Self-Tracking: What the Science Says

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The following is a summary of the scientific literature on the effectiveness of tracking and quantified self tools. This page is a continuous work in progress. If you'd like to suggest an article, please email us here.


Note: Reference in this site to any specific author, organization, method or results is for the information and convenience of the public, and does not constitute endorsement, recommendation, or favoring by Fitnescity.


The mundane experience of everyday calorie trackers: Beyond the metaphor of Quantified Self

Gabija DidžiokaitėPaula SaukkoChristian Greiffenhagen

Published: March 24, 2017

In this article, we build on the work of Ruckenstein and Pantzar, who have demonstrated how our understanding of self-tracking has been influenced by the metaphor of the Quantified Self (QS). To complicate this very selective picture of self-tracking, we shift the focus in understanding self-tracking from members of the QS community to the experiences of ‘ordinary man and woman’. Therefore, we interviewed ‘everyday calorie trackers’, people who had themselves started using MyFitnessPal calorie counting app but were not part of any tracking community. Our analysis identifies three main themes – goals, use and effect – which highlight the mundane side of self-tracking, where people pursuing everyday, limited goals engage in basic self-tracking and achieve temporary changes. These experiences contrast with the account of self-tracking in terms of long-term, experimental analysis of data on the self or ‘biohacking’, which dominates the QS metaphor in the academic literature.


Orthosomnia: Are Some Patients Taking the Quantified Self Too Far?

Kelly Glazer Baron, PhD, MPH1; Sabra Abbott, MD, PhD2; Nancy Jao, MS2; Natalie Manalo, MD2; Rebecca Mullen, MS2

1Rush University Medical School, Chicago IL; 2Feinberg School of Medicine, Northwestern University, Chicago, IL

Published: March 2017

The use of wearable sleep tracking devices is rapidly expanding and provides an opportunity to engage individuals in monitoring of their sleep patterns. However, there are a growing number of patients who are seeking treatment for self-diagnosed sleep disturbances such as insufficient sleep duration and insomnia due to periods of light or restless sleep observed on their sleep tracker data. The patients' inferred correlation between sleep tracker data and daytime fatigue may become a perfectionistic quest for the ideal sleep in order to optimize daytime function. To the patients, sleep tracker data often feels more consistent with their experience of sleep than validated techniques, such as polysomnography or actigraphy. The challenge for clinicians is balancing educating patients on the validity of these devices with patients' enthusiasm for objective data. Incorporating the use of sleep trackers into cognitive behavioral therapy for insomnia will be important as use of these devices is rapidly expanding among our patient population.


Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information

Xiao Li, Jessilyn Dunn, Denis Salins, Gao Zhou, Wenyu Zhou, Sophia Miryam Schüssler-Fiorenza Rose, Dalia Perelman, Elizabeth Colbert, Ryan Runge, Shannon Rego, Ria Sonecha, Somalee Datta, Tracey McLaughlin, Michael P. Snyder 

Published: January 2017

A new wave of wearable sensors allows frequent and continuous measurements of body functions (physiology), including heart rate, skin temperature, blood oxygen levels, and physical activity. We investigated the ability of wearable sensors to follow physiological changes that occur over the course of a day, during illness and other activities. Data from these sensors revealed personalized differences in daily patterns of activities. Interestingly, we discovered striking changes in particular environments such as airline flights. Blood oxygen levels decreased during high-altitude flights, and this decrease was associated with fatigue. By combining sensor information with frequent medical measurements, we made two important health-related observations. First, wearable sensors were useful in identifying the onset of Lyme disease and inflammation. From this observation, we then developed a computational algorithm for personalized disease detection using such sensors. Second, we found that wearable sensors can reveal physiological differences between insulin-sensitive and insulin-resistant individuals, raising the possibility that these sensors could help detect risk for type 2 diabetes. Overall, these results indicate that the information provided by wearable sensors is physiologically meaningful and actionable. Wearable sensors are likely to play an important role in managing health.


Motivation and User Engagement in Fitness Tracking: Heuristics for Mobile Healthcare Wearables

Stavros Asimakopoulos 1,* ,Grigorios Asimakopoulos 2 and Frank Spillers 3.

