Kevin Patrick - US grants

Description (adapted from the investigator's abstract): Improved physical activity (PA) and nutrition behaviors in adolescents show great promise to reduce risk of cancers and other disease. Fewer than 20 percent of adolescents meet recommendations for fat or fruits and vegetable consumption, and only 50 percent of adolescent girls and 67 percent of boys meet recommendations for vigorous PA. In this project we will evaluate an integrated clinical and home-based intervention to improve PA and nutrition behaviors in adolescents. PACE+ has 3 integrated components: a computer assessment and action planner; provider counseling and 12 months of extended phone & mail contact. Pilot study results (n=117) demonstrate that PACE+ shows substantial promise in maintaining healthy and/or improving poor nutrition & PA behaviors. We will recruit 768 male and female adolescents age 11 through 15 seen in 6 healthcare settings. Subjects will be randomly assigned within practices to 2 successive one-year "doses" of PACE+ or a comparison condition involving counseling for sun protection behaviors. PACE+ assesses 4 behaviors: 1) dietary fat, 2) fruits & vegetable consumption, 3) moderate PA, and 4) vigorous PA. PACE+ also assesses stage of change and psychosocial mediators of behavior change. PACE+ guides the adolescent to select 1 nutrition and 1 PA target behavior for which they develop action plans to discuss during the provider encounter. The provider endorses or modifies the action plan and encourages participation in the extended phone and mail intervention. Phone counseling, mailed and print materials guide the adolescent to use cognitive & behavioral skills to make changes in target nutrition and PA behaviors. At 6 months (midway through the extended component of PACE+) participants are reassessed and receive stage-appropriate intervention on the remaining 2 diet & PA behaviors. The sun comparison condition has theory-based computer, provider, phone & mail components controlling for attention and other non-specific intervention effects. Primary behavioral outcomes, secondary outcomes, and selected mediators and process variables will be measured prior to the first office visit and at 6, 12, and 24 months. Primary outcomes will be measured using the 7-day physical activity recall and the 3-day food records of fruits, vegetables and fat intake at 12 months. Secondary outcomes include adiposity, fitness, BMI, psychosocial mediators of change, body image, and other measures. This study will be the first to evaluate a combined physical activity and nutrition intervention for youth that revolves around the primary health care setting. The PACE+ intervention is particularly innovative in that 3 components--computer, provider counseling, and an extended home-based intervention--are unified through a common theoretical framework.

Improved physical activity (PA) and dietary behaviors show great promise to reduce risk of cancers, CVD, NIDDM and other diseases. Improvements in PA and nutrition are particularly important for the overweight, a condition now affecting more than 50 percent of Americans. There are few effective programs for treating overweight in primary care. In this project we will evaluate an integrated clinical and home- based intervention to improve PA and dietary behaviors in overweight (BMI 25-29.9) women. PACE+ has three integrated components, a computer assessment and action planner, provider counseling; and 12 months of extended phone and mail contact. Pilot study results (n=173) demonstrate that PACE+ shows substantial promise in improving dietary and PA behaviors We will recruit 360 overweight women age 18 to 45 seen in 4 healthcare settings. Subjects will be randomly assigned to PACE+ or a usual care, delayed treatment control comparison condition. PACE+ targets three primary and three secondary behaviors: Primary: a) dietary quality (fruits and vegetables, vitamin C, carotenoids, and fiber); b) total dietary fat as a percent of energy consumed; and c) energy expenditure from moderate and vigorous physical activity during leisure-time. Secondary: a) recreational media use; b) overeating; c) saturated fat as percent of energy consumed. PACE+ also assesses stage of change and psychosocial mediators of behavior change. The PACE+ computer program guides patients to select one dietary and one PA target behavior for which they develop action or maintenance plans to discuss with the provider. The provider endorses or modifies the action plan and encourages participation in the extended phone and mail intervention. Phone counseling, mail and print materials guide the patient to use cognitive and behavioral skills to make changes in target behaviors. At six months subjects are reassessed over the phone and then continue to receive stage-appropriate intervention to address their new diet and PA goals. Primary outcomes ((a) a combined measure of energy expended in moderate and vigorous physical activity during leisure; ) an index of dietary quality encompassing increased fruits and vegetables and nutrient indicators of these foods; and (c) total dietary fat as a percent of energy consumed) will be assessed at baseline and 12 months with 7-day PA recall and food frequency questionnaires. Secondary outcomes and mediators of behavior change will be measured at baseline, 6, 12 and 24 months. Secondary outcomes include objective and self-report measures of PA and recreational media use and self-reported dietary behaviors (overeating; saturated fat intake as a percent of total energy consumed), BMI, skinfolds, waist circumference, psychosocial mediators of change. Exploratory assessment of plasma carotenoids and 24-hour dietary recall will be performed on a sub-sample of subjects to validate self report measures. The PACE+ intervention is particularly innovative in that three components - computer, primary care provider counseling, and an extended home-based intervention - are unified through a common theoretical framework.

