Fecundability in relation to use of mobile computing apps to track the menstrual cycle.
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ABSTRACT:
SUBMITTER: Stanford JB
PROVIDER: S-EPMC7518709 | biostudies-literature |
REPOSITORIES: biostudies-literature
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| S-EPMC8561243 | biostudies-literature
Project description:PurposeThe aim of this study was to evaluate the association between menstrual cycle characteristics in early life and adulthood and fecundability.MethodsPregnancy Study Online (PRESTO) is an Internet-based preconception cohort study of pregnancy planners from the United States and Canada. During the preconception period, we enrolled 2189 female pregnancy planners aged 21-45 years who had been attempting conception for ≤6 cycles. Women self-reported menstrual cycle characteristics via an online baseline questionnaire, and pregnancy status was ascertained through bimonthly follow-up questionnaires. Proportional probabilities models were used to estimate fecundability ratios (FRs) and 95% confidence intervals (CIs), adjusting for potential confounders.ResultsCompared with usual menstrual cycle lengths of 27-29 days, cycle lengths of <25 (FR = 0.81, 95% CI: 0.54-1.22) and 25-26 days (FR = 0.92, 95% CI: 0.75-1.14) were associated with reduced fecundability. Compared with women who reached menarche at the age of 12-13 years, those who reached menarche at <12 years had reduced fecundability (FR = 0.87, 95% CI: 0.76-0.99). Women whose cycles never regularized after menarche (FR = 0.93, 95% CI: 0.81-1.06) had slightly reduced fecundability compared with women whose cycles regularized within 2 years of menarche. Bleed length and heaviness of bleeding were not appreciably associated with fecundability.ConclusionsMenstrual cycle characteristics, specifically cycle length and age at menarche, may act as markers of fertility potential among pregnancy planners.
| S-EPMC4964792 | biostudies-literature
Project description:For most women of reproductive age, assessing menstrual health and fertility typically involves regular visits to a gynecologist or another clinician. While these evaluations provide critical information on an individual's reproductive health status, they typically rely on memory-based self-reports, and the results are rarely, if ever, assessed at the population level. In recent years, mobile apps for menstrual tracking have become very popular, allowing us to evaluate the reliability and tracking frequency of millions of self-observations, thereby providing an unparalleled view, both in detail and scale, on menstrual health and its evolution for large populations. In particular, the primary aim of this study was to describe the tracking behavior of the app users and their overall observation patterns in an effort to understand if they were consistent with previous small-scale medical studies. The secondary aim was to investigate whether their precision allowed the detection and estimation of ovulation timing, which is critical for reproductive and menstrual health. Retrospective self-observation data were acquired from two mobile apps dedicated to the application of the sympto-thermal fertility awareness method, resulting in a dataset of more than 30 million days of observations from over 2.7 million cycles for two hundred thousand users. The analysis of the data showed that up to 40% of the cycles in which users were seeking pregnancy had recordings every single day. With a modeling approach using Hidden Markov Models to describe the collected data and estimate ovulation timing, it was found that follicular phases average duration and range were larger than previously reported, with only 24% of ovulations occurring at cycle days 14 to 15, while the luteal phase duration and range were in line with previous reports, although short luteal phases (10 days or less) were more frequently observed (in up to 20% of cycles). The digital epidemiology approach presented here can help to lead to a better understanding of menstrual health and its connection to women's health overall, which has historically been severely understudied.
| S-EPMC6635432 | biostudies-literature
Project description:ObjectivesThis study explored what smartphone health applications (apps) are used by patients, how they learn about health apps, and how information about health apps is shared.MethodsPatients seeking care in an academic ED were surveyed about the following regarding their health apps: use, knowledge, sharing, and desired app features. Demographics and health information were characterized by summary statistics.ResultsOf 300 participants, 212 (71%) owned smartphones, 201 (95%) had apps, and 94 (44%) had health apps. The most frequently downloaded health apps categories were exercise 46 (49%), brain teasers 30 (32%), and diet 23 (24%). The frequency of use of apps varied as six (6%) of health apps were downloaded but never used, 37 (39%) apps were used only a few times, and 40 (43%) health apps were used once per month. Only five apps (2%) were suggested to participants by health care providers, and many participants used health apps intermittently (55% of apps ≤ once a month). Participants indicated sharing information from 64 (59%) health apps, mostly within social networks (27 apps, 29%) and less often with health care providers (16 apps, 17%).ConclusionsWhile mobile health has experienced tremendous growth over the past few years, use of health apps among our sample was low. The most commonly used apps were those that had broad functionality, while the most frequently used health apps encompassed the topics of exercise, diet, and brain teasers. While participants most often shared information about health apps within their social networks, information was less frequently shared with providers, and physician recommendation played a small role in influencing patient use of health apps.
