312 related articles for article (PubMed ID: 29258977)
21. Impact of a Mobile Phone Intervention to Reduce Sedentary Behavior in a Community Sample of Adults: A Quasi-Experimental Evaluation.
Kendzor DE; Shuval K; Gabriel KP; Businelle MS; Ma P; High RR; Cuate EL; Poonawalla IB; Rios DM; Demark-Wahnefried W; Swartz MD; Wetter DW
J Med Internet Res; 2016 Jan; 18(1):e19. PubMed ID: 26810027
[TBL] [Abstract][Full Text] [Related]
22. Depression Screening Using Daily Mental-Health Ratings from a Smartphone Application for Breast Cancer Patients.
Kim J; Lim S; Min YH; Shin YW; Lee B; Sohn G; Jung KH; Lee JH; Son BH; Ahn SH; Shin SY; Lee JW
J Med Internet Res; 2016 Aug; 18(8):e216. PubMed ID: 27492880
[TBL] [Abstract][Full Text] [Related]
23. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study.
Radin JM; Wineinger NE; Topol EJ; Steinhubl SR
Lancet Digit Health; 2020 Feb; 2(2):e85-e93. PubMed ID: 33334565
[TBL] [Abstract][Full Text] [Related]
24. Using Smartphone Sensor Data to Assess Inhibitory Control in the Wild: Longitudinal Study.
Tseng VW; Costa JDR; Jung MF; Choudhury T
JMIR Mhealth Uhealth; 2020 Dec; 8(12):e21703. PubMed ID: 33275106
[TBL] [Abstract][Full Text] [Related]
25. Wearable Sensor/Device (Fitbit One) and SMS Text-Messaging Prompts to Increase Physical Activity in Overweight and Obese Adults: A Randomized Controlled Trial.
Wang JB; Cadmus-Bertram LA; Natarajan L; White MM; Madanat H; Nichols JF; Ayala GX; Pierce JP
Telemed J E Health; 2015 Oct; 21(10):782-92. PubMed ID: 26431257
[TBL] [Abstract][Full Text] [Related]
26. Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications.
Shawen N; Lonini L; Mummidisetty CK; Shparii I; Albert MV; Kording K; Jayaraman A
JMIR Mhealth Uhealth; 2017 Oct; 5(10):e151. PubMed ID: 29021127
[TBL] [Abstract][Full Text] [Related]
27. Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions.
Bae S; Chung T; Ferreira D; Dey AK; Suffoletto B
Addict Behav; 2018 Aug; 83():42-47. PubMed ID: 29217132
[TBL] [Abstract][Full Text] [Related]
28. Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study.
Bae SW; Suffoletto B; Zhang T; Chung T; Ozolcer M; Islam MR; Dey AK
JMIR Form Res; 2023 May; 7():e39862. PubMed ID: 36809294
[TBL] [Abstract][Full Text] [Related]
29. Can Mobile Phone Apps Influence People's Health Behavior Change? An Evidence Review.
Zhao J; Freeman B; Li M
J Med Internet Res; 2016 Oct; 18(11):e287. PubMed ID: 27806926
[TBL] [Abstract][Full Text] [Related]
30. A Real-Time Mobile Intervention to Reduce Sedentary Behavior Before and After Cancer Surgery: Usability and Feasibility Study.
Low CA; Danko M; Durica KC; Kunta AR; Mulukutla R; Ren Y; Bartlett DL; Bovbjerg DH; Dey AK; Jakicic JM
JMIR Perioper Med; 2020 Mar; 3(1):e17292. PubMed ID: 33393915
[TBL] [Abstract][Full Text] [Related]
31. Applying Mobile Technology to Sustain Physical Activity After Completion of Cardiac Rehabilitation: Acceptability Study.
Elnaggar A; von Oppenfeld J; Whooley MA; Merek S; Park LG
JMIR Hum Factors; 2021 Sep; 8(3):e25356. PubMed ID: 34473064
[TBL] [Abstract][Full Text] [Related]
32. Smartphone-based behavioral monitoring and patient-reported outcomes in adults with rheumatic and musculoskeletal disease.
Mollard E; Pedro S; Schumacher R; Michaud K
BMC Musculoskelet Disord; 2022 Jun; 23(1):566. PubMed ID: 35690753
[TBL] [Abstract][Full Text] [Related]
33. Evaluation of a mobile phone-based, advanced symptom management system (ASyMS) in the management of chemotherapy-related toxicity.
Kearney N; McCann L; Norrie J; Taylor L; Gray P; McGee-Lennon M; Sage M; Miller M; Maguire R
Support Care Cancer; 2009 Apr; 17(4):437-44. PubMed ID: 18953579
[TBL] [Abstract][Full Text] [Related]
34. mPulse Mobile Sensing Model for Passive Detection of Impulsive Behavior: Exploratory Prediction Study.
Wen H; Sobolev M; Vitale R; Kizer J; Pollak JP; Muench F; Estrin D
JMIR Ment Health; 2021 Jan; 8(1):e25019. PubMed ID: 33502330
[TBL] [Abstract][Full Text] [Related]
35. Mobile-Based Oral Chemotherapy Adherence-Enhancing Interventions: Scoping Review.
Skrabal Ross X; Gunn KM; Patterson P; Olver I
JMIR Mhealth Uhealth; 2018 Dec; 6(12):e11724. PubMed ID: 30578182
[TBL] [Abstract][Full Text] [Related]
36. The accuracy of passive phone sensors in predicting daily mood.
Pratap A; Atkins DC; Renn BN; Tanana MJ; Mooney SD; Anguera JA; Areán PA
Depress Anxiety; 2019 Jan; 36(1):72-81. PubMed ID: 30129691
[TBL] [Abstract][Full Text] [Related]
37. The mobile phone as a tool in improving cancer care in Nigeria.
Odigie VI; Yusufu LM; Dawotola DA; Ejagwulu F; Abur P; Mai A; Ukwenya Y; Garba ES; Rotibi BB; Odigie EC
Psychooncology; 2012 Mar; 21(3):332-5. PubMed ID: 22383275
[TBL] [Abstract][Full Text] [Related]
38. Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild.
Wahle F; Kowatsch T; Fleisch E; Rufer M; Weidt S
JMIR Mhealth Uhealth; 2016 Sep; 4(3):e111. PubMed ID: 27655245
[TBL] [Abstract][Full Text] [Related]
39. Mobile phone use, school electromagnetic field levels and related symptoms: a cross-sectional survey among 2150 high school students in Izmir.
Durusoy R; Hassoy H; Özkurt A; Karababa AO
Environ Health; 2017 Jun; 16(1):51. PubMed ID: 28577556
[TBL] [Abstract][Full Text] [Related]
40. Physical Activity Assessment Between Consumer- and Research-Grade Accelerometers: A Comparative Study in Free-Living Conditions.
Dominick GM; Winfree KN; Pohlig RT; Papas MA
JMIR Mhealth Uhealth; 2016 Sep; 4(3):e110. PubMed ID: 27644334
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]