8 research outputs found

    Robustness and findings of a web-based system for depression assessment in a university work context

    Full text link
    [EN] Depression is associated with absenteeism and presentism, problems in workplace relationships and loss of productivity and quality. The present work describes the validation of a web-based system for the assessment of depression in the university work context. The basis of the system is the Spanish version of the Beck Depression Inventory (BDI-II). A total of 185 participants completed the BDI-II web-based assessment, including 88 males and 97 females, 70 faculty members and 115 staff members. A high level of internal consistency reliability was confirmed. Based on the results of our web-based BDI-II, no significant differences were found in depression severity between gender, age or workers' groups. The main depression risk factors reported were: Changes in sleep, Loss of energy, Tiredness or fatigue and Loss of interest. However significant differences were found by gender in Changes in appetite, Difficulty of concentration and Loss of interest in sex; males expressed less loss of interest in sex than females with a statistically significant difference. Our results indicate that the data collected is coherent with previous BDI-II studies. We conclude that the web-based system based on the BDI-II is psychometrically robust and can be used to assess depression in the university working community.Funding for this study was provided by the authors' various departments, and partially by the CrowdHealth Project (Collective Wisdom Driving Public Health Policies [727560]), the MTS4up project (DPI2016-80054-R) and patient-centered pathways of early palliative care, supportive ecosystems and appraisal standard (825750).Asensio-Cuesta, S.; Bresó, A.; Sáez Silvestre, C.; Garcia-Gomez, JM. (2019). Robustness and findings of a web-based system for depression assessment in a university work context. International Journal of Environmental research and Public Health. 16(4):1-17. https://doi.org/10.3390/ijerph16040644S117164Depression [Internet]. World Health Organization http://www.who.int/mediacentre/factsheets/fs369/en/Chang, S. M., Hong, J.-P., & Cho, M. J. (2011). Economic burden of depression in South Korea. Social Psychiatry and Psychiatric Epidemiology, 47(5), 683-689. doi:10.1007/s00127-011-0382-8Greenberg, P. E., Fournier, A.-A., Sisitsky, T., Pike, C. T., & Kessler, R. C. (2015). The Economic Burden of Adults With Major Depressive Disorder in the United States (2005 and 2010). The Journal of Clinical Psychiatry, 76(02), 155-162. doi:10.4088/jcp.14m09298Health and Safety at Work in Europe (1999–2007): A Statistical Portrait. Luxembourg. Publications Office of the European Union https://ec.europa.eu/eurostat/documents/3217494/5718905/KS-31-09-290-EN.PDF/88eef9f7-c229-40de-b1cd-43126bc4a946Lee, Y., Rosenblat, J. D., Lee, J., Carmona, N. E., Subramaniapillai, M., Shekotikhina, M., … McIntyre, R. S. (2018). Efficacy of antidepressants on measures of workplace functioning in major depressive disorder: A systematic review. Journal of Affective Disorders, 227, 406-415. doi:10.1016/j.jad.2017.11.003Schmidt, S., Roesler, U., Kusserow, T., & Rau, R. (2012). Uncertainty in the workplace: Examining role ambiguity and role conflict, and their link to depression—a meta-analysis. European Journal of Work and Organizational Psychology, 23(1), 91-106. doi:10.1080/1359432x.2012.711523Cuijpers, P., & Smit, F. (2004). Subthreshold depression as a risk indicator for major depressive