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      Usability Study of Mainstream Wearable Fitness Devices: Feature Analysis and System Usability Scale Evaluation

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          Abstract

          Background

          Wearable devices have the potential to promote a healthy lifestyle because of their real-time data monitoring capabilities. However, device usability is a critical factor that determines whether they will be adopted on a large scale. Usability studies on wearable devices are still scarce.

          Objective

          This study aims to compare the functions and attributes of seven mainstream wearable devices and to evaluate their usability.

          Methods

          The wearable devices selected were the Apple Watch, Samsung Gear S, Fitbit Surge, Jawbone Up3, Mi Band, Huawei Honor B2, and Misfit Shine. A mixed method of feature comparison and a System Usability Scale (SUS) evaluation based on 388 participants was applied; the higher the SUS score, the better the usability of the product.

          Results

          For features, all devices had step counting, an activity timer, and distance recording functions. The Samsung Gear S had a unique sports track recording feature and the Huawei Honor B2 had a unique wireless earphone. The Apple Watch, Samsung Gear S, Jawbone Up3, and Fitbit Surge could measure heart rate. All the devices were able to monitor sleep, except the Apple Watch. For product characteristics, including attributes such as weight, battery life, price, and 22 functions such as step counting, activity time, activity type identification, sleep monitoring, and expandable new features, we found a very weak negative correlation between the SUS scores and price ( r=−.10, P=.03) and devices that support expandable new features ( r=−.11, P=.02), and a very weak positive correlation between the SUS scores and devices that support the activity type identification function ( r=.11, P=.02). The Huawei Honor B2 received the highest score of mean 67.6 (SD 16.1); the lowest Apple Watch score was only 61.4 (SD 14.7). No significant difference was observed among brands. The SUS score had a moderate positive correlation with the user’s experience (length of time the device was used) ( r=.32, P<.001); participants in the medical and health care industries gave a significantly higher score (mean 61.1, SD 17.9 vs mean 68.7, SD 14.5, P=.03).

          Conclusions

          The functions of wearable devices tend to be homogeneous and usability is similar across various brands. Overall, Mi Band had the lowest price and the lightest weight. Misfit Shine had the longest battery life and most functions, and participants in the medical and health care industries had the best evaluation of wearable devices. The perceived usability of mainstream wearable devices is unsatisfactory and customer loyalty is not high. A consumer’s SUS rating for a wearable device is related to their personal situation instead of the device brand. Device manufacturers should put more effort into developing innovative functions and improving the usability of their products by integrating more cognitive behavior change techniques.

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          Most cited references47

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          IBM computer usability satisfaction questionnaires: Psychometric evaluation and instructions for use

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            The role played by perceived usability, satisfaction and consumer trust on website loyalty

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              Acceptance of Commercially Available Wearable Activity Trackers Among Adults Aged Over 50 and With Chronic Illness: A Mixed-Methods Evaluation

              Background Physical inactivity and sedentary behavior increase the risk of chronic illness and death. The newest generation of “wearable” activity trackers offers potential as a multifaceted intervention to help people become more active. Objective To examine the usability and usefulness of wearable activity trackers for older adults living with chronic illness. Methods We recruited a purposive sample of 32 participants over the age of 50, who had been previously diagnosed with a chronic illness, including vascular disease, diabetes, arthritis, and osteoporosis. Participants were between 52 and 84 years of age (mean 64); among the study participants, 23 (72%) were women and the mean body mass index was 31 kg/m2. Participants tested 5 trackers, including a simple pedometer (Sportline or Mio) followed by 4 wearable activity trackers (Fitbit Zip, Misfit Shine, Jawbone Up 24, and Withings Pulse) in random order. Selected devices represented the range of wearable products and features available on the Canadian market in 2014. Participants wore each device for at least 3 days and evaluated it using a questionnaire developed from the Technology Acceptance Model. We used focus groups to explore participant experiences and a thematic analysis approach to data collection and analysis. Results Our study resulted in 4 themes: (1) adoption within a comfort zone; (2) self-awareness and goal setting; (3) purposes of data tracking; and (4) future of wearable activity trackers as health care devices. Prior to enrolling, few participants were aware of wearable activity trackers. Most also had been asked by a physician to exercise more and cited this as a motivation for testing the devices. None of the participants planned to purchase the simple pedometer after the study, citing poor accuracy and data loss, whereas 73% (N=32) planned to purchase a wearable activity tracker. Preferences varied but 50% felt they would buy a Fitbit and 42% felt they would buy a Misfit, Jawbone, or Withings. The simple pedometer had a mean acceptance score of 56/95 compared with 63 for the Withings, 65 for the Misfit and Jawbone, and 68 for the Fitbit. To improve usability, older users may benefit from devices that have better compatibility with personal computers or less-expensive Android mobile phones and tablets, and have comprehensive paper-based user manuals and apps that interpret user data. Conclusions For older adults living with chronic illness, wearable activity trackers are perceived as useful and acceptable. New users may need support to both set up the device and learn how to interpret their data.
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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                November 2018
                08 November 2018
                : 6
                : 11
                : e11066
                Affiliations
                [1 ] IT Center Second Affiliated Hospital, School of Medicine, Zhejiang University Hangzhou China
                [2 ] College of Information Engineering China Jiliang University Hangzhou China
                [3 ] Sichuan College of Traditional Chinese Medicine Mianyang China
                [4 ] Affiliated Hospital of Stomatology, Southwest Medical University Luzhou China
                [5 ] International School Southwest Medical University Luzhou China
                [6 ] Peking University Third Hospital Beijing China
                [7 ] Shenzhen HIT Campus Harbin Institute of Technology Shenzhen China
                [8 ] Center for Medical Informatics Peking University Beijing China
                [9 ] School of Medical Informatics and Engineering Southwest Medical University Luzhou China
                Author notes
                Corresponding Author: Jianbo Lei jblei@ 123456hsc.pku.edu.cn
                Author information
                http://orcid.org/0000-0002-0551-6706
                http://orcid.org/0000-0002-3426-1482
                http://orcid.org/0000-0001-6597-6336
                http://orcid.org/0000-0001-5616-8791
                http://orcid.org/0000-0003-1190-5422
                http://orcid.org/0000-0003-0271-8246
                http://orcid.org/0000-0002-1744-0235
                Article
                v6i11e11066
                10.2196/11066
                6250954
                30409767
                7ff195b6-ff79-45a5-b71f-fb995f567f91
                ©Jun Liang, Deqiang Xian, Xingyu Liu, Jing Fu, Xingting Zhang, Buzhou Tang, Jianbo Lei. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 08.11.2018.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/.as well as this copyright and license information must be included.

                History
                : 17 May 2018
                : 13 June 2018
                : 3 August 2018
                : 20 October 2018
                Categories
                Original Paper
                Original Paper

                wearable devices,usability,system usability scale,function comparison,fitness

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