| S-EPMC4464901 | biostudies-literature
Project description:BackgroundMobile apps are a popular strategy for reducing mobile phone use and preventing maladaptive mobile phone use (MMPU). Previous research efforts have been made to understand the features of apps that have the potential to reduce mobile phone use and MMPU. However, there has been a lack of a comprehensive examination of the effectiveness of such apps and their features.ObjectiveThis paper investigated existing apps designed to reduce mobile phone use and prevent MMPU and examined the evidence of their effectiveness. The research aimed to provide a comprehensive analysis of app features that can reduce mobile phone use and MMPU, while also assessing their effectiveness. In addition, we explored users' perceptions of these apps and the various features the apps offer to understand potential adoption issues and identify opportunities.MethodsThis study used 3 methods: a review of scientific evidence, content analysis, and sentiment analysis.ResultsOur study comprehensively examine the common features of 13 apps designed to reduce mobile phone use. We extracted and classified the features into 7 types: self-tracking, social tracking, goal setting, blocking, gamification, simplification, and assessment. The effectiveness of these apps in reducing mobile phone use and MMPU varied from weak to strong. On the basis of content analysis, self-tracking and goal setting were the most frequently used features, whereas gamification and assessment were used the least frequently. The intervention strategies that effectively reduce mobile phone use and MMPU included using grayscale mode, app limit features, and mixed interventions. Overall, users tended to accept these apps, as indicated by sentiment scores ranging from 61 to 86 out of 100.ConclusionsThis study demonstrates that app-based management has the potential to reduce mobile phone use and MMPU. However, further research is required to evaluate the effectiveness of app-based interventions. Collaborations among researchers, app developers, mobile phone manufacturers, and policy makers could enhance the process of delivering, evaluating, and optimizing apps aimed at reducing mobile phone use and MMPU.
| S-EPMC10498313 | biostudies-literature
Project description:Food intake and usual dietary intake are among the key determinants of health to be assessed in medical research and important confounding factors to be accounted for in clinical studies. Although various methods are available for gathering dietary data, those based on innovative technologies are particularly promising. With combined cost-effectiveness and ease of use, it is safe to assume that mobile technologies can now optimize tracking of eating occasions and dietary behaviors. Yet, choosing a dietary assessment tool that meets research objectives and data quality standards remains challenging. In this paper, we describe the purposes of collecting dietary data in medical research and outline the main considerations for using mobile dietary assessment tools based on participant and researcher expectations.
| S-EPMC7333076 | biostudies-literature
Project description:BackgroundMobile health (mHealth) apps that support individuals pursuing health and wellness goals, such as weight management, stress management, smoking cessation, and self-management of chronic conditions have been on the rise. Despite their potential benefits, the use of these tools has been limited, as most users stop using them just after a few times of use. Under this circumstance, achieving the positive outcomes of mHealth apps is less likely.ObjectiveThe objective of this study was to understand continued use of mHealth apps and individuals' decisions related to this behavior.MethodsWe conducted a qualitative longitudinal study on continued use of mHealth apps. We collected data through 34 pre- and postuse interviews and 193 diaries from 17 participants over two weeks.ResultsWe identified 2 dimensions that help explain continued use decisions of users of mHealth apps: users' assessment of mHealth app and its capabilities (user experience) and their persistence at their health goals (intent). We present the key factors that influence users' assessment of an mHealth app (interface design, navigation, notifications, data collection methods and tools, goal management, depth of knowledge, system rules, actionable recommendations, and user system fit) and relate these factors to previous literature on behavior change technology design. Using these 2 dimensions, we developed a framework that illustrated 4 decisions users might make after initial interaction with mHealth apps (to abandon use, limit use, switch app, and continue use). We put forth propositions to be explored in future research on mHealth app use.ConclusionsThis study provides insight into the factors that shape users' decisions to continue using mHealth apps, as well as other likely decision scenarios after the initial use experience. The findings contribute to extant knowledge of mHealth use and provide important implications for design of mHealth apps to increase long-term engagement of the users.
