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Longitudinal population studies (LPS) have a long history of providing insights into how individual consumption patterns and other lifestyle choices affect health and socio-economic outcomes. LPS typically operate as research databanks, integrating rich and deep phenotypic data - covering diverse aspects of individual, family and household status - with genomic data and linked records on health and socio-economic outcomes. However, individual consumption and behavioural choices are traditionally studied solely using self-report methods which are prone to known biases. We propose to enrich LPS databanks with a new form of digital footprint data - individual shopping history records. These are collected by supermarkets through "loyalty" card schemes and can provide a new perspective on real world behaviours and history of consumption. However, as a novel class of data in the context of longitudinal research, our ability to assess the quality and completeness of the data is unknown, as is our ability to effectively triangulate between self-reported and linked data. This paper describes a protocol for linking individual level shopping history data into a LPS using Avon Longitudinal Study of Parents and Children (ALSPAC) as a testbed. The protocol covers the process of establishing participant fair processing, an ethical and legal basis for the linkage framework itself, and how these data will be integrated into the ALSPAC databank. It does not cover the subsequent research use of these data. The protocol was built on an extensive participant engagement and acceptability work and has been approved by the ALSPAC Law and Ethics committee., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Skatova A and Boyd A.)

When users interact with their mobile devices, they leave behind unique digital footprints that can be viewed as predictive proxies that reveal an array of users' characteristics, including their demographics. Predicting users' demographics based on mobile usage can provide significant benefits for service providers and users, including improving customer targeting, service personalization, and market research efforts. This study uses machine learning algorithms and mobile usage data from 235 demographically diverse users to examine the accuracy of predicting their sociodemographic attributes (age, gender, income, and education) from mobile usage metadata, filling the gap in the current literature by quantifying the predictive power of each attribute and discussing the practical applications and privacy implications. According to the results, gender can be most accurately predicted (balanced accuracy = 0.862) from mobile usage footprints, whereas predicting users' education level is more challenging (balanced accuracy = 0.719). Moreover, the classification models were able to classify users based on whether their age or income was above or below a certain threshold with acceptable accuracy. The study also presents the practical applications of inferring demographic attributes from mobile usage data and discusses the implications of the findings, such as privacy and discrimination risks, from the perspectives of different stakeholders.

The utilization of healthcare services serves as a barometer for current and future health outcomes. Even in countries with modern healthcare IT infrastructure, however, fragmentation and interoperability issues hinder the (short-term) monitoring of utilization, forcing policymakers to rely on secondary data sources, such as surveys. This deficiency may be particularly problematic during public health crises, when ensuring proper and timely access to healthcare acquires special importance. We show that, in specific contexts, online pharmacies' digital footprint data may contain a strong signal of healthcare utilization. As such, online pharmacy data may enable utilization surveillance, i.e., the monitoring of short-term changes in utilization levels in the population. Our analysis takes advantage of the scenario created by the first wave of the Covid-19 pandemic in Mainland China, where the virus' spread lead to pervasive and deep reductions of healthcare service utilization. Relying on a large sample of online pharmacy transactions with full national coverage, we first detect variation that is strongly consistent with utilization reductions across geographies and over time. We then validate our claims by contrasting online pharmacy variation against credit-card transactions for medical services. Using machine learning methods, we show that incorporating online pharmacy data into the models significantly improves the accuracy of utilization surveillance estimates., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Text analyses of social media posts are a promising source of mental health information. This study used natural language processing to explore distinct language patterns on Twitter related to self-reported anxiety diagnosis., Methods: A total of 233.000 tweets made by 605 users (300 reporting anxiety diagnosis and 305 not) over six months were comparatively analysed, considering user behavior, Linguistic Inquiry Word Count (LIWC), and sentiment analysis. Twitter users with a self-disclosed diagnosis of anxiety were classified as 'anxious' to facilitate group comparisons., Results: Supervised machine learning models showed a high prediction accuracy (Naïve Bayes 81.1 %, Random Forests 79.8 %, and LASSO-regression 79.4 %) in identifying Twitter users' self-disclosed diagnosis of anxiety. Additionally, a Latent Profile Analysis (LPA) identified four profiles characterized by high sentiment (31 % anxious participants), low sentiment (68 % anxious), self-immersed (80 % anxious), and normative behavior (38 % anxious)., Conclusion: The digital footprint of self-disclosed anxiety on Twitter posts presented a high frequency of words conveying either negative sentiment, a low frequency of positive sentiment, a reduced frequency of posting, and lengthier texts. These distinct patterns enabled highly accurate prediction of anxiety diagnosis. On this basis, appropriately resourced, awareness raising, online mental health campaigns are advocated., Competing Interests: Declaration of Competing Interest The authors of the present study do not report any conflict of interest., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

