Mobile health

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Editor-In-Chief: C. Michael Gibson, M.S., M.D. [1] Associate Editor(s)-in-Chief: Stephanie Peters; Sierra Foster; Monica Khurana; Ross Miller; Matt Greenstein; Lauren Schuessler

Overview

Mobile health, also called mHealth, is a component of eHealth. “To date, no standardized definition of mHealth has been established...the Global Observatory for eHealth (GOe) defined mHealth or mobile health as medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices. mHealth involves the use and capitalization on a mobile phone’s core utility of voice and short messaging service (SMS) as well as more complex functionalities and applications including general packet radio service (GPRS), third and fourth generation mobile telecommunications (3G and 4G systems), global positioning system (GPS), and Bluetooth technology”[1].

In an age where technology plays a significant role in all aspects of our lives, it seems reasonable to believe that mHealth will become an integral part of our healthcare.

One observational study showed that the vast majority of the patients have a smartphone and have an interest in utilizing it to improve their health, even in a population with lower socioeconomic status where access may be of concern[2].

A possible limitation is accuracy of the accelerometer of smartphones to estimate energy expenditure by the wearer[3].

Usability and functionality of apps has been questioned[4].

Effectiveness may be helped by gamification[5].

Effectiveness

Complex trials are being started[6].

Patient compliance

Regarding medication compliance: Mobile phone strategies may or may not improve medicine adherence:

  • Regarding primary prevention of cardiovascular disease, mobile phone-based interventions have low-quality evidence of increasing compliance with medications[7]
  • Regarding medication adherence, text messaging approximately doubles the odds of adherence according to a meta analysis of 16 randomized control trials of patients with chronic disease. The review concluded that this intervention improved adherence rates from 50% to 67.8% or an absolute increase of 17.8%[8]. More recent trails confirm benefit[9].
  • Regarding physical activity, a 2018 proof of concept randomized controlled trial found fitness trackers target behavioral changes to including goal setting, and social support and comparison as motivators within the concept of social cognitive therapy allowing planning, problem solving and behavioral practice. Counseling with motivational interviewing is necessary in addition to the fitness tracker to increase behaviors[10]. These results are similar to a prior systematic review of overweight people[11].

Patient engagement

mHealth may increase patient engagement. One prospective randomized control trial found that the participants utilizing a mobile application to track their chronic disease had improvement in health self-management as compared to the the control participants who were not utilizing a mobile application[12].

Patient outcomes

There is not yet strong evidence that mobile phone messaging interventions improve health outcomes, but they may improve a patient’s ability to self-manage their disease.

  • A Cochrane Review published in 2012 of four studies that utilized mobile phone messaging interventions in the management of chronic diseases (diabetes mellitus, hypertension, and asthma) demonstrated that there was no significant difference in glycemic control, blood pressure control or FVC/FEV1 values between intervention and control groups. The reviewed study that focused on asthma did however find that there was a significant difference in the pooled asthma symptom score, favoring the text messaging intervention over usual care. All four reviewed studies showed evidence of either improved disease self-monitoring or better medication adherence with the utilization of mobile phone messaging interventions[13].

Regarding coronary artery disease, a randomized controlled trial found reduction of risk factors associated with using mobile health[14].

Regarding hypertension, a randomized controlled trial found no benefit[15].

Diabetes

Meta-analyses published in 2019[16], including a review of randomized trials published through May, 2017[17] and an AHRQ technical brief of any study design published through July, 2017[18][19] found benefit from mHealth when augmented by health care providers or study personnel,

  • A trial omitted from the above reviews also found greater hemoglobin A1c decline[20].
  • A ore recent trail also found reduction in HbA1c[21]

Physical activity

mHealth increases physical activity per a systematic review in 2020[22] and earlier in 2016[11][23]. Text messaging may give short term improvement[24].

Performance feedback provided from mobile devices about progress towards goals may increase motivation according to trials[25][26] and reviews[27][28].

Weight loss and obesity

A systematic review for the Community Guide found that electronic activity monitors increase physical activity[11][29].

