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3 case studies for implementing wearables in clinical trials

Posted by on 09 October 2019
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We take a look at three case studies where wearables are being used in clinical trials and try to separate reality from hype. This is an extract from a longer whitepaper - download the full 'Using Wearables in Clinical Trials' whitepaper here.

Digital phenotyping was first defined in 2015 by Jukka- Pekka Onnela and John Torous, as the “moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices”[1, 2]. The term in itself was introduced shortly afterwards in a commentary article in Nature Biotechnology by Sachin H. Jain and John Brownstein [3]. In their comment, they locked the notion that human interactions with digital technology would capture patient phenotypes and, in doing so, enhance health and wellness. Mobile devices like smartphones and its wide range of body sensors, can streamline the collection of distinctive real-life and long-term data, significant not only to our personal health, but also on a population level.

With real-time self-assessment surveys on our phones, it is now possible to collect time-stamped symptom records in a more convenient and less annoying way than using previous ecological momentary assessment (EMA) approaches [4]. Using phone sensors, such as GPS, to determine time-based mobility patterns; or voice recordings, to detect speech and vocal signs; the phone offers objective measures of behavior in a simpler way than previous actigraphy methods [5]. The detection of pulse, galvanic skin conductance, temperature and ambient light among other capabilities, collect real-time physiological data in a practical way [4]. There is a growing recognition, among clinicians, researchers and funders, that digital phenotyping can provide invaluable additional data from patients using technology that is already used for personal reasons by the majority of us [6].

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The applications mentioned above are also attractive for drug development in both early- and late‐stage clinical trials. Collecting dense data from trial participants using wearables in natural settings - often not collectible otherwise - may fundamentally change how clinical trials are designed and conducted [7]. However, researchers face many challenges in the clinic, from scientific procedures to regulatory, legal, and operational difficulties. Let’s look at some examples…

1. ACTonHEALTH study protocol

The condition of being overweight is an increasing problem worldwide and is becoming an epidemic both in Europe and the USA; where more than 60% of adults are overweight, and obesity affects around 35% of the population [8]. The ACTonHEALTH study protocol is an ongoing randomized clinical trial that aims at comparing the effect of the current standard in obesity treatment to an Acceptance and Commitment Therapy (ACT) with the use of wearable technology at different times, measuring physical activity and also the psychological well-being of the participants [9]. The researchers claim that the benefits of this experimental design seem to overcome the downsides; however, the complexity of the clinical study with multiple independent variables demands a high number of participants, and the findings could be frail if the conclusions are not clear.

The issue of transparency in data processing and analysis in this kind of setting is crucial, in order to build trust and guarantee reproducibility [4]. Another limitation of the ACTonHEALTH study is the absence of a real group without activity trackers. Even when the wearables collect data in “silent” mode, without providing any feedback, participants wear the devices. We might think that the effect of simply wearing a similar device is small, but it is a parameter that cannot be checked directly [9].

2. MHealth screening to prevent strokes

This same problem has been raised in the mSToPS trial (mHealth Screening to Prevent Strokes), where it was stated that the monitored cohort could have been driven to more aggressively seek clinical evaluation, in contrast to the observational cohortn [10]. Wearables have been highly successful for exploring cardiac health and detecting arrhythmias outside of the clinic [11]. The mSToPS randomized clinical trial has shown that among individuals at an increased risk for Atrial Fibrillation (AF), a homebased self-applied wearable electrocardiogram (ECG) patch can improve the early diagnosis of a stroke relative to routine care [10].

Stroke is a major healthcare problem, with a staggering human and economic toll, resulting in 134 000 deaths annually in the USA, with 15-30% survivors becoming perpetually disabled [12]. In the mSToPS trial, it was reported that monitored individuals had greater initiation of anticoagulant therapy, and an increase use of health care resources [10]. Interestingly, only a limited number of the invited suitable individuals successfully enrolled in the trial; and, a substantial number of those who were initially interested in participating, changed their minds and never wore a patch [10]. When combining these two limitations, only a minority of the invited population was successfully monitored (1.7%), raising the question on how to best retain participants in this type of clinical trials [13].

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The reasons behind non-adherence are complex and include several factors like socio-economic status, access to health care, proper communication with healthcare professionals, and patients' education level, for example [14]. This issue of participant non-adherence and retainability has been studied by the ACTIVE REWARD trial (A Clinical Trial Investigating Effects of a Randomized Evaluation of Wearable Activity Trackers with Financial Rewards), where researchers tried to evaluate if an economic stimulus would improve the adherence of ischemic heart disease patients to a physical activity goal [15]. A “loss-frame” financial incentive [16] allocated money upfront to a virtual account, which would be lost if the goals were not achieved. The results were impressive: there was a 50% relative increase in physical activity by patients using wearable devices, when a loss-frame financial incentive was used together with personalized goal setting [15]. Interestingly, the standard economic approach of “gainframe”, which is rewarding individuals only after physical activity goals are achieved, was not effective as reported in a previous study by the same group [17].