| S-EPMC6740166 | biostudies-literature
Project description:Are maternal preconception lipid levels associated with fecundability?Fecundability was reduced for all abnormal female lipid levels including total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and total triglyceride levels.Subfecundity affects 7-15% of the population and lipid disorders are hypothesized to play a role since cholesterol acts as a substrate for the synthesis of steroid hormones. Evidence illustrating this relationship at the mechanistic level is mounting but few studies in humans have explored the role of preconception lipids in fecundity.A secondary analysis of the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial (2007-2011), a block-randomized, double-blind, placebo-controlled trial.A total of 1228 women, with 1-2 prior pregnancy losses and without a diagnosis of infertility, attempting pregnancy for up to six menstrual cycles were recruited from clinical sites in Utah, New York, PA and Colorado. Time to pregnancy was the number of menstrual cycles to pregnancy as determined by positive hCG test or ultrasound. Individual preconception lipoproteins were measured at baseline, prior to treatment randomization and dichotomized based on clinically accepted cut-points as total cholesterol ?200 mg/dl, LDL-C ?130 mg/dl, HDL-C <50 mg/dl and triglycerides ?150 mg/dl.There were 148 (12.3%) women with elevated total cholesterol, 94 (7.9%) with elevated LDL-C, 280 (23.2%) with elevated triglycerides and 606 (50.7%) with low HDL-C. The fecundability odds ratio (FOR) was reduced for all abnormal lipids before and after confounder adjustment, indicating reduced fecundability. Total cholesterol ?200 mg/dl was associated with 24% (FOR: 0.76, 95% CI: 0.59, 0.97) and 29% (FOR: 0.71, 95% CI: 0.55, 0.93) reduced fecundability for hCG-detected and ultrasound-confirmed pregnancy, respectively, compared with total cholesterol <200 mg/dl. There was a 19-36% decrease in the probability of conception per cycle for women with abnormal lipoprotein levels, though additional adjustment for central adiposity and BMI attenuated observed associations.Although the FOR is a measure of couple fecundability, we had only measures of female lipid levels and can therefore not confirm the findings from a previous study indicating the independent role of male lipids in fecundity. The attenuated estimates and decreased precision after adjustment for central adiposity and obesity indicate the complexity of potential causal lipid pathways, suggesting other factors related to obesity besides dyslipidemia likely contribute to reduced fecundability.Our results are consistent with one other study relating preconception lipid concentrations to fecundity and expand these findings by adding critically important information about individual lipoproteins. As lipid levels are modifiable they may offer an inexpensive target to improve female fecundability.This study was funded by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The authors have declared that no conflicts of interest exist.#NCT00467363.
| S-EPMC6075456 | biostudies-literature
Project description:Gunderson's and Holling's adaptive cycle metaphor provides a qualitative description of the development of a dynamically evolving complex system. According to the metaphor, a complex system alternately passes through phases of stability and predictability and phases of reorganization and stochasticity. So far, there have been no attempts to quantify the underlying notions in a way which is independent of the concrete realization of the system. We propose a method which can be applied in a generic way to estimate a system's position within the adaptive cycle as well as to identify drivers of change. We demonstrate applicability and flexibility of our method by three different case studies: Analyzing data obtained from a simulation of a model of interaction of abstract genotypes, we show that our approach is able to capture the nature of these interactions. We then study European economies as systems of economic state variables to illustrate the ability of system comparison. Finally, we identify drivers of change in a plant ecosystem in the prairie-forest. We hereby confirm the conceptual dynamics of the adaptive cycle and thus underline its usability in understanding system dynamics.
| S-EPMC7584630 | biostudies-literature
Project description:BackgroundIn recent years, the use of mobile phone weight-management apps has increased significantly. Weight-management apps have been found effective in promoting health and managing weight. However, data on user perception and on barriers to app usage are scarce.ObjectiveThis study aimed to investigate the use of weight-management apps and barriers to use as well as reasons for discontinuing use in a sample of mobile phone users in Saudi Arabia.MethodsMobile phone users aged 18 years and above from the general public in Saudi Arabia completed a Web-based survey. The survey included questions on weight-management app usage patterns, user perceptions concerning weight management, efficacy of weight-management apps, and reasons for discontinuing use. Participants were classified into normal weight (body mass index [BMI]: 18.5 to 24.9 kg/m2) and overweight or obese (BMI: ≥25.0 kg/m2).ResultsThe survey included 1191 participants; 513 of them used weight-management apps. More overweight or obese respondents used these apps compared with normal weight respondents (319/513, 62.2% vs 194/513, 37.8%, respectively). App features that overweight or obese users were most interested in were mainly the possibility to be monitored by a specialist and barcode identification of calorie content, whereas normal weight users mostly preferred availability of nutrition information of food items. Reasons for discontinuing use among overweight or obese respondents were mainly that monitoring by a specialist was not offered (80/236, 33.9%) and the app was not in the local language (48/236, 20.3%). Among normal weight users, the main reason for noncontinuance was the app language (45/144, 31.3%) and difficulty of use (30/144, 20.8%).ConclusionsTo better address the needs of both normal weight and overweight or obese adults, improved app designs that offer monitoring by a specialist are needed. Developers may consider ways of overcoming barriers to use, such as language, by developing local language apps, which can improve the efficacy of such apps and help spread their use.
| S-EPMC6406230 | biostudies-literature