The Karma system is currently undergoing maintenance (Monday, January 29, 2018).
The maintenance period has been extended to 8PM EST.

Karma Credits will not be available for redeeming during maintenance.
Advertisement

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 28.06.16 in Vol 18, No 6 (2016): Jun

This paper is in the following e-collection/theme issue:

    Original Paper

    Health-Specific Information and Communication Technology Use and Its Relationship to Obesity in High-Poverty, Urban Communities: Analysis of a Population-Based Biosocial Survey

    1Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, United States

    2Kaiser Permanente Northern California, Division of Research, Oakland, CA, United States

    3Robert Wood Johnson Clinical Scholars Program, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States

    4The University of Chicago, Department of Obstetrics and Gynecology, Chicago, IL, United States

    5University of Chicago, Chicago, IL, United States

    6Concordia University-Chicago, Chicago, IL, United States

    7Holbrook Consulting, Chicago, IL, United States

    8The University of Chicago, Department of Obstetrics and Gynecology, and Medicine-Geriatrics, Chicago, IL, United States

    9The University of Chicago Comprehensive Cancer Center, Chicago, IL, United States

    10The University of Chicago Maclean Center on Clinical Medical Ethics, Chicago, IL, United States

    11The University of Chicago Urban Health Initiative, Chicago, IL, United States

    Corresponding Author:

    Anjali Gopalan, MD, MS

    Kaiser Permanente Northern California

    Division of Research

    2000 Broadway

    Oakland, CA, 94612

    United States

    Phone: 1 510 891 3458

    Fax:1 510 891 3508

    Email:


    ABSTRACT

    Background: More than 35% of American adults are obese. For African American and Hispanic adults, as well as individuals residing in poorer or more racially segregated urban neighborhoods, the likelihood of obesity is even higher. Information and communication technologies (ICTs) may substitute for or complement community-based resources for weight management. However, little is currently known about health-specific ICT use among urban-dwelling people with obesity.

    Objective: We describe health-specific ICT use and its relationship to measured obesity among adults in high-poverty urban communities.

    Methods: Using data collected between November 2012 and July 2013 from a population-based probability sample of urban-dwelling African American and Hispanic adults residing on the South Side of Chicago, we described patterns of ICT use in relation to measured obesity defined by a body mass index (BMI) of ≥30 kg/m2. Among those with BMI≥30 kg/m2, we also assessed the association between health-specific ICT use and diagnosed versus undiagnosed obesity as well as differences in health-specific ICT use by self-reported comorbidities, including diabetes and hypertension.

    Results: The survey response rate was 44.6% (267 completed surveys/598.4 eligible or likely eligible individuals); 53.2% were African American and 34.6% were Hispanic. More than 35% of the population reported an annual income of less than US $25,000. The population prevalence of measured obesity was 50.2%. People with measured obesity (BMI≥30 kg/m2) were more likely to report both general (81.5% vs 67.0%, P=.04) and health-specific (61.1% vs 41.2%, P=.01) ICT use. In contrast, among those with measured obesity, being told of this diagnosis by a physician was not associated with increased health-specific ICT use. People with measured obesity alone had higher rates of health-specific use than those with comorbid hypertension and/or diabetes diagnoses (77.1% vs 60.7% vs 47.4%, P=.04).

    Conclusions: In conclusion, ICT-based health resources may be particularly useful for people in high-poverty urban communities with isolated measured obesity, a population that is at high risk for poor health outcomes.

    J Med Internet Res 2016;18(6):e182

    doi:10.2196/jmir.5741

    KEYWORDS



    Introduction

    Obesity and obesity-related chronic diseases are leading drivers of health care costs in the United States [1]. Over the past 35 years, the prevalence of obesity has more than doubled; currently, 35% of American adults are obese (defined as body mass index, BMI, ≥30 kg/m2) [1]. Certain populations are disproportionately affected by obesity, including African American and Hispanic adults and people living in resource-poor, high-poverty, and more racially segregated urban communities [1-5]. Many major technology corporations, payers, and health care systems are investing in information and communication technologies (ICTs), such as mobile apps and Web-based patient portals, to prevent and better manage obesity and related chronic conditions [6-10]. Growing, but limited, evidence demonstrates that ICT-based interventions can positively affect health behaviors and obesity-related outcomes [6,11,12]. Small, clinic-based trials of mobile apps and other Web-based decision support and monitoring tools have demonstrated improved outcomes for specific chronic health conditions, including short-term weight loss [6,11]. Although these findings are promising, the ability of health-specific ICT-based resources to impact health outcomes will depend not only on efficacy in clinical trials but on actual use among people with obesity, especially those residing in communities with limited health resources [13].

