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 GA 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.
Introduction
With the increasing prevalence of chronic multimorbidity, enhanced patient self-management is a priority in improving outcomes and reducing healthcare costs. With the advent of smartphones and tablets in 2007, now used by more than 80% of Australians,1 downloadable mobile health applications (apps) have become popular as a means for enabling patients with chronic disease to participate in more effective self-management. Funders and managers of healthcare systems are increasingly interested in the potential for apps to improve chronic disease self-management and reduce hospitalisations and healthcare costs.
In 2017, the number of health apps released from iTunes and Google Play exceeded 300 000,2 with nearly 25% dealing with disease self-management.3 One-third of adults in the US with smartphones or tablets use health apps to achieve health behaviour goals and help with medical decision making.4 However, although international standards exist with regard to software engineering, privacy, security and usability of mobile apps in general (e.g. International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC) standards), there are currently few widely accepted criteria to ensure health apps are accurate in content, evidence based or efficacious.5 The efficacy of most commercially available apps in improving self-management and clinical outcomes related to chronic diseases has never been evaluated in clinical trials6 and although quality assessment tools exist for health-related websites,7,8 no universally agreed approach exists for evaluating app clinical efficacy and safety. Involvement of users and clinical experts in development is highly variable, and adherence of apps to available clinical evidence ranges from 0% to 87%.9 Most apps omit components that motivate patients to make lifestyle changes, instead providing information already available in paper form.10,11 Concerns also exist about interface design, interactivity, connectivity,12 the privacy and security of stored or transferred personal health information13,14 and risks to patient safety from inaccurate or poorly designed apps.15 In contrast with drugs or implantable devices, the US Food and Drug Administration (FDA)16 and the Australian Therapeutic Goods Administration (TGA)17 have no mandated regulatory oversight of health apps unless they are directly connected to regulated medical equipment. However, both agencies,18,19 together with the Australian Digital Health Agency (ADHA),20 have recently signalled their intention to focus attention on any software device, including apps, used to diagnose, prevent or manage disease.
In January 2018, the Queensland Policy and Advisory Committee on new Technology (QPACT), in partnership with Healthcare Evaluation and Assessment of Technology (HEAT) Team of Queensland Health (QH) and eHealth Queensland, established the mHealth Applications Evaluation Program (mHealth Apps Program) as a pilot initiative. The program welcomes submissions from clinicians who require funds to subject apps of their choosing to a rigorous field evaluation of quality, safety and benefit in order to receive official QH endorsement as an app that QH clinicians can recommend or ‘prescribe’ to their patients with confidence. The program was in rapid need of an evaluative framework and a better understanding of the efficacy and optimal design characteristics of mobile apps. The aims of this study were to: (1) assess the efficacy of currently available apps on chronic disease self-management; (2) identify design attributes that influence app usability and potential to confer benefit; and (3) review methods for evaluating app quality and utility and develop a provisional evaluative framework.
Methods
Objective no. 1: efficacy of apps in optimising chronic disease self-management
Criteria chosen by the mHealth program for app submissions (Table 1) determined the scope of literature reviews. PubMed, Cochrane Library and EMBASE were searched between January 2007 and January 2018 for systematic reviews (as defined using formal criteria21) of studies using search terms (and synonyms) of ‘smartphone’, ‘mobile applications’, ‘mobile health’ and terms for each of 11 chronic diseases, chosen for their burden of hospital utilisation,22 namely asthma, chronic obstructive lung disease (COPD), diabetes, chronic pain, serious mental health disorders, alcohol and substance addiction, heart failure (HF), ischaemic heart disease, cancer, cognitive impairment and chronic kidney disease (CKD).
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Inclusion criteria comprised systematic reviews of randomised controlled trials (RCTs) or observational studies that evaluated patient-facing interactive apps related to disease self-management, reported at least one measure of efficacy and were conducted in developed countries. Reference lists of included reviews were checked for other relevant studies. Reviews not written in English, those that analysed digital interventions not meeting our definition of a mobile app (i.e. exclusive use of text messaging, interactive voice response, desktop applications, websites) or those that examined apps used for diagnosis, screening, risk assessment, primary prevention or health promotion were excluded from the analysis.
