==== Front PLoS One PLoS One plos plosone PLoS ONE 1932-6203 Public Library of Science San Francisco, CA USA 10.1371/journal.pone.0241368 PONE-D-20-03877 Research Article Medicine and Health Sciences Public and Occupational Health Physical Activity Medicine and Health Sciences Surgical and Invasive Medical Procedures Cardiovascular Procedures Cardiac Surgery Biology and Life Sciences Physiology Biological Locomotion Walking Medicine and Health Sciences Vascular Medicine Vascular Diseases Peripheral Vascular Disease Medicine and Health Sciences Public and Occupational Health Physical Activity Physical Fitness Exercise Medicine and Health Sciences Sports and Exercise Medicine Exercise Biology and Life Sciences Sports Science Sports and Exercise Medicine Exercise Computer and Information Sciences Software Engineering Computer Software Engineering and Technology Software Engineering Computer Software Medicine and Health Sciences Surgical and Invasive Medical Procedures Medicine and Health Sciences Surgical and Invasive Medical Procedures Cardiovascular Procedures Coronary Artery Bypass Grafting Smart bracelet to assess physical activity after cardiac surgery: A prospective study Smart bracelet to assess physical activity after cardiac surgeryHauguel-Moreau Marie ConceptualizationInvestigationValidationVisualizationWriting – original draft1 Naudin Cécile Data curationFormal analysisInvestigationMethodologySoftware2 N’Guyen Lee Data curationFormal analysisInvestigationMethodologyValidation23 https://orcid.org/0000-0003-1381-6773Squara Pierre Data curationFormal analysisInvestigationMethodologyProject administrationResourcesWriting – review & editing23* Rosencher Julien ConceptualizationInvestigationMethodologyResourcesWriting – original draft1 Makowski Serge ConceptualizationProject administrationResourcesValidationWriting – review & editing1 Beverelli Fabrice ConceptualizationMethodologySoftwareSupervisionVisualizationWriting – review & editing1 1 Cardiology Department, CMC Ambroise Paré, Neuilly sur Seine, France 2 Research Department, CMC Ambroise Paré, Neuilly sur Seine, France 3 Critical Care Department, CMC Ambroise Paré, Neuilly sur Seine, France Ballotta Andrea Editor IRCCS Policlinico S.Donato, ITALY Competing Interests: The authors have declared that no competing interests exist. * E-mail: pierre.squara@orange.fr 1 12 2020 2020 15 12 e024136812 2 2020 30 9 2020 © 2020 Hauguel-Moreau et al2020Hauguel-Moreau et alThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Objectives Little is known about the physical activity of patients after cardiac surgery. This study was designed to assess this activity using a connected bracelet. Methods In this prospective, monocentric study, patients scheduled for cardiac surgery were offered to wear an electronic bracelet. The main objective was to measure the physical activity recovery. Secondary objectives were the predictors of the correct use of the monitoring system, of the physical recovery and, if any, the relationship between physical activity and out-of-hospital morbidity. Results One hundred patients were included. Most patients (86%) were interested in participating in the study. The compliance to the device and to the study protocol was good (94%). At discharge, the mean number of daily steps was 1454 ± 145 steps, increasing quite homogeneously, reaching 5801±1151 steps at Day 60. The best fit regression curve gave a maximum number of steps at 5897±119 (r2 = 0.97). The 85% level of activity was achieved at Day 30±3. No predictor of noncompliance was found. At discharge, age was independently associated with a lower number of daily steps (p <0.001). At Day 60, age, peripheral arterial disease and cardio-pulmonary bypass duration were independently associated with a lower number of daily steps (p = 0.039, p = 0.041 and p = 0.033, respectively). Conclusions After cardiac surgery, wearing a smart bracelet recording daily steps is simple, well tolerated and suitable for measuring physical activity. Standard patients achieved around 6000 daily steps 2 months after discharge. 