1 Academy for Digital Entertainment, NHTV Breda University of Applied Sciences, Breda 4817 JT, The Netherlands

2 Business Management Division, Universidad Carlos III de Madrid, Madrid 28903, Spain

3 Experience Dynamics, Inc., Portland, OR 97213, USA

Published: January 2017

Wearable fitness trackers have gained a new level of popularity due to their ambient data gathering and analysis. This has signalled a trend toward self-efficacy and increased motivation among users of these devices. For consumers looking to improve their health, fitness trackers offer a way to more readily gain motivation via the personal data-based insights the devices offer. However, the user experience (UX) that accompanies wearables is critical to helping users interpret, understand, gain motivation and act on their data. Despite this, there is little evidence as to specific aspects of fitness tracker user engagement and long-term motivation. We report on a 4-week situated diary study and Healthcare Technology Self-efficacy (HTSE) questionnaire assessment of 34 users of two popular American fitness trackers: JawBone and FitBit. The study results illustrate design implications and requirements for fitness trackers and other self-efficacy mobile healthcare applications.


Effectiveness of activity trackers with and without incentives to increase physical activity (TRIPPA): a randomised controlled trial

Prof Eric A Finkelstein, PhD, Benjamin A Haaland, PhD, Marcel Bilger, PhD, Aarti Sahasranaman, PhD, Robert A Sloan, PhD, Ei Ei Khaing Nang, PhD, Prof Kelly R Evenson, PhD

Published: December 2016

The cash incentive was most effective at increasing MVPA bout min per week at 6 months, but this effect was not sustained 6 months after the incentives were discontinued. At 12 months, the activity tracker with or without charity incentives were effective at stemming the reduction in MVPA bout min per week seen in the control group, but we identified no evidence of improvements in health outcomes, either with or without incentives, calling into question the value of these devices for health promotion. Although other incentive strategies might generate greater increases in step activity and improvements in health outcomes, incentives would probably need to be in place long term to avoid any potential decrease in physical activity resulting from discontinuation.


Can a Free Wearable Activity Tracker Change Behavior? The Impact of Trackers on Adults in a Physician-Led Wellness Group

Lisa Gualtieri, PhD, ScM,1 Sandra Rosenbluth, MS,1 and Jeffrey Phillips, MD2.

1Tufts University School of Medicine, Department of Public Health and Community Medicine, Boston, MA, United States.

2Family Doctors, LLC, Swampscott, MA, United States.

Published: November 2016

Wearable activity trackers (trackers) are increasingly popular devices used to track step count and other health indicators. Trackers have the potential to benefit those in need of increased physical activity, such as adults who are older and face significant health challenges. These populations are least likely to purchase trackers and most likely to face challenges in using them, yet may derive educational, motivational, and health benefits from their use once these barriers are removed.

Our findings suggest that adding trackers to wellness groups comprising primarily older adults with chronic medical conditions can support education and behavior change to be more physically active. The trackers increased participant self-efficacy by providing a tangible, visible reminder of a commitment to increasing activity and immediate feedback on step count and progress towards a daily step goal. While acceptance was high and attitudes ultimately positive, training and support are needed and short-term drop-off in participant use is to be expected. Future research will further consider the potential of trackers in older adults with chronic medical conditions who are unlikely to purchase them, and studies will use larger samples, continue over a longer period of time, and evaluate outcomes independent of a wellness group.


Feasibility and Effectiveness of Using Wearable Activity Trackers in Youth: A Systematic Review

Nicola D Ridgers, BSc (Hons), MSc, PhD,1 Melitta A McNarry, BSc (Hons), PhD,2 and Kelly A Mackintosh, BSc (Hons), MSc, PhD2

Published: November 2016

The proliferation and popularity of wearable activity trackers (eg, Fitbit, Jawbone, Misfit) may present an opportunity to integrate such technology into physical activity interventions. While several systematic reviews have reported intervention effects of using wearable activity trackers on adults’ physical activity levels, none to date have focused specifically on children and adolescents.

There is a paucity of research concerning the effectiveness and feasibility of wearable activity trackers as a tool for increasing children’s and adolescents’ physical activity levels. While there are some preliminary data to suggest these devices may have the potential to increase activity levels through self-monitoring and goal setting in the short term, more research is needed to establish longer-term effects on behavior.


Effect of Wearable Technology Combined With a Lifestyle Intervention on Long-term Weight Loss: The IDEA Randomized Clinical Trial.

Jakicic JMDavis KKRogers RJKing WCMarcus MDHelsel DRickman ADWahed ASBelle SH.

