Citizen neuroscience: Wearable technology and open software to study the human brain in its natural habitat

Citizen science allows the public to participate in various stages of scientific research, including study design, data acquisition, and data analysis. Citizen science has a long history in several fields of the natural sciences, and with recent developments in wearable technology, neuroscience has also become more accessible to citizen scientists. This development was largely driven by the influx of minimal sensing systems in the consumer market, allowing more do-it-yourself (DIY) and quantified-self (QS) investigations of the human brain. While most subfields of neuroscience require sophisticated monitoring devices and laboratories, the study of sleep characteristics can be performed at home with relevant noninvasive consumer devices. The strong influence of sleep quality on waking life and the accessibility of devices to measure sleep are two primary reasons citizen scientists have widely embraced sleep research. Their involvement has evolved from solely contributing to data collection to engaging in more collaborative or autonomous approaches, such as instigating ideas, formulating research inquiries, designing research protocols and methodology, acting upon their findings, and disseminating results. In this article, we introduce the emerging field of citizen neuroscience, illustrating examples of such projects in sleep research. We then provide overviews of the wearable technologies for tracking human neurophysiology and various open-source software used to analyse them. Finally, we discuss the opportunities and challenges in citizen neuroscience projects and suggest how to improve the study of the human brain outside the laboratory.

Are individuals with disabilities using wearable devices? A secondary data analysis of 2017 BRFSS

PURPOSE

The study aims to investigate the prevalence of individuals with disabilities who reported using wearable devices, to examine the association between wearable device usage and disability status, and to determine the characteristic of individuals with disabilities associated with wearable device usage using the 2017 Behavioural Risk Factor Surveillance System (BRFSS) through secondary data analysis.

MATERIALS AND METHODS

Data from the 2017 BRFSS of eight states were used in the analysis. Descriptive analysis, chi-square analysis, and multivariable logistic regressions were performed. Subsample analyses were also conducted for individuals with disabilities and different types of disability, including visual impairments, hearing impairments, cognitive disability, independent living disability, self-care disability, and mobility disability on wearable device usage.

RESULTS

14.6% (95% CI [11.7, 17.5]) of participants with disabilities were wearable device users. Individuals with disabilities were .63 (95% CI [.48, .83], p < 0.001) and .67 (95% CI [.50, .90], p = 0.007) times the odds of individuals without disabilities in using wearable devices, respectively, according to unadjusted and adjusted logistic regression. Individuals with mobility disability were less likely to utilise wearable devices than their counterparts. Among individuals with disabilities, those who were age 65 years or older had a lower odds of using wearable devices (OR = .55, 95% CI [0.35, 0.85), p = 0.007).

CONCLUSION

Individuals with disabilities are using wearable devices in collecting various health-related information. Further research is needed to determine reasons why individuals with disabilities are not using wearable devices and how individuals with disabilities are using wearable devices.IMPLICATIONS FOR REHABILITATIONWearable devices can track various health-related information such as physical activity levels, sleep patterns, calories intakes, and chronic health conditions.Using nationally represent data, individuals with disabilities have access and utilise wearable devices in free living setting.Compare to individuals without disabilities, individuals with disabilities are less likely to utilise wearable devices in free living setting.Further research is needed to determine the accessibility of wearable devices for individuals with disabilities and its usage in rehabilitation setting.

Patients' Information Needs Related to a Monitoring Implant for Heart Failure: Co-designed Study Based on Affect Stories

BACKGROUND

RealWorld4Clinic is a European consortium that is currently developing an implantable monitoring device for acute heart failure prevention.

OBJECTIVE

This study aimed to identify the main issues and information needs related to this new cardiac implant from the patients' perspective.

METHODS

A total of 3 patient collaborators were recruited to help us design the study. During 4 remotely held meetings (each lasting for 2 hours), we defined the main questions and hypotheses together. Next, 26 additional interviews were conducted remotely to test these hypotheses. During both phases, we used affect stories, which are life narratives focusing on affect and the relationship between patients and the care ecosystem, to highlight the main social issues that should be addressed by the research according to the patients.

RESULTS

Context of diagnosis, age, and severity of illness strongly influence patient experience. However, these variables do not seem to influence the choice regarding being implanted, which relies mostly on the individual patient's trust in their physicians. It seems that the major cause of anxiety for the patient is not the implant but the disease itself, although some people may initially be concerned over the idea of becoming a cyborg. Remote monitoring of cardiac implants should draw on existing remote disease management programs focusing on a long-term relationship between the patient and their medical team.

CONCLUSIONS

Co-design with affect stories is a useful method for quickly identifying the main social issues related to information about a new health technology.

Quantified Sleep: Self-Tracking Technologies and the Reshaping of 21st-Century Subjectivity

Taking sleep-tracking as its case study, this article seeks to theorise the understandings of the self that are at stake in the the Quantified Self (QS) movement and everyday self-tracking practices by bringing together a cultural theorist's and a philosopher's perspectives. We situate the rise of sleep-tracking practices within the sleep crisis discourse, namely, the sense that in today's society sleep disorders are on the rise and sleep deprivation is rife. Through analyses of self-trackers' blogs about sleep, sleep-tracking technologies' marketing information, and the functionalities of these devices and apps, we argue that the drive to self-improve at the heart of self- and sleep-tracking props up an understanding of the self centred around achievement. This understanding ends up devaluing sleep and risks contributing to the sleep crisis. We show how these paradoxes can be further understood from an epistemological perspective. Self- and sleep-tracking are arguably practices that seek to obtain knowledge by trading referential expert knowledge for self-referential nonexpert knowledge and that strive for self-optimisation by self-sabotaging achievement subjectivity. We conclude that the use of self-tracking technologies magnifies what is essentially a crisis of subjectivity.

The Effect of Presentation Characteristics of "Quantified Self" Data on Consumers' Continuance Participation Intention: An Empirical Study Based on Health-Related Apps

PURPOSE

The COVID-19 pandemic has greatly influenced the health and lifestyles of individuals. Increasing numbers of consumers now participate in quantified self (QS) process to learn more about their health-related behaviors. Understanding how to increase consumers' QS continuance participation intention is critical. Drawing on Social Cognitive Theory and Self-Construal Theory, this study investigates how the presentation characteristics of QS data and consumers' self-construal can influence their continuance participation intention during QS process.

METHODS

Three between-subjects scenario simulation experiments were conducted to examine the influence mechanisms of the presentation mode and type of QS data and self-construal on consumers' continuance participation intention.

