Cited by 420Mobile Health and mHealth ApplicationsTechnology Use by Older AdultsPhysical Activity and Health

The DOI matches its Crossref record; 78 of the 79 references it cites were found in a registry or an index.

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

Open access

Mercer, Kathryn, Giangregorio, Lora, Schneider, Eric, Chilana, Parmit, Li, Melissa, Grindrod, Kelly·2016·JMIR mHealth and uHealth

DOI 10.2196/mhealth.4225PMC PMC4749845

Open this paper in CoChat — free account

Abstract

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/m(2). 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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.2196/mhealth.4225

Cites

78 of 78 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups.
Tong, A., Sainsbury, P., & Craig, J. (2007) · International Journal for Quality in Health Care · doi:10.1093/intqhc/mzm042 · Cited by 42,509
User Acceptance of Computer Technology: A Comparison of Two Theoretical Models
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989) · Management Science · doi:10.1287/mnsc.35.8.982 · Cited by 25,709
International physical activity questionnaire: 12-country reliability and validity
CRAIG, C. L., MARSHALL, A. L., SJ??STR??M, M., BAUMAN, A. E., BOOTH, M. L., AINSWORTH, B. E., PRATT, M., EKELUND, U., YNGVE, A., SALLIS, J. F., et al. (2003) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000078924.61453.fb · Cited by 21,619
‘Small Changes' to Diet and Physical Activity Behaviors for Weight Management
Hills, A. P., Byrne, N. M., Lindstrom, R. C., & Hill, J. O. (2013) · Obesity Facts · doi:10.1159/000345030 · Cited by 9,526
Applied Thematic Analysis
Guest, G., MacQueen, K., & Namey, E. (2012) · doi:10.4135/9781483384436 · Cited by 7,924
Physical Activity and Public Health: Updated Recommendation for Adults From the American College of Sports Medicine and the American Heart Association
(2007) · Circulation · doi:10.1161/circulationaha.107.185649 · Cited by 6,545
Physical Activity and Public Health in Older Adults
Nelson, M. E., Rejeski, W. J., Blair, S. N., Duncan, P. W., Judge, J. O., King, A. C., Macera, C. A., & Castaneda‐Sceppa, C. (2007) · Circulation · doi:10.1161/circulationaha.107.185650 · Cited by 3,057

Show all 79 references

Research works printed but not linked: 12

Macera, C, 2005, Prev Chronic Dis, 2, A17
10.1177/1010539513506603 · DOI
Lauritzen, J, 2013, Stud Health Technol Inform, 192, 759
Schmalzried, T, 1998, J Bone Joint Surg Am, 80, 54, 10.1302/0301-620X.80B1.7739 · DOI
ConsolvoSKlasnjaPMcDonaldDAvrahamiDFroehlichJLeGrandLLibbyRMosherKLandayJFlowers or a robot army? Encouraging awareness & activity with personal, mobile displaysUbiComp'08200810th International Conference on Ubiquitous ComputingSept 21-24, 2008Seoul, South KoreaNew YorkACM Press5463
10.2196/med20.2667 · DOI
LiIBeyond counting steps: Using context to improving monitoring of physical activity200911th International Conference on Ubiquitous Computing (Proc. UbiComp’09)2009Orlando, FLUbiComp 2009
Fogg, B, 2003, Persuasive Technology: Using Computers to Change What We Think and Do
Haddon, L, 2003, Machines that Become Us: The Social Context of Personal Communication Technology, 43
10.1287/mnsc.35.8.982 · DOI · Paper page
Pan, C, 2003, J Educ Media Lib Sci, 41, 181
10.1186/1756-0500-3-250 · DOI · Paper page

Sorted by citations

Items 101–200 of 420

Digital age and the Public eHealth perspective: Prevailing health app use among Austrian Internet users
Naszay, M., Stockinger, A., Jungwirth, D., & Haluza, D. (2017) · Informatics for Health and Social Care · doi:10.1080/17538157.2017.1399131 · Cited by 34
Acceptability and Effects of Commercially Available Activity Trackers for Chronic Pain Management Among Older African American Adults
Janevic, M. R., Shute, V., Murphy, S. L., & Piette, J. D. (2019) · Pain Medicine · doi:10.1093/pm/pnz215 · Cited by 35
Review of applications and user perceptions of smart home technology for health and environmental monitoring
Rhee, J. H., Hoon, J., Seo, J., & Cha, S. H. (2022) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwac030 · Cited by 33
One drop | Mobile on iPhone and apple watch: an evaluation of hbA1c improvement associated with tracking self-care
Osborn, C. Y., van Ginkel, J. R., Marrero, D. G., Rodbard, D., Huddleston, B., & Dachis, J. (2017) · JMIR mHealth and uHealth · doi:10.2196/mhealth.8781 · Cited by 31
Psychological barriers of using wearable devices by seniors: A mixed-methods study
Javdan, M., Ghasemaghaei, M., & Abouzahra, M. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107615 · Cited by 34
Technology-Based Motivation Support for Seniors’ Physical Activity—A Qualitative Study on Seniors’ and Health Care Professionals’ Views
Ehn, M., Johansson, A., & Revenäs, Å. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16132418 · Cited by 37
Application of Activity Trackers among Nursing Home Residents—A Pilot and Feasibility Study on Physical Activity Behavior, Usage Behavior, Acceptance, Usability and Motivational Impact
Auerswald, T., Meyer, J., von Holdt, K., & Voelcker‐Rehage, C. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17186683 · Cited by 34
It’s not about the capture, it’s about what we can learn”: a qualitative study of experts’ opinions and experiences regarding the use of wearable sensors to measure gait and physical activity
Keogh, A., Taraldsen, K., Caulfield, B., & Vereijken, B. (2021) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-021-00874-8 · Cited by 37
Patients’ experiences with commercially available activity trackers embedded in physiotherapy treatment: a qualitative study
Ummels, D., Beekman, E., Moser, A., Braun, S. M., & Beurskens, A. J. H. M. (2019) · Disability and Rehabilitation · doi:10.1080/09638288.2019.1590470 · Cited by 31
Cascading Feedback: A Longitudinal Study of a Feedback Ecosystem for Telemonitoring Patients with Chronic Disease
Brohman, M. K., Addas, S., Dixon, J., & Pinsonneault, A. (2020) · MIS Quarterly · doi:10.25300/misq/2020/15089 · Cited by 44
User Experience & Usability of Wearable Health Device: A Bibliometric Analysis of 2014–2023
Hu, L., Chen, Y., Cao, E., & Hu, W. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2357905 · Cited by 39
The feasibility and RE-AIM evaluation of the TAME health pilot study
Lewis, Z. H., Ottenbacher, K. J., Fisher, S. R., Jennings, K., Brown, A. F., Swartz, M. C., Martínez, E., & Lyons, E. (2017) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-017-0560-5 · Cited by 32
A Personalized Physical Activity Program With Activity Trackers and a Mobile Phone App for Patients With Metastatic Breast Cancer: Protocol for a Single-Arm Feasibility Trial
Delrieu, L., Pérol, O., Fervers, B., Friedenreich, C. M., Vallance, J. K., Febvey‐Combes, O., Pérol, D., Canada, B., Roitmann, E., Dufresne, A., et al. (2018) · JMIR Research Protocols · doi:10.2196/10487 · Cited by 31
How Are Wearable Activity Trackers Adopted in Older Adults? Comparison between Subjective Adoption Attitudes and Physical Activity Performance
Lee, B. C., Xie, J., Ajisafe, T., & Kim, S.-H. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17103461 · Cited by 30
Older adults’ experiences of social isolation and loneliness: Can virtual touring increase social connectedness? A pilot study
Oppert, M. L., Ngo, M., Lee, G., Billinghurst, M., Banks, S., & Tolson, L. (2023) · Geriatric Nursing · doi:10.1016/j.gerinurse.2023.08.001 · Cited by 41
Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping Review
Moorthy, P., Weinert, L., Schüttler, C., Svensson, L., Sedlmayr, B., Müller, J., & Nagel, T. (2024) · JMIR mhealth and uhealth · doi:10.2196/52179 · Cited by 34
Association between Self-Reported and Accelerometer-Based Estimates of Physical Activity in Portuguese Older Adults
