A web-based non-intrusive ambient system to measure and classify activities of daily living.

BACKGROUND The number of older adults in the global population is increasing. This demographic shift leads to an increasing prevalence of age-associated disorders, such as Alzheimer's disease and other types of dementia. With the progression of the disease, the risk for institutional care increases, which contrasts with the desire of most patients to stay in their home environment. Despite doctors' and caregivers' awareness of the patient's cognitive status, they are often uncertain about its consequences on activities of daily living (ADL). To provide effective care, they need to know how patients cope with ADL, in particular, the estimation of risks associated with the cognitive decline. The occurrence, performance, and duration of different ADL are important indicators of functional ability. The patient's ability to cope with these activities is traditionally assessed with questionnaires, which has disadvantages (eg, lack of reliability and sensitivity). Several groups have proposed sensor-based systems to recognize and quantify these activities in the patient's home. Combined with Web technology, these systems can inform caregivers about their patients in real-time (e.g., via smartphone). OBJECTIVE We hypothesize that a non-intrusive system, which does not use body-mounted sensors, video-based imaging, and microphone recordings would be better suited for use in dementia patients. Since it does not require patient's attention and compliance, such a system might be well accepted by patients. We present a passive, Web-based, non-intrusive, assistive technology system that recognizes and classifies ADL. METHODS The components of this novel assistive technology system were wireless sensors distributed in every room of the participant's home and a central computer unit (CCU). The environmental data were acquired for 20 days (per participant) and then stored and processed on the CCU. In consultation with medical experts, eight ADL were classified. RESULTS In this study, 10 healthy participants (6 women, 4 men; mean age 48.8 years; SD 20.0 years; age range 28-79 years) were included. For explorative purposes, one female Alzheimer patient (Montreal Cognitive Assessment score=23, Timed Up and Go=19.8 seconds, Trail Making Test A=84.3 seconds, Trail Making Test B=146 seconds) was measured in parallel with the healthy subjects. In total, 1317 ADL were performed by the participants, 1211 ADL were classified correctly, and 106 ADL were missed. This led to an overall sensitivity of 91.27% and a specificity of 92.52%. Each subject performed an average of 134.8 ADL (SD 75). CONCLUSIONS The non-intrusive wireless sensor system can acquire environmental data essential for the classification of activities of daily living. By analyzing retrieved data, it is possible to distinguish and assign data patterns to subjects' specific activities and to identify eight different activities in daily living. The Web-based technology allows the system to improve care and provides valuable information about the patient in real-time.

Invisible social control as predictor of daily negative affect and smoking after a self-set quit date

The dual-effects model of social control proposes that social control leads to increased psychological distress but also to better health practices. However, findings are inconsistent, and recent research suggests that the most effective control is unnoticed by the receiver (i. e., invisible). Yet, investigations of the influence of invisible control on daily negative affect and smoking have been limited. Using daily diaries, we investigated how invisible social control was associated with negative affect and smoking. Overall, 100 smokers (72.0 % men, age M = 40.48, SD = 9.82) and their nonsmoking partners completed electronic diaries from a self-set quit date for 22 consecutive days, reporting received and provided social control, negative affect, and daily smoking. We found in multilevel analyses of the within-person process that on days with higher-than-average invisible control, smokers reported more negative affect and fewer cigarettes smoked. Findings are in line with the assumptions of the dual-effects model of social control: Invisible social control increased daily negative affect and simultaneously reduced smoking at the within-person level.

The joint influence of gender and amount of smoking on weight gain one year after smoking cessation

Weight gain is often associated with smoking cessation and may discourage smokers from quitting. This study estimated the weight gained one year after smoking cessation and examined the risk factors associated with weight gain in order to identify socio-demographic groups at higher risk of increased weight after quitting. We analyzed data from 750 adults in two randomized controlled studies that included smokers motivated to quit and found a gradient in weight gain according to the actual duration of abstinence during follow-up. Subjects who were abstinent for at least 40 weeks gained 4.6 kg (SD = 3.8) on average, compared to 1.2 kg (SD = 2.6) for those who were abstinent less than 20 weeks during the 1-year follow-up. Considering the duration of abstinence as an exposure variable, we found an age effect and a significant interaction between sex and the amount of smoking before quitting: younger subjects gained more weight than older subjects; among light smokers, men gained more weight on average than women one year after quitting, while the opposite was observed among heavy smokers. Young women smoking heavily at baseline had the highest risk of weight gain after quitting.

