Going digital: a narrative overview of the effects, quality and utility of mobile apps in chronic disease self-management
Ian A. Scott A B H , Paul Scuffham C , Deepali Gupta A D , Tanya M. Harch E , John Borchi E and Brent Richards F G
+ Author Affiliations
- Author Affiliations
A Princess Alexandra Hospital, 199 Ipswich Road, Woolloongabba, Brisbane 4102, Australia. Email: deepali.gupta@health.qld.gov.au
B School of Clinical Medicine, University of Queensland, 37 Trent Street, Woolloongabba, Brisbane 4102, Australia.
C Menzies Health Institute Queensland, Griffith University (Nathan campus), 170 Kessels Road, Nathan, Brisbane 4111, Australia. Email: p.scuffham@griffith.edu.au
D Queen Elizabeth II Hospital, Troughton Rd and Kessels Rd, Coopers Plains, Brisbane, 4108, Australia.
E eHealth Queensland, 2/315 Brunswick St, Fortitude Valley, Brisbane 4006, Australia. Email: tanya.harch@health.qld.gov.au; john.borchi@health.qld.gov.au
F Gold Coast University Hospital, 1 Hospital Boulevard, Southport 4215, Australia. Email: brent.richards@health.qld.gov.au
G Griffith University (Gold Coast campus), Parklands Drive, Southport 4215, Australia.
H Corresponding author. Email: ian.scott@health.qld.gov.au
Australian Health Review 44(1) 62-82 https://doi.org/10.1071/AH18064
Submitted: 6 April 2018 Accepted: 4 September 2018 Published: 13 November 2018
Journal Compilation © AHHA 2020 Open Access CC BY-NC-ND
Abstract
Objective Smartphone health applications (apps) are being increasingly used to assist patients in chronic disease self-management. The effects of such apps on patient outcomes are uncertain, as are design features that maximise usability and efficacy, and the best methods for evaluating app quality and utility.
Methods In assessing efficacy, PubMed, Cochrane Library and EMBASE were searched for systematic reviews (and single studies if no systematic review was available) published between January 2007 and January 2018 using search terms (and synonyms) of ‘smartphone’ and ‘mobile applications’, and terms for each of 11 chronic diseases: asthma, chronic obstructive lung disease (COPD), diabetes, chronic pain, serious mental health disorders, alcohol and substance addiction, heart failure, ischaemic heart disease, cancer, cognitive impairment, chronic kidney disease (CKD). With regard to design features and evaluation methods, additional reviews were sought using search terms ‘design’, ‘quality,’ ‘usability’, ‘functionality,’ ‘adherence’, ‘evaluation’ and related synonyms.
Results Of 13 reviews and six single studies assessing efficacy, consistent evidence of benefit was seen only with apps for diabetes, as measured by decreased glycosylated haemoglobin levels (HbA1c). Some, but not all, studies showed benefit in asthma, low back pain, alcohol addiction, heart failure, ischaemic heart disease and cancer. There was no evidence of benefit in COPD, cognitive impairment or CKD. In all studies, benefits were clinically marginal and none related to morbid events or hospitalisation. Twelve design features were identified as enhancing usability. An evaluation framework comprising 32 items was formulated.
Conclusion Evidence of clinical benefit of most available apps is very limited. Design features that enhance usability and maximise efficacy were identified. A provisional ‘first-pass’ evaluation framework is proposed that can help decide which apps should be endorsed by government agencies following more detailed technical assessments and which could then be recommended with confidence by clinicians to their patients.
What is known about the topic? Smartphone health apps have attracted considerable interest from patients and health managers as a means of promoting more effective self-management of chronic diseases, which leads to better health outcomes. However, most commercially available apps have never been evaluated for benefits or harms in clinical trials, and there are currently no agreed quality criteria, standards or regulations to ensure health apps are user-friendly, accurate in content, evidence based or efficacious.
What does this paper add? This paper presents a comprehensive review of evidence relating to the efficacy, usability and evaluation of apps for 11 common diseases aimed at assisting patients in self-management. Consistent evidence of benefit was only seen for diabetes apps; there was absent or conflicting evidence of benefit for apps for the remaining 10 diseases. Benefits that were detected were of marginal clinical importance, with no reporting of hard clinical end-points, such as mortality or hospitalisations. Only a minority of studies explicitly reported using behaviour change theories to underpin the app intervention. Many apps lacked design features that the literature identified as enhancing usability and potential to confer benefit. Despite a plethora of published evaluation tools, there is no universal framework that covers all relevant clinical and technical attributes. An inclusive list of evaluation criteria is proposed that may overcome this shortcoming.
What are the implications for practitioners? The number of smartphone apps will continue to grow, as will the appetite for patients and clinicians to use them in chronic disease self-management. However, the evidence to date of clinical benefit of most apps already available is very limited. Design features that enhance usability and clinical efficacy need to be considered. In making decisions about which apps should be endorsed by government agencies and recommended with confidence by clinicians to their patients, a comprehensive but workable evaluation framework needs to be used by bodies assuming the roles of setting and applying standards.
References
[1] Mackay M. Australian mobile phone lifestyle index. 2014. Available at: https://www.dtcollective.org.au/LiteratureRetrieve.aspx?ID=180479 [verified 22 September 2018].[2] Jahns RG. The 8 drivers and barriers that will shape the mHealth app market in the next 5 years. r2g Mobile Health Economics. 2014. Available at: https://research2guidance.com/8-drivers-and-barriers-which-will-shape-mhealth-market-in-the-next-5-years/ [verified 22 September 2018].
[3] Aitken M, Lyle J. Patient adoption of mHealth: use, evidence and remaining barriers to mainstream acceptance. Parsippany, NJ: IMS Institute for Healthcare Informatics; 2015.