    The 2012 Pew Internet Health Tracking Survey examined the relationship between types of ICT use, including seeking information online about conditions, medications, or the experiences of others, and self-reported chronic disease [14]. In this study, controlling for age, income, education, ethnicity, and overall health status, people who self-reported a diagnosis of chronic disease, including hypertension, diabetes, heart, and lung disease, were less likely to report any ICT use (the frequency of these activities was not described) [14]. However, among people who did report ICT use, those with one or more chronic diseases were more likely to report the use of ICT for health-specific reasons compared with those without a chronic condition [14]. Obesity, designated by the American Medical Association as a chronic condition after the study’s completion, was not included among the chronic conditions examined in the Pew survey [15]. Also missing from the Pew survey are any biometric data regarding chronic disease status, specifically BMI. A population-based survey that collected individual-level data on general and health-specific ICT use and chronic disease status, including both self-reported obesity diagnoses and objective obesity status (anthropometric measures) presented an opportunity to address these gaps in the Pew data [16].

    On Chicago’s South Side, 55% of the population (approximately 528,000) lives at or below 200% federal poverty level; 77% of residents are African American, 13% are Hispanic [17]. African American and Hispanic people have disproportionately high rates of obesity [2]. Vital statistics data for the region suggest higher rates of premature and overall mortality related to obesity-related chronic diseases compared with more affluent areas of Chicago [18]. As part of a larger strategy to mitigate health inequities in the region, community leaders, residents, and university researchers with the South Side Health and Vitality Studies conducted a population health survey to establish prevalence estimates for obesity and other chronic diseases and to ascertain what kinds of community-based and ICT resources residents use to manage their health [19]. We hypothesized that residents use ICT resources to substitute for gaps in community-based resources and that ICT use varies by health condition. Understanding the feasibility of ICT-based health resources to reach people with obesity in high-poverty communities is critical to maximizing the potential of these resources to impact health.

    In this study, we first describe overall patterns of current ICT use by measured obesity status. Based on the Pew findings for other chronic conditions, we hypothesized that although overall ICT use would be lower among respondents with measured obesity, health-specific ICT use would be higher. Second, among people with measured obesity (BMI≥30 kg/m2), we described health-specific ICT use comparing those with and without a physician’s diagnosis of obesity and by the presence of self-reported comorbid conditions, specifically diabetes and hypertension. The Health Belief Model suggests that willingness to perform a health behavior, such as using health-specific ICT resources, depends on the perceived need for action [20]. Thus, obese people who were never told by a physician they had obesity or an obesity-related comorbid condition may be less motivated to use ICT-based health resources than people with a physician’s diagnosis or who have one or more of these comorbid diagnoses [20]. We, therefore, hypothesized that health-specific ICT use would be higher among people with BMI≥30 kg/m2 who have been diagnosed as obese by a physician and those with measured obesity who also self-report a diagnosis of comorbid hypertension and/or diabetes. Lastly, based on the study findings and extant literature, we propose a conceptual framework describing the relationship between obesity and health-specific ICT use that we hope will inform the design and translation of ICT-based interventions targeting obesity management.


    Methods

    This analysis is based on data from the South Side Health and Vitality Studies (SSHVS) [16]. SSHVS is a family of interrelated, community-engaged research studies that aim to inform efforts to promote and maintain population health on Chicago’s South Side [21]. SSHVS aims to describe population health in the region and the ways in which residents use ICT to access health-related community resources or substitute for gaps in local resources. All participants provided written documentation of informed consent. This study was designed in partnership with volunteer community members [19] and was approved by the University of Chicago Institutional Review Board (IRB). The primary data collection for this research and the activity of the University of Chicago researchers were conducted under the approved University of Chicago IRB protocol. Secondary data analysis was conducted by AG following a human subjects research exemption granted by University of Pennsylvania IRB. Other co-authors had no access to the study subjects or individual-level data.

    Study Population

    Individuals eligible for this study included those 35 years of age or older, English or Spanish speaking, and residing within the target region.

    Sampling

    Study participants were sampled from 2 distinct regions, a total of 7 census tracts, on the South Side of Chicago. The northwest region was almost entirely African American (98%), based on 2010 US Census data [22]. The southeast region was majority Hispanic (83%). We employed a single-frame, two-stage sampling design. First, an address-based probability sample of household units was generated by randomly selecting household units from a list of all residential postal addresses in the regions purchased from Marketing Systems Group’s Genesys Division, 2012 [23]. Then, if more than one individual in a household was 35 years or older, one individual was randomly selected.

    Data Collection

    Eligible participants were recruited between November 2012 and July 2013 through mailed letters, telephone calls, and home visits. Once informed consent was obtained, participants took part in an in-person, interviewer-administered structured interview, lasting approximately 1 hour. Participants could choose to complete the interview in English or Spanish. The interview collected sociodemographic characteristics, details on ICT device ownership and use, and self-reported medical history. Physical measures were obtained, including height, weight, waist circumference, systolic and diastolic blood pressure, and finger-stick dried blood specimens.

