Improving patient engagement in self-measured blood pressure monitoring using a mobile health technology

Applications and outcomes of implementing telemedicine for hypertension management in COVID-19 pandemic: A systematic review

INTRODUCTION

COVID-19 presented a significant challenge for patients with hypertension in terms of access to care. However, telemedicine offered the healthcare system opportunities that had previously been underutilized. Therefore, this study aims to systematically review the applications and outcomes of telemedicine for hypertension management during the COVID-19 pandemic.

METHOD

A structured search was conducted in accordance with PRISMA guidelines across multiple databases, including PubMed, Cochrane, Web of Science, and Scopus. The search was limited to studies published from December 2019 until May 2023, resulting in a total of 3727 studies. After quality appraisal using the CASP checklists version 2018, 29 articles were included in the final review. Data analysis was performed using thematic analysis.

RESULTS

Most of the studies reviewed had used the proprietary platforms (N = 14) and 11 studies had used public platforms such as social messengers or email. Also 9 studies relied on phone calls (N = 9) to record and transmit the clinical data. Some studies had applied two different approaches (proprietary/public platforms and phone). six articles (20.7%) focused only on hypertension control, while 23 articles (79.3%) examined hypertension as a comorbidity with other diseases. Also, the study identified 88 unique concepts, 15 initial themes, and six final themes for outcomes of using telemedicine for hypertension management during the COVID-19 pandemic. These themes include BP control, BP measurement and recording, medication management, mental health, care continuity and use and acceptance.

CONCLUSION

Telemedicine provides patients with hypertension with the opportunity to engage in medical consultations in a more convenient and comfortable manner, with the same validity as in-person visits. Telemedicine facilitates the creation of a connected network to support patients with high BP at any time and in any location. Limitations and issues may arise due to patients and healthcare staff's unfamiliarity with telemedicine. These issues can be resolved through the ongoing use and continuous feedback.

Engagement in mHealth-Prompted Self-Measured Blood Pressure Monitoring Among Participants Recruited From a Safety-Net Emergency Department: Secondary Analysis of the Reach Out Trial

Background

Hypertension, a key modifiable risk factor for cardiovascular disease, is more prevalent among Black and low-income individuals. To address this health disparity, leveraging safety-net emergency departments for scalable mobile health (mHealth) interventions, specifically using text messaging for self-measured blood pressure (SMBP) monitoring, presents a promising strategy. This study investigates patterns of engagement, associated factors, and the impact of engagement on lowering blood pressure (BP) in an underserved population.

Objective

We aimed to identify patterns of engagement with prompted SMBP monitoring with feedback, factors associated with engagement, and the association of engagement with lowered BP.

Methods

This is a secondary analysis of data from Reach Out, an mHealth, factorial trial among 488 hypertensive patients recruited from a safety-net emergency department in Flint, Michigan. Reach Out participants were randomized to weekly or daily text message prompts to measure their BP and text in their responses. Engagement was defined as a BP response to the prompt. The k-means clustering algorithm and visualization were used to determine the pattern of SMBP engagement by SMBP prompt frequency-weekly or daily. BP was remotely measured at 12 months. For each prompt frequency group, logistic regression models were used to assess the univariate association of demographics, access to care, and comorbidities with high engagement. We then used linear mixed-effects models to explore the association between engagement and systolic BP at 12 months, estimated using average marginal effects.

Results

For both SMBP prompt groups, the optimal number of engagement clusters was 2, which we defined as high and low engagement. Of the 241 weekly participants, 189 (78.4%) were low (response rate: mean 20%, SD 23.4) engagers, and 52 (21.6%) were high (response rate: mean 86%, SD 14.7) engagers. Of the 247 daily participants, 221 (89.5%) were low engagers (response rate: mean 9%, SD 12.2), and 26 (10.5%) were high (response rate: mean 67%, SD 8.7) engagers. Among weekly participants, those who were older (>65 years of age), attended some college (vs no college), married or lived with someone, had Medicare (vs Medicaid), were under the care of a primary care doctor, and took antihypertensive medication in the last 6 months had higher odds of high engagement. Participants who lacked transportation to appointments had lower odds of high engagement. In both prompt frequency groups, participants who were high engagers had a greater decline in BP compared to low engagers.

Conclusions

Participants randomized to weekly SMBP monitoring prompts responded more frequently overall and were more likely to be classed as high engagers compared to participants who received daily prompts. High engagement was associated with a larger decrease in BP. New strategies to encourage engagement are needed for participants with lower access to care.

mHealth Intervention for Elevated Blood Pressure Among College Students: Single-Arm Intervention Study

BACKGROUND

Current evidence reveals a growing pattern of hypertension among young adults, significantly increasing their risk for cardiovascular disease later in life. Young adults, particularly those of college age, often develop risk factors related to lifestyle choices in diet, exercise, and alcohol consumption. Developing useful interventions that can assist with screening and possible behavioral modifications that are suitable and appealing to college-aged young adults could help with early identification and intervention for hypertension. Recent studies indicate mobile health (mHealth) apps are acceptable and effective for communication and message delivery among this population.

