms of responding to messages and severity is related to both engagement in the text messaging as well as the live intervention. When we asked people in some qualitative work, some development work, whether they found the messages helpful, some English speakers told us that the messages forced them to check in with themselves, that they triggered self-examination. The last quote, I think, is very indicative of where a lot of our patients are. Sometimes I am so busy, I hardly stop and think about how I feel. Now that I'm getting texts, I stop and think every day. When it stopped, I missed it. My life is so crazy, I need a reminder to think about how I feel. So, I think that a lot of the clinicians in the audience would probably agree that these are all things that we want our patients to do. We want them to be self-reflective. Then we asked the Spanish speakers the same thing. They mentioned some examples here. They mentioned motivation. They also mentioned, when I was in a difficult situation and I received a message, I felt much better. I felt cared for and supported. My mood even improved. Somebody else also mentions, I realize that someone cares for me and I don't feel alone. There are people that care about my health. We start to notice some differences in terms of between English and Spanish speakers, where Spanish speakers are really talking about this relational aspect of the intervention, of the communication. So think back to the supportive accountability, to feeling that there's a bond, there's a connection with an individual. And these types of findings have really led me to believe that the human connection is valuable and central to the digital health experience. And it may be even more so for folks from Latinx backgrounds. This is where the consideration of culture is really valuable because we can have the exact same intervention be perceived very different based on one's cultural values. And we know that broadly speaking, folks from Latinx backgrounds have more of a relational value and less individualistic than English speakers, by and large. These are broad strokes, but the literature bears that out for the most part. Some of the barriers that people reported are that texting wasn't easy or texts were an added burden. One person did mention that they were not being sent by a human and they didn't like that. Some of the factors that we saw were related to responses are education levels and whether or not people texted before the intervention started. So again, familiarity with the intervention and education levels. So lessons learned so far throughout this work is that patients who text attend more and stay longer. Prior day mood rating predicts attendance. Mood ratings via text can approximate PHQ-9 scores. And patients like interacting with automated texts generally. We're now working, just to give you a little sneak preview on what we're working on now, doing a texting intervention for folks with depression and diabetes and trying to personalize these messages by sending messages that are increasing individual steps. So here they have a smartphone intervention that collects their data on a daily basis. And then we have a machine learning algorithm that can start to individualize messages a bit to ones that are more motivating based on the data that we're collecting on step count. And so there what we want to do is personalize using an algorithm. And I think the reason I bring this up is because it's an example of what I think is an innovative intervention that is starting in a resource constrained environment. And if it works there, we can really target this population and can be more broadly relevant. Here are some references that you should have access to as well. And thank you very much for your attention and your participation. I appreciate it. Thank you so much, Dr. Aguilar for that interesting talk. So Adrian, let me start to roll through some of the questions. Can you talk a little bit about personalizing the text and how do you do that on a large scale? That's a great question. So in this study, we didn't do a lot of personalization besides, let's say, adding an individual's name and responding to some of the mood ratings. And we did that through the examples that I mentioned. Some of the things that we're looking to do going forward is to try to tie, so I alluded to the study of physical activity and we're trying to use, we're testing out that approach right now to see if we can tie which messages are related to higher mood rating and then sending more of those types of messages. Some studies, some researchers have tried to have individuals write their own messages, but those have given mixed results. So the approach that we're trying out is this kind of machine learning approach going forward. I very much agree that personalization is key. I think it's the approach that things like chatbots that use kind of different AI algorithms, Wobot is one example, that does a little bit more personalization around what people are sending. This was kind of a more basic approach. Yeah, I remember a ways back, National Cancer Institute did one with smoking, for texting for quit smoking. And they basically sprinkled throughout some specific things that were motivators for individuals. So they did an initial like interview with folks, sort of found out some of their reasons that they wanted to quit and made up like, I don't know, 20 texts. And they mix those in with all of the regular texts for that person. That's another idea. Yeah, definitely. I think the, you know, anything that we can find that is most motivating for individuals. So another approach could be not something that we've done, but something I've thought about is, you know, utilizing information from, let's say, a motivational interviewing maybe live or even digitally, and integrating those, let's say, values or motivations into an intervention. And that's kind of one of the, it's to some extent, it's one of the approaches we're taking in our future work. I like it. All right. Can you explain the comparison between the mood ratings and the PHQ-9 scores? If the texts are only assessing mood, what about the other symptoms that the PHQ-9 assesses, like sleep, cognition, suicide ideation, et cetera? Right. So admittedly, it's definitely a crude comparison. And we don't get the nuance of the different PHQ-9 questions, right? But that being said, the total score on the PHQ-9 is often one of the things that's used to look at overall functioning or overall symptomology. So if that's one of the goals, then a simple text or mood rating could be beneficial to get an overall view. Obviously, if we want to target specific symptoms, like sleep, for example, we would want to ask for that more specifically. It's always a trade-off in terms of how many questions you ask, how much information you want to gather versus how much time and effort it takes a patient to do. So one of the things that the qualitative feedback said was sometimes it was at inopportune times. Can you speak a little bit about how you timed the release of the text? Were people able to have any control over when they received that? Did you do at random times, different days? How did you time those? Yeah. So the way it worked for the most part is that they were randomly sent out from 9 a.m. to 9 p.m. So there was a possibility that for some people it would arrive at a time when they were at work or something like that. We tried to let people know not to feel pressured to respond right away. That being said, some people still felt that pressure. And I think we've noticed there's kind of a cultural element to that to some extent as well, where many of our Spanish speakers feel bad not responding right away. So maybe that sense of accountability is maybe particularly strong. I think there's a trade-off in terms of... We always have this trade-off between patient preference and how the intervention is developed. Because sometimes we know... Obviously if somebody is kind of at work or something like that, we could work the messages to potentially not send during a period. But it's also possible that somebody might say, well, I'm not a morning person. Don't send them in the morning. But that may be the time that could be the most important for motivating somebody. So in our work going forward, what we're doing is we're actually making time a factor in our algorithm. So we're sending messages at times where people are more responsive to them, and therefore adding yet another layer of personalization. So now that we're in this COVID era, and there's a lot more done via telehealth, and now also that there are health portals where we can contact and send emails to our healthcare providers, we're used to this almost like a holding. There's some sort of holding, connection between our clinicians and us between sessions. And I'm wondering if you think a little bit about your texting as a way to be tangentially connected to your clinician or to your patient between sessions, and what do you think about that? Absolutely. And that's the feedback. So the nice thing is that I was able to see a lot of this firsthand as a clinician. Admittedly, in some ways, it's not the ideal setup, but it really allowed me to see firsthand how this functions. And what a lot of patients often would tell me, particularly when we had glitches where, let's say the messages didn't send out for a couple of days, patients would come back and say, Dr. Aguilera, you forgot about us. And it really made clear to me that the function of the messages was about them feeling that I was sending them, that there was some real connection there. And so I started to imagine a bunch of mini clones of myself texting out messages at various times a day to a variety of my patients. And it really felt like it was able to expand the work that I was doing. And I think that that's where I see a lot of value in this approach. And maybe it's not going to be exactly text, maybe it's emails, it's going to vary based on patients, but it's still rooted in the evidence-based intervention. It's rooted in the relationship that a client has with their patient, but it's expanding that without, importantly, increasing the burden on the clinician to actually sit and send all of these messages or emails regularly. So, you know, this is a question sent in right now that we get a lot when we're talking about communicating with clients over text. And I know you were careful not to ask about suicide, but someone wrote in, what if a client responds with a low feeling score, like very low depression or are suicidal? Are those being reviewed in real time? That's a great question. So given the context of this study, particularly because we were seeing people, you know, on a weekly basis, we made it very clear with patients that the texts were not a direct form of communication with their clinician, that any sort of crisis reporting should happen through regular channels, through calling, usually done through phone call versus text. So we made that very clear. That being said, we still had some safeguards in place. So in particular, we have a system on the back end where if certain words are triggered, some examples are gun, kill, bridge, you know, since we're in the Bay Area, things like that, that might have some sort of indication of harm, self-harm, I would get a notice to go into the system and read the message. And that kind of, those alerts can be, we can put in any words we want in there. So that's one way to do it. And on a weekly basis, at the very least, or before session, you know, we, a clinician would go in there and receive the data. So let me ask you one more question. It's a little long, so, but it's about your data. So let's, did text messaging move people from one group to the other, or did all groups overall benefit from text messaging? With the clustering groups, you said one group, red, was highly engaged in text and attended groups, attended more groups compared to the other groups. But are we seeing a self-selection here? Would the red group have done better than the others anyway, even without text messaging? It's possible. I mean, it's possible that they, you know, but that's the case in a lot of our interventions. I think that, you know, a lot of our interventions, you know, there's some folks who you give them anything, you give them a book, you give them, you know, a couple of sessions and they'll take it and run with it. But then the, and there's a, maybe another group of folks that you give them an intervention and it's, you know, it's going to be really difficult to kind of, you know, we might call treatment resistant. I don't know if I love the term, but that, that's true. But then there's kind of a lot of folks in the middle where needing more, maybe a little bit more engagement, needing a higher dosage in a sense of the intervention, needing more of a, of a nudge, I think could potentially help. I think, you know, what I see as most probably the clearest value that we can show is increasing the level of attendance to sessions. So we know that the intervention works based on all of the data that's, you know, preceded this work. And we know that attending sessions is related to outcomes generally. So if we're able to show that we can, that we can increase that, that I have at least some level of confidence that we're making some impact broadly. I definitely think that's a fair conclusion. All right, we'll stop the Q and A there. Thank you so much, Dr. Aguilera. Thank you for joining us today. Until next time, take care. Thank you for participating in today's free course from SMI Advisor. We know that you may have additional questions on this topic and SMI Advisor is here to help. Education is only one of the free resources that SMI Advisor offers. Let's briefly review all SMI Advisor has to offer on this topic and many others. 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equity

diversity

digital mental health interventions

depression

text messaging intervention

cultural tailoring

disparities

access

cognitive behavioral therapy

technology