In instances where no reviews for a particular condition could be found, individual studies were searched using the same search strategy and selection criteria, but excluding conference abstracts or case reports. Where multiple reviews dealing with the same topic were retrieved, the most recently published reviews were selected; other reviews were included if they provided additional informative data derived from thematic or subgroup analyses.
Data extracted from each review or individual study comprised: date of publication, number and type of studies, sample size, assessment of risk of bias (as defined by authors), app description, measures of efficacy, including meta-analyses of pooled data, assessment of patient adherence, usability and reference to behavioural change theory23,24 underpinning the app, and the authors’ interpretive comments on study strengths and limitations and overall results. In assessing efficacy, we made no attempt to prespecify what constituted clinically meaningful benefits in terms of absolute changes in effect measures, because this will vary according to subjective interpretations of clinicians and users and the outcomes and conditions of interest.
Objective no. 2: design attributes that affect app usability and efficacy
Information about app design attributes affecting adherence, usability and potential to confer benefit were sought from the reviews retrieved, with additional reviews retrieved using app search terms that included ‘quality’, ‘content’, ‘usability’, ‘feasibility’, ‘acceptability’, ‘functionality’, ‘adherence’ and ‘design.’ In the absence of any validated usability score or taxonomy for mobile health apps, we considered all app design features that other researchers had proposed as being important on the basis of prima facie evidence.
Objective no. 3: methods for evaluating app quality and utility
Additional reviews were also sought using app search terms combined with ‘quality assessment’, ‘evaluation’ and related synonyms. Findings were used to construct a comprehensive but practical evaluation framework.
Results
Objective no. 1: efficacy of apps on chronic disease self-management
In total, 49 studies were retrieved,11,25–72 comprising 16 reviews11,25,29,31–39,42,51,60,64 and 33 individual studies.26–28,30,40,41,43–50,52–59,61–63,65–72 Key findings are presented below; more detailed evidence tables are provided in Appendix 1.
Asthma
In a review of 12 RCTs of digital aids,25 three app trials were reported; one saw a 45% increase in the number of patients with well-controlled asthma,26 another saw no effects on several outcomes27 and the third reported improvements in asthma-related quality of life and an 80% decrease in emergency department visits.28
Chronic obstructive pulmonary disease
In a review of three RCTs of digital interventions,29 a single app trial reported no difference in COPD-related quality of life; no other outcomes were reported.30
Diabetes
Four reviews (one with 16 RCTs involving apps,31 another with 12 RCTs,32 a third with 10 RCTs33 and a fourth with six RCTs34) all reported reductions in glycosylated haemoglobin levels (HbA1c) for all diabetics (mean reductions ranging from 0.48% to 0.51%) and those with type 2 diabetes (0.49–0.83%). Other reviews35,36 yielded mixed results, with only one-third of included studies reporting significant reductions in HbA1c.
