428 research outputs found
Consumer Smartwatches for Collecting Self-Report and Sensor Data:App Design and Engagement
No full textLongitudinal data from patients' natural environments would benefit chronic disease care, yet most devices cannot collect sensor data alongside patient-reported outcomes. Here we describe Koalap, a consumer cellular smartwatch application that collects patient-reported outcomes alongside physical activity data from various sensors. Additionally, we show preliminary results indicating high engagement of our 26 participants with knee osteoarthritis. Our future work will show whether data collection with consumer smartwatches is feasible in terms of user engagement, acceptability, data quality and consistency.</p
Are weather conditions associated with chronic musculoskeletal pain? Review of results and methodologies
No full textMany people believe that weather influences chronic musculoskeletal pain. Previous studies on this association are narratively reviewed, with particular focus on comparing methodologies and summarising study findings in light of study quality. We searched 5 databases (Medline, Embase, Web of Science, PsycINFO, and Scopus) for observational studies on the association between weather variables and self-reported musculoskeletal pain severity. Of 4707 located articles, 43 were eligible for inclusion. The majority (67%) found some association between pain and a weather variable. Temperature, atmospheric pressure, relative humidity, and precipitation were most often investigated. For each weather variable, some studies found an association with pain (in either direction), and others did not. Most studies (86%) had a longitudinal study design, usually collecting outcome data for less than a month, from fewer than 100 participants. Most studies blinded participants to study aims but were at a high risk of misclassification of exposure and did not meet reporting requirements. Pain severity was most often self-reported (84%) on a numeric rating scale or visual analog scale. Weather data were collected from local weather stations, usually on the assumption that participants stayed in their home city. Analysis methods, preparation of weather data, and adjustment for covariates varied widely between studies. The association between weather and pain has been difficult to characterise. To obtain more clarity, future studies should address 3 main limitations of the previous literature: small sample sizes and short study durations, misclassification of exposure, and approach to statistical analysis (specifically, multiple comparisons and adjusting for covariates)
Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study
No full textBackground: Smartphone location data can be used for observational health studies (to determine participant exposure or behavior) or to deliver a location-based health intervention. However, missing location data are more common when using smartphones compared to when using research-grade location trackers. Missing location data can affect study validity and intervention safety. Objective: The objective of this study was to investigate the distribution of missing location data and its predictors to inform design, analysis, and interpretation of future smartphone (observational and interventional) studies. Methods: We analyzed hourly smartphone location data collected from 9665 research participants on 488,400 participant days in a national smartphone study investigating the association between weather conditions and chronic pain in the United Kingdom. We used a generalized mixed-effects linear model with logistic regression to identify whether a successfully recorded geolocation was associated with the time of day, participants’ time in study, operating system, time since previous survey completion, participant age, sex, and weather sensitivity. Results: For most participants, the app collected a median of 2 out of a maximum of 24 locations (1760/9665, 18.2% of participants), no location data (1664/9665, 17.2%), or complete location data (1575/9665, 16.3%). The median locations per day differed by the operating system: participants with an Android phone most often had complete data (a median of 24/24 locations) whereas iPhone users most often had a median of 2 out of 24 locations. The odds of a successfully recorded location for Android phones were 22.91 times higher than those for iPhones (95% CI 19.53-26.87). The odds of a successfully recorded location were lower during weekends (odds ratio [OR] 0.94, 95% CI 0.94-0.95) and nights (OR 0.37, 95% CI 0.37-0.38), if time in study was longer (OR 0.99 per additional day in study, 95% CI 0.99-1.00), and if a participant had not used the app recently (OR 0.96 per additional day since last survey entry, 95% CI 0.96-0.96). Participant age and sex did not predict missing location data. Conclusions: The predictors of missing location data reported in our study could inform app settings and user instructions for future smartphone (observational and interventional) studies. These predictors have implications for analysis methods to deal with missing location data, such as imputation of missing values or case-only analysis. Health studies using smartphones for data collection should assess context-specific consequences of high missing data, especially among iPhone users, during the night and for disengaged participants
Birmingham News sleeve BN0010843
No full textSergeant C. D. Milwee / Chief Jamie Moore / Raymond Belcher / Birmingham Police Week / [Work order included
Cloudy with a Chance of Pain:Engagement and subsequent attrition of daily data entry in a smartphone pilot study of weather, disease severity, and physical activity in patients with rheumatoid arthritis.