[4] Bhuyan SS, Lu N, Chandak A, Kim H, Wyant D, Bhatt J, Kedia S, Chang CF. Use of mobile health applications for health seeking behaviour among US adults. J Med Syst 2016; 40 153
| Use of mobile health applications for health seeking behaviour among US adults.Crossref | GoogleScholarGoogle Scholar |
[5] Powell AC, Landman AB, Bates DW. In search of a few good apps. JAMA 2014; 311 1851–2.
| In search of a few good apps.Crossref | GoogleScholarGoogle Scholar |
[6] Boulos MN, Brewer AC, Karimkhani C, Buller DB, Dellavalle RP. Mobile medical and health apps: state of the art, concerns, regulatory control and certification. Online J Public Health Inform 2014; 5 229
[7] Risk A, Dzenowagis J. Review of Internet health information quality initiatives. J Med Internet Res 2001; 3 E28
| Review of Internet health information quality initiatives.Crossref | GoogleScholarGoogle Scholar |
[8] Gagliardi A, Jadad AR. Examination of instruments used to rate quality of health information on the internet: chronicle of a voyage with an unclear destination. BMJ 2002; 324 569–73.
| Examination of instruments used to rate quality of health information on the internet: chronicle of a voyage with an unclear destination.Crossref | GoogleScholarGoogle Scholar |
[9] Subhi Y, Bube SH, Bojsen SR, Thomsen ASS, Konge L. Expert involvement and adherence to medical evidence in medical mobile phone apps: a systematic review. JMIR Mhealth Uhealth 2015; 3 e79
| Expert involvement and adherence to medical evidence in medical mobile phone apps: a systematic review.Crossref | GoogleScholarGoogle Scholar |
[10] Goyal S, Cafazzo JA. Mobile phone apps for diabetes management: current evidence and future developments. QJM 2013; 106 1067–9.
| Mobile phone apps for diabetes management: current evidence and future developments.Crossref | GoogleScholarGoogle Scholar |
[11] Wallace LS, Dhingra LK. A systematic review of smartphone applications for chronic pain available for download in the United States. J Opioid Manag 2014; 10 63–8.
| A systematic review of smartphone applications for chronic pain available for download in the United States.Crossref | GoogleScholarGoogle Scholar |
[12] Reynoldson C, Stones C, Allsop M, Gardner P, Bennett MI, José Closs S, Jones R, Knapp P. Assessing the quality and usability of smartphone apps for pain self-management. Pain Med 2014; 15 898–909.
| Assessing the quality and usability of smartphone apps for pain self-management.Crossref | GoogleScholarGoogle Scholar |
[13] Martínez-Pérez B, De La Torre-Diez I, Lopez-Coronado M. Privacy and security in mobile phone apps: a review and recommendations. J Med Syst 2015; 39 181
| Privacy and security in mobile phone apps: a review and recommendations.Crossref | GoogleScholarGoogle Scholar |
[14] Pustozerov E, von Jan U, Albrecht UV. Evaluation of mHealth applications security based on application permissions. Stud Health Technol Inform 2016; 226 241–4.
[15] Lewis TL, Wyatt JC. mHealth and mobile medical apps: a framework to assess risk and promote safer use. J Med Internet Res 2014; 16 e210
| mHealth and mobile medical apps: a framework to assess risk and promote safer use.Crossref | GoogleScholarGoogle Scholar |
[16] U.S. Department of Health and Human Services, Food and Drug Administration. Mobile medical applications: guidance for industry and Food and Drug Administration staff. 2015. Available at: http://www.fda.gov/downloads/MedicalDevices/./UCM263366.pdf [verified 13 January 2018]
[17] Australian Government Department of Health. Therapeutic goods administration. n. Regulation of medical software and mobile medical ‘apps’. 2013. Available at: https://www.tga.gov.au/regulation-medical-software-and-mobile-medical-apps [verified 13 January 2018]
[18] U.S. Department of Health and Human Services, Food and Drug Administration. Software as a medical device (SAMD): Clinical evaluation. Guidance for industry and Food and Drug Administration Staff. 2017. Available at: https://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/UCM524904.pdf [verified 21 September 2018].
[19] Hau J. Update on software as a medical device (SaMD). The TGA and IMDRF perspectives. 2017. Available at: https://www.tga.gov.au/sites/default/files/update-on-software-as-a-medical-device-samd.pdf [verified 20 January 2018].
[20] Australian Digital Health Agency (ADHA). Safe, seamless and secure: evolving health and care to meet the needs of modern Australia. Australia’s national digital health strategy. Canberra: ADHA; 2017.
