This paper presents the simultaneous utilization of video images and inertial signals that are captured at the same time via a video camera and a wearable inertial sensor within a fusion framework in order to achieve a more robust human action recognition compared to the situations when each sensing modality is used individually. The data captured by these sensors are turned into 3D video images and 2D inertial images that are then fed as inputs into a 3D convolutional neural network and a 2D convolutional neural network, respectively, for recognizing actions. Two types of fusion are considered—Decision-level fusion and feature-level fusion. Experiments are conducted using the publicly available dataset UTD-MHAD in which simultaneous video images and inertial signals are captured for a total of 27 actions. The results obtained indicate that both the decision-level and feature-level fusion approaches generate higher recognition accuracies compared to the approaches when each sensing modality is used individually. The highest accuracy of 95.6% is obtained for the decision-level fusion approach.  Human action recognition has been extensively studied in the literature and has already been incorporated into commercial products. There are a wide range of applications of human action recognition including human–machine interface, e.g., ,2,3,4, iVision and inertial sensing modalities have been used individually to achieve human action recognition, e.g., ,21,22,23Furthermore, wearable inertial sensors have been used to achieve human action recognition. Similarly, these sensors are cost-effective and widely available. The main advantage of these sensors include their wearability—Thus, they are not limited to a specific field of view. Often, 3-axis acceleration signals from their accelerometers and 3-axis angular velocity signals from their gyroscopes are used for conducting human action recognition, e.g., ,21,22,23Basically, no sensing modality is perfect: no sensing modality provides the entire information associated with various actions. In previous works on fusing two sensing modalities ,28,29,30The rest of the paper is organized as follows: This section gives a brief description of the dataset utilized and the way the video images and inertial signals are fed into the deep learning models described in The UTD-MHAD dataset was collected by a Kinect camera and a wearable inertial sensor at the same time. The dataset consists of video images with a resolution of 640 × 480 and depth images with a resolution of 320 × 240 at 30 frames per second captured by the Kinect camera. It also consists of 3-axis acceleration and 3-axis angular velocity signals captured at the same time at a sampling rate of 50Hz by the inertial sensor. The details of the inertial sensor are provided in [Each of the actions were performed by eight subjects , and each action was repeated four times. Hence, there are a total of 864 action clips, with three of the data corrupted action clips not included. Each action clip consists of videos, depth videos, skeleton joint positions, and inertial sensor signals. In this work, the videos and the inertial signals are used.For the video data, since different actions have different video clip durations, 32 frames across each video clip are selected to form a consistent 3D input to the 3D convolutional neural network discussed in the next section. To gain computational efficiency, each frame is resized to 320 × 240 pixels. As a result, each action clip has the same spatial and temporal size forming a 320 × 240 × 32 3D video data volume. An example 3D video data volume formed in this manner is shown in For the inertial data, an eight-column input image is formed by combining the 3-axis acceleration signals, the 3-axis angular velocity signals, the overall acceleration signal, and the overall angular velocity signal at a sampling frequency of 50Hz. Thus, input images to the 2D convolutional neural network discussed in the next section are of size 8 × 50. An example inertial signals input image is shown in A 3D convolutional neural network is used to learn the video data for the 27 actions of the UTD-MHAD dataset. The architecture of this model is a simplified version of the model discussed in  and is sFor the inertial data, a 2D CNN is used. As inertial sensor signals are time-series signals, the 2D CNN model developed in  for speeA brief overview of the layers is described next. Interested readers are referred to many references that are available on deep neural networks, for example ,35,36. TBesides the above decision-level fusion approach of combining the decisions of two deep neural networks, one for video and one for inertial sensing, a feature-level fusion approach is also considered in this work. 256 features are extracted from the video network model and the inertial network model dropout layers as indicated in This section presents the results of the experiments conducted to examine the effectiveness of the introduced fusion approach when the two sensing modalities of video images and inertial signals are used simultaneously. A leave-one-out cross validation was performed, meaning that the data from one subject was used for testing, and the data from the remaining seven subjects were used for training. This process was repeated for each of the subjects. Then, an average was taken across the subjects. The average accuracy of the following four approaches were obtained: (1) only using video sensing modality, (2) only using inertial sensing modality, (3) feature-level fusion of video and inertial sensing modalities, and (4) decision-level fusion of video and inertial sensing modalities. The confusion matrix of the video sensing modality only and the inertial sensing modality only are shown in From In this paper, for the first time, the simultaneous utilization of video and inertial sensing modalities were considered within a fusion framework to achieve human action recognition based on deep learning models. The following four approaches were compared in terms of recognition accuracies: using only video data as input to a deep neural network, using only inertial data as input to a deep neural network, using both video data and inertial data as inputs to two deep neural networks within a decision-level fusion framework, and using both video data and inertial data as inputs to two deep neural networks within a feature-level fusion framework. The experiments conducted based on the publicly available UTD-MHAD dataset have shown that the decision-level fusion approach provided the highest recognition accuracy of 95.6%—The fusion of the two sensing modalities exhibited more robust recognition outcome in terms of misclassification errors compared to the situations when each sensing modality was used individually.


However, these studies are based on 2 dimensional (2D) sections and results are presented with a few sections. The present study aims to show PSD-95 distribution in 3 dimensions (3D) without slicing the brain tissue of C57BL/6 mice into sections using the advanced CUBIC technique.Postsynaptic density – 95 kDa protein (PSD95) is an important molecule on the postsynaptic membrane. It interacts with many other proteins and plays a pivotal role in learning and memory formation. Its distribution in the brain has been studied previously using in situ Immunofluorescent staining using a PSD-95 antibody was performed on a half of the mouse brain after clarifying it using the advanced CUBIC protocol. The brain tissue was imaged using a Zeiss Z1 light sheet microscope and 3D reconstruction was completed using the Arivis Vision 4 dimensional (4D) software.hybridization and immunohistochemical studies in the mouse. The signal can be easily followed in the 3D and their spatial relationship with adjacent structures clearly demarcated. In the present study, some fiber bundles also showed strong PSD-95 signal, which is different from what was shown in previous studies and need to be confirmed in future studies.The majority of brain nuclei have similar distribution pattern to what has been reported from in situ The online version of this article (10.1186/s13041-018-0393-4) contains supplementary material, which is available to authorized users. Postsynaptic density protein of 95 kDa (PSD-95) is a scaffolding protein encoded by the disc large homolog 4 (DLG4) gene. It is highly enriched in the postsynaptic membrane and interacts with multiple synaptic proteins  through Brains of 12-week old C57BL/6 male mice were fixed with 4% paraformaldehyde and dissected before being cut into two halves sagittally. Immunofluorescent staining using a PSD-95 antibody was performed on a half of the brains after clarifying them using the advanced CUBIC protocol [hybridization [In the forebrain, we found strong positive PSD-95 signal in the internal plexiform and the mitral cell layers of the olfactory bulb Fig.  and b, wdization , 8, 9 andization .Fig. 1FlVentral to the cerebral cortex, which had weak PSD-95 signal, moderate signal was observed in the corpus callosum except the area dorsal to the hippocampus Fig.  and c. TStrong signal was also found in the anterior part of the anterior commissure, the fornix, the optic nerve, and the cerebral peduncle ventral to the substantia nigra Fig.  and c. Thybridization and immunohistochemical studies.Positive signal was observed in the majority of midbrain nuclei and nuclei in the rostral hindbrain (the rest of the hindbrain and cerebellum were cut off). Positive signal in the midbrain and the hypothalamus tended to follow the course of large fibers travelling rostrocaudally. In the middle part of the midbrain in sagittal sections, a small band of signal appeared to travel dorsoventrally. Ventral to this band, positive signals converge towards the ventral surface of the hindbrain Fig. . These fThe present study showed the expression of PSD-95 in the mouse brain using an emerging 3D technique – advanced CUBIC and this study confirmed some previous reports about PSD-95 in the majority of brain nuclei . We showed that some fiber bundles were positive for PSD95, which has not been reported in previous studies. To verify this concern, we used Western blot (not shown) and confirmed the specificity of our PSD95 antibody. Our finding might be true due to the fact that lipid of the neuropil has been removed by the clearing solution and the antibody can easily bind to the epitope of PSD-95, whereas in conventional sections, there is no such a step and the interaction between PSD-95 and its antibody may be blocked to some extent, especially in a fiber bundle where the fibers are tightly bound to each other.Additional file 1: 3D video of fluorescent PSD-95 signal reconstructed using Arivis. Strong fluorescent signal was observed in the large fiber bundles such as the anterior commissure, fornix, stria terminalis of the thalamus, and corpus callosum. Weak to moderate signal was observed in a large number of nuclei in the forebrain and midbrain. (AVI 34006 kb)hybridization studies [hybridization studies.The present study did not include the entire hindbrain due to difficulty in mounting the clarified tissue, which was very soft, to the glass capillary for imaging. Based on the findings from the rostral hindbrain, which are similar to what has been shown in in situ  studies , 9, it iThe advanced CUBIC is an efficient technique in clarifying the mouse brain tissue


