Understanding the Effect of Adding Automated and Human Coaching to a Mobile Health Physical Activity App for Afghanistan and Iraq Veterans: Protocol for a Randomized Controlled Trial of the Stay Strong Intervention

Lorraine R Buis, Felicia A McCant, Jennifer M Gierisch, Lori A Bastian, Eugene Z Oddone, Caroline R Richardson, Hyungjin Myra Kim, Richard Evans, Gwendolyn Hooks, Reema Kadri, Courtney White-Clark, Laura J Damschroder, Lorraine R Buis, Felicia A McCant, Jennifer M Gierisch, Lori A Bastian, Eugene Z Oddone, Caroline R Richardson, Hyungjin Myra Kim, Richard Evans, Gwendolyn Hooks, Reema Kadri, Courtney White-Clark, Laura J Damschroder

Abstract

Background: Although maintaining a healthy weight and physical conditioning are requirements of active military duty, many US veterans rapidly gain weight and lose conditioning when they separate from active-duty service. Mobile health (mHealth) interventions that incorporate wearables for activity monitoring have become common, but it is unclear how to optimize engagement over time. Personalized health coaching, either through tailored automated messaging or by individual health coaches, has the potential to increase the efficacy of mHealth programs. In an attempt to preserve conditioning and ward off weight gain, we developed Stay Strong, a mobile app that is tailored to veterans of recent conflicts and tracks physical activity monitored by Fitbit Charge 2 devices and weight measured on a Bluetooth-enabled scale.

Objective: The goal of this study is to determine the effect of activity monitoring plus health coaching compared with activity monitoring alone.

Methods: In this randomized controlled trial, with Stay Strong, a mobile app designed specifically for veterans, we plan to enroll 350 veterans to engage in an mHealth lifestyle intervention that combines the use of a wearable physical activity tracker and a Bluetooth-enabled weight scale. The Stay Strong app displays physical activity and weight data trends over time. Enrolled participants are randomized to receive the Stay Strong app (active comparator arm) or Stay Strong + Coaching, an enhanced version of the program that adds coaching features (automated tailored messaging with weekly physical activity goals and up to 3 telephone calls with a health coach-intervention arm) for 1 year. Our primary outcome is change in physical activity at 12 months, with weight, pain, patient activation, and depression serving as secondary outcome measures. All processes related to recruitment, eligibility screening, informed consent, Health Insurance Portability and Accountability Act authorization, baseline assessment, randomization, the bulk of intervention delivery, and outcome assessment will be accomplished via the internet or smartphone app.

Results: The study recruitment began in September 2017, and data collection is expected to conclude in 2019. A total of 465 participants consented to participate and 357 (357/465, 77%) provided baseline levels of physical activity and were randomized to 1 of the 2 interventions.

Conclusions: This novel randomized controlled trial will provide much-needed findings about whether the addition of telephone-based human coaching and other automated supportive-coaching features will improve physical activity compared with using a smartphone app linked to a wearable device alone.

Trial registration: ClinicalTrials.gov NCT02360293; https://ichgcp.net/clinical-trials-registry/NCT02360293 (Archived by WebCite at http://www.webcitation.org/75KQeIFwh).

International registered report identifier (irrid): DERR1-10.2196/12526.

Keywords: cell phones; exercise; mobile phone; telemedicine; veterans.

Conflict of interest statement

Conflicts of Interest: None declared.

©Lorraine R Buis, Felicia A McCant, Jennifer M Gierisch, Lori A Bastian, Eugene Z Oddone, Caroline R Richardson, Hyungjin Myra Kim, Richard Evans, Gwendolyn Hooks, Reema Kadri, Courtney White-Clark, Laura J Damschroder. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 29.01.2019.

Figures

Figure 1
Figure 1
Theoretical framework. PA: physical activity; SS: Stay Strong.
Figure 2
Figure 2
The Stay Strong intervention and data flow.
Figure 3
Figure 3
Participant flow through recruitment, enrollment, and randomization. HIPAA: Health Insurance Portability and Accountability Act. Note: a: Unable to sync Fitbit (N=12), did not pair with study account (N=12), unable to setup device due to secure environment (N=1), unable to contact (N=9), unable to setup Fitbit with Stay Strong app (N=10), unable to comply or follow study procedures (N=5); b: Changed mind about participating (N=3).