85% of this activity is reached in the first month. Clinical trial registry number NCT03113565 no specific funding except our annual state grant (MERRI)Hauguel-Moreau Marie This study was funded by internal resources (MERRI). Data AvailabilityData are available from: Smart bracelet to assess physical activity after cardiac surgery: a prospective study, Dryad, Dataset, https://doi.org/10.5061/dryad.547d7wm6m.Data Availability Data are available from: Smart bracelet to assess physical activity after cardiac surgery: a prospective study, Dryad, Dataset, https://doi.org/10.5061/dryad.547d7wm6m. ==== Body Introduction Rehabilitation after cardiac surgery improves physical and psychological outcomes. A recent Cochrane review reported improved quality of life, reduced hospital readmissions and mortality [1]. Core components of cardiac rehabilitation include patient assessment, exercise training, diet counseling, risk factor control, patient education and psychosocial management with exercise training [2, 3]. In addition, promoting early training exercise, such as daily walking, may reduce muscle atrophy and recovery time [4, 5]. Hospital discharge is getting earlier and earlier after cardiac surgery [6]. Patient follow-up is a concern as major complications mainly occur within the first month. Decreased physical activity is an independent predictor for a complicated postoperative recovery in patients aged 65 years or older undergoing elective cardiac surgery [7]. The use of connected bracelets and smartwatches is increasing in medicine following significant improvements into both hardware and software components [8]. They seem to be well tolerated by the patients. A portable wearable wristband-type hand orthotic was rated overall satisfactory by stroke survivors [9]. This study was designed to measure the physical activity of patients after cardiac surgery using a connected bracelet and further, to see if this activity may be predicted by the perioperative status and/or may predict the occurrence of complications. Materials and methods Study population BECSUP (Bracelet Electronique Connecté pour le SUivi des Patients après chirurgie cardiaque) was a monocentric prospective study conducted in patients scheduled for cardiac surgery in one teaching cardiothoracic hospital (Centre medico-chirurgical Ambroise Paré, Neuilly-sur-Seine, France). Patients older than 18 years old scheduled for cardiac surgery were screened for inclusion, regardless of the type of intervention. Non-inclusion criteria were pregnancy, refusal of consent, technical inability to use the device, inability to understand the protocol, and pre-existing disability that does not allow walking normally (not linked to cardiac pathology leading to surgery programmed). Since we looked to evaluate the number of daily steps usually performed by a standard patient, we excluded patients with in-hospital or extra-hospital serious adverse events. A cardiac rehabilitation program was proposed to all patients by the medical team. Patients who did not wear the bracelet for at least 30 days were secondarily excluded as well as patients with severe postoperative complications. All patients gave written informed consent. The study protocol was approved on January 2017 by an ethics committee (CPP Ile-De-France 7, Kremlin-Bicêtre) and published under n° NCT03113465 on clinicaltrials.gov. A patient representative was included in the research team. This was an exploratory study and it was decided to include 100 patients. Material We used the Withings Go® electronic bracelet as an activity tracker. It was worn on the patient’s wrist during the whole day, from discharge (Day 0) to Day 60. Following data were recorded: ID number, date, time, and number of steps per day. The bracelet was connected to an anonymous secured database. Data were hourly and automatically wireless transferred to the patient’s mobile phone (or tablet) and to a follow-up application (CardioReport®, Medireport, Paris). Procedure All patients were encouraged to wear the electronic bracelet before hospital discharge. An application was downloaded on their smartphone (or tablet) by the study team whenever possible and checked for appropriate functioning. Clear explanations about the functioning of the bracelet were provided to patients by the research team. Patient and hospitalization data were reported to an