Recent developments in technology are promising for providing home-based exercise programs.The objective of this study was to evaluate the feasibility and patient experience of a home-based rehabilitation program after total hip arthroplasty (THA) delivered using videos on a tablet personal computer (PC) and a necklace-worn motion sensor to continuously monitor mobility-related activities.We enrolled 30 independently living patients aged 18-75 years who had undergone THA as a treatment for primary or secondary osteoarthritis (OA) between December 2015 and February 2017. Patients followed a 12-week exercise program with video instructions on a tablet PC and daily physical activity registration through a motion sensor. Patients were asked to do strengthening and walking exercises at least 5 days a week. There was weekly phone contact with a physiotherapist. Adherence and technical problems were recorded during the intervention. User evaluation was done in week 4 (T1) and at the end of the program (T2).Overall, 26 patients completed the program. Average adherence for exercising 5 times a week was 92%. Reasons mentioned most often for nonadherence were vacation or a day or weekend off 25% (33/134) and work 15% (20/134). The total number of technical issues was 8. The average score on the user evaluation questionnaire (range 0-5) was 4.6 at T1 and 4.5 at T2. The highest score was for the subscale “coaching” and the lowest for the subscale “sensor.”A home-based rehabilitation program driven by a tablet app and mobility monitoring seems feasible for THA patients. Adherence was good and patient experience was positive. The novel technology was well accepted. When the home-based rehabilitation program proves to be effective, it could be used as an alternative to formal physiotherapy. However, further research on its effectiveness is needed. Surgical treatment by means of total hip arthroplasty (THA) is most often indicated in end-stage hip osteoarthritis (OA). At present, THA is considered one of the most successful, effective, and cost-effective surgical treatments available. Hence, a total of 29,937 primary THAs were performed in the Netherlands in 2017 . FurtherIn this context, it is important to look at how technical innovations can be supportive of such home-based programs. Recent technological developments such as wearable sensors and tablet use with mobile internet are promising for providing home-based programs . The useThe use of computers and tablets is rising among older adults in the Netherlands . The ownA 6-month prospective cohort study was conducted to test the feasibility and patient experience of a home-based rehabilitation program. Patients participated in a 12-week, home-based exercise program after THA, following video instructions on a tablet PC. Physical activity was registered daily through a necklace-worn motion sensor, and patients were contacted weekly by telephone to receive coaching from a physiotherapist. The phone calls were aimed at motivating participants, discussing barriers to exercise and exercise load, and answering questions concerning guidelines in terms of movement and load after surgery. Measurements were taken preoperatively (T0) and at 4 weeks (T1), 12 weeks (T2), and 6 months postoperatively (T3). The study was approved by the Medical Ethical Committee of University Medical Center Groningen (METc2014/399).We included 30 independently living patients aged 18-75 years who had undergone THA as treatment for primary or secondary OA. Patients were waiting for THA at either the Martini Hospital Groningen or the Medical Center Leeuwarden in the Netherlands. Exclusion criteria were as follows: (1) revision surgery; (2) medical conditions that disallow independent living; (3) cognitive impairment; and (4) inability to sufficiently read and understand Dutch. Patients were included from December 2015 to February 2017 and were required to sign a written informed consent form to be able to participate.The duration of the program was 12 weeks. Patients started the program within 7 days of the surgery. Patients performed exercises independently at home using the tablet PC for instructions. The program included strengthening and walking exercises based on increasing the muscle force, balance, and functionality. The exercises comprised movements that trained abductors, flexors, and extensors of the affected hip. The content of the program was based on previous research ,10 and oPatients were asked to exercise at least 5 days a week, with rest days on Thursday and Sunday. Strengthening exercises were performed 3 times a week. The instructions for the exercises were provided by videos on the tablet PC, which patients had to imitate. The sessions started with exercise bouts of 10 minutes, which progressively went up to 45 minutes during the 12 weeks of the program. The first step-in level of the program consisted of light and easy exercises. Difficulty and exercise duration were increased across levels very gradually. The exercise burden increased by adding more repetitions, more exercises, and longer training time as well as by incorporating the use of ankle weights. Instructions for walking exercises had no video and showed a descriptive message only. Patients started by walking three 5-minute blocks each day, progressing up to a total of 30-minute walking per day  was used as an indicator that a patient could train at a higher level. There was weekly telephone support from a physiotherapist. During this phone call, the physiotherapist and the patient evaluated the progress and agreed on whether to train at a higher level. The program consisted of 12 levels, each week intended for a level of increasing difficulty.During the intervention, the physiotherapist made 3 home visits. On the first visit, participants received an explanation about the exercises and use of the tablet. The second and third visits were, respectively, at weeks 4 and 12 postoperatively and included physical tests and filling out questionnaires.Patients received exercise instructions through a tablet PC, a Dell Latitude 10 running the Windows 8 operating system. Exercise instructions were provided through a Web-based app. The app provided exercise instructions and gave participants feedback on their training performance. Exercise completion and app use were recorded to track adherence. The app was designed to be as easy as possible so that people with no tablet experience could participate. Internet connection was provided by the subjects’ own home Wi-Fi.The physiotherapist used a coach app that showed daily registration of completion degree or interruption of exercise bouts. Answers on the evaluation questions (about pain and perceived intensity) were also shown at the end of the week. The physiotherapist was able to change the level of the exercises through this app.The necklace-worn sensor  weighed Preoperative demographic data, height, weight, medical history, and pre- and postoperative complications were recorded. Factors that might have influenced patients’ ability to independently perform a home-based program using novel technology were assessed through a questionnaire. Furthermore, questions were asked about the previous and current use of PCs and smartphones.Adherence to the rehabilitation program was evaluated on the basis of the completion of the planned exercises as indicated by watching the exercise videos and reading the instruction messages. Program adherence was considered sufficient when it exceeded 70%. Reasons why patients did not perform the planned exercises were recorded by the physiotherapist during the weekly phone calls.User evaluation was performed with a questionnaire adapted from the sensing and action to support mobility in ambient assisted living subject evaluation form ,13. The Technical issues that interrupted the execution of the program were logged during the program. All phone calls and extra home visits were registered along with the reasons for these calls or visits.Statistical analyses were performed using IBM SPSS Statistics 22.0 (IBM). Descriptive statistics were used to portray the main characteristics of the research group.In total, 9 men and 21 women participated in the study. Mean age was 64 (SD 6.7) years. A total of 26 patients completed the program. Four patients dropped out in the first 2 weeks: 3 patients dropped out because of severe back pain, preference to visit a regular physical therapist, and reoperation after a fall and the fourth patient performed postoperatively worse than expected; this patient was insecure, needed more direct personal coaching, and went to a regular physical therapist. Because of sustained back pain, 2 patients finished the program 4 weeks before the official end and went to a regular physical therapist. There were no exercise-induced injuries. Of the 26 patients who completed the program, 3 did not participate in the 6-month measurement because of surgery of the other hip (THA and a fracture) and illness.For all patients, average adherence to exercising 5 times a week was 92%. For all weeks, the adherence was sufficient (>70%), except for strengthening exercises on week 12 . After wIn this study, of the 26 patients who completed the program, 5 (19%) completed all levels of the program, 11 (42%) reached level 11, 5 (19%) reached level 10, 2 (8%) reached level 9, and 3 (12%) reached level 8 of the program. Not raising the exercise level at the end of the week occurred 43 times in total and occurred mostly in the first 2 weeks of the program. Staying on the same level occurred 23 times  in weeks 1-4 and 20 times  after week 4. The total number of technical issues was 8. Of them, 5 issues included errors in the server of the app. These problems were mostly solved within a few hours, so people were able to complete the exercises for that day. Three issues required an extra home visit to be solved—an unstable Wi-Fi connection, a broken tablet PC, and a disconnection of the sensor and the tablet PC.The average score on the user evaluation questionnaire (range 0-5) was 4.6 at T1 and 4.5 at T2. The highest score was for the subscale “coaching” and the lowest score was for “sensor” . For theOverall, 19 patients gave suggestions for improvements or other comments at the end of the user evaluation questionnaire. Among all, 9 patients mentioned that they liked being able to rehabilitate from home (and that they did not have to travel) and felt motivated by the rehabilitation program; 4 patients would have liked an extra home visit in the first few weeks to check the performance of the exercises (mentioned by 2 patients) and the walking pattern (mentioned by 2 patients). Furthermore, 5 patients recommended more diversity in the exercises; 7 patients mentioned that the duration of the program was a bit too long, especially when they felt their recovery was complete and they had started working again, and 8 patients reported that they experienced the daily wearing of the sensor as uncomfortable because the sensor was big (mentioned by 4 patients) and because the cord was irritating (mentioned by 4 patients).The results of this study provide support for the feasibility of a home-based telemonitored rehabilitation program for patients after a THA. Adherence to the program was good and user evaluation was positive, and there were only 8 technical issues during the intervention.A total of 30 patients were included in the study, and 26 patients completed the program. Because of pre-existing back pain, 2 patients finished the program 4 weeks before the official end. The back pain was unrelated to the intervention. There were no exercise-induced injuries during the intervention. This indicates that patients after THA can perform a rehabilitation program safely at home. Furthermore, previous studies concluded that unsupervised home exercise is safe for a majority of THA patients .Average adherence to exercise 5 times a week was 92%, which was higher than our goal of 70%. However, we must note that after week 8, there was a decrease. Overall, adherence rate for our program is higher than that for similar 12-week programs, such as those by Chang et al and Mikkelsen et al ,15, who The reasons mentioned most often for nonadherence were vacation (or a day or weekend off) and work. Both reasons were mentioned mainly in the last 3 weeks of the intervention, which explains the decrease in adherence after week 8. Some people even suggested that the program could be shortened. Internet problems concerned 10% (13/134) of the reasons for nonadherence, although this applied only for 2 patients in a short period (6-7 days).Patients were positive about the program, giving an average score of 4.6 (range 0-5) at T1 and 4.5 at T2 on the user evaluation questionnaire. Patients liked that they could rehabilitate from home (and that they did not have to travel) and felt motivated by the program. The remote support by weekly phone contact with the coach was appreciated by patients. The importance of the weekly phone contact is in line with a previous study reporting that motivation and coaching is an important parameter for home-based exercise performance and enhanced adherence .The rehabilitation program consisted of 12 levels each week, intended for a level of increasing difficulty and exercise duration. Despite the various levels offered, 5 patients suggested more diversity in the exercises. Furthermore, 4 patients would have liked an extra home visit in the first few weeks to check performance of the exercises and their walking pattern. These comments correspond with the lowest scores in the subscale “rehabilitation program” of the evaluation questionnaire for the statements: “The rehabilitation program is effective for improving my walking pattern” and “The level of the exercises was adapted to my possibilities.” Of all, 7 patients mentioned that the program duration was a bit too long; 6 of these patients started working again 6-8 weeks postoperatively. It appears difficult to combine the program with work, even though patients could choose for themselves the time of day to exercise. A recommended adjustment is a more individualized program with additional exercise diversity and when necessary extra support, possibly in the form of a home visit, to improve the walking pattern. Another recommendation is adjusting the duration of the program to patients’ goal achievement.Patients were positive about the technology and gave an average score of 4.8 and 4.1 (range 0-5) for the use of the tablet and sensor, respectively. All patients used their own home Wi-Fi. Geraedts et al reported that adherence to their home-based exercise program and dropping out were strongly influenced by the stability of the mobile internet connection . Based oAustin et al supported unsupervised home exercise as an effective rehabilitation strategy, which is cost effective as well, for most THA patients compared with formal physiotherapy . The stuA limitation of the study was the small number of patients, although this was a deliberate choice to test the feasibility of the program for the first time. In addition, patients who had agreed to participate in the study had some computer experience already and were probably more motivated than average patients, which led to some bias. Nonetheless, the wide variety in educational level, age, and living and work situation seem to have provided a representative group.A home-based rehabilitation program driven by a tablet app and mobility monitoring seems feasible for THA patients. Adherence to the program was good, and patient experience was positive. In addition, the novel technology was accepted well. When the home-based rehabilitation program also proves to be effective, it could be an alternative to formal physiotherapy. However, further research is needed into the effectiveness.


International evidence shows that lifestyle interventions can effectively reduce the risk of developing diabetes in people with non-diabetic hyperglycaemia (NDH). A candidate intervention that has potential to be rolled out at population level is health coaching. Digital interventions offer the means to potentially enhance user satisfaction with health coaching and improve efficiencies. We used a randomised controlled trial to test whether a digitally-enabled health coaching intervention that included an online dashboard and telephone health coaching improved user satisfaction and cost-efficiencies compared with a telephone only health coaching intervention. The primary outcome was satisfaction measured by Client Satisfaction Questionnaire (CSQ-8). 103 participants with NDH were allocated to the telephone coaching only intervention and 106 participants with NDH were allocated to the digital and telephone coaching intervention. In an intention-to-treat analysis satisfaction was higher in participants allocated to the digital and telephone coaching intervention than those allocated to the telephone only intervention, but the difference was not significant. There were no significant differences between the groups on secondary outcomes . From a service commissioning perspective the mean incremental cost of the digitally-enabled intervention was £236 . Call times, including administration, were longer for participants allocated to the digitally-enabled intervention. The results show that user satisfaction with digitally-enabled intervention is broadly equivalent with that of telephone delivered interventions in the context of routinely delivered diabetes prevention programmes. There is scope for future work that assesses how economies of scale can be achieved at larger user bases. Non-diabetic hyperglycaemia (NDH) is a high-risk category of developing diabetes and is a term used to describe pre-diabetic conditions associated with decreased ability of the body to regulate glucose effectively, such as impaired glucose regulation (IGR) and impaired fasting glucose and/or impaired glucose tolerance.6 When targeted at people with NDH, lifestyle interventions that promote physical activity, diet modification and weight loss can reduce the risk of diabetes by 58%.8 However, while the use of lifestyle interventions to prevent diabetes in people with NDH are recommended in national guidelines, they are usually resource intensive to deliver and are unlikely to be cost-effective and implementable at scale across routine settings.9There is unequivocal evidence that lifestyle interventions can reduce the risk of developing diabetes.10 Health coaching that includes modelling behaviour and goal setting, has emerged as a promising candidate platform that can support the delivery of effective lifestyle interventions for people with long term conditions,11 including diabetes12 and possibly people with NDH.13 With the flexibility of being accessible by telephone, health coaching can potentially reach a significant proportion of the target population.Identifying cost-effective lifestyle interventions that can be deployed in routine settings to prevent diabetes in high risk populations such as those with NDH is therefore a health policy priority.14 Web-based interventions have been especially used to promote behaviour change and manage mental health problems.16 And there is good evidence that technology assisted primary care interventions that combine either the internet, personal computer, and/or a mobile device are effective in supporting people to lose weight.17 Fully automated interventions that support web-based and mobile facilitated goal setting and personalised behaviour change have also proven effective in reducing cardiovascular risk among overweight and obese adults and in improving glycaemic control and reducing other risk factors for diabetes, but gains are short-lived.19It is possible that reach and efficiency of telephone health coaching could be enhanced further by digitally-enabled components. The last decade has seen increased use and availability of second generation web-based interventions that include program and interactive content, multimedia materials and guidance and feedback.20 In the context of web-based interventions user satisfaction and experience ratings are important metrics that go beyond developer assessments of design and build quality and potentially provide critical feedback about usability and acceptability relevant to healthcare providers.21 Satisfaction is increasingly used as an outcome of interest in evaluations of web-based health interventions and there is growing recognition that satisfaction can capture user perception of their experience of web-based interventions.23While there is an emerging consensus about best practice in developing and evaluating effective web-based interventions, there is less emphasis on understanding how end-user satisfaction can inform successful implementation in routine settings.In the context of the CATFISH trial a telephone-only health coaching intervention for people with NDH was established as routine care and the aim of the trial was therefore to compare two active treatments to determine if a digitally-enabled health coaching intervention improved user experience and led to higher efficiency. Specifically the CATFISH trial aims to assess whether a digitally-enabled telephone coaching intervention (IGR3) is more acceptable than an existing telephone-only coaching intervention (IGR2) for people with NDH. Secondary objectives of the trial are to (1) determine whether the delivery of the IGR3 intervention is more efficient than the existing commissioned IGR2 intervention; (2) to explore the cost-effectiveness of IGR3 in comparison with IGR2; (3) to qualitatively explore and compare user and provider experience of IGR3 and IGR2 interventions; and (4) to explore the impact, if any, of IGR3 compared with IGR2 on clinical outcomes relevant to diabetes prevention in people with NDHBetween July 2015 and May 2016 there were 853 referrals from general practice in Salford to Care Call. Of these 253 (29.7%) verbally consented to be contacted by the research team at the University of Manchester about participating in the CATFISH trial. Of those that consented to be contacted 210 (83%) agreed to take part in the CATFISH trial and were assessed for eligibility and invited to a baseline assessment following receipt of their written informed consent. The first participant was recruited on 30th June 2015 and the last participant was recruited on 25th May 2016. One participant was withdrawn from the trial before allocation because they were found to have type 2 diabetes. 