DESCRIPTION (provided by applicant): Improved physical activity (PA) and dietary behaviors show great promise to reduce risk of cancers, CVD, NIDDM and other diseases. Improvements in PA and nutrition are particularly important for the overweight, a condition now affecting more than 50 percent of Americans. There are few effective programs for treating overweight in primary care. In this project we will evaluate an integrated clinical and web-based intervention to improve PA and dietary behaviors in overweight (BMI 25-34.9) men age 18 through 55 years. PACEi has three integrated components: a) preclinic-visit web-based assessment and progress planner; b) provider counseling; c) 12 months of web-based, e-mail & limited phone contact. Pilot study results demonstrate that a phone & mail-based version of PACEi has promise in improving dietary & PA behaviors. An NCI-funded randomized, controlled trial of PACEi among overweight women is currently underway. Phase 1 will involve formative research to structure PACEi to the unique needs of men and their physicians. In Phase 2 we will recruit 360 overweight men seen in 4 healthcare settings. Subjects will be randomly assigned to PACEi or a usual care, delayed treatment control condition. PACEi targets 3 primary and 3secondary behaviors: Primary: a) energy expenditure from moderate and vigorous physical activity during leisure-time; b) fruit & vegetable servings; c) dietary saturated fat as percent of energy consumed. Secondary: a) fiber intake; b) total energy intake; c) total dietary fat as a percent of energy consumed. PACEi also assesses stage of change and psychosocial mediators of behavior change. The PACEi approach guides patients to select one dietary and one PA target behavior for which they develop action plans to discuss with the provider. The provider endorses or modifies the action plan and encourages participation in the continued intervention. Web-tutorials, continuous web access, e-mail interaction, and phone counseling every three months guide the patient to use cognitive & behavioral skills to change target behaviors. The program enables participants to receive tailored & stage-appropriate interventions to address their diet & PA goals. Primary outcomes ((a) energy expended in moderate and vigorous physical activity during leisure; (b) servings of fruits and vegetables; and (c) dietary saturated fat as a percent of energy consumed) will be assessed at baseline and 12 months with the International Physical Activity Questionnaire (IPAQ) and a food frequency questionnaire developed for men at the Fred Hutchinson Cancer Research Center. Secondary outcomes and mediators of behavior change will be measured at baseline, 6, 12 and 24 months. Secondary & exploratory outcomes include: objective and self-report measures of PA, recreational media use & dietary behaviors (fiber; total energy intake; fat intake as a percent of total energy consumed); and BMI, skinfolds, waist circumference, body composition, and psychosocial mediators of change. The PACE+ intervention is particularly innovative because its three components - pre-visit web assessment, primary care provider counseling, and the extended web-based intervention - are unified through a common behavioral theoretical framework.

DESCRIPTION (provided by applicant): Improved physical activity (PA) and dietary behaviors and reductions in overweight or obesity show great promise to reduce the risk of type 2 diabetes in adults. However, very few interventions have been reported that address this issue, and none that are useful for primary care providers or that address reducing diabetes risk in adolescents. We propose a randomized controlled trial to evaluate whether an integrated primary care and web-based intervention, PACEi-DP, can produce initial and sustained improvements in anthropometric, behavioral, metabolic, and physiological outcomes in adolescents who meet the ADA criteria for "high risk" for type 2 diabetes. PACEi-DP is a 1- year intervention involving: a) pre-primary care visit web assessment and progress planning; b) clinician counseling; c) 12 months of web-based, phone and/or group-based follow-up. Pilot studies based upon selected elements of PACEi-DP demonstrate its promise in improving dietary & PA behaviors and in stabilizing BMI in overweight adolescents. We will recruit 93 adolescents, age 12 to 16 years, who meet the ADA criteria for high risk of Type 2 diabetes. Subjects will be recruited from 5 healthcare settings and the community and randomly assigned to one of three conditions: 1) usual medical care; 2) a web-based version of PACEi-DP where the follow-up component involves asynchronous web-based contact with subjects and their parent/guardian; or 3) a multi-modal PACEi-DP where the follow-up component adds phone and group contact. PACEi-DP will target 4 behaviors: 1) total energy expenditure from moderate and vigorous PA; 2) sedentary behavior and recreational media use; 3) Fruit/Vegetable/Fiber consumption (5 or more servings/day of fruits/vegetables and 3 or more servings/day of whole grains or legumes); and 4) total fat as percent of energy consumed. The intervention guides patients to select PA & diet target behaviors for which they develop action plans to discuss with the clinician. The clinician endorses or modifies the action plan and encourages participation in the ongoing intervention. Web-tutorials, continuous web access, e-mail interaction, and (in Group 3) phone counseling and group meetings guide patients and parent/guardian to use cognitive & behavioral skills to change behaviors. PACEi-DP enables participants to receive tailored, stage-appropriate intervention on their diet & PA goals. The primary outcome will be the effect of PACEi-DP on BMI at 12 months. Secondary outcomes (at 6 and 12 mo.) will be: a) metabolic and physiological measures of insulin resistance (fasting insulin, fasting blood glucose, blood lipids, microalbuminuria, acanthosis nigricans, and blood pressure; b) anthropometric measures ( percent body fat by DEXA (at 12 months), waist/hip ratios; c) behavioral measures (moderate & vigorous PA; total energy expenditure; CSA; measures of sedentary behavior & recreational media use; servings of fruits, vegetables & fiber; and total fat as a percent of energy consumed. Exploratory measures will include psychosocial mediators of change; measures of parent/guardians' BMI and waist/hip ratios, and process, satisfaction & cost-effectiveness measures of each study arm. The PACEi-DP intervention is particularly innovative because its three components - pre-visit web assessment and behavior change planning, primary care provider counseling, and the ongoing web or web/phone/group intervention -are unified through a common behavioral theoretical framework.