eCRF. Patients were contacted by phone call at Day 30 and Day 60 to collect pre-determined events (Table 3) and to answer a satisfaction questionnaire (Table 4). If no activity was detected on a patient device for more than 5 days, the patient was contacted to establish the reason for inactivity. Objectives The main objective of the study was to measure the physical activity after cardiac surgery using the connected bracelet. The primary outcome was the patient’s number of daily steps. Secondary objectives were 1) to determine if different perioperative criteria were predictive of the correct use of the monitoring system, 2) to determine the predictors of a physical recovery in line with the goals and 3) to determine if out-of-hospital morbidity and mortality can be predicted from the collected information. Statistical analysis Statistical analyses were performed using R software version 3.2.4 (The R Foundation for Statistical Computing; Vienna, Austria). Categorical variables were reported as numbers (percentages), and continuous variables as means ± SDs if normally distributed or medians (25th to 75th interquartile ranges) if not normally distributed. Categorical variables were compared using the chi-square test or Fisher exact test. Statistical significance was determined by a p-value of <0.05. A non-linear regression model was applied on daily steps per day to evaluate the maximum estimate days and when 85% of the maximal daily steps were achieved. Multivariable associations between predictors and daily steps were examined using a linear regression model. Selection of candidate variables for this model was based on clinical rationale. Results Study population Between September 2017 and June 2018, 621 patients were admitted for scheduled cardiac surgery. After applying the non-inclusion criteria, 137 patients signed informed consent and 37 were secondarily excluded (see consort flow chart, Fig 1). Finally, 100 patients were analyzed. All of them followed at least one month of standard rehabilitation program in dedicated hospitals: 84 as residents and 16 as ambutatory patients. 10.1371/journal.pone.0241368.g001Fig 1 Study consort flow chart. The baseline characteristics of the study population are provided in Table 1. Patients were at standard risk of mortality and adverse events, with most having normal preoperative status and being discharged by Day 9 [8, 10] after intervention. There were no significant differences in these data between the analyzed patients (n = 100) and excluded patients (n = 37). 10.1371/journal.pone.0241368.t001Table 1 Main patient perioperative characteristics. Study patients Excluded p n = 100 n = 37 Age, (years) 63.8 [58.7–71.4] 63.0 [55.5–68.5] NS Gender, (F/M) 15/85 7/30 NS BMI, (kg/m2) 26.6 [24.3–29.8] 26.6 [22.9–29.1] NS Angina:   Stable angina, (%) 37 24 NS   ACS, (%) 8 14 NS Hypertension, (%) 60 51 NS Chronic lung disease, (%) 18 30 NS Diabetes mellitus, (%) 16 30 NS Smoker, (%) 58 49 NS Peripheral vascular disease or stroke, (%) 6 11 NS Preoperative LVEF, (%) 65 [60–70] 65 [57–68] NS Preoperative PA pressure, (mmHg) 30 [28–36] 33 [31–39] NS Preoperative creatinine, (μmol/l) 85 [75–100] 82 [73–95] NS Euroscore 2 0.8 [0.6–1.0] 0.8 [0.6–1.1] NS Surgery performed:   Isolated CABG, (%) 54 43 NS   Isolated AVR, (%) 19 27 NS   Isolated MV repair, (%) 15 5 NS   Isolated MVR, (%) 2 8 NS   TV annuloplasty, (%) 0 3 NS   Aorta repair, (%) 1 0 NS   Cardiac myxoma resection, (%) 1 3 NS   CABG + AVR, (%) 4 0 NS   CABG + MV repair, (%) 0 3 NS   AVR + TV annuloplasty, (%) 1 0 NS   MV + TV annuloplasty, (%) 3 6 NS CPB, (min) 77 [65–89] 65 [52–90] NS LOS, (days) 11 [10–13] 11 [10–14] NS BMI: Body Mass Index; LVEF: Left Ventricle Ejection Fraction; PA: Pulmonary Artery; CABG: Coronary Artery Bypass Graft; AVR: Aortic Valve Replacement; MV: Mitral Valve; MV: Mitral Valve Repair; TV: Tricuspid Valve; CPB: Cardio Pulmonary Bypass duration; LOS: Length of Stay. Values are reported as medians [IQRs]. Daily steps At discharge (Day 0 of the study), the mean number of daily steps was 1454±145 steps. This physical activity increased progressively with a quite homogeneous interpatient profile (Fig 2), reaching 5801±580 steps at Day 60. The inter-patient variability was moderate, with the 2SD highest activity less than 50% above the 2SD lowest activity. The best-fit regression curve showed a maximum daily number of steps at 5897±119 (r2 = 0.97). The 85% level of the maximum number of steps was achieved at 30 [27–33] days after discharge (Fig 2). 