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  Demographics of Users, Social &...

  Oct 23, 2018

  Oncology Patient Interest in the Use of New Technologies to Manage Their Disease: Cross-Sectional Survey

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  Authors List:

  • Roberto Collado-Borrell  
  • Vicente Escudero-Vilaplana  
  • Antonio Calles  
  • Estela Garcia-Martin  
  • Belen Marzal-Alfaro  
  • Eva Gonzalez-Haba  
  • Ana Herranz-Alonso  
  • Maria Sanjurjo-Saez  

  Abstract:

  Background: Information and communication technologies (ICTs) in oncology can revolutionize the medical care of cancer patients. ICTs can promote patients’ empowerment and real-time disease monitoring. There is limited information about the impact of ICTs in cancer patients or their level of interest in using these tools for greater management of their condition. Objective: This study aimed to understand the ICT usage profile in hematology-oncology patients to identify their needs and determine their level of interest in these technologies as a means of managing their disease. Methods: A 28-item questionnaire was drawn up by a multidisciplinary team including pharmacists and oncologists. The questions were organized into 3 blocks, which were as follows: block A—sociodemographic characteristics; block B—use of ICTs when searching for health-related information; and block C—usage preferences for health apps. Hematology-oncology patients receiving treatment between May and July 2017 were included. A paper copy of the questionnaire was handed over to patients in either the day hospital or the pharmaceutical care consultancy in pharmacy services. Results: A total of 650 questionnaires were handed out, with a participation of 94.0% (611/650). Patient sociodemographic characteristics were as follows: mean age was 57.8 years (age range: 19-91). Of 611 participants, 40.7% (249/611) had a university education, and 45.1% (276/611) of participants reported their overall state of health to be good. Results from use of ICTs when searching for health-related information were as follows: 87.1% (532/611) of participants were interested in being informed about health-related matters. Of all participants, 75.5% (532/611) sought information from health professionals and 61.3% (375/611) on the internet. Before going to their doctor’s appointment, 21.8% (133/611) of patients looked up information about their disease or treatment on the internet. This access to the internet rose to 50.9% (311/611) after their first medical appointment with their oncologist. Usage preferences for health apps were as follows: 82.7% (505/611) had a smartphone, whereas 20.3% (124/611) had a health app installed. Overall, 81.5% (498/611) would use an app if their health professional recommended it to them, but 39.6% (242/611) were not willing to pay for it. Conclusions: The hematology-oncology patients showed a great deal of interest in searching for health-related information by means of ICTs, especially using smartphones and apps. The issues that drew the most interest in terms of apps were appointment management, advice on disease management, and communication with health professionals. Free access to these features and the recommendation by a health professional are important factors when it comes to their use. Therefore, the health care provider is a key element in the recommendation of ICTs, providing their knowledge and experience concerning their correct usage.

  • J Med Internet Res 2018;20(10):e11006

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  Web-based and Mobile Health...

  Oct 23, 2018

  Effect of an eHealth Intervention to Reduce Sickness Absence Frequency Among Employees With Frequent Sickness Absence: Randomized Controlled Trial

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  • Annette Notenbomer  
  • Corné Roelen  
  • Johan Groothoff  
  • Willem van Rhenen  
  • Ute Bültmann  

  Abstract:

  Background: Frequent sickness absence—that is, 3 or more episodes of sickness absence in 1 year—is a problem for employers and employees. Many employees who have had frequent sickness absence in a prior year also have frequent sickness absence in subsequent years: 39% in the first follow-up year and 61% within 4 years. Moreover, 19% have long-term sickness absence (≥6 weeks) in the first follow-up year and 50% within 4 years. We developed an electronic health (eHealth) intervention, consisting of fully automated feedback and advice, to use either as a stand-alone tool (eHealth intervention–only) or combined with consultation with an occupational physician (eHealth intervention–occupational physician). Objective: This study aimed to evaluate the effect of the eHealth intervention, with or without additional occupational physician consultation, to reduce sickness absence frequency for employees with frequent sickness absence, versus care as usual (CAU). Methods: This study was a three-armed randomized controlled trial. Employees with frequent sickness absence received invitational letters, which were distributed by their employers. The primary outcome measure was the number of register-based sickness absence episodes 12 months after completing the baseline questionnaire. Secondary outcome measures were register-based total sickness absence days and self-assessed burnout, engagement, and work ability. In a process evaluation 3 months after baseline, we examined adherence to the intervention and additional actions such as general practitioner and occupational physician visit, communication with the manager, and lifestyle change. Results: A total of 82 participants were included in the analyses, 21 in the eHealth intervention–only group, 31 in the eHealth intervention–occupational physician group, and 30 in the CAU group. We found no significant difference in sickness absence frequency between the groups at 1-year follow-up. Sickness absence frequency decreased in the eHealth intervention–only group from 3 (interquartile range, IQR 3-4) to 1 episode (IQR 0.3-2.8), in the eHealth intervention–occupational physician group from 4 (IQR 3-5) to 3 episodes (IQR 1-4), and in the CAU group from 3 (IQR 3-4) to 2 episodes (IQR 1-3). For secondary outcomes, we found no significant differences between the intervention groups and the control group. The process evaluation showed that only 3 participants from the eHealth intervention–occupational physician group visited the occupational physician on invitation. Conclusions: Among employees with frequent sickness absence, we found no effect from the eHealth intervention as a stand-alone tool in reducing sickness absence frequency, nor on total sickness absence days, burnout, engagement, or work ability. This might be due to low adherence to the intervention because of insufficient urgency to act. We cannot draw any conclusion on the effect of the eHealth intervention tool combined with an occupational physician consultation (eHealth intervention–occupational physician), due to very low adherence to the occupational physician consultation. An occupational physician consultation could increase a sense of urgency and lead to more focus and appropriate support. As this was the first effectiveness study among employees with frequent sickness absence, strategies to improve recruitment and adherence in occupational eHealth are included. Trial Registration: Netherlands Trial Register NTR4316; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4316 (Archived by WebCite at http://www.webcitation.org/713DHhOFU).

  • J Med Internet Res 2018;20(10):e10821

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  Web-based and Mobile Health...

  Oct 22, 2018

  A Mobile Videoconference-Based Intervention on Stress Reduction and Resilience Enhancement in Employees: Randomized Controlled Trial

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  Authors List:

  • Johanna Inyang Kim  
  • Je-Yeon Yun  
  • Heyeon Park  
  • Suk-Young Park  
  • Youngsheen Ahn  
  • Hansol Lee  
  • Tae-Kwon Kim  
  • Sooran Yoon  
  • Young-Joon Lee  
  • Sohee Oh  
  • John W. Denninger  
  • Bung-Nyun Kim  
  • Jeong-Hyun Kim  

  Abstract:

  Background: Videoconferencing-based treatments have shown great potential in increasing engagement and compliance by decreasing the barriers of time and distance. In general, employees tend to experience a lot of stress, but find it difficult to visit a clinic during office hours. Objective: The purpose of this study was to investigate the effectiveness of a mobile videoconference-based intervention for stress reduction and resilience enhancement in employees. Methods: In total, 81 participants were randomly allocated to one of the three conditions: mobile videoconferencing, in-person, and self-care; of these, 72 completed the study. All participants underwent assessment via self-reported questionnaires before, immediately after, and 1 month after the intervention. Intervention lasted for 4 weeks and consisted of elements of cognitive behavioral therapy, positive psychology, and meditation. Changes in clinical variables regarding stress and resilience across time were compared between treatment conditions. Results: There were significant condition × time effects on variables measuring perceived stress, resilience, emotional labor, and sleep, demonstrating significantly differential effects across time according to treatment condition. Moreover, there were significant effects of condition on perceived stress and occupational stress. There were no significant differences in any variable between the mobile videoconferencing and in-person conditions at 1 month after the intervention. Conclusions: Results indicate that both mobile videoconferencing and in-person interventions were comparably effective in decreasing stress and enhancing resilience. Further studies with a larger sample size and a longer follow-up period are warranted to investigate the long-term effect of mobile videoconferencing interventions. Trial Registration: ClinicalTrials.gov identifier NCT03256682; https://clinicaltrials.gov/ct2/show/NCT03256682 (Archived by WebCite at http://www.webcitation.org/71W77bwnR)

  • J Med Internet Res 2018;20(10):e10760

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  Special Issue on Computing and Mental...