Published: September 2016

Effective long-term treatments are needed to address the obesity epidemic. Numerous wearable technologies specific to physical activity and diet are available, but it is unclear if these are effective at improving weight loss.

Among young adults with a BMI between 25 and less than 40, the addition of a wearable technology device to a standard behavioral intervention resulted in less weight loss over 24 months. Devices that monitor and provide feedback on physical activity may not offer an advantage over standard behavioral weight loss approaches.


The Rise of Consumer Health Wearables: Promises and Barriers

Lukasz Piwek, David A. Ellis, Sally Andrews, Adam Joinson

Published: February 2, 2016

While many champion wearables as data-rich devices that will revolutionise 21st century medicine, it remains highly probable that, like many technological trends, these mass-marketed gadgets will drift into obscurity. However, given their continued popularity, particularly amongst those who already maintain a watchful eye over their lifestyle, health practitioners may need to prepare themselves for an increase in patients who bring wearable data to their next consultation. This may generate additional confusion and anxiety for both practitioner and patient. More worryingly, the margin of error can be high when patients without medical training attempt to attribute symptoms to a specific stream of data from devices that may themselves be unreliable. Drawing a parallel with patient-obtained diagnoses via Google, less than 5% of surveyed health care providers felt that any Internet self-diagnosis was helpful [1]. Alternatively, if frameworks are in place allowing wearable devices to be integrated into health care systems, this could, in turn, kick-start the development of validation programmes that would sit alongside appropriate training for health care professionals. This knowledge and understanding could then be disseminated to patients as validated devices become standardised, providing both individual and aggregated data for patients, governments, and health care providers. Moving forward, practitioners and researchers should try to work together and open a constructive dialogue on how to approach and accommodate these technological advances in a way that ensures wearable technology can become a valuable asset for health care in the 21st century.


Systematic review of the validity and reliability of consumer-wearable activity trackers

Kelly R. EvensonMichelle M. GotoRobert D. Furberg

University of North Carolina, RTI International.

Published: December 2015

Consumer-wearable activity trackers are electronic devices used for monitoring fitness- and other health-related metrics. The purpose of this systematic review was to summarize the evidence for validity and reliability of popular consumer-wearable activity trackers (Fitbit and Jawbone) and their ability to estimate steps, distance, physical activity, energy expenditure, and sleep.

This systematic review indicated higher validity of steps, few studies on distance and physical activity, and lower validity for energy expenditure and sleep. The evidence reviewed indicated high interdevice reliability for steps, distance, energy expenditure, and sleep for certain Fitbit models. As new activity trackers and features are introduced to the market, documentation of the measurement properties can guide their use in research settings.


A rest-activity biomarker to predict response to SSRIs in major depressive disorder

W. Vaughn McCall

Published: May 2015

Most adults with Major Depressive Disorder (MDD) will not experience a remission with the first antidepressant trial. No practical biomarkers presently exist to predict responsiveness to antidepressants. Herein we report pilot data for a rest-activity biomarker of antidepressant response.

Fifty-eight medication-free adults with MDD underwent a week-long collection of actigraphic data before beginning a 9 week open label trial of fluoxetine, coupled with blinded randomized assignment to eszopiclone/placebo. Depression severity was repeatedly measured with the Hamilton Rating Scale for Depression (HRSD). Baseline actigraphic data was analyzed with functional data analysis to create smoothed 24-h curves of activity. The time of the lowest point of activity (the bathyphase) was calculated for each patient, as well the mean difference between bedtime and the bathyphase (BBD). At the end of treatment, patients were characterized as treatment responders (50% reduction in HRSD) or non-responders, and receiver operating curves were calculated to find the optimal cut point of the BBD for prediction of treatment response.

The best cut point for BBD was at 260.2 min, resulting in an effect size of 1.45, and with a positive predictive value of 0.75 and a negative predictive value of 0.88.

We conclude that actigraphically-determined measures of rest-activity patterns show promise as potential biomarker predictors of antidepressant response. However, this conclusion is based upon a small number of patients who received only one choice of antidepressant, for a single trial. Replication with a larger sample is needed.