RESULTS

The study found: (1) the presentation mode (horizontal comparison vs vertical comparison) and type (descriptive vs analytic) of QS data had significant interaction effects on consumers' continuance participation intention; (2) consumers' self-construal (interdependent vs independent) and the presentation mode of QS data had obvious interaction effects on their continuance participation intention; and (3) consumers' self-construal and the presentation type of QS data had interaction influences on their continuance participation intention.

CONCLUSION

This research combined Social Cognitive Theory and Self-Construal Theory to analyze the influence mechanisms of the presentation characteristics of QS data and consumers' self-construal on their continuance participation intention. These findings not only expand the research field and the scope of application of Social Cognitive Theory, but also provide new insights for the study of consumers' QS problems. They have reference value for the optimization of the presentation features of QS data, and for improving the match between QS data presentation and consumers' self-construal types, to motivate continued participation in QS process.

Factors influencing actual usage of fitness tracking devices: Empirical evidence from the UTAUT model

This research investigates factors influencing the actual usage of wearable fitness devices. Based on the Unified Theory of Acceptance and Use of Technology, the authors propose that privacy concerns, social influence, data accuracy, device engagement, and user efficacy impact the actual usage of wearable fitness devices via performance and effort expectancy. Based on 124 responses using the structural equation approach, most hypotheses were supported. The social influence had the strongest indirect effect through performance expectancy, while user efficacy had the strongest indirect effect through effort expectancy. Data accuracy and device engagement had a positive influence on actual usage and privacy concerns negatively affected the device's use.

How Self-tracking and the Quantified Self Promote Health and Well-being: Systematic Review

BACKGROUND

Self-tracking technologies are widely used in people's daily lives and health care. Academic research on self-tracking and the quantified self has also accumulated rapidly in recent years. Surprisingly, there is a paucity of research that reviews, classifies, and synthesizes the state of the art with respect to self-tracking and the quantified self.

OBJECTIVE

Our objective was to identify the state of the art of self-tracking and the quantified self in terms of health and well-being.

METHODS

We have undertaken a systematic literature review on self-tracking and the quantified self in promoting health and well-being. After a rigorous literature search, followed by inclusions, exclusions, and the application of article quality assessment protocols, 67 empirical studies qualified for the review.

RESULTS

Our results demonstrate that prior research has focused on 3 stakeholders with respect to self-tracking and the quantified self, namely end users, patients and people with illnesses, and health care professionals and caregivers. We used these stakeholder groups to cluster the research themes of the reviewed studies. We identified 11 research themes. There are 6 themes under the end-user cluster: user motivation and goal setting, usage and effects of self-tracking, continuance intention and long-term usage, management of personal data, rejection and discontinuance, and user characteristics. The patient and people with illnesses cluster contains three themes: usage experience of patients and people with illnesses, management of patient-generated data, and advantages and disadvantages in the clinical context. The health care professional and caregiver cluster contains two themes: collaboration among patients, health care professionals, and caregivers, and changes in the roles of patients and professionals. Moreover, we classified the future research suggestions given in the literature into 5 directions in terms of research designs and research topics. Finally, based on our reflections on the observations from the review, we suggest four future research directions: (1) users' cognitions and emotions related to processing and interpreting the information produced by tracking devices and apps; (2) the dark side of self-tracking (eg, its adverse psychosocial consequences); (3) self-tracking as a societal phenomenon; and (4) systemic impacts of self-tracking on health care and the actors involved.

CONCLUSIONS

This systematic literature review contributes to research and practice by assisting future research activities and providing practitioners with a concise overview of the state of the art of self-tracking and the quantified self.

Five-year pediatric use of a digital wearable fitness device: lessons from a pilot case study

Objectives

Wearable fitness devices are increasingly being used by the general population, with many new applications being proposed for healthy adults as well as for adults with chronic diseases. Fewer, if any, studies of these devices have been conducted in healthy adolescents and teenagers, especially over a long period of time. The goal of this work was to document the successes and challenges involved in 5 years of a wearable fitness device use in a pediatric case study.

Materials and methods

Comparison of 5 years of step counts and minutes asleep from a teenaged girl and her father.

Results

At 60 months, this may be the longest reported pediatric study involving a wearable fitness device, and the first simultaneously involving a parent and a child. We find step counts to be significantly higher for both the adult and teen on school/work days, along with less sleep. The teen walked significantly less towards the end of the 5-year study. Surprisingly, many of the adult’s and teen’s sleeping and step counts were correlated, possibly due to coordinated behaviors.

Discussion

We end with several recommendations for pediatricians and device manufacturers, including the need for constant adjustments of stride length and calorie counts as teens are growing.

Conclusion

With periodic adjustments for growth, this pilot study shows these devices can be used for more accurate and consistent measurements in adolescents and teenagers over longer periods of time, to potentially promote healthy behaviors.

The Exposome in the Era of the Quantified Self

Human health is regulated by complex interactions among the genome, the microbiome, and the environment. While extensive research has been conducted on the human genome and microbiome, little is known about the human exposome. The exposome comprises the totality of chemical, biological, and physical exposures that individuals encounter over their lifetimes. Traditional environmental and biological monitoring only targets specific substances, whereas exposomic approaches identify and quantify thousands of substances simultaneously using nontargeted high-throughput and high-resolution analyses. The quantified self (QS) aims at enhancing our understanding of human health and disease through self-tracking. QS measurements are critical in exposome research, as external exposures impact an individual's health, behavior, and biology. This review discusses both the achievements and the shortcomings of current research and methodologies on the QS and the exposome and proposes future research directions.

Usage of eHealth/mHealth Services among Young Czech Adults and the Impact of COVID-19: An Explorative Survey

Various mHealth/eHealth services play an increasingly important role in healthcare systems and personal lifestyle management. Yet, the relative popularity of these services among the young population of the Czech Republic was not known. Therefore, we carried out an on-line survey with a convenience sample (n = 299) of young adults aged 18–29 and living in the Czech Republic. To this end, we adapted the survey instrument which was previously used in a similar study conducted in a different cultural context (Hong Kong). In our study, we found out that health tutorial activities (i.e., acquiring information on diet, exercise, fitness) were the most common among our respondents (M = 2.81, SD = 1.14). These were followed by health information seeking activities (i.e., acquiring information on medical problems) (M = 2.63, SD = 0.89) and medical services (i.e., the eHealth/mHealth services that provide infrastructural support, such as ePrescription and doctor appointment organizers) (M = 2.18, SD = 0.97). Based on the grouping according to gender and existing health condition, pairwise comparisons showed statistically significant differences. We also briefly analyzed the influence of the COVID-19 pandemic on the examined activities. Based on their relative popularity, we suggest leveraging the potential of health tutorial activities to improve public health.