Examining invisible social control as predictor of daily smoking and negative affect after a self-set quit date

Objectives: The dual-effects model of social control proposes that social control leads to better health practices, but also arouses psychological distress. However, findings are inconsistent in relation to health behavior and psychological distress. Recent research suggests that the most effective control is unnoticed by the receiver (i.e., invisible). There is some evidence that invisible social control is beneficial for positive and negative affective reactions. Yet, investigations of the influence of invisible social control on daily smoking and distress have been limited. In daily diaries, we investigated how invisible social control is associated with number of cigarettes smoked and negative affect on a daily basis. Methods: Overall, 99 smokers (72.0% men, mean age M = 40.48, SD = 9.82) and their non-smoking partners completed electronic diaries from a self-set quit date for 22 consecutive days within the hour before going to bed, reporting received and provided social control, daily number of cigarettes smoked, and negative affect. Results: Multilevel analyses indicated that between-person levels of invisible social control were associated with lower negative affect, whereas they were unrelated to number of cigarettes smoked. On days with higher-than-average invisible social control, smokers reported less cigarettes smoked and more negative affect. Conclusions: Between-person level findings indicate that invisible social control can be beneficial for negative affect. However, findings on the within-person level are in line with the assumptions of the dual-effects model of social control: Invisible social control reduced daily smoking and simultaneously increased daily negative affect within person.

Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers

Activities of daily living (ADL) are important for quality of life. They are indicators of cognitive health status and their assessment is a measure of independence in everyday living. ADL are difficult to reliably assess using questionnaires due to self-reporting biases. Various sensor-based (wearable, in-home, intrusive) systems have been proposed to successfully recognize and quantify ADL without relying on self-reporting. New classifiers required to classify sensor data are on the rise. We propose two ad-hoc classifiers that are based only on non-intrusive sensor data. METHODS: A wireless sensor system with ten sensor boxes was installed in the home of ten healthy subjects to collect ambient data over a duration of 20 consecutive days. A handheld protocol device and a paper logbook were also provided to the subjects. Eight ADL were selected for recognition. We developed two ad-hoc ADL classifiers, namely the rule based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. For comparison, two state-of-the-art [Naïves Bayes (NB), Random Forest (RF)] classifiers have also been used. All classifiers were validated with the collected data sets for classification and recognition of the eight specific ADL. RESULTS: Out of a total of 1,373 ADL, the RBI classifier correctly determined 1,264, while missing 109 and the CAR determined 1,305 while missing 68 ADL. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively, outperforming both RF and NB. CONCLUSIONS: The performance of the classifiers varied significantly and shows that the classifier plays an important role in ADL recognition. Both RBI and CAR classifier performed better than existing state-of-the-art (NB, RF) on all ADL. Of the two ad-hoc classifiers, the CAR classifier was more accurate and is likely to be better suited than the RBI for distinguishing and recognizing complex ADL.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Treatment intensification with insulin glargine in patients with inadequately controlled type 2 diabetes improves glycaemic control with a high treatment satisfaction and no weight gain

PRINCIPLES We aimed to evaluate the efficacy of, and treatment satisfaction with, insulin glargine administered with SoloSTAR® or ClikSTAR® pens in patients with type 2 diabetes mellitus managed by primary care physicians in Switzerland. METHODS A total of 327 patients with inadequately controlled type 2 diabetes were enrolled by 72 physicians in this prospective observational study, which aimed to evaluate the efficacy of a 6-month course of insulin glargine therapy measured as development of glycaemic control (glycosylated haemoglobin [HbA1c] and fasting plasma glucose [FPG]) and weight change. We also assessed preference for reusable or disposable pens, and treatment satisfaction. RESULTS After 6 months, the mean daily dose of insulin glargine was 27.7±14.3 U, and dose titration was completed in 228 (72.4%) patients. Mean HbA1c decreased from 8.9%±1.6% (n=327) to 7.3%±1.0% (n=315) (p<0.0001), and 138 (43.8%) patients achieved an HbA1c≤7.0%. Mean FPG decreased from 10.9±4.5 to 7.3±1.8 mmol/l (p<0.0001). Mean body weight did not change (85.4±17.2 kg vs 85.0±16.5 kg; p=0.11). Patients' preference was in favour of the disposable SoloStar® pen (80%), as compared with the reusable ClickStar® pen (20%). Overall, 92.6% of physicians and 96.3% of patients were satisfied or very satisfied with the insulin glargine therapy. CONCLUSIONS In patients with type 2 diabetes insulin glargine administered by SoloSTAR® or ClikSTAR® pens, education on insulin injection and on self-management of diabetes was associated with clinically meaningful improvements in HbA1c and FPG without a mean collective weight gain. The vast majority of both patients and primary care physicians were satisfied with the treatment intensification.

