Going beyond killer apps: building a better mHealth evidence base
TL;DR: Five interrelated reasons as to why mHealth has underdelivered are discussed and these include the diversity of users and the inability to address their varied problems results, which results in low adoption of apps.
Abstract: mHealth relates to the provision of health-related services via a mobile device. It comprises multidimensional elements including provider, patient and administrative applications. Applications include consumer education and behaviour change, wearable sensors and point-of-care diagnostics, disease and population registries, electronic health records, decision support, provider tools (communication, workflow management, professional education) and healthcare management (human resources, financial monitoring, supply chain logistics).1
Although mHealth has potential to strengthen health systems worldwide, the evidence base is immature, and consequently, the opportunities to advance knowledge remain limited.2 3 Mobile devices and apps have become essential tools for disruptive change in many industries, but thus far, this has not happened in healthcare. Here, we discuss five interrelated reasons as to why mHealth has underdelivered and highlight challenges and opportunities for mHealth researchers.
Disruptors often rely on a ‘killer app’—a highly popular application that users will consider indispensable for their needs. At last count, there were nearly 260 000 health apps on the market,4 but most downloads are never opened and consistent use is extremely rare. Further, these apps are often disease siloed, focus mainly on behaviour change, gloss over privacy issues and are not integrated into any overarching healthcare structure. Such apps struggle to achieve large-scale adoption because of their failure to address the needs of diverse stakeholders.5 Most apps are consumer facing, whereas healthcare systems tend to be provider facing. This important distinction may explain why the ‘killer app’ approach is not the correct mindset. The diversity of users and the inability to address their varied problems results …
Citations
More filters
20 Aug 2020
TL;DR: The epidemiology, mechanisms, diagnosis and treatment of comorbid depression in patients with medical diseases, including major depressive disorder, are discussed.
Abstract: Depression is one of the most common comorbidities of many chronic medical diseases including cancer and cardiovascular, metabolic, inflammatory and neurological disorders. Indeed, the prevalence of depression in these patient groups is often substantially higher than in the general population, and depression accounts for a substantial part of the psychosocial burden of these disorders. Many factors can contribute to the occurrence of comorbid depression, such as shared genetic factors, converging biological pathways, social factors, health behaviours and psychological factors. Diagnosis of depression in patients with a medical disorder can be particularly challenging owing to symptomatic overlap. Although pharmacological and psychological treatments can be effective, adjustments may need to be made for patients with a comorbid medical disorder. In addition, symptoms or treatments of medical disorders may interfere with the treatment of depression. Conversely, symptoms of depression may decrease adherence to treatment of both disorders. Thus, comprehensive treatment plans are necessary to optimize care.
191 citations
TL;DR: In this paper, the authors provide a comprehensive update on the overall field of digital psychiatry, covering three areas: the relevance of recent technological advances to mental health research and care, by detailing how smartphones, social media, artificial intelligence and virtual reality present new opportunities for "digital phenotyping" and remote intervention.
176 citations
TL;DR: Future work in this growing area should incorporate active psychological treatment, address continuity of care across the perinatal period, and consider clinical sustainability to realize the potential of mHealth.
Abstract: Background: The perinatal period is a vulnerable time during which depression and anxiety commonly occur. If left untreated or undertreated, there may be significant adverse effects; therefore, access to rapid, effective treatment is essential. Treatments for mild-to-moderate symptoms according to a stepped-care approach involve psychoeducation, peer support, and psychological therapy, all of which have been shown to be efficaciously delivered through digital means. Women experience significant barriers to care because of system- and individual-level factors, such as cost, accessibility, and availability of childcare. The use of mobile phones is widespread in this population, and the delivery of mental health services via mobile phones has been suggested as a means of reducing barriers.
Objective: This study aimed to understand the extent, range, and nature of mobile health (mHealth) tools for prevention, screening, and treatment of perinatal depression and anxiety in order to identify gaps and inform opportunities for future work.
