The detection of psychiatric illness by questionnaire

Measuring Health: A Guide to Rating Scales and Questionnaires

Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors These sensors include GPS sensors, vision sensors (ie, cameras), audio sensors (ie, microphones), light sensors, temperature sensors, direction sensors (ie, magnetic compasses), and acceleration sensors (ie, accelerometers) The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10- second intervals We then used the resulting training data to induce a predictive model for activity recognition This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively---just by having them carry cell phones in their pockets Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (eg, sending calls directly to voicemail if a user is jogging) and generating a daily/weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise

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Activity recognition using cell phone accelerometers

Providing accurate and opportune information on people's activities and behaviors is one of the most important tasks in pervasive computing. Innumerable applications can be visualized, for instance, in medical, security, entertainment, and tactical scenarios. Despite human activity recognition (HAR) being an active field for more than a decade, there are still key aspects that, if addressed, would constitute a significant turn in the way people interact with mobile devices. This paper surveys the state of the art in HAR based on wearable sensors. A general architecture is first presented along with a description of the main components of any HAR system. We also propose a two-level taxonomy in accordance to the learning approach (either supervised or semi-supervised) and the response time (either offline or online). Then, the principal issues and challenges are discussed, as well as the main solutions to each one of them. Twenty eight systems are qualitatively evaluated in terms of recognition performance, energy consumption, obtrusiveness, and flexibility, among others. Finally, we present some open problems and ideas that, due to their high relevance, should be addressed in future research.

A Survey on Human Activity Recognition using Wearable Sensors

The recommendations in this 2015 American Heart Association (AHA) Guidelines Update for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care are based on an extensive evidence review process that was begun by the International Liaison Committee on Resuscitation (ILCOR) after the publication of the 2010 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations 1,2 and was completed in February 2015.3,4

In this in-depth evidence review process, ILCOR examined topics and then generated a prioritized list of questions for systematic review. Questions were first formulated in PICO (population, intervention, comparator, outcome) format,5 and then search strategies and inclusion and exclusion criteria were defined and a search for relevant articles was performed. The evidence was evaluated by the ILCOR task forces by using the standardized methodological approach proposed by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group.6

The quality of the evidence was categorized based on the study methodologies and the 5 core GRADE domains of risk of bias, inconsistency, indirectness, imprecision, and other considerations (including publication bias). Then, where possible, consensus-based treatment recommendations were created.

To create this 2015 Guidelines Update, the AHA formed 15 writing groups, with careful attention to manage conflicts of interest, to assess the ILCOR treatment recommendations and to write AHA treatment recommendations by using the AHA Class of Recommendation (COR) and Level of Evidence (LOE) system. The recommendations made in the Guidelines are informed by the ILCOR recommendations and GRADE classification, in the context of the delivery of medical care in North America. The AHA writing group made new recommendations only on topics specifically reviewed by ILCOR in 2015. This chapter delineates instances where the AHA writing group developed recommendations that are significantly stronger or weaker than the ILCOR statements. In the online …

Part 8: Post-cardiac arrest care: 2015 American Heart Association guidelines update for cardiopulmonary resuscitation and emergency cardiovascular care

An approach is provided for providing automatic determination of an emissions footprint. Movement data of a mobile device associated with a user is acquired. One or more modes of travel by the user are determined based on the movement data. An emissions footprint of the user is determined based on the determined one or more modes of travel.

Method and apparatus for determining emission values

eHealth studies typically suffer from high attrition rates. Objective To investigate type 2 diabetes and heart disease patients’ adherence to a self-management intervention that combined health coaching and telemonitoring. Methods Renewing Health Finland was a 12-month randomized controlled trial to improve quality of life (QoL) and/or HbA1c of 595 patients with chronic conditions. The intervention consisted of (1) weekly measurement of health parameters (2) health coaching every 4–6 weeks. Adherence to telemonitoring was defined as the percentage of weeks with at least one reported health measurement. Adherence to coaching was defined as the number of received calls. Results The median percentage of monitored weeks was 65 % without time-dependent attrition. 66 % of participants received 7–11 calls that corresponds to the predefined coaching schedule. Adherence did not correlate with QoL or HbA1c. Discussion Our results indicate that the intervention in the Renewing Health Finland trial was delivered almost with planned intensity.

Is Home Telemonitoring Feasible in the Care of Chronic Diseases - Insights into Adherence to a Self-management Intervention in Renewing Health Finland Trial

Objectives:Pharmacy dispensing claims data provide longitudinal records of prescriptions and refill events. Previous studies in psychiatric patients have utilised these data to derive indicators of...

Correlation between symptoms and functioning in psychiatric patients and temporal patterns of medication refills derived from pharmacy prescription claims.

Already in the beginning of the last decade it was noticed that our society is ageing and new kind of partnership will be needed to develop both services and technology. Therefore new concepts must be developed and they must be taken into a use to ensure that people needing services can also reach them. Elderly people are eager to use new services and new technology if it will support their daily living at their own home. In the coming years it is even more important than earlier that private and public sector are in close cooperation and are together responsible for the development work concerning also health care and social welfare applications. Because of the complexity of the topic there is a need to use different kind of methods and so to achieve multidisciplinary information to be used by decision makers. INTRODUCTION Technical Research Centre of Finland (VTT) and Technical University of Tampere, Finland (TUT) started a three years project, ‘Smart Home in a Suitcase, new technologies supporting telemedicine, home care, rehabilitation and safety at home’ in December 2005. This project, called UUTE, will end by the end of 2008. Partners representing public health care and special health care organisations in the project are: Pirkanmaa Hospital District, Tampere University Hospital and Lempaala Health Care Centre. Private companies represent information technology, building development for seniors, technology assisting independent living and home care services. A company providing first aid (ambulance) and safety services and a company interested in balance and walking problems joined the project during spring 2007. AIM OF THE PROJECT The aim of the project is to develop a general technical platform for personally adaptable solutions that will support a person in different living conditions at home. It should support a person to follow-up his or her physical condition during home exercises or at a training centre. One basic objective is that health care personnel can support an outdoor patient at his homecare after hospitalization phase. Because of the challenge to provide good health care services including rehabilitation and safety services for a growing number of elderly people, more co-operation is needed between public, private and so called third sector actors. In Finland, municipalities are responsible for arranging the health care and welfare services, but they can organise the services together with different kind of partners. As one example of the possible network in the future can be seen the co-operation between actors in our project as illustrated in the Fig. 1 (integration into services provided by the participating organisations). METHODS The project has used different kind of research methods from the viewpoints of technical, medical and human sciences. Methods used were: a) Management group meetings, b) interviews, c) scenarios, d) questionnaires, e) Workshops and seminars and f) Testing and piloting the prototype with out patient and elderly people. RESULTS AND DISCUSSION So far the results show that it is beneficial if ICT based applications and services meant for elderly people are developed in co-operation with different kind of actors. The need of services varies a lot among elderly people. Because of the variation it was important to take into account physical, mental and social factors when developing new technical solutions and services based on technical development. It was found out that it is important to discuss with the representatives of health care personnel if you like to carry out a proper research process among patients and personnel. The personnel should be informed on the aim the project because nowadays the staff is working under quite a pressure and the time is limited for extra work. Motivation is a big issue that must be considered. It is also very important to take into account the opinions and expectations of patients and elderly people if the aim is that they are willing to use new services based on technology. Both professionals and elderly people are eager to participate in different kind of development work if they are motivated and interested enough and if they see that in the future there might be new ways to do work or new ways to get and use services at home. UUTE home platform Safety and emergency services Home and housing support Home care and elderly care Exercise and rehabilitation services Health care services Relatives Support groups and self-care Fig.1. Integration into services provided by the participating organisations Gateway provided by companies Gateway provided by research consortium Tel. +358 20 722 3329 Fax +358 20 722 3499 tuula.petakoski-hult@vtt.fi www.vtt.fi Funding: Tekes, the Finnish Funding Agency for Technology and Innovation, VTT Technical Research Centre of Finland, Pikosystems Inc., TietoEnator Inc., YH Lansi Inc., UPM-Kymmene Plc, Turun Orthofysio Inc., Arcticare Technologies Inc., Suomen Ensiapupalvelu Inc. VTT TECHNICAL RESEARCH CENTRE OF FINLAND Visiting address: Tekniikankatu 1, TAMPERE P.O. Box 1300 FIN-02044 VTT, Finland

Miikka Ermes

Papers

Method and apparatus for determining emission values

Is Home Telemonitoring Feasible in the Care of Chronic Diseases - Insights into Adherence to a Self-management Intervention in Renewing Health Finland Trial

Correlation between symptoms and functioning in psychiatric patients and temporal patterns of medication refills derived from pharmacy prescription claims.

The development of ICT-based services for ageing people needs co-operation