    Defining Information and Communication Technology Use

    General ICT use was defined as any use of cell phones for texting, emailing, going online, or downloading apps, or any use of the Internet (accessed via computer or cell phone). Health-specific ICT activities included the following activities: looking up health information online, using a health-related mobile app, Web-based purchasing of medications, Web-based communication with providers, participation in online health support groups, and Web-based management of health records and/or benefits. Questions included on the survey instrument relating to health-specific ICT use were primarily based on a prior national-level survey, the Health Information National Trends Survey, which was modified by the study team to increase cultural appropriateness and aid comprehension (Textbox 1) [24]. On the basis of review of our survey instrument by community informants, an additional question was added asking those who reported looking up health information online the following question: “Do you ever use the Internet to find health information because you did not want to ask a doctor?” This question was included to explore a hypothesis that people might use the Internet for information to avoid embarrassing discussions or to compensate for limited time with health care providers. For all of these health-specific activities, use was primarily defined as any amount of engagement (ranging from daily to less than monthly) or no engagement. The distribution of activity frequency was also described. The dichotomous categorization of health-specific ICT use was done to allow examination of associations between selected activities and participant characteristics (small cell counts would prevent statistical testing) and because the “right” amount of each of the examined activities is unknown and likely varies greatly. This categorization also reflected that used in the aforementioned Pew survey that studied ICT use and self-reported chronic conditions [14].


    Textbox 1. Survey instrument used to assess selected health-specific information and communication technology activities (questions 1-5 were modified from the 2003 Health Information National Trends Survey, item HC-26, and question 5a was constructed based on input from community stakeholders).
    View this box

    Defining Obesity and Other Chronic Disease Variables

    Body mass index was calculated using measured height and weight collected during in-home interviews [25]. For the primary analysis, measured obesity was defined as a binary variable: nonobese (BMI<30 kg/m2) and obese (BMI≥30 kg/m2; Figure 1). Those with missing BMI data (n=12) were excluded. To examine the effect of being diagnosed as obese by a physician, respondents who were categorized as obese by their measured BMI were then stratified by their response to the survey question “Has a medical doctor ever told you that you have excess weight or obesity?” (Figure 1). The resultant binary variable categories were labeled “diagnosed obese” and “undiagnosed obese.” To assess differences based on a diagnosis of comorbid chronic conditions, diabetes and hypertension were defined as binary variables based on survey responses to the following two questions: (1) “Has a medical doctor ever told you that you have diabetes?” and (2) “Has a medical doctor ever told you that you have high blood pressure or hypertension?” Given the significant overlap between the 3 examined conditions, the 3 categories were defined as follows: measured obesity only, measured obesity plus self-reported hypertension or diabetes, and measured obesity, self-reported hypertension, and self-reported diabetes.

    Figure 1. Population-based probability sample enrollment flowchart. BMI: body mass index.
    View this figure

    Statistical Analysis

    All analyses for this population-based probability sample were weighted to account for differential selection probabilities and differential nonresponse. The response rate and the cooperation rate were calculated using the American Association for Public Opinion Research (AAPOR) definitions for response rate (RR3) and cooperation rate (COOP3) [26]. The response rate describes the number of completed surveys in relation to the total number of eligible individuals (we assumed an eligibility rate of 63% for 261 individuals of unknown eligibility; Figure 1), whereas the cooperation rate describes the number of completed surveys in relation to the number of eligible individuals ever contacted [26]. Descriptive statistics were calculated to summarize sociodemographic characteristics by obesity status and diagnosis of obesity. Differences in general and health-specific ICT use among the people with and without measured obesity were examined. Among those with measured obesity, the associations between health-specific ICT use and diagnosis of obesity were also compared. Lastly, differences in health-specific ICT use among those with measured obesity only were compared with those reporting a diagnosis of hypertension or diabetes and those with all 3 conditions. Chi-square tests were used to test for statistical differences between or among groups. All data analyses were performed using Stata version 12.1 (2011, StataCorp, College Station, TX, USA).


    Results

    The response rate was 44.6% (267 completed surveys/598.4 eligible or likely eligible individuals) and the cooperation rate was 61.5% (267 completed surveys/434 eligible individuals contacted). In total, 267 individuals participated in the biosocial study.

    Sociodemographic Characteristics and ICT Use by Measured Obesity Status

    Associations between sociodemographic characteristics and measured obesity (BMI≥30 kg/m2) status in the population are summarized in Table 1. Measured obesity was more prevalent among women than men (63.2% vs 36.8%, P=.01). Measured obesity status did not differ by income, education, or race or ethnicity. The majority of people reported seeing a physician in the past year (79.8%) and this did not differ by measured obesity status (82.5% obese vs 78.6% nonobese, P=.55). However, individuals with measured obesity were more likely to report a source of regular care than those without obesity (96.7% obese vs 88.6% nonobese, P=.04).

    The prevalence of general ICT use in the population was high (75.0%) but was more common among people with obesity when compared with people without obesity (81.5% obese vs 67% nonobese, P=.04; Table 2). Half of all respondents (51.7%) reported some type of health-specific ICT use, but it was more common among people with measured obesity (61.1% obese vs 41.2% nonobese, P=.01). The most common health-specific ICT activity in the population was looking up health-related information online (47.2%); this activity was more common among the people with obesity (54.4% obese vs 38.7% nonobese, P=.04). Other health-related activities, including Web-based medical record access (8.8%), participation in online health-related support groups (9.3%), and Web-based communication with providers (9.7%), were infrequent and did not differ by obesity status (Table 2). Health-specific mobile app use was also uncommon (7.6%) and did not differ by measured obesity status. A supplementary table describes the distribution of frequencies for the examined health-specific ICT activities (see Multimedia Appendix 1).

    Sociodemographic Characteristics and ICT Use by Obesity Diagnosis

    Among people with measured obesity, a physician’s diagnosis of obesity was associated with educational attainment; people with obesity who had been diagnosed by a physician were more likely to have achieved a high school diploma or have passed a general educational development (GED) test (41.2% diagnosed vs 24.6% undiagnosed, P=.01) or some college experience (41.2% diagnosed vs 26.0% undiagnosed, P=.01; Table 3). Obese individuals with an obesity diagnosis were also more likely to report a source of regular health care (100% diagnosed vs 89.1% undiagnosed, P=.01). Obesity diagnosis was similar across the other examined sociodemographic characteristics (Table 3). Rates of ICT use by obesity diagnosis were not significantly different for general (85.6% diagnosed vs 72.0% undiagnosed, P=.15) or health-specific use (65.9% diagnosed vs 49.8% undiagnosed, P=.15). Although no statistically significant differences were noted in use of Web-based resources to look up health-related information (60.7% diagnosed vs 39.6% undiagnosed, P=.06), people with diagnosed obesity were more likely to look up information online as a means to avoid asking a doctor (32.0% diagnosed vs 5.0% undiagnosed, P<.001). No other differences in rates of specific health-related ICT activities by obesity diagnosis status were noted.

    Table 1. Sociodemographic characteristics of the population and by measured obesity status (results of weighted analysis).
    View this table
    Table 2. Information and communication technology–based activities of the population and by measured obesity status (results of weighted analysis).
    View this table
    Table 3. Sociodemographic characteristics of the measured obese population and by obesity diagnosis status (results of weighted analysis).
    View this table

    ICT Use, Measured Obesity, and Self-Reported Diagnosis of Comorbid Chronic Conditions

    Among people with measured obesity, a self-reported diagnosis of hypertension (46.8%) or diabetes (16.8%) was prevalent. Only 36.8% of people with measured obesity had no diagnosis of diabetes or hypertension; 20.5% of the population had all 3 conditions. Isolated measured obesity was associated with higher rates of health-specific ICT use than measured obesity plus comorbid diabetes and/or hypertension diagnosis (77.1% obesity only vs 47.4% obesity and hypertension or diabetes vs 60.7% obesity, hypertension, and diabetes, P=.04; Table 4). Examining rates of specific health-related activities, a statistically significant difference was only noted for accessing Web-based health benefits information (27.6% obesity only vs 18.0% obesity and hypertension or diabetes vs 4.0% obesity, hypertension, and diabetes, P=.04).

    Table 4. Comparing information and communication technology activities by presence of comorbid conditions (results of weighted analysis).
    View this table

    Discussion

    This study describes ICT use in high-poverty African American and Hispanic communities on Chicago’s South Side with a disproportionate burden of obesity and obesity-related diseases and examines the association between obesity and ICT use. To our knowledge, this is the only study to ascertain measured BMI, self-reported obesity diagnoses, and ICT use from the same sample. This design, albeit limited by a relatively small sample size, enabled us to generate three new findings. First, we found that ICT use patterns differed by measured obesity status; people with obesity had statistically significant higher rates of both general ICT and health-specific ICT use compared with people without obesity. Second, among people with measured obesity, a physician’s diagnosis of obesity was not associated with higher rates of health-specific ICT use or use of Web-based health-related information sources, but it was associated with a higher rate of using Web-based resources to avoid asking questions of a doctor. Finally, an unexpected association between comorbidity burden and health-specific ICT use was found. The highest rates of ICT use were among people with measured obesity only, as compared with those with measured obesity who reported one or two common comorbidities.

    In contrast to the Pew study findings that showed lower ICT use among people with other common chronic conditions [14], our study found obesity to be associated with a higher likelihood of general ICT use. Obesity, for some, is associated with difficulties with mobility and other physical activities—factors both potentially related to increased rates of ICT use [27-30]. The physical limitations imposed by obesity may result in a vicious cycle of restricted mobility, greater use of ICT, increasing weight, and increasing physical limitation. These problems are likely exacerbated in high-poverty communities with limited access to health care and other health-promoting resources (eg, fresh food, safe spaces for exercise) [3]. The new finding of relatively high rates of health-specific ICT use among people with obesity in a high-poverty urban community may just reflect greater use of all types of ICT.

