Effect of Mobile Phone Text Messaging Self-Management Support for Patients With Diabetes or Coronary Heart Disease in a Chronic Disease Management Program (SupportMe) on Blood Pressure: Pragmatic Randomized Controlled Trial

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Abstract

Background

Maintaining engagement and support for patients with chronic diseases is challenging. SMS text messaging programs have complemented patient care in a variety of situations. However, such programs have not been widely translated into routine care.

Objective

We aimed to examine the implementation and utility of a customized SMS text message–based support program for patients with type 2 diabetes (T2D), coronary heart disease, or both within a chronic disease integrated care program.

Methods

We conducted a 6-month pragmatic parallel-group, single-blind randomized controlled trial that recruited people with T2D or coronary heart disease. Intervention participants received 4 semipersonalized SMS text messages per week providing self-management support to supplement standard care. Preprogrammed algorithms customized content based on participant characteristics, and the messages were sent at random times of the day and in random order by a fully automated SMS text messaging engine. Control participants received standard care and only administrative SMS text messages. The primary outcome was systolic blood pressure. Evaluations were conducted face to face whenever possible by researchers blinded to randomization. Participants with T2D were evaluated for glycated hemoglobin level. Participant-reported experience measures were evaluated using questionnaires and focus groups and summarized using proportions and thematic analysis.

Results

A total of 902 participants were randomized (n=448, 49.7% to the intervention group and n=454, 50.3% to the control group). Primary outcome data were available for 89.5% (807/902) of the participants. At 6 months, there was no difference in systolic blood pressure between the intervention and control arms (adjusted mean difference=0.9 mm Hg, 95% CI −1.1 to 2.1; P=.38). Of 642 participants with T2D, there was no difference in glycated hemoglobin (adjusted mean difference=0.1%, 95% CI −0.1% to 0.3%; P=.35). Self-reported medication adherence was better in the intervention group (relative risk=0.82, 95% CI 0.68-1.00; P=.045). Participants reported that the SMS text messages were useful (298/344, 86.6%) and easily understood (336/344, 97.7%) and motivated change (217/344, 63.1%). The lack of bidirectional messaging was identified as a barrier.

Conclusions

The intervention did not improve blood pressure in this cohort, possibly because of high clinician commitment to improved routine patient care as part of the chronic disease management program as well as favorable baseline metrics. There was high program engagement, acceptability, and perceived value. Feasibility as part of an integrated care program was demonstrated. SMS text messaging programs may supplement chronic disease management and support self-care.

Trial Registration

Australian New Zealand Clinical Trials Registry ACTRN12616001689460; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=371769&isReview=true

International Registered Report Identifier (IRRID)

RR2-10.1136/bmjopen-2018-025923

Related collections

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Regression methods were used to select and score 12 items from the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) to reproduce the Physical Component Summary and Mental Component Summary scales in the general US population (n=2,333). The resulting 12-item short-form (SF-12) achieved multiple R squares of 0.911 and 0.918 in predictions of the SF-36 Physical Component Summary and SF-36 Mental Component Summary scores, respectively. Scoring algorithms from the general population used to score 12-item versions of the two components (Physical Components Summary and Mental Component Summary) achieved R squares of 0.905 with the SF-36 Physical Component Summary and 0.938 with SF-36 Mental Component Summary when cross-validated in the Medical Outcomes Study. Test-retest (2-week)correlations of 0.89 and 0.76 were observed for the 12-item Physical Component Summary and the 12-item Mental Component Summary, respectively, in the general US population (n=232). Twenty cross-sectional and longitudinal tests of empirical validity previously published for the 36-item short-form scales and summary measures were replicated for the 12-item Physical Component Summary and the 12-item Mental Component Summary, including comparisons between patient groups known to differ or to change in terms of the presence and seriousness of physical and mental conditions, acute symptoms, age and aging, self-reported 1-year changes in health, and recovery for depression. In 14 validity tests involving physical criteria, relative validity estimates for the 12-item Physical Component Summary ranged from 0.43 to 0.93 (median=0.67) in comparison with the best 36-item short-form scale. Relative validity estimates for the 12-item Mental Component Summary in 6 tests involving mental criteria ranged from 0.60 to 107 (median=0.97) in relation to the best 36-item short-form scale. Average scores for the 2 summary measures, and those for most scales in the 8-scale profile based on the 12-item short-form, closely mirrored those for the 36-item short-form, although standard errors were nearly always larger for the 12-item short-form.