Domingos, C., Santos, N. C., & Pêgo, J. M. (2021) · Sensors · doi:10.3390/s21072258 · Cited by 32
Measuring Free-Living Physical Activity With Three Commercially Available Activity Monitors for Telemonitoring Purposes: Validation Study
Breteler, M. J. M., Janssen, J. H., Spiering, W., Kalkman, C. J., van Solinge, W. W., & Dohmen, D. (2019) · JMIR Formative Research · doi:10.2196/11489 · Cited by 29
Integrating user behavior with engineering design of point-of-care diagnostic devices: theoretical framework and empirical findings
Nayak, S., Guo, T. W., Lopez-Ríos, J., Lentz, C. L., Arumugam, S., Hughes, J., Dolezal, C. L., Linder, V., Carballo‐Diéguez, A., Balán, I. C., et al. (2019) · Lab on a Chip · doi:10.1039/c9lc00188c · Cited by 28
Designing Digital Health Technology to Support Patients Before and After Bariatric Surgery: Qualitative Study Exploring Patient Desires, Suggestions, and Reflections to Support Lifestyle Behavior Change
Robinson, A., Husband, A., Slight, R. D., & Slight, S. P. (2021) · JMIR Human Factors · doi:10.2196/29782 · Cited by 26
Helping Elderly Users Report Pain Levels: A Study of User Experience with Mobile and Wearable Interfaces
Rodríguez, I., Cajamarca, G., Herskovic, V., Fuentes, C., & Campos, M. (2017) · Mobile Information Systems · doi:10.1155/2017/9302328 · Cited by 27
The use of accelerometer-based wearable activity monitors in clinical settings: current practice, barriers, enablers, and future opportunities
Maher, C. A., Szeto, K., & Arnold, J. B. (2021) · BMC Health Services Research · doi:10.1186/s12913-021-07096-7 · Cited by 26
Can we improve cognitive function among adults with osteoarthritis by increasing moderate-to-vigorous physical activity and reducing sedentary behaviour? Secondary analysis of the MONITOR-OA study
Falck, R. S., Best, J. R., LI, L. C., Chan, P., Feehan, L. M., & Liu-Ambrose, T. Y. L. (2018) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-018-2369-z · Cited by 27
An Objective Methodology for the Selection of a Device for Continuous Mobility Assessment
Bonci, T., Keogh, A., Din, S. D., Scott, K., & Mazzà, C. (2020) · Sensors · doi:10.3390/s20226509 · Cited by 26
Feasibility of a Sensor-Controlled Digital Game for Heart Failure Self-management: Randomized Controlled Trial
Radhakrishnan, K., Julien, C., Baranowski, T., O’Hair, M., Lee, G., Main, A. S. D., Allen, C., Viswanathan, B., Thomaz, E., & Kim, M. T. (2021) · JMIR Serious Games · doi:10.2196/29044 · Cited by 26
Exercise in Obesity—the Role of Technology in Health Services: Can This Approach Work?
Dobbie, L. J., Tahrani, A. A., Alam, U., James, J., Wilding, J. P. H., & Cuthbertson, D. J. (2021) · Current Obesity Reports · doi:10.1007/s13679-021-00461-x · Cited by 26
Willingness of Older Adults to Share Mobile Health Data with Researchers
Seifert, A., Christen, M., & Martin, M. (2018) · GeroPsych · doi:10.1024/1662-9647/a000181 · Cited by 27
Exploring influencing factors of technology use for active and healthy ageing support in older adults
Vaziri, D. D., Giannouli, E., Frisiello, A., Kaartinen, N., Wieching, R., Schreiber, D., & Wulf, V. (2019) · Behaviour and Information Technology · doi:10.1080/0144929x.2019.1637457 · Cited by 26
Exploring Acceptance of Digital Health Technologies for Managing Non-Communicable Diseases Among Older Adults: A Systematic Scoping Review
Anisha, S. A., Sen, A., Ahmad, B., & Bain, C. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02166-3 · Cited by 30
A Scale to Assess the Methodological Quality of Studies Assessing Usability of Electronic Health Products and Services: Delphi Study Followed by Validity and Reliability Testing
Silva, A. G., Simões, P., Santos, R., Queirós, A., Rocha, N. P., & Rodrigues, M. J. F. (2019) · Journal of Medical Internet Research · doi:10.2196/14829 · Cited by 23
Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK
Assi, K. C., Meegahapola, L., Droz, W., Kun, P., GÖTZEN, A. D., Bidoglia, M., Stares, S., GASKELL, G. D., Chagnaa, A., Ganbold, A., et al. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581190 · Cited by 25
Seamless connectivity architecture and methods for IoT and wearable devices
Celić, L., & Magjarević, R. (2019) · Automatika · doi:10.1080/00051144.2019.1660036 · Cited by 24
A feasibility study of a peer-facilitated physical activity intervention in methadone maintenance
Abrantes, A. M., Noppen, D. V., Bailey, G. L., Uebelacker, L. A., Buman, M. P., & Stein, M. D. (2021) · Mental health and physical activity · doi:10.1016/j.mhpa.2021.100419 · Cited by 23