No full textBackground: The increasing ownership of smartphones provides major opportunities for epidemiological research through self-reported and passively collected data.Objective: This pilot study aimed to codesign a smartphone app to assess associations between weather and joint pain in patients with rheumatoid arthritis (RA) and to study the success of daily self-reported data entry over a 60-day period and the enablers of and barriers to data collection.Methods: A patient and public involvement group (n=5) and 2 focus groups of patients with RA (n=9) supported the codesign of the app collecting self-reported symptoms. A separate “capture app” was designed to collect global positioning system (GPS) and continuous raw accelerometer data, with the GPS-linking providing local weather data. A total of 20 patients with RA were then recruited to collect daily data for 60 days, with entry and exit interviews. Of these, 17 were loaned an Android smartphone, whereas 3 used their own Android smartphones.Results: Of the 20 patients, 6 (30%) withdrew from the study: 4 because of technical challenges and 2 for health reasons. The mean completion of daily entries was 68% over 2 months. Patients entered data at least five times per week 65% of the time. Reasons for successful engagement included a simple graphical user interface, automated reminders, visualization of data, and eagerness to contribute to this easily understood research question. The main barrier to continuing engagement was impaired battery life due to the accelerometer data capture app. For some, successful engagement required ongoing support in using the smartphones.Conclusions: This successful pilot study has demonstrated that daily data collection using smartphones for health research is feasible and achievable with high levels of ongoing engagement over 2 months. This result opens important opportunities for large-scale longitudinal epidemiological research.<br/
RAZOR: a phase II open randomized trial of screening plus goserelin and raloxifene versus screening alone in premenopausal women at increased risk of breast cancer
Full text linkBackground: Ovarian suppression in
premenopausal women is known to reduce breast cancer risk. This study
aimed to assess uptake and compliance with ovarian suppression using the
luteinizing hormone releasing hormone (LHRH) analogue, goserelin, with
add-back raloxifene, as a potential regimen for breast cancer
prevention.Methods: Women at ≥30%
lifetime risk breast cancer were approached and randomized to
mammographic screening alone (C-Control) or screening in addition to
monthly subcutaneous injections of 3.6 mg goserelin and continuous 60 mg
raloxifene daily orally (T-Treated) for 2 years. The primary endpoint
was therapy adherence. Secondary endpoints were toxicity/quality of
life, change in bone density, and mammographic density.Results:
A total of 75/950 (7.9%) women approached agreed to randomization. In
the T-arm, 20 of 38 (52%) of women completed the 2-year period of study
compared with the C-arm (27/37, 73.0%). Dropouts were related to
toxicity but also the wish to have established risk-reducing procedures
and proven chemoprevention. As relatively few women completed the study,
data are limited, but those in the T-arm reported significant increases
in toxicity and sexual problems, no change in anxiety, and less cancer
worry. Lumbar spine bone density declined by 7.0% and visually assessed
mammographic density by 4.7% over the 2-year treatment period.</p
Engagement and Participant Experiences with Consumer Smartwatches for Health Research: a Longitudinal, Observational Feasibility Study
No full textBackground: Wearables provide opportunities for frequent health data collection and symptom monitoring. The feasibility of using consumer cellular smartwatches to provide information both on symptoms and contemporary sensor data has not yet been investigated. Objectives: To investigate the feasibility and acceptability of using cellular smartwatches to capture multiple patient-reported outcomes per day alongside continuous physical activity data over a 3 month period in people living with knee osteoarthritis.Methods: For the KOALAP (Knee OsteoArthritis: Linking Activity and Pain) study a novel cellular smartwatch application for health data collection was developed. Participants (age ≥ 50 years; self-diagnosed knee osteoarthritis) received a smartwatch (Huawei Watch 2) with the KOALAP app. When worn, the watch collected sensor data and prompted participants to self-report outcomes multiple times per day. Participants were invited for a baseline and follow-up interview to discuss their motivations and experiences. Engagement with the watch was measured using daily watch wear time and the percentage completion of watch questions. Interview transcripts were analyzed using grounded thematic analysis. Results: Twenty-six people participated in the study. Good use and engagement was observed over 3 months: most participants wore the watch on 75% of days or more, for a median of 11 hours. The number of active participants declined over the study duration, especially in the final week. Among participants who remained active, neither watch time nor question completion percentage declined over time. Participants were mainly motivated to learn about their symptoms and enjoyed the self-tracking aspects of the watch. Barriers to full engagement were battery life limitations, technical problems and unfulfilled expectations of the watch. Participants reported that they would have liked to report symptoms more than four/five times per day.Conclusions: This study shows that capture of patient-reported outcomes multiple times per day with linked sensor data from a smartwatch is feasible over at least a 3 month period
Weather Patterns Associated With Pain In Chronic-Pain Sufferers
No full textThe belief that weather influences people's health has been prevalent for millennia. Recent studies on the relationship between weather and pain for those who suffer from chronic pain remain indeterminate, with some studies finding strong effects and others finding no effects; most studies face limitations to their study design or dataset size. To address these limitations, a UK-wide smartphone study Cloudy with a Chance of Pain was conducted over 15 months with 10,584 citizen scientists who suffer from chronic pain, producing the largest dataset both in duration and number of participants. Compared to other similar citizen-science studies, our retention of participants was substantially better, with 15% still entering data nearly every day after 200 days. Analysis of the dataset using synoptic climatology and compositing revealed the daily weather associated with a prevalence of high pain and low pain across the population. Specifically, our results indicate that the top 10% of days with a high percentage of participants (about 20%) experiencing a pain event (represented here by a +1 change or greater in their pain level on a 5-point scale; referred to as a high-pain day) were associated with below-normal pressure, above-normal humidity, higher precipitation rate, and stronger wind. In contrast, the bottom 10% of days with a small percentage of participants (about 10%) experienced a pain event (a low-pain day) were associated with above-normal pressure and below-normal humidity, lower precipitation rate, and weaker wind. Thus, these synoptic weather patterns support the beliefs of many participants who said that low pressure—and its accompanying weather—was associated with a pain event.<br/
Eastland pictured at breakfast meeting with colleagues
No full textJohn C. Stennis and Jamie Whitten also picturedhttps://egrove.olemiss.edu/joephoto_e/1347/thumbnail.jp
Chemical applications of escience to interfacial spectroscopy
No full textThis report is a summary of works carried out by the author between October 2003 and September 2004, in the first year of his PhD studie
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