disorder: a systematic review of prospective studies. Acta Psychiatrica Scandinavica, 109(5), 325-331. doi:10.1111/j.1600-0447.2004.00301.xRihmer, Z. (2001). Can better recognition and treatment of depression reduce suicide rates? A brief review. European Psychiatry, 16(7), 406-409. doi:10.1016/s0924-9338(01)00598-3Nogueira-Martins, L. A., Fagnani Neto, R., Macedo, P. C. M., Cítero, V. A., & Mari, J. J. (2004). The mental health of graduate students at the Federal University of São Paulo: a preliminary report. Brazilian Journal of Medical and Biological Research, 37(10), 1519-1524. doi:10.1590/s0100-879x2004001000011Ibrahim, A. K., Kelly, S. J., Adams, C. E., & Glazebrook, C. (2013). A systematic review of studies of depression prevalence in university students. Journal of Psychiatric Research, 47(3), 391-400. doi:10.1016/j.jpsychires.2012.11.015Levecque, K., Anseel, F., De Beuckelaer, A., Van der Heyden, J., & Gisle, L. (2017). Work organization and mental health problems in PhD students. Research Policy, 46(4), 868-879. doi:10.1016/j.respol.2017.02.008Zhong, J., You, J., Gan, Y., Zhang, Y., Lu, C., & Wang, H. (2009). Job Stress, Burnout, Depression Symptoms, and Physical Health among Chinese University Teachers. Psychological Reports, 105(3_suppl), 1248-1254. doi:10.2466/pr0.105.f.1248-1254The International Test Commission. (2006). International Guidelines on Computer-Based and Internet-Delivered Testing. International Journal of Testing, 6(2), 143-171. doi:10.1207/s15327574ijt0602_4Reevy, G. M., & Deason, G. (2014). Predictors of depression, stress, and anxiety among non-tenure track faculty. Frontiers in Psychology, 5. doi:10.3389/fpsyg.2014.00701McLean, L., & Connor, C. M. (2015). Depressive Symptoms in Third‐Grade Teachers: Relations to Classroom Quality and Student Achievement. Child Development, 86(3), 945-954. doi:10.1111/cdev.12344Griffiths, K. M., Christensen, H., Jorm, A. F., Evans, K., & Groves, C. (2004). Effect of web-based depression literacy and cognitive–behavioural therapy interventions on stigmatising attitudes to depression. British Journal of Psychiatry, 185(4), 342-349. doi:10.1192/bjp.185.4.342HASLAM, C., ATKINSON, S., BROWN, S., & HASLAM, R. (2005). Anxiety and depression in the workplace: Effects on the individual and organisation (a focus group investigation). Journal of Affective Disorders, 88(2), 209-215. doi:10.1016/j.jad.2005.07.009Finkelstein, J., & Lapshin, O. (2007). Reducing depression stigma using a web-based program. International Journal of Medical Informatics, 76(10), 726-734. doi:10.1016/j.ijmedinf.2006.07.004BECK, A. T. (1961). An Inventory for Measuring Depression. Archives of General Psychiatry, 4(6), 561. doi:10.1001/archpsyc.1961.01710120031004Montgomery, S. A., & Åsberg, M. (1979). A New Depression Scale Designed to be Sensitive to Change. British Journal of Psychiatry, 134(4), 382-389. doi:10.1192/bjp.134.4.382Kroenke, K., Spitzer, R. L., Williams, J. B. W., & Löwe, B. (2010). The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review. General Hospital Psychiatry, 32(4), 345-359. doi:10.1016/j.genhosppsych.2010.03.006ZUNG, W. W. K. (1965). A Self-Rating Depression Scale. Archives of General Psychiatry, 12(1), 63. doi:10.1001/archpsyc.1965.01720310065008Ginting, H., Näring, G., van der Veld, W. M., Srisayekti, W., & Becker, E. S. (2013). Validating the Beck Depression Inventory-II in Indonesia’s general population and coronary heart disease patients. International Journal of Clinical and Health Psychology, 13(3), 