Methods: Using a scoping review framework, 4 databases were searched for terms related to mobile phones, perinatal period, and either depression or anxiety. A total of 477 unique records were retrieved, 81 of which were reviewed by full text. Peer-reviewed publications were included if they described the population as women pregnant or up to 1 year postpartum and a tool explicitly delivered via a mobile phone for preventing, screening, or treating depression or anxiety. Studies published in 2007 or earlier, not in English, or as case reports were excluded.
Results: A total of 26 publications describing 22 unique studies were included (77% published after 2017). mHealth apps were slightly more common than texting-based interventions (12/22, 54% vs 10/22, 45%). Most tools were for either depression (12/22, 54%) or anxiety and depression (9/22, 41%); 1 tool was for anxiety only (1/22, 4%). Interventions starting in pregnancy and continuing into the postpartum period were rare (2/22, 9%). Tools were for prevention (10/22, 45%), screening (6/22, 27%), and treatment (6/22, 27%). Interventions delivered included psychoeducation (16/22, 73%), peer support (4/22, 18%), and psychological therapy (4/22, 18%). Cost was measured in 14% (3/22) studies.
Conclusions: Future work in this growing area should incorporate active psychological treatment, address continuity of care across the perinatal period, and consider clinical sustainability to realize the potential of mHealth.
Trial Registration:
48 citations
Cites background from "Going beyond killer apps: building ..."
...Consideration should also be given to focusing on testing the principles of the intervention versus the technology that is used to allow tools to adapt, while remaining adherent to the treatment embedded within them [74]....
[...]
TL;DR: This work sought to identify measures which are used in randomised controlled trials and argued that ‘value of information’ (VoI) is such a metric – it is calculated as the difference between the ‘expected utility’ of alternative options.
Abstract: By what measure should a policy maker choose between two mediums that deliver the same or similar message or service? Between, say, video consultation or a remote patient monitoring application (i.e. patient-facing digital health innovations) and in-person consultation? To answer this question, we sought to identify measures which are used in randomised controlled trials. But first we used two theories to frame the effects of patient-facing digital health innovations on – 1) transaction costs (i.e. the effort, time and costs required to complete a clinical interaction); and 2) process outcomes and clinical outcomes along the care cascade or information value chain, such that the ‘value of information’ (VoI) is different at each point in the care cascade or value chain. From the trials, we identified three categories of measures: outcome (process or clinical), satisfaction, and cost. We found that although patient-facing digital health innovations tend to confer much of their value by altering process outcomes, satisfaction, and transaction costs, these measures are inconsistently assessed. Efforts to determine the relative value of and choose between mediums of service delivery should adopt a metric (i.e. mathematical combination of measures) that capture all dimensions of value. We argue that ‘value of information’ (VoI) is such a metric – it is calculated as the difference between the ‘expected utility’ (EU) of alternative options. But for patient-facing digital health innovations, ‘expected utility’ (EU) should incorporate the probability of achieving not only a clinical outcome, but also process outcomes (depending on the innovation under consideration); and the measures of utility should include satisfaction and transaction costs; and also changes in population access to services, and health system capacity to deliver more services, which may result from reduction in transaction costs.
45 citations
TL;DR: Personal factors and features of the device influenced the experience of using mobile health to support physical activity and the two mechanisms through which mobile health use facilitated physical activity were strengthening of motivation and changes in self-awareness and strategising.
Abstract: Objective
Despite evidence supporting physical activity in primary and secondary prevention, many individuals do not meet recommended levels. Mobile health is a field with a growing evidence base and is proposed as a convenient method for delivering health interventions. Despite qualitative exploration of stakeholder perspectives, there is a lack of synthesis to inform evidence-based design. This study aims to resolve this by identifying and synthesising qualitative research on the experience of using mobile health applications to promote physical activity.
Method
A systematic review focused on qualitative research, mobile health and physical activity was conducted in October 2017 using CINAHL, ERIC, EMBASE, MEDLINE and PsycINFO databases. The protocol was registered with the Prospero database (Registration: CRD42018080610). Results were synthesised as a meta-ethnography.
Results
Fifteen studies were included, covering a variety of populations, including people with diabetes, obesity, and serious mental illness. Five themes emerged: (a) personal factors and the experience of using mobile health, (b) mobile health and changes in thinking that support physical activity, (c) the experience of mobile health features, including prompts, goal setting and gamification, (d) the experience of personalised mobile health and physical activity, (e) technical and user issues in mobile health and their effect on experience.