    Unlike other chronic conditions (eg, hypertension and diabetes), obesity is an outward-facing condition. The social stigma of obesity may lead individuals to ICT-based resources rather than medical care for their health needs [31]. Our findings of higher use of Web-based information resources to avoid asking doctors, among those with diagnosed obesity, along with past work in other stigmatized health conditions, support this possibility. A national survey of adult Internet users found that users with a stigmatized condition (eg, depression, sexually transmitted diseases) were more likely than those with a less stigmatized condition (eg, diabetes, back pain) to report using the Internet as a health information source and as a tool to communicate with clinicians [32]. Obesity was not included among the stigmatized conditions in this study.

    Regardless of the drivers behind increased health-specific ICT use, our study suggests that obesity may be a useful target for health-specific ICT-based interventions. The high prevalence of obesity among residents on Chicago’s South Side and in other high-poverty, minority communities, along with the high rate of health-specific use, indicates an already online population with high health needs and risks. ICT-based resources could potentially not only aid in the self-care and management of obesity but also serve as an entry point to provide information and support for routine preventive care and other important health topics in this population.

    Among studies of health-related ICT use, this survey was unique in its combination of ICT use measures with anthropometric measures and assessment of self-reported chronic diseases [14,33]. This design enabled closer study of the association between physician-diagnosed obesity and health-specific ICT use. Counter to our hypothesis, obese people who reported a physician’s diagnosis of obesity were not more likely to report health-specific ICT use. This result differs from past evidence demonstrating the effect of physician input on health behaviors. In a randomized controlled trial of adults in a primary care setting, individuals randomized to receiving physicians’ advice on quitting smoking, reducing fat consumption, and increasing exercise were more likely to believe these topics were relevant to them and more likely to report attempting to quit smoking and making some dietary changes [34]. The absence of differences in health-specific ICT use by provider diagnosis may again reflect differences in obesity as a condition. Unlike diabetes, dyslipidemia, and hypertension diagnosed by blood test or expert measurement, obesity is easily self-diagnosed making the physician’s diagnosis less surprising and, potentially, less important.

    Obesity status may also be more subject to individual perceptions than other chronic conditions. Past research has demonstrated that people have difficulty in assessing ideal weight [35]. Beyond that, differences in obesity perception by race and ethnicity are well documented in the literature. Using pooled cross-sectional data from National Health and Nutrition Examination Survey, Dorsey et al [36] observed differences in weight perception when comparing non-Hispanic black adults to non-Hispanic white adults; non-Hispanic black adults with obesity who did not perceive themselves as obese had lower odds of desiring weight loss. Given this, it may be that a physician’s obesity diagnosis has a different effect when it is discordant with existing cultural norms and an individual’s perception of his or her weight. If that is the case, the need to seek out health resources for obesity, including ICT-based health resources, may be less affected by a provider’s diagnosis.

    Comorbid diagnoses of diabetes and/or hypertension were not found to be associated with a higher likelihood of health-specific ICT use among individuals with measured obesity. This finding contrasts with findings from a 2007 phone survey of US adults that demonstrated a positive correlation between the number of chronic conditions (did not include obesity) and engagement in selected health-specific ICT activities [37]. However, this study’s population was younger (>60% aged less than 50 years), had higher educational attainment, and a much lower prevalence of chronic conditions. Also, the racial and ethnic characteristics of the sample were not described. Individuals with obesity in addition to hypertension and/or diabetes are likely to have more frequent in-person contact with health care providers than individuals with obesity alone and may better understand obesity as, or relating to exacerbation of, a chronic medical condition. It is possible that among individuals with obesity and fewer comorbid conditions, less regular provider contact may result in more unanswered health questions and unmet needs, motivating more health-specific ICT use.

    On the basis of the study findings, extant literature, and clinical experience, we propose a preliminary conceptual framework for the relationship between obesity and use of health-specific ICT (Figure 2) [14,20,28,29,31,36,38,39]. The proposed framework is adapted from Andersen’s Model of Health Services Utilization [40]; it incorporates obesity as a specific use case and highlights the still incompletely understood interplay between obesity and ICT use. The proposed model also demonstrates the potential for ICT-based services to act as both a complement to traditional health services by enabling access (eg, an individual uses the Internet to find a weight loss support group) and a partial or complete substitute for traditional health services (eg, individual may use the Internet to connect with a weight loss support group online).

    The study has several limitations. First, the conservative AAPOR response rate calculation (44.6%) is lower than desired, but it is also consistent with or higher than that reported for other similar surveys in this and other urban populations [41-43]. Of note, Pew reports an 11.6% response rate for its widely cited 2012 phone survey (although used different sampling approach, random digit dialing) [14,44]. Second, although the use of high-quality population-based probability sampling serves to balance the relatively small sample size and allow for generalization of the findings beyond just the survey respondents, the racial and ethnic characteristics of the studied population may not generalize to other groups. Next, although we were able to report on broad categories of health-specific technology use in this population, we did not collect comprehensive information on exactly how and why people were using health-related ICT. It is also possible that we did not capture all of the current health-specific ICT activities in which people may engage. While we describe an association between measured obesity and health-specific ICT use, we cannot infer causality using cross-sectional data. As has been postulated, it is possible that higher levels of any type of ICT use (including health-specific use) cause sedentariness and increase the likelihood of obesity [29,30].

    In this high-poverty urban population, the majority of people with measured obesity reported use of technology for health-specific reasons. This high-risk, already online population presents an opportunity for ICT-based health resources to impact health, especially in communities where the burden of obesity is high. However, understanding current use patterns and potential opportunities for health-specific ICT-based resources is only a first step. The critical next step is evaluating the ability of these technology-based resources to meaningfully impact health care and health outcomes in this high-need, high-risk population.

    Figure 2. Proposed conceptual framework for the relationship between obesity and health-specific information and communication technology (ICT) use derived from literature, study results, and clinical experience. Adapted from Andersen’s Behavioral Model of Health Services Utilization, the proposed model incorporates obesity as a specific use case. The dashed lines highlight two incompletely understood domains: (1) the relationship between obesity and health-specific ICT use and (2) the potential dual role of health-specific ICT as both an access point to and a replacement for traditional health resources. BMI: body mass index.
    View this figure

    Acknowledgments

    The authors would like to acknowledge the entire South Side Health and Vitality Studies (SSHVS) research team for their contribution to this work. We would also like to acknowledge the University of Chicago Survey Lab for its role in field operations of the SSHVS population health study, including conducting the survey interviews and biological measure collection.

    The efforts of AG on this manuscript were supported by the Department of Veterans Affairs and by the Kaiser Permanente Northern California Division of Research. Support for the SSHVS population health studies was provided by the University of Chicago Medicine Urban Health Initiative, individual philanthropy to the Lindau Laboratory at the University of Chicago, and the Chicago Core on Biomarkers in Population-Based Aging Research supported by the National Institutes of Health (NIH)/National Institute on Aging grant P30 AG012857 at NORC and the University of Chicago. The efforts of STL, JM, and VE on this manuscript were also supported in part by the NIH/National Institute on Aging grant 1R01 AG 047869-01 at the University of Chicago.

    The contents of this presentation are solely the responsibility of the authors and do not necessarily represent the official views of the US Department of Veterans Affairs, or the US Department of Health and Human Services or any of its agencies.

    Authors' Contributions

    AG contributed to study conception, design, data analysis and interpretation, and manuscript construction. JM contributed to the study design and execution, data analysis and interpretation, and manuscript content. LBG and VE contributed to the data analysis and interpretation, and manuscript content. RM contributed to study design, data interpretation, and manuscript content. RH and MBW contributed to design of the population health study data collection instrument and protocol, community engagement activities in the preparation and dissemination phases of the study, interpretation of the analytic findings, and review and editing of this manuscript. STL led the research team that administered MAPSCorps and the South Side Health and Vitality Studies population health study data collection. STL also provided material support, contributed to design and execution of the study, and oversaw all aspects of data collection, analysis, and interpretation. She has contributed original writing to and edited drafts of the manuscript for important intellectual content. She has provided material support to the execution of this study.

    Conflicts of Interest

    RM has grant support from NIH, National Heart, Lung, and Blood Institute (NHLBI) grant K23 (109083) and NHLBI R01 (122457), but this funding was not used to conduct the study. Her time on this project was part of her leadership role with the Robert Wood Johnson Clinical Scholars program. STL is founder and co-owner of a social impact company NowPow, LLC, developed as the sustainable business model expected by a Centers for Medicare & Medicaid Services (CMS) Health Care Innovation Award (1C1CMS330997-03-00, 2012-15). The CMS award did not directly support the research described in this manuscript. The other authors report no conflicts of interest.

    Multimedia Appendix 1

    Frequency of health-specific information and communication technology activities.