Daily polynya area and ice production in the North Water polynya region

The North Water (NOW) Polynya is a regularly-forming area of open-water and thin-ice, located between northwestern Greenland and Ellesmere Island (Canada) at the northern tip of Baffin Bay. Due to its large spatial extent, it is of high importance for a variety of physical and biological processes, especially in wintertime. Here, we present a long-term remote sensing study for the winter seasons 1978/1979 to 2014/2015. Polynya characteristics are inferred from (1) sea ice concentrations and brightness temperatures from passive microwave satellite sensors (Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager/Sounder (SSM/I-SSMIS)) and (2) thin-ice thickness distributions, which are calculated using MODIS ice-surface temperatures and European Center for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis data in a 1D thermodynamic energy-balance model. Daily ice production rates are retrieved for each winter season from 2002/2003 to 2014/2015, assuming that all heat loss at the ice surface is balanced by ice growth. Two different cloud-cover correction schemes are applied on daily polynya area and ice production values to account for cloud gaps in the MODIS composites. Our results indicate that the NOW polynya experienced significant seasonal changes over the last three decades considering the overall frequency of polynya occurrences, as well as their spatial extent. In the 1980s, there were prolonged periods of a more or less closed ice cover in northern Baffin Bay in winter. This changed towards an average opening on more than 85% of the days between November and March during the last decade. Noticeably, the sea ice cover in the NOW polynya region shows signs of a later-appearing fall freeze-up, starting in the late 1990s. Different methods to obtain daily polynya area using passive microwave AMSR-E/AMSR2 data and SSM/I-SSMIS data were applied. A comparison with MODIS data (thin-ice thickness < 20 cm) shows that the wintertime polynya area estimates derived by MODIS are about 30 to 40% higher than those derived using the polynya signature simulation method (PSSM) with AMSR-E data. In turn, the difference in polynya area between PSSM and a sea ice concentration (SIC) threshold of 70% is fairly low (approximately 10%) when applied to AMSR-E data. For the coarse-resolution SSM/I-SSMIS data, this difference is much larger, particularly in November and December. Instead of a sea ice concentration threshold, the PSSM method should be used for SSM/I-SSMIS data. Depending on the type of cloud-cover correction, the calculated ice production based on MODIS data reaches an average value of 264.4 ± 65.1 km**3 to 275.7 ± 67.4 km**3 (2002/2003 to 2014/2015) and shows a high interannual variability. Our achieved long-term results underline the major importance of the NOW polynya considering its influence on Arctic ice production and associated atmosphere/ocean processes.

Daily MODIS composites of thin-ice thickness and ice-surface temperatures for the Southern Weddell Sea

Based upon high-resolution thermal-infrared Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite imagery in combination with ERA-Interim atmospheric reanalysis data, we derived long-term polynya parameters such as polynya area, thin-ice thickness distribution and ice-production rates from daily cloud-cover corrected thin-ice thickness composites. Our study is based on a thirteen year investigation period (2002-2014) for the austral winter (1 April to 30 September) in the Antarctic Southern Weddell Sea. The focus lies on coastal polynyas which are important hot spots for new-ice formation, bottom-water formation and heat/moisture release into the atmosphere. MODIS has the capability to resolve even very narrow coastal polynyas. Its major disadvantage is the sensor limitation due to cloud cover. We make use of a newly developed and adapted spatial feature reconstruction scheme to account for cloud-covered areas. We find the sea-ice areas in front of Ronne and Brunt Ice Shelf to be the most active with an annual average polynya area of 3018 ± 1298 and 3516 ± 1420 km2 as well as an accumulated volume ice production of 31 ± 13 and 31 ± 12 km**3, respectively. For the remaining four regions, estimates amount to 421 ± 294 km**2 and 4 ± 3 km**3 (Antarctic Peninsula), 1148 ± 432 km**2 and 12 ± 5 km**3 (Iceberg A23A), 901 ± 703 km**2 and 10 ± 8 km**3 (Filchner Ice Shelf) as well as 499 ± 277 km**2 and 5 ± 2 km**3 (Coats Land). Our findings are discussed in comparison to recent studies based on coupled sea-ice/ocean models and passive-microwave satellite imagery, each investigating different parts of the Southern Weddell Sea.