Patient-centered activity monitoring in the self-management of chronic health conditions

Emil Chiauzzi, Carlos Rodarte, Pronabesh DasMahapatra

Published: March 2015

Activity monitoring has the potential to engage patients as advocates in their personalized care, as well as offer health care providers real world assessments of their patients’ daily activity patterns. This potential will be realized as the voice of the chronic disease patients is accounted for in the design of devices, measurements are validated against existing clinical assessments, devices become part of the treatment ‘prescription’, behavior change programs are used to engage patients in self-management, and best practices for clinical integration are defined.


Use and adoption challenges of wearable activity trackers

Patrick C. Shih, Kyungsik Han, Erika Shehan Poole, Mary Beth Rosson, John M. Carroll.

The Pennsylvania State University.

Published: March 2015

In this paper, we reviewed the current research on utilising wearable technology to influence human health behaviour. We focused specifically on methods of data collection, manipulation and representation in wearable ecosystems. As previous studies have shown, wearable applications and the data have the power to drive positive behaviour change within an individual. By utilising methods such as gamification and social interaction, motivation can be created. This motivation increases the possibility of someone changing their health behaviours for the better. But we have found issues with using wearable sensing data as a behavioural driver. Although studies have shown it to be effective in the short term, there are issues regarding data losing its meaning to the user over time. As a response to this, it has been suggested that data and data representations should act as a facilitator for behaviour change. This can be archived by encouraging reflection and presenting the health data to accommodate cognitive theories and support the natural behavioural change process. Using data as a facilitator is showing positive hope for the development of further health wearables, but we believe that even more research is needed. Through outlining previous studies, we believe that there are many opportunities for further research. Personalisation is an area in which more research would be beneficial. A system that can adapt to the user and recognise their needs could help to form a long-term relationship between a user and their health data. Data meaningfulness needs to remain high to ensure longterm retention between the user and their device. We suggest ways that this could be done through non-invasive collection and intelligent interpretation of health data in a way to encourage selfmotivation. Wearable systems need to offer a number of different data manipulation and presentation methods. The methods would then be chosen to reflect which process the system determined to be the most effective. Ideally, research needs to be conducted that can inform the design process of future wearable technology. Ensuring long-term retention needs to be considered from the very beginning of the development process to create effective systems.


Wearable Devices as Facilitators, Not Drivers, of Health Behavior Change

Mitesh S. Patel, MD, MBA, MS1David A. Asch, MD, MBA1Kevin G. Volpp, MD, PhD1

1Philadelphia VA Medical Center, University of Pennsylvania, Philadelphia

Published: February 2015

Several large technology companies including Apple, Google, and Samsung are entering the expanding market of population health with the introduction of wearable devices. This technology, worn in clothing or accessories, is part of a larger movement often referred to as the “quantified self.” The notion is that by recording and reporting information about behaviors such as physical activity or sleep patterns, these devices can educate and motivate individuals toward better habits and better health. The gap between recording information and changing behavior is substantial, however, and while these devices are increasing in popularity, little evidence suggests that they are bridging that gap.


A Primary Care Nurse-Delivered Walking Intervention in Older Adults: PACE (Pedometer Accelerometer Consultation Evaluation)-Lift Cluster Randomised Controlled Trial

Tess Harris, Sally M. Kerry, Christina R. Victor, Ulf Ekelund, Alison Woodcock, Steve Iliffe, Peter H. Whincup, Carole Beighton, Michael Ussher, Elizabeth S. Limb, Lee David, Debbie Brewin, Fredrika Adams, Annabelle Rogers, Derek G. Cook

Published: February 2015

Brisk walking in older people can increase step-counts and moderate to vigorous intensity physical activity (MVPA) in ≥10-minute bouts, as advised in World Health Organization guidelines. Previous interventions have reported step-count increases, but not change in objectively measured MVPA in older people. We assessed whether a primary care nurse-delivered complex intervention increased objectively measured step-counts and MVPA.

The PACE-Lift trial increased both step-counts and objectively measured MVPA in ≥10-minute bouts in 60–75 year olds at 3 and 12 months, with no effect on adverse events. To our knowledge, this is the first trial in this age group to demonstrate objective MVPA increases and highlights the value of individualised support incorporating objective PA assessment in a primary care setting.