DESCRIPTION (provided by applicant): We propose a randomized controlled trial to evaluate "PACE-Primary Care" (PACE-PC), a theory-based topped care program that enables pediatricians and other primary care providers to intervene with obese adolescents to improve their anthropometric, metabolic, physiological, behavioral, and quality of life outcomes. In the US, some 15% of adolescents are now obese. However, there are no evidence-based and lost-effective programs that primary care practitioners can use to treat these children. Study participants will be 110 adolescents age 11-13 years, obese (>95% BMI), and recruited through three large pediatric practices in the South Bay area of San Diego. The PACE-PC stepped care process incorporates recognized guidelines for treatment of obesity in children (Barlow &Dietz, 1998). It integrates clinician counseling, health educator counseling, and phone and mail (or web depending on access and preference) contact tailored to the needs of the om Year 1 can be maintained following the first intervention year, participants will enroll in a less intensive second "Maintenance Year" of PACE-PC consisting of phone, mail (or web) and every other month group meetings. Participants randomized to the comparison "enhanced standard care" condition will receive the community standard of care for obese adolescents consisting of an initial session with the pediatrician and 3 visits with a health counselor to discuss how to improve weight related behaviors. The primary aim of this study is to compare at 12 months, the effects of PACE-PC vs. enhanced standard care on BMI z-score. The secondary aims of this study will be assess the impact of PACE-PC on selected anthropometric measures, body fat, quality of life, metabolic and physiological manifestations of obesity, behavioral measures of diet and physical activity and a fitness test, and to model health outcomes, cost, and cost-effectiveness of the intervention. Exploratory aims will include evaluation of psychosocial mediators of behavior change, measures of depression, self esteem and body image, parental measures (including BMI) and process measures. 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[unreadable] DESCRIPTION (provided by applicant): Overweight is related to several cancers, CVD, and NIDDM, and the prevalence of overweight is increasing rapidly (NIH 1998). This study will develop and then evaluate in a randomized controlled trial a cell phone and web-based application (mDIET) that can be used as an assessment and intervention tool to improve dietary behaviors in overweight and moderately obese women age 25 through 55 years. Forty women will be randomized to the mDIET group or a control group. The mDIET (Mobile Diet Intervention through Electronic Technology) system will include a web-application that shares data and communicates via a server with the cell phone. mDiet will identify unique dietary needs for each participant through an initial assessment focusing on most frequently eaten foods (including fast food) and eating behaviors that may lead to ncreased caloric intake (e.g., food preparation techniques, snacking while watching tv). An expert system will use logic rules to filter this information and create concrete goals to target. These goals are then presented to the user via the cell phone on a daily basis to serve as a prompt for food selection and behavioral improvements. The study will examine whether real-time communication to "pull" information from users and "push" appropriate intervention messages at critical point-of-decision moments will translate into greater goal attainment and dietary improvement. The control group will participate in the same baseline dietary assessment and will receive feedback on their assessment via print materials. The primary outcome will be the effect of the mDIET system on BMI at 4 months. This study will help determine whether this novel combined use of web assessment and cell phone technology can improve the effectiveness of dietary interventions by facilitating self-monitoring, increasing awareness of behaviors, and providing appropriate prompts at critical decision time-points. If the mDIET system is successful, it will be incorporated into a larger R01 study focused on obesity management utilizing cell-phone technologies. [unreadable] [unreadable] [unreadable]

DESCRIPTION (provided by applicant): The central role of places in which physical activity is done is now widely recognized, so it is important to measure both activity and its location. We will develop a Physical Activity and Location Measurement System (PALMS) comprised of an integrated suite of hardware and software that supports real-time capture and subsequent analyses of data on physical activity and energy expenditure (PAEE) from a geospatial perspective. Capability for ecological momentary assessment (EMA) of psychosocial factors related to PAEE context will also be included. No measurement approach exists that is capable of simultaneously and objectively collecting PAEE by combined heart rate and motion (HR+M) and location by Global Positioning Systems (GPS) data. These objective measures will provide significant advantages over currently-available self-reports to help understand relationships between PAEE, the environment, and health-related factors. This project will be performed by an interdisciplinary group of researchers with expertise in physical activity and energy expenditure measurement, active living research, software engineering, wireless sensor networks, cell phone technologies, GPS and geographic information systems (GIS) research and data modeling. The project will occur in four phases: PHASE I (12 mo.): Specify, build and bench test the software architecture that supports data collection from both an HR+M monitor and a highly accurate GPS device as well as a cell phone. Develop data-server and web-server software including new application program interfaces as well as methods to integrate into existing well established GIS systems (e.g. ArcGIS). PHASE II (12 mo.): Perform usability testing of the portable tool, refine the system and then use it to capture a minimum of 45 person-weeks of combined PAEE, GPS and EMA data among a multiethnic sample (n=45) of adolescents (age 12-20), adults (age 21-59) and older adults (age 60+). PHASE III (12 mo.): Use data captured in PHASE II to develop methods of data modeling and analysis appropriate to PAEE and geospatial research. Further improve the tool and software for use in Phase IV and develop User Guide and Web Tutorial for Researchers. PHASE IV (12 mo.): Field test the entire system (PAEE, GPS, EMA data collection, supporting server, and web software) in free-living adolescents, adults and older adults (n=45;min. 45 person-weeks) and test the utility of the system as a support to research on geospatial aspects of PAEE.

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Evaluate the effect, at 12 months, of the two intensities of the PACEi-DP (Pace-Internet for Diabetes Prevention) intervention on Body Mass Index (BMI) among male and female adolescents. BMI is proposed as the primary outcome because among the ADA "high risk" criteria, it is modifiable through weight control and lifestyle interventions. Assess at 6 and 12 months the impact of the intervention on metabolic and physiological manifestations of insulin resistance including fasting insulin, fasting blood glucose, blood lipid levels, microalbuminuria, acanthosis nigricans, and blood pressure. Assess anthropometric measures, and behavioral measures of diet and physical activity including total energy intake, percent calories from dietary fat, fiber, consumption of food, total energy expended and sedentary behaviors/recreational media use. Exploratory aims will assess the impact of the intervention on psychosocial mediators, impact on parents BMI, assess satisfaction with the intervention, and assess health outcomes, cost and cost-effectiveness of each intervention.

