Impact of Home Blood Pressure Data Visualization on Hypertension Medical Decision Making in Primary Care.

PURPOSE: Evidence shows the value of home blood pressure (BP) monitoring in hypertension management. Questions exist about how to effectively incorporate these readings into BP follow-up visits. We developed and implemented a tool that combines clinical and home BP readings into an electronic health record (EHR)-integrated visualization tool. We examined how this tool was used during primary care visits and its effect on physician-patient communication and decision making about hypertension management, comparing it with home BP readings on paper. METHODS: We video recorded the hypertension follow-up visits of 73 patients with 15 primary care physicians between July 2018 and April 2019. During visits, physicians reviewed home BP readings with patients, either directly from paper or as entered into the EHR visualization tool. We used conversation analysis to analyze the recordings. RESULTS: Home BP readings were viewed on paper for 26 patients and in the visualization tool for 47 patients. Access to home BP readings during hypertension management visits, regardless of viewing mode, positioned the physician and patient to assess BP management and make decisions about treatment modification, if needed. Length of BP discussion with the visualization tool was similar to or shorter than that with paper. Advantages of the visualization tool included ease of use, and enhanced and faster sense making and decision making. Successful use of the tool required patients' ability to obtain their BP readings and enter them into the EHR via a portal, and an examination room configuration that allowed for screen sharing. CONCLUSIONS: Reviewing home BP readings using a visualization tool is feasible and enhances sense making and patient engagement in decision making. Practices and their patients need appropriate infrastructure to realize these benefits.

Home blood pressure data visualization for the management of hypertension: using human factors and design principles.

BACKGROUND: Home blood pressure measurements have equal or even greater predictive value than clinic blood pressure measurements regarding cardiovascular outcomes. With advances in home blood pressure monitors, we face an imminent flood of home measurements, but current electronic health record systems lack the functionality to allow us to use this data to its fullest. We designed a data visualization display for blood pressure measurements to be used for shared decision making around hypertension. METHODS: We used an iterative, rapid-prototyping, user-centred design approach to determine the most appropriate designs for this data display. We relied on visual cognition and human factors principles when designing our display. Feedback was provided by expert members of our multidisciplinary research team and through a series of end-user focus groups, comprised of either hypertensive patients or their healthcare providers required from eight academic, community-based practices in the Midwest of the United States. RESULTS: A total of 40 participants were recruited to participate in patient (N = 16) and provider (N = 24) focus groups. We describe the conceptualization and development of data display for shared decision making around hypertension. We designed and received feedback from both patients and healthcare providers on a number of design elements that were reported to be helpful in understanding blood pressure measurements. CONCLUSIONS: We developed a data display for substantial amounts of blood pressure measurements that is both simple to understand for patients, but powerful enough to inform clinical decision making. The display used a line graph format for ease of understanding, a LOWESS function for smoothing data to reduce the weight users placed on outlier measurements, colored goal range bands to allow users to quickly determine if measurements were in range, a medication timeline to help link recorded blood pressure measurements with the medications a patient was taking. A data display such as this, specifically designed to encourage shared decision making between hypertensive patients and their healthcare providers, could help us overcome the clinical inertia that often results in a lack of treatment intensification, leading to better care for the 35 million Americans with uncontrolled hypertension.

Home blood pressure data visualization for the management of hypertension: designing for patient and physician information needs.

