Effect of a mass radio campaign on family behaviours and child survival in Burkina Faso: a repeated cross-sectional, cluster-randomised trial.

BACKGROUND: Media campaigns can potentially reach a large audience at relatively low cost but, to our knowledge, no randomised controlled trials have assessed their effect on a health outcome in a low-income country. We aimed to assess the effect of a radio campaign addressing family behaviours on all-cause post-neonatal under-5 child mortality in rural Burkina Faso. METHODS: In this repeated cross-sectional, cluster randomised trial, clusters (distinct geographical areas in rural Burkina Faso with at least 40 000 inhabitants) were selected by Development Media International based on their high radio listenership (>60% of women listening to the radio in the past week) and minimum distances between radio stations to exclude population-level contamination. Clusters were randomly allocated to receive the intervention (a comprehensive radio campaign) or control group (no radio media campaign). Household surveys were performed at baseline (from December, 2011, to February, 2012), midline (in November, 2013, and after 20 months of campaigning), and endline (from November, 2014, to March, 2015, after 32 months of campaigning). Primary analyses were done on an intention-to-treat basis, based on cluster-level summaries and adjusted for imbalances between groups at baseline. The primary outcome was all-cause post-neonatal under-5 child mortality. The trial was designed to detect a 20% reduction in the primary outcome with a power of 80%. Routine data from health facilities were also analysed for evidence of changes in use and these data had high statistical power. The indicators measured were new antenatal care attendances, facility deliveries, and under-5 consultations. This trial is registered with ClinicalTrial.gov, number NCT01517230. FINDINGS: The intervention ran from March, 2012, to January, 2015. 14 clusters were selected and randomly assigned to the intervention group (n=7) or the control group (n=7). The average number of villages included per cluster was 34 in the control group and 29 in the intervention group. 2269 (82%) of 2784 women in the intervention group reported recognising the campaign's radio spots at endline. Post-neonatal under-5 child mortality decreased from 93·3 to 58·5 per 1000 livebirths in the control group and from 125·1 to 85·1 per 1000 livebirths in the intervention group. There was no evidence of an intervention effect (risk ratio 1·00, 95% CI 0·82-1·22; p>0·999). In the first year of the intervention, under-5 consultations increased from 68 681 to 83 022 in the control group and from 79 852 to 111 758 in the intervention group. The intervention effect using interrupted time-series analysis was 35% (95% CI 20-51; p<0·0001). New antenatal care attendances decreased from 13 129 to 12 997 in the control group and increased from 19 658 to 20 202 in the intervention group in the first year (intervention effect 6%, 95% CI 2-10; p=0·004). Deliveries in health facilities decreased from 10 598 to 10 533 in the control group and increased from 12 155 to 12 902 in the intervention group in the first year (intervention effect 7%, 95% CI 2-11; p=0·004). INTERPRETATION: A comprehensive radio campaign had no detectable effect on child mortality. Substantial decreases in child mortality were observed in both groups over the intervention period, reducing our ability to detect an effect. This, nevertheless, represents the first randomised controlled trial to show that mass media alone can change health-seeking behaviours. FUNDING: Wellcome Trust and Planet Wheeler Foundation

Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment

[EN] Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents' health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident's activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. Based on the post-deployment analysis, a re-deployment phase is performed, and results show the improvement of collected data accuracy in re-deployment phase from 81.57% to 95.53%. To complete our analysis, we apply the well-known CASAS project dataset as a reference to conduct a comparison with our collected results which shows a similar pattern. The collected data further is processed to use the level of activity of the solo-resident for a behaviour assessment.Shirali, M.; Bayo-Monton, JL.; Fernández Llatas, C.; Ghassemian, M.; Traver Salcedo, V. (2020). Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment. Sensors. 20(24):1-25. https://doi.org/10.3390/s20247167S1252024Lutz, W., Sanderson, W., & Scherbov, S. (2001). The end of world population growth. Nature, 412(6846), 543-545. doi:10.1038/35087589United Nations, Department of Economic and Social Affairs, World Population Prospoects 2019 https://population.un.org/wpp/Publications/Files/WPP2019_Highlights.pdfAtzori, L., Iera, A., & Morabito, G. (2017). Understanding the Internet of Things: definition, potentials, and societal role of a fast evolving paradigm. Ad Hoc Networks, 56, 122-140. doi:10.1016/j.adhoc.2016.12.004Cook, D. J., Duncan, G., Sprint, G., & Fritz, R. L. (2018). Using Smart City Technology to Make Healthcare Smarter. Proceedings of the IEEE, 106(4), 708-722. doi:10.1109/jproc.2017.2787688Cook, D. J., & Krishnan, N. (2014). Mining the home environment. Journal of Intelligent Information Systems, 43(3), 503-519. doi:10.1007/s10844-014-0341-4Alaa, M., Zaidan, A. A., Zaidan, B. B., Talal, M., & Kiah, M. L. M. (2017). A review of smart home applications based on Internet of Things. Journal of Network and Computer Applications, 97, 48-65. doi:10.1016/j.jnca.2017.08.017Palipana, S., Pietropaoli, B., & Pesch, D. (2017). Recent advances in RF-based passive device-free localisation for indoor applications. Ad Hoc Networks, 64, 80-98. doi:10.1016/j.adhoc.2017.06.007Chen, G., Wang, A., Zhao, S., Liu, L., & Chang, C.-Y. (2017). Latent feature learning for activity recognition using simple sensors in smart homes. Multimedia Tools and Applications, 77(12), 15201-15219. doi:10.1007/s11042-017-5100-4Tewell, J., O’Sullivan, D., Maiden, N., Lockerbie, J., & Stumpf, S. (2019). Monitoring meaningful activities using small low-cost devices in a smart home. Personal and Ubiquitous Computing, 23(2), 339-357. doi:10.1007/s00779-019-01223-2Krishnan, N. C., & Cook, D. J. (2014). Activity recognition on streaming sensor data. Pervasive and Mobile Computing, 10, 138-154. doi:10.1016/j.pmcj.2012.07.003Wang, A., Chen, G., Wu, X., Liu, L., An, N., & Chang, C.-Y. (2018). Towards Human Activity Recognition: A Hierarchical Feature Selection Framework. Sensors, 18(11), 3629. doi:10.3390/s18113629Liu, Y., Wang, X., Zhai, Z., Chen, R., Zhang, B., & Jiang, Y. (2019). Timely daily activity recognition from headmost sensor events. ISA Transactions, 94, 379-390. doi:10.1016/j.isatra.2019.04.026Viani, F., Robol, F., Polo, A., Rocca, P., Oliveri, G., & Massa, A. (2013). Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation. Proceedings of the IEEE, 101(11), 2381-2396. doi:10.1109/jproc.2013.2266858Rashidi, P., Cook, D. J., Holder, L. B., & Schmitter-Edgecombe, M. (2011). Discovering Activities to Recognize and Track in a Smart Environment. IEEE Transactions on Knowledge and Data Engineering, 23(4), 527-539. doi:10.1109/tkde.2010.148Samsung SmartThings http://www.smartthings.com/Apple HomeKit https://www.apple.com/ios/home/Vera3 Advanced Smart Home Controller http://getvera.com/controllers/vera3/AndroidThings https://developer.android.com/things/index.htmlTeleAlarm Assisted Living http://www.telealarm.com/en/products/assisted-livingBirdie—Connected Sensors around the Home https://birdie.care/AllJoyn Framework https://identity.allseenalliance.org/developersCook, D. J., Crandall, A. S., Thomas, B. L., & Krishnan, N. C. (2013). CASAS: A Smart Home in a Box. Computer, 46(7), 62-69. doi:10.1109/mc.2012.328Skubic, M., Alexander, G., Popescu, M., Rantz, M., & Keller, J. (2009). A smart home application to eldercare: Current status and lessons learned. Technology and Health Care, 17(3), 183-201. doi:10.3233/thc-2009-0551Helal, S., Mann, W., El-Zabadani, H., King, J., Kaddoura, Y., & Jansen, E. (2005). The Gator Tech Smart House: a programmable pervasive space. Computer, 38(3), 50-60. doi:10.1109/mc.2005.107Doctor, F., Hagras, H., & Callaghan, V. (2005). A Fuzzy Embedded Agent-Based Approach for Realizing Ambient Intelligence in Intelligent Inhabited Environments. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 35(1), 55-65. doi:10.1109/tsmca.2004.838488Abowd, G. D., & Mynatt, E. D. (2005). Designing for the Human Experience in Smart Environments. Smart Environments, 151-174. doi:10.1002/047168659x.ch7Technology Integrated Health Management (TIHM) Project https://www.sabp.nhs.uk/tihmAhvar, E., Daneshgar-Moghaddam, N., Ortiz, A. M., Lee, G. M., & Crespi, N. (2016). On analyzing user location discovery methods in smart homes: A taxonomy and survey. Journal of Network and Computer Applications, 76, 75-86. doi:10.1016/j.jnca.2016.09.012Milenkovic, M., & Amft, O. (2013). Recognizing Energy-related Activities Using Sensors Commonly Installed in Office Buildings. Procedia Computer Science, 19, 669-677. doi:10.1016/j.procs.2013.06.089Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Dogan, O., Bayo-Monton, J.-L., Fernandez-Llatas, C., & Oztaysi, B. (2019). Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application. Sensors, 19(3), 557. doi:10.3390/s19030557Schmitter-Edgecombe, M., & Cook, D. J. (2009). Assessing the Quality of Activities in a Smart Environment. Methods of Information in Medicine, 48(05), 480-485. doi:10.3414/me0592Alberdi Aramendi, A., Weakley, A., Aztiria Goenaga, A., Schmitter-Edgecombe, M., & Cook, D. J. (2018). Automatic assessment of functional health decline in older adults based on smart home data. Journal of Biomedical Informatics, 81, 119-130. doi:10.1016/j.jbi.2018.03.009Dawadi, P. N., Cook, D. J., & Schmitter-Edgecombe, M. (2016). Automated Cognitive Health Assessment From Smart Home-Based Behavior Data. IEEE Journal of Biomedical and Health Informatics, 20(4), 1188-1194. doi:10.1109/jbhi.2015.2445754Sprint, G., Cook, D. J., & Schmitter-Edgecombe, M. (2017). Unsupervised Detection and Analysis of Changes in Everyday Physical Activity Data. Intelligent Systems Reference Library, 97-122. doi:10.1007/978-3-319-67513-8_6Taheri Tanjanai, P., Moradinazar, M., & Najafi, F. (2016). Prevalence of depression and related social and physical factors amongst the Iranian elderly population in 2012. Geriatrics & Gerontology International, 17(1), 126-131. doi:10.1111/ggi.12680Zhao, Z., Zhang, M., Yang, C., Fang, J., & Huang, G. Q. (2018). Distributed and collaborative proactive tandem location tracking of vehicle products for warehouse operations. Computers & Industrial Engineering, 125, 637-648. doi:10.1016/j.cie.2018.05.00

