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Sakima A, Akagi Y, Akasaki Y, Fujii T, Haze T, Kawakami-Mori F, Kitajima K, Kobayashi Y, Matayoshi T, Sakaguchi T, Yamazato M, Abe M, Ohya Y, Arima H. Effectiveness of digital health interventions for telemedicine/telehealth for managing blood pressure in adults: a systematic review and meta-analysis. Hypertens Res 2024:10.1038/s41440-024-01792-7. [PMID: 38977877 DOI: 10.1038/s41440-024-01792-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 06/15/2024] [Indexed: 07/10/2024]
Abstract
This systematic review and meta-analysis included randomized controlled trials or observational studies that compare digital health interventions (DHIs) for telemedicine/telehealth versus usual care for managing blood pressure (BP) in adults. We searched PubMed, Cochrane CENTRAL, and IchuShi-Web, and used a random-effects meta-analysis of the weighted mean difference (MD) between the comparison groups to pool data from the included studies. The outcome included the pooled MD of office BP from baseline to each follow-up period. This meta-analysis considered 117 studies with 68677 participants as eligible. The 3-month intervention period reduced office systolic BP (SBP) compared with usual care in 38 studies (MD: -3.21 mmHg [95% confidence interval: -4.51 to -1.90]), with evidence of heterogeneity. Office SBP across intervention periods demonstrated comparable effects (3-, 6- [54 studies], 12- [43 studies], and >12-month periods [9 studies]). The benefits for office diastolic BP were similar to those for office SBP. Additionally, the interventions significantly reduced the office SBP compared with the control, regardless of the mode of intervention delivery (smartphone apps [38 studies], text messages [35 studies], and websites [34 studies]) or type of facility (medical [74 studies] vs. non-medical [33 studies]). The interventions were more effective in 41 hypertension cohorts compared with 66 non-hypertension cohorts (-4.81 mmHg [-6.33, -3.29] vs. -2.17 mmHg [-3.15, -1.19], P = 0.006 for heterogeneity). In conclusion, DHIs for telemedicine/telehealth improved BP management compared with usual care. The effectiveness with heterogeneity should be considered, as prudent for implementing evidence-based medicine. This meta-analysis considered 117 studies with 68677 participants eligible. The DHIs for telemedicine/telehealth reduced office BP compared with usual care, regardless of intervention duration, intervention delivery mode, facility type, and cohort type. Additionally, the DHIs reduced the risk of uncontrolled BP compared with usual care, regardless of intervention duration, intervention delivery mode, and facility type. BP blood pressure, DHI digital health intervention, MD mean difference, RR risk ratio, SBP systolic blood pressure.
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Affiliation(s)
- Atsushi Sakima
- Health Administration Center, University of the Ryukyus, Okinawa, Japan.
| | - Yuya Akagi
- Division of Health Sciences, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yuichi Akasaki
- Department of Cardiovascular Medicine and Hypertension, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan
| | - Takako Fujii
- Department of Preventive Medicine and Public Health, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Tatsuya Haze
- YCU Center for Novel and Exploratory Clinical Trials (Y-NEXT), Yokohama City University Hospital, Kanagawa, Japan
| | - Fumiko Kawakami-Mori
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Mitsui Memorial Hospital, Tokyo, Japan
| | - Ken Kitajima
- Department of Cardiology, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Yusuke Kobayashi
- Co-Creation Innovation Center, Yokohama City University, Kanagawa, Japan
| | | | - Takashi Sakaguchi
- Department of Pediatrics, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | | | - Makiko Abe
- Department of Preventive Medicine and Public Health, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Yusuke Ohya
- University Hospital of the Ryukyus, Okinawa, Japan
| | - Hisatomi Arima
- Department of Preventive Medicine and Public Health, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
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Castro O, Mair JL, Salamanca-Sanabria A, Alattas A, Keller R, Zheng S, Jabir A, Lin X, Frese BF, Lim CS, Santhanam P, van Dam RM, Car J, Lee J, Tai ES, Fleisch E, von Wangenheim F, Tudor Car L, Müller-Riemenschneider F, Kowatsch T. Development of "LvL UP 1.0": a smartphone-based, conversational agent-delivered holistic lifestyle intervention for the prevention of non-communicable diseases and common mental disorders. Front Digit Health 2023; 5:1039171. [PMID: 37234382 PMCID: PMC10207359 DOI: 10.3389/fdgth.2023.1039171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 04/06/2023] [Indexed: 05/28/2023] Open
Abstract
Background Non-communicable diseases (NCDs) and common mental disorders (CMDs) are the leading causes of death and disability worldwide. Lifestyle interventions via mobile apps and conversational agents present themselves as low-cost, scalable solutions to prevent these conditions. This paper describes the rationale for, and development of, "LvL UP 1.0″, a smartphone-based lifestyle intervention aimed at preventing NCDs and CMDs. Materials and Methods A multidisciplinary team led the intervention design process of LvL UP 1.0, involving four phases: (i) preliminary research (stakeholder consultations, systematic market reviews), (ii) selecting intervention components and developing the conceptual model, (iii) whiteboarding and prototype design, and (iv) testing and refinement. The Multiphase Optimization Strategy and the UK Medical Research Council framework for developing and evaluating complex interventions were used to guide the intervention development. Results Preliminary research highlighted the importance of targeting holistic wellbeing (i.e., both physical and mental health). Accordingly, the first version of LvL UP features a scalable, smartphone-based, and conversational agent-delivered holistic lifestyle intervention built around three pillars: Move More (physical activity), Eat Well (nutrition and healthy eating), and Stress Less (emotional regulation and wellbeing). Intervention components include health literacy and psychoeducational coaching sessions, daily "Life Hacks" (healthy activity suggestions), breathing exercises, and journaling. In addition to the intervention components, formative research also stressed the need to introduce engagement-specific components to maximise uptake and long-term use. LvL UP includes a motivational interviewing and storytelling approach to deliver the coaching sessions, as well as progress feedback and gamification. Offline materials are also offered to allow users access to essential intervention content without needing a mobile device. Conclusions The development process of LvL UP 1.0 led to an evidence-based and user-informed smartphone-based intervention aimed at preventing NCDs and CMDs. LvL UP is designed to be a scalable, engaging, prevention-oriented, holistic intervention for adults at risk of NCDs and CMDs. A feasibility study, and subsequent optimisation and randomised-controlled trials are planned to further refine the intervention and establish effectiveness. The development process described here may prove helpful to other intervention developers.