Regarding MyFitnessPal, a randomized control trial in 2014 of 212 patients with a body mass index >25 kg/m^2 aimed at evaluating the efficacy of the application MyFitnessPal for weight loss in the primary care setting demonstrated that the application did not result in increased weight loss compared to usual care. Although it must be noted that most participants didn’t use the application after the first month of the study; 94 participants logged on in the first month compared to only 34 in the sixth month[30][11]. A second randomized controlled trial was also negative[31].

A pilot randomized control trial in 2013 found that a smartphone app may improve adherence and total weight loss after 6 months when compared to a website monitoring group[32]. 128 overweight individuals were randomized to receive a weight management intervention delivered by smartphone app, website, or paper diary. The smartphone app intervention, My Meal Mate (MMM), was developed by the research team using an evidence-based behavioral approach[32].

  • Trial retention was 93% in the smartphone group, 55% in the website group, and 53% in the paper diary group at 6 months.
  • Adherence means were 92 days in the smartphone group, 35 days in the website group, and 29 days in the paper diary group.
  • Mean weight loss and BMI reduction, respectively, at 6 months were -4.6 kg and -1.6 kg/m2 in the smartphone group, -2.9 kg and -1.0 kg/m2 in the paper diary group, and -1.3 kg and -0.5 kg/m2 in the website group.

Diagnostic assistance

A smartphone camera may be able to use photoplethysmography to detect atrial fibrillation according to initial research[33].