OBJECTIVE

The purpose of this study was to examine the feasibility of using a mobile smartphone delivery system that provides tailored messages based on participant self-measured blood pressure (BP) with college-aged young adults.

METHODS

Using a single-arm intervention, pilot study design, the mHealth to Optimize BP Improvement (MOBILE) intervention was implemented with college students aged 18 years to 39 years who had systolic BP >120 mm Hg and diastolic BP ≥80 mm Hg. Participants were required to measure their BP daily for 28 days, submit the readings to the app, and receive preset educational text messages tailored to their BP value and related to encouraging healthy lifestyle modifications. Changes in a participant's BP was evaluated using a mixed regression model, and a postintervention survey evaluated their perspectives on the mHealth intervention.

RESULTS

The participants' (N=9) mean age was 22.64 (SD 4.54) years; 56% (5/9) were overweight, and 11% (1/9) were obese. The average daily participation rate was 86%. Of the 9 participants, 8 completed the survey, and all indicated the intervention was easy to use, found it increased awareness of their individual BP levels, indicated the text messages were helpful, and reported making lifestyle changes based on the study intervention. They also provided suggestions for future implementation of the intervention and program. Overall, no significant changes were noted in BP over the 28 days.

CONCLUSIONS

The mHealth-supported MOBILE intervention for BP monitoring and tailored text messaging was feasible to implement, as our study indicated high rates of participation and acceptability. These encouraging findings support further development and testing in a larger sample over a longer time frame and hold the potential for early identification and intervention among college-aged adults, filling a gap in current research.

Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial

BACKGROUND

Multiple chronic conditions (multimorbidity) are becoming more prevalent among aging populations. Digital health technologies have the potential to assist in the self-management of multimorbidity, improving the awareness and monitoring of health and well-being, supporting a better understanding of the disease, and encouraging behavior change.

OBJECTIVE

The aim of this study was to analyze how 60 older adults (mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged with digital symptom and well-being monitoring when using a digital health platform over a period of approximately 12 months.

METHODS

Principal component analysis and clustering analysis were used to group participants based on their levels of engagement, and the data analysis focused on characteristics (eg, age, sex, and chronic health conditions), engagement outcomes, and symptom outcomes of the different clusters that were discovered.

RESULTS

Three clusters were identified: the typical user group, the least engaged user group, and the highly engaged user group. Our findings show that age, sex, and the types of chronic health conditions do not influence engagement. The 3 primary factors influencing engagement were whether the same device was used to submit different health and well-being parameters, the number of manual operations required to take a reading, and the daily routine of the participants. The findings also indicate that higher levels of engagement may improve the participants' outcomes (eg, reduce symptom exacerbation and increase physical activity).

CONCLUSIONS

The findings indicate potential factors that influence older adult engagement with digital health technologies for home-based multimorbidity self-management. The least engaged user groups showed decreased health and well-being outcomes related to multimorbidity self-management. Addressing the factors highlighted in this study in the design and implementation of home-based digital health technologies may improve symptom management and physical activity outcomes for older adults self-managing multimorbidity.

Mobile health technologies in the prevention and management of hypertension: A scoping review

Introduction

An estimated one billion people globally are currently suffering from hypertension. Prevention and management of hypertension are suboptimal especially in low- and middle-income countries leading to increased complications and deaths. With increased mobile phone coverage globally, this study aims to review mobile health technologies used for the prevention and management of hypertension.

Methods

We conducted a literature search on electronic databases using identified keywords involving "hypertension", "mobile health technology" and their synonyms. Snowballing technique was also used. Papers were screened at two levels by independent reviewers. The targets were studies published in peer-reviewed journals reporting mobile health interventions for hypertension prevention and management. Only primary research studies published in English from January 2017 to April 2024 were included. Google Forms were used to extract the data along with other characteristics, and selected articles were categorised into: mobile application, web-based solutions, and Short Message Service (SMS) and other offline solutions.

Result

The search yielded 184 articles, and 44 studies were included in the review. Most (n = 26) were randomised control trials. Twenty-two studies (22) focused only on mobile applications solutions, 12 on SMS and other offline mHealth, 5 web-based solutions, and 5 combined more than one type of mobile health technology. The United States of America had the majority of studies (n = 17), with 6 studies from other American countries, 11 from Asia and nine from Europe, while only one from Africa. A total of 36 studies reported that mobile health technology significantly improved hypertension care through reduced blood pressure, improved adherence to follow-up visits and medications, and lifestyle changes. SMS and offline mHealth strategies have also demonstrated effectiveness in promoting self-management and reducing racial disparities in hypertension care.