The Impact of Water, Sanitation, and Hygiene on Key Health and Social Outcomes: Review of Evidence

This evidence paper looks at 10 areas identified collaboratively with the United Nations Children's Fund (UNICEF) on which WASH can plausibly have a strong impact: diarrhea, nutrition, complementary food hygiene, female psychosocial stress, violence, maternal and newborn health, menstrual hygiene management, school attendance, oral vaccine performance, and neglected tropical diseases. Together, these areas cover the most significant sector outcomes associated with the distinct life course phases that UNICEF seeks to help to address through its WASH activities

Technology used to recognize activities of daily living in community-dwelling older adults

The use of technology has been suggested as a means of allowing continued autonomous living for older adults, while reducing the burden on caregivers and aiding decision-making relating to healthcare. However, more clarity is needed relating to the Activities of Daily Living (ADL) recognised, and the types of technology included within current monitoring approaches. This review aims to identify these differences and highlight the current gaps in these systems. A scoping review was conducted in accordance with PRISMA-ScR, drawing on PubMed, Scopus, and Google Scholar. Articles and commercially available systems were selected if they focused on ADL recognition of older adults within their home environment. Thirty-nine ADL recognition systems were identified, nine of which were commercially available. One system incorporated environmental and wearable technology, two used only wearable technology, and 34 used only environmental technologies. Overall, 14 ADL were identified but there was variation in the specific ADL recognised by each system. Although the use of technology to monitor ADL of older adults is becoming more prevalent, there is a large variation in the ADL recognised, how ADL are defined, and the types of technology used within monitoring systems. Key stakeholders, such as older adults and healthcare workers, should be consulted in future work to ensure that future developments are functional and useable

Effectiveness of two community-based strategies on disease knowledge and health behaviour regarding malaria, diarrhoea and pneumonia in Ghana.

BACKGROUND: Ghana has developed two community-based strategies that aim to increase access to quality treatment for malaria, diarrhoea and suspected pneumonia, and to improve household and family practices: integrated Community Case Management (iCCM) and Community-based Health Planning and Services (CHPS). The objective of the study was to assess the effectiveness of iCCM and CHPS on disease knowledge and health behaviour regarding malaria, diarrhoea and pneumonia. METHODS: A household survey was conducted two and eight years after implementation of iCCM in the Volta and Northern Regions of Ghana respectively, and more than ten years of CHPS implementation in both regions. The study population included 1356 carers of children under- five years of age who had fever, diarrhoea and/or cough in the two weeks prior to the interview. Disease knowledge was assessed based on the knowledge of causes and identification of signs of severe disease and its association with the sources of health education messages received. Health behaviour was assessed based on reported prompt care seeking behaviour, adherence to treatment regime, utilization of mosquito nets and having improved sanitation facilities, and its association with the sources of health education messages received. RESULTS: Health education messages from community-based agents (CBAs) in the Northern Region were associated with the identification of at least two signs of severe malaria (adjusted Odds Ratio (OR) 1.8, 95%CI 1.0, 3.3, p = 0.04), two practices that can cause diarrhoea (adjusted OR 4.7, 95%CI 1.4, 15.5, p = 0.02) 0and two signs of severe pneumonia (adjusted OR 7.7, 95%CI2.2, 26.5, p = 0.01)-the later also associated with prompt care seeking behaviour (p = 0.04). In the Volta Region, receiving messages on diarrhoea from CHPS was associated with the identification of at least two signs of severe diarrhoea (adjusted OR 3.6, 95%CI 1.4, 9.0), p = 0.02). iCCM was associated with prompt care seeking behaviour in the Volta Region and CHPS with prompt care seeking behaviour in the Northern Region (p < 0.5). CONCLUSIONS: Both iCCM and CHPS were associated with disease knowledge and health behaviour, but this was more pronounced for iCCM and in the Northern Region. HBC should continue to be considered as the strategy through which community-IMCI is implemented

Health-seeking Behaviour of Women Reporting Symptoms of Reproductive Tract Infections

A woman’s access to health care, in physical, social, and psychological contexts, depends on her health beliefs and her socio-economic and demographic background. As in most developing countries, the health system in Pakistan is a combination of modern and traditional medicine, and the nature of care sought again depends on the individual’s health beliefs and background characteristics. This paper thus not only focuses on whether women seek help or not when sick, but also on the differentials that exist in the health-seeking behaviour among women with different backgrounds. It finds that less than half the women reporting any symptom related to reproductive tract infections seek help, while for some symptoms the proportion seeking help goes down to a mere one-fifth. The decision to seek help depends on a woman’s educational and economic status, the extent to which she is worried about the symptom, duration of experiencing the symptom, and inter-spousal communication about the symptom. Lack of finances to access any health service and considering the symptom as something common not needing attention are the two main reasons for not seeking help. The choice of the healthprovider consulted for a symptom is linked to the perceived cause of the symptom, but allopathic doctors are preferred by the majority of women seeking health care