References

  1. World Health Organization (2011). mHealth New horizons for health through mobile technologies ISBN 9789241564250
  2. Ramirez V, Johnson E, Gonzalez C, Ramirez V, Rubino B, Rossetti G (2016). "Assessing the Use of Mobile Health Technology by Patients: An Observational Study in Primary Care Clinics". JMIR Mhealth Uhealth. 4 (2): e41. doi:10.2196/mhealth.4928. PMC 4858592. PMID 27095507.
  3. O'Driscoll R, Turicchi J, Beaulieu K, Scott S, Matu J, Deighton K; et al. (2018). "How well do activity monitors estimate energy expenditure? A systematic review and meta-analysis of the validity of current technologies". Br J Sports Med. doi:10.1136/bjsports-2018-099643. PMID 30194221.
  4. Lum E, Jimenez G, Huang Z, et al. Decision Support and Alerts of Apps for Self-management of Blood Glucose for Type 2 Diabetes. JAMA. 2019;321(15):1530–1532. doi:10.1001/jama.2019.1644
  5. Patel MS, Small DS, Harrison JD, Hilbert V, Fortunato MP, Oon AL; et al. (2021). "Effect of Behaviorally Designed Gamification With Social Incentives on Lifestyle Modification Among Adults With Uncontrolled Diabetes: A Randomized Clinical Trial". JAMA Netw Open. 4 (5): e2110255. doi:10.1001/jamanetworkopen.2021.10255. PMID 34028550 Check |pmid= value (help).
  6. Phillips SM, Collins LM, Penedo FJ, Courneya KS, Welch W, Cottrell A; et al. (2018). "Optimization of a technology-supported physical activity intervention for breast cancer survivors: Fit2Thrive study protocol". Contemp Clin Trials. 66: 9–19. doi:10.1016/j.cct.2018.01.001. PMC 5828903. PMID 29330081.
  7. Palmer MJ, Barnard S, Perel P, Free C (2018). "Mobile phone-based interventions for improving adherence to medication prescribed for the primary prevention of cardiovascular disease in adults". Cochrane Database Syst Rev. 6: CD012675. doi:10.1002/14651858.CD012675.pub2. PMID 29932455.
  8. Thakkar J, Kurup R, Laba TL, Santo K, Thiagalingam A, Rodgers A; et al. (2016). "Mobile Telephone Text Messaging for Medication Adherence in Chronic Disease: A Meta-analysis". JAMA Intern Med. doi:10.1001/jamainternmed.2015.7667. PMID 26831740.
  9. Santo K, Singleton A, Rogers K, Thiagalingam A, Chalmers J, Chow CK; et al. (2018). "Medication reminder applications to improve adherence in coronary heart disease: a randomised clinical trial". Heart. doi:10.1136/heartjnl-2018-313479. PMID 30150326.
  10. Li LC, Sayre EC, Xie H, Falck RS, Best JR, Liu-Ambrose T; et al. (2018). "Efficacy of a Community-Based Technology-Enabled Physical Activity Counseling Program for People With Knee Osteoarthritis: Proof-of-Concept Study". J Med Internet Res. 20 (4): e159. doi:10.2196/jmir.8514. PMC 5952118. PMID 29712630.
  11. 11.0 11.1 11.2 11.3 de Vries HJ, Kooiman TJ, van Ittersum MW, van Brussel M, de Groot M (2016). "Do activity monitors increase physical activity in adults with overweight or obesity? A systematic review and meta-analysis". Obesity (Silver Spring). 24 (10): 2078–91. doi:10.1002/oby.21619. PMID 27670401.
  12. Bloss CS, Wineinger NE, Peters M, Boeldt DL, Ariniello L, Kim JY; et al. (2016). "A prospective randomized trial examining health care utilization in individuals using multiple smartphone-enabled biosensors". PeerJ. 4: e1554. doi:10.7717/peerj.1554. PMC 4715435. PMID 26788432.
  13. de Jongh T, Gurol-Urganci I, Vodopivec-Jamsek V, Car J, Atun R (2012). "Mobile phone messaging for facilitating self-management of long-term illnesses". Cochrane Database Syst Rev. 12: CD007459. doi:10.1002/14651858.CD007459.pub2. PMID 23235644.
  14. Chow CK, Redfern J, Hillis GS, Thakkar J, Santo K, Hackett ML; et al. (2015). "Effect of Lifestyle-Focused Text Messaging on Risk Factor Modification in Patients With Coronary Heart Disease: A Randomized Clinical Trial". JAMA. 314 (12): 1255–63. doi:10.1001/jama.2015.10945. PMID 26393848. Review in: Ann Intern Med. 2016 Jan 19;164(2):JC7
  15. Morawski K, Ghazinouri R, Krumme A, Lauffenburger JC, Lu Z, Durfee E; et al. (2018). "Association of a Smartphone Application With Medication Adherence and Blood Pressure Control: The MedISAFE-BP Randomized Clinical Trial". JAMA Intern Med. doi:10.1001/jamainternmed.2018.0447. PMID 29710289.
  16. Aminuddin HB, Jiao N, Jiang Y, Hong J, Wang W (2019). "Effectiveness of smartphone-based self-management interventions on self-efficacy, self-care activities, health-related quality of life and clinical outcomes in patients with type 2 diabetes: A systematic review and meta-analysis". Int J Nurs Stud. doi:10.1016/j.ijnurstu.2019.02.003. PMID 30827741.