Conclusion

Mobile health technology has the potential to play a significant role in the prevention and management of hypertension. However, there is a need for mobile health solutions for hypertension prevention and management in African countries and other developing countries. Integrating mHealth into primary healthcare delivery would also go a long way in strengthening patient care and reducing the burden on healthcare systems.

Interindividual Variability in Self-Monitoring of Blood Pressure Using Consumer-Purchased Wireless Devices

BACKGROUND

Engagement with self-monitoring of blood pressure (BP) declines, on average, over time but may vary substantially by individual.

OBJECTIVES

We aimed to describe different 1-year patterns (groups) of self-monitoring of BP behaviors, identify predictors of those groups, and examine the association of self-monitoring of BP groups with BP levels over time.

METHODS

We analyzed device-recorded BP measurements collected by the Health eHeart Study-an ongoing prospective eCohort study-from participants with a wireless consumer-purchased device that transmitted date- and time-stamped BP data to the study through a full 12 months of observation starting from the first day they used the device. Participants received no instruction on device use. We applied clustering analysis to identify 1-year self-monitoring, of BP patterns.

RESULTS

Participants had a mean age of 52 years and were male and White. Using clustering algorithms, we found that a model with three groups fit the data well: persistent daily use (9.1% of participants), persistent weekly use (21.2%), and sporadic use only (69.7%). Persistent daily use was more common among older participants who had higher Week 1 self-monitoring of BP frequency and was associated with lower BP levels than the persistent weekly use or sporadic use groups throughout the year.

CONCLUSION

We identified three distinct self-monitoring of BP groups, with nearly 10% sustaining a daily use pattern associated with lower BP levels.

Towards Developing a Voice-activated Self-monitoring Application (VoiS) for Adults with Diabetes and Hypertension

The integration of motivational strategies and self-management theory with mHealth tools is a promising approach to changing the behavior of patients with chronic disease. In this manuscript, we describe the development and current architecture of a prototype voice-activated self-monitoring application (VoiS) which is based on these theories. Unlike prior mHealth applications which require textual input, VoiS app relies on the more convenient and adaptable approach of asking users to verbally input markers of diabetes and hypertension control through a smart speaker. The VoiS app can provide real-time feedback based on these markers; thus, it has the potential to serve as a remote, regular, source of feedback to support behavior change. To enhance the usability and acceptability of the VoiS application, we will ask a diverse group of patients to use it in real-world settings and provide feedback on their experience. We will use this feedback to optimize tool performance, so that it can provide patients with an improved understanding of their chronic conditions. The VoiS app can also facilitate remote sharing of chronic disease control with healthcare providers, which can improve clinical efficacy and reduce the urgency and frequency of clinical care encounters. Because the VoiS app will be configured for use with multiple platforms, it will be more robust than existing systems with respect to user accessibility and acceptability.

Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review

BACKGROUND

Mobile health (mHealth) apps show vast potential in supporting patients and health care systems with the increasing prevalence and economic costs of noncommunicable diseases (NCDs) worldwide. However, despite the availability of evidence-based mHealth apps, a substantial proportion of users do not adhere to them as intended and may consequently not receive treatment. Therefore, understanding the factors that act as barriers to or facilitators of adherence is a fundamental concern in preventing intervention dropouts and increasing the effectiveness of digital health interventions.

OBJECTIVE

This review aimed to help stakeholders develop more effective digital health interventions by identifying factors influencing the continued use of mHealth apps targeting NCDs. We further derived quantified adherence scores for various health domains to validate the qualitative findings and explore adherence benchmarks.

METHODS

A comprehensive systematic literature search (January 2007 to December 2020) was conducted on MEDLINE, Embase, Web of Science, Scopus, and ACM Digital Library. Data on intended use, actual use, and factors influencing adherence were extracted. Intervention-related and patient-related factors with a positive or negative influence on adherence are presented separately for the health domains of NCD self-management, mental health, substance use, nutrition, physical activity, weight loss, multicomponent lifestyle interventions, mindfulness, and other NCDs. Quantified adherence measures, calculated as the ratio between the estimated intended use and actual use, were derived for each study and compared with the qualitative findings.

RESULTS

The literature search yielded 2862 potentially relevant articles, of which 99 (3.46%) were included as part of the inclusion criteria. A total of 4 intervention-related factors indicated positive effects on adherence across all health domains: personalization or tailoring of the content of mHealth apps to the individual needs of the user, reminders in the form of individualized push notifications, user-friendly and technically stable app design, and personal support complementary to the digital intervention. Social and gamification features were also identified as drivers of app adherence across several health domains. A wide variety of patient-related factors such as user characteristics or recruitment channels further affects adherence. The derived adherence scores of the included mHealth apps averaged 56.0% (SD 24.4%).