Self-efficacy and Physical Activity in Overweight and Obese Adults Participating in a Worksite Weight Loss Intervention: Multistate Modeling of Wearable Device Data
Robertson, M. C., Green, C. E., Liao, Y., Durand, C. P., & Basen-Engquist, K. M. (2019) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-19-0907 · Cited by 21
Perspectives of older adults with chronic disease on the use of wearable technology and video games for physical activity
Simmich, J., Mandrusiak, A., Russell, T. G., Smith, S., & Hartley, N. (2021) · Digital Health · doi:10.1177/20552076211019900 · Cited by 22
Process Evaluation of Strong Hearts, Healthy Communities: A Rural Community-Based Cardiovascular Disease Prevention Program
Sriram, U., Sandreuter, K., Graham, M. L., Folta, S. C., Pullyblank, K. L., Paul, L. C., & Seguin‐Fowler, R. A. (2019) · Journal of Nutrition Education and Behavior · doi:10.1016/j.jneb.2018.10.011 · Cited by 22
Physical Activity Monitoring and Acceptance of a Commercial Activity Tracker in Adult Patients with Haemophilia
Carrasco, J. J., Pérez‐Alenda, S., Casaña, J., Soria‐Olivas, E., Bonanad, S., & Querol, F. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16203851 · Cited by 23
Investigation of wearable health tracker version updates
Woolley, S. I., Collins, T., Mitchell, J. L., & Fredericks, D. (2019) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2019-100083 · Cited by 21
The Effectiveness of New Digital Technologies in Increasing Physical Activity Levels and Promoting Active and Healthy Ageing: A Narrative Review
Greco, G., Poli, L., Clemente, F. M., Fischetti, F., & Cataldi, S. (2023) · Health & Social Care in the Community · doi:10.1155/2023/2803620 · Cited by 26
Design factors and preferences in wearable soft robots for movement disabilities
Koo, S. H. (2018) · International Journal of Clothing Science and Technology · doi:10.1108/ijcst-10-2017-0167 · Cited by 21
Features of coping with disease in Iranian multiple sclerosis patients: a qualitative study
Dehghani, A., Dehghan Nayeri, N., & Ebadi, A. (2018) · Journal of Caring Sciences · doi:10.15171/jcs.2018.006 · Cited by 20
Effectiveness of Distance Technology in Promoting Physical Activity in Cardiovascular Disease Rehabilitation: Cluster Randomized Controlled Trial, A Pilot Study
Hakala, S., Kivistö, H., Paajanen, T. I., Kankainen, A., Anttila, M.-R., Heinonen, A. O., & Sjögren, T. (2021) · JMIR Rehabilitation and Assistive Technologies · doi:10.2196/20299 · Cited by 20
Acceptance of e‐health technology among older people: A qualitative study
Rochmawati, E., Kamilah, F., & Iskandar, A. C. (2022) · Nursing and Health Sciences · doi:10.1111/nhs.12939 · Cited by 20
The Unanticipated Use of Fitness Tracking Technologies During Post-COVID Syndrome
Homewood, S., Just, K. O., & Johansson, O. B. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3661617 · Cited by 23
Seniors and Self-tracking Technology
Caldeira, C. M., & Chen, Y. (2019) · Human-computer interaction series · doi:10.1007/978-3-030-06076-3_5 · Cited by 20
A Social Group-Based Information-Motivation-Behavior Skill Intervention to Promote Acceptability and Adoption of Wearable Activity Trackers Among Middle-Aged and Older Adults: Cluster Randomized Controlled Trial
Liao, J., Xiao, H.-Y., Li, X., Shu-hua, S., Liu, S., Yang, Y., & Xu, D. R. (2020) · JMIR mhealth and uhealth · doi:10.2196/14969 · Cited by 22
Succeeding with prolonged usage of consumer-based activity trackers in clinical studies: a mixed methods approach
Henriksen, A., Sand, A.-S., Deraas, T., Grimsgaard, S., Hartvigsen, G., & Hopstock, L. A. (2020) · BMC Public Health · doi:10.1186/s12889-020-09406-w · Cited by 19
Using an Activity Tracker in Healthcare: Experiences of Healthcare Professionals and Patients
Ummels, D., Beekman, E., Braun, S., & Beurskens, A. J. H. M. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18105147 · Cited by 20
BAILAMOS With mHealth Technology! Improving Physical Activity and Well-Being in Middle-Aged and Older Latinxs: A Pre–Post Feasibility Study
Aguiñaga, S., Marques, I. G., Kitsiou, S., Balbim, G. M., Gerber, B. S., Buchholz, S. W., Bustamante, E. E., & Márquez, D. X. (2021) · Health Education & Behavior · doi:10.1177/10901981211027517 · Cited by 19
Experiences of Wearable Technology by Persons with Knee Osteoarthritis Participating in a Physical Activity Counseling Intervention: Qualitative Study Using a Relational Ethics Lens
Leese, J., Macdonald, G., Backman, C. L., Townsend, A. F., Nimmon, L., & LI, L. C. (2021) · JMIR mhealth and uhealth · doi:10.2196/30332 · Cited by 17