References

    1. Buis LR, Kotagal LV, Porcari CE, Rauch SA, Krein SL, Richardson CR. Physical activity in postdeployment Operation Iraqi Freedom/Operation Enduring Freedom veterans using Department of Veterans Affairs services. J Rehabil Res Dev. 2011;48(8):901–11.
    1. Haskell SG, Mattocks K, Goulet JL, Krebs EE, Skanderson M, Leslie D, Justice AC, Yano EM, Brandt C. The burden of illness in the first year home: do male and female VA users differ in health conditions and healthcare utilization. Womens Health Issues. 2011;21(1):92–7. doi: 10.1016/j.whi.2010.08.001.
    1. Possemato K, Wade M, Andersen J, Ouimette P. The impact of PTSD, depression, and substance use disorders on disease burden and health care utilization among OEF/OIF veterans. Psychol Trauma. 2010;2(3):218–23. doi: 10.1037/a0019236.
    1. Littman AJ, Jacobson IG, Boyko EJ, Powell TM, Smith TC, Millennium Cohort Study Team Weight change following US military service. Int J Obes (Lond) 2013 Feb;37(2):244–53. doi: 10.1038/ijo.2012.46.
    1. Rosenberger PH, Ning Y, Brandt C, Allore H, Haskell S. BMI trajectory groups in veterans of the Iraq and Afghanistan wars. Prev Med. 2011 Sep;53(3):149–54. doi: 10.1016/j.ypmed.2011.07.001.
    1. Burke LE, Ma J, Azar KM, Bennett GG, Peterson ED, Zheng Y, Riley W, Stephens J, Shah SH, Suffoletto B, Turan TN, Spring B, Steinberger J, Quinn CC, American Heart Association Publications Committee of the Council on Epidemiology Prevention. Behavior Change Committee of the Council on Cardiometabolic Health. Council on Cardiovascular Stroke Nursing. Council on Functional Genomics Translational Biology. Council on Quality of Care Outcomes Research. Stroke Council Current science on consumer use of mobile health for cardiovascular disease prevention: a scientific statement from the American Heart Association. Circulation. 2015 Sep 22;132(12):1157–213. doi: 10.1161/CIR.0000000000000232.
    1. Bhardwaj NN, Wodajo B, Gochipathala K, Paul ID, Coustasse A. Can mHealth revolutionize the way we manage adult obesity? Perspect Health Inf Manag. 2017 Apr 1;14(Spring):1a.
    1. Bravata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R, Stave CD, Olkin I, Sirard JR. Using pedometers to increase physical activity and improve health: a systematic review. JAMA. 2007 Nov 21;298(19):2296–304. doi: 10.1001/jama.298.19.2296.
    1. Davies CA, Spence JC, Vandelanotte C, Caperchione CM, Mummery WK. Meta-analysis of internet-delivered interventions to increase physical activity levels. Int J Behav Nutr Phys Act. 2012 Apr 30;9:52. doi: 10.1186/1479-5868-9-52.
    1. Richardson CR, Newton TL, Abraham JJ, Sen A, Jimbo M, Swartz AM. A meta-analysis of pedometer-based walking interventions and weight loss. Ann Fam Med. 2008;6(1):69–77. doi: 10.1370/afm.761.
    1. Lewis BA, Napolitano MA, Buman MP, Williams DM, Nigg CR. Future directions in physical activity intervention research: expanding our focus to sedentary behaviors, technology, and dissemination. J Behav Med. 2017 Feb;40(1):112–26. doi: 10.1007/s10865-016-9797-8.
    1. Triantafyllidis A, Filos D, Claes J, Buys R, Cornelissen V, Kouidi E, Chouvarda I, Maglaveras N. Computerised decision support in physical activity interventions: a systematic literature review. Int J Med Inform. 2018 Mar;111:7–16. doi: 10.1016/j.ijmedinf.2017.12.012.
    1. Buchholz SW, Wilbur J, Ingram D, Fogg L. Physical activity text messaging interventions in adults: a systematic review. Worldviews Evid Based Nurs. 2013 Aug;10(3):163–73. doi: 10.1111/wvn.12002.