Towards using high-performance liquid chromatography at home

In order to make high-performance liquid chromatography (HPLC) more widely available at home and in small-scale settings, we have simplified two of its most costly modules, namely the pump and the detector. This should make the setup affordable for home or small laboratory use. A manual HPLC pump was constructed so as to fit into a caulk gun from a local hardware store enabling the generation of 100-150 bar of pressure. In order to limit the pressure drop during the running of a chromatogram, a pulse dampener was developed. We further modified the electrochemical detection (ECD) system so as to use a cheap boron-doped diamond electrode with an overlay of thin filter paper, causing an eluent flow over the electrode by wicking and gravity. Both the pump and the detector are at least ten times cheaper than conventional HPLC modules. Using a home-packed Jupiter^Ⓡ Proteo reversed phase capillary column we show how this low-cost HPLC system generates well resolving chromatograms after direct injection of fresh urine. The ECD did not lose its sensitivity during regular use over more than half a year. For homovanillic acid (HVA), which is of medical interest, we measured a linear dynamic range of two orders of magnitude, a detection limit of HVA in the injected sample of 3 μM and a coefficient of variation <10%. The contribution to peak broadening by the detector was much smaller than the contributions by the injector and by the column. After consumption of table olives containing hydroxytyrosol (HT), its metabolite HVA in the corresponding urine could be measured quantitatively. An approach to quantify HT in table olives is presented, as well. This method provides a new tool for investigating physiology of oneself or of dear ones at home.

Self-Quantification Systems to Support Physical Activity: From Theory to Implementation Principles

Since the emergence of the quantified self movement, users aim at health behavior change, but only those who are sufficiently motivated and competent with the tools will succeed. Our literature review shows that theoretical models for quantified self exist but they are too abstract to guide the design of effective user support systems. Here, we propose principles linking theory and implementation to arrive at a hierarchical model for an adaptable and personalized self-quantification system for physical activity support. We show that such a modeling approach should include a multi-factors user model (activity, context, personality, motivation), a hierarchy of multiple time scales (week, day, hour), and a multi-criteria decision analysis (user activity preference, user measured activity, external parameters). This theoretical groundwork, which should facilitate the design of more effective solutions, has now to be validated by further empirical research.

Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis

BACKGROUND

Self-tracking through mobile health technology can augment the electronic health record (EHR) as an additional data source by providing direct patient input. This can be particularly useful in the context of enigmatic diseases and further promote patient engagement.

OBJECTIVES

This study aimed to investigate the additional information that can be gained through direct patient input on poorly understood diseases, beyond what is already documented in the EHR.

METHODS

This was an observational study including two samples with a clinically confirmed endometriosis diagnosis. We analyzed data from 6,925 women with endometriosis using a research app for tracking endometriosis to assess prevalence of self-reported pain problems, between- and within-person variability in pain over time, endometriosis-affected tasks of daily function, and self-management strategies. We analyzed data from 4,389 patients identified through a large metropolitan hospital EHR to compare pain problems with the self-tracking app and to identify unique data elements that can be contributed via patient self-tracking.

RESULTS

Pelvic pain was the most prevalent problem in the self-tracking sample (57.3%), followed by gastrointestinal-related (55.9%) and lower back (49.2%) pain. Unique problems that were captured by self-tracking included pain in ovaries (43.7%) and uterus (37.2%). Pain experience was highly variable both across and within participants over time. Within-person variation accounted for 58% of the total variance in pain scores, and was large in magnitude, based on the ratio of within- to between-person variability (0.92) and the intraclass correlation (0.42). Work was the most affected daily function task (49%), and there was significant within- and between-person variability in self-management effectiveness. Prevalence rates in the EHR were significantly lower, with abdominal pain being the most prevalent (36.5%).

CONCLUSION

For enigmatic diseases, patient self-tracking as an additional data source complementary to EHR can enable learning from the patient to more accurately and comprehensively evaluate patient health history and status.

Health technology identities and self. Patients' appropriation of an assistive device for self-management of chronic illness

In recent years, assistive technologies have gained acceptance as tools for supporting chronically ill patients in achieving improvements in physical activity. However, various healthcare and sociological studies show contradicting results regarding the physical and social impact of using such devices. This paper explores real-time user appropriation of an assistive monitoring/tracking device, the pedometer, in a healthcare intervention, with a particular focus on the technology identities users attribute to the pedometer. The study site was a rehabilitation programme at a local Danish health centre supporting patients with chronic obstructive pulmonary disease. As part of this empirical study, six focus-group interviews were conducted with patients before and after they used pedometers. The analysis of respondents' accounts shows that monitoring devices become part of users' complex socio-technical ensembles in which the use of the device and its tracking of activity is constantly negotiated through experimentation with type and frequency of use; interpretation of knowledge and experience gained via the device; and negotiation of expectations, wellbeing, and the value of quantified knowledge for the management of chronic illness. On the basis of these findings the paper brings together and advances sociological scholarship on chronic illness, embodiment, the quantified self and technology adoption.

Design and Content Quality of Alcohol-Related German, French and Italian Self-Tracking Applications

Background: Research on the increasing use of mobile technology in the addiction field is mainly focused on data collection and brief interventions. The acceptance and outcomes of autonomous self-tracking and self-governance as key elements for behavior change are under-researched. Purpose/Objectives: The objective of the study was to conduct a quality assessment of design and content features of self-tracking smartphone applications related to alcohol use, available in German, Italian, or French. Methods: A total of 25 self-tracking applications were identified, of which 17 could be assessed with the Mobile App Rating Scale (MARS), the System Usability Scale (SUS), and an additional content quality checklist based on the theoretical self-change framework (n = 13). Results: The scale design analysis showed a rather positive picture. Using the SUS, only six cases were below the reference average (x = 68), and three were clearly above average. Application of the MARS showed higher scores among the self-tracking applications in this study than among the health applications reviewed in the original MARS study. Better design quality goes together with better basic content quality. However, a closer look at the "interactivity scores" and the "risk/information barometer," as well as at the individual subtopics of the 10-point content checklist revealed major shortcomings. Conclusions/Importance: Improvements are necessary for consumer information in app stores, increased availability of alcohol-related self-tracking applications, transparent quality assurance regarding evidence-based content, and user-friendly design quality, to provide guidance for potential users on how to successfully navigate a highly unstable digital environment.