10.1371/journal.pone.0241368.g002Fig 2 Daily steps performed after cardiac surgery. Black points = mean daily steps plus variability (±2SD) as shown by vertical lines. Blue curve = best fit regression, r2 = 0.97. DSmax: Extrapolated maximum of daily steps. Impact of perioperative factors on daily steps Most patients (86%) were interested in participating in the study. The compliance to the device and to the study protocol was good (94%). No predictor of noncompliance was found due to the great proportion of technical issues as compared to noncompliance. At discharge, age was independently associated with a lower number of daily steps (p <0.001). At Day 60, age, peripheral arterial disease and cardio-pulmonary bypass duration were independently associated with a lower number of daily steps at Day 60 (p = 0.039, p = 0.041 and p = 0.033, respectively) (Table 2). 10.1371/journal.pone.0241368.t002Table 2 Multivariate linear regression model predicting daily steps. Day 0 Day 60 Predictors Estimates CI p Estimates CI p (Intercept) 5293 2900–7685 <0.001 11708.80 1927.40–21490.20 0.021 Age (years) -31 -52 –-10 0.005 -92.14 -178.30 –-5.97 0.039 Diabetes mellitus -280 -871–310 0.355 497.74 -1919.55–2915.04 0.687 Smoker 60 -360–481 0.778 267.38 -1453.80–1988.57 0.761 Peripheral vascular disease or stroke -419 -1305–466 0.355 -3822 -7445 –-199 0.041 Preoperative LVEF (%) -21 -47–4 0.101 63 -41–167 0.246 CPB (min.) -5.2 -13–2.7 0.199 -36 -67 –-3.0 0.033 Safety and satisfaction As predicted by the Euroscore and considering the exclusion of patients having severe adverse events, the study mortality was very low (no death and one loss of follow-up). Morbidity was also low with 3% prolonged hospitalization and 6% rehospitalization for any reason (Table 3). Therefore, out-of-hospital morbidity and mortality were not predicted from the collected data. The results of the questionnaire at Day 30 and 60 are given in Table 4. At Day 60, physical activity was limited by dyspnea (as compared to preoperative status) in 7% of the patients and by pain in 4% of the patients. The connected bracelet was seen as useful; 61% of patients considered that it has influenced their rehabilitation and 41% that it changed their way of life. The connected bracelet and process to transfer the information was acceptable; 77% of the patients decided to continue wearing it and only 4% considered it as a constraint. 10.1371/journal.pone.0241368.t003Table 3 Extra-hospital events at Day 30 and Day 60. Y = yes, N = no, and DN = don’t know. DAY 30 DAY 60 Complication occurrence (Y/N/DN) 17 79 4 16 80 4  -Major complications (Y/N/DN) 5 1  -Minor complications (Y/N/DN) 12 15 Related to cardiac surgery (Y/N/DN) 14 3 - 9 5 2 Prolonged hospitalization (Y/N/DN) 3 91 6 - - - Rehospitalization (Y/N/DN) 5 92 3 3 93 4 Death (Y/N/DN) - 99 1 - 99 1 10.1371/journal.pone.0241368.t004Table 4 Patient questionnaire at Day 30 and Day 60. Y = yes, N = no, and DN = don’t know. DAY 30 DAY 60 How do you score your actual physical activity? (better/same/less) 62 7 26 73 16 7 How do you score your actual breathing? (better/same/less) 60 20 13 71 18 7 Have you experienced scar pain? (Y/N/DN) 46 48 6 36 60 4 If yes, is this pain limiting your activity? (Y/N/DN) 13 33 - 7 29 - Do you believe that the bracelet influenced your rehabilitation? (Y/N/DN) 53 38 9 61 32 7 Is the bracelet representing a constraint in your daily life? (Y/N/DN) - 94 6 7 89 4 Is the bracelet changing your way of life? (Y/N/DN) 23 71 6 41 55 4 Would you accept pursuing your monitoring using this bracelet? (Y/N/DN) 83 11 6 77 19 4 Do you believe that other connected devices would do better? (Y/N/DN) 64 19 17 69 20 11 Discussion This study shows that a connected bracelet is easy to use and well accepted by most patients as well as providing a few constraints. Half of them considered that this tool influenced their rehabilitation. The most important limitation for using the connected bracelet was technical. Our results suggest that postcardiac surgery patients achieved around 6000 daily steps 2 months after discharge. The increase in the daily steps average is quite homogeneous, following a quite common curve and reaching 85% of the maximum activity near one month after discharge. As previously reported, age was a constant independent factor affecting the recovery of patients [10]. Peripheral arterial disease, including past stroke, also affected the maximum activity steps that a patient could perform. Cardiopulmonary bypass duration was the last independent factor affecting the number of daily steps. This is not surprising since cardiopulmonary bypass duration is a global indicator of surgical complexity and known to be an independent predictive factor of outcome after cardiac surgery [11]. There were not enough patients to determine if a specific intervention could affect the number of steps per day during recovery. To our knowledge, this study was the first that measures the recovery profile and the level of activity as assessed by daily steps after standard cardiac surgery. Therefore, few data in the literature can be compared to our results [12]. Activity trackers may contribute to better treatments and more positive outcomes [13]. It has also been shown that physical activity assessed by motion sensors in patients with heart failure was linked with exercise capacity and was predictive of the disease severity [14, 15]. Wearing an activity tracker has the potential to increase physical activity participation [16, 17]. We can also speculate that monitoring patient activity will also enable to identify patients at risk for complications or requiring enhanced follow-up [18]. Thus, when better standardization will allow a majority of patients to have access to connected devices, they could become a standard of postoperative follow-up. They could act to motivate the patients (self-coaching). Using the basic system of this study, 60% of the patients considered that it improved their recovery. We can speculate that more sophisticated software will help a majority of patients. Connected bracelet could also help clinicians to target the appropriate daily physical activity, adapted to the specific situation of the patient, and finally provide an overview of the progress of the recovery. We found three independent factors affecting the number of daily steps. Further studies may better indicate which type of patients and/or which patient characteristics may benefit from reinforced protocols. This would promote appropriate interventions either to shorten the rehabilitation or to strengthen it. Limitations A lot of patients were noneligible. From a cohort of 621 patients, only 100 were analyzed. However, 297 patients were unable to participate, mostly due to neurological, psychological or motor limitations. Therefore, a generalization of our results is only suitable for the 224 patients eligible for the study. Technical problems were the main reason for reducing this number to 100. Indeed, a specific recent Android or Apple operating system phone was needed to connect the smart bracelet to the mobile application. The daily wear of a bracelet was the second reason for non-inclusion. In the absence of immediate benefit, 15% of our patients were not interested in wearing a bracelet. Further studies are needed to demonstrate that daily monitoring of patients with a smart device can anticipate adverse events or their severity and to convince the patient of its interest. We assume that this technological selection did not generate different results than a random selection would have given. The number of daily steps performed by our patients before surgery was not measured, since this study was not designed to compare the number of daily steps performed before and after cardiac surgery. However, preoperative walking scores would have been impacted by the heart disease. None of the patients included had a physical disability that prevented them from walking and being able to perform standard exercise. Overall, 62% of the patients at day 30 and 73% at day 60 considered that their physical activity was improved. Wearable activity trackers have become popular for assessing the daily physical activity of individuals. However, the validity of these devices in step counts has raised concerns [19] and debate [20]. Pedometers generated significant errors at slow speeds, slower than 0.6 m/s (2.16 km/h or 1.24 mph) [21, 22]. A recent study compared the ability of ten different tracker activity systems for measuring steps. Although considerable inter-device variability was found, the Withings system that we used was one of the two providing the most accurate measures under three different conditions (i.e., treadmill, over-ground, and 24-hour conditions) [23]. Conclusion After cardiac surgery, wearing a smart bracelet recording daily steps is simple, well tolerated and useful to measure physical activity recovery. Standard postcardiac surgery patients achieved around 3000–5000 daily steps one month after discharge and reached a plateau of around 5000–7000 steps at two months after discharge. Only 45% of the patients able to participate, benefited from this technology, most often for technological reasons. Improving the standardization of connected devices will help to enlarge the proportion of patient candidates for this type of monitoring. Supporting information S1 File TREND statement checklist. (PDF) Click here for additional data file. S2 File (PDF) Click here for additional data file. S3 File (PDF) Click here for additional data file. S4 File (PDF) Click here for additional data file. The authors thanks Messaouda Merzoug, Cécile Naudin and Steve Novak for their help in collecting the data. ==== Refs References 1 Anderson L , Oldridge N , Thompson DR , Zwisler AD , Rees K , Martin N , et al Exercise-Based Cardiac Rehabilitation for Coronary Heart Disease: Cochrane Systematic Review and Meta-Analysis . J Am Coll Cardiol . 2016 ;67 (1 ):1 –12 . Epub 2016/01/15. 10.1016/j.jacc.2015.10.044 .26764059 2 Montalescot G , Sechtem U , Achenbach S , Andreotti F , Arden C , Budaj A , et al 2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology . Eur Heart J . 2013 ;34 (38 ):2949 –3003 . Epub 2013/09/03. 10.1093/eurheartj/eht296 .23996286 3 Piepoli MF , Hoes AW , Agewall S , Albus C , Brotons C , Catapano AL , et al [2016 European guidelines on cardiovascular disease prevention in clinical practice. The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts. Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation ]. Giornale italiano di cardiologia (2006) . 2017 ;18 (7 ):547 –612 . Epub 2017/07/18.28714997 4 Ramos Dos Santos PM , Aquaroni Ricci N , Aparecida Bordignon Suster E , de Moraes Paisani D , Dias Chiavegato L . Effects of early mobilisation in patients after cardiac surgery: a systematic review . Physiotherapy . 2017 ;103 (1 ):1 –12 . Epub 2016/12/10. 10.1016/j.physio.2016.08.003 .27931870 5 Hojskov IE , Moons P , Hansen NV , Greve H , Olsen DB , Cour SL , et al Early physical training and psycho-educational intervention for patients undergoing coronary artery bypass grafting. The SheppHeart randomized 2 x 2 factorial clinical pilot trial . European journal of cardiovascular nursing: journal of the Working Group on Cardiovascular Nursing of the European Society of Cardiology . 2016 ;15 (6 ):425 –37 . Epub 2015/07/19. 6 Papachristofi O , Klein AA , Mackay J , Nashef S , Fletcher N , Sharples LD . Effect of individual patient risk, centre, surgeon and anaesthetist on length of stay in hospital after cardiac surgery: Association of Cardiothoracic Anaesthesia and Critical Care (ACTACC) consecutive cases series study of 10 UK specialist centres . BMJ Open . 2017 ;7 (9 ):e016947 Epub 2017/09/13. 10.1136/bmjopen-2017-016947 28893748 7 van Laar C , TI ST , Noyez L . Decreased physical activity is a predictor for a complicated recovery post cardiac surgery . Health and quality of life outcomes . 2017 ;15 (1 ):5 Epub 2017/01/11. 10.1186/s12955-016-0576-6 28069013 8 Haghi M , Stoll R , Thurow K . A Low-Cost, Standalone, and Multi-Tasking Watch for Personalized Environmental Monitoring . IEEE transactions on biomedical circuits and systems . 2018 Epub 2018/07/17. 10.1109/tbcas.2018.2840347 .30010589 9 Lakshminarayanan K , Wang F , Webster JG , Seo NJ . Feasibility and usability of a wearable orthotic for stroke survivors with hand impairment . Disability and rehabilitation Assistive technology . 