There is a growing number of new digital technologies mediating the experiences of grief and the continuing bonds between the bereaved and their loved ones following death. One of the most recent technological developments is the "griefbot". Based on the digital footprint of the deceased, griefbots allow two-way communication between mourners and the digital version of the dead through a conversational interface or chat. This paper explores the mediational role that griefbots might have in the grieving process vis-à-vis that of other digital technologies, such as social media services or digital memorials on the Internet. After briefly reviewing the new possibilities offered by the Internet in the way people relate with the dead, we delve into the particularities of griefbots, focusing on the two-way communication afforded by this technology and the sense of simulation derived from the virtual interaction between the living and the dead. Discussion leads us to emphasize that, while both the Internet and griefbots bring about a significant spatial and temporal expansion to the grief experience -affording a more direct way to communicate with the dead anywhere and at any time- they differ in that, unlike the socially shared virtual space between mourners and loved ones in most digital memorials, griefbots imply a private conversational space between the mourner and the deceased person. The paper concludes by pointing to some ethical issues that griefbots, as a profit-oriented afterlife industry, might raise for both mourners and the dead in our increasingly digital societies., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

This qualitative study explores mental health clinician perspectives on how information extracted from client interactions with digital devices such as smartphones and the Internet (their digital footprint data) can inform client treatment. The process of learning about an individual's behaviours and psychology from their digital footprint, what has been termed 'digital phenotyping', has emerged in recent years as a field of research with potential to offer insights of clinical value that could be used to predict/detect mental ill-health and inform treatment. This research agenda has largely consisted of quantitative studies exploring statistical associations between smartphone data and psychometric outcomes among relatively small participant cohorts. We on the other hand focus on how the data gathered from smartphones and other digital sources could be converted to pieces of meaningful information that clinicians could directly access and interpret to augment their practice and inform their treatment of clients. Through a reflexive thematic analysis of interviews involving clinical psychologists, this study presents ideas and a framework for understanding how digital phenotyping can inform, augment, and innovate client treatment. In total, five themes concerning the ethics, praxis, and value of digital phenotyping for client treatment are generated., Competing Interests: Declaration of competing interest There are no conflicts of interest to declare., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: The June 2022 US Supreme Court decision to ban abortion care in Dobbs v Jackson Women's Health Organization sparked ominous debate about the privacy and safety of women and families of childbearing age with digital footprints who actively engage in family planning, including abortion and miscarriage care., Objective: To assess the perspectives of a subpopulation of research participants of childbearing age regarding the health-relatedness of their digital data, their concerns about the use and sharing of personal data online, and their concerns about donating data from various sources to researchers today or in the future., Methods: An 18-item electronic survey was developed using Qualtrics and administered to adults (aged ≥18 years) registered in the ResearchMatch database in April 2021. Individuals were invited to participate in the survey regardless of health status, race, gender, or any other mutable or immutable characteristics. Descriptive statistical analyses were conducted using Microsoft Excel and manual queries (single layer, bottom-up topic modeling) and used to categorize illuminating quotes from free-text survey responses., Results: A total of 470 participants initiated the survey and 402 completed and submitted the survey (for an 86% completion rate). Nearly half the participants (189/402, 47%) self-reported to be persons of childbearing age (18 to 50 years). Most participants of childbearing age agreed or strongly agreed that social media data, email data, text message data, Google search history data, online purchase history data, electronic medical record data, fitness tracker and wearable data, credit card statement data, and genetic data are health-related. Most participants disagreed or strongly disagreed that music streaming data, Yelp review and rating data, ride-sharing history data, tax records and other income history data, voting history data, and geolocation data are health-related. Most (164/189, 87%) participants were concerned about fraud or abuse based on their personal information, online companies and websites sharing information with other parties without consent, and online companies and websites using information for purposes that are not explicitly stated in their privacy policies. Free-text survey responses showed that participants were concerned about data use beyond scope of consent; exclusion from health care and insurance; government and corporate mistrust; and data confidentiality, security, and discretion., Conclusions: Our findings in light of Dobbs and other related events indicate there are opportunities to educate research participants about the health-relatedness of their digital data. Developing strategies and best privacy practices for discretion regarding digital-footprint data related to family planning should be a priority for companies, researchers, families, and other stakeholders., (©Rachele Hendricks-Sturrup, Christine Y Lu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.03.2023.)