209 participants were randomised, with 103 allocated to IGR2 and 106 allocated to IGR3. 87% of participants returned a follow-up questionnaire for the primary outcome. The flow of participants is shown in Fig. 2) at baseline, with a mean BMI of 33.9 (SD 7.3). Over a quarter (28.2%) of participants met the threshold on MHI-5 for probable depression at baseline. Participants in the two groups were similar in all respects, except that a higher proportion of females were allocated to IGR2 compared with IGR3 (48.5 vs. 39.6%). Table Participants had a mean age of 58.3 SD 11.4) years and 44% were female. The majority (94%) were from white ethnic backgrounds and just under half (47.5%) were either in full or part-time paid employment. Participants had a mean HbA1c of 44.4 (SD 1.6) mmol/mol, which is equivalent to 6.2% HbA1c. All participants had a BMI in the obese range  withdrew before they received an action planning call. A further 10 participants  did not receive any follow-up calls after the initial action planning call. Twenty-six participants allocated to IGR3 switched to IGR2 during the course of their exposure to the intervention. Ninety-nine participants in IGR2 and 93 participants in IGR3 had at least one follow-up call and the majority of these  had more than five attempted follow-up calls. The mean total health advisor contact time per participant was 142.6 (SD 50.7) minutes for IGR2 and 132.2 (SD 66.5) minutes for IGR3.p = 0.074) in participants allocated to IGR3 compared with those allocated to IGR2. This equates to a small effect size . There is, therefore, weak evidence in favour of IGR3, but not at the 5% level of significance. Both the per-protocol  and complete case  analyses for the primary outcome returned very similar results to the intention to treat analyses: differences in total satisfaction scores between IGR2 and IGR3 were not significant at the 5% level.We collected primary outcome data for 181 (87%) participants. Missing outcome data were therefore imputed for 28 participants. At follow-up, the mean score on the CSQ-8 in IGR2 was 27.1 (SD 5.2), and in IGR3 was 28.4 (SD 4.5). Controlling for relevant covariates, the total score for the CSQ-8 was 1.32 points higher .Secondary outcomes were only analysed and presented for complete cases Table . While sNone available.To be reported in a separate process evaluation.The resources needed to deliver IGR3 over the 9-month observation period included training Care Call staff and monthly IT costs  for action planning was slightly higher in IGR3 compared with IGR2. Among those who had at least one attempted follow-up call the average call time (including administration) was similar in both groups Table . CombiniTable There was no evidence that participants who used a digitally-enabled telephone coachin intervention that included an online dashboard plus telephone health coaching for preventing diabetes were more satisfied with their healthcare than participants who used a telephone only health coaching intervention. While there was an indication that participants who received the digital intervention were more activated, had improved health status and reduced depressive symptoms, the differences compared with participants in the telephone only group were not significant. At follow-up participants in both groups had lower HbA1c and BMI but the between group difference for these outcomes was not significant. There were no significant differences between the groups for other secondary outcomes. Attrition was slightly higher in IGR3 and a quarter of participants in this group switched to IGR2 and received the telephone only intervention. However the per protocol analysis returned similar results to the intention-to-treat analysis suggesting that participants were satisfied with the overall health coaching approach regardless of allocation. Contact time between health coaches and participants was broadly equivalent in both groups. Although both were used for less than the maximum of 180 h allotted for the IGR pathway, IGR3 was not found to improve staff efficiency as expected. From a provider perspective the additional cost of the digital intervention was £236  per participant to deliver, which consisted mostly of the training and platform costs of the online dashboard.24 While there was a signal that participants in IGR3 were more satisfied and more activated than those in IGR2, we cannot be certain this was associated with the availability of the digitally-enabled intervention. Indeed, because there was no difference in uptake and number of follow-up telephone coaching calls between the two groups, it is still possible that any difference in satisfaction and activation may be attributed to the quality of health coaching.Previously it has been shown that satisfaction with telephone only health coaching is associated with higher levels of activation and number of sessions completed among those enrolled in a behaviour change programme to prevent diabetes.25 A similar result was reported at 6-months for a community based diabetes prevention programme run along pragmatic lines in the NHS.26 In CATFISH the overall performance of the IGR Care Call intervention was associated with a mean reduction in HbA1c of 0.10% with no difference between trial groups. This is equivalent to results of more intensive national diabetes prevention programmes such as those run in the United States which showed a mean reduction in HbA1c of 0.10% at 12 months.8 Similarly, weight loss results for the IGR Care Call interventions were equivalent or outperformed results from comparable pragmatic interventions such as Let’s Prevent which reported a mean difference of 0.11 in BMI scores between the intervention group and standard care.26 Larger effects were reported by Block et al who tested the effects of a fully automated digital behaviour change intervention among people with prediabetes.19 In that trial participants who used the web-based intervention reported a mean reduction in HbA1c of 0.26% and a mean reduction of 1.05 in BMI scores at 6-months. While participants were not supported by face-to-face or telephone coaching they did have access to social support via online networks and engaged in team based competitions suggesting that peer support and gamification might be critical to the success of online interventions.While this trial was not set-up as a superiority test for clinical outcomes, it is instructive to compare before and after results achieved in CATFISH with those reported in comparable studies that tested lifestyle interventions for preventing diabetes in routine practice. In updating the review by Dunkley et al, Public Health England reported a pooled reduction in HbA1c of 0.07% for 11 pragmatic lifestyle interventions at 12–18 months follow-up.27 In CATFISH drop out from the web plus telephone group was 24.5% when withdrawals and those who switched to the telephone only group are combined. It is clear that therapeutic contact on the telephone can improve usage and outcomes of web-based interventions.16 However, unlike other web delivered lifestyle interventions that used telephone support we did not see a difference between the two groups in uptake of the telephone element of health coaching and we do not know if telephone coaching improved usage of the digital content of the intervention.28Attrition is typically higher in trials of web-based interventions compared with controls, with drop-outs reported to be 22.5%.29 the digitally-enabled telehealth intervention designed by Hitachi is purported to achieve efficiencies of scaling over telephone only interventions at 3000 users per year with the use of cloud based infrastructure and improved management capacity. In that model the cost of the digitally-enabled intervention would be £383 per person; a fully optimised cost basis is achievable at 10000 users per year costing £309 per person. There is potential to reduce the cost of the digitally-enabled intervention further with the use of a dynamic care pathway whereby users are segmented into three categories of health coaching support. In this dynamic model the cost per person is proposed to be £326 with a user base of 3000 per year, and £292 per person with a user base of 10000.This was not a cost-effectiveness study but we did assess costs attributed to the set-up and delivery of the digital intervention. The higher cost for using IGR3 were mainly associated with the web elements. The expected effect on efficiency was not found in the trial, which could be a result of lack of integration between the telephone and digital components. If the digital platform only served as a notebook, the platform could potentially only add additional burden associated with the training without any intended benefit. With no efficiency savings gained, it would be for the provider to judge if the additional cost per point of improvement on the CSQ-8 is worth paying. A key advantage of digitally-enabled interventions is that they have potential to reach economies of scale, which might take place as the user base grows. Based on modelling used in the digital pilot of the National Diabetes Prevention Programme in the NHS,30 Additionally the CATFISH trial evaluated end-user satisfaction as a primary outcome and thereby underscores the importance of assessing patient experience of interventions.31 As with most trials there is the possibility that we only reached and included a self-selecting cohort of eligible patients and that this group were the most motivated to engage with the intervention. However, participants were blind to treatment allocation and were not aware which intervention was novel; researchers and analysts were also blind to treatment allocation. Finally, drop outs from IGR3, including those who switched to IGR2, was moderately high