BACKGROUND: Nearly half of US adults with diagnosed hypertension have uncontrolled blood pressure. Clinical inertia may contribute, including patient-physician uncertainty about how variability in blood pressures impacts overall control. Better information display may support clinician-patient hypertension decision making through reduced cognitive load and improved situational awareness. METHODS: A multidisciplinary team employed iterative user-centered design to create a blood pressure visualization EHR prototype that included patient-generated blood pressure data. An attitude and behavior survey and 10 focus groups with patients (N = 16) and physicians (N = 24) guided iterative design and confirmation phases. Thematic analysis of qualitative data yielded insights into patient and physician needs for hypertension management. RESULTS: Most patients indicated measuring home blood pressure, only half share data with physicians. When receiving home blood pressure data, 88% of physicians indicated entering gestalt averages as text into clinical notes. Qualitative findings suggest that including a data visualization that included home blood pressures brought this valued data into physician workflow and decision-making processes. Data visualization helps both patients and physicians to have a fuller understanding of the blood pressure 'story' and ultimately promotes the activated engaged patient and prepared proactive physician central to the Chronic Care Model. Both patients and physicians expressed concerns about workflow for entering and using home blood pressure data for clinical care. CONCLUSIONS: Our user-centered design process with physicians and patients produced a well-received blood pressure visualization prototype that includes home blood pressures and addresses patient-physician information needs. Next steps include evaluating a recent EHR visualization implementation, designing annotation functions aligned with users' needs, and addressing additional stakeholders' needs (nurses, care managers, caregivers). This significant innovation has potential to improve quality of care for hypertension through better patient-physician understanding of control and goals. It also has the potential to enable remote monitoring of patient blood pressure, a newly reimbursed activity, and is a strong addition to telehealth efforts.

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.

Patient Judgments About Hypertension Control: The Role of Variability, Trends, and Outliers in Visualized Blood Pressure Data.

BACKGROUND: Uncontrolled hypertension is a significant health problem in the United States, even though multiple drugs exist to effectively treat this chronic disease. OBJECTIVE: As part of a larger project developing data visualizations to support shared decision making about hypertension treatment, we conducted a series of studies to understand how perceptions of hypertension control were impacted by data variations inherent in the visualization of blood pressure (BP) data. METHODS: In 3 Web studies, participants (internet sample of patients with hypertension) reviewed a series of vignettes depicting patients with hypertension; each vignette included a graph of a patient's BP. We examined how data visualizations that varied by BP mean and SD (Study 1), the pattern of change over time (Study 2), and the presence of extreme values (Study 3) affected patients' judgments about hypertension control and the need for a medication change. RESULTS: Participants' judgments about hypertension control were significantly influenced by BP mean and SD (Study 1), data trends (whether BP was increasing or decreasing over time-Study 2), and extreme values (ie, outliers-Study 3). CONCLUSIONS: Patients' judgment about hypertension control is influenced both by factors that are important predictors of hypertension related-health outcomes (eg, BP mean) and factors that are not (eg, variability and outliers). This study highlights the importance of developing data visualizations that direct attention toward clinically meaningful information.

Effect of peer support interventions on cardiovascular disease risk factors in adults with diabetes: a systematic review and meta-analysis.

BACKGROUND: Peer support by persons affected with diabetes improves peer supporter's diabetes self-management skills. Peer support interventions by individuals who have diabetes or are affected by diabetes have been shown to improve glycemic control; however, its effects on other cardiovascular disease risk factors in adults with diabetes are unknown. We aimed to estimate the effect of peer support interventions on cardiovascular disease risk factors other than glycemic control in adults with diabetes. METHODS: We conducted a systematic review and meta-analysis of randomized controlled trials comparing peer support interventions to a control condition in adults affected by diabetes that measured any cardiovascular disease risk factors [Body Mass Index, smoking, diet, physical activity, cholesterol level, glucose control and blood pressure]. Quality was assessed by Cochrane's risk of bias tool. We calculated standardized mean difference effect sizes using random effects models. RESULTS: We retrieved 438 citations from multiple databases including OVID MEDLINE, Cochrane database and Scopus, and author searches. Of 233 abstracts reviewed, 16 articles met inclusion criteria. A random effects model in a total of 3243 participants showed a positive effect of peer support interventions on systolic BP with a pooled effect size of 2.07 mmHg (CI 0.35 mmHg to 3.79 mmHg, p = 0.02); baseline pooled systolic blood pressure was 137 mmHg. There was a non-significant effect of peer support interventions on diastolic blood pressure, cholesterol, body mass index, diet and physical activity. Cardiovascular disease risk factors other than glycemic control outcomes were secondary outcomes in most studies and baseline values were normal or mildly elevated. Only one study reported smoking outcomes. CONCLUSIONS: We found a small (2 mmHg) positive effect of peer support interventions on systolic blood pressure in adults with diabetes whose baseline blood pressure was on average minimally elevated. Additional studies need to be conducted to further understand the effect of peer support interventions on high-risk cardiovascular disease risk factors in adults with diabetes.

