JMIR Formative Research最新文献

Background: The environment shapes health behaviors and outcomes. Studies exploring this influence have been limited to research groups with the geographic information systems expertise required to develop built and social environment measures (eg, groups that include a researcher with geographic information system expertise).

Objective: The goal of this study was to develop an open-source, user-friendly, and privacy-preserving tool for conveniently linking built, social, and natural environmental variables to study participant addresses.

Methods: We built the automatic context measurement tool (ACMT). The ACMT comprises two components: (1) a geocoder, which identifies a latitude and longitude given an address (currently limited to the United States), and (2) a context measure assembler, which computes measures from publicly available data sources linked to a latitude and longitude. ACMT users access both of these components using an RStudio/RShiny-based web interface that is hosted within a Docker container, which runs on a local computer and keeps user data stored in local to protect sensitive data. We illustrate ACMT with 2 use cases: one comparing population density patterns within several major US cities, and one identifying correlates of cannabis licensure status in Washington State.

Results: In the population density analysis, we created a line plot showing the population density (x-axis) in relation to distance from the center of the city (y-axis, using city hall location as a proxy) for Seattle, Los Angeles, Chicago, New York City, Nashville, Houston, and Boston with the distances being 1000, 2000, 3000, 4000, and 5000 m. We found the population density tended to decrease as distance from city hall increased except for Nashville and Houston, 2 cities that are notably more sprawling than the others. New York City had a significantly higher population density than the others. We also observed that Los Angeles and Seattle had similarly low population densities within up to 2500 m of City Hall. In the cannabis licensure status analysis, we gathered neighborhood measures such as age, sex, commute time, and education. We found the strongest predictive characteristic of cannabis license approval to be the count of female children aged 5 to 9 years and the proportion of females aged 62 to 64 years who were not in the labor force. However, after accounting for Bonferroni error correction, none of the measures were significantly associated with cannabis retail license approval status.

Conclusions: The ACMT can be used to compile environmental measures to study the influence of environmental context on population health. The portable and flexible nature of ACMT makes it optimal for neighborhood study research seeking to attribute environmental data to specific locations within the United States.

Background: Aging is a risk factor for falls, frailty, and disability. The utility of wearables to screen for physical performance and frailty at the population level is an emerging research area. To date, there is a limited number of devices that can measure frailty and physical performance simultaneously.

Objective: The aim of this study is to evaluate the accuracy and validity of a continuous digital monitoring wearable device incorporating gait mechanics and heart rate recovery measurements for detecting frailty, poor physical performance, and falls risk in older adults at risk of falls.

Methods: This is a substudy of 156 community-dwelling older adults ≥60 years old with falls or near falls in the past 12 months who were recruited for a fall prevention intervention study. Of the original participants, 22 participants agreed to wear wearables on their ankles. An interview questionnaire involving demographics, cognition, frailty (FRAIL), and physical function questions as well as the Falls Risk for Older People in the Community (FROP-Com) was administered. Physical performance comprised gait speed, timed up and go (TUG), and the Short Physical Performance Battery (SPPB) test. A gait analyzer was used to measure gait mechanics and steps (FRAIL-functional: fatigue, resistance, and aerobic), and a heart rate analyzer was used to measure heart rate recovery (FRAIL-nonfunctional: weight loss and chronic illness).

Results: The participants' mean age was 74.6 years. Of the 22 participants, 9 (41%) were robust, 10 (46%) were prefrail, and 3 (14%) were frail. In addition, 8 of 22 (36%) had at least one fall in the past year. Participants had a mean gait speed of 0.8 m/s, a mean SPPB score of 8.9, and mean TUG time of 13.8 seconds. The sensitivity, specificity, and area under the curve (AUC) for the gait analyzer against the functional domains were 1.00, 0.84, and 0.92, respectively, for SPPB (balance and gait); 0.38, 0.89, and 0.64, respectively, for FRAIL-functional; 0.45, 0.91, and 0.68, respectively, for FROP-Com; 0.60, 1.00, and 0.80, respectively, for gait speed; and 1.00, 0.94, and 0.97, respectively, for TUG. The heart rate analyzer demonstrated superior validity for the nonfunctional components of frailty, with a sensitivity of 1.00, specificity of 0.73, and AUC of 0.83.