Background: Flat feet increase the risk of knee osteoarthritis and contribute to frailty, which may lead to worse life prognoses. The influence of the foot skeletal structure on flat feet is not yet entirely understood. Footprints are often used to evaluate feet. However, footprint-based measurements do not reflect the underlying structures of feet and are easily confounded by soft tissue. Three-dimensional evaluation of the foot shape can reveal the characteristics of flat feet. Therefore, foot shape evaluations have garnered increasing research interest. This study aimed to determine the correlation between the three-dimensional (3D) features of the foot and the measurement results of footprint and to predict the evaluation results of flat feet from the footprint based on the 3D features. Finally, the three-dimensional characteristics of flat feet, which cannot be revealed by footprint, were determined., Methods: A total of 403 individuals (40-89 years) participated in this study. The proposed system was developed to identify seven skeletal features that were expected to be associated with flat feet. The loads on the soles of the feet were measured in a static standing position and with a digital footprint device. Specifically, two footprint indices were calculated: the Chippaux-Smirak index (CSI) and the Staheli index (SI). In the analysis, comparisons between male and female measurement variables were performed using the Student's t test. The relationships between the 3D foot features and footprint index parameters were determined by employing the Pearson correlation coefficient. Multiple linear regression was utilized to identify 3D foot features that were strongly associated with the CSI and SI. Foot features identified as significant in the multivariate regression analysis were compared based on a one-way analysis of variance (ANOVA) with Tukey's post hoc test., Results: The CSI and SI were highly correlated with the instep height (IH) and navicular height (NH) of the 3D foot scanning system and were also derived from multiple regression analysis. In addition to the NH and IH, the indicators of the forefoot, transverse arch width, and transverse arch height were considered. In the flat foot group with CSI values above 62.7%, NH was 13.5% (p < 0.001) for males and 14.9% (p = 0.01) for females, and the axis of the bone distance was 5.3% (p = 0.05) for males and 4.9% (p = 0.10) for females. In particular, for CSI values above 62.7% and NH values below 13%, the axis of the bone distance was large and the foot skeleton was deformed., Conclusions: Decreased navicular bone height could be evaluated with the 3D foot scanning system even when flat feet were not detected from the footprint. The results indicate that the use of quantitative indices for 3D foot measurements is important when evaluating the flattening of the foot. Trial registration number UMIN000037694. Name of the registry: University Hospital Medical Information Network Registry. Date of registration: August 15, 2019., (© 2022. The Author(s).)

The development of precision psychiatry is largely based on multi-module measurements from the molecular, cellular, and behavioral levels, which are integrated to assess neurocognitive performances and clinically observed psychopathology. Nevertheless, quantifying mental activities and functions accurately and continuously has been a major difficulty within this field. This article reviews the latest efforts that utilize mobile apps to collect human-smartphone interaction data and contribute towards digital biomarkers of mental illnesses. The fundamental principles underlying a behavioral analysis with mobile apps were introduced, such as ways to monitor smartphone use under different circumstances and construct long-term patterns and trend changes. Examples were also provided to illustrate the potential applications of mobile apps that gain further insights into traditional research topics in occupational health and sleep medicine. We suggest that, with an optimized study design and analytical approach that accounts for technical challenges and ethical considerations, mobile apps will enhance the systemic understanding of mental illnesses.