  Oct 22, 2018

  Group-Personalized Regression Models for Predicting Mental Health Scores From Objective Mobile Phone Data Streams: Observational Study

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  Authors List:

  • Niclas Palmius  
  • Kate E A Saunders  
  • Oliver Carr  
  • John R Geddes  
  • Guy M Goodwin  
  • Maarten De Vos  

  Abstract:

  Background: Objective behavioral markers of mental illness, often recorded through smartphones or wearable devices, have the potential to transform how mental health services are delivered and to help users monitor their own health. Linking objective markers to illness is commonly performed using population-level models, which assume that everyone is the same. The reality is that there are large levels of natural interindividual variability, both in terms of response to illness and in usual behavioral patterns, as well as intraindividual variability that these models do not consider. Objective: The objective of this study was to demonstrate the utility of splitting the population into subsets of individuals that exhibit similar relationships between their objective markers and their mental states. Using these subsets, “group-personalized” models can be built for individuals based on other individuals to whom they are most similar. Methods: We collected geolocation data from 59 participants who were part of the Automated Monitoring of Symptom Severity study at the University of Oxford. This was an observational data collection study. Participants were diagnosed with bipolar disorder (n=20); borderline personality disorder (n=17); or were healthy controls (n=22). Geolocation data were collected using a custom Android app installed on participants’ smartphones, and participants weekly reported their symptoms of depression using the 16-item quick inventory of depressive symptomatology questionnaire. Population-level models were built to estimate levels of depression using features derived from the geolocation data recorded from participants, and it was hypothesized that results could be improved by splitting individuals into subgroups with similar relationships between their behavioral features and depressive symptoms. We developed a new model using a Dirichlet process prior for splitting individuals into groups, with a Bayesian Lasso model in each group to link behavioral features with mental illness. The result is a model for each individual that incorporates information from other similar individuals to augment the limited training data available. Results: The new group-personalized regression model showed a significant improvement over population-level models in predicting mental health severity (P<.001). Analysis of subgroups showed that different groups were characterized by different features derived from raw geolocation data. Conclusions: This study demonstrates the importance of handling interindividual variability when developing models of mental illness. Population-level models do not capture nuances in how different individuals respond to illness, and the group-personalized model demonstrates a potential way to overcome these limitations when estimating mental state from objective behavioral features.

  • J Med Internet Res 2018;20(10):e10194

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• Letters

  Oct 22, 2018

  Authors’ Reply: Comment on “A New Cuffless Device for Measuring Blood Pressure: A Real-Life Validation Study”

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  Authors List:

  • Sebastian Bredie  
  • Tom van de Belt  
  • Harry van Goor  

  Abstract:

  • J Med Internet Res 2018;20(10):e12477

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• Letters

  Oct 22, 2018

  Blood Pressure Measurement Validation Off the Cuff? Comment on “A New Cuffless Device for Measuring Blood Pressure: A Real-Life Validation Study”

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  Authors List:

  • Noud van Helmond  
  • Timothy B. Plante  
  • Jeffrey I. Joseph  

  Abstract:

  • J Med Internet Res 2018;20(10):e10089

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  Demographics of Users, Social &...

  Oct 22, 2018

  Modeling and Predicting Outcomes of eHealth Usage by European Physicians: Multidimensional Approach from a Survey of 9196 General Practitioners

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  Authors List:

  • Joan Torrent-Sellens  
  • Ángel Díaz-Chao  
  • Ivan Soler-Ramos  
  • Francesc Saigí-Rubió  

  Abstract:

  Background: The literature has noted the need to use more advanced methods and models to evaluate physicians’ outcomes in the shared health care model that electronic health (eHealth) proposes. Objective: The goal of our study was to design and evaluate a predictive multidimensional model of the outcomes of eHealth usage by European physicians. Methods: We used 2012-2013 survey data from a sample of 9196 European physicians (general practitioners). We proposed and tested two composite indicators of eHealth usage outcomes (internal practices and practices with patients) through 2-stage structural equation modeling. Logistic regression (odds ratios, ORs) to model the predictors of eHealth usage outcomes indicators were also calculated. Results: European general practitioners who were female (internal practices OR 1.15, 95% CI 1.10-1.20; practices with patients OR 1.19, 95% CI 1.14-1.24) and younger—aged <35 years (internal practices OR 1.14, 95% CI 1.02-1.26; practices with patients OR 1.32, 95% CI 1.13-1.54) and aged 36-45 years (internal practices OR 1.16, 95% CI 1.06-1.28; practices with patients OR 1.21, 95% CI 1.10-1.33)—had a greater propensity toward favorable eHealth usage outcomes in internal practices and practices with patients. European general practitioners who positively valued information and communication technology (ICT) impact on their personal working processes (internal practices OR 5.30, 95% CI 4.73-5.93; practices with patients OR 4.83, 95% CI 4.32-5.40), teamwork processes (internal practices OR 4.19, 95% CI 3.78-4.65; practices with patients OR 3.38, 95% CI 3.05-3.74), and the doctor-patient relationship (internal practices OR 3.97, 95% CI 3.60-4.37; practices with patients OR 6.02, 95% CI 5.43-6.67) had a high propensity toward favorable effects of eHealth usage on internal practices and practices with patients. More favorable eHealth outcomes were also observed for self-employed European general practitioners (internal practices OR 1.33, 95% CI 1.22-1.45; practices with patients OR 1.10, 95% CI 1.03-1.28). Finally, general practitioners who reported that the number of patients treated in the last 2 years had remained constant (internal practices OR 1.08, 95% CI 1.01-1.17) or increased (practices with patients OR 1.12, 95% CI 1.03-1.22) had a higher propensity toward favorable eHealth usage outcomes. Conclusions: We provide new evidence of predictors (sociodemographic issues, attitudes toward ICT impacts, and working conditions) that explain favorable eHealth usage outcomes. The results highlight the need to develop more specific policies for eHealth usage to address different realities.

  • J Med Internet Res 2018;20(10):e279

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  Participatory Medicine & E-Patients

  Oct 19, 2018

  Improving the Understanding of Test Results by Substituting (Not Adding) Goal Ranges: Web-Based Between-Subjects Experiment

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  Authors List:

  • Aaron M Scherer  
  • Holly O Witteman  
  • Jacob Solomon  
  • Nicole L Exe  
  • Angela Fagerlin  
  • Brian J Zikmund-Fisher  

  Abstract:

  Background: Most displays of laboratory test results include a standard reference range. For some patients (eg, those with chronic conditions), however, getting a result within the standard range may be unachievable, inappropriate, or even harmful. Objective: The objective of our study was to test the impact of including clinically appropriate goal ranges outside the standard range in the visual displays of laboratory test results. Methods: Participants (N=6776) from a demographically diverse Web-based panel viewed hypothetical glycated hemoglobin (HbA1c) test results (HbA1c either 6.2% or 8.2%) as part of a type 2 diabetes management scenario. Test result visual displays included either a standard range (4.5%-5.7%) only, a goal range (6.5%-7.5%) added to the standard range, or the goal range only. The results were displayed in 1 of the following 3 display formats: (1) a table; (2) a simple, two-colored number line (simple line); or (3) a number line with diagnostic categories indicated via colored blocks (block line). Primary outcome measures were comprehension of and negative reactions to test results. Results: While goal range information did not influence the understanding of HbA1c=8.2% results, the goal range only display produced higher levels of comprehension and decreased negative reactions to HbA1c=6.2% test results compared with the no goal range and goal range added conditions. Goal range information was less helpful in the block line condition versus the other formats. Conclusions: Replacing the standard range with a clinically appropriate goal range could help patients better understand how their test results relate to their personal targets.

  • J Med Internet Res 2018;20(10):e11027

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  Participatory Medicine & E-Patients

  Oct 19, 2018

  Older People Negotiating Independence and Safety in Everyday Life Using Technology: Qualitative Study

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  • Randi Stokke  

  Abstract:

  Background: Due to demographic changes with an aging population, there is a demand for technology innovations in care services. However, technology innovations have proven difficult to implement in regular use. To understand the complexity of technology innovations in care practices, we need a knowledge base of the complex and diverse experiences of people interacting with established technologies. Objective: This paper addresses the research gap in relation to understanding the microcontext of co-production of care involving established technologies integrated into care practices. The paper also aims to provide a framework for exploring what really happens when different actors use technology in care practices. Methods: Participant observations and 22 interviews with actors using social alarms were conducted employing the critical incident technique. A stepwise deductive-inductive analysis was then performed. Results: The results reveal how co-production of care assumes different meanings according to how actors use the technology. The results also show how technology innovation changes the dynamics between the actors and rearranges care practices. Independent and safe living is co-produced through performing bricolages and optimizing practice. Additionally, this opens up for unexpected results and bricolages as an integrated part of technology innovations. Conclusions: This study illustrates how care services are always co-produced between the actors involved. By using aspects from science and technology studies, this paper provides a framework for exploring technology in use in care practices. The framework provides tools to unpack and articulate the process of co-producing services.

  • J Med Internet Res 2018;20(10):e10054

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  Web-based and Mobile Health...

  Oct 19, 2018

  Promoting Psychological Well-Being at Work by Reducing Stress and Improving Sleep: Mixed-Methods Analysis

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  Authors List:

  • Denny Meyer  
  • Madawa W Jayawardana  
  • Samuel D Muir  
  • David Yen-Teh Ho  
  • Olivia Sackett  

  Abstract:

  Background: Workplace programs designed to improve the health and psychological well-being of employees are becoming increasingly popular. However, there are mixed reports regarding the effectiveness of such programs and little analysis of what helps people to engage with such programs. Objective: This evaluation of a particularly broad, team-based, digital health and well-being program uses mixed methods to identify the elements of the program that reduce work stress and promote psychological well-being, sleep quality, and productivity of employees. Methods: Participation in the Virgin Pulse Global Challenge program during May to September 2016 was studied. Self-reported stress, sleep quality, productivity, and psychological well-being data were collected both pre- and postprogram. Participant experience data were collected through a third final survey. However, the response rates for the last 2 surveys were only 48% and 10%, respectively. A random forest was used to estimate the probability of the completion of the last 2 surveys based on the preprogram assessment data and the demographic data for the entire sample (N=178,350). The inverse of these estimated probabilities were used as weights in hierarchical linear models in an attempt to address any estimation bias caused by the low response rates. These linear models described changes in psychological well-being, stress, sleep, and productivity over the duration of the program in relation to gender and age, engagement with each of the modules, each of the program features, and participant descriptions of the Virgin Pulse Global Challenge. A 0.1% significance level was used due to the large sample size for the final survey (N=18,653). Results: The final analysis suggested that the program is more beneficial for older people, with 2.9% greater psychological well-being improvements observed on average in the case of women than men (P<.001). With one exception, all the program modules contributed significantly to the outcome measures with the following average improvements observed: psychological well-being, 4.1%-6.0%; quality of sleep, 3.2%-6.9%; work-related stress, 1.7%-6.8%; and productivity, 1.9%-4.2%. However, only 4 of the program features were found to have significant associations with the outcome measures with the following average improvements observed: psychological well-being, 3.7%-5.6%; quality of sleep, 3.4%-6.5%; work-related stress, 4.1%-6.4%; and productivity, 1.6%-3.2%. Finally, descriptions of the Virgin Pulse Global Challenge produced 5 text topics that were related to the outcome measures. Healthy lifestyle descriptions showed a positive association with outcomes, whereas physical activity and step count tracking descriptions showed a negative association with outcomes. Conclusions: The complementary use of qualitative and quantitative survey data in a mixed-methods analysis provided rich information that will inform the development of this and other programs designed to improve employee health. However, the low response rates and the lack of a control group are limitations, despite the attempts to address these problems in the analysis.

  • J Med Internet Res 2018;20(10):e267

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  Demographics of Users, Social &...

  Oct 18, 2018

  Preferences for Health Information Technologies Among US Adults: Analysis of the Health Information National Trends Survey

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  Authors List:

  • Onur Asan  
  • Farion Cooper II  
  • Sneha Nagavally  
  • Rebekah J Walker  
  • Joni S Williams  
  • Mukoso N Ozieh  
  • Leonard E Egede  

  Abstract:

  Background: Emerging health technologies are increasingly being used in health care for communication, data collection, patient monitoring, education, and facilitating adherence to chronic disease management. However, there is a lack of studies on differences in the preference for using information exchange technologies between patients with chronic and nonchronic diseases and factors affecting these differences. Objective: The purpose of this paper is to understand the preferences and use of information technology for information exchange among a nationally representative sample of adults with and without 3 chronic disease conditions (ie, cardiovascular disease [CVD], diabetes, and hypertension) and to assess whether these preferences differ according to varying demographic variables. Methods: We utilized data from the 2012 and 2014 iteration of the Health Information National Trends Survey (N=7307). We used multiple logistic regressions, adjusting for relevant demographic covariates, to identify the independent factors associated with lower odds of using health information technology (HIT), thus, identifying targets for awareness. Analyses were weighted for the US population and adjusted for the sociodemographic variables of age, gender, race, and US census region. Results: Of 7307 participants, 3529 reported CVD, diabetes, or hypertension. In the unadjusted models, individuals with diabetes, CVD, or hypertension were more likely to report using email to exchange medical information with their provider and less likely to not use any of the technology in health information exchange, as well as more likely to say it was not important for them to access personal medical information electronically. In the unadjusted model, additional significant odds ratio (OR) values were observed. However, after adjustment, most relationships regarding the use and interest in exchanging information with the provider were no longer significant. In the adjusted model, individuals with CVD, diabetes, or hypertension were more likely to access Web-based personal health information through a website or app. Furthermore, we assessed adjusted ORs for demographic variables. Those aged >65 years and Hispanic people were more likely to report no use of email to exchange medical information with their provider. Minorities (Hispanic, non-Hispanic black, and Asian people) were less likely to indicate they had no interest in exchanging general health tips with a provider electronically. Conclusions: The analysis did not show any significant association among those with comorbidities and their proclivity toward health information, possibly implying that HIT-related interventions, particularly design of information technologies, should focus more on demographic factors, including race, age, and region, than on comorbidities or chronic disease status to increase the likelihood of use. Future research is needed to understand and explore more patient-friendly use and design of information technologies, which can be utilized by diverse age, race, and education or health literacy groups efficiently to further bridge the patient-provider communication gap.

  • J Med Internet Res 2018;20(10):e277

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  Demographics of Users, Social &...

  Oct 18, 2018

  Assessment of Personal Health Care Management and Chronic Disease Prevalence: Comparative Analysis of Demographic, Socioeconomic, and Health-Related Variables

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  Authors List:

  • Ryan H Sandefer  
  • Bonnie L Westra  
  • Saif S Khairat  
  • David S Pieczkiewicz  
  • Stuart M Speedie  

  Abstract:

  Background: The use of personal health care management (PHM) is increasing rapidly within the United States because of implementation of health technology across the health care continuum and increased regulatory requirements for health care providers and organizations promoting the use of PHM, particularly the use of text messaging (short message service), Web-based scheduling, and Web-based requests for prescription renewals. Limited research has been conducted comparing PHM use across groups based on chronic conditions. Objective: This study aimed to describe the overall utilization of PHM and compare individual characteristics associated with PHM in groups with no reported chronic conditions, with 1 chronic condition, and with 2 or more such conditions. Methods: Datasets drawn from the National Health Interview Survey were analyzed using multiple logistic regression to determine the level of PHM use in relation to demographic, socioeconomic, or health-related factors. Data from 47,814 individuals were analyzed using logistic regression. Results: Approximately 12.19% (5737/47,814) of respondents reported using PHM, but higher rates of use were reported by individuals with higher levels of education and income. The overall rate of PHM remained stable between 2009 and 2014, despite increased focus on the promotion of patient engagement initiatives. Demographic factors predictive of PHM use included people who were younger, non-Hispanic, and who lived in the western region of the United States. There were also differences in PHM use based on socioeconomic factors. Respondents with college-level education were over 2.5 times more likely to use PHM than respondents without college-level education. Health-related factors were also predictive of PHM use. Individuals with health insurance and a usual place for health care were more likely to use PHM than individuals with no health insurance and no usual place for health care. Individuals reporting a single chronic condition or multiple chronic conditions reported slightly higher levels of PHM use than individuals reporting no chronic conditions. Individuals with no chronic conditions who did not experience barriers to accessing health care were more likely to use PHM than individuals with 1 or more chronic conditions. Conclusions: The findings of this study illustrated the disparities in PHM use based on the number of chronic conditions and that multiple factors influence the use of PHM, including economics and education. These findings provide evidence of the challenge associated with engaging patients using electronic health information as the health care industry continues to evolve.

  • J Med Internet Res 2018;20(10):e276

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