    PDF File (Adobe PDF File), 28KB

    References

    1. Centers for Disease Control and Prevention. Adult Obesity Facts   URL: http://www.cdc.gov/obesity/data/adult.html [accessed 2016-03-07] [WebCite Cache]
    2. National Institute of Diabetes and Digestive and Kidney Diseases. Overweight and Obesity Statistics   URL: http:/​/www.​niddk.nih.gov/​health-information/​health-statistics/​Pages/​overweight-obesity-statistics.​aspx [accessed 2016-03-07] [WebCite Cache]
    3. Black JL, Macinko J. The changing distribution and determinants of obesity in the neighborhoods of New York City, 2003-2007. Am J Epidemiol 2010 Apr 1;171(7):765-775 [FREE Full text] [CrossRef] [Medline]
    4. Levine JA. Poverty and obesity in the U.S. Diabetes 2011;60(11):2667-2668. [CrossRef]
    5. Chang VW. Racial residential segregation and weight status among US adults. Soc Sci Med 2006 Sep;63(5):1289-1303. [CrossRef] [Medline]
    6. Quinn CC, Clough SS, Minor JM, Lender D, Okafor MC, Gruber-Baldini A. WellDoc mobile diabetes management randomized controlled trial: change in clinical and behavioral outcomes and patient and physician satisfaction. Diabetes Technol Ther 2008 Jun;10(3):160-168. [CrossRef] [Medline]
    7. Duffey C. 2014 Jul 25. Smartwatches will revolutionise treatment for chronic conditions   URL: http:/​/www.​theguardian.com/​healthcare-network/​2014/​jul/​25/​smartwatches-revolutionise-treatment-chronic-conditions [accessed 2016-05-09] [WebCite Cache]
    8. Dolan B. mobihealthnews. 2013 Jun 13. WellDoc's BlueStar secures first mobile health reimbursement   URL: http://mobihealthnews.com/23026/welldocs-bluestar-secures-first-mobile-health-reimbursement [accessed 2016-05-09] [WebCite Cache]
    9. Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med 2010 Aug 5;363(6):501-504. [CrossRef]
    10. Sequist TD, Taveras EM. Clinic-community linkages for high-value care. N Engl J Med 2014 Dec 4;371(23):2148-2150. [CrossRef] [Medline]
    11. Bennett GG, Herring SJ, Puleo E, Stein EK, Emmons KM, Gillman MW. Web-based weight loss in primary care: a randomized controlled trial. Obesity (Silver Spring) 2010 Feb;18(2):308-313 [FREE Full text] [CrossRef] [Medline]
    12. Park M, Kim H, Kim K. Cellular phone and Internet-based individual intervention on blood pressure and obesity in obese patients with hypertension. Int J Med Inform 2009 Oct;78(10):704-710. [CrossRef] [Medline]
    13. Schulz PJ, Hulsman RL. Assessing the quality of eHealth programs and their impact on health behaviour and health outcomes of users. Patient Educ Couns 2009 Oct;77(1):1-3. [CrossRef]
    14. Pew Research Center. 2013 Nov. The Diagnosis Difference   URL: http://www.pewinternet.org/files/old-media//Files/Reports/2013/PewResearch_DiagnosisDifference.pdf [accessed 2016-05-08] [WebCite Cache]
    15. American Medical Association Annual Meeting. 2013. AMA Adopts New Policies on Second Day of Voting at Annual Meeting   URL: http://www.ama-assn.org/ama/pub/news/news/2013/2013-06-18-new-ama-policies-annual-meeting.page [accessed 2016-05-09] [WebCite Cache]
    16. The University of Chicago. South Side Health & Vitality Studies   URL: https://thestudies.uchicago.edu/node [accessed 2016-03-07] [WebCite Cache]
    17. U.S. Census Bureau. 2012. American Community Survey   URL: http://factfinder.census.gov [accessed 2016-05-08] [WebCite Cache]
    18. City of Chicago Data Portal. Public Health Statistics--Selected underlying causes of death in Chicago, 2006-2010;   URL: https:/​/data.​cityofchicago.org/​Health-Human-Services/​Public-Health-Statistics-Selected-underlying-cause/​j6cj-r444 [accessed 2016-05-09] [WebCite Cache]
    19. Lindau ST, Makelarski JA, Chin MH, Desautels S, Johnson D, Johnson WE, et al. Building community-engaged health research and discovery infrastructure on the South Side of Chicago: science in service to community priorities. Prev Med 2011;52(3-4):200-207 [FREE Full text] [CrossRef] [Medline]
    20. Rosenstock IM, Strecher VJ, Becker MH. Social learning theory and the Health Belief Model. Health Educ Q 1988;15(2):175-183. [Medline]
    21. Lindau ST, James R, Makelarski JA, Sanders E, Johnson D. Comments From the South Side of Chicago on New Haven's Inspiring Initiative. Am J Public Health 2012;102(7):e3-e4. [Medline]
    22. U.S. Census Bureau. United States; 2010. Census Interactive Population Map   URL: http://www.census.gov/2010census/popmap/ [accessed 2016-03-08] [WebCite Cache]
    23. Marketing Systems Group.   URL: http://www.m-s-g.com/Web/genesys/index.aspx [accessed 2016-03-08] [WebCite Cache]
    24. Nelson DE, Kreps GL, Hesse BW, Croyle RT, Willis G, Arora NK, et al. The Health Information National Trends Survey (HINTS): development, design, and dissemination. J Health Commun 2004;9(5):443-60; discussion 81. [CrossRef] [Medline]
    25. Harris KM, Halpern CT, Whitsel E, Hussey J, Tabor J, Entzel P. The National Longitudinal Study of Adolescent to Adult Health. 2009. Research Design General documentation   URL: http://www.cpc.unc.edu/projects/addhealth/design [accessed 2016-05-08] [WebCite Cache]