but we do not know the reasons for this, nor do we know why once engaged in health coaching participants completed their call schedules. These questions will partly be addressed in a separate qualitative process evaluation.The digital intervention was embedded within an existing service that was delivered as part of routine care for people with NDH and in this sense the CATFISH trial responds to the need to address the translational gap between evidence and practice in diabetes prevention.32 Encouraging patients to engage with digital health interventions is a challenge and their reach and scaling are likely to hinge on successful outreach and enrolment activities.33 Where digital interventions are preferred it is critical that inclusion is enhanced to contend with higher rates of digital exclusion among the most deprived and among those who lack the confidence and skills to use online approaches.34 As with other digital interventions there is also need to consider how to maximise adherence and optimise usage and the use of games and SMS alerts, as well as improving accessibility on mobile platforms are possible avenues for further evaluation. Critical too is the need for a better understanding of the optimal combinations of behaviour change techniques and mode of delivery for web-based interventions. A review of 52 online health interventions showed that only usability was associated with effectiveness, but there was little evidence that pointed to the most effective combinations of behaviour change techniques.35 Component network meta-analysis is a relatively new approach to meta-analysis that can unpick the treatment effects of different components includes in complex composite interventions.36 There is scope for further elaboration of the most effective combinations of components for web-based behaviour change interventions using this approach. Definitive testing of candidate interventions would further enhance the evidence base for web-based behaviour change and health promotion interventions.In the context of continued roll out of diabetes prevention programmes, such as that launched by NHS England, it is important to recognise that relatively low intensity interventions such as the ones deployed in CATFISH have an important role to play in translating evidence into practice.We showed that user satisfaction and experience of a digital-enabled telephone coaching intervention that included an online dashboard and telephone health coaching was broadly equivalent to that of a routinely delivered telephone-only coaching intervention for people with NDH. On the basis of user satisfaction there is scope to consider this digitally-enabled telephone coaching intervention as an option for decision makers with responsibility for commissioning diabetes prevention services. However there is still uncertainty about cost-effectiveness of this digitally-enabled telephone coaching intervention and further definitive evaluation is warranted. Furthermore, future work is needed to assess whether economies of scale are achievable outside the context of trial based evaluations where user bases are likely to be much larger.37 Ethical approval was granted by NHS Research Ethics Committee for the East of England (Cambridgeshire and Hertfordshire) (reference no 15/EE/0117).CATFISH was an individually randomised controlled trial conducted at the Salford NDH Care Call  service provided by Salford Royal NHS Foundation Trust, Greater Manchester. The trial protocol has been previously published.38 and HbA1c = 42–47 mmol/mol (6.0–6.4%) or a previous diagnosis of IGR with 1x confirmatory blood test (HbA1c within the previous 6 months).Participants for CATFISH were identified from referrals from primary care or community teams to NDH Care Call. Referral criteria to NDH Care Call were: moderate or high risk on the Leicester Diabetes Risk Score Assessment ToolAdditional eligibility criteria for inclusion in CATFISH were adults aged >18 years and access to a telephone and home internet. We excluded people with a diagnosis of type 2 diabetes (HbA1c of ≥ 48 mmol/mol [≥6.5%]); a diagnosis of gestational diabetes; did not read or speak English; and those incapable of participating as indicated by their GP because of dementia, learning difficulties, vision or motor skills limitations, serious and enduring mental health problems.39 The principal investigator and NDH programme administrator were aware of allocations; research staff and analysts were blinded to allocation. Treatment allocation was concealed from participants at the baseline assessment appointment. It was not possible to blind the health professionals delivering the interventions.Contact details of eligible patients were forwarded to the Care Call programme administrator who asked whether patients were willing to discuss participation in the CATFISH trial. Patients who verbally consented to be contacted about CATFISH were then telephoned by the research team (AB and EB) who checked their eligibility for the trial and invited them to agree to a baseline assessment. Once signed consent forms had been received and the baseline assessment had been completed participants were randomised using a central randomisation service provided by the Manchester Academic Health Science Clinical Trials Unit at the Christie NHS Foundation Trust. Participants were allocated 1:1 by minimisation using BMI and HbA1c at baseline. This technique ensures that treatment groups are very closely similar for several variables, even in small samples.41 The core content for both IGR2 and IGR3 includes education about reducing the risk of developing diabetes, goal setting and action planning, and coaching from health advisors to support patients to achieve their goals and to maintain and review their action plans. Health advisors are NHS Agenda for Change band 4 workers who received at least two months in-house induction training that included the X-pert 6-week diabetes patient education programme which includes education sessions about the prevention of diabetes, and education sessions for newly diagnosed type 2 diabetes patients and insulin starters.42 Additionally all advisors completed a 1-day training course in motivational interviewing provided by Advancing Quality Alliance and delivered by an independent training company.43 Advisors were also given educational materials, took part in telephone role-play calls, observed diabetes clinics, attended medication training with the Diabetes Team, and shadowed calls from trained advisors.The telephone only service was known as IGR2. The digitally-enabled telephone service was known as IGR3. The service specification was adapted from the Diabetes Care Call service and a previously piloted 6 month telephone delivered lifestyle and education programme for people with impaired glucose tolerance.The telephone schedule was similar for IGR2 and IGR3 and comprised an introductory call from the programme administrator, followed by a 30–40 min action planning phone call from a diabetes specialist nurse or dietician [call 1]; 6 × 10–20 min phone calls over six months from the health advisor [calls 2–7]; and a final step-down 10–20 min call at nine months [call 8]. All calls were outbound and heath advisors maintained contact with the same patients throughout the care pathway.Before the action planning call all participants received a copy of the Leicester diabetes booklet. Participants completed ‘My Plan’ which helped them to reflect on how they might make changes to their food choices and activity to manage their risk of diabetes. The action planning call was not scripted and focused on explaining the significance of blood tests results, understanding risk factors for diabetes, exploring participants’ readiness to change and encouraging them to identify SMART goals. Weight loss targets of 5 and 10% were communicated to the participants. The six follow-up calls were guided by scripts modelled on those used by Diabetes Care Call but with an emphasis on diet and exercise and tailoring to ensure that call content was patient-centred and flexible to support their individual goals. The step-down call at 9 months provided an opportunity to negotiate and agree onward action plans and discharge participants back to GP care.12 The dashboard was designed for use on desktop computers only. The patient view included an interactive and dynamic log that enabled participants to track their progress against action plans and monitor their weight, physical activity, and blood glucose. Additionally the dashboard view included a space for participants to message health advisors and a progress tracker to identify completed and future call events (Supplementary 2).The critical innovation in IGR3 was the addition of an online dashboard that was accessible by participants and health advisors. The dashboard was co-designed with patient groups and front-line NHS Health Advisors, based on both Hitachi’s experience of delivering diabetes prevention and well-being programmes in Japan and SRFT experience of developing and delivering a telehealth diabetes prevention programme known as Diabetes Care Call.Before the action planning call, the participants allocated to IGR3 were sent a pedometer to log their activity during the six months exposure to the intervention. Participants allocated to IGR3 also completed an online self-assessment before the action planning call. The IGR3 care pathway was supported by three embedded videos on the online dashboard: Introduction; Guide to Self-Assessment; Guide to the Dashboard (supplementary 3). Health advisors could access logged information to assess the progress of the participants and tailor the follow-up calls accordingly.44 at 9 months post-randomisation. This end point was chosen to coincide with the timing of the scheduled step-down call. The CSQ-8 is an 8-item generic survey instrument used in primary care clinical trials and has been widely used as a measure of user satisfaction of web-based interventions.45 It is scored using a four-point Likert with a range from 8 to 32; higher scores indicate higher satisfaction. The CSQ-8 was measured only at follow-up with computed scores used to calculate the mean difference between groups.The primary outcome was the mean difference in total score of the Client Satisfaction Questionnaire (CSQ-8)46 We used routine test results that were reported between 12 weeks before or 12 weeks after the end of follow-up.Glycated haemoglobin (HbA1c) was measured with a blood test taken from participants after the step-down call at the Barnes Clinical Research Facility, SRFT. Sample type and volume used were fluoride oxalate (yellow), 1 mL; reference range and units were 3.0 to 6.0 mmol/L. Participants who declined a blood test at follow-up were asked to consent to sharing the results of routine blood tests taken at their general practice. HbA1c represents average blood glucose over a 8 to 12 week period.2) was measured using height (m) taken at baseline and weight (kg) measured at follow-up using ISO 9001:2008 standard scales (Seca model 8751) that were calibrated using weights traceable to the National Physics Laboratory Standards of Mass.BMI . The VAS records an individual’s perceived self-rated health, ranging from 0 to 100; higher values indicate better general health. The EQ-5D-5L was used at baseline and follow-up.The five-item EQ-5D-5L is a generic measure of health-related quality of life consisting of the EQ-5D descriptive system and EQ Visual Analogue Scale (EQ VAS).48 We used the recommended score ≤ 60  to indicate the presence of ‘probable depression’;49 continuous scores were used in the analysis. The MHI-5 was collected at baseline and at follow-up.The Mental Health Inventory (MHI-5) is a 5-item scale which measures general mental health, including depression, anxiety, behavioural-emotional control and general positive affect.50 The SDSCA includes items that assess diabetes self-management across core domains related to: general diet, specific diet, exercise, blood-glucose testing, foot care, and smoking. We used items relevant to diabetes prevention and assessed the number of days per week participants engaged in healthy and unhealthy behaviours . We also included an item on number of cigarettes smoked per day. Health experience and self-management data were collected at baseline and at follow-up.We evaluated self-management behaviours using the revised version of the Summary of Diabetes Self-Care Activities (SDSCA) scale.51 We used the short 13 item version.52 The score (0–100) can be used to segment patient populations into four levels of activation but we used the continuous score in the analysis. The PAM-13 was collected at baseline and at follow-up.The Patient Activation Measure (PAM)−13 is a self-report measure of patient knowledge, skills and confidence in self-management for long-term conditions.We powered the study to have 90% power  to detect a standardised effect size of 0.5 on the CSQ-8. Allowing for a 15% attrition rate, 200 participants were needed in total (100 per group). Basic summary statistics  were calculated, by trial group, for each of our primary and secondary outcome variables. For the CSQ-8, data were only available at follow-up, whereas for all other outcomes, data were available at both baseline and follow-up. In order to formally test for post-intervention differences between CSQ-8 scores in the two groups, we conducted linear regression analysis, controlling for participant age-group, gender and baseline measurements of BMI, HbA1c, Patient Activation, MHI-5 and self-rated general health status. The primary analysis was conducted following the intention-to-treat principle. The CSQ-8 was only available for 181 observations whereas between 179 and 181 observations were available for secondary outcomes. The missing data in the outcome and covariates were handled by multiple imputation using chained equations, with 20 replications. To ease interpretation and to allow comparison with published studies, we estimated a standard effect size (Cohen’s d) for the CSQ-8 as the difference in follow-up means divided by the pooled baseline standard deviation for all participants. Missing data for secondary outcomes were not imputed but presented in available cases  perspective using intervention costs and the CSQ-8 to derive an additional cost per additional point of the CSQ-8. CCGs are clinically-led statutory NHS bodies responsible for the planning and commissioning of health care services for their local area. All costs were presented in UK pounds Sterling (£) for the financial year 2015–16. Costs were inflated to 2015–16 price levels where necessary using the Hospital and Community Health Services pay and price inflation index.54 Training to support delivery of IGR3 was provided by Hitachi. It consisted of three 2-h sessions conducted by the IT project manager, IT technical manager and service designer. The Care Call service manager, programme administrator, six diabetes specialist nurses/diabetic dieticians and five health advisors attended the training. The cost of development of IGR3 platform was considered a ‘sunk cost’ and therefore not included.55 The total costs of the training were evenly allocated to the IGR3 participants.A micro-costing exercise was conducted following the methods of technology appraisal recommended by NICE.The online dashboard was costed for the 9 months intervention period using contract pricing provided by Hitachi. This included monthly costs for set up and hosting services, server rental and third line support, including VAT. These fixed costs were evenly allocated to each participant in IGR3.53 . These unit costs were then multiplied by the time spent recorded in the call log or timesheet during the trial period to estimate the costs of delivering the interventions.Unit costs of staff delivering both IGR2 and IGR3 were based on estimates of costs of health and social care compiled by the Personal Social Services Research Unit (PSSRU)56 From a CCG perspective the ICER was interpreted as the additional intervention cost per participant for one point improvement on CSQ-8.Incremental cost-effectiveness analysis combined the intervention costs with the primary outcome to generate an incremental cost-effectiveness ratio (ICER), by dividing the mean difference in costs between the two trial groups by the mean difference in effect.57 assuming that any missing data were missing at random. A chained equation model was developed and predictive mean matching by intervention groups was used as the imputation method, using the ten nearest neighbours to the prediction as a set to draw from. The number of imputations was set to approximately the highest percentage of missing data in all variables included in the imputation model.58 Due to the non-normal distribution of both cost and outcome data, 5000 replacement samplings were generated using bootstrap technique to derive 95% confidence intervals. A sensitivity analysis was undertaken to repeat the CEA using complete cases.Missing data were handled by multiple imputation following Rubin’s rules,Supplementary material tables combinedSupplementary material 2Supplementary material 3


Adults with Down syndrome (DS) are at increased risk for Alzheimer disease dementia, and there is a pressing need for the development of assessment instruments that differentiate chronic cognitive impairment, acute neuropsychiatric symptomatology, and dementia in this population of patients.We adapted a widely used instrument, the Clinical Dementia Rating (CDR) Scale, which is a component of the Uniform Data Set used by all federally funded Alzheimer Disease Centers for use in adults with DS, and tested the instrument among 34 DS patients recruited from the community. The participants were assessed using two versions of the modified CDR—a caregiver questionnaire and an in-person interview involving both the caregiver and the DS adult. Assessment also included the Dementia Scale for Down Syndrome (DSDS) and the Raven’s Progressive Matrices to estimate IQ.Both modified questionnaire and interview instruments captured a range of cognitive impairments, a majority of which were found to be chronic when accounting for premorbid function. Two individuals in the sample were strongly suspected to have early dementia, both of whom had elevated scores on the modified CDR instruments. Among individuals rated as having no dementia based on the DSDS, about half showed subthreshold impairments on the modified CDR instruments; there was substantial agreement between caregiver questionnaire screening and in-person interview of caregivers and DS adults.The modified questionnaire and interview instruments capture a range of impairment in DS adults, including subthreshold symptomatology, and the instruments provide complementary information relevant to the ascertainment of dementia in DS. Decline was seen across all cognitive domains and was generally positively related to age and negatively related to IQ. Most importantly, adjusting instrument scores for chronic, premorbid impairment drastically shifted the distribution toward lower (no impairment) scores. APP) gene is located leading to overexpression of both APP mRNA and amyloid beta (Aβ) peptide [Alzheimer disease (AD) is the most common cause of dementia . Because peptide . Dementi peptide –5.Cognitive and functional decline are the salient indicators of AD , 6. DS aThe Dementia Scale for Down Syndrome (DSDS)  is an inThe Cambridge Examination for Mental Disorders in the Elderly (CAMDEX)  is an inThe Dementia Screening Questionnaire for Individuals with Intellectual Disabilities (DSQIID ) was morIn addition to these advances, it would be desirable to adapt clinical staging instruments that are widely utilized in scientific and clinical work involving AD dementia in the broader population to afford comparability of assessment and staging for scientific and clinical efforts involving individuals with DS. Therefore, we adapted the Clinical Dementia Rating scale (CDR ) for useThe primary goal of this project was to determine whether adaptations of the CDR for DS—by both interview and caregiver report—could reliably capture variation among DS patients when implementing new adaptations of an established dementia rating scale.In the first study phase, families with DS adults aged 18 years and older were recruited from the St. Louis ARC, a non-profit organization that provides support and services for individuals with intellectual and developmental disabilities in the St. Louis region. Potential participants were recruited via letters sent to the families by St. Louis ARC. A total of 189 letters were sent asking families to contact the study. Forty families (21.2% response rate) contacted study staff over the telephone regarding their interest to participate. Verbal consent was obtained from the caregiver over the telephone and he/she was then sent the questionnaire version of the modified CDR instrument (CDR-QDS) by postal mail. All forty CDR-QDS instruments that were mailed were received back for evaluation.In the second study phase, families who completed the CDR-QDS were subsequently invited to come to the laboratory. Families who agreed to participate after verbal explanation over the telephone of this study phase were sent the informed consent document to their homes prior to their laboratory appointment. At the time of the appointment, study staff went over study procedures, answered any questions, and obtained signed informed consent from the caregiver/legal guardian and signed ascent from the DS adult. A question as to whether the DS adult knew why he/she is in the laboratory was asked to both the informant and the DS adult as part of the interview process, to assess capacity.The laboratory appointment involved administration of (1) the modified interview format of the CDR (CDR-IDS) to the informant and to the DS adult; (2) the Dementia Scale for Down Syndrome (DSDS) , for conResults from this study were not given to patient families. This study was approved by the Institutional Review Board at Washington University in St. Louis.Original CDR questions were uniformly modified to ascertain degree of cognitive impairment and the extent to which change had occurred in relation to the participant’s prior baseline. In order to effect this general modification, in some cases, we generated qualifiers for affirmative responses; for example, endorsement of the CDR question “Does he/she have problems with his/her memory or thinking?” triggered a new follow-up question: “Did he/she ALWAYS have a problem with his/her memory or thinking?” (Yes (1)/No (0)). If the DS adult was reported to currently have problems with memory or thinking (current score = 1) but he/she always had this problem , then the CDR-DS rating for cognitive decline was calculated to be zero .current level of functioning were paired with identical companion items eliciting an endorsement of the same item with respect to his/her best ever level of functioning; for example, the CDR question “How often does he/she know the exact day of the month?” has response categories: Usually (0), Sometimes (1), Rarely (2), and Don’t Know. If the DS adult was reported to currently Rarely know the exact day of the month but his/her best ever ability was that he/she Sometimes knew the exact day of the month, then the decline score would be 1 (current score 2–best ever score 1).In other cases, items related to the DS adult’s In still other cases, we changed response categories; for example, the original CDR question “Rate his/her ability to cope with small sums of money ” has response categories: No Loss (0), Some Loss (1), and Severe Loss (2). We substituted No Loss with Never Able (0) and Some Ability/No Loss (0) response categories and kept Some Loss (1) and Severe Loss (2). A difference  score could not be calculated for questions with these response options since change relative to premorbid ability was implicit in the response options.CDR-QDS scores were generated for each of six functional domains of the CDR—memory, judgment and problem solving, orientation, community affairs, home and hobbies, and personal care—by summing over response categories of individual questions/items within each domain. Higher scores reflected greater cognitive decline. Cognitive domain scores were summed for a total CDR-QDS summary score. Unlike the interview instrument described below, we did not generate CDR-QDS global scores because published rules for generating a global score  are baseExamination of CDR-QDS data prompted modifications that were included in the CDR-IDS. In addition, unlike the CDR-QDS where a cognitive decline score was generated for every item, CDR-IDS scoring included clinical judgment as well as direct assessment of the DS adult. Two members of the team (CNLS and OLDR) underwent CDR clinical training through the Knight Alzheimer Disease Research Center at Washington University. One member (OLDR) performed the face-to-face interviews with the informant and the DS adult and the other reviewed the video recordings of the interviews. We each scored the interviews separately and together reached a consensus on disparate scores by discussing rationales for the original scores. Consistent with CDR scoring , each coData were managed using REDCap electronic data capture tools  hosted bOf the 40 returned CDR-QDS questionnaires, 6 were incomplete due to missing data. For the remaining 34 completed questionnaires , mean age of the DS adults was 33.2 years old , 15 were women (44.1%), 30 DS adults lived with the responder (88.2%), and the responder was the mother for 28 of the DS adults (82.4%), with the father and full sibling reporting on 4 and 2 of the DS adults, respectively (11.8% and 5.9%). Respondents reported spending an average of 55.5 h per week with the DS adult .Thirty-three of the 34 families with CDR-QDS data were invited to come to the laboratory. From these 33 families, 22 completed assessments in the laboratory. Of the 11 families who did not come to the laboratory, 5 declined to participate, 4 withdrew from the study after initially agreeing to participate, and 2 families did not respond to repeated calls. Five of the 9 families who declined to participate or withdrew from the study had reported relatively high CDR-QDS scores for the DS adult (≥ 14 where the scoring range was 0–32). The 22 participant families comprised 10 women with DS (45.5%), all non-Hispanic White, of average age 32.8  and average IQ of 59 .History of medical and behavioral problems was obtained from the differential diagnosis screening questions of the DSDS . Of the n = 34 and a coefficient ≥ |0.53|  with n = 22.There was 80% power to detect a correlation coefficient ≥ |0.44|  with Table n = 34); the sample manifested a wide range of scores. The distribution was skewed toward lower scores where 13 individuals had a score of 0 (38.2%), and another 10 individuals had scores of 1 or 2 (29.4%). CDR-QDS summary score mean was 4.85 . There were three individuals with CDR-QDS scores ≥ 20, all of whom were over the age of 30 years.Figure n = 22) when scores were adjusted for premorbid function (adjusted) and not adjusted (unadjusted), which would be consistent with current function. As predicted, adjusting for premorbid ability shifted, in the non-pathological direction, scores that would otherwise be elevated on the standard version of the CDR. CDR-IDS adjusted sum of boxes sample mean was 1.68  and unadjusted mean was 4.41 . The table embedded in the middle panel of Fig. Figure p < 0.05, n = 17) and to total CDR-QDS score .Table p = 0.14, n = 7), home and hobbies , CDR-IDS sum of boxes , and CDR-IDS global score . The sample size was n = 2 to 4 for the remaining four domains. Higher IQ was related to lower (better) cognitive function scores overall for both the CDR-QDS and CDR-IDS instruments with correlations (rho > − 0.45) reaching statistical significance.Older age was not significantly related to CDR-IDS domain, sum of boxes, or global decline scores. Excluding individuals with zero scores resulted in stronger relationships between older age and decline in memory . One person had a DSDS cognitive cutoff score of 2, a threshold for which 6-month reassessment is recommended . One perThe modified CDR-QDS and CDR-IDS instruments captured a range of cognitive decline, provided complementary information and exhibited moderate to strong inter-correlations for the critical domains.Our approach to account for premorbid function is similar to that of the CAMDEX-DS interview and DSQIID questionnaire. In the CAMDEX-DS, endorsement of functional difficulty is followed by a question of whether the difficulties represent deterioration . In the In our study, accounting for premorbid abilities dramatically shifted the score distribution toward lower (no dementia) scores, which clearly demonstrates how not accounting for premorbid abilities would result in a high false positive rate of cognitive impairment. CDR scoring for the typical population emphasizes memory domain scores . This emHigher IQ was related to lower levels of cognitive decline. In the typically developing aging population, greater “cognitive reserve,” a measure that combines IQ, education, and lifelong engagement in cognitive activities , is alsoInclusion of the DSDS was meant to serve as an external validity index, but our sample did not reveal sufficient variability in DSDS scoring, and it is notable that a wider range of scores were elicited among the subjects using the CDR instruments. The vast majority of the sample was rated as having no dementia on the DSDS, but most had non-zero CDR-QDS and CDR-IDS scores; the 2 individuals flagged for reassessment or rated as early dementia on the DSDS had higher CDR-QDS scores, as well as CDR-IDS adjusted global scores consistent with moderate and severe dementia. The DSDS was developed and validated in a community sample of individuals the majority of whom had severe to profound intellectual disability which could result in low sensitivity of the DSDS in detecting dementia in higher functioning individuals , 17. We The participant interview portion of the CDR-IDS was difficult to administer to DS adults who were nonverbal or whose speech was limited or difficult to understand. The original CDR was developed for the typical population and in more extreme cases, it cannot capture the full range of disability. Ability to capture cognitive decline across the range of premorbid impairment in DS individuals, from low to high functioning, with a single instrument is a challenge . In the Based on our data and on personal interaction with DS adults and their family members, ongoing research should continue to focus on elements of caregiver report and direct assessment of the DS individual that will optimize differential diagnosis. The primary goal of this study was to adapt the widely used CDR for assessment of dementia in adults with DS. We have shown that such adaptation was successful in capturing a range of cognitive decline and in shifting the distribution of decline scores toward lower/no impairment by adjusting original CDR questions to take into account premorbid intellectual and functional ability. Future work of the CDR-QDS and CDR-IDS should include comparison with existing instruments such as the CAMDEX-DS and DSQIID, with neuropsychological performance, and with other biomarkers, such as positron emission tomography for amyloid or tau.The modified CDR questionnaire (CDR-QDS) and interview (CDR-IDS) instruments capture a range of impairment in DS adults. Adjustment of cognitive decline scores for premorbid function dramatically shifted the score distribution toward no/low impairment and was an important element in identifying DS adults with suspicion for dementia. The DSDS indicated high suspicion of dementia in two of twenty-two individuals in this sample who had broadly elevated scores on the modified CDR instruments. The modified CDR instruments captured a range of subthreshold impairments in cognitive function that may prove extremely useful in prospective studies of the development of AD in DS. Moreover, future research is warranted to use quantitative scales such as these to help differentiate dementia from other neuropsychiatric problems in DS, including catatonia, adjustment disorder, and a range of psychiatric comorbidities, all of which are potentially treatable and for which inappropriate/premature assignment of a diagnosis of dementia may be extremely misleading and result in a major obstacle to appropriate care and treatment. For DS adults with elevated scores for cognitive impairment in this study, the impairment generally encompassed multiple cognitive domains and was not predominantly within the memory domain, as seen in the typical population.