[21] Moher D, Liberati A, Tetzlaff J, Altman DG, Group TP. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009; 6 e1000097
| Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.Crossref | GoogleScholarGoogle Scholar |
[22] Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, Abraham J, Adair T, Aggarwal R, Ahn SY, Alvarado M, Anderson HR, Anderson LM, Andrews KG, Atkinson C, Baddour LM, Barker-Collo S, Bartels DH, Bell ML, Benjamin EJ, Bennett D, Bhalla K, Bikbov B, Bin Abdulhak A, Birbeck G, Blyth F, Bolliger I, Boufous S, Bucello C, Burch M, Burney P, Carapetis J, Chen H, Chou D, Chugh SS, Coffeng LE, Colan SD, Colquhoun S, Colson KE, Condon J, Connor MD, Cooper LT, Corriere M, Cortinovis M, de Vaccaro KC, Couser W, Cowie BC, Criqui MH, Cross M, Dabhadkar KC, Dahodwala N, De Leo D, Degenhardt L, Delossantos A, Denenberg J, Des Jarlais DC, Dharmaratne SD, Dorsey ER, Driscoll T, Duber H, Ebel B, Erwin PJ, Espindola P, Ezzati M, Feigin V, Flaxman AD, Forouzanfar MH, Fowkes FG, Franklin R, Fransen M, Freeman MK, Gabriel SE, Gakidou E, Gaspari F, Gillum RF, Gonzalez-Medina D, Halasa YA, Haring D, Harrison JE, Havmoeller R, Hay RJ, Hoen B, Hotez PJ, Hoy D, Jacobsen KH, James SL, Jasrasaria R, Jayaraman S, Johns N, Karthikeyan G, Kassebaum N, Keren A, Khoo JP, Knowlton LM, Kobusingye O, Koranteng A, Krishnamurthi R, Lipnick M, Lipshultz SE, Ohno SL, Mabweijano J, MacIntyre MF, Mallinger L, March L, Marks GB, Marks R, Matsumori A, Matzopoulos R, Mayosi BM, McAnulty JH, McDermott MM, McGrath J, Mensah GA, Merriman TR, Michaud C, Miller M, Miller TR, Mock C, Mocumbi AO, Mokdad AA, Moran A, Mulholland K, Nair MN, Naldi L, Narayan KM, Nasseri K, Norman P, O’Donnell M, Omer SB, Ortblad K, Osborne R, Ozgediz D, Pahari B, Pandian JD, Rivero AP, Padilla RP, Perez-Ruiz F, Perico N, Phillips D, Pierce K, Pope CA, Porrini E, Pourmalek F, Raju M, Ranganathan D, Rehm JT, Rein DB, Remuzzi G, Rivara FP, Roberts T, De León FR, Rosenfeld LC, Rushton L, Sacco RL, Salomon JA, Sampson U, Sanman E, Schwebel DC, Segui-Gomez M, Shepard DS, Singh D, Singleton J, Sliwa K, Smith E, Steer A, Taylor JA, Thomas B, Tleyjeh IM, Towbin JA, Truelsen T, Undurraga EA, Venketasubramanian N, Vijayakumar L, Vos T, Wagner GR, Wang M, Wang W, Watt K, Weinstock MA, Weintraub R, Wilkinson JD, Woolf AD, Wulf S, Yeh PH, Yip P, Zabetian A, Zheng ZJ, Lopez AD, Murray CJ, AlMazroa MA, Memish ZA. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012; 380 2095–128. [published erratum appears in Lancet 2013; 381:628]
| Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010.Crossref | GoogleScholarGoogle Scholar |
[23] Abraham C, Mitchie S. A taxonomy of behaviour change techniques used in interventions. Health Psychol 2008; 27 379–87.
| A taxonomy of behaviour change techniques used in interventions.Crossref | GoogleScholarGoogle Scholar |
[24] Michie S, Ashford S, Sniehotta FF, Dombrowski SU, Bishop A, French DP. A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy. Psychol Health 2011; 26 1479–98.
| A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy.Crossref | GoogleScholarGoogle Scholar |
[25] Hui CY, Walton R, McKinstry B, Jackson T, Parker R, Pinnock H. The use of mobile applications to support self-management for people with asthma: a systematic review of controlled studies to identify features associated with clinical effectiveness and adherence. J Am Med Inform Assoc 2017; 24 619–32.
[26] Cingi C, Yorgancioglu A, Cingi CC, Oguzulgen K, Muluk NB, Ulusoy S, Orhon N, Yumru C, Gokdag D, Karakaya G, Çelebi S, Çobanoglu HB, Unlu H, Aksoy MA. The ‘physician on call patient engagement trial’ (POPET): measuring the impact of a mobile patient engagement application on health outcomes and quality of life in allergic rhinitis and asthma patients. Int Forum Allergy Rhinol 2015; 5 487–97.
| The ‘physician on call patient engagement trial’ (POPET): measuring the impact of a mobile patient engagement application on health outcomes and quality of life in allergic rhinitis and asthma patients.Crossref | GoogleScholarGoogle Scholar |
[27] Ryan D, Price D, Musgrave SD, Malhotra S, Lee AJ, Ayansina D, Sheikh A, Tarassenko L, Pagliari C, Pinnock H. Clinical and cost effectiveness of mobile phone supported self monitoring of asthma: multicentre randomised controlled trial. BMJ 2012; 344 e1756
| Clinical and cost effectiveness of mobile phone supported self monitoring of asthma: multicentre randomised controlled trial.Crossref | GoogleScholarGoogle Scholar |
[28] Liu WT, Huang CD, Wang CH, Lee KY, Lin SM, Kuo HP. A mobile telephone-based interactive self-care system improves asthma control. Eur Respir J 2011; 37 310–7.
| A mobile telephone-based interactive self-care system improves asthma control.Crossref | GoogleScholarGoogle Scholar |
[29] McCabe C, McCann M, Brady AM. Computer and mobile technology interventions for self-management in chronic obstructive pulmonary disease. Cochrane Database Syst Rev 2017; 5 CD011425
[30] Tabak M, Vollenbroek-Hutten MM, van der Valk PD, van der Palen J, Hermens HJ. A telerehabilitation intervention for patients with chronic obstructive pulmonary disease: a randomized controlled pilot trial. Clin Rehabil 2014; 28 582–91.
| A telerehabilitation intervention for patients with chronic obstructive pulmonary disease: a randomized controlled pilot trial.Crossref | GoogleScholarGoogle Scholar |
[31] Wu IXY, Kee JCY, Threapleton DE, Ma R, Lam VCK, Lee EKP, Wong SYS, Chung VCH. Effectiveness of smartphone technologies on glycaemic control in patients with type 2 diabetes: systematic review with meta-analysis of 17 trials. Obes Rev 2018; 19 825–38.
| Effectiveness of smartphone technologies on glycaemic control in patients with type 2 diabetes: systematic review with meta-analysis of 17 trials.Crossref | GoogleScholarGoogle Scholar |
[32] Wu Y, Yao X, Vespasiani G, Nicolucci A, Dong Y, Kwong J, Li L, Sun X, Tian H, Li S. Mobile app-based interventions to support diabetes self-management: a systematic review of randomized controlled trials to identify functions associated with glycemic efficacy. JMIR Mhealth Uhealth 2017; 5 e35
| Mobile app-based interventions to support diabetes self-management: a systematic review of randomized controlled trials to identify functions associated with glycemic efficacy.Crossref | GoogleScholarGoogle Scholar |
[33] Hou C, Carter B, Hewitt J, Francisa T, Mayor S. Do mobile phone applications improve glycemic control (HbA1c) in the self-management of diabetes? A systematic review, meta-analysis, and GRADE of 14 randomized trials. Diabetes Care 2016; 39 2089–95.