    26. The Association for Public Opinion Research. 2011. Standard Definitions Final Definitions of Case Codes and Outcome Rates for Surveys   URL: http://www.aapor.org/AAPOR_Main/media/publications/Standard-Definitions20169theditionfinal.pdf [accessed 2016-05-09] [WebCite Cache]
    27. Backholer K, Wong E, Freak-Poli R, Walls HL, Peeters A. Increasing body weight and risk of limitations in activities of daily living: a systematic review and meta-analysis. Obes Rev 2012 May;13(5):456-468. [CrossRef] [Medline]
    28. Alley DE, Chang VW. The changing relationship of obesity and disability, 1988-2004. JAMA 2007 Nov 7;298(17):2020-2027. [CrossRef] [Medline]
    29. Chatterjee A, Ross DC. Milken Institute. 2012 Aug. Waistlines of the World: The Effect of Information and Communications Technology on Obesity   URL: http://www.milkeninstitute.org/publications/view/531 [accessed 2016-05-09] [WebCite Cache]
    30. Vandelanotte C, Sugiyama T, Gardiner P, Owen N. Associations of leisure-time internet and computer use with overweight and obesity, physical activity and sedentary behaviors: cross-sectional study. J Med Internet Res 2009;11(3):e28 [FREE Full text] [CrossRef] [Medline]
    31. Hilbert A, Rief W, Braehler E. Stigmatizing attitudes toward obesity in a representative population-based sample. Obesity (Silver Spring) 2008 Jul;16(7):1529-1534 [FREE Full text] [CrossRef] [Medline]
    32. Berger M, Wagner TH, Baker LC. Internet use and stigmatized illness. Soc Sci Med 2005 Oct;61(8):1821-1827. [CrossRef] [Medline]
    33. Wang JY, Bennett K, Probst J. Subdividing the digital divide: differences in internet access and use among rural residents with medical limitations. J Med Internet Res 2011;13(1):e25 [FREE Full text] [CrossRef] [Medline]
    34. Kreuter MW, Chheda SG, Bull FC. How does physician advice influence patient behavior? Evidence for a priming effect. Arch Fam Med 2000 May;9(5):426-433. [Medline]
    35. Naghshizadian R, Rahnemai-Azar AA, Kella K, Weber MM, Calin ML, Bibi S, et al. Patient perception of ideal body weight and the effect of body mass index. J Obes 2014;2014:491280 [FREE Full text] [CrossRef] [Medline]
    36. Dorsey RR, Eberhardt MS, Ogden CL. Racial and ethnic differences in weight management behavior by weight perception status. Ethn Dis 2010;20(3):244-250. [Medline]
    37. Ayers SL, Kronenfeld JJ. Chronic illness and health-seeking information on the Internet. Health (London) 2007 Jul;11(3):327-347. [CrossRef] [Medline]
    38. Pew Research Center. Cell Phone and Smartphone Ownership Demographics   URL: http://www.pewinternet.org/data-trend/mobile/cell-phone-and-smartphone-ownership-demographics/ [accessed 2016-03-08] [WebCite Cache]
    39. Pew Research Center. Internet User Demographics   URL: http://www.pewinternet.org/data-trend/internet-use/latest-stats/ [accessed 2016-05-08] [WebCite Cache]
    40. Aday LA, Andersen R. A framework for the study of access to medical care. Health Services Research 1974;9(3):208-220.
    41. Schneider J, Makelarski JA, Van HM, Lipton RB, Abramsohn E, Lauderdale DS, et al. Differential access to digital communication technology: association with health and health survey recruitment within an African-American underserviced urban population. J Urban Health 2011 Jun;88(3):479-492 [FREE Full text] [CrossRef] [Medline]
    42. Margellos-Anast H, Shah AM, Whitman S. Prevalence of obesity among children in six Chicago communities: findings from a health survey. Public Health Reports 2008;123(2):117-125.
    43. Thorpe LE, Greene C, Freeman A, Snell E, Rodriguez-Lopez JS, Frankel M, et al. Rationale, design and respondent characteristics of the 2013-2014 New York City Health and Nutrition Examination Survey (NYC HANES 2013-2014). Prev Med Rep 2015;2:580-585 [FREE Full text] [CrossRef] [Medline]
    44. Pew Research Center. 2012 Nov 27. Health Tracking Survey Survey Questions: Revised Topline General documentation   URL: http:/​/www.​pewinternet.org/​files/​old-media/​/Files/Questionnaire/2013/Surveyquestionsandmethodology.​pdf [accessed 2016-05-06] [WebCite Cache]


    Abbreviations

    AAPOR: American Association for Public Opinion Research
    BMI: body mass index
    ICT: information and communication technology
    IRB: Institutional Review Board
    SSHVS: South Side Health and Vitality Studies
    GED: general educational development


    Edited by G Eysenbach; submitted 09.03.16; peer-reviewed by H Yang, R Haase; comments to author 31.03.16; revised version received 09.05.16; accepted 04.06.16; published 28.06.16

    ©Anjali Gopalan, Jennifer A Makelarski, Lori B Garibay, Veronica Escamilla, Raina M Merchant, Marcus B Wolfe Sr, Rebecca Holbrook, Stacy Tessler Lindau. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 28.06.2016.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.