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Affiliation(s)
- Oscar Castro
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Jacqueline Louise Mair
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Alicia Salamanca-Sanabria
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Aishah Alattas
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Roman Keller
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Shenglin Zheng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Ahmad Jabir
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
| | - Xiaowen Lin
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
| | - Bea Franziska Frese
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions,Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Chang Siang Lim
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Prabhakaran Santhanam
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Rob M. van Dam
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington DC, DC, United States
| | - Josip Car
- Centre for Population Health Sciences, LKCMedicine, Nanyang Technological University, Singapore, Singapore
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Jimmy Lee
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
- Research Division, Institute of Mental Health, Singapore, Singapore
- North Region & Department of Psychosis, Institute of Mental Health, Singapore, Singapore
| | - E Shyong Tai
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Elgar Fleisch
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions,Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Florian von Wangenheim
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Lorainne Tudor Car
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Falk Müller-Riemenschneider
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Digital Health Center, Berlin Institute of Health, Charite University Medical Centre Berlin, Berlin, Germany
| | - Tobias Kowatsch
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- School of Medicine, University of St. Gallen, St. Gallen, Switzerland
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Jeem YA, Andriani RN, Nabila R, Emelia DD, Lazuardi L, Koesnanto H. The Use of Mobile Health Interventions for Outcomes among Middle-Aged and Elderly Patients with Prediabetes: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13638. [PMID: 36294218 PMCID: PMC9603799 DOI: 10.3390/ijerph192013638] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/30/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND There are currently limited systematic reviews of mobile health interventions for middle-aged and elderly patients with prediabetes from trial studies. This review aimed to gather and analyze information from experimental studies investigating the efficacy of mobile health usability for outcomes among middle-aged and elderly patients with prediabetes. METHODS We conducted a literature search in five databases: Clinicaltrials.gov, the International Clinical Trials Registry Platform (ICTRP), PubMed, ProQuest, and EBSCO, with a date range of January 2007 to July 2022 written in English, following a registered protocol on PROSPERO (CRD42022354351). The quality and possibility of bias were assessed using the Jadad score. The data extraction and analysis were conducted in a methodical manner. RESULTS A total of 25 studies were included in the qualitative synthesis, with 19 studies using randomized trial designs and 6 studies with non-randomized designs. The study outcomes were the incidence of diabetes mellitus, anthropometric measures, laboratory examinations, measures of physical activity, and dietary behavior. During long-term follow-up, there was no significant difference between mobile health interventions and controls in reducing the incidence of type 2 diabetes. The findings of the studies for weight change, ≥3% and ≥5% weight loss, body mass index, and waist circumference changes were inconsistent. The efficacy of mobile health as an intervention for physical activity and dietary changes was lacking in conclusion. Most studies found that mobile health lacks sufficient evidence to change hbA1c. According to most of these studies, there was no significant difference in blood lipid level reduction. CONCLUSIONS The use of mobile health was not sufficiently proven to be effective for middle-aged and elderly patients with prediabetes.
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Affiliation(s)
- Yaltafit Abror Jeem
- Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
- Faculty of Medicine, Universitas Islam Indonesia, Yogyakarta 55584, Indonesia
| | - Russy Novita Andriani
- Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
- Faculty of Medicine, Universitas Islam Indonesia, Yogyakarta 55584, Indonesia
| | - Refa Nabila
- Faculty of Medicine, Universitas Islam Indonesia, Yogyakarta 55584, Indonesia
| | - Dwi Ditha Emelia
- Faculty of Medicine, Universitas Islam Indonesia, Yogyakarta 55584, Indonesia
| | - Lutfan Lazuardi
- Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
| | - Hari Koesnanto
- Department of Family and Community Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
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