Digital Phenotyping:Operation & Execution

This thesis is centered around the development and application of a digital platform for passive behavioral human subject research.This platform named Behapp (https://behapp.com), developed at the University of Groningen, makes use of mobile ‘apps’ to passively record various attributes related to social and explorative behavior through the smartphones of research participants. The resulting data is detailed, objective and can be received on a longitudinal basis without active input from the participant. It is expected that such a detailed perspective on behavior may contribute significantly to our understanding of various diseases. Feeling (un) well is often closely related to one’s social and explorative behavior.An important and recurring theme of this thesis is the responsible application of this type of research technology. The resulting data is privacy sensitive which is the reason why initially an assessment is made from a legal, technical and organizational perspective to serve as input for the design of the Behapp platform. Once in service quality control comes into view, measurements must accurately reflect real-world behavior. However, mobile platforms such as android evolve rapidly, not always without consequences to data quality. Therefore, continuous evaluations of data quality are deemed a necessity. Lastly, against the backdrop of the pandemic a pre- / post- lockdown comparison of behavior was made over various studies. Here the Behapp platform fulfills one of her primary design goals, to uncover significant changes in behavior

Requirements and operational guidelines for secure and sustainable digital phenotyping:Design and development study

Background: Digital phenotyping, the measurement of human behavioral phenotypes using personal devices, is rapidly gaining popularity. Novel initiatives, ranging from software prototypes to user-ready research platforms, are innovating the field of biomedical research and health care apps. One example is the BEHAPP project, which offers a fully managed digital phenotyping platform as a service. The innovative potential of digital phenotyping strategies resides among others in their capacity to objectively capture measurable and quantitative components of human behavior, such as diurnal rhythm, movement patterns, and communication, in a real-world setting. The rapid development of this field underscores the importance of reliability and safety of the platforms on which these novel tools are operated. Large-scale studies and regulated research spaces (eg, the pharmaceutical industry) have strict requirements for the software-based solutions they use. Security and sustainability are key to ensuring continuity and trust. However, the majority of behavioral monitoring initiatives have not originated primarily in these regulated research spaces, which may be why these components have been somewhat overlooked, impeding the further development and implementation of such platforms in a secure and sustainable way.Objective: This study aims to provide a primer on the requirements and operational guidelines for the development and operation of a secure behavioral monitoring platform.Methods: We draw from disciplines such as privacy law, information, and computer science to identify a set of requirements and operational guidelines focused on security and sustainability. Taken together, the requirements and guidelines form the foundation of the design and implementation of the BEHAPP behavioral monitoring platform.Results: We present the base BEHAPP data collection and analysis flow and explain how the various concepts from security and sustainability are addressed in the design.Conclusions: Digital phenotyping initiatives are steadily maturing. This study helps the field and surrounding stakeholders to reflect upon and progress toward secure and sustainable operation of digital phenotyping–driven research

Social behavior assessment in cognitively impaired older adults using a passive and remote smartphone application

BACKGROUND: In Alzheimer's Disease (AD), loss of social interactions has a major impact on well-being. Therefore, AD patients would benefit from early detection of symptoms of social withdrawal. Current measurement techniques such as questionnaires are subjective and rely on recall, in contradiction to smartphone applications, which measure social behavior passively and objectively. Here, we examine social interactions through passive remote monitoring with the smartphone application BEHAPP in cognitively impaired participants. This study aims to investigate (1) the association between demographic characteristics and BEHAPP outcome variables in cognitively normal (CN) older adults, (2) if social behavior as measured using the passive smartphone app BEHAPP is impaired in cognitively impaired (CI) participants compared to subjects with subjective cognitive decline (SCD), and CN older adults. In addition, we explored in a subset of individuals the association between BEHAPP outcomes and neuropsychiatric symptoms. METHOD: CN (n=209), SCD (n=55) and CI (n=22) participants, older than 45 years, installed the BEHAPP app on their own Android smartphone for 7-42 days. CI participants had a clinical diagnosis of mild cognitive impairment or AD-type dementia. The app continuously measured communication events, application usage and location. Neuropsychiatric Inventory (NPI) total scores were available from 20 SCD and 22 CI participants. RESULT: We found that older cognitively healthy participants called less frequently and made less use of apps. No sex effects were found. Linear models corrected for age, sex and education showed that compared to the CN and SCD groups, CI participants called less unique contacts and contacted the same contacts relatively more often (Figure 1). They also made less use of apps, visited less unique places and traveled less far from home. Higher total NPI scores were associated with more unique stay points and further travelling. Similar behavior patterns were found when correcting for multiple comparisons. CONCLUSION: Cognitively impaired individuals show reduced social activity, as measured by the smartphone application BEHAPP. Neuropsychiatric symptoms seemed only marginally associated with social behavior as measured with BEHAPP. This research shows that a passive and remote smartphone application is able to objectively and passively measure altered social behavior in a cognitively impaired population

Digital phenotyping and the COVID-19 pandemic:Capturing behavioral change in patients with psychiatric disorders

Contains fulltext : 227418.pdf (publisher's version ) (Closed access)The COVID-19 pandemic has led to unprecedented societal changes limiting us in our mobility and our ability to connect with others in person. These unusual but widespread changes provide a unique opportunity for studies using digital phenotyping tools. Digital phenotyping tools, such as mobile passive monitoring platforms (MPM), provide a new perspective on human behavior and hold promise to improve human behavioral research. However, there is currently little evidence that these tools can reliably detect changes in behavior. Considering the Considering the COVID-19 pandemic as a high impact common environmental factor we studied potential impact on behavior of participants using our mobile passive monitoring platform BEHAPP that was ambulatory tracking them during the COVID-19 pandemic. We pooled data from three MPM studies involving Schizophrenia (SZ), Major Depressive Disorder (MDD) and Bipolar Disorder (BD) patients (N = 12). We compared the data collected on weekdays during three weeks prior and three weeks subsequent to the start of the quarantine. We hypothesized an increase in communication and a decrease in mobility. We observed a significant increase in the total time spent on communication applications (median 179 and 243 min per week respectively, p = 0.005), and a significant decrease in the number of unique places visited (median 6 and 3 visits per week respectively, p = 0.007), while the total time spent at home did not change significantly (median 64 and 77 h per week, respectively, p = 0.594). The data provides a proof of principle that digital phenotyping tools can identify changes in human behavior incited by a common external environmental factor.6 p

A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data

The use of smartphone-based location data to quantify behavior longitudinally and passively is rapidly gaining traction in neuropsychiatric research. However, a standardized and validated preprocessing framework for deriving behavioral phenotypes from smartphone-based location data is currently lacking. Here, we present a preprocessing framework consisting of methods that are validated in the context of geospatial data. This framework aims to generate context-enriched location data by identifying stationary, non-stationary, and recurrent stationary states in movement patterns. Subsequently, this context-enriched data is used to derive a series of behavioral phenotypes that are related to movement. By using smartphone-based location data collected from 245 subjects, including patients with schizophrenia, we show that the proposed framework is effective and accurate in generating context-enriched location data. This data was subsequently used to derive behavioral readouts that were sensitive in detecting behavioral nuances related to schizophrenia and aging, such as the time spent at home and the number of unique places visited. Overall, our results indicate that the proposed framework reliably preprocesses raw smartphone-based location data in such a manner that relevant behavioral phenotypes of interest can be derived

Behapp:Digital phenotyping platform & app (iOS, Android)

Behapp is a research instrument for use in (medical) scientific research contexts. Behapp facilitates the collection of personal smartphone-based data that is descriptive of a person's social behavior in terms of mobility and communication

sj-docx-1-amp-10.1177_25152459231202677 – Supplemental material for It’s All About Timing: Exploring Different Temporal Resolutions for Analyzing Digital-Phenotyping Data

Supplemental material, sj-docx-1-amp-10.1177_25152459231202677 for It’s All About Timing: Exploring Different Temporal Resolutions for Analyzing Digital-Phenotyping Data by Anna M. Langener, Gert Stulp, Nicholas C. Jacobson, Andrea Costanzo, Raj R. Jagesar, Martien J. Kas and Laura F. Bringmann in Advances in Methods and Practices in Psychological Science</p

Assessment of Social Behavior Using a Passive Monitoring App in Cognitively Normal and Cognitively Impaired Older Adults:Observational Study

Background: In people with cognitive impairment, loss of social interactions has a major impact on well-being. Therefore, patients would benefit from early detection of symptoms of social withdrawal. Current measurement techniques such as questionnaires are subjective and rely on recall, in contradiction to smartphone apps, which measure social behavior passively and objectively. Objective: This study uses the remote monitoring smartphone app Behapp to assess social behavior, and aims to investigate (1) the association between social behavior, demographic characteristics, and neuropsychiatric symptoms in cognitively normal (CN) older adults, and (2) if social behavior is altered in cognitively impaired (CI) participants. In addition, we explored in a subset of individuals the association between Behapp outcomes and neuropsychiatric symptoms. Methods: CN, subjective cognitive decline (SCD), and CI older adults installed the Behapp app on their own Android smartphone for 7 to 42 days. CI participants had a clinical diagnosis of mild cognitive impairment (MCI) or Alzheimer-type dementia. The app continuously measured communication events, app use and location. Neuropsychiatric Inventory (NPI) total scores were available for 20 SCD and 22 CI participants. Linear models were used to assess group differences on Behapp outcomes and to assess the association of Behapp outcomes with the NPI. Results: We included CN (n=209), SCD (n=55) and CI (n=22) participants. Older cognitively normal participants called less frequently and made less use of apps (P<.05). No sex effects were found. Compared to the CN and SCD groups, CI individuals called less unique contacts (β=–0.7 [SE 0.29], P=.049) and contacted the same contacts relatively more often (β=0.8 [SE 0.25], P=.004). They also made less use of apps (β=–0.83 [SE 0.25], P=.004). Higher total NPI scores were associated with further traveling (β=0.042 [SE 0.015], P=.03). Conclusions: CI individuals show reduced social activity, especially those activities that are related to repeated and unique behavior, as measured by the smartphone app Behapp. Neuropsychiatric symptoms seemed only marginally associated with social behavior as measured with Behapp. This research shows that the Behapp app is able to objectively and passively measure altered social behavior in a cognitively impaired population

A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data

The use of smartphone-based location data to quantify behavior longitudinally and passively is rapidly gaining traction in neuropsychiatric research. However, a standardized and validated preprocessing framework for deriving behavioral phenotypes from smartphone-based location data is currently lacking. Here, we present a preprocessing framework consisting of methods that are validated in the context of geospatial data. This framework aims to generate context-enriched location data by identifying stationary, non-stationary, and recurrent stationary states in movement patterns. Subsequently, this context-enriched data is used to derive a series of behavioral phenotypes that are related to movement. By using smartphone-based location data collected from 245 subjects, including patients with schizophrenia, we show that the proposed framework is effective and accurate in generating context-enriched location data. This data was subsequently used to derive behavioral readouts that were sensitive in detecting behavioral nuances related to schizophrenia and aging, such as the time spent at home and the number of unique places visited. Overall, our results indicate that the proposed framework reliabl