Abstract Background The Consortium of Hospitals Advancing Research on Tobacco (CHART) is a network of six projects and a research coordinating unit funded by the National Heart, Lung, and Blood Institute, the National Cancer Institute, the National Institute on Drug Abuse, and the National Institutes of Health (NIH) Office of Behavioral and Social Science Research. The CHART projects will assess the effectiveness and cost-effectiveness of smoking cessation interventions initiated during hospitalization and continued post-discharge. Methods/design Along with a seventh project funded previously under the NIH Challenge grants, the CHART projects will assess smoking cessation strategies delivered to approximately 10,000 hospitalized smokers across a geographically diverse group of nearly 20 private, public, academic, and community hospitals. The CHART research coordinating unit at Kaiser Permanente Center for Health Research provides organizational and data coordination support, facilitating the development of common measures for combining data from multiple CHART projects. Discussion The targeted enrollment in CHART, if achieved, will represent the largest, most diverse pooled dataset of hospitalized smokers receiving smoking cessation assistance, and is designed to contribute to the dissemination and implementation of smoking cessation interventions provided by hospital systems.

Objective

To create upper-extremity and mobility subdomain scores from the Patient-Reported Outcomes Measurement Information System (PROMIS) physical functioning adult item bank.

Design

Expert reviews were used to identify upper-extremity and mobility items from the PROMIS item bank. Psychometric analyses were conducted to assess empirical support for scoring upper-extremity and mobility subdomains.

Setting

Data were collected from the U.S. general population and multiple disease groups via self-administered surveys.

Participants

The sample (N=21,773) included 21,133 English-speaking adults who participated in the PROMIS wave 1 data collection and 640 Spanish-speaking Latino adults recruited separately.

Interventions

Not applicable.

Main outcome measures

We used English- and Spanish-language data and existing PROMIS item parameters for the physical functioning item bank to estimate upper-extremity and mobility scores. In addition, we fit graded response models to calibrate the upper-extremity items and mobility items separately, compare separate to combined calibrations, and produce subdomain scores.

Results

After eliminating items because of local dependency, 16 items remained to assess upper extremity and 17 items to assess mobility. The estimated correlation between upper extremity and mobility was .59 using existing PROMIS physical functioning item parameters (r=.60 using parameters calibrated separately for upper-extremity and mobility items).

Conclusions

Upper-extremity and mobility subdomains shared about 35% of the variance in common, and produced comparable scores whether calibrated separately or together. The identification of the subset of items tapping these 2 aspects of physical functioning and scored using the existing PROMIS parameters provides the option of scoring these subdomains in addition to the overall physical functioning score.

Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.