Another restraint found by the mSToPS researchers, was the fact that in the US the health plan membership is fluid, with the possibility to change annually for individuals. It ended up that more than 10% of the randomized individuals were no longer members of the assigned health plan after 1 year, which reduced even further the final study group10. Therefore, it is important to notice that at the beginning of a large clinical study, clinical trial long-term claims that are based on participant follow-up are not certain. Besides, the simple fact that participant eligibility was reduced to a cohort of privately insured individuals, limits the relevancy of the results and its significance for a broader population13.

3. The Superpower Glass intervention

The homogenization of the cohort is a common problem reported in mobile health clinical trials that restrict the scope of the results. This was also a limitation found by The Superpower Glass intervention, a randomized clinical trial supporting the effectiveness of wearable behavioral intervention for children with autism spectrum disorder (ASD) [18]. Autism behavioral therapy is effective, but expensive and difficult to access; therefore, mobile technology-based therapy can mitigate wait-lists and be scaled for higher demand [19]. By having the child wear a Google Glass supported by a smartphone app (The Superpower Glass intervention) [20], there were significant improvements in facial engagement and social interaction in the children receiving the mobile technology-based therapy compared with “treatment-as-usual” controls.

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The restricted recruitment of individuals to within driving distance of Stanford University, where the population might be enriched because of the acquaintance with technology, challenges the scope of the results obtained by this intervention. Another limitation reported by the group was a low participant compliance to the recommended treatment dosing, clearly posting a challenge to a hometreatment without the help of professional intervention [13]. In the case of medication adherence in chronic patients, for example, it was reported that adopters of digital health activity trackers tend to be more adherent to hypertension, diabetes, and dyslipidemia medications, and that the adherence increases with tracking frequency [21].

There are new developments in this regard, such as pillboxes with sensors that can track when a patient opened the box as well as containers and syringes that illuminate brighter and brighter as a reminder system [22]. One step into the future, Proteus Digital Health has developed an ingestible sensor applied to a capecitabine chemotherapy drug, that can measure medication adherence patterns and is currently being used to help treat stage 3 and 4 oncological patients at Fairview Health Services together with the University of Minnesota Health professionals [23].

Download the full 'Using Wearables in Clinical Trials' whitepaper here.

ABOUT THE AUTHOR: Dr Catarina Carrao gained a PhD in Biochemistry from Northeastern Ohio Medical and Pharmacy University and an M.Phil in Biochemistry at the University of Beira Interior. She has worked as a researcher at Max F. Perutz Laboratories, Yale Cardiovascular Center at Yale University School of Medicine, and the Center Cardiovascular Research (CCR) at Charité Medical University.


