Perspectives of People Who Are Overweight and Obese on Using Wearable Technology for Weight Management: Systematic Review

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Abstract

Background

Obesity is a large contributor to preventable chronic diseases and health care costs. The efficacy of wearable devices for weight management has been researched; however, there is limited knowledge on how these devices are perceived by users.

Objective

This study aimed to review user perspectives on wearable technology for weight management in people who are overweight and obese.

Methods

We searched the online databases Pubmed, Scopus, Embase, and the Cochrane library for literature published from 2008 onward. We included all types of studies using a wearable device for delivering weight-loss interventions in adults who are overweight or obese, and qualitative data were collected about participants' perspectives on the device. We performed a quality assessment using criteria relevant to different study types. The Cochrane risk of bias tool was used for randomized controlled trials. The Risk of Bias in Non-randomized Studies - of Interventions (ROBINS-I) was used for nonrandomized studies. The Oxman and Guyatt Criteria were used for systematic reviews. We used the critical appraisal checklist for qualitative studies. Data were extracted into a data extraction sheet and thematically analyzed.

Results

We included 19 studies: 5 randomized controlled trials, 6 nonrandomized studies, 5 qualitative studies, and 3 reviews. Mixed perceptions existed for different constructs of wearable technologies, which reflects the differences in the suitability of wearable technology interventions for different individuals in different contexts. This also indicates that interventions were not often tailored to participants' motivations. In addition, very few wearable technology interventions included a thorough qualitative analysis of the participants' view on important features of the intervention that made it successful.

Conclusions

This study highlights the importance of determining the type of intervention most suitable for an individual before the intervention is used. Our findings could help participants find a suitable intervention that is most effective for them. Further research needs to develop a user-centered tool for obtaining comprehensive user feedback.

Trial Registration

PROSPERO CRD42018096932; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=96932

Related collections

Most cited references 19

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Using computer, mobile and wearable technology enhanced interventions to reduce sedentary behaviour: a systematic review and meta-analysis

Aoife Stephenson, Suzanne M McDonough, Marie Murphy … (2017)

Background High levels of sedentary behaviour (SB) are associated with negative health consequences. Technology enhanced solutions such as mobile applications, activity monitors, prompting software, texts, emails and websites are being harnessed to reduce SB. The aim of this paper is to evaluate the effectiveness of such technology enhanced interventions aimed at reducing SB in healthy adults and to examine the behaviour change techniques (BCTs) used. Methods Five electronic databases were searched to identify randomised-controlled trials (RCTs), published up to June 2016. Interventions using computer, mobile or wearable technologies to facilitate a reduction in SB, using a measure of sedentary time as an outcome, were eligible for inclusion. Risk of bias was assessed using the Cochrane Collaboration’s tool and interventions were coded using the BCT Taxonomy (v1). Results Meta-analysis of 15/17 RCTs suggested that computer, mobile and wearable technology tools resulted in a mean reduction of −41.28 min per day (min/day) of sitting time (95% CI -60.99, −21.58, I2 = 77%, n = 1402), in favour of the intervention group at end point follow-up. The pooled effects showed mean reductions at short (≤ 3 months), medium (>3 to 6 months), and long-term follow-up (>6 months) of −42.42 min/day, −37.23 min/day and −1.65 min/day, respectively. Overall, 16/17 studies were deemed as having a high or unclear risk of bias, and 1/17 was judged to be at a low risk of bias. A total of 46 BCTs (14 unique) were coded for the computer, mobile and wearable components of the interventions. The most frequently coded were “prompts and cues”, “self-monitoring of behaviour”, “social support (unspecified)” and “goal setting (behaviour)”. Conclusion Interventions using computer, mobile and wearable technologies can be effective in reducing SB. Effectiveness appeared most prominent in the short-term and lessened over time. A range of BCTs have been implemented in these interventions. Future studies need to improve reporting of BCTs within interventions and address the methodological flaws identified within the review through the use of more rigorously controlled study designs with longer-term follow-ups, objective measures of SB and the incorporation of strategies to reduce attrition. Trial registration The review protocol was registered with PROSPERO: CRD42016038187 Electronic supplementary material The online version of this article (doi:10.1186/s12966-017-0561-4) contains supplementary material, which is available to authorized users.

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A review of efficacious technology-based weight-loss interventions: five key components.