Peer Support Interventions for Adults With Diabetes: A Meta-Analysis of Hemoglobin A1c Outcomes.

PURPOSE: Peer support intervention trials have shown varying effects on glycemic control. We aimed to estimate the effect of peer support interventions delivered by people affected by diabetes (those with the disease or a caregiver) on hemoglobin A1c (HbA1c) levels in adults. METHODS: We searched multiple databases from 1960 to November 2015, including Ovid MEDLINE, the Cochrane Central Register of Controlled Trials, CINAHL, and Scopus. We included randomized controlled trials (RCTs) of adults with diabetes receiving peer support interventions compared with otherwise similar care. Seventeen of 205 retrieved studies were eligible for inclusion. Quality was assessed with the Cochrane risk of bias tool. We calculated the standardized mean difference (SMD) of change in HbA1c level from baseline between groups using a random effects model. Subgroup analyses were predefined. RESULTS: Seventeen studies (3 cluster RCTs, 14 RCTs) with 4,715 participants showed an improvement in pooled HbA1c level with an SMD of 0.121 (95% CI, 0.026-0.217; P = .01; I2 = 60.66%) in the peer support intervention group compared with the control group; this difference translated to an improvement in HbA1c level of 0.24% (95% CI, 0.05%-0.43%). Peer support interventions showed an HbA1c improvement of 0.48% (95% CI, 0.25%-0.70%; P <.001; I2 = 17.12%) in the subset of studies with predominantly Hispanic participants and 0.53% (95% CI, 0.32%-0.73%; P <.001; I2 = 9.24%) in the subset of studies with predominantly minority participants; both were clinically relevant. In sensitivity analysis excluding cluster RCTs, the overall effect size changed little. CONCLUSIONS: Peer support interventions for diabetes overall achieved a statistically significant but minor improvement in HbA1c levels. These interventions may, however, be particularly effective in improving glycemic control for people from minority groups, especially those of Hispanic ethnicity.

A New Paradigm of Technology-Enabled 'Vital Signs' for Early Detection of Health Change for Older Adults.

Environmentally embedded (nonwearable) sensor technology is in continuous use in elder housing to monitor a new set of 'vital signs' that continuously measure the functional status of older adults, detect potential changes in health or functional status, and alert healthcare providers for early recognition and treatment of those changes. Older adult participants' respiration, pulse, and restlessness are monitored as they sleep. Gait speed, stride length, and stride time are calculated daily, and automatically assess for increasing fall risk. Activity levels are summarized and graphically displayed for easy interpretation. Falls are detected when they occur and alerts are sent immediately to healthcare providers, so time to rescue may be reduced. Automated health alerts are sent to healthcare staff, based on continuously running algorithms applied to the sensor data, days and weeks before typical signs or symptoms are detected by the person, family members, or healthcare providers. Discovering these new functional status 'vital signs', developing automated methods for interpreting them, and alerting others when changes occur have the potential to transform chronic illness management and facilitate aging in place through the end of life. Key findings of research in progress at the University of Missouri are discussed in this viewpoint article, as well as obstacles to widespread adoption.

Enhanced registered nurse care coordination with sensor technology: Impact on length of stay and cost in aging in place housing.

BACKGROUND: When planning the Aging in Place Initiative at TigerPlace, it was envisioned that advances in technology research had the potential to enable early intervention in health changes that could assist in proactive management of health for older adults and potentially reduce costs. PURPOSE: The purpose of this study was to compare length of stay (LOS) of residents living with environmentally embedded sensor systems since the development and implementation of automated health alerts at TigerPlace to LOS of those who are not living with sensor systems. Estimate potential savings of living with sensor systems. METHODS: LOS for residents living with and without sensors was measured over a span of 4.8 years since the implementation of sensor-generated health alerts. The group living with sensors (n = 52) had an average LOS of 1,557 days (4.3 years); the comparison group without sensors (n = 81) was 936 days (2.6 years); p = .0006. Groups were comparable based on admission age, gender, number of chronic illnesses, SF12 physical health, SF12 mental health, Geriatric Depression Scale (GDS), activities of daily living, independent activities of daily living, and mini-mental status examination scores. Both groups, all residents living at TigerPlace since the implementation of health alerts, receive registered nurse (RN) care coordination as the standard of care. DISCUSSION: Results indicate that residents living with sensors were able to reside at TigerPlace 1.7 years longer than residents living without sensors, suggesting that proactive use of health alerts facilitates successful aging in place. Health alerts, generated by automated algorithms interpreting environmentally embedded sensor data, may enable care coordinators to assess and intervene on health status changes earlier than is possible in the absence of sensor-generated alerts. Comparison of LOS without sensors TigerPlace (2.6 years) with the national median in residential senior housing (1.8 years) may be attributable to the RN care coordination model at TigerPlace. Cost estimates comparing cost of living at TigerPlace with the sensor technology vs. nursing home reveal potential saving of about $30,000 per person. Potential cost savings to Medicaid funded nursing home (assuming the technology and care coordination were reimbursed) are estimated to be about $87,000 per person. CONCLUSIONS: Early alerts for potential health problems appear to enhance the current RN care coordination care delivery model at TigerPlace, increasing LOS for those living with sensors to nearly twice that of those who did not. Sensor technology with care coordination has cost saving potential for consumers and Medicaid.

Development of the PRE-HIT instrument: patient readiness to engage in health information technology.

BACKGROUND: Technology-based aids for lifestyle change are becoming more prevalent for chronic conditions. Important "digital divides" remain, as well as concerns about privacy, data security, and lack of motivation. Researchers need a way to characterize participants' readiness to use health technologies. To address this need, we created an instrument to measure patient readiness to engage with health technologies among adult patients with chronic conditions. METHODS: Initial focus groups to determine domains, followed by item development and refinement, and exploratory factor analysis to determine final items and factor structure. The development sample included 200 patients with chronic conditions from 6 family medicine clinics. From 98 potential items, 53 best candidate items were examined using exploratory factor analysis. Pearson's Correlation for Test/Retest reliability at 3 months. RESULTS: The final instrument had 28 items that sorted into 8 factors with associated Cronbach's alpha: 1) Health Information Need (0.84), 2) Computer/Internet Experience (0.87), 3) Computer Anxiety (0.82), 4) Preferred Mode of Interaction (0.73), 5) Relationship with Doctor (0.65), 6) Cell Phone Expertise (0.75), 7) Internet Privacy (0.71), and 8) No News is Good News (0.57). Test-retest reliability for the 8 subscales ranged from (0.60 to 0.85). CONCLUSION: The Patient Readiness to Engage in Health Internet Technology (PRE-HIT) instrument has good psychometric properties and will be an aid to researchers investigating technology-based health interventions. Future work will examine predictive validity.

Successful weight loss: how information technology is used to lose.

BACKGROUND: Commercial producers have outpaced traditional academic healthcare in terms of novel repackaging of traditional approaches to weight control for online delivery. Little is known, however, about consumers' experiences with such products and services. We explored ways that people use information technology (IT) to facilitate health-related behavioral change. MATERIALS AND METHODS: Qualitative methods and grounded theory methodology were used to analyze transcripts of audiotaped material from three focus groups, each with 12 participants. Participants were recruited from an ongoing, community-wide weight-loss event, who reported regular or frequent use of IT. RESULTS: Participants frequently used IT applications for completing functional tasks associated with weight loss. In contrast, most participants preferred in-person social support. IT applications facilitated integration of behavior change tasks into everyday life. Despite easy access to a range of social networking Web sites and tools, however, having access to in-person social support was targeted as critical to successful weight loss and well-being. To this end, the role of work peers and work environments was emphasized by these participants. CONCLUSIONS: In terms of patient care, successful health portals may benefit from either developing or integrating existing IT applications that save time and/or provide users with visual feedback on progress toward goals. IT-delivered resources would likely optimize community-based behavioral health interventions that target naturally occurring social groups.

Implementing home blood glucose and blood pressure telemonitoring in primary care practices for patients with diabetes: lessons learned.

BACKGROUND: Prior telemonitoring trials of blood pressure and blood glucose have shown improvements in blood pressure and glycemic targets. However, implementation of telemonitoring in primary care practices may not yield the same results as research trials with extra resources and rigid protocols. In this study we examined the process of implementing home telemonitoring of blood glucose and blood pressure for patients with diabetes in six primary care practices. MATERIALS AND METHODS: Grounded theory qualitative analysis was conducted in parallel with a randomized controlled effectiveness trial of home telemonitoring. Data included semistructured interviews with 6 nurse care coordinators and 12 physicians in six participating practices and field notes from exit interviews with 93 of 108 randomized patients. RESULTS: The three stakeholder groups (patients, nurse care coordinators, and physicians) exhibited some shared themes and some unique to the particular stakeholder group. Major themes were that practices should (1) understand the capabilities and limitations of the technology and the willingness of patient and physician stakeholders to use it, (2) understand the workflow, flow of information, and human factors needed to optimize use of the technology, (3) engage and prepare the physicians, and (4) involve the patient in the process. Although there was enthusiasm for a patient-centered medical home model that included between-visit telemonitoring, there was concern about the support and resources needed to provide this service to patients. CONCLUSIONS: As with many technology interventions, careful consideration of workflow and information flow will help enable effective implementations.

Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes.

OBJECTIVE: Patient self-management support may be augmented by using home-based technologies that generate data points that providers can potentially use to make more timely changes in the patients' care. The purpose of this study was to evaluate the effectiveness of short-term targeted use of remote data transmission on treatment outcomes in patients with diabetes who had either out-of-range hemoglobin A1c (A1c) and/or blood pressure (BP) measurements. MATERIALS AND METHODS: A single-center randomized controlled clinical trial design compared in-home monitoring (n=55) and usual care (n=53) in patients with type 2 diabetes and hypertension being treated in primary care clinics. Primary outcomes were A1c and systolic BP after a 12-week intervention. RESULTS: There were no significant differences between the intervention and control groups on either A1c or systolic BP following the intervention. CONCLUSIONS: The addition of technology alone is unlikely to lead to improvements in outcomes. Practices need to be selective in their use of telemonitoring with patients, limiting it to patients who have motivation or a significant change in care, such as starting insulin. Attention to the need for effective and responsive clinic processes to optimize the use of the additional data is also important when implementing these types of technology.

Clinician Needs for Electronic Health Record Pediatric and Adolescent Weight Management Tools: A Mixed-Methods Study.

BACKGROUND: Clinicians play an important role in addressing pediatric and adolescent obesity, but their effectiveness is restricted by time constraints, competing clinical demands, and the lack of effective electronic health record (EHR) tools. EHR tools are rarely developed with provider input. OBJECTIVES: We conducted a mixed method study of clinicians who provide weight management care to children and adolescents to determine current barriers for effective care and explore the role of EHR weight management tools to overcome these barriers. METHODS: In this mixed-methods study, we conducted three 1-hour long virtual focus groups at one medium-sized academic health center in Missouri and analyzed the focus group scripts using thematic analysis. We sequentially conducted a descriptive statistical analysis of a survey emailed to pediatric and family medicine primary care clinicians (n = 52) at two private and two academic health centers in Missouri. RESULTS: Surveyed clinicians reported that they effectively provided health behavior lifestyle counseling at well-child visits (mean of 60 on a scale of 1-100) and child obesity visits (63); however, most felt the current health care system (27) and EHR tools (41) do not adequately support pediatric weight management. Major themes from the clinician focus groups were that EHR weight management tools should display data in a way that (1) improves clinical efficiency, (2) supports patient-centered communication, (3) improves patient continuity between visits, and (4) reduces documentation burdens. An additional theme was (5) clinicians trust patient data entered in real time over patient recalled data. CONCLUSION: Study participants report that the health care system status quo and currently available EHR tools do not sufficiently support clinicians working to manage pediatric or adolescent obesity and provide health behavior counseling. Clinician input in the development and testing of EHR weight management tools provides opportunities to address barriers, inform content, and improve efficiencies of EHR use.