    1. Burke LE, Conroy MB, Sereika SM, Elci OU, Styn MA, Acharya SD, Sevick MA, Ewing LJ, Glanz K. The effect of electronic self-monitoring on weight loss and dietary intake: a randomized behavioral weight loss trial. Obesity (Silver Spring) 2011 Feb;19(2):338–44. doi: 10.1038/oby.2010.208. doi: 10.1038/oby.2010.208.
    1. Changizi M, Kaveh MH. Effectiveness of the mHealth technology in improvement of healthy behaviors in an elderly population-a systematic review. Mhealth. 2017 Nov 27;3:51. doi: 10.21037/mhealth.2017.08.06. doi: 10.21037/mhealth.2017.08.06.
    1. Flores Mateo G, Granado-Font E, Ferré-Grau C, Montaña-Carreras X. Mobile phone apps to promote weight loss and increase physical activity: a systematic review and meta-analysis. J Med Internet Res. 2015 Nov 10;17(11):e253. doi: 10.2196/jmir.4836.
    1. Lee JA, Choi M, Lee SA, Jiang N. Effective behavioral intervention strategies using mobile health applications for chronic disease management: a systematic review. BMC Med Inform Decis Mak. 2018 Feb 20;18(1):12. doi: 10.1186/s12911-018-0591-0.
    1. Pfaeffli Dale L, Dobson R, Whittaker R, Maddison R. The effectiveness of mobile-health behaviour change interventions for cardiovascular disease self-management: a systematic review. Eur J Prev Cardiol. 2016 May;23(8):801–17. doi: 10.1177/2047487315613462.
    1. Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight: a systematic review. J Cardiovasc Nurs. 2013;28(4):320–9. doi: 10.1097/JCN.0b013e318250a3e7.
    1. Wang J, Wang Y, Wei C, Yao NA, Yuan A, Shan Y, Yuan C. Smartphone interventions for long-term health management of chronic diseases: an integrative review. Telemed J E Health. 2014 Jun;20(6):570–83. doi: 10.1089/tmj.2013.0243.
    1. Whitehead L, Seaton P. The effectiveness of self-management mobile phone and tablet apps in long-term condition management: a systematic review. J Med Internet Res. 2016 May 16;18(5):e97. doi: 10.2196/jmir.4883.
    1. Zhao J, Freeman B, Li M. Can mobile phone apps influence people?s health behavior change? An evidence review. J Med Internet Res. 2016 Dec 31;18(11):e287. doi: 10.2196/jmir.5692.
    1. Birkhoff SD, Smeltzer SC. Perceptions of smartphone user-centered mobile health tracking apps across various chronic illness populations: an integrative review. J Nurs Scholarsh. 2017 Jul;49(4):371–8. doi: 10.1111/jnu.12298.
    1. Buis LR, Hirzel L, Turske SA, Des Jardins TR, Yarandi H, Bondurant P. Use of a text message program to raise type 2 diabetes risk awareness and promote health behavior change (part II): assessment of participants' perceptions on efficacy. J Med Internet Res. 2013 Dec 19;15(12):e282. doi: 10.2196/jmir.2929.
    1. Kivelä K, Elo S, Kyngäs H, Kääriäinen M. The effects of health coaching on adult patients with chronic diseases: a systematic review. Patient Educ Couns. 2014 Nov;97(2):147–57. doi: 10.1016/j.pec.2014.07.026.
    1. Oliveira JS, Sherrington C, Amorim AB, Dario AB, Tiedemann A. What is the effect of health coaching on physical activity participation in people aged 60 years and over? A systematic review of randomised controlled trials. Br J Sports Med. 2017 Oct;51(19):1425–32. doi: 10.1136/bjsports-2016-096943.
    1. Olsen JM, Nesbitt BJ. Health coaching to improve healthy lifestyle behaviors: an integrative review. Am J Health Promot. 2010;25(1):e1–e12. doi: 10.4278/ajhp.090313-LIT-101.
    1. Goode AP, Hall KS, Batch BC, Huffman KM, Hastings SD, Allen KD, Shaw RJ, Kanach FA, McDuffie JR, Kosinski AS, Williams Jr JW, Gierisch JM. The impact of interventions that integrate accelerometers on physical activity and weight loss: a systematic review. Ann Behav Med. 2017 Feb;51(1):79–93. doi: 10.1007/s12160-016-9829-1.
    1. Goodrich DE, Larkin AR, Lowery JC, Holleman RG, Richardson CR. Adverse events among high-risk participants in a home-based walking study: a descriptive study. Int J Behav Nutr Phys Act. 2007 May 23;4:20. doi: 10.1186/1479-5868-4-20.
    1. Richardson CR, Brown BB, Foley S, Dial KS, Lowery JC. Feasibility of adding enhanced pedometer feedback to nutritional counseling for weight loss. J Med Internet Res. 2005 Nov 17;7(5):e56. doi: 10.2196/jmir.7.5.e56.
    1. Vibrent Health. 2018. [2019-01-10]. Vibrent
    1. Holtz B, Krein SK, Bentley DR, Hughes ME, Giardino ND, Richardson CR. Comparison of veteran experiences of low-cost, home-based diet and exercise interventions. J Rehabil Res Dev. 2014;51(1):149–60. doi: 10.1682/JRRD.2013.04.0088.
    1. Amico KR, Harman JJ, O'Grady MA. Attrition and related trends in scientific rigor: a score card for ART adherence intervention research and recommendations for future directions. Curr HIV/AIDS Rep. 2008 Nov;5(4):172–85.
    1. Fisher JD, Amico KR, Fisher WA, Harman JJ. The information-motivation-behavioral skills model of antiretroviral adherence and its applications. Curr HIV/AIDS Rep. 2008 Nov;5(4):193–203.
    1. Fisher JD, Fisher WA. Changing AIDS-risk behavior. Psychol Bull. 1992 May;111(3):455–74.
    1. Steinhardt MA, Dishman RK. Reliability and validity of expected outcomes and barriers for habitual physical activity. J Occup Med. 1989 Jun;31(6):536–46.
    1. Ashford S, Edmunds J, French DP. What is the best way to change self-efficacy to promote lifestyle and recreational physical activity? A systematic review with meta-analysis. Br J Health Psychol. 2010 May;15(Pt 2):265–88. doi: 10.1348/135910709X461752.
    1. DuVall C, Dinger MK, Taylor EL, Bemben D. Minimal-contact physical activity interventions in women: a pilot study. Am J Health Behav. 2004;28(3):280–6.
    1. Kelly S, Melnyk BM, Belyea M. Predicting physical activity and fruit and vegetable intake in adolescents: a test of the information, motivation, behavioral skills model. Res Nurs Health. 2012 Apr;35(2):146–63. doi: 10.1002/nur.21462.
    1. Pearson ES. Goal setting as a health behavior change strategy in overweight and obese adults: a systematic literature review examining intervention components. Patient Educ Couns. 2012 Apr;87(1):32–42. doi: 10.1016/j.pec.2011.07.018.
    1. Rhodes RE, Dickau L. Moderators of the intention-behaviour relationship in the physical activity domain: a systematic review. Br J Sports Med. 2013 Mar;47(4):215–25. doi: 10.1136/bjsports-2011-090411.
    1. Williams SL, French DP. What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour--and are they the same? Health Educ Res. 2011 Apr;26(2):308–22. doi: 10.1093/her/cyr005.
    1. Vohs KD, Baumeister RF, editors. Handbook Of Self-Regulation: Research, Theory, and Applications. New York, NY: Guilford Press; 2004. Self-regulation of action and affect; pp. 3–24.
    1. Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, Eccles MP, Cane J, Wood CE. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013 Aug;46(1):81–95. doi: 10.1007/s12160-013-9486-6.
    1. Henriksen A, Haugen Mikalsen M, Woldaregay AZ, Muzny M, Hartvigsen G, Hopstock LA, Grimsgaard S. Using fitness trackers and smartwatches to measure physical activity in research: analysis of consumer wrist-worn wearables. J Med Internet Res. 2018 Mar 22;20(3):e110. doi: 10.2196/jmir.9157.
    1. Dooley EE, Golaszewski NM, Bartholomew JB. Estimating accuracy at exercise intensities: a comparative study of self-monitoring heart rate and physical activity wearable devices. JMIR Mhealth Uhealth. 2017 Mar 16;5(3):e34. doi: 10.2196/mhealth.7043.