Daily ice production and polynya area in the Laptev Sea region during 1979-2009

Polynyas in the Laptev Sea are examined with respect to recurrence and interannual wintertime ice production.We use a polynya classification method based on passive microwave satellite data to derive daily polynya area from long-term sea-ice concentrations. This provides insight into the spatial and temporal variability of open-water and thin-ice regions on the Laptev Sea Shelf. Using thermal infrared satellite data to derive an empirical thin-ice distribution within the thickness range from 0 to 20 cm, we calculate daily average surface heat loss and the resulting wintertime ice formation within the Laptev Sea polynyas between 1979 and 2008 using reanalysis data supplied by the National Centers for Environmental Prediction, USA, as atmospheric forcing. Results indicate that previous studies significantly overestimate the contribution of polynyas to the ice production in the Laptev Sea. Average wintertime ice production in polynyas amounts to approximately 55 km3 ± 27% and is mostly determined by the polynya area, wind speed and associated large-scale circulation patterns. No trend in ice production could be detected in the period from 1979/80 to 2007/08.

Abundance, biomass, ingestion rates and daily ratios of Salpa thompsoni individuals sampled during POLARSTERN cruise ANT-XVIII/5b to the Eastern Belingshausen Sea

Distribution, density, and feeding dynamics of the pelagic tunicate Salpa thompsoni have been investigated during the expedition ANTARKTIS XVIII/5b to the Eastern Bellingshausen Sea on board RV Polarstern in April 2001. This expedition was the German contribution to the field campaign of the Southern Ocean Global Ocean Ecosystems Dynamics Study (SO-GLOBEC). Salps were found at 31% of all RMT-8 and Bongo stations. Their densities in the RMT-8 samples were low and did not exceed 4.8 ind/m**2 and 7.4 mg C/m**2. However, maximum salp densities sampled with the Bongo net reached 56 ind/m**2 and 341 mg C/m**2. A bimodal salp length frequency distribution was recorded over the shelf, and suggested two recent budding events. This was also confirmed by the developmental stage composition of solitary forms. Ingestion rates of aggregate forms increased from 2.8 to 13.9 µg (pig)/ind/day or from 0.25 to 2.38 mg C/ind/day in salps from 10 to 40 mm oral-atrial length, accounting for 25-75% of body carbon per day. Faecal pellet production rates were on average 0.08 pellet/ind/h with a pronounced diel pattern. Daily individual egestion rates in 13 and 30 mm aggregates ranged from 0.6 to 4.8 µg (pig)/day or from 164 to 239 µg C/day. Assimilation efficiency ranged from 73 to 90% and from 65 to 76% in 13 and 30 mm aggregates, respectively. S. thompsoni exhibited similar ingestion and egestion rates previously estimated for low Antarctic (~50°S) habitats. It has been suggested that the salp population was able to develop in the Eastern Bellingshausen Sea due to an intrusion into the area of the warm Upper Circumpolar Deep Water

Daily pan-Arctic sea-ice lead maps for 2003-2015, with links to maps in netCDF format

The presence of sea-ice leads represents a key feature of the Arctic sea ice cover. Leads promote the flux of sensible and latent heat from the ocean to the cold winter atmosphere and are thereby crucial for air-sea-ice-ocean interactions. We here apply a binary segmentation procedure to identify leads from MODIS thermal infrared imagery on a daily time scale. The method separates identified leads into two uncertainty categories, with the high uncertainty being attributed to artifacts that arise from warm signatures of unrecognized clouds. Based on the obtained lead detections, we compute quasi-daily pan-Arctic lead maps for the months of January to April, 2003-2015. Our results highlight the marginal ice zone in the Fram Strait and Barents Sea as the primary region for lead activity. The spatial distribution of the average pan-Arctic lead frequencies reveals, moreover, distinct patterns of predominant fracture zones in the Beaufort Sea and along the shelf-breaks, mainly in the Siberian sector of the Arctic Ocean as well as the well-known polynya and fast-ice locations. Additionally, a substantial inter-annual variability of lead occurrences in the Arctic is indicated.