2017 ;12 (2 ):175 –83 . Epub 2016/01/07. 10.3109/17483107.2015.1111945 26735630 10 Rahmanian PB , Kroner A , Langebartels G , Ozel O , Wippermann J , Wahlers T . Impact of major non-cardiac complications on outcome following cardiac surgery procedures: logistic regression analysis in a very recent patient cohort . Interactive cardiovascular and thoracic surgery . 2013 ;17 (2 ):319 –26 ; discussion 26–7. Epub 2013/05/15. 10.1093/icvts/ivt149 23667066 11 Tkebuchava S , Tasar R , Lehmann T , Faerber G , Diab M , Breuer M , et al Predictors of Outcome for Aortic Valve Reimplantation Including the Surgeon-A Single-Center Experience . Thorac Cardiovasc Surg . 2018 Epub 2018/11/30. 10.1055/s-0038-1675594 .30485895 12 Cook DJ , Thompson JE , Prinsen SK , Dearani JA , Deschamps C . Functional recovery in the elderly after major surgery: assessment of mobility recovery using wireless technology . The Annals of thoracic surgery . 2013 ;96 (3 ):1057 –61 . Epub 2013/09/03. 10.1016/j.athoracsur.2013.05.092 .23992697 13 Nogic J , Thein PM , Cameron J , Mirzaee S , Ihdayhid A , Nasis A . The utility of personal activity trackers (Fitbit Charge 2) on exercise capacity in patients post acute coronary syndrome [UP-STEP ACS Trial]: a randomised controlled trial protocol . BMC cardiovascular disorders . 2017 ;17 (1 ):303 Epub 2017/12/30. 10.1186/s12872-017-0726-8 29284402 14 Jehn M , Prescher S , Koehler K , von Haehling S , Winkler S , Deckwart O , et al Tele-accelerometry as a novel technique for assessing functional status in patients with heart failure: feasibility, reliability and patient safety . International journal of cardiology . 2013 ;168 (5 ):4723 –8 . Epub 2013/08/22. 10.1016/j.ijcard.2013.07.171 .23962782 15 Jehn M , Schmidt-Trucksass A , Hanssen H , Schuster T , Halle M , Koehler F . Association of physical activity and prognostic parameters in elderly patients with heart failure . Journal of aging and physical activity . 2011 ;19 (1 ):1 –15 . Epub 2011/02/03. 10.1123/japa.19.1.1 .21285472 16 Brickwood KJ , Watson G , O’Brien J , Williams AD . Consumer-Based Wearable Activity Trackers Increase Physical Activity Participation: Systematic Review and Meta-Analysis . JMIR mHealth and uHealth . 2019 ;7 (4 ):e11819 Epub 2019/04/13. 10.2196/11819 30977740 17 Brakenridge CL , Fjeldsoe BS , Young DC , Winkler EA , Dunstan DW , Straker LM , et al Evaluating the effectiveness of organisational-level strategies with or without an activity tracker to reduce office workers’ sitting time: a cluster-randomised trial . The international journal of behavioral nutrition and physical activity . 2016 ;13 (1 ):115 Epub 2016/11/07. 10.1186/s12966-016-0441-3 27814738 18 Pevnick JM , Birkeland K , Zimmer R , Elad Y , Kedan I . Wearable technology for cardiology: An update and framework for the future . Trends in cardiovascular medicine . 2018 ;28 (2 ):144 –50 . Epub 2017/08/19. 10.1016/j.tcm.2017.08.003 28818431 19 Daligadu J , Pollock CL , Carlaw K , Chin M , Haynes A , Thevaraajah Kopal T , et al Validation of the Fitbit Flex in an Acute Post-Cardiac Surgery Patient Population . Physiotherapy Canada Physiotherapie Canada . 2018 ;70 (4 ):314 –20 . Epub 2019/02/13. 10.3138/ptc.2017-34 30745716 20 Lee JM , Byun W , Keill A , Dinkel D , Seo Y . Comparison of Wearable Trackers’ Ability to Estimate Sleep . International journal of environmental research and public health . 2018 ;15 (6 ). Epub 2018/06/20. 10.3390/ijerph15061265 29914050 21 Ehrler F , Weber C , Lovis C . Influence of Pedometer Position on Pedometer Accuracy at Various Walking Speeds: A Comparative Study . Journal of medical Internet research . 2016 ;18 (10 ):e268 Epub 2016/10/08. 10.2196/jmir.5916 27713114 22 Fokkema T , Kooiman TJ , Krijnen WP , CP VDS , M DEG . Reliability and Validity of Ten Consumer Activity Trackers Depend on Walking Speed . Medicine and science in sports and exercise . 2017 ;49 (4 ):793 –800 . Epub 2017/03/21. 10.1249/MSS.0000000000001146 .28319983 23 An HS , Jones GC , Kang SK , Welk GJ , Lee JM . How valid are wearable physical activity trackers for measuring steps? European journal of sport science . 2017 ;17 (3 ):360 –8 . Epub 2016/12/04. 10.1080/17461391.2016.1255261 .27912681