Escherichia coli (STEC) O157:H7, to investigate this. Our study provides evidence that phage resistance profiles are well maintained at the sub-lineage level with variation in profiles within sub-lineages uncommon. This indicates that phage resistance heterogeneity happened early on in the STEC O157:H7 natural history and that occasional “wobbles” do not often outcompete the stable lineage unless combined with a competitive advantage. We discuss an example of this in the acquisition of stx2a that, while an important virulence factor, also conveys increased phage cross-resistance. We also discuss the role of phage resistance in co-occurrence of the three stable lineages worldwide and whether differing phage resistance is maintaining diversity.Bacterial–host interactions are non-linear and actually threefold, involving significant selection through predatory lytic bacteriophages in the host environment. In studies of human and animal gut microbiome bacteria, it is important to consider phage in all host–pathogen interactions. We use an important zoonotic pathogen, Shiga toxigenic  Bacteria are ubiquitous colonizers of plant and animal hosts. The increasing availability of large bacterial genome datasets is revealing variation in strains and species that reflect genetic isolation in, and adaptation to, the host . It is tEscherichia coli (STEC) serotype O157:H7 is a zoonotic, foodborne pathogen that causes gastrointestinal disease in humans. Despite being relatively rare compared with other bacterial gastrointestinal infections, for example, Campylobacter and Salmonella, STEC O157:H7 infections are of significant public health concern due to potential disease severity. Symptoms range from abdominal cramps and nausea to severe bloody diarrhea and, in 5–14% of cases, infection leads to hemolytic uremic syndrome (HUS), a severe and potentially fatal condition  can be divided into subtypes stx1a-1d and stx2a-2g  for more than 20 years as an epidemiological tool for the rapid detection and subsequent investigation of outbreaks. Following the implementation of whole genome sequencing at PHE, retrospective studies revealed that each bacterial sub-lineage broadly correlates with certain phage types (PT); lineage I/ll comprises PT2, sub-lineage Ic correlates with PT21/28, and the recent emergence of sub-lineage IIb coincided with a change in the dominant PT associated with this sub-lineage from PT4 or PT1 to PT8 (Bacteriophage display different host (bacterium) specificity with some phage having broad host ranges and others capable of only infecting a limited strain set. This phenomenon is exploited by phage typing, an 1 to PT8 . The O151 to PT8 .In the same way that pathogenic bacteria and viruses are in a continual struggle to survive the human immune system of their hosts, bacteriophage are in a simultaneous arms race with bacteria to survive and pass on their genetic information through infection of their bacterial hosts. We propose that this dynamic has made a significant contribution to the population structure and therefore the evolutionary history of STEC O157:H7. Phage predation has posed an evolutionary pressure on this pathotype and certain phage resistance profiles have become fixed in the population. STEC O157:H7 is susceptible to lysogenic phage and is known to exclude some prophage through phage–phage interactions and superinfection immunity . HorizonThe interaction of phage and bacterium within the reservoir host (ruminants) is a key factor in shaping the STEC O157:H7 population to which humans are exposed. However, there are multiple phenotypes that affect the population structure, such as super shedding, colonization in the terminal rectum, and host immunity. The population structure observed in human disease is not the natural ecology where selection takes place and different strain diversity is seen in cattle to people. However, we can use measurements of strains causing disease in humans as markers of prevalence in cows. Strains causing disease in humans also have to be successful in their primary ruminant hosts and can provide indications of the selective environment of the primary host.Heterogeneity in phage resistance profiles within the population structure of STEC O157:H7 can potentially be linked to short-term changes in accessory genome but can also become fixed as stable lineage associations. Here, by combining information from phage typing profiles  and wholPhage resistance and susceptibility in STEC O157:H7 is a complex phenotype involving the interaction of large numbers of genes and gene families . AlthougPhage resistance and susceptibility was regarded as fixed when all members of a clade displayed the same phenotype. As seen in Within lineages, phage resistance profiles and phylogenetic clades are generally consistent. For this to be the case, there must be phage resistance phenotype fixation within phylogenetic clades. Therefore, it is likely that most phage resistance diversity happened early on in the STEC O157:H7 history. Occasionally we see “wobbles” introduced to the phenotype, but this is unlikely to outcompete other strains unless a competitive advantage is also provided.stx-encoding prophages and was not associated with any change in stx-profile. In one example identified during an outbreak of STEC O157:H7 in the UK in 2013, where the contaminated food vehicle was identified as herbs imported from the Mediterranean region, resistance to the group 3 coliphages was shown to be horizontally transferred on a plasmid . However, the point mutation is a synonymous mutation, so it does not change the amino acid coding sequence. This decreases the likelihood of an interference of phage receptor binding through protein folding and conformational changes but may play a role in internal cell messaging or regulation of the gene. Exploratory studies into the role of this SNP in phage susceptibility are warranted. T4-like bacteriophage have also been shown to interact with the LPS layer of bacteria as a binding site so further work to investigate mechanisms of resistance should also focus on mutations in LPS proteins.The most common and well-studied mechanism of phage resistance is through adaptation of outer membrane proteins that act as receptors for tailed bacteriophage. This is a mechanism that has been studied in detail since the early days of molecular and evolutionary biology . OmpC isstx1a that is associated with increased risk of bloody diarrhea but are largely susceptible to all the T4 coliphage in the phage typing scheme. One hypothesis is that sub-lineage IIc occupies a different reservoir or environmental niche to sub-lineage Ic characterized by the lack of a T4 coliphage lytic predator. Strains from both lineages can be isolated from the same cows but are often associated with different outbreak vehicles   . In thisOur analysis showed that of the STEC O157:H7 sub-lineages currently causing the greatest burden of gastrointestinal disease in the human population in the UK, specifically sub-lineages Ic, IIc, and IIb, sub-lineage Ic displayed cross-resistance to the T4 coliphages but not the T7 coliphages, whereas sub-lineage IIc and the emerging sub-lineage IIb were susceptible to all the T4 coliphages but resistant to the T7 coliphages. Therefore, the dominant lineage I and II sub-lineages have opposing phage resistance profiles with opposite phenotypes for the four phage groups tested. This observation provides some insight into the coexistence of different sub-lineages of STEC O157:H7 in the UK and the need to occupy separate niches—potentially even within the same host rumen. The dominant lineage II clones are likely to succeed in niches where T7-like phages are abundant and the dominant lineage I clone is likely to succeed in T4 coliphage-rich environments.Alternatively, mechanisms of lytic phage resistance could be under Negative Frequency Dependent Selection (NFDS), which has recently been associated with bacterial accessory gene content . NFDS maChromosomal background determinants of phage susceptibility phenotypes are likely to be established and fixed in specific lineages and sub-lineages at the time of divergence, but transient susceptibility and resistance profiles observed in these lineages may be shaped by the current niches that they occupy. This relationship between clone and phage population in environmental niche is likely to be a significant driver in shaping the population structure of bacteria including STEC O157:H7. Further phage susceptibility testing would expand the work described in this perspective. Additional testing of different lytic phages that infect STEC O157:H7 may provide a better understanding on how phage populations in different environmental niches and animal reservoirs influence the evolution and emergence of the STEC O157:H7 sub-lineages causing the greatest burden of gastrointestinal disease. In conclusion, our results have shown that predatory phage effects in the primary ruminant host affect strains causing disease in the secondary human host and may maintain diversity in strains observed in human disease worldwide.Sequencing reads from 580 isolates of STEC O157:H7 from human cases in England and Wales, submitted to the Gastrointestinal Bacterial Reference Unit (GBRU) in 2017, were downloaded from the sequence read archive. The reads were trimmed  and mappPublic Health England (PHE) have used phage typing as a way of broadly discriminating and clustering strains of STEC O157 for more than 20 years. This involves a panel of 16 typing phages (TP) that the strains are assayed against to produce a resistance profile corresponding to phage type . GroupinBriefly, STEC O157:H7 strains for typing are flooded on a Difco nutrient agar plate with 1 ml of broth culture. Dried plates are spotted with 10 µl of each of the 16 typing phages following a template and incubated overnight. Profiles of lysis for each of the typing phages are then recorded the next day.Stx profiling is an important assay in public health surveillance of STEC O157 and is carried out routinely at PHE who provided data for this study. Shiga toxin subtyping is performed through a mapping technique to reference Shiga toxin sequences as previously described . Reads that mapped to more than one place in the reference set  were removed from the resultant SAM file using Samtools  . This reThe datasets generated for this study can be found in Supplementary table 1, listed in Table S1.LC conceived, designed and carried out the research. TD and CJ provided data. All authors contributed to writing the manuscript.LC is supported under the University of Bath prize fellowship scheme. SS is a principal investigator for the MRC CLIMB consortium (MR/L015080/1). TD and CJ are funded through PHE.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.