In an era marked by the digitalization of psychiatry, American researcher Ben Berners-Lee has conducted an observational study on a digital mental health trial, offering an intriguing glimpse into the complexities and contradictions of digital psychiatry. His study, titled “Reconciling healthism and techno-solutionism: An observational study of a digital mental health trial,” was published in the journal Sociology of Health & Illness.
The digital mental health trial in question uses a smart system to draw correlations between data acquired from wearable devices and mood self-reports, further offering behavior modification recommendations for mood improvement. According to Berners-Lee, descriptions of the trial emphasize the strong role of the intrinsically motivated, responsible participant and the empowering influence of the machine learning (ML)-based technology.
Interestingly, this dynamic extends what Berners-Lee calls the “neoliberal paradox,” where freedom, in order to be preserved, must be constantly adjusted through discipline. As a result of the contradictory nature of this relationship, disagreements about the balance of agency between the objective machine learning system and the empowered participant are frequent among laboratory members.
Neoliberalism and surveillance have been steady companions in the field of mental healthcare. With mental health apps becoming commonplace, the urgency to understand their theoretical and practical underpinnings intensifies.
Ben Berners-Lee unveils the neoliberal ideology that fuels these apps by providing an in-depth account of a digital mental health trial. The study offers insight into the creation, execution, and constant adjustments needed in digital mental health trials to maintain the balance between intention and ideology.
“In advance of the trial, it was characterized as data-driven, evidence-based, and highly personalized—a way of helping participants understand themselves through data and thus learn new strategies for improving their mood. By this account, the outcome of the intervention would be determined by the combination of two primary factors: the participants themselves, who are characterized as capable and intrinsically motivated, and the ML [machine learning] system, which would empower participants to achieve their mental health potential.”
Before observing the digital mental health trial, Berners-Lee conducted a literature review that emphasized the current critical work in translational psychiatry, digital phenotyping, and surveillance capitalism. Guided by this literature, Berners-Lee picks ‘healthism’ and ‘techno-solutionism’ to describe the phenomena he saw during his research.
“Health discourses that emphasize the responsible individual have been discussed by sociologists under the banner of ‘healthism,’ and those that focus on the promising epistemological power of new technology are often called ‘techno-solutionism.’”
Berners-Lee acknowledges that health consciousness is a useful lens to understand the history of health technology. However, he notes that it risks painting an image of an ideal neoliberal subject—a controlling rather than empowering presence for the user.
Berners-Lee gathered observational data for exploration over nine months using a trace ethnography method. He observed the laboratory practices of a translational psychiatry lab, which at the trial’s outset was described as a “personalized, evidence-based, data-driven, machine-learning-based intervention for mental health in mildly to moderately depressed patients.”
The trial involved several stages for each participant:
- Screening for eligibility in terms of age and exclusionary medical conditions.
- Completing an EEG test.
- Getting fitted with a smartwatch and downloading a mental health application.
“The smartwatch will automatically monitor steps and other movements using onboard gyroscopes and accelerometers. The mobile application will elicit participant self-assessments of mood, exercise, and diet. These prompts by the mobile app are called ecological momentary assessments (EMAs).”
In the following 15-day phase, participants passively generated biometric data using the smartwatch and actively reported data concerning their mood, exercise, and diet. This data was sent to a ‘data team,’ which developed a machine learning prediction model. Following this, the team selected a Personal Wellness Plan (PWP) to promote optimal mental health.
“The participant will receive ‘PWP guidance’ on how to interpret the ML model’s output and implement the proposed behavioral modification plan. Before this process can begin, however, the data team first will explain the ML model’s outputs to the PIs, and the PIs will then explain them to PWP guides. These guides will prepare for guidance the process by completing a series of trainings that simulate the guidance situation. Guidance sessions start with the selection of and introduction to the participant’s assigned plan: sleep, diet, positivity or exercise. After familiarising the participant with the overview of the plan and the relevant handouts, subsequent coaching sessions consist of setting weekly goals and evaluating the previous week’s behaviours.”
The personalization of the wellness plan aimed to stimulate ‘intrinsic motivation,’ according to Berners-Lee. If there’s a lack of inherent buy-in, the app’s purpose is defeated. The app is designed to translate everyday life data into quantifiable figures, and if the participant doesn’t trust the feedback as relevant or personalized, they won’t implement the intervention. Creating a meaningful intervention becomes a joint effort—if one party disagrees, the output and outcomes become meaningless.
“The two components, the individual participant and insightful data system that empowers that participant combine to constitute the force of the intervention as it is described in formal accounts. The former component, the participants themselves, falls into the domain of psychiatric expertise, which includes theories of motivation, mood, and well-being that are specific to the experience of the individual. The latter component, the ML system, falls into the domain of computer science and computational modeling.”
However, how the app resolves potential friction becomes crucial. During the trial, Berners-Lee discovered:
“…The two PIs, however, one with a background in psychiatry and the other with expertise in neuroscience and computational modeling, do not entirely agree on the balance of the two key components. In one participant’s case, transcribed as Excerpt 1, the algorithm found that variables relating to both diet and social connection had a relationship to mood. When the two PIs are preparing the PWP guide by picking a plan and instructing them on how to propose the plan to the participant, they come to a disagreement. This disagreement relates to the question of the comparative importance of data insights and intrinsic participant motivation.”
Disagreements and mismatches are not uncommon between mental health and computational machine learning, as seen in other healthcare wearable trials. It’s a delicate balance: the participant must be free enough to follow the app’s advice while also recognizing their inability to make rational and healthy choices independently.
One of the most intriguing findings of the study is the crucial yet undervalued role of guides. These are the individuals who assist participants in interpreting the ML outputs and implementing system recommendations. While these guides play a significant role in the trial, they are often relegated to the sidelines and depicted as replaceable in formal accounts.
Berners-Lee’s study shows that the work of guides is generally relegated to the technological aspect of the trial, which affects the manner in which they describe and perform their work. This finding, Berners-Lee argues, sheds light on how new forms of human labor can be dismissed or rendered invisible in digital health interventions.
Beyond its contribution to sociological methods and theories, Berners-Lee’s study offers valuable insights for study design and computational system design. The study advocates for a co-design approach for health technology that focuses on participant-guide interactions. This would accommodate shifts in relationships between people, professions, and technologies and elicit valuable participant or patient feedback in the design process.
In essence, Berners-Lee’s research underlines the urgent need for further critical social scientific research in the field of digital phenotyping. It calls for a more comprehensive understanding of cognitive behavioral therapy’s role in the work of guides and lab members and a deeper exploration of participants’ lived experiences that are often excluded from the formal accounts of such trials.
Berners‐Lee, B. (2023). Reconciling healthism and techno‐solutionism: An observational study of a digital mental health trial. Sociology of Health & Illness. (Link)