What Factors Predict the Adoption of Type 2 Diabetes Patients to Wearable Activity Trackers—Application of Diffusion of Innovation Theory
Chen, P., Shen, Y., Li, Z., Sun, X., Feng, X. L., & Fisher, E. B. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2021.773293 · Cited by 23
Continuously Healthy, Continuously Used? –A Thematic Analysis of User Perceptions on Consumer Health Wearables
Matt, C., Becker, M. Y., Munich, L., Kolbeck, A., Munich, L., Heß, T., & Munich, L. (2019) · Pacific Asia journal of the Association for Information Systems · doi:10.17705/1pais.11105 · Cited by 23
Healthier Life with Digital Companions: Effects of Reflection-Level and Statement-Type of Messages on Behavior Change via a Perceived Companion
Jang, J., & Kim, J. (2019) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2019.1615722 · Cited by 18
Exploring Families’ Acceptance of Wearable Activity Trackers: A Mixed-Methods Study
Creaser, A., Hall, J., Costa, S., Bingham, D., & Clemes, S. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19063472 · Cited by 18
Chronic pain self-management in middle-aged and older adults: A collective intelligence approach to identifying barriers and user needs in eHealth interventions
O’Reilly, P. M. R., Harney, O., Hogan, M., Mitchell, C., McGuire, B. E., & Slattery, B. W. (2022) · Digital Health · doi:10.1177/20552076221105484 · Cited by 16
Open-Source, Step-Counting Algorithm for Smartphone Data Collected in Clinical and Nonclinical Settings: Algorithm Development and Validation Study
Strączkiewicz, M., Keating, N. L., Thompson, E., Matulonis, U. A., Campos, S. M., Wright, A. A., & Onnela, J.-P. “. (2023) · JMIR Cancer · doi:10.2196/47646 · Cited by 18
The feasibility of the Prostate cancer: Evidence of Exercise and Nutrition Trial (PrEvENT) dietary and physical activity modifications: a qualitative study
Shingler, E., Hackshaw‐McGeagh, L., Robles, L. A., Persad, R. A., Koupparis, A. J., Rowe, E., Shiridzinomwa, C., Bahl, A. K., Martin, R. M., & Lane, J. A. (2017) · Trials · doi:10.1186/s13063-017-1828-4 · Cited by 18
User Acceptance Factors for mHealth
Pan, A., & Zhao, F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91244-8_14 · Cited by 18
Promoting physical activity in regional and remote cancer survivors (PPARCS) using wearables and health coaching: randomised controlled trial protocol
Hardcastle, S. J., Hince, D., Jiménez‐Castuera, R., Boyle, T., Cavalheri, V., Makin, G. B., Tan, P., Salfinger, S. G., Tan, J., Mohan, G. R., et al. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-028369 · Cited by 18
Exploring Smart Health Wearable Adoption Among Singaporean Older Adults Based on Self-Determination Theory: Web-Based Survey Study
Kang, H., Yang, T., Banu, N., Ng, S. W. T., & Lee, J. K. (2025) · JMIR Aging · doi:10.2196/69008 · Cited by 19
Design of an Integrated Acceptance Framework for Older Users and eHealth: Influential Factor Analysis
Yu, J., de Antonio, A., & Villalba‐Mora, E. (2021) · Journal of Medical Internet Research · doi:10.2196/31920 · Cited by 17
Wearable Devices Beyond Activity Trackers in Youth With Obesity: Summary of Options
Chimatapu, S. N., Mittelman, S. D., Habib, M., Osuna-Garcia, A., & Vidmar, A. P. (2023) · Childhood Obesity · doi:10.1089/chi.2023.0005 · Cited by 17
Patient perspectives on the use of mobile apps to support heart failure management: A qualitative descriptive study
Sivakumar, B., Lemonde, M., Stein, M., Mak, S. S., Al‐Hesayen, A., & Arcand, J. (2023) · PLoS ONE · doi:10.1371/journal.pone.0285659 · Cited by 17
Usability evaluation for the Amulet Wearable Device in rural older adults with obesity
Batsis, J. A., Zagaria, A. B., Kotz, D. F., Bartels, S. J., Boateng, G., Proctor, P., Halter, R. J., & Carpenter-Song, E. A. (2018) · Gerontechnology · doi:10.4017/gt.2018.17.3.003.00 · Cited by 17
Enabling Remote Patient Monitoring Through the Use of Smart Thermostat Data in Canada: Exploratory Study
Sahu, K. S., Oetomo, A., & Morita, P. P. (2020) · JMIR mhealth and uhealth · doi:10.2196/21016 · Cited by 20
Effect of personal activity intelligence (PAI) monitoring in the maintenance phase of cardiac rehabilitation: a mixed methods evaluation
Hannan, A. L., Hing, W. A., Coombes, J. S., Gough, S., Climstein, M., Adsett, G., Jayasinghe, R., & Furness, J. W. (2021) · BMC Sports Science Medicine and Rehabilitation · doi:10.1186/s13102-021-00350-9 · Cited by 17
Adoption Criteria Evaluation of Activity Tracking Wristbands for University Students