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Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015

Haidong Wang, Mohsen Naghavi, Christine Allen … (2017)

Summary Background Improving survival and extending the longevity of life for all populations requires timely, robust evidence on local mortality levels and trends. The Global Burden of Disease 2015 Study (GBD 2015) provides a comprehensive assessment of all-cause and cause-specific mortality for 249 causes in 195 countries and territories from 1980 to 2015. These results informed an in-depth investigation of observed and expected mortality patterns based on sociodemographic measures. Methods We estimated all-cause mortality by age, sex, geography, and year using an improved analytical approach originally developed for GBD 2013 and GBD 2010. Improvements included refinements to the estimation of child and adult mortality and corresponding uncertainty, parameter selection for under-5 mortality synthesis by spatiotemporal Gaussian process regression, and sibling history data processing. We also expanded the database of vital registration, survey, and census data to 14 294 geography–year datapoints. For GBD 2015, eight causes, including Ebola virus disease, were added to the previous GBD cause list for mortality. We used six modelling approaches to assess cause-specific mortality, with the Cause of Death Ensemble Model (CODEm) generating estimates for most causes. We used a series of novel analyses to systematically quantify the drivers of trends in mortality across geographies. First, we assessed observed and expected levels and trends of cause-specific mortality as they relate to the Socio-demographic Index (SDI), a summary indicator derived from measures of income per capita, educational attainment, and fertility. Second, we examined factors affecting total mortality patterns through a series of counterfactual scenarios, testing the magnitude by which population growth, population age structures, and epidemiological changes contributed to shifts in mortality. Finally, we attributed changes in life expectancy to changes in cause of death. We documented each step of the GBD 2015 estimation processes, as well as data sources, in accordance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). Findings Globally, life expectancy from birth increased from 61·7 years (95% uncertainty interval 61·4–61·9) in 1980 to 71·8 years (71·5–72·2) in 2015. Several countries in sub-Saharan Africa had very large gains in life expectancy from 2005 to 2015, rebounding from an era of exceedingly high loss of life due to HIV/AIDS. At the same time, many geographies saw life expectancy stagnate or decline, particularly for men and in countries with rising mortality from war or interpersonal violence. From 2005 to 2015, male life expectancy in Syria dropped by 11·3 years (3·7–17·4), to 62·6 years (56·5–70·2). Total deaths increased by 4·1% (2·6–5·6) from 2005 to 2015, rising to 55·8 million (54·9 million to 56·6 million) in 2015, but age-standardised death rates fell by 17·0% (15·8–18·1) during this time, underscoring changes in population growth and shifts in global age structures. The result was similar for non-communicable diseases (NCDs), with total deaths from these causes increasing by 14·1% (12·6–16·0) to 39·8 million (39·2 million to 40·5 million) in 2015, whereas age-standardised rates decreased by 13·1% (11·9–14·3). Globally, this mortality pattern emerged for several NCDs, including several types of cancer, ischaemic heart disease, cirrhosis, and Alzheimer's disease and other dementias. By contrast, both total deaths and age-standardised death rates due to communicable, maternal, neonatal, and nutritional conditions significantly declined from 2005 to 2015, gains largely attributable to decreases in mortality rates due to HIV/AIDS (42·1%, 39·1–44·6), malaria (43·1%, 34·7–51·8), neonatal preterm birth complications (29·8%, 24·8–34·9), and maternal disorders (29·1%, 19·3–37·1). Progress was slower for several causes, such as lower respiratory infections and nutritional deficiencies, whereas deaths increased for others, including dengue and drug use disorders. Age-standardised death rates due to injuries significantly declined from 2005 to 2015, yet interpersonal violence and war claimed increasingly more lives in some regions, particularly in the Middle East. In 2015, rotaviral enteritis (rotavirus) was the leading cause of under-5 deaths due to diarrhoea (146 000 deaths, 118 000–183 000) and pneumococcal pneumonia was the leading cause of under-5 deaths due to lower respiratory infections (393 000 deaths, 228 000–532 000), although pathogen-specific mortality varied by region. Globally, the effects of population growth, ageing, and changes in age-standardised death rates substantially differed by cause. Our analyses on the expected associations between cause-specific mortality and SDI show the regular shifts in cause of death composition and population age structure with rising SDI. Country patterns of premature mortality (measured as years of life lost [YLLs]) and how they differ from the level expected on the basis of SDI alone revealed distinct but highly heterogeneous patterns by region and country or territory. Ischaemic heart disease, stroke, and diabetes were among the leading causes of YLLs in most regions, but in many cases, intraregional results sharply diverged for ratios of observed and expected YLLs based on SDI. Communicable, maternal, neonatal, and nutritional diseases caused the most YLLs throughout sub-Saharan Africa, with observed YLLs far exceeding expected YLLs for countries in which malaria or HIV/AIDS remained the leading causes of early death. Interpretation At the global scale, age-specific mortality has steadily improved over the past 35 years; this pattern of general progress continued in the past decade. Progress has been faster in most countries than expected on the basis of development measured by the SDI. Against this background of progress, some countries have seen falls in life expectancy, and age-standardised death rates for some causes are increasing. Despite progress in reducing age-standardised death rates, population growth and ageing mean that the number of deaths from most non-communicable causes are increasing in most countries, putting increased demands on health systems. Funding Bill & Melinda Gates Foundation.

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Mobile Telephone Text Messaging for Medication Adherence in Chronic Disease: A Meta-analysis.

Jay Thakkar, Rahul Kurup, Tracey-Lea Laba … (2016)

Adherence to long-term therapies in chronic disease is poor. Traditional interventions to improve adherence are complex and not widely effective. Mobile telephone text messaging may be a scalable means to support medication adherence.

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Author and article information

Contributors

Ngai Wah Cheung:

ORCID: https://orcid.org/0000-0001-6323-8006

Department of Diabetes & EndocrinologyWestmead HospitalHawkesbury RdWestmead, 2145Australia61 288916796wah.cheung@sydney.edu.au

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J Med Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: 2023

Publication date (Electronic): 16 June 2023

Volume: 25

Electronic Location Identifier: e38275

Affiliations

[1 ] Department of Diabetes & Endocrinology Westmead Hospital Westmead Australia

[2 ] Westmead Applied Research Centre Faculty of Medicine & Health University of Sydney Westmead Australia

[3 ] School of Health Sciences Faculty of Medicine & Health University of Sydney Sydney Australia

[4 ] Department of Cardiology Westmead Hospital Westmead Australia

[5 ] Blacktown Hospital Blacktown Australia

[6 ] Department of Respiratory Medicine Westmead Hospital Westmead Australia

[7 ] See Acknowledgments

Author notes

Corresponding Author: Ngai Wah Cheung wah.cheung@ 123456sydney.edu.au

Author information

Ngai Wah Cheung https://orcid.org/0000-0001-6323-8006

Julie Redfern https://orcid.org/0000-0001-8707-5563

Aravinda Thiagalingam https://orcid.org/0000-0002-7763-7806

Tien-Ming Hng https://orcid.org/0000-0001-6813-8813

Simone Marschner https://orcid.org/0000-0002-5484-9144

Rabbia Haider https://orcid.org/0000-0002-2912-9989

Sonia Faruquie https://orcid.org/0009-0004-3853-7070

Amy Von Huben https://orcid.org/0000-0001-6168-9719

Shelley She https://orcid.org/0000-0002-4411-5673

Daniel McIntyre https://orcid.org/0000-0003-4854-4050

Jin-Gun Cho https://orcid.org/0000-0002-0735-4691

Clara K Chow https://orcid.org/0000-0003-4693-0038

Article

Publisher ID: v25i1e38275

DOI: 10.2196/38275

PMC ID: 10337246

PubMed ID: 37327024

SO-VID: 160eef93-917a-475f-92d1-f79eb692648a

Copyright © ©Ngai Wah Cheung, Julie Redfern, Aravinda Thiagalingam, Tien-Ming Hng, Simone Marschner, Rabbia Haider, Sonia Faruquie, Amy Von Huben, Shelley She, Daniel McIntyre, Jin-Gun Cho, Clara K Chow, The SupportMe Investigators. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.06.2023.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 26 March 2022

Date revision requested : 19 May 2022

Date revision received : 7 September 2022

Date accepted : 21 February 2023

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