A major impediment to understanding human-environment interactions is that data on social systems are not collected in a way that is easily comparable to natural systems data. While many environmental variables are collected with high frequency, gridded in time and space, social data is typically conducted irregularly, in waves that are far apart in time. These efforts typically engage respondents for hours at a time, and suffer from decay in participants' ability to recall their experiences over long periods of time. Systematic use of mobile and smartphones has the potential to transcend these challenges, with a critical first step being an evaluation of where survey respondents experience the greatest recall decay. We present results from, to our knowledge, the first systematic evaluation of recall bias in components of a household survey, using the Open Data Kit (ODK) platform on Android smartphones. We tasked approximately 500 farmers in rural Bangladesh with responding regularly to components of a large household survey, randomizing the frequency of each task to be received weekly, monthly, or seasonally. We find respondents' recall of consumption and experience (such as sick days) to suffer much more greatly than their recall of the use of their households' time for labor and farm activities. Further, we demonstrate a feasible and cost-effective means of engaging respondents in rural areas to create and maintain a true socioeconomic "baseline" to mirror similar efforts in the natural sciences. Keywords High-frequency data collection. Android smartphone. Microtasks for micropayments. Bangladesh One of the most significant challenges impeding our understanding of humanenvironment interactions is that quantitative data on social systems are not collected

The advent of cheap smartphones in rural areas across the globe presents an opportunity to change the mode with which researchers engage hard-to-reach populations. In particular, smartphones allow researchers to connect with respondents more frequently than standard household surveys, opening a new window into important short-term variability in key measures of household and community wellbeing. In this paper, we present early results from a pilot study in rural Bangladesh using a ‘microtasks for micropayments’ model to collect a range of community and household living standards data using Android smartphones. We find that more frequent task repetition with shorter recall periods leads to more inclusive reporting, improved capture of intra-seasonal variability, and earlier signals of events such as illness. Payments in the form of mobile talk time and data provide a positive development externality in the form of expanded access to mobile internet and social networks. Taken to scale, programs such as this have potential to transform data collection in rural areas, providing near-real-time windows into the development of markets, the spread of illnesses, or the diffusion of ideas and innovations.

MMI can play a vital role in improving nurses' compliance with the standard infection control practices. Such context-specific interventions, which are crucial for preventing healthcare-associated infections and for decreasing occupational hazards, should be replicated in resource-poor countries for achieving universal health coverage by 2030.

The original version of this article unfortunately contained a mistake. The name of BMd. Ehsanul Haque Tamal^is now corrected in the author group of this article. The original article has been corrected.

Improving quality of life of farmers in rapidly changing rural economies remains a challenge. In low income settings, agricultural lean seasons lead to a fall in consumption and nutrition that affect longer term well-being trajectories. However, human well-being goes beyond material wealth, and increasingly subjective well-being is measured to reflect whether personal objectives are being met across a range of life domains. However, resource constraints mean surveys are usually carried out once a year, or at most, once a season. Here, we investigate whether life satisfaction reported annually is representative of assessments throughout the year, with a focus on the influence of the agricultural cycle on scores. We do so using data from a novel, mobile phone-based survey that collected 10,032 observations of life satisfaction reported weekly for one calendar year in land-owning farmers in Bangladesh. The data show that most individuals report stable and midrange life satisfaction. Smaller groups show consistently low, consistently high, or fluctuating levels of satisfaction. Using a cluster analysis, we define natural groups based on levels and stability of satisfaction. Social-demographics as well as material wealth predict membership of these groups showing the relative and culturally embedded nature of subjective well-being. Agricultural activities throughout the year are significantly associated with reported life satisfaction, but not always consistent with low seasons: land preparation and harvest are associated with increased life satisfaction; weeding and irrigation are associated with lower satisfaction. Furthermore, we show that the periods of activity during the agricultural cycle most likely to be associated with satisfaction vary depending on whether the individual reports high, low, or variable life satisfaction. Thus, we suggest, to improve well-being in low-income rural areas, analysis should include people's propensity to be satisfied, as this alters sensitivity to changes in other life domains.