  1. Torous J, Staples P and Onnela JP. Realizing the potential of mobile mental health: new methods for new data in psychiatry. Curr Psychiatry Rep. 2015;17:602.
  2. Torous J, Staples P, Shanahan M, Lin C, Peck P, Keshavan M and Onnela JP. Utilizing a Personal Smartphone Custom App to Assess the Patient Health Questionnaire-9 (PHQ-9) Depressive Symptoms in Patients With Major Depressive Disorder. JMIR Ment Health. 2015;2:e8.
  3. Jain SH, Powers BW, Hawkins JB and Brownstein JS. The digital phenotype. Nat Biotechnol. 2015;33:462-3.
  4. Torous J, Onnela JP and Keshavan M. New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices. Transl Psychiatry. 2017;7:e1053.
  5. Bot BM, Suver C, Neto EC, Kellen M, Klein A, Bare C, Doerr M, Pratap A, Wilbanks J, Dorsey ER, Friend SH and Trister AD. The mPower study, Parkinson disease mobile data collected using ResearchKit. Sci Data. 2016;3:160011.
  6. Warraich H.J.; Califf RMK, H.M. The digital transformation of medicine can revitaliza the patent-clinicial relationship. Nature Digital Medicine. 2018;1.
  7. Izmailova ES, Wagner JA and Perakslis ED. Wearable Devices in Clinical Trials: Hype and Hypothesis. Clin Pharmacol Ther. 2018;104:42-52.
  8. Lifshitz F and Lifshitz JZ. Globesity: the root causes of the obesity epidemic in the USA and now worldwide. Pediatr Endocrinol Rev. 2014;12:17-34.
  9. Cattivelli R, Castelnuovo G, Musetti A, Varallo G, Spatola CAM, Riboni FV, Usubini AG, Tosolin F, Manzoni GM, Capodaglio P, Rossi A, Pietrabissa G and Molinari E. ACTonHEALTH study protocol: promoting psychological flexibility with activity tracker and mHealth tools to foster healthful lifestyle for obesity and other chronic health conditions. Trials. 2018;19:659.
  10. Steinhubl SR, Waalen J, Edwards AM, Ariniello LM, Mehta RR, Ebner GS, Carter C, Baca-Motes K, Felicione E, Sarich T and Topol EJ. Effect of a Home-Based Wearable Continuous ECG Monitoring Patch on Detection of Undiagnosed Atrial Fibrillation: The mSToPS Randomized Clinical Trial. JAMA. 2018;320:146-155.
  11. Dunn J, Runge R and Snyder M. Wearables and the medical revolution. Per Med. 2018;15:429-448.
  12. Roger VL, Go AS, Lloyd-Jones DM, Adams RJ, Berry JD, Brown TM, Carnethon MR, Dai S, de Simone G, Ford ES, Fox CS, Fullerton HJ, Gillespie C, Greenlund KJ, Hailpern SM, Heit JA, Ho PM, Howard VJ, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Makuc DM, Marcus GM, Marelli A, Matchar DB, McDermott MM, Meigs JB, Moy CS, Mozaffarian D, Mussolino ME, Nichol G, Paynter NP, Rosamond WD, Sorlie PD, Stafford RS, Turan TN, Turner MB, Wong ND, Wylie-Rosett J, American Heart Association Statistics C and Stroke Statistics S. Heart disease and stroke statistics--2011 update: a report from the American Heart Association. Circulation. 2011;123:e18-e209.
  13. Baca-Motes K, Edwards AM, Waalen J, Edmonds S, Mehta RR, Ariniello L, Ebner GS, Talantov D, Fastenau JM, Carter CT, Sarich TC, Felicione E, Topol EJ and Steinhubl SR. Digital recruitment and enrollment in a remote nationwide trial of screening for undiagnosed atrial fibrillation: Lessons from the randomized, controlled mSToPS trial. Contemp Clin Trials Commun. 2019;14:100318.
  14. Brown MT and Bussell JK. Medication adherence: WHO cares? Mayo Clin Proc. 2011;86:304-14.
  15. Chokshi NP, Adusumalli S, Small DS, Morris A, Feingold J, Ha YP, Lynch MD, Rareshide CAL, Hilbert V and Patel MS. Loss- Framed Financial Incentives and Personalized Goal-Setting to Increase Physical Activity Among Ischemic Heart Disease Patients Using Wearable Devices: The ACTIVE REWARD Randomized Trial. J Am Heart Assoc. 2018;7.
  16. Bosone Lm, F. When, How and Why is Loss-Framing More Effective than Gain- and Non-Gain-Framing in the Promotion of Detection Behaviors? International Review of Social Psychology. 2017;30:184-192.
  17. Patel MS, Volpp KG, Rosin R, Bellamy SL, Small DS, Fletcher MA, Osman-Koss R, Brady JL, Haff N, Lee SM, Wesby L, Hoffer K, Shuttleworth D, Taylor DH, Hilbert V, Zhu J, Yang L, Wang X and Asch DA. A Randomized Trial of Social Comparison Feedback and Financial Incentives to Increase Physical Activity. Am J Health Promot. 2016;30:416-24.
  18. Voss C, Schwartz J, Daniels J, Kline A, Haber N, Washington P, Tariq Q, Robinson TN, Desai M, Phillips JM, Feinstein C, Winograd T and Wall DP. Effect of Wearable Digital Intervention for Improving Socialization in Children With Autism Spectrum Disorder: A Randomized Clinical Trial. JAMA Pediatr. 2019.
  19. Buescher AV, Cidav Z, Knapp M and Mandell DS. Costs of autism spectrum disorders in the United Kingdom and the United States. JAMA Pediatr. 2014;168:721-8.
  20. Washington P, Voss C, Kline A, Haber N, Daniels J, Fazel A, De T, Feinstein C, Winograd T and Wall D. Superpowerglass: A wearable aid for the at-home therapy of children with autism. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2017;1:112.
  21. Quisel T, Foschini L, Zbikowski SM and Juusola JL. The Association Between Medication Adherence for Chronic Conditions and Digital Health Activity Tracking: Retrospective Analysis. J Med Internet Res. 2019;21:e11486.
  22. Christian J DN, Jordan M, et al. . Digital Health and Patient Registries: Today, Tomorrow, and the Future. 21st Century Patient Registries: Registries for Evaluating Patient Outcomes: A User’s Guide: 3rd Edition Agency for Healthcare Research and Quality (US); 2018.
  23. Proteus Digital Health I. Proteus Digital Health® Launches Digital Oncology Medicines to Improve Patient Outcomes 2019
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