Shaygan, A., Güngör, D. Ö., Kutgun, H., & Daneshi, A. (2017) · Portland International Conference on Management of Engineering and Technology (PICMET) · doi:10.23919/picmet.2017.8125443 · Cited by 16
Electronic Wearable Device Use for Physical Activity in Older Adults: A Qualitative Study
Li, M., McPhillips, M., Szanton, S. L., Wenzel, J. A., & Li, J. (2022) · Work Aging and Retirement · doi:10.1093/workar/waac023 · Cited by 18
Self-citation
Benefits of a Wearable Activity Tracker with Safety Features for Older Adults: An Intervention Study
Touzery, S. H., Šetinc, M., & Dolničar, V. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192315723 · Cited by 20
Formative Evaluation of Consumer-Grade Activity Monitors Worn by Older Adults: Test-Retest Reliability and Criterion Validity of Step Counts
Maganja, S. A., Clarke, D. C., Lear, S. A., & Mackey, D. C. (2020) · JMIR Formative Research · doi:10.2196/16537 · Cited by 16
Analysis of Older Adults in Spanish Care Facilities, Risk of Falling and Daily Activity Using Xiaomi Mi Band 2
Miranda-Duro, M. D. C., Nieto-Riveiro, L., Concheiro-Moscoso, P., Groba, B., Pousada, T., Canosa, N., & Pereira, J. (2021) · Sensors · doi:10.3390/s21103341 · Cited by 16
Evaluating Health Care Provider Perspectives on the Use of Mobile Apps to Support Patients With Heart Failure Management: Qualitative Descriptive Study
Sivakumar, B., Lemonde, M., Stein, M., Goldstein, S., Mak, S. S., & Arcand, J. (2022) · JMIR Cardio · doi:10.2196/40546 · Cited by 17
Brief Report: Active Ingredients for Adherence to a Tracker-Based Physical Activity Intervention in Older Adults
Swartz, M. C., Lewis, Z. H., Swartz, M. D., Martínez, E., & Lyons, E. (2017) · Journal of Applied Gerontology · doi:10.1177/0733464817739350 · Cited by 16
Snacktivity™ to promote physical activity and reduce future risk of disease in the population: protocol for a feasibility randomised controlled trial and nested qualitative study
Daley, A., Griffin, R. A., Moakes, C. A., Sanders, J. P., Skrybant, M., Ives, N. J., Maylor, B. D., Greenfield, S., Gokal, K., Parretti, H. M., et al. (2023) · Pilot and Feasibility Studies · doi:10.1186/s40814-023-01272-8 · Cited by 16
A Social Needs Assessment Tool for an Urban Latino Population
Careyva, B. A., Hamadani, R., Friel, T. J., & Coyne, C. A. (2017) · Journal of Community Health · doi:10.1007/s10900-017-0396-6 · Cited by 15
Transforming work through human sensing: a confined space monitoring application
Kiehl, Z. A., Durkee, K. T., Halverson, K. C., Christensen, J. C., & Hellstern, G. (2019) · Structural Health Monitoring · doi:10.1177/1475921719840994 · Cited by 15
Impacts of Risks Over Benefits in the Adoption of Self-Tracking Technologies
Moya, J. D., Pallud, J., & Wamba, S. F. (2021) · Journal of Global Information Management · doi:10.4018/jgim.20211101.oa10 · Cited by 16
Ambulatory cardiac bio-signals: From mirage to clinical reality through a decade of progress
Periyaswamy, T., & Balasubramanian, M. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.07.007 · Cited by 14
Bridging Community, History, and Culture in Personal Informatics Tools
Martin-Hammond, A. M., & Purnell, T. S. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3492848 · Cited by 14
Context-aware electromagnetic design for continuously wearable biosymbiotic devices
Stuart, T., Yin, X., Chen, S. J., Farley, M., McGuire, D. T., Reddy, N., Thien, R., DiMatteo, S., Fumeaux, C., & Gutruf, P. (2023) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2023.115218 · Cited by 14
Enhancing behavioral change among lung cancer survivors participating in a lifestyle risk reduction intervention: a qualitative study
Somayaji, D., Blok, A. C., Hayman, L. L., Colson, Y. L., Jaklisch, M., & Cooley, M. E. (2019) · Supportive Care in Cancer · doi:10.1007/s00520-018-4631-1 · Cited by 14
Measure It Super Simple (MISS) activity tracker: (re)design of a user-friendly interface and evaluation of experiences in daily life
Ummels, D., Braun, S. M., Stevens, A., Beekman, E., & Beurskens, A. J. H. M. (2020) · Disability and Rehabilitation Assistive Technology · doi:10.1080/17483107.2020.1815089 · Cited by 13
Is wearable technology an activity motivator, or a fad that wears thin?
Salmon, J., & Ridgers, N. D. (2017) · The Medical Journal of Australia · doi:10.5694/mja16.01242 · Cited by 14
Performance and Pleasure: Exploring the Perceived Usefulness and Appeal of Physical Activity Data Visualizations with Older Adults