Efforts to infer personality from digital footprints have focused on behavioral stability at the trait level without considering situational dependency. We repeated a classic study of intraindividual consistency with secondary data (five data sets) containing 28,692 days of smartphone usage from 780 people. Using per-app measures of pickup frequency and usage duration, we found that profiles of daily smartphone usage were significantly more consistent when taken from the same user than from different users ( d > 1.46). Random-forest models trained on 6 days of behavior identified each of the 780 users in test data with 35.8% accuracy for pickup frequency and 38.5% accuracy for duration frequency. This increased to 73.5% and 75.3%, respectively, when success was taken as the user appearing in the top 10 predictions (i.e., top 1%). Thus, situation-dependent stability in behavior is present in our digital lives, and its uniqueness provides both opportunities and risks to privacy.

There remains a significant mismatch between the complexity and variability of symptoms and disabilities in Parkinson's disease (PD), and the capabilities of existing validated assessment tools to objectively measure and monitor them. However, with the advances of circuit and sensor technologies, it is now possible to apply the concept of digital phenotyping to PD, providing a moment-by-moment quantification of individual patient phenotypes using personal digital devices, such as smartphones. Such technology holds considerable potential if a patient-centered multidisciplinary team is able to select digital outcomes that are not only clinically relevant, but also provide measurement-based care results that support individual patient clinical decision making. However, it is likely to be a long road, requiring large collaborative efforts to undertake a number of essential steps before full integration and synchronization of these outcomes into patient management platforms that can deliver individualized data to patients, caregivers, and treating neurologists. In the meantime, both neurologists and patients can empower themselves with digital technologies, working as a team to define the ways that new technologies can be most powerfully employed in PD management. Once digital phenotyping becomes feasible and widely adopted in PD communities, it is likely to expand our understanding of individual PD patients' lives and priorities, leading to targeted treatments and better outcomes for PD patients and their families., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

It is a challenge to accurately quantify short-term dynamic human impact on the environment, which is the key to ecosystem and biodiversity conservation. Human's digital footprints are widely used as a proxy of dynamic human impact. This study developed a method to accurately and objectively map the dynamic human's digital footprints in the Tibetan Plateau using the geospatial big datasets, including the numbers of smartphone location request, microblog check-ins, and geo-tagged flicker photos. We developed a method to calculate the fused digital footprint intensity (FDFI) by integrating the location information in the three datasets. The magnitude of the FDFI was converted to a footprint intensity score (FIS), which was then used to infer the human impact. Results show that the average FIS values in Qinghai and Tibet are low (0.12 and 0.04, respectively). The grids with a positive FIS only account for 5.99% of the Tibetan Plateau and are mainly found in the cities and along the transportation networks. The FIS is also strongly correlated to land use and the positive values are mainly found in the built-up and agricultural lands. All other land use categories tend to have near zero FIS values. We concluded that human activities overall show very limited impact on the Tibetan Plateau and most of the impact is found in the built-up and agricultural lands., (Copyright © 2019 Elsevier B.V. All rights reserved.)

In recent years the involvement of technology in psychiatric treatment and its usefulness is increasing. The main advantages of its use lie in the possibility of collecting passively data with greater temporal granularity from each patient individually, since these devices are in direct contact almost every minute of the day with them. The variety of data collected by the all the smartphone sensors allows for a better understanding of the patient behavior through what is called the digital phenotype. So the use of a continuous monitoring system for patients at risk of suicide becomes a very useful tool for improving the quality of life of patients and for the early detection of suicide attempts.