Chronic pain
Although four reviews were retrieved assessing more than 300 apps,11,37–39 none contained any trials of efficacy. A single app RCT showed an improvement in pain control, functionality and quality of life of patients with low back pain,40 whereas another app involving women with widespread pain that included diaries and therapist feedback showed no effects at 11 months.41
Serious mental health disorders
In a review of 18 studies,42 14 app studies included four RCTs. Of two RCTs of depression apps, one showed no effect,43 whereas another RCT reported improved mental health scores and reduced lost or unproductive days.44 An RCT of an app for psychotic disorders reported ‘positive effects’,45 whereas another RCT for bipolar disorder reported no effects.46 A single-arm study of an app for schizophrenia and schizoaffective disorder was associated with significant reduction in symptoms.47 A review of 24 app studies in children and adolescents with various mental disorders48 included two RCTs, both showing no effects.49,50
Alcohol and substance addiction
In a review of five studies with three app RCTs,51 one RCT showed increased frequency of drinking in men but not women compared with controls.52 The second saw 1.37 fewer risky drinking days among app patients over 12 months,53 whereas the third noted less drinking over 14 days but no effect on heavy episodic drinking.54
Heart failure
No reviews were found, but, of two RCTs, one using a tablet computer reported 2.2 fewer HF-related days in hospital per patient and improved HF-related quality of life and physical function over 3 months.56 The second RCT using home telemonitoring reported no difference in hospital days and, instead, an increase in health care utilisation with more nurse visits and telephone contacts.56
Ischaemic heart disease
No reviews were found but, of three RCTs of apps targeting patients following acute coronary events, one reported improved medication adherence but no effect on smoking rates, physical activity or quality of life at 6 months.57 Another showed slight weight reduction and improved quality of life at 6 weeks.58 The third RCT showed weight loss and improved blood pressure and blood sugar control, but no change in medication adherence, smoking rates, physical activity or quality of life.59
Cancer
In a review of five studies60 that reported three app trials, a controlled trial involving prostate cancer patients receiving radiotherapy reported lower levels of fatigue, nausea, insomnia, urinary symptoms and emotional dysfunction.61 An RCT involving breast cancer patients showed no effect on physical function or quality of life,62 whereas a pre-post study involving survivors of breast and endometrial cancer noted weight reduction but no change in physical activity or quality of life.63
Cognitive impairment
In a review of 24 studies,64 two RCTs65,66 and one controlled trial67 of apps focused on cognitive training showed improved cognition, whereas one RCT did not.68 Three other RCTs, one of serious games coupled with cognitive training,69 one with a multicomponent program70 and another of engagement,71 showed no effects on various outcomes.
Chronic kidney disease
No reviews were found. Only one small pre-post cohort study of an app reported mean reductions in home blood pressure readings from baseline values.72
Objective no. 2: design attributes that affect app usability and efficacy
Eight reviews were considered relevant.35,64,73–78 In the review of diabetes apps by Fu et al.35 seven usability studies reported poor (38%) to average (80%) rates of use, due primarily to product design flaws (e.g. screen layout, system capability and reliability, and general characteristics), need for manual data entry, patients having to take more time and making more errors than expected when exporting and correcting blood sugar levels, difficulty encountered in system navigation whenever tasks required multiple steps and the absence of personalised feedback functions or social networking functions. No study considered confounders such as a patient’s history of, and levels of motivation in, using digital technologies. Only 51 of 295 (17.3%) cancer apps,73 12 of 117 (10.3%) depression apps74 and a minority of chronic pain apps75 provide skill-building tools to assist in self-management. On the basis of these and other reviews68,76–78 we identified several design features for enhancing usability (Table 2).
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Objective no. 3: methods for evaluating app quality and utility
Methods for evaluating apps fall into three broad categories, as summarised in Table 3,79 each providing a different type of evidence but with limitations. For example, some lists of criteria focus on user ratings (ease of use, reliability, aesthetics) but may exclude reference to independent expert clinician involvement or assessment.80 Others cover development, implementation and integration, but omit evaluation of key usability attributes or privacy and security.81 Evaluation guidelines from the Health Care Information and Management Systems Society consider efficiency, effectiveness, user satisfaction and platform optimisation, but exclude accuracy and appropriateness of app health information.82 Such omissions pose risks to patient safety and explicit app risk assessment has been proposed.15 Various rating scales,83,84 scoring systems,85,86 checklists,87–91 toolkits,92,93 questionnaires,94 development guides95 and reporting standards for app studies96,97 all promote more transparent and objective reporting and evaluation. The existence of so many instruments suggests no single one meets all needs. Several government (https://www.vichealth.vic.gov.au/media-and-resources/vichealth-apps; https://www.healthnavigator.org.nz/app-library), private (http://myhealthapps.net/; https://imedicalapps.com/#; https://practicalapps.ca/) and developer (https://www.ourmobilehealth.com/; https://www.medappcare.com/en/) websites (all accessed 13 September 2018) also exist that post apps that have been assessed using various in-house rating methods.