CONCLUSIONS

This study contributes to the scarce scientific evidence on factors that positively or negatively influence adherence to mHealth apps and is the first to quantitatively compare adherence relative to the intended use of various health domains. As underlying studies mostly have a pilot character with short study durations, research on factors influencing adherence to mHealth apps is still limited. To facilitate future research on mHealth app adherence, researchers should clearly outline and justify the app's intended use; report objective data on actual use relative to the intended use; and, ideally, provide long-term use and retention data.

A Systematic Review of the Role of Telemedicine in Blood Pressure Control: Focus on Patient Engagement

Purpose of Review

To systematically review and synthesize the existing evidence on the effects of different telemedicine interventions on improving patient engagement among patients with hypertension. Patient engagement is defined as patients’ knowledge, skills, ability, and willingness to manage their healthcare within the context of interventions designed to promote positive patient behaviors.

Recent Findings

Telemedicine is a rapidly growing method of healthcare services delivery. Telemedicine interventions are mainly used to facilitate communication between the patient and provider, measure, record, and track blood pressure, and educate and train patients about managing their blood pressure. Findings from several studies indicate the evidence of patient engagement, adherence to the care plan, improvement in knowledge about blood pressure, and patient satisfaction with telemedicine interventions for blood pressure.

Summary

Telemedicine interventions need to be customized depending on patient demographics and socioeconomic characteristics such as age and education level to ensure optimal patient engagement.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11906-022-01186-5.

mHealth Interventions for Self-management of Hypertension: Framework and Systematic Review on Engagement, Interactivity, and Tailoring

BACKGROUND

Engagement is essential for the effectiveness of digital behavior change interventions. Existing systematic reviews examining hypertension self-management interventions via mobile apps have primarily focused on intervention efficacy and app usability. Engagement in the prevention or management of hypertension is largely unknown.

OBJECTIVE

This systematic review explores the definition and role of engagement in hypertension-focused mobile health (mHealth) interventions, as well as how determinants of engagement (ie, tailoring and interactivity) have been implemented.

METHODS

A systematic review of mobile app interventions for hypertension self-management targeting adults, published from 2013 to 2020, was conducted. A total of 21 studies were included in this systematic review.

RESULTS

The engagement was defined or operationalized as a microlevel concept, operationalized as interaction with the interventions (ie, frequency of engagement, time or duration of engagement with the program, and intensity of engagement). For all 3 studies that tested the relationship, increased engagement was associated with better biomedical outcomes (eg, blood pressure change). Interactivity was limited in digital behavior change interventions, as only 7 studies provided 2-way communication between users and a health care professional, and 9 studies provided 1-way communication in possible critical conditions; that is, when abnormal blood pressure values were recorded, users or health care professionals were notified. The tailoring of interventions varied at different aspects, from the tailoring of intervention content (including goals, patient education, advice and feedback from health professionals, reminders, and motivational messages) to the tailoring of intervention dose and communication mode. Tailoring was carried out in a number of ways, considering patient characteristics such as goals, preferences, disease characteristics (eg, hypertension stage and medication list), disease self-management experience levels, medication adherence rate, and values and beliefs.

CONCLUSIONS

Available studies support the importance of engagement in intervention effectiveness as well as the essential roles of patient factors in tailoring, interactivity, and engagement. A patient-centered engagement framework for hypertension self-management using mHealth technology is proposed here, with the intent of facilitating intervention design and disease self-management using mHealth technology.

Exploring and Characterizing Patient Multibehavior Engagement Trails and Patient Behavior Preference Patterns in Pathway-Based mHealth Hypertension Self-Management: Analysis of Use Data

Background

Hypertension is a long-term medical condition. Mobile health (mHealth) services can help out-of-hospital patients to self-manage. However, not all management is effective, possibly because the behavior mechanism and behavior preferences of patients with various characteristics in hypertension management were unclear.

Objective

The purpose of this study was to (1) explore patient multibehavior engagement trails in the pathway-based hypertension self-management, (2) discover patient behavior preference patterns, and (3) identify the characteristics of patients with different behavior preferences.

Methods

This study included 863 hypertensive patients who generated 295,855 use records in the mHealth app from December 28, 2016, to July 2, 2020. Markov chain was used to infer the patient multibehavior engagement trails, which contained the type, quantity, time spent, sequence, and transition probability value (TP value) of patient behavior. K-means algorithm was used to group patients by the normalized behavior preference features: the number of behavioral states that a patient performed in each trail. The pages in the app represented the behavior states. Chi-square tests, Z-test, analyses of variance, and Bonferroni multiple comparisons were conducted to characterize the patient behavior preference patterns.