  17. Hou C, Xu Q, Diao S, Hewitt J, Li J, Carter B (2018). "Mobile phone applications and self-management of diabetes: A systematic review with meta-analysis, meta-regression of 21 randomized trials and GRADE". Diabetes Obes Metab. doi:10.1111/dom.13307. PMID 29582538.
  18. Veazie et al. (2018) Mobile Health Applications for Self-Management of Diabetes
  19. Veazie S, Winchell K, Gilbert J, Paynter R, Ivlev I, Eden KB; et al. (2018). "Rapid Evidence Review of Mobile Applications for Self-management of Diabetes". J Gen Intern Med. doi:10.1007/s11606-018-4410-1. PMID 29740786.
  20. Hsu WC, Lau KH, Huang R, Ghiloni S, Le H, Gilroy S; et al. (2016). "Utilization of a Cloud-Based Diabetes Management Program for Insulin Initiation and Titration Enables Collaborative Decision Making Between Healthcare Providers and Patients". Diabetes Technol Ther. 18 (2): 59–67. doi:10.1089/dia.2015.0160. PMID 26645932.
  21. Kim EK, Kwak SH, Jung HS, Koo BK, Moon MK, Lim S; et al. (2018). "The Effect of a Smartphone-Based, Patient-Centered Diabetes Care System in Patients With Type 2 Diabetes: A Randomized, Controlled Trial for 24 Weeks". Diabetes Care. doi:10.2337/dc17-2197. PMID 30377185.
  22. dLaranjo L, Ding D, Heleno B, Kocaballi B, Quiroz JC, Tong HL, Chahwan B, Neves AL, Gabarron E, Dao KP, Rodrigues D, Neves GC, Antunes ML, Coiera E, Bates DW (2020). "Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression,". Br J Sports Med. doi:10.1136/bjsports-2020-102892. PMID 33355160 Check |pmid= value (help). .
  23. Community Guide (2017). Physical Activity: Interventions Including Activity Monitors for Adults with Overweight or Obesity
  24. Agboola, Stephen; Jethwani, Kamal; Lopez, Lenny; Searl, Meghan; O’Keefe, Sandra; Kvedar, Joseph (2016-11-18). "Text to Move: A Randomized Controlled Trial of a Text-Messaging Program to Improve Physical Activity Behaviors in Patients With Type 2 Diabetes Mellitus". Journal of Medical Internet Research. 18 (11): –307. doi:10.2196/jmir.6439. ISSN 1438-8871. PMID 27864165. Retrieved 2016-11-18.
  25. Ozemek C, Strath SJ, Riggin K, Harber MP, Imboden MT, Kaminsky LA (2020). "Pedometer Feedback Interventions Increase Daily Physical Activity in Phase III Cardiac Rehabilitation Participants". J Cardiopulm Rehabil Prev. 40 (3): 183–188. doi:10.1097/HCR.0000000000000472. PMID 31714397.
  26. Kaminsky LA, Jones J, Riggin K, Strath SJ (2013). "A pedometer-based physical activity intervention for patients entering a maintenance cardiac rehabilitation program: a pilot study". Cardiovasc Diagn Ther. 3 (2): 73–9. doi:10.3978/j.issn.2223-3652.2013.03.03. PMC 3839193. PMID 24282749.
  27. Braakhuis HEM, Berger MAM, Bussmann JBJ (2019). "Effectiveness of healthcare interventions using objective feedback on physical activity: A systematic review and meta-analysis". J Rehabil Med. 51 (3): 151–159. doi:10.2340/16501977-2522. PMID 30843082.
  28. McEwan D, Harden SM, Zumbo BD, Sylvester BD, Kaulius M, Ruissen GR; et al. (2016). "The effectiveness of multi-component goal setting interventions for changing physical activity behaviour: a systematic review and meta-analysis". Health Psychol Rev. 10 (1): 67–88. doi:10.1080/17437199.2015.1104258. PMID 26445201.. This review is based on goal setting theory (Latham & Locke, 1991; Locke & Latham, 2002; Locke et al., 1981)
  29. Community Guide (2017). Physical Activity: Interventions Including Activity Monitors for Adults with Overweight or Obesity
  30. Laing BY, Mangione CM, Tseng CH, Leng M, Vaisberg E, Mahida M; et al. (2014). "Effectiveness of a smartphone application for weight loss compared with usual care in overweight primary care patients: a randomized, controlled trial". Ann Intern Med. 161 (10 Suppl): S5–12. doi:10.7326/M13-3005. PMC 4422872. PMID 25402403.
  31. Jospe MR, Roy M, Brown RC, Williams SM, Osborne HR, Meredith-Jones KA; et al. (2017). "The Effect of Different Types of Monitoring Strategies on Weight Loss: A Randomized Controlled Trial". Obesity (Silver Spring). 25 (9): 1490–1498. doi:10.1002/oby.21898. PMID 28703448.
  32. 32.0 32.1 Carter MC, Burley VJ, Nykjaer C, Cade JE (2013). "Adherence to a smartphone application for weight loss compared to website and paper diary: pilot randomized controlled trial". J Med Internet Res. 15 (4): e32. doi:10.2196/jmir.2283. PMC 3636323. PMID 23587561.
  33. Gill S, Bunting KV, Sartini C, Cardoso VR, Ghoreishi N, Uh HW; et al. (2022). "Smartphone detection of atrial fibrillation using photoplethysmography: a systematic review and meta-analysis". Heart. doi:10.1136/heartjnl-2021-320417. PMID 35277454 Check |pmid= value (help).