The widespread use and deep reach of smartphones motivate the use of mobile applications to continuously monitor the relationship between circadian system, individual sleep patterns, and environmental effects. We selected 61 adults with 14-day data from the "Know Addiction" database. We developed an algorithm to identify the "sleep time" based on the smartphone behaviors. The total daily smartphone use duration and smartphone use duration prior to sleep onset were identified respectively. We applied mediation analysis to investigate the effects of total daily smartphone use on sleep through pre-sleep use (PS). The results showed participants' averaged pre-sleep episodes within 1 h prior to sleep are 2.58. The duration of three pre-sleep uses (PS 1 ∼ 3 ) maybe a more representative index for smartphone use before sleep. Both total daily duration and the duration of the last three uses prior to sleep of smartphone use significantly delayed sleep onset, midpoint of sleep and reduced total sleep time. One hour of increased smartphone use daily, delays the circadian rhythm by 3.5 min, and reduced 5.5 min of total sleep time (TST). One hour of increased pre-sleep smartphone use delayed circadian rhythm by 1.7 min, and reduced 39 s of TST. The mediation effects of PS 1 ∼ 3 significantly impacted on these three sleep indicators. PS 1 ∼ 3 accounted for 14.3% of total daily duration, but the proportion mediated of delayed circadian rhythm was 44.0%. We presented "digital chronotype" with an automatic system that can collect high temporal resolution data from naturalistic settings with high ecological validity. Smartphone screen time, mainly mediated by pre-sleep use, delayed the circadian rhythm and reduced the total sleep time., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Background: Patients are increasingly turning to online resources to inquire about individual physicians and to gather health information. However, little research exists studying the online presence of neurosurgeons across the country. This study aimed to characterize these online profiles and assess the scope of neurosurgeons' digital identities., Methods: Medicare-participating neurologic surgeons from the United States and Puerto Rico were identified using the Centers for Medicare and Medicaid Services (CMS) Physician Comparable Downloadable File. Each physician was characterized by his or her medical education, graduation year, city of practice, gender, and affiliation with an academic institution. Using a Google-based custom search tool, the top 10 search results for each physician were extracted and categorized as 1 of the following: 1) physician, hospital, or healthcare system controlled, 2) third-party or government controlled, 3) social media-based, 4) primary journal article, or 5) other., Results: Among the physicians within the CMS database, 4751 self-identified as being neurosurgeons, yielding a total of 45,875 uniform resource locator search results pertinent to these physicians. Of the 4751 neurosurgeons, 2317 (48.8%) and 2434 (51.2%) were classified as academic and nonacademic neurosurgeons, respectively. At least 1 search result was obtained for every physician. Hospital, healthcare system, or physician-controlled websites (18,206; 39.7%) and third-party websites (17,122; 37.3%) were the 2 most commonly observed domain types. Websites belonging to social media platforms accounted for 4843 (10.6%) search results, and websites belonging to peer-reviewed academic journals accounted for 1888 (4.1%) search results. The frequency with which a third-party domain appeared as the first search result was higher for nonacademic neurosurgeons than for academic neurosurgeons., Conclusions: In general, neurosurgeons lacked a controllable online presence within their first page of Google Search results. Third-party physician rating websites constituted about half of the search results, and a relative lack of social media websites was apparent. Still, numerous opportunities exist for neurosurgeons to address this disparity., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: As a result of digitalization, data is available about almost every aspect of our lives. Personal data collected by individuals themselves or stored by organizations interacting with people is known as a digital footprint. The purpose of this study was to identify prerequisites for collecting and using digital data that could be valuable for health data analytics and new health services., Methods: Researchers and their contacts involved in a nationwide research project focusing on digital health in Finland were asked to participate in a pilot study on collecting their own personal data from various organizations of their own choice, such as retail chains, banks, insurance companies, and healthcare providers. After the pilot, a qualitative inquiry was adopted to collect semi-structured interview data from twelve active participants in the pilot. Interviews comprised themes such as the experiences of collecting personal data, as well as the usefulness of the data in general and for the participants themselves. Interview data was then analyzed thematically., Results: Even if the participants had an academic background and were highly motivated to collect and use their data, they faced many challenges, such as quite long delays in the provision of the data, and the unresponsiveness of some organizations. Regarding the usefulness of the acquired personal data, our results show that participants had high expectations, but they were disappointed with the small amount of data and its irrelevant content. For the most part, the data was not in a format that would be useful for health data analytics and new health services. Participants also found that there were actual mistakes in their health data reports., Conclusions: The study revealed that collecting and using digital footprint data, even by knowledgeable individuals, is not an easy task. As the usefulness of the acquired personal health data mainly depended on its form and usability for services or solutions relevant to an individual, rather than on the data being valuable as such, more emphasis should be placed on providing the data in a reusable form.