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We contend that any evaluation method should consider clinical and technical attributes of an app that we have identified from this review as being important, while not being too general or complex or time consuming with need for training, or too specific to a particular health condition or outcome. After analysing the above reports and recent reviews,98–101 we derived a list of criteria (Table 4) to which yes/no or scalar (Likert) responses could gauge the extent to which an app meets each criterion. Obtaining informed responses to each criterion requires retrieving relevant data using strategies listed in Table 5.102 We suggest these criteria may suffice as a ‘first-pass’ assessment by panels involving clinicians in deciding whether the app justifies more detailed technical assessments under a second-phase due-diligence process.
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Discussion
To the best of our knowledge, this is the first review of evidence relating to the efficacy, usability and evaluation of apps that assist patients to self-manage chronic diseases. Limitations of this review are that, due to time and resource constraints, we did not search for and analyse every individual study of every app, but instead relied on systematic reviews, when these were available, some of which differed in inclusion criteria and conclusions. However, we do not feel any important study was overlooked and, in particular, RCTs with less risk of bias were diligently sought and given emphasis. Space limitations did not allow us to provide full descriptions of each app, some having multiple components, but readers can refer to the references provided for more detail. We view our framework as being a provisional ‘first-pass’ framework that may help select app submissions that warrant second-phase due-diligence reviews to which more detailed assessments of technical attributes relating to interoperability, security and privacy may be added.
Improving the evidence base of app efficacy
The rapid pace of the technological evolution of apps and the slowness and expense of gold-standard research designs, such as RCTs, make assessments of efficacy problematic. Evaluating apps is complex given the combination of content, user, platform, links and interface attributes, and determining whether benefits are attributable to a total app package or specific components. Apps can be released, updated, modified or removed by commercial developers as studies with fixed protocols evaluating a specific app (or type of app) are underway, rendering the results potentially obsolete by the time of public release. Control groups in comparative trials may be contaminated by being exposed to similar or changing digital interventions during the course of a study. In addition, results of new clinical studies may change the evidence on which the core content and behaviour change functions contained in an app are based, such that the app itself, despite having been studied, becomes invalid. Finally, apps investigated in research settings are often not the same as those that are disseminated commercially.
Alternative study designs have been suggested that aim to overcome some of these issues, albeit with limitations. These include multiphase optimisation (MOST) strategies (factorial or fractional designs and adaptive trials),103 n-of-1 studies that involve repeated measurement of many individuals over time in understanding within-person behaviour before and then after using an app, or using an app and then having it withdrawn,104 or triangulation of ‘fit-for-purpose’ studies that investigate sociocultural, organisational, cognitive and other contextual determinants of effects using surveys, focus group discussions and ethnographic studies.105
Relationship between app use and efficacy
In many reviews, more focus is given to assessing usability than to testing efficacy. We are concerned that apps rated by many users as highly usable and that become popular may be viewed by new users as apps that predictably confer health benefits. This is akin to ‘I really like and use the app so therefore it must be doing me good’. Design attributes that discourage people from using an app in the way intended will certainly compromise efficacy, but the reverse is not true. Usability is a multifaceted construct that requires app design to be informed by behaviour change theory, because information alone is insufficient to change behaviour.106 In this review, only a minority of studies explicitly reported using behaviour change theories to underpin app design, and adherence and retention rates were frequently <80%. Although many apps make reference to behavioural theories, self-monitoring alone is often the dominant function rather than incorporating proactive skills development in self-management, which may substantially enhance efficacy.107 Using apps pertaining to mental health and addiction problems as examples, apps need to incorporate a behavioural conditioning plan and interactive framework that include rapid access to expert help at times of crisis and that mitigate limited attention span and curtail time spent on devices by emphasising non-app-based activities.108 Personalised information, real-time feedback and access to expert consultation when required are features highly valued by users. There is a need for a checklist of usability and functionality attributes that apps should aspire to in maximising user engagement, which this review has helped create.