    1. Stahl SE, An HS, Dinkel DM, Noble JM, Lee JM. How accurate are the wrist-based heart rate monitors during walking and running activities? Are they accurate enough? BMJ Open Sport Exerc Med. 2016 Apr 25;2(1):e000106. doi: 10.1136/bmjsem-2015-000106.
    1. Fitbit, Inc. 2018. [2019-01-11]. What are active minutes? .
    1. Sallis Jf, Hovell Mf. Determinants of exercise behavior. Exerc Sport Sci Rev. 1990;18:307–30.
    1. Sallis JF, Hovell MF, Hofstetter CR. Predictors of adoption and maintenance of vigorous physical activity in men and women. Prev Med. 1992 Mar;21(2):237–51.
    1. Borm GF, Fransen J, Lemmens WA. A simple sample size formula for analysis of covariance in randomized clinical trials. J Clin Epidemiol. 2007 Dec;60(12):1234–8. doi: 10.1016/j.jclinepi.2007.02.006.
    1. Shuger SL, Barry VW, Sui X, McClain A, Hand GA, Wilcox S, Meriwether RA, Hardin JW, Blair SN. Electronic feedback in a diet- and physical activity-based lifestyle intervention for weight loss: a randomized controlled trial. Int J Behav Nutr Phys Act. 2011 May 18;8:41. doi: 10.1186/1479-5868-8-41.
    1. Williams GC, Grow VM, Freedman ZR, Ryan RM, Deci EL. Motivational predictors of weight loss and weight-loss maintenance. J Pers Soc Psychol. 1996 Jan;70(1):115–26.
    1. Cohen S, Mermelstein R, Kamarck T, Hoberman HM. Measuring the functional components of social support. In: Sarason IG, Sarason BR, editors. Social Support: Theory, Research and Applications. Dordrecht: Springer; 1985. pp. 73–94.
    1. Coleman KJ, Ngor E, Reynolds K, Quinn VP, Koebnick C, Young DR, Sternfeld B, Sallis RE. Initial validation of an exercise "vital sign" in electronic medical records. Med Sci Sports Exerc. 2012 Nov;44(11):2071–6. doi: 10.1249/MSS.0b013e3182630ec1.
    1. Villejo RE, Humphrey LL, Kirschenbaum DS. Affect and self-regulation in binge eaters: effects of activating family images. Int J Eat Disord. 1997 Apr;21(3):237–49.
    1. Ajzen I. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior 1. J Appl Soc Psychol. 2002;32(4):665–83. doi: 10.1111/j.1559-1816.2002.tb00236.x.
    1. Sallis JF, Grossman RM, Pinski RB, Patterson TL, Nader PR. The development of scales to measure social support for diet and exercise behaviors. Prev Med. 1987 Nov;16(6):825–36.
    1. Paxton AE, Strycker LA, Toobert DJ, Ammerman AS, Glasgow RE. Starting the conversation performance of a brief dietary assessment and intervention tool for health professionals. Am J Prev Med. 2011 Jan;40(1):67–71. doi: 10.1016/j.amepre.2010.10.009.
    1. Macera CA, Ham SA, Yore MM, Jones DA, Ainsworth BE, Kimsey CD, Kohl HW. Prevalence of physical activity in the United States: behavioral risk factor surveillance system, 2001. Prev Chronic Dis. 2005 Apr;2(2):A17.
    1. Kinsinger LS, Jones KR, Kahwati L, Harvey R, Burdick M, Zele V, Yevich SJ. Design and dissemination of the MOVE! Weight-management program for veterans. Prev Chronic Dis. 2009 Jul;6(3):A98.
    1. Ware Jr J, Kosinski M, Keller SD. A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996 Mar;34(3):220–33.
    1. Kroenke K, Strine TW, Spitzer RL, Williams JB, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009 Apr;114(1-3):163–73. doi: 10.1016/j.jad.2008.06.026.
    1. Centers for Disease Control and Prevention. CDC; 2013. [2019-01-11]. BRFSS Questionnaires .
    1. Goulet JL, Brandt C, Crystal S, Fiellin DA, Gibert C, Gordon AJ, Kerns RD, Maisto S, Justice AC. Agreement between electronic medical record-based and self-administered pain numeric rating scale: clinical and research implications. Med Care. 2013 Mar;51(3):245–50. doi: 10.1097/MLR.0b013e318277f1ad.
    1. Keller S, Bann CM, Dodd SL, Schein J, Mendoza TR, Cleeland CS. Validity of the brief pain inventory for use in documenting the outcomes of patients with noncancer pain. Clin J Pain. 2004;20(5):309–18.
    1. Pickard AS, De Leon MC, Kohlmann T, Cella D, Rosenbloom S. Psychometric comparison of the standard EQ-5D to a 5 level version in cancer patients. Med Care. 2007 Mar;45(3):259–63. doi: 10.1097/01.mlr.0000254515.63841.81.
    1. Stewart AL. In: Measuring functioning and well-being: the medical outcomes study approach. Ware Jr JE, editor. Measuring functioning and well-being: the medical outcomes study approach: Duke University Press; 1992. May 27, p. 456.
    1. Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Alcohol Use Disorders Identification Test. Arch Intern Med. 1998 Sep 14;158(16):1789–95.
    1. Nelson DH, Holtzman D, Bolen J, Stanwyck CA, Mack KA. Reliability and validity of measures from the Behavioral Risk Factor Surveillance System (BRFSS) Soz Praventivmed. 2001;46(Suppl 1):S3–42.
    1. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989 Sep;13(3):319–40. doi: 10.2307/249008.
    1. Zulman DM, Piette JD, Jenchura EC, Asch SM, Rosland AM. Facilitating out-of-home caregiving through health information technology: survey of informal caregivers' current practices, interests, and perceived barriers. J Med Internet Res. 2013 Jul 10;15(7):e123. doi: 10.2196/jmir.2472.
    1. Centers for Disease Control and Prevention. 2018. [2019-01-11]. Barriers to being active quiz what keeps you from being more active? .
    1. Centers for Disease Control. National Center for Chronic Disease Prevention and Health Promotion. Division of Nutrition and Physical Activity . Promoting Physical Activity: A Guide for Community Action. Vermont: Human Kinetics; 2018. p. 408.
    1. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001 Sep;16(9):606–13.
    1. Hibbard JH, Mahoney ER, Stockard J, Tusler M. Development and testing of a short form of the patient activation measure. Health Serv Res. 2005 Dec;40(6 Pt 1):1918–30. doi: 10.1111/j.1475-6773.2005.00438.x.
    1. Hibbard JH, Mahoney ER, Stock R, Tusler M. Do increases in patient activation result in improved self-management behaviors? Health Serv Res. 2007 Aug;42(4):1443–63. doi: 10.1111/j.1475-6773.2006.00669.x.
    1. Geneen LJ, Moore RA, Clarke C, Martin D, Colvin LA, Smith BH. Physical activity and exercise for chronic pain in adults: an overview of Cochrane Reviews. Cochrane Database Syst Rev. 2017 Dec 24;4:CD011279. doi: 10.1002/14651858.CD011279.pub3.
    1. Agarwal S, LeFevre AE, Lee J, L'Engle K, Mehl G, Sinha C, Labrique A, WHO mHealth Technical Evidence Review Group Guidelines for reporting of health interventions using mobile phones: mobile health (mHealth) evidence reporting and assessment (mERA) checklist. Br Med J. 2016 Mar 17;352:i1174. doi: 10.1136/bmj.i1174.
    1. Cowan LT, Van Wagenen SA, Brown BA, Hedin RJ, Seino-Stephan Y, Hall PC, West JH. Apps of steel: are exercise apps providing consumers with realistic expectations?: a content analysis of exercise apps for presence of behavior change theory. Health Educ Behav. 2013 Apr;40(2):133–9. doi: 10.1177/1090198112452126.
    1. 2016 American Community Survey Public Use Microdata Sample. 2016. [2019-01-11]. Key Statistics by Veteran Status and Period of Service .
    1. Pew Research Center. 2018. Feb 5, [2019-01-08]. Mobile Fact Sheet
    1. Littman AJ, Damschroder LJ, Verchinina L, Lai Z, Kim HM, Hoerster KD, Klingaman EA, Goldberg RW, Owen RR, Goodrich DE. National evaluation of obesity screening and treatment among veterans with and without mental health disorders. Gen Hosp Psychiatry. 2015;37(1):7–13. doi: 10.1016/j.genhosppsych.2014.11.005.

Source: PubMed

3
Subscribe