235-242. doi:10.1016/s1697-2600(13)70028-0Kojima, M., Furukawa, T. A., Takahashi, H., Kawai, M., Nagaya, T., & Tokudome, S. (2002). Cross-cultural validation of the Beck Depression Inventory-II in Japan. Psychiatry Research, 110(3), 291-299. doi:10.1016/s0165-1781(02)00106-3Kapci, E. G., Uslu, R., Turkcapar, H., & Karaoglan, A. (2008). Beck Depression Inventory II: evaluation of the psychometric properties and cut-off points in a Turkish adult population. Depression and Anxiety, 25(10), E104-E110. doi:10.1002/da.20371Aratake, Y., Tanaka, K., Wada, K., Watanabe, M., Katoh, N., Sakata, Y., & Aizawa, Y. (2007). Development of Japanese Version of the Checklist Individual Strength Questionnaire in a Working Population. Journal of Occupational Health, 49(6), 453-460. doi:10.1539/joh.49.453Kühner, C., Bürger, C., Keller, F., & Hautzinger, M. (2007). Reliabilität und Validität des revidierten Beck-Depressionsinventars (BDI-II). Der Nervenarzt, 78(6), 651-656. doi:10.1007/s00115-006-2098-7Holländare, F., Andersson, G., & Engström, I. (2010). A Comparison of Psychometric Properties Between Internet and Paper Versions of Two Depression Instruments (BDI-II and MADRS-S) Administered to Clinic Patients. Journal of Medical Internet Research, 12(5), e49. doi:10.2196/jmir.1392Potential of the Internet for Personality Research https://www.sciencedirect.com/science/article/pii/B978012099980450006XCarlbring, P., Brunt, S., Bohman, S., Austin, D., Richards, J., Öst, L.-G., & Andersson, G. (2007). Internet vs. paper and pencil administration of questionnaires commonly used in panic/agoraphobia research. Computers in Human Behavior, 23(3), 1421-1434. doi:10.1016/j.chb.2005.05.002Schulenberg, S. E., & Yutrzenka, B. A. (2001). Equivalence of computerized and conventional versions of the Beck Depression Inventory-II (BDI-II). Current Psychology, 20(3), 216-230. doi:10.1007/s12144-001-1008-

    Obrasci tjelesne aktivnosti tijekom srednje škole

    Get PDF
    Aim The main aim of the doctoral thesis is to describe the patterns of physical activity during high-school. Three specific goals for three independent studies (Study 1, Study 2 and Study 3) were set. Study 1 aimed to evaluate: (1) the objectively assessed physical activity (PA) patterns in urban 15-year-old male and female adolescents according to school type and (2) to assess the differences in PA between school days and weekend days. Study 2 aimed to evaluate PA, SBs and SP changes between 1st and 2nd grade of secondary school in urban adolescents. Study 3 aimed to evaluate the extent of tracking of physical activity (PA), sports participation (SP) and sedentary behaviors (SB) over 4 years of secondary school education among the Croatian Physical Activity in Adolescence Longitudinal Study (CRO-PALS) cohort. Study 1 methods In this cross-sectional study, participants were 187 secondary-school male and female adolescents (61.4% females) attending grammar and vocational schools. Patterns of PA were objectively evaluated using a multi-sensor body monitor for 5 consecutive days. Confounders assessed included biological age, socio-economic status, sum of 4 skinfolds, maximal temperature and the amount of rainfall. Study 1 results Males and females from grammar schools achieved higher total daily energy expenditure (TEE) and active energy expenditure (AEE) compared to their peers from vocational schools (TEE: 50 ± 12 kcal/kg/day vs. 47 ± 12 kcal/kg/day, p = 0.02; AEE: 23 ± 5 kcal/kg/day vs. vocational = 21 ± 6 kcal/kg/day, p = 0.04). No differences in time spent in light (LPA), moderate (MPA) or vigorous (VPA) physical activity were noted between the two groups (p = 0.16–0.43). Next, a significant decline in TEE and MPA between school days and weekends was observed (p< 0.001 and p = 0.02, respectively), while VPA remained the same throughout the week (p = 0.76). Weekly patterns of PA did not show differences by school type or gender (p for interactions = 0.21–0.50). In addition, significantly lower amount of MPA was accumulated during weekends compared to school days, resulting in lower TEE, regardless of school type or gender. Study 1 conclusion Policies and strategies on PA in adolescents should focus vocational schools and weekend days. Study 2 methods In this one year follow-up study, participants were 81 secondary-school students (28 boys and 53 girls) aged 15.5 years at the baseline. PA was assessed with the SenseWear Armband multi-sensor activity monitor, while SBs were assessed by using School Health Action, Planning and Evaluation System (SHAPES) physical activity questionnaire. SHAPES questionnaire was supplemented with 2 questions inquiring about SP in organized sports in school and outside of school. Study 2 results PA decreased markedly in both genders between the 1st and 2nd grade of secondary school. Total energy expenditure was reduced by 13 kcal/kg/day on average in boys and by 10 kcal/kg/day in girls (p for both <0.001), while mean daily active energy expenditure decreased by 7 kcal/kg/day (p<0.001) and 3 kcal/kg/day (p=0.04) in boys and girls, respectively. Similarly, the amount of moderate physical activity declined by 49 min/day in boys and 21 min/day in girls (p for both <0.001). At the same time vigorous physical activity was cut by 14 min/day (p<0.001) and 3 min/day (p=0.003) in boys and girls, respectively. Conversely, time spent in SBs did not show any change. Study 2 conclusion In conclusion, a decline in PA between 1st and 2nd grade of secondary school was marked, but was not accompanied with an increase in SBs. Policies aimed at increasing PA should be targeting the period of entering secondary school in order to offset the observed drop in PA. Study 3 methods In this investigation, participants were 844 secondary school students (15.6 years at baseline; 49% girls). SHAPES questionnaire was used to assess PA, SP and SB at ages 15, 16, 17, and 18 and continuous tracking was assessed by stability coefficients and odds ratios calculated using generalized estimating equations. Study 3 results Tracking coefficients for the duration of moderate and vigorous PA and physical activity energy expenditure (PAEE) were similar in both genders and indicated moderate tracking (0.49-0.61), while the stability of SB tended to be somewhat higher over the 4 years of follow-up (0.60-0.72 in boys and 0.60-0.70 in girls). In addition, youth that participated in sports at baseline had16 to 28 time higher odds of continued participation at follow-up, depending on sport type and gender. Finally, both low physical activity and high screen time showed strong tracking in both genders. Study 3 conclusion In conclusion, PA and SB tracked moderately between age 15 and 18, the tracking of SB being slightly stronger compared to PA. Moreover, strong tracking of low PA and high screen time indicates that detection of these risk factors at the beginning of secondary school should be strongly recommended.Cilj Glavni cilj ove doktorske disertacije je proučiti obrasce tjelesne aktivnosti tijekom srednje škole. Tri specifična cilja su postavljena za tri nezavisne studije (Studija 1, Studija 2 i Studija 3). Studija 1 je imala cilj za utvrditi: (1) obrasce