Evaluation of health alerts from an early illness warning system in independent living.

Passive sensor networks were deployed in independent living apartments to monitor older adults in their home environments to detect signs of impending illness and alert clinicians so they can intervene and prevent or delay significant changes in health or functional status. A retrospective qualitative deductive content analysis was undertaken to refine health alerts to improve clinical relevance to clinicians as they use alerts in their normal workflow of routine care delivery to older adults. Clinicians completed written free-text boxes to describe actions taken (or not) as a result of each alert; they also rated the clinical significance (relevance) of each health alert on a scale of 1 to 5. Two samples of the clinician's written responses to the health alerts were analyzed after alert algorithms had been adjusted based on results of a pilot study using health alerts to enhance clinical decision-making. In the first sample, a total of 663 comments were generated by seven clinicians in response to 385 unique alerts; there are more comments than alerts because more than one clinician rated the same alert. The second sample had a total of 142 comments produced by three clinicians in response to 88 distinct alerts. The overall clinical relevance of the alerts, as judged by the content of the qualitative comments by clinicians for each alert, improved from 33.3% of the alerts in the first sample classified as clinically relevant to 43.2% in the second. The goal is to produce clinically relevant alerts that clinicians find useful in daily practice. The evaluation methods used are described to assist others as they consider building and iteratively refining health alerts to enhance clinical decision making.

Information needs and information-seeking behaviour analysis of primary care physicians and nurses: a literature review.

BACKGROUND: The increase in the adoption of electronic health records (EHR) has contributed to physicians and nurses experiencing information overload. To address the problem of information overload, an assessment of the information needs of physicians and nurses will assist in understanding what they view as useful information to make patient care more efficient. OBJECTIVE: To analyse studies that assessed the information needs and information-seeking behaviour of physicians and nurses in a primary care setting to develop a better understanding of what information to present to physicians when they making clinical decisions. METHOD: A literature review of studies was conducted with a comprehensive search in PubMed, cinahl, scopus, as well as examination of references from relevant papers and hand-searched articles to identify articles applicable to this review. RESULTS: Of the papers reviewed the most common information needs found among physicians and nurses were related to diagnoses, drug(s) and treatment/therapy. Colleagues remain a preferred information source among physicians and nurses; however, a rise in Internet usage is apparent. CONCLUSION: Physicians and nurses need access to the Internet and job-specific resources to find practitioner-oriented information. In addition, effective usage of resources is important for improving patient care.

Infrastructure Features Associated With Increased Department Research Capacity.

PURPOSE: Although researchers have identified factors associated with research capacity in academic medicine departments, less is known about how a department builds research capacity over time. The Association of Departments of Family Medicine's Research Capacity Scale (RCS) can be used by departments to self-categorize into five capacity levels. Our current study aimed to describe the distribution of infrastructure features and to evaluate how the addition of infrastructure features influences movement of a department along the RCS. METHOD: An online survey was sent to US family medicine department chairs in August 2021. Survey questions asked chairs to categorize their department's research capacity in 2018 and 2021 and also about the presence of infrastructure resources and changes in these features across 6 years. RESULTS: The response rate was 54.2%. Departments identified substantial variation in research capacity. Most departments classified into the middle three levels. Departments in higher levels were more likely than those at lower levels to have any of the infrastructure resources in 2021. Department size, as measured by full time faculty, was highly associated with department level. From 2018 and 2021, 43% of responding departments climbed at least one level. Of these, more than half added three or more infrastructure features. Adding a PhD researcher was the feature most associated with increasing research capacity (P<.001). CONCLUSIONS: Most departments that increased their research capacity added multiple additional infrastructure features. For chairs of departments without a PhD researcher, this additional resource may be the most impactful investment to increase research capacity.