Results

Markov chain analysis revealed 3 types of behavior transition (1-way transition, cycle transition, and self-transition) and 4 trails of patient multibehavior engagement. In perform task trail (PT-T), patients preferred to start self-management from the states of task blood pressure (BP), task drug, and task weight (TP value 0.29, 0.18, and 0.20, respectively), and spent more time on the task food state (35.87 s). Some patients entered the states of task BP and task drug (TP value 0.20, 0.25) from the reminder item state. In the result-oriented trail (RO-T), patients spent more energy on the ranking state (19.66 s) compared to the health report state (13.25 s). In the knowledge learning trail (KL-T), there was a high probability of cycle transition (TP value 0.47, 0.31) between the states of knowledge list and knowledge content. In the support acquisition trail (SA-T), there was a high probability of self-transition in the questionnaire (TP value 0.29) state. Cluster analysis discovered 3 patient behavior preference patterns: PT-T cluster, PT-T and KL-T cluster, and PT-T and SA-T cluster. There were statistically significant associations between the behavior preference pattern and gender, education level, and BP.

Conclusions

This study identified the dynamic, longitudinal, and multidimensional characteristics of patient behavior. Patients preferred to focus on BP, medications, and weight conditions and paid attention to BP and medications using reminders. The diet management and questionnaires were complicated and difficult to implement and record. Competitive methods such as ranking were more likely to attract patients to pay attention to their own self-management states. Female patients with lower education level and poorly controlled BP were more likely to be highly involved in hypertension health education.

Assessment of Hypertension Control Among Adults Participating in a Mobile Technology Blood Pressure Self-management Program

IMPORTANCE

It is unclear whether mobile technology hypertension self-management programs are associated with blood pressure (BP) control.

OBJECTIVE

To examine whether engagement with a hypertension self-management program with a BP monitor and connected smartphone application with clinically based digital coaching was associated with BP control during a follow-up period of as long as 3 years.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study enrolled US adults with elevated BP or hypertension between January 1, 2015, and July 1, 2020. The hypertension self-management program was provided through the participant's (or their spouse's) employer health plan.

EXPOSURES

Program engagement, defined by average number of application sessions.

MAIN OUTCOMES AND MEASURES

Systolic and diastolic BP measured by a US Food and Drug Administration-cleared BP monitor, with categories defined as normal (systolic BP, <120 mm Hg), elevated (systolic BP, 120-129 mm Hg), stage 1 hypertension (systolic BP, 130-139 mm Hg), and stage 2 hypertension (systolic BP ≥140 mm Hg). Other measures included age, gender, depression, anxiety, diabetes, high cholesterol, smoking, geographic region, area deprivation index, self-reported weight, and device-measured physical activity (steps per day).

RESULTS

Among 28 189 participants (median [IQR] age, 51 [43-58] years; 9424 women [40.4%]; 13 902 men [59.6%]), median (IQR) baseline systolic BP was 129.5 mm Hg (120.5-139.6 mm Hg) and diastolic BP was 81.7 mm Hg (75.7-88.4 mm Hg). Median systolic BP at 1 year improved at least 1 category for 495 of 934 participants (53.0%) with baseline elevated BP, 673 of 966 (69.7%) with baseline stage 1 hypertension, and 920 of 1075 (85.7%) with baseline stage 2 hypertension. Participants in the program for 3 years had a mean (SEM) systolic BP reduction of 7.2 (0.4), 12.2 (0.7), and 20.9 (1.7) mm Hg compared with baseline for those starting with elevated, stage 1 hypertension, and stage 2 hypertension, respectively. Greater engagement was associated with lower systolic BP over time (high-engagement group: 131.2 mm Hg; 95% CI, 115.5-155.8 mm Hg; medium-engagement group: 133.4 mm Hg; 95% CI 116.3-159.5 mm Hg; low-engagement group: 135.5 mm Hg; 95% CI, 117.3-164.8 mm Hg; P < .001); these results persisted after adjusting for age, gender, depression, anxiety, diabetes, high cholesterol, smoking, area deprivation index rank, and US region, which was partially mediated by greater physical activity. A very high BP (systolic BP >180 mm Hg) was observed 11 637 times from 3778 participants. Greater engagement was associated with lower risk of very high BP; the estimated probability of a very high BP was greater in the low-engagement group (1.42%; 95% CI, 1.26%-1.59%) compared with the medium-engagement group (0.79%; 95% CI, 0.71%-0.87%; P < .001) and the high-engagement group (0.53%; 95% CI, 0.45%-0.60%; P < .001 for comparison with both groups).

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that a mobile technology hypertension self-management program can support long-term BP control and very high BP detection. Such programs may improve real-world BP monitoring and control.

Patterns for Patient Engagement with the Hypertension Management and Effects of Electronic Health Care Provider Follow-up on These Patterns: Cluster Analysis

Background

Hypertension is a long-term medical condition. Electronic and mobile health care services can help patients to self-manage this condition. However, not all management is effective, possibly due to different levels of patient engagement (PE) with health care services. Health care provider follow-up is an intervention to promote PE and blood pressure (BP) control.

Objective

This study aimed to discover and characterize patterns of PE with a hypertension self-management app, investigate the effects of health care provider follow-up on PE, and identify the follow-up effects on BP in each PE pattern.