Cheryl Gore-Felton, Anna Khaylis, Themis Yiaslas … (2010)

Obesity is highly prevalent among American adults and has negative health and psychosocial consequences. The purpose of this article was to qualitatively review studies that used technology-based interventions for weight loss and to identify specific components of these interventions that are effective in facilitating weight loss. We conducted a narrow, qualitative review, focusing on articles published in the last 10 years that used an experimental or pre/posttest design and used a technology-based intervention for weight loss. Among the 21 studies reviewed, we identified the following five components that we consider to be crucial in technology-based weight-loss interventions that are successful in facilitating weight loss: self monitoring, counselor feedback and communication, social support, use of a structured program, and use of an individually tailored program. Short-term results of technologically driven weight-loss interventions using these components have been promising, but long-term results have been mixed. Although more longitudinal studies are needed for interventions implementing these five components, the interface of technology and behavior change is an effective foundation of a successful, short-term weight-loss program and may prove to be the basis of long-term weight loss.

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A comparison of wearable fitness devices

Kanitthika Kaewkannate, Soochan Kim (2016)

Background Wearable trackers can help motivate you during workouts and provide information about your daily routine or fitness in combination with your smartphone without requiring potentially disruptive manual calculations or records. This paper summarizes and compares wearable fitness devices, also called “fitness trackers” or “activity trackers.” These devices are becoming increasingly popular in personal healthcare, motivating people to exercise more throughout the day without the need for lifestyle changes. The various choices in the market for wearable devices are also increasing, with customers searching for products that best suit their personal needs. Further, using a wearable device or fitness tracker can help people reach a fitness goal or finish line. Generally, companies display advertising for these kinds of products and depict them as beneficial, user friendly, and accurate. However, there are no objective research results to prove the veracity of their words. This research features subjective and objective experimental results, which reveal that some devices perform better than others. Methods The four most popular wristband style wearable devices currently on the market (Withings Pulse, Misfit Shine, Jawbone Up24, and Fitbit Flex) are selected and compared. The accuracy of fitness tracking is one of the key components for fitness tracking, and some devices perform better than others. This research shows subjective and objective experimental results that are used to compare the accuracy of four wearable devices in conjunction with user friendliness and satisfaction of 7 real users. In addition, this research matches the opinions between reviewers on an Internet site and those of subjects when using the device. Results Withings Pulse is the most friendly and satisfactory from the users’ viewpoint. It is the most accurate and repeatable for step and distance tracking, which is the most important measurement of fitness tracking, followed by Fitbit Flex, Jawbone Up24, and Misfit Shine. In contrast, Misfit Shine has the highest score for design and hardware, which is also appreciated by users. Conclusions From the results of experiments on four wearable devices, it is determined that the most acceptable in terms of price and satisfaction levels is the Withings Pulse, followed by the Fitbit Flex, Jawbone Up24, and Misfit Shine.

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Author and article information

Contributors

Edward Meinert:

ORCID: https://orcid.org/0000-0003-2484-3347

Digitally Enabled PrevenTative Health Research GroupDepartment of PaediatricsUniversity of OxfordLevel 2, Children's HospitalJohn Radcliffe HospitalOxford, OX3 9DUUnited Kingdom44 7824446808e.meinert14@imperial.ac.uk

Journal

Journal ID (nlm-ta): JMIR Mhealth Uhealth

Journal ID (iso-abbrev): JMIR Mhealth Uhealth

Journal ID (publisher-id): JMU

Title: JMIR mHealth and uHealth

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2291-5222

Publication date Collection: January 2020

Publication date (Electronic): 13 January 2020

Volume: 8

Issue: 1

Electronic Location Identifier: e12651

Affiliations

[1 ] Digitally Enabled PrevenTative Health Research Group Department of Paediatrics University of Oxford Oxford United Kingdom

[2 ] Department of Primary Care and Public Health, School of Public Health Imperial College London London United Kingdom

Author notes

Corresponding Author: Edward Meinert e.meinert14@ 123456imperial.ac.uk

Author information

Ruiqi Hu https://orcid.org/0000-0002-1522-7334

Michelle Helena van Velthoven https://orcid.org/0000-0003-1245-8759

Edward Meinert https://orcid.org/0000-0003-2484-3347

Article

Publisher ID: v8i1e12651

DOI: 10.2196/12651

PMC ID: 6996738

PubMed ID: 31929104

SO-VID: ca1b2c86-347b-40a0-816c-d0e97d9e0734

License:

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

Date received : 30 October 2018

Date revision requested : 21 November 2018

Date revision received : 22 January 2019

Date accepted : 15 November 2019

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