Conclusion
Personal factors and features of the device influenced the experience of using mobile health to support physical activity. The two mechanisms through which mobile health use facilitated physical activity were strengthening of motivation and changes in self-awareness and strategising. Experiences were not entirely unproblematic as technical issues and adverse effects related to self-monitoring were noted. This synthesis provides insight into the experience of mobile health and is useful for researchers and healthcare practitioners interested in designing user-informed mobile health interventions for promoting physical activity.
45 citations
References
More filters
TL;DR: An evidence-based, theory-informed, and pragmatic framework to help predict and evaluate the success of a technology-supported health or social care program, which has several potential uses and could be applied across a range of technological innovations in health and social care.
Abstract: Background: Many promising technological innovations in health and social care are characterized by nonadoption or abandonment by individuals or by failed attempts to scale up locally, spread distantly, or sustain the innovation long term at the organization or system level. Objective: Our objective was to produce an evidence-based, theory-informed, and pragmatic framework to help predict and evaluate the success of a technology-supported health or social care program. Methods: The study had 2 parallel components: (1) secondary research (hermeneutic systematic review) to identify key domains, and (2) empirical case studies of technology implementation to explore, test, and refine these domains. We studied 6 technology-supported programs-video outpatient consultations, global positioning system tracking for cognitive impairment, pendant alarm services, remote biomarker monitoring for heart failure, care organizing software, and integrated case management via data sharing-using longitudinal ethnography and action research for up to 3 years across more than 20 organizations. Data were collected at micro level (individual technology users), meso level (organizational processes and systems), and macro level (national policy and wider context). Analysis and synthesis was aided by sociotechnically informed theories of individual, organizational, and system change. The draft framework was shared with colleagues who were introducing or evaluating other technology-supported health or care programs and refined in response to feedback. Results: The literature review identified 28 previous technology implementation frameworks, of which 14 had taken a dynamic systems approach (including 2 integrative reviews of previous work). Our empirical dataset consisted of over 400 hours of ethnographic observation, 165 semistructured interviews, and 200 documents. The final nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework included questions in 7 domains: the condition or illness, the technology, the value proposition, the adopter system (comprising professional staff, patient, and lay caregivers), the organization(s), the wider (institutional and societal) context, and the interaction and mutual adaptation between all these domains over time. Our empirical case studies raised a variety of challenges across all 7 domains, each classified as simple (straightforward, predictable, few components), complicated (multiple interacting components or issues), or complex (dynamic, unpredictable, not easily disaggregated into constituent components). Programs characterized by complicatedness proved difficult but not impossible to implement. Those characterized by complexity in multiple NASSS domains rarely, if ever, became mainstreamed. The framework showed promise when applied (both prospectively and retrospectively) to other programs. Conclusions: Subject to further empirical testing, NASSS could be applied across a range of technological innovations in health and social care. It has several potential uses: (1) to inform the design of a new technology; (2) to identify technological solutions that (perhaps despite policy or industry enthusiasm) have a limited chance of achieving large-scale, sustained adoption; (3) to plan the implementation, scale-up, or rollout of a technology program; and (4) to explain and learn from program failures.
990 citations
"Going beyond killer apps: building ..." refers background in this paper
...Incorporation of theory-informed frameworks for understanding factors associated with adoption and non-adoption are therefore essential to evaluate programmes and generate knowledge that can be applied to other settings.(14) A third and unexplored area is postmarketing surveillance mechanisms to safeguard against unintended consequences derived from mHealth-related activities....
[...]
TL;DR: This new framework lays out 12 common mHealth applications used as health systems strengthening innovations across the reproductive health continuum and describes how these applications can be applied in the context of a women's health care system.
Abstract: This new framework lays out 12 common mHealth applications used as health systems strengthening innovations across the reproductive health continuum.