Mixed-methods study of development and design needs for CommitFit, an adolescent mHealth App.

Background: Adolescent obesity remains a significant public health issue within the United States. Mobile application technology growth and popularity offer new opportunities for research and health improvement. The development of a consolidated mobile health application (mHealth app) for adolescents on these platforms has the potential to improve health outcomes. Thus, this study describes the co-development process working with adolescent users. The aims are as follows: (I) to explore the visual design and functional requirements when developing the CommitFit mHealth app, (II) to uncover the gamification techniques that incentivize adolescents to set and achieve healthy lifestyle goals, and (III) to identify adolescent expectations when using the CommitFit mHealth application. Methods: In this mixed method study, we used semi-structured interviews/task analysis and surveys of adolescents (aged 13 to 15 years) to understand their user requirements and design preferences during the development of the CommitFit mHealth app. Interviews were conducted online, via Zoom. The survey included the user design industry-standard System Usability Scale (SUS) paired with a supplemental questionnaire on the specific features and functionalities of the CommitFit mHealth app. Participants were recruited from the electronic health record from the University of Missouri Healthcare system. Results: Ten adolescents, aged 13 to 15 years (average of 13.6 years), were interviewed and surveyed to explore adolescent preferences with visual app design and functionality. Our inductive thematic analysis found that adolescents preferred colorful, user-friendly interfaces paired with gamification in the CommitFit mHealth app. Our analysis of SUS survey data validated our user-centered and human-system design and adolescents confirmed their design, feature, and functionality preferences. Overall, adolescent users were able to confirm their preference to have educational resources, goal recommendations, leaderboard, points, reminders, and an avatar in the app. Conclusions: Adolescent feedback is crucial in the successful development of our adolescent-targeted mHealth app, CommitFit. Adolescents preferred vibrant colors, easy-to-use interface, gamification, customizable and personalized, and mature graphics. Adolescents were especially motivated by gamification techniques to maintain their interest in the application and their health behavior goals. Additional research is now needed to explore the clinical effectiveness of the CommitFit mHealth app, as a health and lifestyle intervention.

Automated technology to speed recognition of signs of illness in older adults.

Our team has developed a technological innovation that detects changes in health status that indicate impending acute illness or exacerbation of chronic illness before usual assessment methods or self-reports of illness. We successfully used this information in a 1-year prospective study to alert health care providers so they could readily assess the situation and initiate early treatment to improve functional independence. Intervention participants showed significant improvements (as compared with the control group) for the Short Physical Performance Battery gait speed score at Quarter 3 (p = 0.03), hand grip-left at Quarter 2 (p = 0.02), hand grip-right at Quarter 4 (p = 0.05), and the GAITRite functional ambulation profile score at Quarter 2 (p = 0.05). Technological methods such as these could be widely adopted in older adult housing, long-term care settings, and in private homes where older adults wish to remain independent for as long as possible.

Development and Pilot Analysis of the Nursing Home Health Information Technology Maturity Survey and Staging Model.

The current research includes a psychometric test of a nursing home (NH) health information technology (HIT) maturity survey and staging model. NHs were assembled based on HIT survey scores from a prior study representing NHs with low (20%), medium (60%), and high (20%) HIT scores. Inclusion criteria were NHs that completed at least two annual surveys over 4 years. NH administrators were excluded who participated in the Delphi panel responsible for instrument recommendations. Recruitment occurred from January to May 2019. Administrators from 121 of 429 facilities completed surveys. NHs were characteristically for-profit, medium bed size, and metropolitan. A covariance matrix demonstrated that all dimensions and domains were significantly correlated, except HIT capabilities and integration in administrative activities. Cronbach's alpha was very good (0.86). Principal component analysis revealed all items loaded intuitively onto four components, explaining 80% variance. The HIT maturity survey and staging model can be used to assess nine dimensions and domains, total HIT maturity, and stage, leading to reliable assumptions about NH HIT. [Research in Gerontological Nursing, 15(2), 93-99.].