Methods

PE was represented as the number of days that a patient recorded self-measured BP per week. The study period was the first 4 weeks for a patient to engage in the hypertension management service. K-means algorithm was used to group patients by PE. There was compliance follow-up, regular follow-up, and abnormal follow-up in management. The follow-up effect was calculated by the change in PE (CPE) and the change in systolic blood pressure (CSBP, SBP) before and after each follow-up. Chi-square tests and z scores were used to ascertain the distribution of gender, age, education level, SBP, and the number of follow-ups in each cluster. The follow-up effect was identified by analysis of variances. Once a significant effect was detected, Bonferroni multiple comparisons were further conducted to identify the difference between 2 clusters.

Results

Patients were grouped into 4 clusters according to PE: (1) PE started low and dropped even lower (PELL), (2) PE started high and remained high (PEHH), (3) PE started high and dropped to low (PEHL), and (4) PE started low and rose to high (PELH). Significantly more patients over 60 years old were found in the PEHH cluster (P≤.05). Abnormal follow-up was significantly less frequent (P≤.05) in the PELL cluster. Compliance follow-up and regular follow-up can improve PE. In the clusters of PEHH and PELH, the improvement in PE in the first 3 weeks and the decrease in SBP in all 4 weeks were significant after follow-up. The SBP of the clusters of PELL and PELH decreased more (–6.1 mmHg and –8.4 mmHg) after follow-up in the first week.

Conclusions

Four distinct PE patterns were identified for patients engaging in the hypertension self-management app. Patients aged over 60 years had higher PE in terms of recording self-measured BP using the app. Once SBP reduced, patients with low PE tended to stop using the app, and a continued decline in PE occurred simultaneously with the increase in SBP. The duration and depth of the effect of health care provider follow-up were more significant in patients with high or increased engagement after follow-up.

Internet use habits and influenza vaccine uptake among US adults: results from seven years (2012-2018) of the National Health Interview Survey

STUDY DESIGN

This is a Cross-sectional data analysis study.

OBJECTIVES

Our goal was to examine the association between internet use habits and influenza vaccination uptake using a nationally representative sample of non-institutionalised US adults.

STUDY DESIGN

This is a Cross-sectional data analysis study.

METHODS

We pooled data from seven years (2012-2018) of the National Health Interview Survey for secondary data analysis (N = 220,570). We estimated influenza vaccination uptake among different population groups. We performed multivariable logistic regression models with influenza vaccination uptake as a dichotomous dependent variable.

RESULTS

Influenza vaccination uptake was highest among those who used the internet for formal health information and communication with a provider (55.1%), and lowest among those internet users who did not use the internet for any type of formal or informal health information and communication (35.6%). About 45.2% of non-internet users received an influenza vaccination during the last 12 months. After controlling for covariates, compared with those who did not use the internet, adults who used the internet for formal health information and communication with providers were 1.52 times more likely to uptake an influenza vaccine (odds ratio [OR] = 1.52; 95% confidence interval [CI] = 1.45-1.59). Internet users who did not use the internet for any health information were significantly less likely to get vaccinated against influenza (OR = 0.92; 95% CI = 0.88-0.96).

CONCLUSIONS

It appears that internet use habit impacts influenza vaccination uptake. Internet users who do not use the internet for any formal or informal health information tend to have lower rates of influenza vaccine uptake than other groups. Customised interventions for different populations based on their internet use habits can help increase the national influenza vaccination rate and other immunisation efforts for contagious diseases.

Association between real-world home blood pressure measurement patterns and blood pressure variability among older individuals with hypertension: A community-based blood pressure variability study

Home blood pressure (BP) monitoring is a useful tool for hypertension management. BP variability (BPV) has been associated with an increased risk of cardiovascular events. However, little is known about the correlation between BPV and different measurement patterns of long-term home BP monitoring. This longitudinal cohort study aimed to assess the associations between dynamic BP measurement patterns and BPV. A total of 1128 participants (mean age, 77.4 ± 9.3 years; male, 51%) with 23 269 behavior measuring units were included. We used sliding window sampling to classify the home BP data with a regular 6-month interval into units in a sliding manner until the data are not continuous. Three measurement patterns (stable frequent [SF], stable infrequent [SI], and unstable [US]) were assessed based on the home BP data obtained within the first 3 months of the study, and the data in the subsequent 3 months were used to assess the BPV of that unit. We used linear mixed-effects model to assess the association between BP measurement patterns and BPV with adjustment for possible confounding factors including average BP. Average real variability and coefficient variability were used as measures of the BPV. No significant differences were observed in average BP between the SF, SI, and US patterns. However, BPV in the SF group was significantly lower than that in the US and SI groups (all p-values < .05). The BPV in SI and US groups was not significantly different. A stable and frequent BP measuring pattern was independently associated with a lower BPV.