objektivno mjerene tjelesne aktivnosti kod petnaestogodišnjaka oba spola s obzirom na tip školovanja i (2) razlike u tjelesnoj aktivnosti između školskih dana i dana vikenda. Studija 2 imala je za utvrditi promjene u razini tjelesne aktivnosti i sedentarnih ponašanja između prvog i drugog razreda srednje škole. Studija 3 imala je za cilj utvrditi praćenje tjelesne aktivnosti, sudjelovanja u sportu i sedentarnih ponašanja tijekom četiri godine srednje škole. Studija 1 metode U ovoj poprečno-presječnoj studiji sudjelovalo je 187 muških i ženskih ispitanika (61.4% djevojaka) koji su pohađali gimnazije i strukovne škole. Obrasci tjelesne aktivnosti mjereni su objektivno koristeći multisenzorni uređaj tijekom 5 uzastopnih dana. Kovarijati korišteni u studiji bili su biološka dob, socioekonomski status, potkožno masno tkivo, maksimalna temperatura i količina padalina. Studija 1 rezultati Dječaci i djevojčice iz gimnazija ostvarili su veću ukupnu dnevnu količinu utrošene energije (UEP) i aktivne količine utrošene energije (KUE) s obzirom na učenike iz strukovnih škola (UEP: 50 ± 12 kcal/kg/danu vs. 47 ± 12 kcal/kg/danu, p = 0.02; KUE: 23 ± 5 kcal/kg/danu vs. strukovne = 21 ± 6 kcal/kg/danu, p = 0.04). Nisu pronađene značajne razlike u niskoj, umjerenoj i visokoj tjelesnoj aktivnosti između dvije grupe (p = 0.16–0.43). Nadalje, značajno smanjenje UEP i umjerene tjelesne aktivnosti je pronađeno (p< 0.001 and p = 0.02), dok je razina visoke razine tjelesne aktivnosti ostala nepromijenjena (p = 0.76). Tjedni obrasci tjelesne aktivnosti se nisu razlikovali po tipu škole ili spolu (p za interakciju = 0.21-0.50). Značajno manja razina umjerene tjelesne aktivnosti je zabilježena tijekom dana vikenda s obzirom na školske dane, rezultirajući manjom potrošnjom ukupne količine energije, bez obzira na tip škole i spol. Studija 1 zaključak Strategije za tjelesnu aktivnost bi se trebale koncentrirati na povećanje razine tjelesne aktivnosti u strukovnim školama i tijekom dana vikenda. Studija 2 metode U ovoj jednogodišnjoj studiji, ispitanici su bili dječaci i djevojke (81, 28 dječaka i 53 djevojke) u prosječnoj dobi od 15.5 godina na početku. Tjelesna aktivnost je mjerena uz pomoć Senswear multisenzornog uređaja, dok su se sedentarna ponašanja mjerila uz pomoć SHAPES upitnika. Upitniku su se dodala dva pitanja o sudjelovanju u sportu u školi i van škole. Studija 2 rezultati Razina tjelesne aktivnosti značajno se smanjila u oba spola između prvog i drugog razreda srednje škole. Ukupna energetska potrošnja smanjila se za 13 kcal/kg/danu u prosjeku kod dječaka i za 10 kcal/kg/danu kod djevojaka (p vrijednost za oboje <0.001), dok se prosječna dnevna aktivna količina utrošene energije smanjila za 7 kcal/kg/danu (p<0.001) i za 3 kcal/kg/danu (p=0.04) kod dječaka i djevojaka. Slično, količina umjerene tjelesne aktivnosti smanjila se za 49 min/danu kod dječaka i za 21 min/danu kod djevojaka (p za oboje <0.001). U isto vrijeme, razina visoke razine tjelesne aktivnosti smanjila se za 14 min/danu (p<0.001) i za 3 min/danu (p=0.003) kod dječaka i djevojaka. Vrijeme provedeno u sedentarnim ponašanjima se nije značajno promijenilo. Studija 2 zaključak U zaključku, smanjenje razine tjelesne aktivnosti između prvog i drugog razreda srednje škole je bilo značajno, ali nije bilo popraćeno povećanjem razine sedentarnih ponašanja. Strategije kojima je za cilj povećanje razine tjelesne aktivnosti umjesto vremena provedenog u sedentarnim ponašanjima bi trebale ciljati populaciju učenika koji upisuju srednju školu. Studija 3 metode U ovoj studiji, ispitanici su bili 844 učenika srednjih škola (prosječna dob 15.6 godina na početku, 49% djevojaka). SHAPES upitnik je korišten za prikupljanje informacija o razini tjelesne aktivnosti, sudjelovanja u sportu i sedentarnih ponašanja u dobi 15, 16, 17 i 18 godina na temelju kojeg je izračunato sudjelovanje koeficijentima praćenja i omjerima vjerojatnosti koristeći generalizirane jednadžbe. Studija 3 rezultati Koeficijenti praćenja za trajanje umjerene i visoke razine tjelesne aktivnosti bili su slični za oba spola i označavali su umjereno praćenje (0.49-0.61), dok je stabilnost sedentarnih ponašanja bila nešto viša tijekom četverogodišnjeg praćenja (0.60-0.72 u dječaka i 0.60-0.70 u djevojčica). Mladi koji su sudjelovali u sportu na početku su imali 16 do 28 puta veću vjerojatnost da će se nastaviti baviti sportom, s obzirom na tip sporta i spol. Konačno, niska razina tjelesne aktivnosti i visoka razina vremena provedenog pred računalom, televizijom i mobitelom je pokazala jako praćenje u oba spola. Studija 3 zaključak U zaključku, tjelesna aktivnost i sedentarna ponašanja pokazala su umjereno praćenje između 15.-te i 18.-te godine, gdje je praćenje bilo malo jače kod sedentarnog ponašanja s obzirom na tjelesnu aktivnost. Nadalje, jako praćenje niske razine tjelesne aktivnosti i visoke razine vremena provedenog pred računalom, televizijom i mobitelom naglašava da je otkrivanje tih rizičnih čimbenika preporučeno na početku srednje škole

    Smartphone sensors for monitoring cancer-related Quality of Life: App design, EORTC QLQ-C30 mapping and feasibility study in healthy subjects

    Full text link
    [EN] Quality of life (QoL) indicators are now being adopted as clinical outcomes in clinical trials on cancer treatments. Technology-free daily monitoring of patients is complicated, time-consuming and expensive due to the need for vast amounts of resources and personnel. The alternative method of using the patients¿ own phones could reduce the burden of continuous monitoring of cancer patients in clinical trials. This paper proposes monitoring the patients¿ QoL by gathering data from their own phones. We considered that the continuous multiparametric acquisition of movement, location, phone calls, conversations and data use could be employed to simultaneously monitor their physical, psychological, social and environmental aspects. An open access phone app was developed (Human Dynamics Reporting Service (HDRS)) to implement this approach. We here propose a novel mapping between the standardized QoL items for these patients, the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and define HDRS monitoring indicators. A pilot study with university volunteers verified the plausibility of detecting human activity indicators directly related to QoL.Funding for this study was provided by the authors' various departments, and partially by the CrowdHealth Project (Collective Wisdom Driving Public Health Policies (727560)) and the MTS4up project (DPI2016-80054-R).Asensio Cuesta, S.; Sánchez-García, Á.; Conejero, JA.; Sáez Silvestre, C.; Rivero-Rodriguez, A.; Garcia-Gomez, JM. (2019). Smartphone sensors for monitoring cancer-related Quality of Life: App design, EORTC QLQ-C30 mapping and feasibility study in healthy subjects. International Journal of Environmental research and Public Health. 16(3):1-18. https://doi.org/10.3390/ijerph16030461S118163Number of Smartphone Users Worldwide from 2014 to 2020 (in Billions)https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/Mirkovic, J., Kaufman, D. R., & Ruland, C. M. (2014). Supporting Cancer Patients in Illness Management: Usability Evaluation of a Mobile App. JMIR mHealth and uHealth, 2(3), e33. doi:10.2196/mhealth.3359Xing Su, Hanghang Tong, & Ping Ji. (2014). Activity recognition with smartphone sensors. Tsinghua Science and Technology, 19(3), 235-249. doi:10.1109/tst.2014.6838194Schmitz Weiss, A. (2013). Exploring News Apps and Location-Based Services on the Smartphone. Journalism & Mass Communication Quarterly, 90(3), 435-456. doi:10.1177/1077699013493788Higgins, J. P. (2016). Smartphone Applications for Patients’ Health and Fitness. The American Journal of Medicine, 129(1), 11-19. doi:10.1016/j.amjmed.2015.05.038Rivenson, Y., Ceylan Koydemir, H., Wang, H., Wei, Z., Ren, Z., Günaydın, H., … Ozcan, A. (2018). Deep Learning Enhanced Mobile-Phone Microscopy. ACS Photonics, 5(6), 2354-2364. doi:10.1021/acsphotonics.8b00146Priye, A., Ball, C. S., & Meagher, R. J. (2018). Colorimetric-Luminance Readout for Quantitative Analysis of Fluorescence Signals with a Smartphone CMOS Sensor. Analytical Chemistry, 90(21), 12385-12389. doi:10.1021/acs.analchem.8b03521Measuring Quality of Life for Cancer Patients: Where Are We Today and Where Are We Headed Tomorrow?http://blog.mdsol.com/measuring-quality-of-life-for-cancer-patients-where-are-we-today-and-where-are-we-headed-tomorrow/Zulueta, J., Piscitello, A., Rasic, M., Easter, R., Babu, P., Langenecker, S. A., … Leow, A. (2018). Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study. Journal of Medical Internet Research, 20(7), e241. doi:10.2196/jmir.9775Caruso, R., GiuliaNanni, M., Riba, M. B., Sabato, S., & Grassi, L. (2017). Depressive Spectrum Disorders in Cancer: Diagnostic Issues and Intervention. A Critical Review. Current Psychiatry Reports, 19(6). doi:10.1007/s11920-017-0785-7THE WHOQOL GROUP. (1998). Development of the World Health Organization WHOQOL-BREF Quality of Life Assessment. Psychological Medicine, 28(3), 551-558. doi:10.1017/s0033291798006667Basic Issues Concerning Health-Related Quality of Life. (2017). Central European Journal of Urology, 70(2). doi:10.5173/ceju.2017.923Sloan, J. A. (2011). Metrics to Assess Quality of Life After Management of Early-Stage Lung Cancer. The Cancer Journal, 17(1), 63-67. doi:10.1097/ppo.0b013e31820e15dcBordoni, R., Ciardiello, F., von Pawel, J., Cortinovis, D., Karagiannis, T., Ballinger, M., … Rittmeyer, A. (2018). Patient-Reported Outcomes in OAK: A Phase III Study of Atezolizumab Versus Docetaxel in Advanced Non–Small-cell Lung Cancer. Clinical Lung Cancer, 19(5), 441-449.e4. doi:10.1016/j.cllc.2018.05.011Hartkopf, A. D., Graf, J., Simoes, E., Keilmann, L., Sickenberger, N., Gass, P., … Wallwiener, M. (2017). Electronic-Based Patient-Reported Outcomes: Willingness, Needs, and Barriers in Adjuvant and Metastatic Breast Cancer Patients. JMIR Cancer, 3(2), e11. doi:10.2196/cancer.6996Wallwiener, M., Matthies, L., Simoes, E., Keilmann, L., Hartkopf, A. D., Sokolov, A. N., … Brucker, S. Y. (2017). Reliability of an e-PRO Tool of EORTC QLQ-C30 for Measurement of Health-Related Quality of Life in Patients With Breast Cancer: Prospective Randomized Trial. Journal of Medical Internet Research, 19(9), e322. doi:10.2196/jmir.8210Gresham, G., Hendifar, A. E., Spiegel, B., Neeman, E., Tuli, R., Rimel, B. J., … Shinde, A. M. (2018). Wearable activity monitors to assess performance status and predict clinical outcomes in advanced cancer patients. npj Digital Medicine, 1(1). doi:10.1038/s41746-018-0032-6BOHANNON, R. W. (1997). Comfortable and maximum walking speed of adults aged 20—79 years: reference values and determinants. Age and Ageing, 26(1), 15-19. doi:10.1093/ageing/26.1.15Pérez-García, V. M., Fitzpatrick, S., Pérez-Romasanta, L. A., Pesic, M., Schucht, P., Arana, E., & Sánchez-Gómez, P. (2016). Applied mathematics and nonlinear sciences in the war on cancer. Applied Mathematics and Nonlinear Sciences, 1(2), 423-436. doi:10.21042/amns.2016.2.00036Shin, W., Song, S., Jung, S.