ライフサイエンスコーパス: mobile phone

1 that provides service to collect data using mobile phones).

2 ers and nonusers (owning only old-technology mobile phones).

3 ne during clinic visits, home visits, and by mobile phone.

4 portable, and can transfer test results via mobile phone.

5 ental health care, yet most have access to a mobile phone.

6 ovement in nurse/surgeon communication using mobile phones.

7 exposures in the head of users of hand-held mobile phones.

8 vironmental monitoring and motion sensors in mobile phones.

9 pants to answer the mRS questionnaire in the mobile phones.

10 a mobile phone application in their personal mobile phones.

11 (over $70 M USD unrecovered in computers and mobile phones, 2006-2014) due to operations that fail to

12 d weekly personalised text messages to their mobile phones about diet quality and physical activity f

13 As mobile phone access continues to expand globally, opport

14 tion testing (mobiNAAT) platform utilizing a mobile phone and droplet magnetofluidics to deliver NAAT

15 Using both mobile phone and GPS data, we discover the existence of

16 ey typically use the communication between a mobile phone and its nearest antenna tower to infer posi

17 that allows monetary value to be stored on a mobile phone and sent to other users via text messages,

18 des (OLEDs) are in widespread use in today's mobile phones and are likely to drive the next generatio

19 tributions of commuting fluxes per link from mobile phones and census sources are similar and highly

20 g with the availability of data derived from mobile phones and other dynamic data sources.

21 ward trips, and backward slips-while wearing mobile phones and previously validated, dedicated accele

22 le direct communication and powering between mobile phones and printed e-tags.

23 Here, we quantify participant activity using mobile phones and relate activity measured during real w

24 we discuss recent developments on the use of mobile phones and similar devices for biosensing applica

25 n, reducing the limit of detection (LOD) for mobile phones and webcams from 1000 nM to 10nM.

26 A mobile phone app (Allergy Diary, Google Play Store and A

27 A mobile phone "app" was also capable of reading the test

28 childhood obesity and integrate them into a mobile phone application (App).

29 Three months after inclusion, the mobile phone application automatically prompted the stud

30 The mobile phone application contained a set of 20 questions

31 ients and/or caregivers were equipped with a mobile phone application in their personal mobile phones

32 Measurements are wirelessly transferred to a mobile phone application that geo-tags the data and tran

33 We used a time-stamped-picture mobile phone application to record all food intake acros

34 Despite an explosion of mobile phone applications aimed at physical activity and

35 tiation for treatment-eligible participants, mobile phone appointment reminders, health educational p

36 , we demonstrate that commercially available mobile phones are a powerful tool for acoustically mappi

37 ion and communication technology, especially mobile phones, are nearly equal in magnitude to the mean

38 The results show good use of a mobile phone as an analytical instrument.

39 equency electromagnetic fields (RF-EMF) from mobile-phone base stations and the development of nonspe

40 We modeled far-field RF-EMF exposure from mobile-phone base stations at the home addresses of the

41 In contrast to modeled RF-EMF exposure from mobile-phone base stations, perceived exposure was assoc

42 , we propose and demonstrate a new miniature mobile phone based system for ELISA (MELISA).

43 ensity, radiation based on travel times, and mobile-phone based).

44 Mobile phone-based automatic assessments of mRS performe

45 Mobile phone-based health interventions (mHealth) have b

46 We find that mobile phone-based mobility estimates predict the geogra

47 We therefore developed a point-of-care mobile phone-based platform that can quickly characteriz

48 terventions that incorporate traditional and mobile-phone-based education will help create smoke-free

49 roscope in combination with a cost-effective mobile-phone-based microscope can generate color images

50 it on-site diagnostics with a cost-effective mobile-phone-based multimodal microscope.

51 ascertain the most cost-effective method of mobile-phone-based reminder.

52 Mobile-phone-based reminders of scheduled HIV appointmen

53 Mobile-phone-based smoking cessation intervention has be

54 c ways to capture human mobility measured by mobile phones; both severely overestimate the spatial sp

55 from a mixture of metals typically found in mobile phones by extraction into toluene from an aqueous

56 Mobile phone call data provide a new, first-order source

57 igital data sources, such as medical claims, mobile phone call data records, and geographically tagge

58 phone call, or concomitant text message and mobile phone call increase attendance at medical appoint

59 eys, (ii) proxy mobility data extracted from mobile phone call records, and (iii) the radiation model

60 er reminders sent to carers by text message, mobile phone call, or concomitant text message and mobil

61 -scale data analysis techniques to study the mobile phone calling activity of people in large cities

62 The recent emergence of mobile phone calling data and associated locational info

63 nactivity periods in the people's aggregated mobile phone calling patterns and infer these to represe

64 we use a unique 18-mo dataset that combines mobile phone calls and survey data to track changes in t

65 the head not being exclusively used for all mobile phone calls, the results were similar.

66 ntific collaborations, Twitter mentions, and mobile phone calls.

67 t samples using a spotting automatic system, mobile phone camera and a computer with developed softwa

68 ld condenser and a 20x objective lens with a mobile phone camera to create an inexpensive, portable a

69 hat accelerometry-based technologies such as mobile phones can be used to evaluate real world activit

70 nication technology such as the Internet and mobile phones can deliver behavioral components for STD/

71 than on census commuting networks, once the mobile phone commuting network is considered in the epid

72 ances and computer devices (SACD), including mobile phones, contain significant amounts of precious m

73 ical and electronic equipment (WEEE) such as mobile phones contains a plethora of metals of which gol

74 ing seasonal and spatial data on travel from mobile phone data allows us to characterize seasonal flu

75 Here, we use spatially explicit mobile phone data and malaria prevalence information fro

76 Additionally, as a general rule, the mobile phone data are not linked to demographic or socia

77 of population travel (fluxes) inferred from mobile phone data are predictive of disease transmission

78 r conditions on social relationships and how mobile phone data can be used to investigate the influen

79 We show that commuting networks from mobile phone data capture the empirical commuting patter

80 set, and the mobility fluxes extracted from mobile phone data collected in a western European countr

81 e we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subsc

82 n different spatial scales and use anonymous mobile phone data from nearly 15 million individuals to

83 c overestimation of commuting traffic in the mobile phone data is observed.

84 on human resting or sleeping patterns using mobile phone data of a large number of individuals.

85 Mobile phone data provide a unique source of information

86 Mobile phone data therefore offer a valuable previously

87 Here, we use mobile phone data to quantify seasonal travel and direct

88 radiation model showing higher accuracy than mobile phone data when the seed is central in the networ

89 udy real anomalous events using country-wide mobile phone data, finding that information flow during

90 Combining data on human movement from mobile phone data-derived population fluxes with data on

91 1.21, as observed in recent studies based on mobile phone data.

92 Here, by exploiting three different mobile phone datasets that capture simultaneously these

93 Devices included are mobile phones, desktop and laptop computers, monitors, c

94 l analysis directly to "the cloud" using any mobile phone, for use in resource-limited settings.

95 ntees broad compatibility with any available mobile phone (from low-end phones to smartphones) or cel

96 Mechanically flexible mobile phones have been long anticipated due to the rapi

97 Data collected from mobile phones have the potential to provide insight into

98 ge-scale rabies surveillance system based on mobile phones in southern Tanzania.

99 These findings provide evidence that mobile phone intervention may be a useful tool for promo

100 ealth care facilities were randomized to the mobile phone intervention or to standard care (control).

101 Compared with the control group, mobile phone intervention was associated with significan

102 Next, we reconfigured the mobile phone into a fluorescence imager by adding a low-

103 h) optical detectors, such as those found on mobile phones, is a limiting factor for many mHealth cli

104 in the intervention group received frequent mobile phone messages compared with controls who receive

105 e 2 diabetes was lower in those who received mobile phone messages than in controls: 50 (18%) partici

106 atients were randomly assigned to either the mobile phone messaging intervention (n=271) or standard

107 mputer-generated randomisation sequence to a mobile phone messaging intervention or standard care (co

108 Mobile phone messaging is an effective and acceptable me

109 Mobile phone messaging is an inexpensive alternative way

110 We aimed to assess whether mobile phone messaging that encouraged lifestyle change

111 our samples can be imaged and analysed using mobile phone microscopy, achieving a new milestone for t

112 Given the ubiquity of mobile phones, mobile health interventions offer promise

113 Mobile phones (MPs) now have an extremely high penetrati

114 We collaborated with the largest mobile phone operator in Haiti (Digicel) and analyzed th

115 many fluorophores in multiple wavelengths, a mobile phone or a webcam as a detector, and capillary tu

116 handheld format that is compatible with any mobile phone or network worldwide guarantees that sophis

117 arly well suited to mobile devices (watches, mobile phones or tablets), which require the combination

118 ting, including for example integration with mobile phones, or exhibited the potential for such opera

119 ted CPR could be increased with the use of a mobile-phone positioning system that could instantly loc

120 A mobile-phone positioning system that was activated when

121 A mobile-phone positioning system to dispatch lay voluntee

122 The mobile-phone positioning system was activated in 667 out

123 f the modified Rankin Scale (mRS) based on a mobile phone questionnaire may serve as an alternative t

124 communication patterns in a large dataset of mobile phone records and show the existence of temporal

125 Mobile phone records can provide vast quantities of spat

126 f-reports of physical proximity deviate from mobile phone records depending on the recency and salien

127 Here, we analyze a dataset of mobile phone records of approximately 150,000 users in S

128 ed treatment adherence support delivered via mobile phone short message system (SMS) text messages on

129 lour of the sky as well as the strength of a mobile phone signal.

130 Mobile phone STD/HIV interventions using text-messaging

131 n a nationwide cohort study, 355,701 private mobile phone subscribers in Denmark from 1987 to 1995 we

132 mobility data from approximately 40 million mobile phone subscribers.

133 Mobile phone technology has been successful in changing

134 ements in knowledge of obesity aetiology and mobile phone technology have created the opportunity to

135 (aged 16-49 years) who could receive secure mobile phone text messages were randomly assigned (1:1:1

136 ed smoking cessation programme delivered via mobile phone text messaging on continuous abstinence, wh

137 Smoking cessation programmes delivered via mobile phone text messaging show increases in self-repor

138 pendent telephone randomisation system, to a mobile phone text messaging smoking cessation programme

139 Owing to the capabilities of mobile phones (their cameras, connectivity, portability,

140 The use of a handy mobile phone to remotely control the releasing process a

141 wever, novel strategies including the use of mobile phones to ease stockouts, task-shifting to commun

142 of sample size, and data collection through mobile phones to improve timeliness of reporting and all

143 microchips in modern computer systems--from mobile phones to large-scale data centres.

144 These range from web browsers in mobile phones to the most popular micro service platform

145 morning peak hour obtained from billions of mobile phone traces to comprehensively analyse urban tra

146 vailable geodatabases and a large dataset of mobile phone traces.

147 Here, an extensive mobile phone usage data set for Kenya was processed to e

148 systems, finding that protein functions and mobile phone usage occupy distinct regions of the phase

149 We show that an individual's past history of mobile phone use can be used to infer his or her socioec

150 een explored through the analysis of a large mobile phone use dataset.

151 ne instance, observed and noted the personal mobile phone use of a visitor.

152 ch researchers evaluated the associations of mobile phone use with the risks of brain, acoustic neuro

153 However, our model used reported side of mobile phone use, which is potentially in fl uenced by r

154 roma, or parotid gland tumors in relation to mobile phone use.

155 ion in the investigation of brain tumors and mobile phone use.

156 A mobile phone user interface operating in tandem with a p

157 s of a full calendar year of data for 22,696 mobile phone users (53.2 million call logs) in Lisbon, P

158 Our main analysis included 792 regular mobile phone users diagnosed with a glioma between 2000

159 l) and analyzed the movements of 1.9 million mobile phone users during the period from 42 d before, t

160 We modeled the mobility of mobile phone users in order to study the fundamental spr

161 ized call detail records of over one million mobile phone users in Portugal.

162 e study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-m

163 c and demographic information of millions of mobile phone users with their communication patterns to

164 ne the communication patterns of millions of mobile phone users, allowing us to simultaneously study

165 ncreased skin cancer risk was observed among mobile phone users.

166 studying the mobility patterns of anonymized mobile phone users.

167 sitioning system that could instantly locate mobile-phone users and dispatch lay volunteers who were

168 te rate (PR) maps and call data records from mobile phones, using a steady-state analysis of a malari

169 s to store, send, and receive money on their mobile phone via text message.

170 om a Global System for Mobile Communications mobile phone was used to update the display.

171 This paper compares observational data from mobile phones with standard self-report survey data.

172 We show that even low-cost mobile phones with very basic functionality are capable