Deep learning-based ambient assisted living for self-management of cardiovascular conditions

According to the World Health Organization, cardiovascular diseases contribute to 17.7 million deaths per year and are rising with a growing ageing population. In order to handle these challenges, the evolved countries are now evolving workable solutions based on new communication technologies such as ambient assisted living. In these solutions, the most well-known solutions are wearable devices for patient monitoring, telemedicine and mHealth systems. This systematic literature review presents the detailed literature on ambient assisted living solutions and helps to understand how ambient assisted living helps and motivates patients with cardiovascular diseases for self-management to reduce associated morbidity and mortalities. Preferred reporting items for systematic reviews and meta-analyses technique are used to answer the research questions. The paper is divided into four main themes, including self-monitoring wearable systems, ambient assisted living in aged populations, clinician management systems and deep learning-based systems for cardiovascular diagnosis. For each theme, a detailed investigation shows (1) how these new technologies are nowadays integrated into diagnostic systems and (2) how new technologies like IoT sensors, cloud models, machine and deep learning strategies can be used to improve the medical services. This study helps to identify the strengths and weaknesses of novel ambient assisted living environments for medical applications. Besides, this review assists in reducing the dependence on caregivers and the healthcare systems.

Outcome measures used in the smartphone applications for the management of low back pain: a systematic scoping review

PURPOSE

Smartphone applications (SPApps) have become a key tool for the self-management of low back pain (LBP). However, the scientific evidence behind the outcome measures used in SPApps for LBP is never investigated before. Therefore, this systematic review firstly assess the quality of the free SPApps for LBP, secondly examines the outcome measures used and thirdly evaluates the outcome measures against the International Classification of Functioning, Disability and Health (ICF) core set classifications for LBP.

METHODS

A systematic scoping review was conducted in the iTunes and Google Play™ on-line stores for LBP SPApps which are free to download. These searches were conducted using keywords suggested by the Cochrane Back and Neck Group. SPApps were screened and downloaded to assess the quality using the Mobile App Rating Scale (MARS). SPApps using outcome measures were reviewed separately to evaluate whether their outcome measures represented any of the ICF components for LBP.

RESULTS

The overall quality of the apps has a mean MARS score of 2.5/5. Out of 74 apps reviewed, only four apps had outcome measures that could be linked to ICF components for LBP. Two of the four categories comprising the LBP core set were well represented.

CONCLUSION

The overall quality of the SPApps for LBP is low. Only very few SPApps offer outcome measures to monitor their effectiveness in the management of LBP. There is very limited evidence to show that the outcome measures used in the apps represents all the four core sets of LBP criteria set by ICF.

Using a Mobile App-Based International Classification of Functioning, Disability, and Health Set to Assess the Functioning of Spinal Cord Injury Patients: Rasch Analysis

Background

The International Classification of Functioning, Disability, and Health (ICF) is a unified system of functioning terminology that has been used to develop electronic health records and assessment instruments. Its application has been limited, however, by its complex terminology, numerous categories, uncertain operationalization, and the training required to use it well. Together is a mobile health app designed to extend medical support to the families of spinal cord injury (SCI) patients in China. The app’s core framework is a set of only 31 ICF categories. The app also provides rating guidelines and automatically transforms routine assessment results to the terms of the ICF qualifiers.

Objective

The goal of the research is to examine the suitability of the ICF set used in the app Together for use as an instrument for assessing the functioning of SCI patients.

Methods

A cross-sectional study was conducted including 112 SCI patients recruited before discharge from four rehabilitation centers in China between May 2018 and October 2019. Nurses used the app to assess patient functioning in face-to-face interviews. The resulting data were then subjected to Rasch analysis.

Results

After deleting two categories (family relationships and socializing) and one personal factor (knowledge about spinal cord injury) that did not fit the Rasch model, the body functions and body structures, activities and participation, and contextual factors components of the ICF exhibited adequate fit to the Rasch model. All three demonstrated acceptable person separation indices. The 28 categories retained in the set were free of differential item functioning by gender, age, education level, or etiology.

Conclusions

Together overcomes some of the obstacles to practical application of the ICF. The app is a reliable assessment tool for assessing functioning after spinal cord injury.

Provider perceptions of mHealth engagement for low-resourced, safety-net communities

OBJECTIVE

Evaluate nurses' and other health care professionals' (HCPs) perceptions about implementing mobile health technology (mHealth) in clinical practice to support health care delivery for low-resourced, safety-net communities.

DESIGN

Qualitative exploratory study using data collected from focus group sessions. Respondents addressed four topics: (1) technology's role in health care delivery; (2) barriers to incorporating mHealth data in clinical practice; (3) need for mHealth Clinical Practice Guide (CPG); and (4) mHealth's potential to improve health care access for marginalized communities.

SAMPLE

Thirty HCPs providing services to community health center patients in Washington State and Washington, DC.

MEASUREMENTS

Thematic analysis of qualitative data.