Implications for app evaluation and endorsement
Lack of evidence of app efficacy, poor descriptions of processes and data sources used to develop the app and discrepancies between information generated on apps and evidence-based guidelines are key issues in evaluation.109 Of note, apps requiring purchase are not necessarily more evidence based than free apps.110 With regard to efficacy testing, our overview highlights several shortcomings in the current evidence base: a paucity of rigorous efficacy trials with adequate sample sizes for most conditions; inconsistency of measured outcomes, with many based on subjective patient self-report, with relatively few hard end-points (e.g. mortality, clinical events, hospitalisations); insufficient duration for assessing long-term effects; minimal reference to adherence rates and underlying behavioural change techniques; questionable fidelity of patient compliance with data input and use of algorithms; and limited generalisability given heterogeneity in populations, interventions, outcome measures and conflicting estimates of effect. The exceptions to many of these limitations are diabetes apps, for which sufficient and consistent data confirm efficacy in optimising blood sugar control, although effects on other outcomes await assessment. For all conditions, there was little focus on the needs and competencies of older patients111 and culturally diverse groups,112 or on cost-effectiveness, including costs of misinformation transmitted, of addressing resulting problems or of the diverse workforce required for app implementation and monitoring.113
Methods for evaluating the quality and utility of an app must be capable of assessing all relevant attributes of technical integrity, usability and efficacy in an explicit and transparent manner. This review suggests an evaluation framework that could be refined and tested for construct validity and internal consistency. Potential users of the framework may include the ADHA, TGA, state-based government digital health agencies and networks, app developers and any other stakeholder group who has responsibilities in health app evaluation and endorsement. In Queensland, the mHealth Apps Program, under the auspices of QPACT, HEAT, and eHealth Queensland, is being resourced to further develop the framework and its application to all future submissions from developers and interested clinicians for QPACT funding of field trials of mobile health apps within QH facilities.
Conclusion
The numbers 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 of the apps already available is very limited. Design features that enhance users’ desire to use the app, and thereby derive most benefit, need to be considered in maximising efficacy. In making decisions about which apps should be endorsed by government agencies and recommended with confidence by clinicians to their patients, a workable evaluation framework needs to be used by those agencies that assume the role of formulating and applying app standards. The provisional framework presented in this report serves as a starting point for such work.
Competing interests
The authors declare that they have no competing interests.
Acknowledgements
There was no funding source for this research. The authors are all members of the Queensland Health mHealth Apps Working Group. The views expressed in this article should not be regarded as official policy of Queensland Health or any of its divisions, Queensland Policy and Advisory Committee on new Technology, Healthcare Evaluation and Assessment of Technology Team or eHealth Queensland. The views expressed are solely those of the authors.
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Appendix 1. Evidence table for systematic reviews and individual trials of apps for chronic disease management
3D, three dimensional; A-CHESS, Addiction-Comprehensive Health Enhancement Support System; ACS, acute coronary syndrome; BDI, Beck’s Depression Inventory; BMI, body mass index; BP, blood pressure; BSL, blood sugar level; CAP-CR, Care Assessment Platform of cardiac rehabilitation; CCQ, Clinical COPD Questionnaire; COPD, chronic obstructive pulmonary disease; DBP, diastolic blood pressure; EBAC, estimated blood alcohol concentration; ED, emergency department; EMAs, ecological momentary assessments; EQ5D, EuroQol 5D version; ESRD, end-stage renal disease; FACT-G, Functional Assessment of Cancer G version; GP, general practitioner; HADS, Hospital Anxiety and Depression Scale; HF, heart failure; HIS, home intervention system; HRQoL, health-related quality of life; IQR, interquartile range; IRR, incidence rate ratio; KDS, Kessler Psychological Distress Scale; LDL-C, low-density lipoprotein cholesterol; LVEF, left ventricular ejection fraction; m app, mobile health application; MD, mean difference; MI, myocardial infarction; NYHA, New York Heart Association; PANSS, positive and negative symptom scale; PEFR, peak expiratory flow rate; PHQ, Patient Health Questionnaire; QoL, quality of life; RCT, randomised controlled trial; RR, relative risk; SBP, systolic blood pressure; SDS, self-directed support; SF, Short Form; SGRQ, St George Respiratory Questionnaire; SMD, standardised mean difference; SR, systematic review; T1DM, type 1 diabetes mellitus; T2DM, type 2 diabetes mellitus; TCR, traditional, centre-based cardiac rehabilitation; TMG, telemonitoring group; WEL, Weight Efficacy Lifestyle Questionnaire
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