-Y., Lee, E., Kim, Z., Moon, H.-G., … Lee, J. E. (2017). The association between physical activity and health-related quality of life among breast cancer survivors. Health and Quality of Life Outcomes, 15(1). doi:10.1186/s12955-017-0706-9Wearable Fitness Monitors Useful in Cancer Treatment, Study Findswww.sciencedaily.com/releases/2018/05/180501130856.htmBade, B. C., Brooks, M. C., Nietert, S. B., Ulmer, A., Thomas, D. D., Nietert, P. J., … Silvestri, G. A. (2016). Assessing the Correlation Between Physical Activity and Quality of Life in Advanced Lung Cancer. Integrative Cancer Therapies, 17(1), 73-79. doi:10.1177/1534735416684016Fortner, B. V., Stepanski, E. J., Wang, S. C., Kasprowicz, S., & Durrence, H. H. (2002). Sleep and Quality of Life in Breast Cancer Patients. Journal of Pain and Symptom Management, 24(5), 471-480. doi:10.1016/s0885-3924(02)00500-6Mishra, S. I., Scherer, R. W., Snyder, C., Geigle, P., & Gotay, C. (2014). Are Exercise Programs Effective for Improving Health-Related Quality of Life Among Cancer Survivors? A Systematic Review and Meta-Analysis. Oncology Nursing Forum, 41(6), E326-E342. doi:10.1188/14.onf.e326-e342Ratcliff, C. G., Lam, C. Y., Arun, B., Valero, V., & Cohen, L. (2014). Ecological momentary assessment of sleep, symptoms, and mood during chemotherapy for breast cancer. Psycho-Oncology, 23(11), 1220-1228. doi:10.1002/pon.3525Cox, S. M., Lane, A., & Volchenboum, S. L. (2018). Use of Wearable, Mobile, and Sensor Technology in Cancer Clinical Trials. JCO Clinical Cancer Informatics, (2), 1-11. doi:10.1200/cci.17.00147Brown, W., Yen, P.-Y., Rojas, M., & Schnall, R. (2013). Assessment of the Health IT Usability Evaluation Model (Health-ITUEM) for evaluating mobile health (mHealth) technology. Journal of Biomedical Informatics, 46(6), 1080-1087. doi:10.1016/j.jbi.2013.08.001Darlow, S., & Wen, K.-Y. (2016). Development testing of mobile health interventions for cancer patient self-management: A review. Health Informatics Journal, 22(3), 633-650. doi:10.1177/1460458215577994Martin Sanchez, F., Gray, K., Bellazzi, R., & Lopez-Campos, G. (2014). Exposome informatics: considerations for the design of future biomedical research information systems. Journal of the American Medical Informatics Association, 21(3), 386-390. doi:10.1136/amiajnl-2013-001772Kim, H. H., Lee, S. Y., Baik, S. Y., & Kim, J. H. (2015). MELLO: Medical lifelog ontology for data terms from self-tracking and lifelog devices. International Journal of Medical Informatics, 84(12), 1099-1110. doi:10.1016/j.ijmedinf.2015.08.005Kessel, K. A., Vogel, M. M., Alles, A., Dobiasch, S., Fischer, H., & Combs, S. E. (2018). Mobile App Delivery of the EORTC QLQ-C30 Questionnaire to Assess Health-Related Quality of Life in Oncological Patients: Usability Study. JMIR mHealth and uHealth, 6(2), e45. doi:10.2196/mhealth.9486Elsbernd, A., Hjerming, M., Visler, C., Hjalgrim, L. L., Niemann, C. U., Boisen, K., & Pappot, H. (2018). Cocreated Smartphone App to Improve the Quality of Life of Adolescents and Young Adults with Cancer (Kræftværket): Protocol for a Quantitative and Qualitative Evaluation. JMIR Research Protocols, 7(5), e10098. doi:10.2196/1009
    corecore