The Effect of Smartphone App–Based Interventions for Patients With Hypertension: Systematic Review and Meta-Analysis

Xu, Hongxuan; Long, Huanyu

doi:10.2196/21759

Cited by 85 publications

(72 citation statements)

References 40 publications

(31 reference statements)

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“…Compared with web-based health services delivered from desktops and laptops, mHealth services have the advantage of interacting with individuals with greater frequency and flexibility, without being limited by time and place [ 3 ]. Mobile technologies, especially smartphone-based apps, can improve the efficiency of health care delivery, ultimately make health care more effective [ 4 - 6 ] and help people to better control their chronic conditions [ 7 , 8 ]. However, despite the numerous benefits of mobile health (mHealth) apps, relatively little is known about whether older adults perceive that these apps confer such benefits.…”

Section: Introductionmentioning

confidence: 99%

Perception and Initial Adoption of Mobile Health Services of Older Adults in London: Mixed Methods Investigation

Pan¹,

Dong²,

Bryan-Kinns³

2021

JMIR Aging

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Background Advances in mobile technology and public needs have resulted in the emergence of mobile health (mHealth) services. Despite the potential benefits of mHealth apps, older adults face challenges and barriers in adopting them. Objective The aims of this study are to understand older adults’ perception of mHealth services and to discover the barriers that older adults face in the initial adoption of mHealth apps. Methods This paper systematically analyzed main determinants related to mHealth services and investigated them through questionnaires, interviews, and a workshop. Two studies were carried out in London. In study 1, the questionnaires with follow-up interviews were conducted based on the literature review to uncover older adults’ perception (including perceived usefulness, perceived ease of use, and perceived behavioral control) of mHealth services. Study 2 was a workshop helping older adults to trial selected mHealth apps. The workshop was conducted by the first author (JP) with assistance from 5 research students. The barriers that older adults faced in the initial adoption period were observed. The interviews and workshop were audiotaped and transcribed. Descriptive statistics and the thematic analysis technique were used for data analysis. Results In total, 30 older adults in London completed the questionnaires and interviews in study 1. The results of study 1 show that the lack of obvious advantage, low reliability, scary information, and the risk of privacy leakage would decrease older adults’ perceived usefulness of mHealth services; the design of app interface would directly affect the perceived ease of use; and aging factors, especially the generation gap, would create barriers for older users. In total, 12 participants took part in the workshop of study 2, including 8 who took part in study 1. The results of study 2 identified that access to technology, the way of interaction, the risk of money loss, heavy workload of using an mHealth app, and different lifestyle are influential factors to older adults’ adoption of mHealth services. Conclusions The perceptions of mHealth services of older adults were investigated; the barriers that older adults may face in the initial adoption stage were identified. On the basis of the synthesis of these results, design suggestions were proposed, including technical improvement, free trial, information clarification, and participatory design. They will help inform the design of mHealth services to benefit older adults.

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Section: Introductionmentioning

confidence: 99%

Perception and Initial Adoption of Mobile Health Services of Older Adults in London: Mixed Methods Investigation

Pan¹,

Dong²,

Bryan-Kinns³

2021

JMIR Aging

View full text Add to dashboard Cite

show abstract

“…Finally, regarding clinical conditions, CVD/events ( 41 , 52 , 59 , 62 , 80 – 82 , 92 , 93 , 95 , 96 ) and psychiatric disorders [depression ( 46 , 80 , 81 , 84 , 87 , 92 , 93 ) and anxiety ( 59 , 80 , 81 , 84 , 92 , 93 )] were the most common. Some outcomes, such as smoking cessation, were reviewed either as objective measures ( 94 ) or self-reported ( 39 , 79 , 80 , 83 , 93 – 97 , 105 ).…”

Section: Resultsmentioning

confidence: 99%

What Are We Measuring When We Evaluate Digital Interventions for Improving Lifestyle? A Scoping Meta-Review

Castro

Ribeiro-Alves

Oliveira

et al. 2022

Front. Public Health

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Background: Lifestyle Medicine (LM) aims to address six main behavioral domains: diet/nutrition, substance use (SU), physical activity (PA), social relationships, stress management, and sleep. Digital Health Interventions (DHIs) have been used to improve these domains. However, there is no consensus on how to measure lifestyle and its intermediate outcomes aside from measuring each behavior separately. We aimed to describe (1) the most frequent lifestyle domains addressed by DHIs, (2) the most frequent outcomes used to measure lifestyle changes, and (3) the most frequent DHI delivery methods.Methods: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR) Extension for Scoping Reviews. A literature search was conducted using MEDLINE, Cochrane Library, EMBASE, and Web of Science for publications since 2010. We included systematic reviews and meta-analyses of clinical trials using DHI to promote health, behavioral, or lifestyle change.Results: Overall, 954 records were identified, and 72 systematic reviews were included. Of those, 35 conducted meta-analyses, 58 addressed diet/nutrition, and 60 focused on PA. Only one systematic review evaluated all six lifestyle domains simultaneously; 1 systematic review evaluated five lifestyle domains; 5 systematic reviews evaluated 4 lifestyle domains; 14 systematic reviews evaluated 3 lifestyle domains; and the remaining 52 systematic reviews evaluated only one or two domains. The most frequently evaluated domains were diet/nutrition and PA. The most frequent DHI delivery methods were smartphone apps and websites.Discussion: The concept of lifestyle is still unclear and fragmented, making it hard to evaluate the complex interconnections of unhealthy behaviors, and their impact on health. Clarifying this concept, refining its operationalization, and defining the reporting guidelines should be considered as the current research priorities. DHIs have the potential to improve lifestyle at primary, secondary, and tertiary levels of prevention—but most of them are targeting clinical populations. Although important advances have been made to evaluate DHIs, some of their characteristics, such as the rate at which they become obsolete, will require innovative research designs to evaluate long-term outcomes in health.

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“…Amongst the most important benefits identified by the patients are easy access to personalized information, convenience, better information on their health, and the ability to communicate more easily with healthcare professionals [ 5 , 6 ]. So far, most studies have focused on patients with cancer [ 7 , 8 , 9 ], patients with cardiovascular diseases [ 10 , 11 ], or older adults with [ 12 ] or without cognitive impairment [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Is mHealth a Useful Tool for Self-Assessment and Rehabilitation of People with Multiple Sclerosis? A Systematic Review

et al. 2021

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The development of mobile technology and mobile Internet offers new possibilities in rehabilitation and clinical assessment in a longitudinal perspective for multiple sclerosis management. However, because the mobile health applications (mHealth) have only been developed recently, the level of evidence supporting the use of mHealth in people with multiple sclerosis (pwMS) is currently unclear. Therefore, this review aims to list and describe the different mHealth available for rehabilitation and self-assessment of pwMS and to define the level of evidence supporting these interventions for functioning problems categorized within the International Classification of Functioning, Disability and Health (ICF). In total, 36 studies, performed with 22 different mHealth, were included in this review, 30 about rehabilitation and six for self-assessment, representing 3091 patients. For rehabilitation, most of the studies were focusing on cognitive function and fatigue. Concerning the efficacy, we found a small but significant effect of the use of mHealth for cognitive training (Standardized Mean Difference (SMD) = 0.28 [0.12; 0.45]) and moderate effect for fatigue (SMD = 0.61 [0.47; 0.76]). mHealth is a promising tool in pwMS but more studies are needed to validate these solutions in the other ICF categories. More replications studies are also needed as most of the mHealth have only been assessed in one single study.

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The Effect of Smartphone App–Based Interventions for Patients With Hypertension: Systematic Review and Meta-Analysis

Cited by 85 publications

References 40 publications

Perception and Initial Adoption of Mobile Health Services of Older Adults in London: Mixed Methods Investigation

Perception and Initial Adoption of Mobile Health Services of Older Adults in London: Mixed Methods Investigation

What Are We Measuring When We Evaluate Digital Interventions for Improving Lifestyle? A Scoping Meta-Review

Is mHealth a Useful Tool for Self-Assessment and Rehabilitation of People with Multiple Sclerosis? A Systematic Review

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