RESULTS

Themes included:(1) mHealth's ability to provide customized reminders and data accuracy; (2) patients' mistrust of technology; (3) the possibility of linking community resources to address the social determinants of health;(4) mHealth's potential to improve patient-provider communication.

CONCLUSION

Health care professionals support incorporating mHealth inpatient care but suggest that an mHealth CPG would improve its potential for facilitating health care delivery in low-resourced communities.

Characteristics of Future Models of Integrated Outpatient Care

Replacement of fee-for-service with capitation arrangements, forces physicians and institutions to minimize health care costs, while maintaining high-quality care. In this report we described how patients and their families (or caregivers) can work with members of the medical care team to achieve these twin goals of maintaining-and perhaps improving-high-quality care and minimizing costs. We described how increased self-management enables patients and their families/caregivers to provide electronic patient-reported outcomes (i.e., symptoms, events) (ePROs), as frequently as the patient or the medical care team consider appropriate. These capabilities also allow ongoing assessments of physiological measurements/phenomena (mHealth). Remote surveillance of these communications allows longer intervals between (fewer) patient visits to the medical-care team, when this is appropriate, or earlier interventions, when it is appropriate. Systems are now available that alert medical care providers to situations when interventions might be needed.

Effect of Health Information Technologies on Cardiovascular Risk Factors among Patients with Diabetes

PURPOSE OF REVIEW

To identify a common effect of health information technologies (HIT) on the management of cardiovascular disease (CVD) risk factors among people with type 2 diabetes (T2D) across randomized control trials (RCT).

RECENT FINDINGS

CVD is the most frequent cause of morbidity and mortality among patients with diabetes. HIT are effective in reducing HbA1c; however, their effect on cardiovascular risk factor management for patients with T2D has not been evaluated. We identified 21 eligible studies (23 estimates) with measurement of SBP, 20 (22 estimates) of DBP, 14 (17 estimates) of HDL, 14 (17 estimates) of LDL, 15 (18 estimates) of triglycerides, and 10 (12 estimates) of weight across databases. We found significant reductions in SBP, DBP, LDL, and TG, and a significant improvement in HDL associated with HIT. As adjuvants to standard diabetic treatment, HIT can be effective tools for improving CVD risk factors among patients with T2D, especially in those whose CVD risk factors are not at goal.

The Effects of Smartphone Applications on Patients Self-care with Hypertension: A Systematic Review Study

Introduction:

High blood pressure or hypertension is one of the chronic diseases causing other serious diseases and syndromes. Active involvement of the patient in the management of the disease is crucial in improving self-care and clinical outcomes. Mobile technology is nowadays used widely to improve the self-care process in people with chronic diseases such as hypertension.

Aim:

The objective of this study was to provide an overview of the existing research evaluating the impact of mobile applications on the self-care of patients with hypertension.

Methods:

The Scopus and PubMed databases were investigated using a comprehensive search strategy from the beginning of 2010 to 2019. All controlled clinical trial studies as well as quasi-experimental studies used mobile as a device for improving the self-care and conducted on patients with hypertension were included in the study. The studies were reviewed by two independent individuals.

Results:

Out of 1032 studies found, 6 studies were finally reviewed after applying the inclusion criteria. Out of 6 studies reviewed, three studies confirmed the effect of using mobile applications on lowering blood pressure. Other studies reported a decline in blood pressure, while statistically significant were not shown.

Conclusion:

The results showed that mobile apps have positive potential on improving the self-care behavior of patients with hypertension, but the evidences presenting their impact are varied. Different reports for efficiency of mobile phone apps for the self-care modification was due to diverse condition of studies for mobile intervention on the patients with hypertension.

Advanced and Accurate Mobile Health Tracking Devices Record New Cardiac Vital Signs

Cardiovascular disease remains the leading cause of death and disease worldwide. As demands on an already resource-constrained healthcare system intensify, disease prevention in the future will likely depend on out-of-office monitoring of cardiovascular risk factors. Mobile health tracking devices that can track blood pressure and heart rate, in addition to new cardiac vital signs, such as physical activity level and pulse wave velocity (PWV), offer a promising solution. An initial barrier is the development of accurate and easily-scalable platforms. In this study, we made a customized smartphone app and used mobile health devices to track PWV, blood pressure, heart rate, physical activity, sleep duration, and multiple lifestyle risk factors in ≈250 adults for 17 continual weeks. Eligible participants were identified by a company database and then were consented and enrolled using only a smartphone app, without any special training given. Study participants reported high overall satisfaction, and 73% of participants were able to measure blood pressure and PWV, <1 hour apart, for at least 14 of 17 weeks. The study population's blood pressure, PWV, heart rate, activity levels, sleep duration, and the interrelationships among these measurements were found to closely match either population averages or values obtained from studies performed in a controlled setting. As a proof-of-concept, we demonstrated the accuracy and ease, as well as many challenges, of using mHealth technology to accurately track PWV and new cardiovascular vital signs at home.