[unreadable] DESCRIPTION (provided by applicant): We propose to develop a clinic- and home-based behavioral intervention to promote weight management and improve physical activity, diet and sedentary behaviors among preadolescent and adolescent youth who are survivors of childhood ALL (defined as being off therapy for at least 2 years without relapse). Children who survive acute lymphoblastic leukemia (ALL) have an increased risk for development of overweight and obesity with the prevalence in this population reported as high as 57%. Identified mechanisms that may contribute to the risk of obesity in the childhood ALL population include factors related to their underlying ALL and its treatment as well as modifiable factors such as excess energy intake, reduced energy expenditure, and sedentary lifestyle. Because of the unique health and psychosocial challenges faced by these children interventions to prevent and treat their obesity need to be developed and tested. The experimental intervention we propose to evaluate will be tailored specifically for pediatric survivors of ALL, be grounded in formative work with this group and their caregivers, and build upon prior successful research on weight reduction and related behaviors by the research team. We hypothesize that because the intervention will the tailored to the unique needs of childhood survivors of ALL, it will promote greater change in BMI z-score, a metric of relative weight status, as compared to an intervention developed for otherwise healthy children. [unreadable] [unreadable] Testing of the intervention will be performed via a small, randomized, controlled trial (RCT) among at risk for overweight and overweight (> 85% BMI for age and sex) preadolescent and adolescent youth (10-16 years) with a prior history of childhood acute lymphoblastic leukemia. The RCT will compare the experimental intervention to an intervention that has been proven efficacious in increasing healthy behaviors and reducing excess weight among healthy overweight and obese adolescents. Our primary aim is to determine, at 4 months, the impact of this intervention on BMI z-score as compared to an Enhanced Standard Intervention (ESI). Secondary aims are to determine the impact of the intervention vs. ESI on self-reported behavioral measures of diet and physical activity, selected metabolic and physiologic measures, quality of life and depression measurements, and use and acceptability of the intervention by participants, their families and participating clinical staff. [unreadable] [unreadable] Children who survive ALL have an increased risk for development of overweight and obesity that in part is due to the treatment for their ALL. No proven interventions exist to prevent or treat overweight in this unique population and research on the importance of tailoring behavioral interventions to the characteristics of the underlying population suggest that weight control programs successful among healthy children might not work for this group. This research aims to develop and pilot test a specific weight control intervention for pediatric survivors of ALL. [unreadable] [unreadable] [unreadable]

DESCRIPTION (provided by applicant): We propose a randomized controlled trial to evaluate how well an intervention, Planned Care for Obesity &Risk Reduction (Planned CORR), supports primary care treatment of obesity in adults with at least one cardiovascular risk factor (CVRF). We will assess how well it creates initial and sustained improvements in BMI and metabolic, anthropometric and behavioral outcomes in obese adults, age 25 to 70 who have one or more of the following CVRFs: hypertension, smoking or the metabolic syndrome. Planned CORR integrates intervention components based on behavior change theory with a delivery strategy based on the Chronic Care Model (Wagner et al, 2001;also called the "Planned Care Model") and is designed to incorporate intervention principles used successfully to treat obesity while at the same time supporting the management of one or more CVRFs. Planned CORR is intended to be compatible with a variety of primary care settings including those with minimal resources to devote to patient education and where most obese adults receive their usual medical care. After formative work to ensure that intervention components are useful to intended participants, we will recruit 274 women and men, age 25-70 who meet the entry criteria of obesity (Class I ⅈBMI 30-39.9) plus one or more CVRFs. We anticipate that approximately 40% will be Hispanic. Planned CORR is a stepped care intervention that begins with computer-assisted assessment and tailored action planning, physician or nurse practitioner counseling, and an intensive "first step" of 4 months of lifestyle modification delivered via monthly in-person sessions and phone calls and weekly web or mail (based on preference) tutorials. CVRFs are managed concurrently according to recognized evidence-based protocols. Successive 4-month steps involve more, the same, or less intensive intervention depending on clinical response. The primary aim is to evaluate the effects at 12 &24 months of Planned CORR on BMI when compared to enhanced usual care. Secondary aims will evaluate: CVRF outcomes;physiological, metabolic, behavioral, and psychosocial factors related to obesity and CVRFs;quality of life &depression;patient, provider &staff satisfaction;and process measures relevant to implementation in primary care. PUBLIC HEALTH RELEVANCE: The aim of this project is to explore how to improve the way primary care providers provide services to their patients who are overweight and who also have other important medical conditions or health risks such as hypertension, smoking or high cholesterol. Because these patients require a complicated treatment course that includes regular use of medications and individual or group health behavior counseling, their needs are often poorly addressed. This study will explore the use of a systems-based approach to improve these services.