Conclusions: Agreement between the gait and heart rate analyzers and the functional components of the FRAIL scale, gait speed, and FROP-Com was significant. In addition, there was significant agreement between the heart rate analyzer and the nonfunctional components of the FRAIL scale. The gait and heart rate analyzers could be used in a screening test for frailty and falls in community-dwelling older adults but require further improvement and validation at the population level.

Background: Heavy menstrual bleeding (HMB) affects up to 37% of adolescents. Without recognition, HMB can lead to other medical conditions resulting in diminished health-related quality of life. WeThrive, a new mobile health (mHealth) app, implements the pictorial bleeding assessment chart to identify HMB, and the adolescent Menstrual Bleeding Questionnaire to measure the effects of HMB on adolescents' health-related quality of life. If HMB is identified, WeThrive will connect users to local clinics for further assessment of their menstrual bleeding with a health care provider.

Objective: This study aimed to describe adolescents' experiences using WeThrive app.

Methods: This qualitative study was approved by the local Research Ethics Board in Halifax, Nova Scotia, and informed consent was provided by all participants. Individual semistructured interviews were held via videoconference with adolescents younger than 18 years, who had at least 1 menstrual period and had used WeThrive at least once. Interview transcripts were thematically analyzed by 2 investigators (MP and NMN) independently, and the κ statistic was calculated to determine the strength of correlation in themes.

Results: Five adolescents (mean age 15.5, range 13-18 years), participated in the interviews. All participants stated that WeThrive helps them better understand their menstrual periods by predicting period onset, recognizing menstrual symptoms, and identifying HMB. Four themes were identified: (1) the importance of visual features and usability, (2) newly obtained knowledge using WeThrive, (3) feature use depends on menstrual health, and (4) trustworthiness. There was substantial agreement on the identified themes (κ=0.73).

Conclusions: WeThrive is visually appealing, and trustworthy, and helps users better understand their menstrual periods, including identifying HMB. By identifying HMB early, WeThrive has the potential to improve the recognition of bleeding disorders and iron deficiency in adolescents. WeThrive is a useful tool to help adolescents better understand their menstrual periods.

Background: Hypertension is a major risk factor for cardiovascular disease, affecting over a billion people worldwide. Mobile health (mHealth) apps have emerged as effective tools for managing hypertension, offering capabilities for monitoring blood pressure, fostering lifestyle changes, and improving treatment adherence.

Objective: This study aimed to explore patient-users' perspectives on the hypertension care mHealth app Hypertension.APP, focusing on its accessibility, expected benefits, potential risks, and role in hypertension management in Germany.

Methods: A qualitative study was conducted involving semistructured interviews with 20 patient-users of a hypertension care mHealth app, Hypertension.APP. Participants were recruited between January and June 2023 using purposive sampling. Verbatim transcripts were analyzed using qualitative content analysis.

Results: Participants primarily discovered the app independently, driven by recent hypertension diagnoses and insufficient information from health care professionals regarding effective self-management strategies for their blood pressure. They valued the app for its continuous monitoring and feedback capabilities, aiding in understanding their condition and making lifestyle adjustments. Risks were perceived as minimal, mainly concerning data privacy and potential overreliance on the app. The app became integral to patient-users' hypertension management by offering consistent information and support. The integration into formal health care was limited, as patient-users felt that health care professionals did not accept the use of the technology or might have even felt intimidated to use it.

Conclusions: Among the sample studied, mHealth apps like Hypertension.APP were valued for their continuous monitoring and educational content, aiding in hypertension management. The findings suggest potential benefits of mHealth apps for effective hypertension care among patients who are health- and digitally literate as well as self-effective. There is a critical need for better integration of these apps into routine health care practices, as perceived by the app users. Given the small and specific sample of this qualitative study, further quantitative research with a broader and more varied participant group is necessary to validate these findings.

Trial registration: Deutsches Register Klinischer Studien DRKS00029761; https://tinyurl.com/r33ru22s.