| Do mobile phone applications improve glycemic control (HbA1c) in the self-management of diabetes? A systematic review, meta-analysis, and GRADE of 14 randomized trials.Crossref | GoogleScholarGoogle Scholar |
[34] Cui M, Wu X, Mao J, Wang X, Nie M. T2DM self-management via smartphone applications: a systematic review and meta-analysis. PLoS One 2016; 11 e0166718
| T2DM self-management via smartphone applications: a systematic review and meta-analysis.Crossref | GoogleScholarGoogle Scholar |
[35] Fu H, McMahon SK, Gross CR, Adam TJ, Wyman JF. Usability and clinical efficacy of diabetes mobile applications for adults with type 2 diabetes: a systematic review. Diabetes Res Clin Pract 2017; 131 70–81.
| Usability and clinical efficacy of diabetes mobile applications for adults with type 2 diabetes: a systematic review.Crossref | GoogleScholarGoogle Scholar |
[36] Holmen H, Wahl AK, Cvancarova Småstuen M, Ribu L. Tailored communication within mobile apps for diabetes self-management: a systematic review. J Med Internet Res 2017; 19 e227
| Tailored communication within mobile apps for diabetes self-management: a systematic review.Crossref | GoogleScholarGoogle Scholar |
[37] Lalloo C, Jibb LA, Rivera J, Agarwal A, Stinson JN. ‘There’s a pain app for that’: review of patient-targeted smartphone applications for pain management. Clin J Pain 2015; 31 557–63.
| ‘There’s a pain app for that’: review of patient-targeted smartphone applications for pain management.Crossref | GoogleScholarGoogle Scholar |
[38] Machado GC, Pinheiro MB, Lee H, Ahmed OH, Hendrick P, Williams C, Kamper SJ. Smartphone apps for the self-management of low back pain: a systematic review. Best Pract Res Clin Rheumatol 2016; 30 1098–109.
| Smartphone apps for the self-management of low back pain: a systematic review.Crossref | GoogleScholarGoogle Scholar |
[39] Nicholl BI, Sandal LF, Stochkendahl MJ, McCallum M, Suresh N, Vasseljen O. Digital support interventions for the self-management of low back pain: a systematic review. J Med Internet Res 2017; 19 e179
| Digital support interventions for the self-management of low back pain: a systematic review.Crossref | GoogleScholarGoogle Scholar |
[40] Irvine AB, Russell H, Manocchia M, Mino DE, Cox GT, Morgan R, Gau JM, Birney AJ, Ary DV. Mobile-web app to self-manage low back pain: randomized controlled trial. J Med Internet Res 2015; 17 e1
| Mobile-web app to self-manage low back pain: randomized controlled trial.Crossref | GoogleScholarGoogle Scholar |
[41] Kristjánsdóttir ÓB, Fors EA, Eide E, Finset A, Stensrud TL, van Dulmen S, Wigers SH, Eide H. A smartphone-based intervention with diaries and therapist feedback to reduce catastrophizing and increase functioning in women with chronic widespread pain. Part 2: 11-month follow-up results of a randomized trial. J Med Internet Res 2013; 15 e72
| A smartphone-based intervention with diaries and therapist feedback to reduce catastrophizing and increase functioning in women with chronic widespread pain. Part 2: 11-month follow-up results of a randomized trial.Crossref | GoogleScholarGoogle Scholar |
[42] Batra S, Baker RA, Wang T, Forma F, DiBiasi F, Peters-Strickland T. Digital health technology for use in patients with serious mental illness: a systematic review of the literature. Med Devices (Auckl) 2017; 10 237–51.
| Digital health technology for use in patients with serious mental illness: a systematic review of the literature.Crossref | GoogleScholarGoogle Scholar |
[43] Ly KH, Trüschel A, Jarl L, Magnusson S, Windahl T, Johansson R, Carlbring P, Andersson G. Behavioural activation versus mindfulness-based guided self-help treatment administered through a smartphone application: a randomised controlled trial. BMJ Open 2014; 4 e003440
| Behavioural activation versus mindfulness-based guided self-help treatment administered through a smartphone application: a randomised controlled trial.Crossref | GoogleScholarGoogle Scholar |
[44] Watts S, Mackenzie A, Thomas C, Griskaitis A, Mewton L, Williams A, Andrews G. CBT for depression: a pilot RCT comparing mobile phone vs. computer. BMC Psychiatry 2013; 13 49
| CBT for depression: a pilot RCT comparing mobile phone vs. computer.Crossref | GoogleScholarGoogle Scholar |
[45] Forchuk C, Reiss JP, O’Regan T, Ethridge P, Donelle L, Rudnick A. Client perceptions of the Mental Health Engagement Network: a qualitative analysis of an electronic personal health record. BMC Psychiatry 2015; 15 250
| Client perceptions of the Mental Health Engagement Network: a qualitative analysis of an electronic personal health record.Crossref | GoogleScholarGoogle Scholar |
[46] Faurholt-Jepsen M, Ritz C, Frost M, Mikkelsen RL, Christensen EM, Bardram J, Vinberg M, Kessing LV. Mood instability in bipolar disorder type I versus type II: continuous daily electronic self-monitoring of illness activity using smartphones. J Affect Disord 2015; 186 342–9.
| Mood instability in bipolar disorder type I versus type II: continuous daily electronic self-monitoring of illness activity using smartphones.Crossref | GoogleScholarGoogle Scholar |
[47] Ben-Zeev D, Brenner CJ, Begale M, Duffecy J, Mohr DC, Mueser KT. Feasibility, acceptability, and preliminary efficacy of a smartphone intervention for schizophrenia. Schizophr Bull 2014; 40 1244–53.
| Feasibility, acceptability, and preliminary efficacy of a smartphone intervention for schizophrenia.Crossref | GoogleScholarGoogle Scholar |
[48] Grist R, Porter J, Stallard P. Mental health mobile apps for preadolescents and adolescents: a systematic review. J Med Internet Res 2017; 19 e176
| Mental health mobile apps for preadolescents and adolescents: a systematic review.Crossref | GoogleScholarGoogle Scholar |
[49] Reid SC, Kauer SD, Hearps SJ, Crooke AH, Khor AS, Sanci LA, Patton GC. A mobile phone application for the assessment and management of youth mental health problems in primary care: health service outcomes from a randomised controlled trial of mobile type. BMC Fam Pract 2013; 14 84
| A mobile phone application for the assessment and management of youth mental health problems in primary care: health service outcomes from a randomised controlled trial of mobile type.Crossref | GoogleScholarGoogle Scholar |
[50] Veldhuis J. Media models matter in context: negotiated media effects of idealized body images. PhD Thesis, Vrije Universiteit Amsterdam Faculteit der Social Wetenschappen; 2014. Available at: https://research.vu.nl/en/publications/media-models-matter-in-context-negotiated-media-effects-of-ideali [verified 21 September 2018].
[51] Meredith SE, Alessi SM, Petry NM. Smartphone applications to reduce alcohol consumption and help patients with alcohol use disorder: a state-of-the-art review. Adv Health Care Technol 2015; 1 47–54.
[52] Gajecki M, Berman AH, Sinadinovic K, Rosendahl I, Andersson C. Mobile phone brief intervention applications for risky alcohol use among university students: a randomized controlled study. Addict Sci Clin Pract 2014; 9 11
| Mobile phone brief intervention applications for risky alcohol use among university students: a randomized controlled study.Crossref | GoogleScholarGoogle Scholar |
[53] Gustafson DH, McTavish FM, Chih MY, Atwood AK, Johnson RA, Boyle MG. A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA Psychiatry 2014; 71 566–72.
| A smartphone application to support recovery from alcoholism: a randomized clinical trial.Crossref | GoogleScholarGoogle Scholar |
[54] Witkiewitz K, Desai SA, Bowen S, Leigh BC, Kirouac M, Larimer ME. Development and evaluation of a mobile intervention for heavy drinking and smoking among college students. Psychol Addict Behav 2014; 28 639–50.
| Development and evaluation of a mobile intervention for heavy drinking and smoking among college students.Crossref | GoogleScholarGoogle Scholar |
[55] Hägglund E, Lyngå P, Frie F, Ullman B, Persson H, Melin M, Hagerman I. Patient-centred home-based management of heart failure. Findings from a randomised clinical trial evaluating a tablet computer for self-care, quality of life and effects on knowledge. Scand Cardiovasc J 2015; 49 193–9.
| Patient-centred home-based management of heart failure. Findings from a randomised clinical trial evaluating a tablet computer for self-care, quality of life and effects on knowledge.Crossref | GoogleScholarGoogle Scholar |
[56] Vuorinen AL, Leppänen J, Kaijanranta H, Kulju M, Heliö T, van Gils M, Lähteenmäki J. Use of home telemonitoring to support multidisciplinary care of heart failure patients in Finland: randomized controlled trial. J Med Internet Res 2014; 16 e282
| Use of home telemonitoring to support multidisciplinary care of heart failure patients in Finland: randomized controlled trial.Crossref | GoogleScholarGoogle Scholar |
[57] Johnston N, Bodegard J, Jerström S, Åkesson J, Brorsson H, Alfredsson J. Effects of interactive patient smartphone support app on drug adherence and lifestyle changes in myocardial infarction patients: a randomized study. Am Heart J 2016; 178 85–94.
| Effects of interactive patient smartphone support app on drug adherence and lifestyle changes in myocardial infarction patients: a randomized study.Crossref | GoogleScholarGoogle Scholar |
[58] Varnfield M, Karunanithi M, Lee CK, Honeyman E, Arnold D, Ding H, Smith C, Walters DL. Smartphone-based home care model improved use of cardiac rehabilitation in postmyocardial infarction patients: results from a randomised controlled trial. Heart 2014; 100 1770–9.
| Smartphone-based home care model improved use of cardiac rehabilitation in postmyocardial infarction patients: results from a randomised controlled trial.Crossref | GoogleScholarGoogle Scholar |
[59] Blasco A, Carmona M, Fernández-Lozano I, Salvador CH, Pascual M, Sagredo PG, Somolinos R, Muñoz A, García-López F, Escudier JM, Mingo S, Toquero J, Moñivas V, González MA, Fragua JA, López-Rodríguez F, Monteagudo JL, Alonso-Pulpón L. Evaluation of a telemedicine service for the secondary prevention of coronary artery disease. J Cardiopulm Rehabil Prev 2012; 32 25–31.
| Evaluation of a telemedicine service for the secondary prevention of coronary artery disease.Crossref | GoogleScholarGoogle Scholar |
[60] Rincon E, Monteiro-Guerra F, Rivera-Romero O, Dorronzoro-Zubiete E, Sanchez-Bocanegra CL, Gabarron E. Mobile phone apps for quality of life and well-being assessment in breast and prostate cancer patients: systematic review. JMIR Mhealth Uhealth 2017; 5 e187
| Mobile phone apps for quality of life and well-being assessment in breast and prostate cancer patients: systematic review.Crossref | GoogleScholarGoogle Scholar |
[61] Sundberg K, Wengström Y, Blomberg K, Hälleberg-Nyman M, Frank C, Langius-Eklöf A. Early detection and management of symptoms using an interactive smartphone application (Interaktor) during radiotherapy for prostate cancer. Support Care Cancer 2017; 25 2195–204.
| Early detection and management of symptoms using an interactive smartphone application (Interaktor) during radiotherapy for prostate cancer.Crossref | GoogleScholarGoogle Scholar |
[62] Uhm KE, Yoo JS, Chung SH, Lee JD, Lee I, Kim JI, Lee SK, Nam SJ, Park YH, Lee JY, Hwang JH. Effects of exercise intervention in breast cancer patients: is mobile health (mHealth) with pedometer more effective than conventional program using brochure? Breast Cancer Res Treat 2017; 161 443–52.