DESCRIPTION (Provided by the applicant): To accelerate progress toward conducting comparative effectiveness (CE) research in cancer prevention and control, we have formed a consortium of investigators with expertise in behavioral research, cyberinformatics, telematics, oncology, and clinical trials to create and test an innovative infrastructure called CYCORE: CYberinfrastructure for COmparative effectiveness REsearch. The overarching goal of CYCORE is to develop a comprehensive, state of the art cyberplatform that will enable large-scale and robust CE research across the neoplastic continuum, i.e., from cancer prevention, to cancer treatment and ultimately, to cancer control and survivorship care. For this Grand Opportunity grant, we propose to design, and validate within a community of cancer investigators and patients, a prototype cyberinfrastructure (CI) that supports the acquisition, storage, quality assurance, visualization, analysis, and sharing of clinical, genetic, physiologic, and behavioral data for cancer-related trials. The specific aims for this project include: 1) obtain input from key stakeholders (cancer researchers, clinicians and patients) regarding needs and preferences for the CI through an interactive, iterative process;2) create the CI for data aggregation, integration, processing, mining, storage and retrieval;3) build data acquisition hardware and software, including interfaces with multiple data sources, physiological sensors, and digitization methods for self-reported data;4) develop applications to be used in conjunction with the CI, including implementation of novel brain-based device methods for data analysis;and, 5) Conduct feasibility studies with the new technology with cancer patients to identify needs for CI improvement and enhancement. This project will build capacity and accelerate CE research in two important ways: 1) through the creation of a system enabling two-way, real-time interaction between researchers, health care providers, and patients and their families, to enhance research participation, adherence to treatments and other interventions and, ultimately, result in improved outcomes;and, 2) through the establishment of a sustained and viable consortium that is able to validate this technology in settings that assure its future use and provide continued oversight to assure that CYCORE stays abreast of continuing scientific advances. PUBLIC HEALTH RELEVANCE: To accelerate progress toward conducting comparative effectiveness (CE) research in cancer prevention and control, we have formed a consortium of investigators with expertise in behavioral research, cyber-informatics, telematics, oncology, and clinical trials to create and test an innovative infrastructure called CYCORE: CYberinfrastructure for COmparative effectiveness REsearch. The overarching goal of CYCORE is to develop a comprehensive, state of the art cyberplatform that will enable large-scale and robust CE research across the neoplastic continuum, i.e., from cancer prevention, to cancer treatment and ultimately, to cancer control and survivorship care. For this Grand Opportunity grant, we propose to design, and validate within a community of cancer investigators and patients, a prototype cyberinfrastructure (CI) that supports the acquisition, storage, quality assurance, visualization, analysis, and sharing of clinical, genetic, physiologic, and behavioral data for cancer-related trials.

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Compare at 12 months the effect of year 1 of PACE-PC vs Enhanced Standard Care on BMI z-score among obese >95th percentile for age male and female adolescents age 11-13.

DESCRIPTION (provided by applicant): The problem of weight gain and obesity is important in young adulthood. As individuals traverse life paths from adolescence into early adulthood, whether or not they attend college, join the military or enter the workforce out of high school, they encounter multiple stressors and influences that contribute to weight gain. Weight gain in turn leads to increase risk of cardiovascular disease, diabetes and other health problems. Little is known about how to intervene to prevent weight gain or enable weight loss in this population. We propose to conduct formative research to develop an intervention, Social/Mobile Approach to Reduce Weight (SMART) to prevent weight gain and promote weight loss in young adults at risk for weight gain. We will then conduct a randomized controlled trial in 406 participants to evaluate the effects on weight status and other metabolic, behavioral and psychosocial outcomes of SMART at 12 and 24 months. SMART will be a theory- based intervention that combines web, mobile phone and social media components into an engaging weight control program. The project will build on our prior work in intervention research to improve weight-related behaviors and weight outcomes and in mobile phone based behavioral interventions. We will perform formative research on SMART in San Diego and in collaboration with researchers at Stanford University. This will be followed by development of the final SMART software architecture and system. Then a 2-year randomized controlled trial will be conducted to evaluate the effects of SMART on weight-related outcomes. The primary aims of the study will be differences in weight status (kg) at 24 months among two groups: a) Those with a normal weight at baseline to evaluate how well SMART supports the prevention of weight gain;and b) Overweight/obese participants at baseline in whom weight loss will be evaluated. Sufficient numbers of participants in both groups will be recruited to evaluate these outcomes. Secondary and exploratory aims of the study will be to examine other health related outcomes and process measures related to intervention use and satisfaction. RELEVANCE (See instructions): Overweight/obesity are highly prevalent conditions in the US impacting approximately 66% of the adult population. Reducing the overall incidence of obesity is a major public health concern, yet little is known about how to do this with young adults. This study will address this gap in knowledge. (End of Abstract)

DESCRIPTION (provided by applicant): We propose a randomized controlled trial to evaluate how well a weight loss intervention based primarily on text messages sent to/from mobile phones, mDIET, promotes weight loss and weight loss maintenance in overweight and obese men and women age 21 through 60. This study is based on promising formative research and a pilot randomized controlled trial (RCT) recently concluded among 75 overweight/obese men and women (mean age: 49.9) with the support of an NCI R21. To our knowledge the mDIET pilot study was the first RCT to address overweight/obesity with an intervention that is based primarily on the use of text (SMS) messages. At 16 weeks we found a significant between-group difference in weight with mDIET users losing 3.16% more weight (2.88 kg) than controls and very high acceptability. SMS messaging is becoming increasingly common and can be viewed on essentially all mobile phone platforms. Thus, the population reach of behavioral interventions using this approach can be extensive. In this project we will expand and strengthen the expert system logic underlying mDIET, strengthen content targeted at energy intake behaviors that contribute to weight loss and develop additional content related to physical activity and sedentary behavior -- all changes aimed at enabling mDIET to promote and sustain a 5-10% weight loss at 12 months. We will conduct additional formative research among those whose first language is Spanish, and develop mDIET for both English and Spanish-language speaking men and women. Then, 309 overweight/obese (BMI 25.1-39.9) women and men, age 21-60 will be randomized to one of three 12-month conditions: a) an SMS only version of mDIET;b) An SMS+Phone version of mDIET that includes regular brief counseling calls from a trained weight loss specialist;or c) a usual care Standard Print-based weight intervention. We will enroll approximately 30% in the Spanish language preference group to assure our ability to perform meaningful exploratory analyses in this population. mDIET is based in behavioral theory and can support delivery of tailored messages to each user. The primary outcome will be the effect of mDIET on percent weight loss at 12 months. Secondary outcomes will include assessments at 6 and 12 months of mDIET effects on proportion of each group that lose at least 5% of initial body weight, behavioral, and psychosocial constructs, quality of life &depression, satisfaction with the intervention and process measures related to use of intervention elements. PUBLIC HEALTH RELEVANCE: This project explores whether a program based mainly on the use of automated text messages sent to and from a person's mobile phone can help overweight/obese people lose weight. Messages are sent a few times each day based upon expert system rules and promote improved behaviors important to weight loss and weight loss maintenance. Some messages request a reply encouraging continued engagement and interaction with the program. Because of the widespread use of text messages, if found efficacious the public health impact of this program could be great.

The joint National Cancer Institute (NCI)/NSF workshop on A Cyberinfrastructure Platform for Collaborative Research and Application in Public Health and Health Services: A Seeded Cloud Approach, to be held on January 10-11, 2011 at the San Diego Supercomputer Center, UC San Diego, is aimed at setting the stage for a sustained research effort encompassing the theme of public health and health services, which will cut across technical and socio-behavioral boundaries. The NIH/NCI component of this effort provides the motivating applications and use cases, while the NSF component provides the access to the cyberinfrastructure, computer science, and social and behavioral science research and education communities. The workshop discussions will be driven by use cases drawn from a broad set of areas including, Emergency Medical Services; Personal and Mobile Applications; Behavior and Environment; Cancer; and, Social Networking and Communication.

The results of the symposium are expected to provide input into technical and socio-behavioral parameters for developing a collaborative platform for research and application. In addition, an important goal of the workshop will be to concretize the ideas and concepts into a regional platform, e.g. for the California region, and to initiate a seeded cloud to enable research on public health and health services topics in that region. The workshop will identify a broad set of research needs and opportunities that must be addressed to advance cyberinfrastructure for healthcare services; population health; and, computer and information systems.

DESCRIPTION (provided by applicant): We propose to field test and validate PALMS (Physical Activity Location Measurement System), a behavioral measurement tool that we have developed with support from the Gene-Environment Initiative (GEI). We will do this in an ongoing R01 study among church going Latinas in San Diego County. PALMS uses data from concurrently worn Global Positioning System (GPS) devices and accelerometers to ascertain location, time and type of physical activity (PA) on an essentially continuous basis. By the time this proposed project begins we will have validated the processing algorithms in PALMS in a highly controlled study supported by a GEI Opportunity Fund Grant. We have also processed 7-day GPS and accelerometer data from over 2000 participants (700 adults; 800 adolescents; and 500 children) enabling us to statistically demonstrate the value of collecting GPS data in addition to accelerometer data. However, we have not yet assessed PALMS in an ongoing cohort (or prospective) study and thus have no information on staff or participant burden in real world use. The field based validation study we propose herein will be the logical next step to ensure that PALMS is feasible to use-by both research staff and research participants-in typical research settings, and that it provides valid information about what it is intended to measure: the geospatial and temporal characteristics of physical activity. Importantly, we have selected to perform this research in collaboration with Dr. Elva Arredondo, PI of an R01 examining a place based physical activity intervention among Latinos in San Diego. Dr. Arredondo is a Co-investigator on a multi-site NHLBI-funded prospective cohort study, the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), a major national study with one of its four sites in San Diego (others are in Miami, New York and Chicago). The PI of the San Diego HCHS/SOL is Dr. Greg Talavera and he is a proposed co-investigator on the project we propose. Thus, this project will help set the stage for HCHS/SOL investigators to incorporate PALMS into the HCHS/SOL. In addition, place based interventions like Dr. Arredondo's R01, that are designed to improve PA environments and encourage PA in the most supportive environments, can greatly benefit from more precise measures of PA in time and space. In this study we will: a) Demonstrate the usability of PALMS in a community-based R01 trial through two rounds of assessment with participants and research staff; b) Demonstrate that PALMS can collect more precise and valid data on continuous combined measures of physical activity (via accelerometer) and location- x-time (via GPS) than current survey methods; and c) that these data provide added value to researchers as they have greater ability to predict location-x-PA relationships than current GIS or survey based methods.

In response to a healthcare crisis of epidemic proportions, thousands of software developers have been innovating new personal healthcare applications and technologies that leverage advances in medical and computing technology. Despite the endless streams of personal data that these tools process -- weight, activity, diet, heart rate, etc. -- they are relatively data poor. Left out of these applications is a comprehensive set of users' clinical electronic medical records, genomic data, comparative data with relevant subpopulations, and data on environmental influences important to health and quality of life.

There are numerous barriers to incorporating such data in applications, the dominant factors being the tremendous volume and heterogeneity of such data, much of it streaming in real-time and spread across disparate stakeholder platforms. A related problem is drawing inferences from these data. With the advances in databases and machine learning proposed, we envision a new era of health and healthcare where patients, providers and consumers are empowered by data access and applicability that we characterize as personalized population health. In particular, we anticipate a new category of healthcare applications that infer one's health status - and help execute interventions - in the perspective of one's entire life history and context.

This project is conducting fundamental and applied research in support of a platform, called DELPHI, that enables integrated access and analysis of all data relevant to health, and consequently promotes more rapid development of empowering, data-driven health apps and tools by a broad community of health-related software developers. The platform supports an integrated "whole health information model" of the individual that provides developers a single point of access that both (a) hides distribution and data heterogeneities, and (b) facilitates drawing inferences from these "noisy" data. The platform enables novel forms of analyses based on contextual and statistical metadata. Scalability is achieved through theoretically proven and newly proposed database and machine learning techniques. Our research is driven by three disparate case studies and field trials: a clinician-facing type-1 diabetes intervention, a patient and consumer-facing hypertension application, and a regional population health asthma and respiratory disease scenario.

