Global Perspectives on Digital Health

Shubhanan Upadhyay (00:00)
Episode 11, Global Perspectives on Digital Health podcast. If you are working on mental health, digital biomarkers for mental health, health data poverty, ethical approaches to AI, responsible AI, then you're going to want to listen to this episode.

around mental health and research gaps globally and some of the publications just put out there. So very, very excited to have this episode. Why does this matter? Why should you be listening to this? Well, number one, there's been a massive proliferation of digital solutions that serve

people's mental health all around the world. There are wide, wide variations in practice, policy, definitions, cultural contexts, healthcare workflow context, healthcare delivery context, and practice. And so it's really a tough space, I would say,

In particular also, and thirdly, lower and middle income country settings and underserved communities also in higher income settings are not that visible in research

So very, very excited about the key insights that they've got on what can people who are in policy and decision makers and also vendors and builders take away from this work.

Really, fits with what we're trying to do at the Global Perspectives on Digital Health podcast. And DIME are a great flag bearer or a lighthouse organization for like the nuances of what it takes to do good implementation of digital health. So I hope you find it useful. Let's get into this.

Shubhanan Upadhyay (01:34)
Lucy Cesnakova thank you so much for joining us. You work with DiME and I'm really, really excited to hear about the work that you've done. could you tell us a little bit about yourself and the work of DiME?

Lucy Cesnakova (01:45)
Yeah, absolutely. Hello, Shubs. So nice to meet you and everyone who's listening. My name is Lucy Cesnakova I work with DIME, Digital Medicine in Society. And I lead projects that are focused on improvement and innovations in digital measurements and digital endpoints, which is a wide topic, but we aim to pick the problems that are most important to research, I'm...

not promising that we are solving everything, but we are providing those step stones that help others to do important, meaningful research in this field.

Shubhanan Upadhyay (02:21)
Awesome, yeah, and I know a lot about the work of DiME really, big lighthouse leaders in terms of helping the whole industry understand what good implementation looks like, what considerations need to be made. And so, yeah, I've always loved my interactions with DiME and Jen as well, Jen Goldsack, who is your CEO. So really, really important the work that you're doing. So glad to see that it's also having an impact.

and wider than where I'm used to seeing you, which is like in the US context

Maybe we could start with telling us a little bit about the why. Why was this needed? What kind of gap was this addressing?

Lucy Cesnakova (02:55)
Right, so this research was focused on exploring how we can advance adoption of digital health technologies for mental health. And we were basically kind of exploring what is already out there, how does it work and what can be improved? are the main gaps and...

themes topic that would help us to move the whole field forward, right? And the overarching topic is that there is an immense potential, of course, for digital to aid research and care for mental health conditions, if it is done right and meaningfully. So it's nothing new and obviously digital solutions have been here to help evaluate intervening medical mental health conditions for quite some time. However, the evidence shows that it

mostly has been done in isolation, so small pilots, small studies, small groups, and some kind of larger support and initiative towards the adoption would be great to overcome the gaps that we have identified.

Shubhanan Upadhyay (03:57)
Yep. And as you're saying that as well, I feel like there's a framing to talk about as well. So we're in like March, 2025, and there's kind of been global seismic shifts.

in the way healthcare is kind of perceived to be delivered, how healthcare workers are going to be part of this and kind of just like the political determinants of that, right? So the UK has just made a massive change in abolishing NHS England. So that's a big seismic change. The US as well in terms of how politically it's thinking about healthcare and also the kind of cuts within the US on USAID has had massive cascading effects on.

healthcare. And so the work that you're doing is kind of now also within this context. Why is that important? Well, I think the cross section that you've got, which is of course, including the UK and the US, and people with lived experience healthcare workers and many others, but also these other contexts that you've brought in.

And so what struck me across these geographies to be able to then extract what's similar, what is different. Because as we're going towards a world that's trying to look more inwards

We are still heterogeneous cultures. And so how do we make sure that we get these considerations right?

Lucy Cesnakova (05:17)
Yeah, absolutely. So in this research, think what was great was that we aimed for, as you said, global representation from our experts that we talked to, surveyed, and explored this topic with. We talked with healthcare providers, clinicians, with lived experience people, so the patients, with researchers.

There were also some policymakers or administrators, so like many different kinds of experts that have insights into the topic and also many different geographies. not only very like high income, like the US and UK, but we also tapped into Africa, a little bit of Asia, Europe as well. And it was really great to see where

we can learn from one another and what are kind of some like universal similarities and where you know maybe some approaches can complement and inspire some other parts of the world.

Shubhanan Upadhyay (06:14)
so you set out this goal. you've touched on how you wanted to get that representation. But do you have any key learnings or things to share from the methodology of the work that you've done?

as well.

Lucy Cesnakova (06:25)
From the methodology part, we did three kind of activities. First was scoping literature reviews. So we looked at what evidence is out there and published and what people have been researching and looking to. And that kind of provided the state of the art, kind of the research evidence. And we also talked to the experts and people, lived experiences, et cetera.

in one-on-one interviews and we also surveyed them to get their insights in kind of like a modified Delphi approach where we not only provided like survey ABCD options and you know like tells their preference but also

provided the chance to get their qualitative feedback. So we learned a lot from these open text and open answer responses. And in the end, we kind of closed everything with debriefing focus groups where we talk about, what were the main learnings? What were the main areas where maybe their opinions differed, right? Or maybe some things that were not matching, let's say across different.

expert types or geographies, etc. So we discussed that as well and everything is written up very nicely in the report that we have published.

Shubhanan Upadhyay (07:38)
Awesome, and we'll publish that in the show notes as well and provide links to that. It's very, very extensive and there's so many, many insights to get through. so any big challenges in the methodology, in the work that you did, whether it was kind of the interview process, gathering and synthesizing the insights and then kind of the work that you did to kind of then turn these into something that was actionable.

Lucy Cesnakova (08:01)
You know, I think for me, what was most challenging was that we had such a vast amount of data collected from many different streams that do like, not only analyze, but synthesize these learnings into a form that would be digestible and presentable. And that would offer some clear, let's say recommendations for the next steps, et cetera, was, well, it was.

both challenging but also very interesting to work on, right? Because once you get like deep into that, then you you kind of start seeing those connections and those things that are like having those aha moments that, you know, what is important and what should be coming next.

Shubhanan Upadhyay (08:41)
Mm-hmm.

So I'd love to kind of really get into what stood out for you, what were kind of your biggest takeaways. Because we talked about, I think you put it really well, part of the things that are valuable about this was, A, finding the universal kind of commonalities, but also B, what were the key

contextual or local nuances from certain countries that were very definitely different between and discreetly different. So if you could cover those two, that would be amazing. And if you've got any examples, that'd be super.

Lucy Cesnakova (09:14)
Right. So I'll start with the commonalities, right? So in this research, explored one of the things that we explored is which areas are important in mental health conditions that can be targeted or measured by digital health technologies or sensor based technologies. And almost universally, those that came up as like the highly appreciated ones where

Shubhanan Upadhyay (09:30)
Mm-hmm.

Lucy Cesnakova (09:38)
sleep, physical activity and social behavior. So this is something that are that is these three are determinants of the mental health conditions that are universal across the world and also those that a lot of research has been done. There is a lot of sensors out there that can measure it. And so as for the next steps, you know, to improve on these, should

focus on validating these technologies in those respective populations so that we have a meaningful, let's say, measurement or intervention, right? So this was something that was kind of universal from the point of view of the research, from the point of view of the implementation and adoption. One of the interesting things that was coming up universally were cost and access. And this was interesting because it was not only in the areas and regions that were

say low income or poor, but also in areas that were also, you know, high income countries like USA or UK, which suggests that also in these countries, there is a lot of heterogeneity in terms of access to these technologies. Many people still view it as, let's say luxury items or something very advanced that they don't have the reach to have or to own or to use in their regular life.

And this should not be something viewed as a problem of poorer countries, right? But it should be looked at, you know, universally like, okay, if I want to advance adoption of some kind of technology, how am I going to make sure that the right people get access to it at the right time for the right reason?

so there are ways to tackle this so that the technologies that researchers would plan to use in such communities would not be the most high and the most expensive tech, right? So you can, for example, build solutions that allow use of older models of, let's say, phones, watches, etc.

things that are, let's say, low cost to manufacture. And so they can be manufactured in larger quantities and shipped and used in different populations, or have some kind of subsidies or grants that would cover some of the costs in this area. there are ways to tackle this. And these technologies doesn't always need to be thought of as like the most high-end expensive, let's say, newest Apple Watch, right?

Shubhanan Upadhyay (12:04)
Mm hmm.

Yeah. And you mentioned this thing, was it the right intervention for the right people at the right time? that it? Can you just say it again?

Lucy Cesnakova (12:12)
Yes.

Yeah, the right solution or intervention for the right person at the right time, right? Because the journey in mental health is oftentimes very long and has a lot of twists and turns. People need to adjust their medications, sometimes change it. They do therapy maybe. So it's a little bit different when you have, let's say, blood pressure and then you just like...

Shubhanan Upadhyay (12:18)
I think that's key, right? Yeah.

Lucy Cesnakova (12:37)
require some setting up of the medication, but you know what I mean, like it's a little bit more intricate and a little bit more delicate So that's why also like that's one of the considerations that we are putting out there for use of the technologies in the clinical care and clinical practice is that they need to be designed for ease of use, for comfort, for that long

term use and wear so that it's usable for the people, both patients and clinicians who are interacting with them and will bring that benefit over time that they are intended to.

Shubhanan Upadhyay (13:10)
Yeah, over time.

And I think, I mean, that's another aspect of that data that, you know, can easily get overlooked. It's not just about the vertical types of biomarkers, but actually the value is also the time over time. So if you invest in making sure that you have something that has engagement adoption over time, you then get back the value of longitudinal data.

Lucy Cesnakova (13:28)
Yes, absolutely.