Vargemidis, D., Gerling, K., Abeele, V. V., & Geurts, L. (2023) · ACM Transactions on Accessible Computing · doi:10.1145/3615664 · Cited by 13
Use of Physical Activity Monitoring Devices by Families in Rural Communities: Qualitative Approach
Sharaievska, I., Battista, R. A., & Zwetsloot, J. J. (2018) · JMIR Pediatrics and Parenting · doi:10.2196/10658 · Cited by 13
Monitoring Patients Reported Outcomes after Valve Replacement Using Wearable Devices: Insights on Feasibility and Capability Study: Feasibility Results
Ocagli, H., Lorenzoni, G., Lanera, C., Schiavo, A., D’Angelo, L., Liberti, A. D., Besola, L., Cibin, G., Martinato, M., Azzolina, D., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18137171 · Cited by 14
Mapping sedentary behaviour (MAPS-B) in winter and spring using wearable sensors, indoor positioning systems, and diaries in older adults who are pre-frail and frail: A feasibility longitudinal study
Rodrigues, I. B., Tariq, S., Kouroukis, A., Swance, R., Adachi, J. D., Bray, S. R., Fang, Q., Ioannidis, G., Kobsar, D., Kobsar, D., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0290197 · Cited by 13
Wearable Activity Monitor Use Is Associated With the Aerobic Physical Activity Guidelines and Walking Among Older Adults
Zytnick, D., Kumar, G. S., Folta, S. C., Reid, K. F., Tybor, D. J., & Chomitz, V. R. (2021) · American Journal of Health Promotion · doi:10.1177/0890117120985834 · Cited by 13
Wearable activity trackers and mobilization after major head and neck cancer surgery: You can't improve what you don't measure
Twomey, R., Culos-Reed, S. N., Daun, J. T., Ferber, R., & Dort, J. C. (2020) · International Journal of Surgery · doi:10.1016/j.ijsu.2020.10.032 · Cited by 12
Navigating the Maze of Routine Disruption: Exploring How Older Adults Living Alone Navigate Barriers to Establishing and Maintaining Physical Activity Habits
Yang, M., & Moffatt, K. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642842 · Cited by 13
Usability Evaluation of Wearable Smartwatches Using Customized Heuristics and System Usability Scale Score
Alshamari, M., & Althobaiti, M. M. (2024) · Future Internet · doi:10.3390/fi16060204 · Cited by 20
Pulmonary hypertension and home-based (PHAHB) exercise intervention: protocol for a feasibility study
McCormack, C. M., Kehoe, B., Hardcastle, S. J., McCaffrey, N., McCarren, A. L., Gaine, S., McCullagh, B. N., & Moyna, N. M. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-045460 · Cited by 14
Active wearable device utilization improved physical performance and IGF-1 among community-dwelling middle-aged and older adults: a 12-month prospective cohort study
Lee, W., Peng, L., Lin, M.-H., Loh, C., & Chen, L. (2021) · Aging · doi:10.18632/aging.203383 · Cited by 13
Toward a Unified mHealth Platform: A Survey of Current User Challenges and Expectations
Philip, B. J., Abdelrazek, M., Barnett, S., Bonti, A., & Grundy, J. (2023) · IEEE Access · doi:10.1109/access.2023.3249786 · Cited by 13
Wearable Activity Trackers Supporting Elderly Living Independently
Urbauer, P., Frohner, M., David, V., & Sauermann, S. (2018) · International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI) · doi:10.1145/3218585.3218679 · Cited by 12
Understanding digital health ecosystem from Australian citizens’ perspective: A scoping review
Alvandi, A. O., Bain, C., & Burstein, F. (2021) · PLoS ONE · doi:10.1371/journal.pone.0260058 · Cited by 15
Mobile-Health based physical activities co-production policies towards cardiovascular diseases prevention: findings from a mixed-method systematic review
Palozzi, G., & Antonucci, G. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-07637-8 · Cited by 12
Exploring factors affecting the acceptance of fall detection technology among older adults and their families: a content analysis
Huang, H.-H., Chang, M.-H., Chen, P.-T., Lin, C., Sung, P., Chen, C., & Fan, S. (2024) · BMC Geriatrics · doi:10.1186/s12877-024-05262-0 · Cited by 11
The Rise of Digital Health and Potential Implications for Pharmacy Practice
Clark, M., Clark, T. R., Bhatti, A., & Aungst, T. D. (2017) · Journal of Contemporary Pharmacy Practice · doi:10.37901/jcphp16-00012 · Cited by 11
Satisfaction and Acceptability of Telemonitored Home-Based Exercise in Patients With Intermittent Claudication: Pragmatic Observational Pilot Study
Cornelis, N., Buys, R., Dewit, T., Benoit, D., Claes, J., Fourneau, I., & Cornelissen, V. (2021) · JMIR Rehabilitation and Assistive Technologies · doi:10.2196/18739 · Cited by 11