The lived experiences of adolescents with autism spectrum disorder: a personal constructivist and family systems approach

Individuals with Autism Spectrum Disorder (ASD) experience persistent and significant social communicative impairments, as well as restricted and repetitive behaviours and/or interests. There is currently a dearth of literature investigating the experiences of adolescents with ASD, despite research findings indicating adolescence is a particularly challenging period for these individuals. In addition to the significant influence of ASD on the individual, having a family member with the condition can have chronic and pervasive effects on individual family members and the family unit as a whole. However, research investigating the experiences of families who have an adolescent member with ASD is scarce. The overall aim of this thesis is to investigate the lived experiences of adolescents with ASD and their families. More specifically, the studies explore the positive and challenging aspects of adolescence; the coping strategies used by family members; and the distribution of roles and responsibilities within the family and their impact on individual and family functioning. A qualitative approach was used to interview multiple family members, including adolescents with ASD, mothers, fathers, and adolescent siblings. The thesis is informed by Family Systems and Personal Constructivist frameworks. Chapters 2 and 3 examine these frameworks in relation to understanding adolescents with ASD and their families. Conceptual and methodological components of these frameworks were used to guide subsequent qualitative investigations, which are presented in Chapters 4-7. More specifically, Chapters 4 and 5 focus on the experiences of adolescent boys and girls with ASD, respectively; Chapter 6 investigates the experiences of parents; and Chapter 7 identifies issues pertinent to adolescent neurotypically developing siblings. The findings of these qualitative investigations highlight a range of adolescent-specific issues, covering physical, cognitive, emotional, social, and sexual domains, which may provide guidance to clinicians, researchers, and family members supporting adolescents with ASD and their families. Chapter 8 utilises Personal Constructivist methodology to investigate the dependency patterns of adolescents with ASD. Findings indicate that the adolescents had various ways of dispersing their dependencies amongst resources and differed in the types of support most utilised. Additionally, by including family members, the study was able to investigate family awareness of the adolescents’ preferences. The findings presented in this chapter offer a novel approach to understanding the experiences of families living with ASD, given the adoption of the combined Family Systems and Personal Constructivist framework. The thesis concludes with a synthesis of the key findings of the conceptual and qualitative investigations. It considers research limitations of the studies conducted and discusses implications for future research and clinical practice. Overall, the thesis findings address an important gap in literature and have the potential to make significant contributions to the field of clinical psychology by directly informing clinical interventions for adolescents with ASD and their families

A Behaviour Monitoring System (BMS) for Ambient Assisted Living

Unusual changes in the regular daily mobility routine of an elderly person at home can be an indicator or early symptom of developing health problems. Sensor technology can be utilised to complement the traditional healthcare systems to gain a more detailed view of the daily mobility of a person at home when performing everyday tasks. We hypothesise that data collected from low-cost sensors such as presence and occupancy sensors can be analysed to provide insights on the daily mobility habits of the elderly living alone at home and to detect routine changes. We validate this hypothesis by designing a system that automatically learns the daily room-to-room transitions and permanence habits in each room at each time of the day and generates alarm notifications when deviations are detected. We present an algorithm to process the sensors' data streams and compute sensor-driven features that describe the daily mobility routine of the elderly as part of the developed Behaviour Monitoring System (BMS). We are able to achieve low detection delay with confirmation time that is high enough to convey the detection of a set of common abnormal situations. We illustrate and evaluate BMS with synthetic data, generated by a developed data generator that was designed to mimic different user's mobility profiles at home, and also with a real-life dataset collected from prior research work. Results indicate BMS detects several mobility changes that can be symptoms of common health problems. The proposed system is a useful approach for learning the mobility habits at the home environment, with the potential to detect behaviour changes that occur due to health problems, and therefore, motivating progress toward behaviour monitoring and elder's care.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT—Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio