Literature DB >> 26911820

Mobile health devices: will patients actually use them?

Ryan J Shaw1, Dori M Steinberg2, Jonathan Bonnet3, Farhad Modarai3, Aaron George3, Traven Cunningham4, Markedia Mason, Mohammad Shahsahebi3, Steven C Grambow5, Gary G Bennett2, Hayden B Bosworth6.   

Abstract

Although mobile health (mHealth) devices offer a unique opportunity to capture patient health data remotely, it is unclear whether patients will consistently use multiple devices simultaneously and/or if chronic disease affects adherence. Three healthy and three chronically ill participants were recruited to provide data on 11 health indicators via four devices and a diet app. The healthy participants averaged overall weekly use of 76%, compared to 16% for those with chronic illnesses. Device adherence declined across all participants during the study. Patients with chronic illnesses, with arguably the most to benefit from advanced (or increased) monitoring, may be less likely to adopt and use these devices compared to healthy individuals. Results suggest device fatigue may be a significant problem. Use of mobile technologies may have the potential to transform care delivery across populations and within individuals over time. However, devices may need to be tailored to meet the specific patient needs.
© The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  data collection; health apps; health promotion; informatics; mHealth; mobile health; self-monitoring

Mesh:

Year:  2016        PMID: 26911820      PMCID: PMC4901379          DOI: 10.1093/jamia/ocv186

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   4.497


  11 in total

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Authors:  M M Funnell; R M Anderson
Journal:  JAMA       Date:  2000-10-04       Impact factor: 56.272

2.  Self-monitoring of blood glucose in type 2 diabetes and long-term outcome: an epidemiological cohort study.

Authors:  S Martin; B Schneider; L Heinemann; V Lodwig; H-J Kurth; H Kolb; W A Scherbaum
Journal:  Diabetologia       Date:  2005-12-17       Impact factor: 10.122

3.  A remote patient monitoring system for congestive heart failure.

Authors:  Myung-kyung Suh; Chien-An Chen; Jonathan Woodbridge; Michael Kai Tu; Jung In Kim; Ani Nahapetian; Lorraine S Evangelista; Majid Sarrafzadeh
Journal:  J Med Syst       Date:  2011-05-25       Impact factor: 4.460

4.  Masked hypertension assessed by ambulatory blood pressure versus home blood pressure monitoring: is it the same phenomenon?

Authors:  George S Stergiou; Eleanna V Salgami; Dimitris G Tzamouranis; Leonidas G Roussias
Journal:  Am J Hypertens       Date:  2005-06       Impact factor: 2.689

Review 5.  Health behavior models in the age of mobile interventions: are our theories up to the task?

Authors:  William T Riley; Daniel E Rivera; Audie A Atienza; Wendy Nilsen; Susannah M Allison; Robin Mermelstein
Journal:  Transl Behav Med       Date:  2011-03       Impact factor: 3.046

6.  Time requirements for diabetes self-management: too much for many?

Authors:  Louise B Russell; Dong-Churl Suh; Monika A Safford
Journal:  J Fam Pract       Date:  2005-01       Impact factor: 0.493

7.  Consistent self-monitoring of weight: a key component of successful weight loss maintenance.

Authors:  Meghan L Butryn; Suzanne Phelan; James O Hill; Rena R Wing
Journal:  Obesity (Silver Spring)       Date:  2007-12       Impact factor: 5.002

8.  Weight rhythms: weight increases during weekends and decreases during weekdays.

Authors:  Anna-Leena Orsama; Elina Mattila; Miikka Ermes; Mark van Gils; Brian Wansink; Ilkka Korhonen
Journal:  Obes Facts       Date:  2014-01-31       Impact factor: 3.942

9.  Using pedometers to increase physical activity in overweight and obese women: a pilot study.

Authors:  Sebely Pal; Cheryl Cheng; Garry Egger; Colin Binns; Robert Donovan
Journal:  BMC Public Health       Date:  2009-08-25       Impact factor: 3.295

Review 10.  Are behavioral interventions effective in increasing physical activity at 12 to 36 months in adults aged 55 to 70 years? A systematic review and meta-analysis.

Authors:  Nicola Hobbs; Alan Godfrey; Jose Lara; Linda Errington; Thomas D Meyer; Lynn Rochester; Martin White; John C Mathers; Falko F Sniehotta
Journal:  BMC Med       Date:  2013-03-19       Impact factor: 8.775

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  25 in total

1.  Feasibility of a text-based smoking cessation intervention in rural older adults.

Authors:  D Noonan; S Silva; J Njuru; T Bishop; L J Fish; L A Simmons; S H Choi; K I Pollak
Journal:  Health Educ Res       Date:  2018-02-01

2.  A Machine Learning Approach to Classifying Self-Reported Health Status in a Cohort of Patients With Heart Disease Using Activity Tracker Data.

Authors:  Yiwen Meng; William Speier; Chrisandra Shufelt; Sandy Joung; Jennifer E Van Eyk; C Noel Bairey Merz; Mayra Lopez; Brennan Spiegel; Corey W Arnold
Journal:  IEEE J Biomed Health Inform       Date:  2019-06-11       Impact factor: 5.772

3.  Telehealth-Based Health Coaching Increases m-Health Device Adherence and Rate of Weight Loss in Obese Participants.

Authors:  Michelle Alencar; Kelly Johnson; Virginia Gray; Rashmi Mullur; Elizabeth Gutierrez; Patricia Dionico
Journal:  Telemed J E Health       Date:  2019-04-17       Impact factor: 3.536

Review 4.  User-centered design for technology-enabled services for eating disorders.

Authors:  Andrea K Graham; Jennifer E Wildes; Madhu Reddy; Sean A Munson; C Barr Taylor; David C Mohr
Journal:  Int J Eat Disord       Date:  2019-07-16       Impact factor: 4.861

Review 5.  Wearables for Neurologic Conditions: Considerations for Our Patients and Research Limitations.

Authors:  Mia T Minen; Eric J Stieglitz
Journal:  Neurol Clin Pract       Date:  2021-08

6.  The New Digital Divide For Digital BioMarkers.

Authors:  John Torous; Jorge Rodriguez; Adam Powell
Journal:  Digit Biomark       Date:  2017-06-12

7.  Symptom Monitoring in Children With Life-Threatening Illness: A Feasibility Study Using mHealth.

Authors:  Jacqueline Vaughn; Nirmish Shah; Sharron L Docherty; Qing Yang; Ryan J Shaw
Journal:  ANS Adv Nurs Sci       Date:  2021 Jul-Sep 01       Impact factor: 1.824

8.  A Longitudinal Study of Fitbit Usage Behavior Among College Students.

Authors:  Cheng Wang; Omar Lizardo; David S Hachen
Journal:  Cyberpsychol Behav Soc Netw       Date:  2022-02-02

9.  Evaluating utility and compliance in a patient-based eHealth study using continuous-time heart rate and activity trackers.

Authors:  William Speier; Eldin Dzubur; Mary Zide; Chrisandra Shufelt; Sandy Joung; Jennifer E Van Eyk; C Noel Bairey Merz; Mayra Lopez; Brennan Spiegel; Corey Arnold
Journal:  J Am Med Inform Assoc       Date:  2018-10-01       Impact factor: 7.942

10.  Usability of a Wrist-Worn Smartwatch in a Direct-to-Participant Randomized Pragmatic Clinical Trial.

Authors:  Michael Galarnyk; Giorgio Quer; Kathryn McLaughlin; Lauren Ariniello; Steven R Steinhubl
Journal:  Digit Biomark       Date:  2019-12-20
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