Heart Rate Measures From Wrist-Worn Activity Trackers in a Laboratory and Free-Living Setting: Validation Study

Background

Wrist-worn activity trackers are popular, and an increasing number of these devices are equipped with heart rate (HR) measurement capabilities. However, the validity of HR data obtained from such trackers has not been thoroughly assessed outside the laboratory setting.

Objective

This study aimed to investigate the validity of HR measures of a high-cost consumer-based tracker (Polar A370) and a low-cost tracker (Tempo HR) in the laboratory and free-living settings.

Methods

Participants underwent a laboratory-based cycling protocol while wearing the two trackers and the chest-strapped Polar H10, which acted as criterion. Participants also wore the devices throughout the waking hours of the following day during which they were required to conduct at least one 10-min bout of moderate-to-vigorous physical activity (MVPA) to ensure variability in the HR signal. We extracted 10-second values from all devices and time-matched HR data from the trackers with those from the Polar H10. We calculated intraclass correlation coefficients (ICCs), mean absolute errors, and mean absolute percentage errors (MAPEs) between the criterion and the trackers. We constructed decile plots that compared HR data from Tempo HR and Polar A370 with criterion measures across intensity deciles. We investigated how many HR data points within the MVPA zone (≥64% of maximum HR) were detected by the trackers.

Results

Of the 57 people screened, 55 joined the study (mean age 30.5 [SD 9.8] years). Tempo HR showed moderate agreement and large errors (laboratory: ICC 0.51 and MAPE 13.00%; free-living: ICC 0.71 and MAPE 10.20%). Polar A370 showed moderate-to-strong agreement and small errors (laboratory: ICC 0.73 and MAPE 6.40%; free-living: ICC 0.83 and MAPE 7.10%). Decile plots indicated increasing differences between Tempo HR and the criterion as HRs increased. Such trend was less pronounced when considering the Polar A370 HR data. Tempo HR identified 62.13% (1872/3013) and 54.27% (5717/10,535) of all MVPA time points in the laboratory phase and free-living phase, respectively. Polar A370 detected 81.09% (2273/2803) and 83.55% (9323/11,158) of all MVPA time points in the laboratory phase and free-living phase, respectively.

Conclusions

HR data from the examined wrist-worn trackers were reasonably accurate in both the settings, with the Polar A370 showing stronger agreement with the Polar H10 and smaller errors. Inaccuracies increased with increasing HRs; this was pronounced for Tempo HR.

Information Literacy in Food and Activity Tracking Among Parkrunners, People With Type 2 Diabetes, and People With Irritable Bowel Syndrome: Exploratory Study

BACKGROUND

The tracking, or logging, of food intake and physical activity is increasing among people, and as a result there is increasing evidence of a link to improvement in health and well-being. Crucial to the effective and safe use of logging is a user's information literacy.

OBJECTIVE

The aim of this study was to analyze food and activity tracking from an information literacy perspective.

METHODS

An online survey was distributed to three communities via parkrun, diabetes.co.uk and the Irritable Bowel Syndrome Network.

RESULTS

The data showed that there were clear differences in the logging practices of the members of the three different communities, as well as differences in motivations for tracking and the extent of sharing of said tracked data. Respondents showed a good understanding of the importance of information accuracy and were confident in their ability to understand tracked data, however, there were differences in the extent to which food and activity data were shared and also a lack of understanding of the potential reuse and sharing of data by third parties.

CONCLUSIONS

Information literacy in this context involves developing awareness of the issues of accurate information recording, and how tracked information can be applied to support specific health goals. Developing awareness of how and when to share data, as well as of data ownership and privacy, are also important aspects of information literacy.

Assembling the 'Fitbit subject': A Foucauldian-sociomaterialist examination of social class, gender and self-surveillance on Fitbit community message boards

The rise of fitness-tracking devices such as the Fitbit in personal health and wellness is emblematic of the use of data-gathering health and fitness technologies by institutions to create a surveillance regime. Using postings on Fitbit community message boards and the theoretical frames of Michel Foucault and sociomaterialist scholars, the goal of this article is to analyse the experiences of those who choose to self-track using a Fitbit and the constellation of barriers and facilitators (human and non-human) related to social class and gender that enable and constrain one's ability to use a Fitbit as intended. First, we examine the social class assumptions of Fitbit as a risk management tool in the workplace, illustrating what elements must come together - both human and non-human - to create an environment that enables walking throughout the workday to combat the risks of sedentary work. Second, we explore the ways that Fitbit users 'confessed' to their past inactivity and how gendered home labour differently enables and constrains some of the users' abilities to act on their confessions. Ultimately, one's ability to engage in the idealized use of the Fitbit in the minds of its users, or what we term the 'Fitbit subject assemblage', is structured by numerous material and social factors that must be taken into account when examining the mechanics of power in fitness tracking.

The Quantified Relationship

The growth of self-tracking and personal surveillance has given rise to the Quantified Self movement. Members of this movement seek to enhance their personal well-being, productivity, and self-actualization through the tracking and gamification of personal data. The technologies that make this possible can also track and gamify aspects of our interpersonal, romantic relationships. Several authors have begun to challenge the ethical and normative implications of this development. In this article, we build upon this work to provide a detailed ethical analysis of the Quantified Relationship (QR). We identify eight core objections to the QR and subject them to critical scrutiny. We argue that although critics raise legitimate concerns, there are ways in which tracking technologies can be used to support and facilitate good relationships. We thus adopt a stance of cautious openness toward this technology and advocate the development of a research agenda for the positive use of QR technologies.

Exploring Entertainment Medicine and Professionalization of Self-Care: Interview Study Among Doctors on the Potential Effects of Digital Self-Tracking

Background

Nowadays, digital self-tracking devices offer a plethora of possibilities to both healthy and chronically ill users who want to closely examine their body. This study suggests that self-tracking in a private setting will lead to shifting understandings in professional care. To provide more insight into these shifts, this paper seeks to lay bare the promises and challenges of self-tracking while staying close to the everyday professional experience of the physician.

Objective

The aim of this study was to (1) offer an analysis of how medical doctors evaluate self-tracking methods in their practice and (2) explore the anticipated shifts that digital self-care will bring about in relation to our findings and those of other studies.