| Effects of exercise intervention in breast cancer patients: is mobile health (mHealth) with pedometer more effective than conventional program using brochure?Crossref | GoogleScholarGoogle Scholar |
[63] McCarroll ML, Armbruster S, Pohle-Krauza RJ, Lyzen AM, Min S, Nash DW, Roulette GD, Andrews SJ, von Gruenigen VE. Feasibility of a lifestyle intervention for overweight/obese endometrial and breast cancer survivors using an interactive mobile application. Gynecol Oncol 2015; 137 508–15.
| Feasibility of a lifestyle intervention for overweight/obese endometrial and breast cancer survivors using an interactive mobile application.Crossref | GoogleScholarGoogle Scholar |
[64] Bateman DR, Srinivas B, Emmett TW, Schleyer TK, Holden RJ, Hendrie HC, Callahan CM. Categorizing health outcomes and efficacy of mHealth apps for persons with cognitive impairment: a systematic review. J Med Internet Res 2017; 19 e301
| Categorizing health outcomes and efficacy of mHealth apps for persons with cognitive impairment: a systematic review.Crossref | GoogleScholarGoogle Scholar |
[65] Chan SC, Chan CC, Derbie AY, Hui I, Tan DG, Pang MY, Lau SCL, Fong KNK. Chinese calligraphy writing for augmenting attentional control and working memory of older adults at risk of mild cognitive impairment: a randomized controlled trial. J Alzheimers Dis 2017; 58 735–46.
| Chinese calligraphy writing for augmenting attentional control and working memory of older adults at risk of mild cognitive impairment: a randomized controlled trial.Crossref | GoogleScholarGoogle Scholar |
[66] Mansbach WE, Mace RA, Clark KM. The efficacy of a computer-assisted cognitive rehabilitation program for patients with mild cognitive deficits: a pilot study. Exp Aging Res 2017; 43 94–104.
| The efficacy of a computer-assisted cognitive rehabilitation program for patients with mild cognitive deficits: a pilot study.Crossref | GoogleScholarGoogle Scholar |
[67] Han JW, Oh K, Yoo S, Kim E, Ahn K-H, Son Y-J, Kim TH, Chi YK, Kim KW. Development of the ubiquitous spaced retrieval-based memory advancement and rehabilitation training program. Psychiatry Investig 2014; 11 52–8.
| Development of the ubiquitous spaced retrieval-based memory advancement and rehabilitation training program.Crossref | GoogleScholarGoogle Scholar |
[68] Barnes DE, Yaffe K, Belfor N, Jagust WJ, DeCarli C, Reed BR, Kramer JH. Computer-based cognitive training for mild cognitive impairment: results from a pilot randomized, controlled trial. Alzheimer Dis Assoc Disord 2009; 23 205–10.
| Computer-based cognitive training for mild cognitive impairment: results from a pilot randomized, controlled trial.Crossref | GoogleScholarGoogle Scholar |
[69] Finn M, McDonald S. Computerised cognitive training for older persons with mild cognitive impairment: a pilot study using a randomised controlled trial design. Brain Impair 2011; 12 187–99.
| Computerised cognitive training for older persons with mild cognitive impairment: a pilot study using a randomised controlled trial design.Crossref | GoogleScholarGoogle Scholar |
[70] Hattink BJ, Meiland FJ, Overmars-Marx T, de Boer M, Ebben PW, van Blanken M, Verhaeghe S, Stalpers-Croeze I, Jedlitschka A, Flick SE, van den Leeuw J, Karkowski I, Dröes RM. The electronic, personalizable Rosetta system for dementia care: exploring the user-friendliness, usefulness and impact. Disabil Rehabil Assist Technol 2016; 11 61–71.
| The electronic, personalizable Rosetta system for dementia care: exploring the user-friendliness, usefulness and impact.Crossref | GoogleScholarGoogle Scholar |
[71] Van der Ploeg ES, Eppingstall B, O’Connor DW. Internet video chat (Skype) family conversations as a treatment of agitation in nursing home residents with dementia. Int Psychogeriatr 2016; 28 697–8.
| Internet video chat (Skype) family conversations as a treatment of agitation in nursing home residents with dementia.Crossref | GoogleScholarGoogle Scholar |
[72] Ong SW, Jassal SV, Miller JA, Porter EC, Cafazzo JA, Seto E. Integrating a smartphone-based self-management system into usual care of advanced CKD. Clin J Am Soc Nephrol 2016; 11 1054–62.
| Integrating a smartphone-based self-management system into usual care of advanced CKD.Crossref | GoogleScholarGoogle Scholar |
[73] Bender JL, Yue RY, To MJ, Deacken L, Jadad AR. A lot of action, but not in the right direction: systematic review and content analysis of smartphone applications for the prevention, detection, and management of cancer. J Med Internet Res 2013; 15 e287
| A lot of action, but not in the right direction: systematic review and content analysis of smartphone applications for the prevention, detection, and management of cancer.Crossref | GoogleScholarGoogle Scholar |
[74] Huguet A, Rao S, McGrath PJ, Wozney L, Wheaton M, Conrod J, Rozario S. A systematic review of cognitive behavioral therapy and behavioral activation apps for depression. PLoS One 2016; 11 e0154248
| A systematic review of cognitive behavioral therapy and behavioral activation apps for depression.Crossref | GoogleScholarGoogle Scholar |
[75] Rosser BA, Eccleston C. Smartphone applications for pain management. J Telemed Telecare 2011; 17 308–12.
| Smartphone applications for pain management.Crossref | GoogleScholarGoogle Scholar |
[76] Brzan PP, Rotman E, Pajnkihar M, Klanjsek P. Mobile applications for control and self-management of diabetes: a systematic review. J Med Syst 2016; 40 210
| Mobile applications for control and self-management of diabetes: a systematic review.Crossref | GoogleScholarGoogle Scholar |
[77] Zhao J, Freeman B, Li M. Can mobile phone apps influence people’s health behavior change? An evidence review. J Med Internet Res 2016; 18 e287
| Can mobile phone apps influence people’s health behavior change? An evidence review.Crossref | GoogleScholarGoogle Scholar |
[78] Spring B, Gotsis M, Paiva A, Spruijt-Metz D. Healthy apps: mobile devices for continuous monitoring and intervention. IEEE Pulse 2013; 4 34–40.
| Healthy apps: mobile devices for continuous monitoring and intervention.Crossref | GoogleScholarGoogle Scholar |
[79] Jake-Schoffman DE, Silfee VJ, Waring ME, Boudreaux ED, Sadasivam RS, Mullen SP, Carey JL, Hayes RB, Ding EY, Bennett GG, Pagoto SL. Methods for evaluating the content, usability, and efficacy of commercial mobile health apps. JMIR Mhealth Uhealth 2017; 5 e190
| Methods for evaluating the content, usability, and efficacy of commercial mobile health apps.Crossref | GoogleScholarGoogle Scholar |
[80] Handel MJ. mHealth (mobile health)-using apps for health and wellness. Explore (NY) 2011; 7 256–61.
| mHealth (mobile health)-using apps for health and wellness.Crossref | GoogleScholarGoogle Scholar |
[81] Khoja S, Durrani H, Scott RE, Sajwani A, Piryani U. Conceptual framework for development of comprehensive e-health evaluation tool. Telemed J E Health 2013; 19 48–53.
| Conceptual framework for development of comprehensive e-health evaluation tool.Crossref | GoogleScholarGoogle Scholar |
[82] mHIMSS App Usability Work Group. Selecting a mobile app: evaluating the usability of medical applications. Chicago, IL: Health Care Information Management Systems Society; 2012. Available at: https://www.himss.org/selecting-mobile-app-evaluating-usability-medical-applications-0 [verified 21 September 2018]
[83] Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile apps rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth 2015; 3 e27
| Mobile apps rating scale: a new tool for assessing the quality of health mobile apps.Crossref | GoogleScholarGoogle Scholar |
[84] Jin M, Kim J. Development and evaluation of an evaluation tool for healthcare smartphone applications. Telemed J E Health 2015; 21 831–7.
| Development and evaluation of an evaluation tool for healthcare smartphone applications.Crossref | GoogleScholarGoogle Scholar |
[85] Chyjek K, Farag S, Chen KT. Rating pregnancy wheel applications using the APPLICATIONS scoring system. Obstet Gynecol 2015; 125 1478–83.
| Rating pregnancy wheel applications using the APPLICATIONS scoring system.Crossref | GoogleScholarGoogle Scholar |
[86] Baumel A, Faber K, Mathur N, Kane JM, Muench F. Enlight: a comprehensive quality and therapeutic potential evaluation tool for mobile and web-based ehealth interventions. J Med Internet Res 2017; 19 e82
| Enlight: a comprehensive quality and therapeutic potential evaluation tool for mobile and web-based ehealth interventions.Crossref | GoogleScholarGoogle Scholar |
[87] McNiel P, McAthtur EC. Evaluating health mobile apps: information literacy in undergraduate and graduate nursing courses. J Nurs Educ 2016; 55 480–2.
| Evaluating health mobile apps: information literacy in undergraduate and graduate nursing courses.Crossref | GoogleScholarGoogle Scholar |
[88] Meurk C, Leung J, Hall W, Head BW, Whiteford H. Establishing and governing e-mental health care in Australia: a systematic review of challenges and a call for policy-focused research. J Med Internet Res 2016; 18 e10
| Establishing and governing e-mental health care in Australia: a systematic review of challenges and a call for policy-focused research.Crossref | GoogleScholarGoogle Scholar |
[89] Maheu MM, Pulier ML, Roy S. Finding, evaluating, and using smartphone applications. In: Koocher GP, Norcross JC, Breene BA, editors. Psychologists’ desk reference. 3rd edn. New York: Oxford University Press; 2013. pp. 704–8.
[90] Wyatt JC, Thimbleby H, Rastall P, Hoogewerf J, Wooldridge D, Williams J. What makes a good clinical app? Introducing the RCP Health Informatics Unit checklist. Clin Med (Lond) 2015; 15 519–21.
| What makes a good clinical app? Introducing the RCP Health Informatics Unit checklist.Crossref | GoogleScholarGoogle Scholar |
[91] Anderson K, Burford O, Emmerton L. App Chronic disease checklist: protocol to evaluate mobile apps for chronic disease self-management. JMIR Res Protoc 2016; 5 e204
| App Chronic disease checklist: protocol to evaluate mobile apps for chronic disease self-management.Crossref | GoogleScholarGoogle Scholar |
[92] Maheu MM, Nicolucci V, Pulier ML, Wall KM, Frye TJ, Hudlick E. The Interactive Mobile App Review Toolkit (IMART): a clinical practice-oriented system. J Technol Behav Sci 2017; 1 3–15. [published erratum appears in: J Technol Behav Sci 2017; 2 (3-4):211]
| The Interactive Mobile App Review Toolkit (IMART): a clinical practice-oriented system.Crossref | GoogleScholarGoogle Scholar |
[93] The Royal Australian College of General Practitioners (RACGP). mHealth in general practice: a toolkit for effective and secure use of mobile technology. Melbourne: RACGP; 2016.
[94] Yasini M, Beranger J, Desmarais P, Perez L, Marchand G. mHealth quality: aA process to seal the qualified mobile health apps. Stud Health Technol Inform 2016; 228 205–9.
[95] Dialogue Consulting. Guidelines for developing healthy living apps. Melbourne: VicHealth; 2015.
[96] Eysenbach G. CONSORT-EHEALTH Group. CONSORT-EHEALTH: improving and standardizing evaluation reports of web-based and mobile health interventions. J Med Internet Res 2011; 13 e126
| CONSORT-EHEALTH Group. CONSORT-EHEALTH: improving and standardizing evaluation reports of web-based and mobile health interventions.Crossref | GoogleScholarGoogle Scholar |
[97] Agarwal S, LeFevre AE, Lee J, L’Engle K, Mehl G, Sinha C, Labrique A. Guidelines for reporting of health interventions using mobile phones: mobile health (mHealth) evidence reporting and assessment (mERA) checklist. BMJ 2016; 352 i1174
| Guidelines for reporting of health interventions using mobile phones: mobile health (mHealth) evidence reporting and assessment (mERA) checklist.Crossref | GoogleScholarGoogle Scholar |
[98] BinDhim NF, Hawkey A, Trevena L. A systematic review of quality assessment methods for smartphone health apps. Telemed J E Health 2015; 21 97–104.
| A systematic review of quality assessment methods for smartphone health apps.Crossref | GoogleScholarGoogle Scholar |
[99] Chan S, Torous J, Hinton L, Yellowlees P. Towards a framework for evaluating mobile mental health apps. Telemed J E Health 2015; 21 1038–41.
| Towards a framework for evaluating mobile mental health apps.Crossref | GoogleScholarGoogle Scholar |
[100] Grundy QH, Wang Z, Bero LA. Challenges in assessing mobile health app quality. A systematic review of prevalent and innovative methods. Am J Prev Med 2016; 51 1051–9.
| Challenges in assessing mobile health app quality. A systematic review of prevalent and innovative methods.Crossref | GoogleScholarGoogle Scholar |
[101] McKay FH, Cheng C, Wright A, Shill J, Stephens H, Uccellini M. Evaluating mobile phone applications for health behaviour change: a systematic review. J Telemed Telecare 2018; 24 22–30.
| Evaluating mobile phone applications for health behaviour change: a systematic review.Crossref | GoogleScholarGoogle Scholar |
[102] Boudreaux ED, Waring ME, Hayes RB, Sadasivam RS, Mullen S, Pagoto S. Evaluating and selecting mobile health apps: strategies for healthcare providers and healthcare organizations. Transl Behav Med 2014; 4 363–71.
| Evaluating and selecting mobile health apps: strategies for healthcare providers and healthcare organizations.Crossref | GoogleScholarGoogle Scholar |
[103] Collins LM, Murphy SA, Strecher V. The multiphase optimisation strategy (MOST) and the sequential multiple assignment randomiised trial (SMART): new methods for more potent eHealth interventions. Am J Prev Med 2007; 32 S112–8.
| The multiphase optimisation strategy (MOST) and the sequential multiple assignment randomiised trial (SMART): new methods for more potent eHealth interventions.Crossref | GoogleScholarGoogle Scholar |
[104] McDonald S, Quinn F, Vieira R, O’Brien N, White M, Johnston DW, Sniehotta FF. The state of the art and future opportunities for using longitudinal n-of-1 methods in health behaviour research: a systematic literature overview. Health Psychol Rev 2017; 11 307–23.
| The state of the art and future opportunities for using longitudinal n-of-1 methods in health behaviour research: a systematic literature overview.Crossref | GoogleScholarGoogle Scholar |
[105] Kaplan B. Evaluating informatics applications – some alternative approaches: theory, social interactionism, and call for methodological pluralism. Int J Med Inform 2001; 64 39–56.
| Evaluating informatics applications – some alternative approaches: theory, social interactionism, and call for methodological pluralism.Crossref | GoogleScholarGoogle Scholar |
[106] Michie S, Johnston M. Theories and techniques of behaviour change: developing a cumulative science of behaviour change. Health Psychol Rev 2012; 6 1–6.
| Theories and techniques of behaviour change: developing a cumulative science of behaviour change.Crossref | GoogleScholarGoogle Scholar |
[107] Payne HE, Lister C, West JH, Bernhardt JM. Behavioral functionality of mobile apps in health interventions: a systematic review of the literature. JMIR Mhealth Uhealth 2015; 3 e20
| Behavioral functionality of mobile apps in health interventions: a systematic review of the literature.Crossref | GoogleScholarGoogle Scholar |
[108] Bakker D, Kazantzis N, Rickwood D, Rickard N. Mental health smartphone apps: review and evidence-based recommendations for future developments. JMIR Ment Health 2016; 3 e7
| Mental health smartphone apps: review and evidence-based recommendations for future developments.Crossref | GoogleScholarGoogle Scholar |
[109] Chomutare T, Fernandez-Luque L, Arsand E, Hartvigsen G. Features of mobile diabetes applications: review of the literature and analysis of current applications compared against evidence-based guidelines. J Med Internet Res 2011; 13 e65
| Features of mobile diabetes applications: review of the literature and analysis of current applications compared against evidence-based guidelines.Crossref | GoogleScholarGoogle Scholar |
[110] Pagoto S, Schneider K, Jojic M, DeBiasse M, Mann D. Evidence-based strategies in weight-loss mobile apps. Am J Prev Med 2013; 45 576–82.
| Evidence-based strategies in weight-loss mobile apps.Crossref | GoogleScholarGoogle Scholar |
[111] Arnhold M, Quade M, Kirch W. Mobile applications for diabetics: a systematic review and expert-based usability evaluation considering the special requirements of diabetes patients age 50 years or older. J Med Internet Res 2014; 16 e104
| Mobile applications for diabetics: a systematic review and expert-based usability evaluation considering the special requirements of diabetes patients age 50 years or older.Crossref | GoogleScholarGoogle Scholar |
[112] Sarkar U, Gourley GI, Lyles CR, Tieu L, Clarity C, Newmark L, Singh K, Bates DW. Usability of commercially available mobile applications for diverse patients. J Gen Intern Med 2016; 31 1417–26.
| Usability of commercially available mobile applications for diverse patients.Crossref | GoogleScholarGoogle Scholar |
[113] Roess A. The promise, growth, and reality of mobile health – another data-free zone. N Engl J Med 2017; 377 2010–1.
| The promise, growth, and reality of mobile health – another data-free zone.Crossref | GoogleScholarGoogle Scholar |