Intellectual Merit

DELPHI is yielding fundamental advances in databases and machine learning that enable a wide community of programmers - from full-time professional to relative novices - to program on top of a "live", streaming population-scale medical dataset. Additionally, these techniques are being evaluated in at least three realistic field trials, yielding new insights on both the nature of computing on medical "big data" and the techniques we have proposed to make it tractable.

Broader Impact

This will be demonstrated through a personal well-being and population health applications ecosystem, with three immediate beneficiaries: 1) The San Diego Beacon Community, a model for health information exchanges currently under development nationally. 2) Governmental and non-profit agencies who serve as an example of public/private partnerships to promote community-wide health. 3) Private industry, in this case Qualcomm Life's/2net platform where we demonstrate how to utilize existing services in novel ways to handle health data. Finally, this project will serve as a training ground in personalized population health for graduate students, post docs and medical residents.

Current healthcare diagnostics and assessment systems are limited by health data, which is sporadic, periodic, and incomplete. Wireless devices and health sensor technologies are increasing in use for continuous monitoring and assessment of key physiologic, psychological, and environmental variables and reduce the current gaps in health data. Uptake of such data by current health systems has been slow because of the reliance upon the physician/healthcare team to interpret and manage incoming data. Nevertheless, the large streams of data generated by these devices in conjunction with traditional clinical data (Electronic Medical Records) have the potential provide real and important insights into patient health and behavior. To address this gap, this proposal will develop SenseHealth -- a novel software platform that will automatically process and incorporate volumes of real-time data from sensors tailored to the individual in the context of personal electronic medical records and available environmental data. Such data will be integrated into the clinical care workflow to enable system usability, feasibility, and ultimately utility. A core component of the cyberinfrastructure is a collection of quantitative, predictive models that are sensitive to concerns across age, diseases, and health and variety of patient situations (ranging from low priority with no consequence on patient management to high priority requiring emergency evaluation), and sensor failures. The models will be integrated with a distributed real-time stream data processing system and a complex event stream processing engine to process sensor data in a scalable and fault-tolerant manner. Research at Rady Children's Hospital of San Diego, an affiliate of UCSD will be leveraged to develop these models. In each of the following studies, clinically relevant events (i.e. events that require clinical intervention) will be identified and disease specific models will be developed that will predict clinical relevance or the need for intervention. Incoming data and resulting clinical management activity from studies using various types of health sensors will be evaluated in two different patient populations: (1) MyGlucoHealth application for evaluating the use of a Bluetooth-enabled glucometer (for blood sugar measurements) in 40 youths with Type 1 diabetes, and (2) Asthma Tracking application for evaluating the ability of a metered dose inhaler (MDI) tracking device to track asthma medication use in 50 mild-to-moderate asthma subjects over a period of 6 months. The models will then be evaluated using multiple sensor streams in youth with diabetes (The Diabetes Management Integrated Technology Research Initiative (DMITRI) study) and in a prospective study in youth with asthma to determine their validity, efficacy, and utility in identifying patient scenarios of concern.

The SenseHealth system architecture will consist of four major components (1) Health and environmental sensors linked with (2) smartphone applications that communicate with (3) a back-end Data Center comprised of data storage and clusters doing and real-time analytics and data visualization, which will then provide a comprehensive health picture to users/clients via (4) tailored, programmed user/client applications. For these continuous sensing applications, managing sensors and smartphone in an energy-efficient manner is critical. SenseHealth will include a novel context-aware power management framework that uses both the application-level context (e.g., sensor data) and the dynamic environmental or system-level context (e.g., battery level, next phone charging opportunity prediction, or bandwidth availability) to adaptively control the state of hardware components and deliver a consistent performance (e.g. data accuracy, latency). In particular, data sampling protocols will be energy-aware and will be designed to sample data accurately but only as necessary to provide relevant clinical information. SenseHealth will use Storm, an open source distributed real-time computation system to process the data in a scalable and fault-tolerant manner. The aforementioned predictive models will be implemented in ESPER, an open-source complex event processing (CEP) engine. The models will use ESPER's rich Event Processing Language (EPL) to express filtering, aggregation, and joins, possibly over sliding windows of multiple event streams and pattern semantics to express complex temporal causality among events and trigger custom actions when event conditions occur among event streams. Finally, SenseHealth will fuse sensor and clinical data in a visual format that will increase interpretability and comprehension independent of literacy levels and will provide feedback and ultimately intervention support that is timely and relevant to the user (patient and clinician) based on comprehensive knowledge of data. Open source software visualization tools developed at Calit2 that leverage advances in scaled display wall technology will serve as the foundation for the data visualization component. NSF-funded DELPHI project will provide the data center component to store health sensor data and provide access to SenseHealth algorithm-processed data and visualization protocols.

The research itself will have direct impact on two patient communities, but the broader impacts of the proposed research will extend well beyond them. The proposed open software platform will be built with flexibility to allow for alternative programming with plug-and-play data processing algorithms as required for various sensors/data sources/clinical scenarios. The results from the proposed development activities and prototyping experiments will be of tremendous value to medical professionals, scientists and engineers who are engaged in planning and developing sensor-based systems for continuous health monitoring. The developed software products will be publicly available as open source products under the Apache license. The tools developed from this proposal will be designed to be extensible so that other sensors as well as models can easily be integrated and impact a broader range of healthcare applications. SenseHealth is an essential step toward providing a real-time 360-degree snapshot of health to optimize patient-centered, evidence-based decisions and to empower patients to participate in their own healthcare. The project team will contribute to training a diverse next generation of scientists by involving undergraduate students in the development process, both for computer science techniques and medical science research. The exciting aspect of this proposed work is that wellness is a very tangible and important factor even at young age. The education program will be structured to excite students, particularly those from traditionally underrepresented groups such as minorities and females, about multi-disciplinary research. Through the UCSD's COSMOS program, simple, fun and hands-on experiences for these students will be designed to allow them to understand importance of self-health assessment and disease management at an early age. The team is involved heavily in Graduate Medical Education at UCSD and will promote use of SenseHealth to integrate health data into current health systems in fellowship training activities. This proposal also funds for one graduate student.