475 citations
"Going beyond killer apps: building ..." refers background in this paper
...Applications include consumer education and behaviour change, wearable sensors and point-of-care diagnostics, disease and population registries, electronic health records, decision support, provider tools (communication, workflow management, professional education) and healthcare management (human resources, financial monitoring, supply chain logistics).(1) Although mHealth has potential to strengthen health systems worldwide, the evidence base is immature, and consequently, the opportunities to advance knowledge remain limited....
[...]
01 Jan 1970
TL;DR: In this paper, the authors examined and synthesized the existing mHealth literature to assess the current state of mHealth knowledge and identify barriers and gaps, and the mHealth Alliance commissioned an in-depth exploration of the policy barriers and research gaps facing mHealth.
Abstract: There is growing momentum and enthusiasm to capitalize on the rapid spread of telecommunications infrastructure and uptake of mobile phones and mobile broadband services in low and middle income countries to support the achievement of global, national, district, community, and individual level health priorities. Still in its infancy, mHealth, the use of mobile technologies for health, runs the risk of not realizing its full potential due to small‐scale implementations and pilot projects with limited reach. To help shed light on these issues, the mHealth Alliance commissioned an in‐depth exploration of the policy barriers and research gaps facing mHealth. This White Paper, written by a team of researchers at the Center for Global Health and Economic Development at the Earth Institute, Columbia University, examines and synthesizes the existing mHealth literature to assess the current state of mHealth knowledge and identify barriers and gaps.
296 citations
TL;DR: In this article, a systematic review assessed the effect of mHealth interventions that support pregnant women during the antenatal, birth and postnatal period in low and middle-income countries (LMIC).
Abstract: INTRODUCTION: Maternal and neonatal mortality remains high in many low- and middle-income countries (LMIC). Availability and use of mobile phones is increasing rapidly with 90% of persons in developing countries having a mobile-cellular subscription. Mobile health (mHealth) interventions have been proposed as effective solutions to improve maternal and neonatal health. This systematic review assessed the effect of mHealth interventions that support pregnant women during the antenatal, birth and postnatal period in LMIC. METHODS: The review was registered with Prospero (CRD42014010292). Six databases were searched from June 2014-April 2015, accompanied by grey literature search using pre-defined search terms linked to pregnant women in LMIC and mHealth. Quality of articles was assessed with an adapted Cochrane Risk of Bias Tool. Because of heterogeneity in outcomes, settings and study designs a narrative synthesis of quantitative results of intervention studies on maternal outcomes, neonatal outcomes, service utilization, and healthy pregnancy education was conducted. Qualitative and quantitative results were synthesized with a strengths, weaknesses, opportunities, and threats analysis. RESULTS: In total, 3777 articles were found, of which 27 studies were included: twelve intervention studies and fifteen descriptive studies. mHealth interventions targeted at pregnant women increased maternal and neonatal service utilization shown through increased antenatal care attendance, facility-service utilization, skilled attendance at birth, and vaccination rates. Few articles assessed the effect on maternal or neonatal health outcomes, with inconsistent results. CONCLUSION: mHealth interventions may be effective solutions to improve maternal and neonatal service utilization. Further studies assessing mHealth's impact on maternal and neonatal outcomes are recommended. The emerging trend of strong experimental research designs with randomized controlled trials, combined with feasibility research, government involvement and integration of mHealth interventions into the healthcare system is encouraging and can pave the way to improved decision making on best practice implementation of mHealth interventions.
279 citations
TL;DR: The authors highlight three widely held misconceptions that they believe are holding back the field, and they reconceptualize the issues to strengthen the path toward implementation and accelerate innovation.
Abstract: An increasingly large body of randomized controlled trials has demonstrated the efficacy of mental health technologies, such as Web-based and mobile interventions, to prevent and treat mental disorders and increase psychological well-being. However, there is little evidence that these tools can be successfully implemented in clinical settings. The authors highlight three widely held misconceptions that they believe are holding back the field, and they reconceptualize the issues to strengthen the path toward implementation and accelerate innovation.
195 citations
"Going beyond killer apps: building ..." refers background in this paper
...Such apps struggle to achieve large-scale adoption because of their failure to address the needs of diverse stakeholders.(5) Most apps are consumer facing, whereas healthcare systems tend to be provider facing....
[...]