Guest blog, Tech Week

Blog – Digital phenotyping in dementia and neurology: we have questions

Blog by Dr Alan Cronemberger Andrade

Reading Time: 7 minutes

Digital interactions could be useful for tracking health states, especially for brain disorders. The problem is that this kind of data may be harder to protect, and less controlled by ourselves. People with dementia, and other cognitive and behavioural problems, are vulnerable to data insecurity. In this blog, I describe what is digital phenotyping, the good and bad aspects of using it, and some future perspectives behind ethical and methodological difficulties. Healthcare professionals and non-clinical researchers need to talk more about that.

Big data, artificial intelligence, blockchains, machine learning. All these definitions related to the “digital world” gained momentum – fortunately, or not – in the last few years. This occurred especially after the rapid new needs of the Covid-19 pandemic.

Direct social contact and communication was more difficult from 2020 to 2022 and we needed more from the online digital tools to solve the problems for our dementia patients. Now, we rely more on video calls, telemedicine, and online shopping than ever. They are really here to stay, and I can’t see things turning back. And you, what do you think?

The hurdle they bring to us is also that our privacy, an important human right, is always being put in proof in online services. In the UK, for example, news points out that ease in digital privacy laws are actively being discussed again, questioning individuals’ controls over their data.

Digital technologies are part of our everyday life and social interactions

There is much interest in studying these interactions, trying to build a so-called digital phenotype. But, do the research regulation and informed consent is enough for approved research and for business use cases?  What is really digital phenotyping? How strict should these regulations be in any case? Are people suffering from cognitive problems and dementia addressed and protected? We have some questions, but not many answers.

In the next sections, I will describe what is digital phenotyping, the good and bad aspects of using this kind of approach for dementia and mental health problems, and some future perspectives considering the ethical and methodological characteristics behind that.

What is digital phenotyping

We know, or can imagine, that we have some “digital fingerprints” everywhere: that smartwatch your mom gave you, your nice mobile to connect to the 4G or 5G internet, the online chatting you share with your grandpa, and the complaint form you fill to return a broken delivered good.

As a broad definition, the digital phenotype refers to all the moment-by-moment quantification characteristics of the individual-level human phenotype using data from personal digital devices. The promise of digital phenotyping is that those objective measures happen in the context of a patient’s lived experience, reflecting how we function in the world, not in our clinics.

This type of data collection may converge with neuroprosthetics and other types of biological data, but is believed to have better ecological validity, i.e. the information is taken out while performing daily tasks beyond a lab-controlled environment.

The (real) goods

This type of data is especially useful in diseases that alter not an easily identifiable cause, but rather the human function. This is really the case for various neurological or neuropsychiatric diseases, such as dementia, schizophrenia, or depression. The applications for brain disease and mental health are real.

As for dementia, a type of problem that alters cognitive functioning and daily living, these applications and derived techniques may be important in various aspects of disease diagnostics, tracking, and even treatments monitoring. Research has already shown that digital technologies could be used to track mood in depression, for example.

Efforts in dementia research are underway. This type of method could provide a non-invasive brain and general health, cognitive, and behavioural evaluation data. This may have real-world implications in digital health trials and rehabilitation. “This approach is especially important for the wide spectrum of diseases characterised by functional limitations”, says an article published in 2015, one of the first to characterise the term.

Digital phenotyping has new data generated continuously to reflect changing patterns of behaviour. As an example, we can trace a parallel between this and genetics data. Differences between techniques in patients genotyping and digital phenotyping are shown in the table below. The table is about how these aspects differentiate, tracing a parallel analysis between the genetic vs. digital phenotyping characteristics.

Genotyping data = single dataset / collected once / unchangeable, immutable / often knowledge of collection / non-negligible cost, expertise / marginal benefits from triangulation - Digital phenotyping data = complex, mult-layered, modal datasets / collection ongoing / changeable, mutable / sometimes knowledge of collection / negligible cost, expertise / substantial benefits from triangulation

Table 1. Differences between genotyping vs. digital phenotyping data. The author says: “although sophisticated data analysis often requires considerable infrastructure and expertise, the cost of processing and analysing each additional data point is usually negligible”. Adapted from Ignacio Perez-Pozuelo et al (2021). Image credits: mohamed_hassan and kreatikar.


The (ugly) bads

On the other hand, concerns about data protection are not anecdotal. The numbers don’t lie. A recent survey found that 62% of companies in the whole Americas experienced a data breach or cyber incident in 2021. Also in 2021, Brazil experienced a data leakage with more users compromised than its entire population. In the UK, understandable concerns have been directed to patient information systems within the NHS.

All this human interactions with technology could be potentially under (bad) surveillance. And, nothing more precious than our behaviours, cognitive skills, and body ability can be tracked. They can potentially be used not in our favour. Perez-Pozuelo et al (2021) gives an example of third-party companies using healthcare data shared under business associate agreements. They also affirm:

“Several complex issues must first be resolved, such as who owns, controls, and can use personal health data to derive wider insights; the formats and standards that should underpin how these data are shared; and how the range of potential uses of personal data are explained and justified to data subjects.”  Digital phenotyping and sensitive health data: implications for data governance, Perez-Pozuelo et al. (2021)

The (untamed) future

Although public entities and the central governments hold a lot of our data, our day-to-day digital interactions are somewhat not in their power, but usually stored and managed by tech companies. Their responsibility is very big.

The question that comes to my mind is that, if this data privacy is really a collective human right, and could bring benefits to all of us, how are the current efforts to secure these de-identified databases for public research beyond the companies walls? Should they be used as a research collective good? How would this be implemented? Is this possible? Lots of questions.

I really believe a way better data handling balance is possible, and rules should be placed clearly. Well built digital technologies could bring immeasurable value for the communities, patients, caregivers, and also for the research teams and private entities. The needs will eventually shape the technology adjustments, considering the fact we have a general positive view for digital technology solutions for our dementia patients. We should participate more in the technology development processes for healthcare.

Some nice efforts and calls for action are in place. It is already possible to find statements and consensus about using digital phenotyping for mental health in Pubmed. Table 2 shows some findings of a research group from Center for Biomedical Ethics and Stanford Law School (Stanford University).

Consensus statements on ethics of mental health applications of digital phenotyping 1) Evidence of validity for the intended use ●Algorithms incorporated have to be thoroughly evaluated in terms of performance and accuracy ● Implement processes for review of digital phenotyping tools’ effectiveness after implementation ● Digital phenotyping tools that are intended for use in health care should use relevant standards ● Digital phenotyping tools for mental health applications should respond to real-world needs 2) Transparency ● Explanations of the processes, risks, limitations, and results ● Processes involved in the collection, storage, and dissemination of raw data, and the architecture of the algorithms, should be explainable 3) Accountability ● Development and use of digital phenotyping tools (e.g., plans for data collection or validation) should be reviewed for potential ethical issues by an independent interdisciplinary group ● Provision of appropriate educational and training materials for ethical review boards 4) Consent ● Consent should be required from individuals when their personal data are collected ● Consent for collection of digital phenotyping data should include information at a sixth-grade level regarding the types of data collected ● Include relevant stakeholders in efforts to formulate and disseminate relevant information for disclosure 5) Data security and privacy ● Data and findings that are identifying should not be collected, used or shared with third parties without the informed consent ● Sharing of data to advance scientific research and the validity of the tools remains an important goal ● If data will be shared with third-party researchers, clear information, written at sixth-grade reading level, must be given ● The individual user also must have an option to opt out of sharing their data with third parties ● Raw data that is non identifying, and non identifying summary statistics, may be shared without consent ● There should be periodic review to re-evaluate whether identifying information can be drawn from the raw data ● Raw data should always be encrypted when stored or transmitted; potential identifiers in data ● Standards and approaches to minimise risk of re identification of individuals, such as differential privacy measures, should be implemented ● The security standards for data storage, sharing, and use of the individual’s data, as well as the process for monitoring compliance with these standards, should be clearly defined ● Security reviews and audits of data practices should also be implemented 6) Fairness ● Encourage collaborative research and partnerships to develop ways to identify and minimize bias or discrimination ● Conduct research into and implement methods to mitigate bias in different levels of algorithm development ● Identify the specific ways that mental health and clinical care may impact the potential for bias in these areas

Table 2. Data table adapted from Martinez-Martin et al (2021), JMIR mHealth and uHealth.


More than health personnel, interdisciplinary researchers are the one that seem more concerned about these ethical and social implications, and interdisciplinarity may be also an important aid to solve the puzzle. Legislators should be approached and acknowledged about the situation. We, as healthcare providers in mental health, and academics involved in research and education, should educate more, and talk more about that.

Alan Cronemberger Andrade Profile Picture

Dr Alan Cronemberger Andrade


Dr Alan Cronemberger Andrade is a Neurologist and MSc Student in Neurology and Neuroscience at the Federal University of São Paulo in Brazil. He takes care of patients with neurological problems in diverse settings, and studies how digital technology interacts with the human brain in health and disease, focused on dementia and related disorders. His aim is to find how useful digital technologies could be in the near future, helping dementia patients and their caregivers. He loves writing, travelling, and reading about curious facts of ancient history.


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