Absolutely. And the report makes a very interesting case in comparing the research use versus the care use, right?

So the clinical research aspect is oftentimes shorter in duration and people are willing to tolerate a little bit more discomfort over this short period of time. However, when you translate the same technology into care that is longer term, then a little bit different considerations apply

Shubhanan Upadhyay (13:43)
Mm-hmm.

Any other universal commonalities before we get into the kind of local nuances and context?

Lucy Cesnakova (14:05)
also one of the common universal commonalities was accessibility, right? And that's basically design of any kind of intervention or technology that is tailored to that specific population and their needs.

I will give you a great example that was coming up a lot. So for example, people with depression, sometimes when their depression spikes up, they have very low motivation, right? And they don't even want to get out of bed, not even go and plug some device into charging, right? So for example, a long battery life that can withstand these periods of lower motivation was highlighted as important for depression, just so

that it is a very specific aspect of that specific population.

Shubhanan Upadhyay (14:52)
Nice one. And so I guess like a meta concept is, if you really understand people that you're trying to serve, you kind of need that level of empathy of like getting to that granular level of detail of the understanding and build for their ups and downs behaviorally, right? That will happen as a normal part of that lived experience.

Lucy Cesnakova (15:09)
Yes, absolutely.

And you mentioned empathy, but also this can be something that can be targeted with some kind of co-development and co-design. basically inviting your future users into the development so that they can provide their perspectives and their insights so that from the beginning you are designing technology for that particular population.

Shubhanan Upadhyay (15:20)
Yeah.

And especially in mental

health where there's such, I mean, I feel like there's even a greater need to do that in this because of the things we talked about at the beginning with such variability. A really good example of one of the podcasts with Jana that I did, co-designing for young people for mental health solutions So yeah, absolutely that recommendation is spot on. How about local kind of nuances and local key...

things that you saw that were like unique to a certain country or area.

Lucy Cesnakova (16:03)
So

this might not be, I don't know whether this applies, but it's very interesting that from the views of professionals from low and middle income countries, more emphasis was being put on kind of the community, the youth prevention awareness, the kind of approach to life that oftentimes is missing in like more developed countries. For example, we learned that oftentimes people

need to get better understanding about the mental health condition itself and thinking about any digital innovation as connection to it is something very abstract and new. So that need for education and awareness building is very strongly supported. And I think that's something that would be beneficial also all around the world, right? Because even in

Shubhanan Upadhyay (16:38)
Mm-hmm.

Lucy Cesnakova (16:53)
more developed countries, I think that there might be less understanding about the conditions themselves and how they can manifest and how the people feel, how they experience them so that they know what to do before they try to think about any kind of, let's say, digital innovations, iterations, iterations, et cetera.

Shubhanan Upadhyay (17:09)
Yeah,

absolutely. And I guess like another one that's kind of linked to the same kind of concept, is an insight that applies locally, but kind of applies in a meta way everywhere, is like local stigma, each has its own unique way of the stigma kind of manifesting. But the stigma applies to kind of almost all settings in a different way. So did you get any insights on on that particularly?

Lucy Cesnakova (17:33)
yes. So when we were exploring, you know, what kind of characteristics this new, let's say devices or technologies should have, like one of them was that they shouldn't be very glaring, very visible so that they would not bring attention to that particular condition. Like for example, let's say I have a special type of

Shubhanan Upadhyay (17:51)
Mm-hmm.

Lucy Cesnakova (17:55)
patch or a watch and it means that I have a schizophrenia and I don't want people to know that, right? So when designing these technologies to avoid that unnecessary stigmatization, the kind of sleek unobtrusive design should be a part of the thinking as well.

Shubhanan Upadhyay (18:13)
Yeah, Was there anything that really surprised you? Was there anything like, wow, I was not expecting that.

Lucy Cesnakova (18:20)
Let's see. a big one was that even so in all of the countries, focus or the emphasis on data privacy was pretty big. So people are already understanding this that, you know, like their data and

Their data can go to different places, many different people or organizations can have access to it. So they want that to be protected and explained how that is done. And also that there is on the contrary, a lot of trust for sharing this data with clinical practitioners.

Shubhanan Upadhyay (18:46)
Yep.

The other thing I would ask is what are your next, are there any next steps on this?

Lucy Cesnakova (19:01)
So we hope that this research will inspire future work that will go more in depth in specific aspects or conditions because it was very, I would say, general. We covered general recommendations, general gaps and conditions. But we would welcome for future researchers to go deeper, but also

larger. So if there's a new like research development, not to focus only on small pilots and feasibility studies, but like go into larger studies validations, etc.

Shubhanan Upadhyay (19:33)
Amazing. I think we got a lot of really, valuable insight there. We'll also, if you want to dive deeper into this, people can go into that and we'll link that out. it just leaves me to say, Lucy, thank you for sharing these valuable insights. Really, really great to see this type of research occurring and thankful that you spent some time talking to us about this. you. Thank

Lucy Cesnakova (19:51)
Thank you very much Have a great day.