Methods

A total of 12 in-depth semistructured interviews with general practitioners (GPs) and cardiologists were conducted in Flanders, Belgium, from November 2015 to November 2016. Thematic analysis was applied to examine the transcripts in an iterative process.

Results

Four major themes arose in our body of data: (1) the patient as health manager, (2) health obsession and medicalization, (3) information management, and (4) shifting roles of the doctors and impact on the health care organization. Our research findings show a nuanced understanding of the potentials and pitfalls of different forms of self-tracking. The necessity of contextualization of self-tracking data and a professionalization of self-care through digital devices come to the fore as important overarching concepts.

Conclusions

This interview study with Belgian doctors examines the potentials and challenges of self-monitoring while focusing on the everyday professional experience of the physician. The dialogue between our dataset and the existing literature affords a fine-grained image of digital self-care and its current meaning in a medical-professional landscape.

Development and Validation of a Taxonomy for Characterizing Measurements in Health Self-Quantification

Background

The use of wearable tools for health self-quantification (SQ) introduces new ways of thinking about one’s body and about how to achieve desired health outcomes. Measurements from individuals, such as heart rate, respiratory volume, skin temperature, sleep, mood, blood pressure, food consumed, and quality of surrounding air can be acquired, quantified, and aggregated in a holistic way that has never been possible before. However, health SQ still lacks a formal common language or taxonomy for describing these kinds of measurements. Establishing such taxonomy is important because it would enable systematic investigations that are needed to advance in the use of wearable tools in health self-care. For a start, a taxonomy would help to improve the accuracy of database searching when doing systematic reviews and meta-analyses in this field. Overall, more systematic research would contribute to build evidence of sufficient quality to determine whether and how health SQ is a worthwhile health care paradigm.

Objective

The aim of this study was to investigate a sample of SQ tools and services to build and test a taxonomy of measurements in health SQ, titled: the classification of data and activity in self-quantification systems (CDA-SQS).

Methods

Eight health SQ tools and services were selected to be examined: Zeo Sleep Manager, Fitbit Ultra, Fitlinxx Actipressure, MoodPanda, iBGStar, Sensaris Senspod, 23andMe, and uBiome. An open coding analytical approach was used to find all the themes related to the research aim.

Results

This study distinguished three types of measurements in health SQ: body structures and functions, body actions and activities, and around the body.

Conclusions

The CDA-SQS classification should be applicable to align health SQ measurement data from people with many different health objectives, health states, and health conditions. CDA-SQS is a critical contribution to a much more consistent way of studying health SQ.

Information Quality Challenges of Patient-Generated Data in Clinical Practice

A characteristic trend of digital health has been the dramatic increase in patient-generated data being presented to clinicians, which follows from the increased ubiquity of self-tracking practices by individuals, driven, in turn, by the proliferation of self-tracking tools and technologies. Such tools not only make self-tracking easier but also potentially more reliable by automating data collection, curation, and storage. While self-tracking practices themselves have been studied extensively in human-computer interaction literature, little work has yet looked at whether these patient-generated data might be able to support clinical processes, such as providing evidence for diagnoses, treatment monitoring, or postprocedure recovery, and how we can define information quality with respect to self-tracked data. In this article, we present the results of a literature review of empirical studies of self-tracking tools, in which we identify how clinicians perceive quality of information from such tools. In the studies, clinicians perceive several characteristics of information quality relating to accuracy and reliability, completeness, context, patient motivation, and representation. We discuss the issues these present in admitting self-tracked data as evidence for clinical decisions.

A Comparison of Discovered Regularities in Blood Glucose Readings across Two Data Collection Approaches Used with a Type 1 Diabetic Youth

BACKGROUND

Type 1 diabetes requires frequent testing and monitoring of blood glucose levels in order to determine appropriate type and dosage of insulin administration. This can lead to thousands of individual measurements over the course of a lifetime of a single individual, of which very few are retained as part of a permanent record. The third author, aged 9, and his family have maintained several years of written records since his diagnosis with Type 1 diabetes at age 20 months, and have also recently begun to obtain automated records from a continuous glucose monitor.

OBJECTIVES

This paper compares regularities identified within aggregated manually-collected and automatically-collected blood glucose data visualizations by the family involved in monitoring the third author's diabetes.

METHODS

7,437 handwritten entries of the third author's blood sugar readings were obtained from a personal archive, digitized, and visualized in Tableau data visualization software. 6,420 automatically collected entries from a Dexcom G4 Platinum continuous glucose monitor were obtained and visualized in Dexcom's Clarity data visualization report tool. The family was interviewed three times about diabetes data management and their impressions of data as presented in data visualizations. Interviews were audiorecorded or recorded with handwritten notes.

RESULTS

The aggregated visualization of manually-collected data revealed consistent habitual times of day when blood sugar measurements were obtained. The family was not fully aware that their existing life routines and the third author's entry into formal schooling had created critical blind spots in their data that were often unmeasured. This was realized upon aggregate visualization of CGM data, but the discovery and use of these visualizations were not realized until a new healthcare provider required the family to find and use them. The lack of use of CGM aggregate visualization was reportedly because the default data displays seemed to provide already abundant information for in-the-moment decision making for diabetes management.

CONCLUSIONS

Existing family routines and school schedules can shape if and when blood glucose data are obtained for T1D youth. These routines may inadvertently introduce blind spots in data, even when it is collected and recorded systematically. Although CGM data may be superior in its overall density of data collection, families do not necessarily discover nor use the full range of useful data visualization features. To support greater awareness of youth blood sugar levels, families that manually obtain youth glucose data should be advised to avoid inadvertently creating data blind spots due to existing schedules and routines. For families using CGM technology, designers and healthcare providers should consider implementing better cues and prompts that will encourage families to discover and utilize aggregate data visualization capabilities.

The Social Context of "Do-It-Yourself" Brain Stimulation: Neurohackers, Biohackers, and Lifehackers

The "do-it-yourself" (DIY) brain stimulation movement began in earnest in late 2011, when lay individuals began building stimulation devices and applying low levels of electricity to their heads for self-improvement purposes. To date, scholarship on the home use of brain stimulation has focused on characterizing the practices of users via quantitative and qualitative studies, and on analyzing related ethical and regulatory issues. In this perspective piece, however, I take the opposite approach: rather than viewing the home use of brain stimulation on its own, I argue that it must be understood within the context of other DIY and citizen science movements. Seen in this light, the home use of brain stimulation is only a small part of the "neurohacking" movement, which is comprised of individuals attempting to optimize their brains to achieve enhanced performance. Neurohacking itself is an offshoot of the "life hacking" (or "quantified self") movement, in which individuals self-track minute aspects of their daily lives in order to enhance productivity or performance. Additionally, the home or DIY use of brain stimulation is in many ways parallel to the DIY Biology (or "biohacking") movement, which seeks to democratize tools of scientific experimentation. Here, I describe the place of the home use of brain stimulation with regard to neurohackers, lifehackers, and biohackers, and suggest that a policy approach for the home use of brain stimulation should have an appreciation both of individual motivations as well as the broader social context of the movement itself.

Online Self-Tracking Groups to Increase Fruit and Vegetable Intake: A Small-Scale Study on Mechanisms of Group Effect on Behavior Change

BACKGROUND

Web-based interventions with a self-tracking component have been found to be effective in promoting adults' fruit and vegetable consumption. However, these interventions primarily focus on individual- rather than group-based self-tracking. The rise of social media technologies enables sharing and comparing self-tracking records in a group context. Therefore, we developed an online group-based self-tracking program to promote fruit and vegetable consumption.

OBJECTIVE

This study aims to examine (1) the effectiveness of online group-based self-tracking on fruit and vegetable consumption and (2) characteristics of online self-tracking groups that make the group more effective in promoting fruit and vegetable consumption in early young adults.

METHODS

During a 4-week Web-based experiment, 111 college students self-tracked their fruit and vegetable consumption either individually (ie, the control group) or in an online group characterized by a 2 (demographic similarity: demographically similar vs demographically diverse) × 2 (social modeling: incremental change vs ideal change) experimental design. Each online group consisted of one focal participant and three confederates as group members or peers, who had their demographics and fruit and vegetable consumption manipulated to create the four intervention groups. Self-reported fruit and vegetable consumption were assessed using the Food Frequency Questionnaire at baseline and after the 4-week experiment.

RESULTS

Participants who self-tracked their fruit and vegetable consumption collectively with other group members consumed more fruits and vegetables than participants who self-tracked individually (P=.01). The results did not show significant main effects of demographic similarity (P=.32) or types of social modeling (P=.48) in making self-tracking groups more effective in promoting fruit and vegetable consumption. However, additional analyses revealed the main effect of performance discrepancy (ie, difference in fruit and vegetable consumption between a focal participant and his/her group members during the experiment), such that participants who had a low performance discrepancy from other group members had greater fruit and vegetable consumption than participants who had a high performance discrepancy from other group members (P=.002). A mediation test showed that low performance discrepancy led to greater downward contrast (b=-0.78, 95% CI -2.44 to -0.15), which in turn led to greater fruit and vegetable consumption.

CONCLUSIONS

Online self-tracking groups were more effective than self-tracking alone in promoting fruit and vegetable consumption for early young adults. Low performance discrepancy from other group members lead to downward contrast, which in turn increased participants' fruit and vegetable consumption over time. The study highlighted social comparison processes in online groups that allow for sharing personal health information. Lastly, given the small scale of this study, nonsignificant results with small effect sizes might be subject to bias.

Personal Activity Trackers and the Quantified Self

Personal activity trackers are an inexpensive and easy way for people to record their physical activity and simple biometric data. As these devices have increased in availability and sophistication, their use in daily life and in medicine has grown. This column will briefly explore what these devices are, what types of data they can track, and how that data can be used. It will also discuss potential problems with trackers and how librarians can help patients and physicians manage and protect activity data. A brief list of currently available activity trackers is also included.

Computational Approaches Toward Integrating Quantified Self Sensing and Social Media

The growing amount of data collected by quantified self tools and social media hold great potential for applications in personalized medicine. Whereas the first includes health-related physiological signals, the latter provides insights into a user's behavior. However, the two sources of data have largely been studied in isolation. We analyze public data from users who have chosen to connect their MyFitnessPal and Twitter accounts. We show that a user's diet compliance success, measured via their self-logged food diaries, can be predicted using features derived from social media: linguistic, activity, and social capital. We find that users with more positive affect and a larger social network are more successful in succeeding in their dietary goals. Using a Granger causality methodology, we also show that social media can help predict daily changes in diet compliance success or failure with an accuracy of 77%, that improves over baseline techniques by 17%. We discuss the implications of our work in the design of improved health interventions for behavior change.

Refining the Concepts of Self-quantification Needed for Health Self-management. A Thematic Literature Review

BACKGROUND

Questions like 'How is your health? How are you feeling? How have you been?' now can be answered in a different way due to innovative health self-quantification apps and devices. These apps and devices generate data that enable individuals to be informed and more responsible about their own health.

OBJECTIVES

The aim of this paper is to review studies on health SQ, firstly, exploring the concepts that are associated with the users' interaction with and around data for managing health; and secondly, the potential benefits and challenges that are associated with the use of such data to maintain or promote health, as well as their impact on the users' certainty or confidence in taking effective actions upon such data.

METHODS

To answer these questions, we conducted a comprehensive literature review to build our study sample. We searched a number of electronic bibliographic databases including Scopus, Web of Science, Medline, and Google Scholar. Thematic analysis was conducted for each study to find all the themes that are related to our research aims.

RESULTS

In the reviewed literature, conceptualisation of health SQ is messy and inconsistent. Personal tracking, personal analytics, personal experimentation, and personal health activation are different concepts within the practice of health SQ; thus, a new definition and structure is proposed to set out boundaries between them. Using the data that are generated by SQS for managing health has many advantages but also poses many challenges.

CONCLUSIONS

Inconsistency in conceptualisation of health SQ - as well as the challenges that users experience in health self-management - reveal the need for frameworks that can describe the users' health SQ practice in a holistic and consistent manner. Our ongoing work toward developing these frameworks will help researchers in this domain to gain better understanding of this practice, and will enable more systematic investigations which are needed to improve the use of SQS and their data in health self-management.

Survalytics: An Open-Source Cloud-Integrated Experience Sampling, Survey, and Analytics and Metadata Collection Module for Android Operating System Apps

BACKGROUND

We describe here Survalytics, a software module designed to address two broad areas of need. The first area is in the domain of surveys and app analytics: developers of mobile apps in both academic and commercial environments require information about their users, as well as how the apps are being used, to understand who their users are and how to optimally approach app development. The second area of need is in the field of ecological momentary assessment, also referred to as experience sampling: researchers in a wide variety of fields, spanning from the social sciences to psychology to clinical medicine, would like to be able to capture daily or even more frequent data from research subjects while in their natural environment.

OBJECTIVE

Survalytics is an open-source solution for the collection of survey responses as well as arbitrary analytic metadata from users of Android operating system apps.

METHODS

Surveys may be administered in any combination of one-time questions and ongoing questions. The module may be deployed as a stand-alone app for experience sampling purposes or as an add-on to existing apps. The module takes advantage of free-tier NoSQL cloud database management offered by the Amazon Web Services DynamoDB platform to package a secure, flexible, extensible data collection module. DynamoDB is capable of Health Insurance Portability and Accountability Act compliant storage of personal health information.

RESULTS

The provided example app may be used without modification for a basic experience sampling project, and we provide example questions for daily collection of blood glucose data from study subjects.

CONCLUSIONS

The module will help researchers in a wide variety of fields rapidly develop tailor-made Android apps for a variety of data collection purposes.

Activity Theory as a Theoretical Framework for Health Self-Quantification: A Systematic Review of Empirical Studies

BACKGROUND

Self-quantification (SQ) is a way of working in which, by using tracking tools, people aim to collect, manage, and reflect on personal health data to gain a better understanding of their own body, health behavior, and interaction with the world around them. However, health SQ lacks a formal framework for describing the self-quantifiers' activities and their contextual components or constructs to pursue these health related goals. Establishing such framework is important because it is the first step to operationalize health SQ fully. This may in turn help to achieve the aims of health professionals and researchers who seek to make or study changes in the self-quantifiers' health systematically.

OBJECTIVE

The aim of this study was to review studies on health SQ in order to answer the following questions: What are the general features of the work and the particular activities that self-quantifiers perform to achieve their health objectives? What constructs of health SQ have been identified in the scientific literature? How have these studies described such constructs? How would it be possible to model these constructs theoretically to characterize the work of health SQ?

METHODS

A systematic review of peer-reviewed literature was conducted. A total of 26 empirical studies were included. The content of these studies was thematically analyzed using Activity Theory as an organizing framework.

RESULTS

The literature provided varying descriptions of health SQ as data-driven and objective-oriented work mediated by SQ tools. From the literature, we identified two types of SQ work: work on data (ie, data management activities) and work with data (ie, health management activities). Using Activity Theory, these activities could be characterized into 6 constructs: users, tracking tools, health objectives, division of work, community or group setting, and SQ plan and rules. We could not find a reference to any single study that accounted for all these activities and constructs of health SQ activity.

CONCLUSIONS

A Health Self-Quantification Activity Framework is presented, which shows SQ tool use in context, in relation to the goals, plans, and competence of the user. This makes it easier to analyze issues affecting SQ activity, and thereby makes it more feasible to address them. This review makes two significant contributions to research in this field: it explores health SQ work and its constructs thoroughly and it adapts Activity Theory to describe health SQ activity systematically.

Restoration in Its Natural Context: How Ecological Momentary Assessment Can Advance Restoration Research

More and more people use self-tracking technologies to track their psychological states, physiology, and behaviors to gain a better understanding of themselves or to achieve a certain goal. Ecological Momentary Assessment (EMA) also offers an excellent opportunity for restorative environments research, which examines how our physical environment (especially nature) can positively influence health and wellbeing. It enables investigating restorative health effects in everyday life, providing not only high ecological validity but also opportunities to study in more detail the dynamic processes playing out over time on recovery, thereby bridging the gap between laboratory (i.e., short-term effects) and epidemiological (long-term effects) research. We have identified four main areas in which self-tracking could help advance restoration research: (1) capturing a rich set of environment types and restorative characteristics; (2) distinguishing intra-individual from inter-individual effects; (3) bridging the gap between laboratory and epidemiological research; and (4) advancing theoretical insights by measuring a more broad range of effects in everyday life. This paper briefly introduces restorative environments research, then reviews the state of the art of self-tracking technologies and methodologies, discusses how these can be implemented to advance restoration research, and presents some examples of pioneering work in this area.

Self-tracking the microbiome: where do we go from here?

The quantified self community brings together enthusiasts who are using technological devices to monitor their health and social media to share their personal data with others online. In light of the growing popularity of this movement, self-trackers are challenging the health-care system by raising important questions about data ownership and risk-taking. As we enter a new era of consumer genomics, a significant number of quantified self (QS) individuals are now interested in the monitoring of their microbiome and performing personal interventions. In this paper, we discuss the scientific validity of experiments involving serial observations of a single individual as opposed to randomized clinical trials. We look at self-tracking from an ethical standpoint by questioning the risks and assessing the potential benefits for personalized medicine in general and for microbiome research in particular.

Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data Science

The last several years have seen an explosion of interest in wearable computing, personal tracking devices, and the so-called quantified self (QS) movement. Quantified self involves ordinary people recording and analyzing numerous aspects of their lives to understand and improve themselves. This is now a mainstream phenomenon, attracting a great deal of attention, participation, and funding. As more people are attracted to the movement, companies are offering various new platforms (hardware and software) that allow ever more aspects of daily life to be tracked. Nearly every aspect of the QS ecosystem is advancing rapidly, except for analytic capabilities, which remain surprisingly primitive. With increasing numbers of qualified self participants collecting ever greater amounts and types of data, many people literally have more data than they know what to do with. This article reviews the opportunities and challenges posed by the QS movement. Data science provides well-tested techniques for knowledge discovery. But making these useful for the QS domain poses unique challenges that derive from the characteristics of the data collected as well as the specific types of actionable insights that people want from the data. Using a small sample of QS time series data containing information about personal health we provide a formulation of the QS problem that connects data to the decisions of interest to the user.

Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches

As the availability and use of wearables increases, they are becoming a promising platform for context sensing and context analysis. Smartwatches are a particularly interesting platform for this purpose, as they offer salient advantages, such as their proximity to the human body. However, they also have limitations associated with their small form factor, such as processing power and battery life, which makes it difficult to simply transfer smartphone-based context sensing and prediction models to smartwatches. In this paper, we introduce an energy-efficient, generic, integrated framework for continuous context sensing and prediction on smartwatches. Our work extends previous approaches for context sensing and prediction on wrist-mounted wearables that perform predictive analytics outside the device. We offer a generic sensing module and a novel energy-efficient, on-device prediction module that is based on a semantic abstraction approach to convert sensor data into meaningful information objects, similar to human perception of a behavior. Through six evaluations, we analyze the energy efficiency of our framework modules, identify the optimal file structure for data access and demonstrate an increase in accuracy of prediction through our semantic abstraction method. The proposed framework is hardware independent and can serve as a reference model for implementing context sensing and prediction on small wearable devices beyond smartwatches, such as body-mounted cameras.

More Than Telemonitoring: Health Provider Use and Nonuse of Life-Log Data in Irritable Bowel Syndrome and Weight Management

Background

The quantified self, self-monitoring or life-logging movement is a trend to incorporate technology into data acquisition on aspects of a person's daily life in terms of inputs (eg food consumed), states (eg mood), and performance (mental and physical). Consumer self-monitoring mobile phone apps have been widely studied and used to promote healthy behavior changes. Data collected through life-logging apps also have the potential to support clinical care.

Objective

We sought to develop an in-depth understanding of providers’ facilitators and barriers to successfully integrating life-log data into their practices and creating better experiences. We specifically investigated three research questions: How do providers currently use patient-collected life-log data in clinical practice? What are provider concerns and needs with respect to this data? What are the constraints for providers to integrate this type of data into their workflows?

Methods

We interviewed 21 health care providers—physicians, dietitians, a nurse practitioner, and a behavioral psychologist—who work with obese and irritable bowel syndrome patients. We transcribed and analyzed interviews according to thematic analysis and an affinity diagramming process.

Results

Providers reported using self-monitoring data to enhance provider-patient communication, develop personalized treatment plans, and to motivate and educate patients, in addition to using them as diagnostic and adherence tools. However, limitations associated with current systems and workflows create barriers to regular and effective review of this data. These barriers include a lack of time to review detailed records, questions about providers' expertise to review it, and skepticism about additional benefits offered by reviewing data. Current self-monitoring tools also often lack flexibility, standardized formats, and mechanisms to share data with providers.

Conclusions

Variations in provider needs affect tracking and reviewing needs. Systems to support diagnosis might require better reliability and resolution, while systems to support interaction should support collaborative reflection and communication. Automatic synthesis of data logs could help providers focus on educational goals while communication of contextual information might help providers better understand patient values. We also discuss how current mobile apps and provider systems do, and do not, support these goals, and future design opportunities to realize the potential benefits of using life-logging tools in clinical care.

The quantified self: closing the gap between general knowledge and particular case?

RATIONALE, AIMS AND OBJECTIVES

This paper addresses the movements 'evidence-based' (EBM) and 'personalized' (PM) medicine. The former is being criticized for failing to do justice to clinical complexity and human individuality. The latter aims at tailoring medical knowledge for every patient in a personalized fashion. Instrumental to this effort is the technological development engendering unlimited amounts of data about bodily fragments. The aim of this article is to stimulate a debate about the notion of the body and knowledge in medicine.

METHODS

An authentic sickness history is used as a vantage point for a more comprehensive account of biomedicine.

RESULTS

The analysis of the sickness history demonstrates how biomedical logic guided all approaches in the care for this particular patient. Each problem was identified and treated separately, whereby neglecting the interaction between body parts and systems, and between the woman's bodily condition and her experiences. The specialists involved seemed to look for phenomena that fit categories of disorders 'belonging' to their field. These approaches engendered unintended effects: chronification, poly-pharmacy and multi-morbidity, leading to an unsustainable increase in medical costs.

CONCLUSIONS

The article elucidates how the status that professionals ascribe to the body has vital implications for what they regard as relevant and how they interpret the information they have collected. On this ground, we challenge both the prevailing and tacitly accepted separation between the physical body and human experience and the view of knowledge underpinning EBM and PM. The growing molecularization of the body veils decisive sources of human illness.

The use of self-quantification systems for personal health information: big data management activities and prospects

BACKGROUND

Self-quantification is seen as an emerging paradigm for health care self-management. Self-quantification systems (SQS) can be used for tracking, monitoring, and quantifying health aspects including mental, emotional, physical, and social aspects in order to gain self-knowledge. However, there has been a lack of a systematic approach for conceptualising and mapping the essential activities that are undertaken by individuals who are using SQS in order to improve health outcomes. In this paper, we propose a new model of personal health information self-quantification systems (PHI-SQS). PHI-SQS model describes two types of activities that individuals go through during their journey of health self-managed practice, which are 'self-quantification' and 'self-activation'.

OBJECTIVES

In this paper, we aimed to examine thoroughly the first type of activity in PHI-SQS which is 'self-quantification'. Our objectives were to review the data management processes currently supported in a representative set of self-quantification tools and ancillary applications, and provide a systematic approach for conceptualising and mapping these processes with the individuals' activities.

METHOD

We reviewed and compared eleven self-quantification tools and applications (Zeo Sleep Manager, Fitbit, Actipressure, MoodPanda, iBGStar, Sensaris Senspod, 23andMe, uBiome, Digifit, BodyTrack, and Wikilife), that collect three key health data types (Environmental exposure, Physiological patterns, Genetic traits). We investigated the interaction taking place at different data flow stages between the individual user and the self-quantification technology used.

FINDINGS

We found that these eleven self-quantification tools and applications represent two major tool types (primary and secondary self-quantification systems). In each type, the individuals experience different processes and activities which are substantially influenced by the technologies' data management capabilities.

CONCLUSIONS

Self-quantification in personal health maintenance appears promising and exciting. However, more studies are needed to support its use in this field. The proposed model will in the future lead to developing a measure for assessing the effectiveness of interventions to support using SQS for health self-management (e.g., assessing the complexity of self-quantification activities, and activation of the individuals).

Using MapMyFitness to Place Physical Activity into Neighborhood Context

It is difficult to obtain detailed information on the context of physical activity at large geographic scales, such as the entire United States, as well as over long periods of time, such as over years. MapMyFitness is a suite of interactive tools for individuals to track their workouts online or using global positioning system in their phones or other wireless trackers. This method article discusses the use of physical activity data tracked using MapMyFitness to examine patterns over space and time. An overview of MapMyFitness, including data tracked, user information, and geographic scope, is explored. We illustrate the utility of MapMyFitness data using tracked physical activity by users in Winston-Salem, NC, USA between 2006 and 2013. Types of physical activities tracked are described, as well as the percent of activities occurring in parks. Strengths of MapMyFitness data include objective data collection, low participant burden, extensive geographic scale, and longitudinal series. Limitations include generalizability, behavioral change as the result of technology use, and potential ethical considerations. MapMyFitness is a powerful tool to investigate patterns of physical activity across large geographic and temporal scales.