DESCRIPTION (provided by applicant): We propose to continue and significantly extend the research we began in 2010, with support from the National Cancer Institute, to develop a system that improves the capture of patient-reported as well as objectively measured data from patients in cancer clinical trials. The system, CYCORE (CYberinfrastructure for COmparative effectiveness REsearch), is a robust software-based prototype for a user-friendly, cyberinfrastructure (CI) that supports the acquisition, storage, visualization, analysis, and sharing of data important for cancer-related comparative effectiveness research (CER). Our initial project began with the intent to solve the problem of how to collect data on patients enrolled in cancer clinical trials that would improve the ability of cancer researchers to gather patient-reported data on such things as symptoms, quality of life, performance status and physiological parameters that signal how well participants are doing with their treatment. We have developed an initial, highly successful prototype of CYCORE, tested it in several use- cases, and now have a small but highly enthusiastic community of users. What we propose herein is based both on our assessment of critical needs for scalability and extensibility of the system as well as on user input about what they find useful in CYCORE and what features they would like to see in subsequent versions. Hence, we endeavor to position CYCORE uniquely to support a rapidly growing interest in cancer-related CER. Our vision for CYCORE is to support an expanded community of researchers in a variety of research settings. In this project, we focus on the following Aims: Aim 1: Improve scalability and performance: Exploit the emerging trends in service-oriented architectures, virtualization, and cloud computing to scale our infrastructure to accommodate both existing and emerging sensors, processing, analysis and reporting tools. Aim 2: Expand capability for new sensors and address fault tolerance and self-healing. Develop and test the acquisition of data from new sensors, while increasing the system's reliability. Aim 3: Introduce a new cancer control use case for CYCORE Aim 4: Incorporate new algorithms that support the detection and analysis of outcomes important in cancer CER. Provide a generic infrastructure for algorithm definition and execution and implement some of the algorithms needed for the pilot trials (e.g., dehydration, performance status, movement, and smoking cessation). Aim 5: Improve security, privacy and data sharing capabilities of CYCORE - optimize how CYCORE handles issues related to data security, anonymization, and confidentiality in order to facilitate data sharing. Integrate with external systems to ingest data into CYCORE and also to export data for sharing with other researchers. Aim 6: Develop and Implement a model for sustainability - develop a sustainability model for CYCORE to ensure support for programming, project and outreach staff in the future. Aim 7: Expand and enrich the community of CYCORE users. Expand, support and nurture the community of CYCORE users.

All cyber-physical systems (CPS) depend on properly calibrated sensors to sense the surrounding environment. Unfortunately, the current state of the art is that calibration is often a manual and expensive operation; moreover, many types of sensors, especially economical ones, must be recalibrated often. This is typically costly, performed in a lab environment, requiring that sensors be removed from service. MetaSense will reduce the cost and management burden of calibrating sensors. The basic idea is that if two sensors are co-located, then they should report similar values; if they do not, the least-recently-calibrated sensor is suspect. Building on this idea, this project will provide an autonomous system and a set of algorithms that will automate the detection of calibration issues and preform recalibration of sensors in the field, removing the need to take sensors offline and send them to a laboratory for calibration. The outcome of this project will transform the way sensors are engineered and deployed, increasing the scale of sensor network deployment. This in turn will increase the availability of environmental data for research, medical, personal, and business use. MetaSense researchers will leverage this new data to provide early warning for factors that could negatively affect health. In addition, graduate student engagement in the research will help to maintain the STEM pipeline.

This project will leverage large networks of mobile sensors connected to the cloud. The cloud will enable using large data repositories and computational power to cross-reference data from different sensors and detect loss of calibration. The theory of calibration will go beyond classical models for computation and physics of CPS. The project will combine big data, machine learning, and analysis of the physics of sensors to calculate two factors that will be used in the calibration. First, MetaSense researchers will identify measurement transformations that, applied in software after the data collection, will generate calibrated results. Second, the researchers will compute the input for an on-board signal-conditioning circuit that will enable improving the sensitivity of the physical measurement. The project will contribute research results in multiple disciplines. In the field of software engineering, the project will contribute a new theory of service reconfiguration that will support new architecture and workflow languages. New technologies are needed because the recalibration will happen when the machine learning algorithms discover calibration errors, after the data has already been collected and processed. These technologies will support modifying not only the raw data in the database by applying new calibration corrections, but also the results of calculations that used the data. In the field of machine learning, the project will provide new algorithms for dealing with spatiotemporal maps of noisy sensor readings. In particular, the algorithms will work with Gaussian processes and the results of the research will provide more meaningful confidence intervals for these processes, substantially increasing the effectiveness of MetaSense models compared to the current state of the art. In the field of pervasive computing, the project will build on the existing techniques for context-aware sensing to increase the amount of information available to the machine learning algorithms for inferring calibration parameters. Adding information about the sensing context is paramount to achieve correct calibration results. For example, a sensor that measures air pollution inside a car on a highway will get very different readings if the car window is open or closed. Finally, the project will contribute innovations in sensor calibration hardware. Here, the project will contribute innovative signal-conditioning circuits that will interact with the cloud system and receive remote calibration parameters identified by the machine learning algorithms. This will be a substantial advance over current circuits based on simple feedback loops because it will have to account for the cloud and machine learning algorithms in the loop and will have to perform this more complex calibration with power and bandwidth constraints. Inclusion of graduate students in the research helps to maintain the STEM pipeline.