    Accuracy of Smartphone Applications and Wearable Devices for Tracking Physical Activity Data

    Meredith A. Case, BA1Holland A. Burwick2Kevin G. Volpp, MD, PhD3; et alMitesh S. Patel, MD, MBA, MS3

    1Perelman School of Medicine, University of Pennsylvania, Philadelphia; 2Amherst College, Amherst, Massachusetts; 3Center for Health Equity Research and Promotion, Philadelphia VA Medical Center, Philadelphia, Pennsylvania

    Published: February 2015

    Despite the potential of pedometers to increase physical activity and improve health,1 there is little evidence of broad adoption by the general population. In contrast, nearly two-thirds of adults in the United States own a smartphone and technology advancements have enabled these devices to track health behaviors such as physical activity and provide convenient feedback. New wearable devices that may have more consumer appeal have also been developed.

    Even though these devices and applications might better engage individuals in their health, for example through workplace wellness programs, there has been little evaluation of their use. The objective of this study was to evaluate the accuracy of smartphone applications and wearable devices compared with direct observation of step counts, a metric successfully used in interventions to improve clinical outcomes.


    Studying the Role of Wearable Health-Tracking Devices in Raising Users’ Self-Awareness and Motivating Physical Activities

    Grace Shin MS, Mohammad H. Jarrahi, PhD University of North Carolina at Chapel Hill, NC

    Workshop on Interactive Systems in Healthcare (WISH) 2014, 

    Published: October 2014

    The empirical work presented here focuses on the use of activity-tracking devices and the way they are adopted by users in practice. Our findings demonstrated two distinct forms of affordances generated by the use of the devices. For all the participants, the devices served as valuable informational tools by quantifying and making their daily physical activities visible. What’s more, for a few participants, the device provided motivational affordances, encouraging more physical activities. To delineate the reasons behind these differing affordances, our study further highlights consequential individual differences in terms of perception (personal context) and motivation (self-motivated vs. social motivation) that affect how individuals use the device to motivate themselves.


    Electronic feedback in a diet- and physical activity-based lifestyle intervention for weight loss: a randomized controlled trial

    Sara L Shuger, Vaughn W Barry, Xuemei Sui, Amanda McClain, Gregory A Hand, Sara Wilcox, Rebecca A Meriwether, James W Hardin and Steven N Blair.

    Published: May 2011

    The SenseWear™ Armband (SWA) (BodyMedia, Inc. Pittsburgh, PA) is a physical activity and lifestyle monitor that objectively and accurately measures free-living energy balance and sleep and includes software for self-monitoring of daily energy expenditure and energy intake. The real-time feedback of the SWA can improve individual self-monitoring and, therefore, enhance weight loss outcomes.

    Continuous self-monitoring from wearable technology with real-time feedback may be particularly useful to enhance lifestyle changes that promote weight loss in sedentary overweight or obese adults. This strategy, combined with a group-based behavioral intervention, may yield optimal weight loss.


    Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking

    Melanie Swan

    Published: February 2009

    The growing presence of patient-driven health care models may be central to the evolving health ecosystem. Individuals are starting to better manage their health, independently, with peers, in large aggregated online affinity communities and in consultative co-care with medical professionals. Tools, demographics and financial incentives may combine to accelerate the achievement of improved health outcomes for all ages. Individuals and groups of individuals as new classes of participants in the health ecosystem could be beneficial at many levels from the practical, inspiring the launch of resources, services and businesses, to the theoretical, helping to inform the general inquiry of health and to supplement the traditional scientific method with empirical data.

    More health resources and alternatives are starting to be available, consumers can control more of their own data and are becoming empowered to make their own choices; traditional medicine is no longer the exclusive source of health solutions. The individual can obtain relevant information more readily and act upon it. Health information databases and patient registries by condition are emerging as a significant public resource.

    The emerging patient-driven technology-enabled health care models have focal points at every node of the wellness cycle, particularly at earlier stages, targeting prevention rather than therapy. Uptake could advance quickly given the more open attitudes of younger generations regarding trust and privacy and their facility in using Internet models for information-seeking, communication and action-taking. Self-collected digital data could be an input to quantitative analysis, predictive outcomes and biosimulation. Consolidated reflection on reductionist self-measurement activities could be extrapolated into new perspectives such as a shift in the overall conceptualization of health, and the meaning of wellness to the individual and society.

    For both consumers and all manner of medical and public health and environmental research professionals, this could be a time of great opportunity. There is a potential chance to learn and apply the emerging models, to invent new tools, to reach out to a global peer audience in collaboration, to embrace technological change and to make progress on systemic challenges that may have previously appeared intractable.