Authors: Mercer, K., Giangregorio, L., Schneider, E., Chilana, P., Li, M., & Grindrod, K.
Affiliations: University of Waterloo (CA)
Wingate University (US)
Corresponding author: Grindrod, Kelly
ORCID: Mercer, Kathryn
Giangregorio, Lora
Schneider, Eric
Chilana, Parmit
Li, Melissa
Grindrod, Kelly
Venue: JMIR mHealth and uHealth
Publisher: JMIR Publications Inc.
Published: 2016-01-27
Volume: 4
Issue: 1
Pages: e7
ISSN: 2291-5222 (ISSN-L)
DOI: 10.2196/mhealth.4225
PMC: PMC4749845
OpenAlex: W2254170564
Cited by: 419
References: 79
Citations by year: 2026: 18 · 2025: 38 · 2024: 28 · 2023: 36 · 2022: 50 · 2021: 71 · 2020: 47 · 2019: 40 · 2018: 42 · 2017: 39 · 2016: 10
FWCI: 81.22
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: JMIR mhealth and uhealth (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
UWSpace (University of Waterloo) (submittedVersion, cc-by)
Topics: Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Health Sciences › Medicine › Geriatrics and Gerontology › Technology Use by Older Adults
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
Keywords: wearable activity trackers, older adults, chronic disease, technology acceptance, usability, pedometers, physical activity promotion, sedentary behavior, Fitbit, misfit, jaw bone, self-monitoring, goal setting, technology adoption, user experience, diabetes mellitus, arthritis, osteoporosis, vascular disease, physical inactivity prevention

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks