Name: measuring the switch cost of smartphone use while walking
Uploaded: 2020-04-30T14:00:00.000+00:00
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Os autores reconhecem o apoio financeiro do Conselho de Pesquisa em Ciências Sociais e Humanas do Canadá (SSHERC).

<ol>
	<li>Monsell, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Task+switching.">Task switching.</a> Trends in Cognitive Sciences. 7 (3), 134-140 (2003).</li><li>Haga, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+using+a+Smart+Phone+on+Pedestrians'+Attention+and+Walking.">Effects of using a Smart Phone on Pedestrians' Attention and Walking.</a> Procedia Manufacturing. 3, 2574-2580 (2015).</li><li>Hatfield, J., Murphy, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effects+of+mobile+phone+use+on+pedestrian+crossing+behaviour+at+signalised+and+unsignalised+intersections.">The effects of mobile phone use on pedestrian crossing behaviour at signalised and unsignalised intersections.</a> Accident Analysis, Prevention. 39 (1), (2007).</li><li>Stavrinos, D., Byington, K. W., Schwebel, D. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distracted+walking:+Cell+phones+increase+injury+risk+for+college+pedestrians.">Distracted walking: Cell phones increase injury risk for college pedestrians.</a> Journal of Safety Research. 42 (2), 101-107 (2011).</li><li>Nasar, J., Hecht, P., Wener, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mobile+telephones,+distracted+attention,+and+pedestrian+safety.">Mobile telephones, distracted attention, and pedestrian safety.</a> Accident Analysis, Prevention. 40 (1), 69-75 (2008).</li><li>Hyman, I. E., Boss, S. M., Wise, B. M., McKenzie, K. E., Caggiano, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Did+you+see+the+unicycling+clown?+Inattentional+blindness+while+walking+and+talking+on+a+cell+phone.">Did you see the unicycling clown? Inattentional blindness while walking and talking on a cell phone.</a> Applied Cognitive Psychology. 24 (5), 597-607 (2010).</li><li>Pourchon, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Is+augmented+reality+leading+to+more+risky+behaviors?+An+experiment+with+pok&#233;mon+go.">Is augmented reality leading to more risky behaviors? An experiment with pok&#233;mon go.</a> Proceedings of the International Conference on HCI in Business, Government, and Organizations. , 354-361 (2017).</li><li>Thornton, I. M., Rensink, R. A., Shiffrar, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Active+versus+Passive+Processing+of+Biological+Motion.">Active versus Passive Processing of Biological Motion.</a> Perception. 31 (7), 837-853 (2002).</li><li>Neider, M. B., McCarley, J. S., Crowell, J. A., Kaczmarski, H., Kramer, A. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pedestrians,+vehicles,+and+cell+phones.">Pedestrians, vehicles, and cell phones.</a> Accident Analysis, Prevention. 42, 589-594 (2010).</li><li>Wylie, G., Allport, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Task+switching+and+the+measurement+of+"switch+costs".">Task switching and the measurement of "switch costs".</a> Psychological Research. 63 (3-4), 212-233 (2000).</li><li>Johansson, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visual+perception+of+biological+motion+and+a+model+for+its+analysis.">Visual perception of biological motion and a model for its analysis.</a> Perception, Psychophysics. 14 (2), 201-211 (1973).</li><li>Cavanagh, P., Labianca, A. T., Thornton, I. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Attention-based+visual+routines:+Sprites.">Attention-based visual routines: Sprites.</a> Cognition. 80 (1-2), 47-60 (2001).</li><li>Troje, N. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Retrieving+Information+from+Human+Movement+Patterns.">Retrieving Information from Human Movement Patterns.</a> Understanding Events. , 308-334 (2008).</li><li>Klimesch, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+alpha+and+theta+oscillations+reflect+cognitive+and+memory+performance:+A+review+and+analysis.">EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis.</a> Brain Research Reviews. 29 (2-3), 169-195 (1999).</li><li>Jensen, O., Gelfand, J., Kounios, J., Lisman, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oscillations+in+the+Alpha+Band+(9-12+Hz)+Increase+with+Memory+Load+during+Retention+in+a+Short-term+Memory+Task.">Oscillations in the Alpha Band (9-12 Hz) Increase with Memory Load during Retention in a Short-term Memory Task.</a> Cerebral Cortex. 12 (8), 877-882 (2002).</li><li>Busch, N. A., Herrmann, C. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Object-load+and+feature-load+modulate+EEG+in+a+short-term+memory+task.">Object-load and feature-load modulate EEG in a short-term memory task.</a> NeuroReport. 14 (13), 1721-1724 (2003).</li><li>Herrmann, C. S., Senkowski, D., R&#246;ttger, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phase-Locking+and+Amplitude+Modulations+of+EEG+Alpha.">Phase-Locking and Amplitude Modulations of EEG Alpha.</a> Experimental Psychology. 51 (4), 311-318 (2004).</li><li>Pfurtscheller, G., Aranibar, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Event-related+cortical+desynchronization+detected+by+power+measurements+of+scalp+EEG.">Event-related cortical desynchronization detected by power measurements of scalp EEG.</a> Electroencephalography and Clinical Neurophysiology. 42 (6), 817-826 (1977).</li><li>Sauseng, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+alpha+synchronization+and+functional+coupling+during+top-down+processing+in+a+working+memory+task.">EEG alpha synchronization and functional coupling during top-down processing in a working memory task.</a> Human Brain Mapping. 26 (2), 148-155 (2005).</li><li>Pope, A. T., Bogart, E. H., Bartolome, D. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biocybernetic+system+evaluates+indices+of+operator+engagement+in+automated+task.">Biocybernetic system evaluates indices of operator engagement in automated task.</a> Biological Psychology. 40 (1-2), 187-195 (1995).</li><li>Scerbo, M. W., Freeman, F. G., Mikulka, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+brain-based+system+for+adaptive+automation.">A brain-based system for adaptive automation.</a> Theoretical Issues in Ergonomics Science. 4 (1-2), 200-219 (2003).</li><li>Charland, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+the+Multiple+Dimensions+of+Engagement+to+Characterize+Learning:+A+Neurophysiological+Perspective.">Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective.</a> Journal of Visualized Experiments. (101), e52627 (2015).</li><li>Courtemanche, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Texting+while+walking:+An+expensive+switch+cost.">Texting while walking: An expensive switch cost.</a> Accident Analysis, Prevention. 127, 1-8 (2019).</li><li>Townsend, J. T., Ashby, F. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The stochastic modeling of elementary psychological processes. , (1983).</li><li>Jung, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Removal+of+eye+activity+artifacts+from+visual+event-related+potentials+in+normal+and+clinical+subjects.">Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects.</a> Clinical Neurophysiology. 111, 1745-1758 (2000).</li><li>Luck, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> An Introduction to the Event-related Potential Technique (Cognitive Neuroscience). , (2005).</li><li>Steiger, J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tests+for+comparing+elements+of+a+correlation+matrix.">Tests for comparing elements of a correlation matrix.</a> Psychological Bulletin. 87 (2), 245-251 (1980).</li><li>L&#233;ger, P. -. M., Davis, F., Riedl, R., vom Brocke, J., L&#233;ger, P. -. M., Randolph, A., Fischer, T. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Task+Switching+and+Visual+Discrimination+in+Pedestrian+Mobile+Multitasking:+Influence+of+IT+Mobile+Task+Type.">Task Switching and Visual Discrimination in Pedestrian Mobile Multitasking: Influence of IT Mobile Task Type.</a> Information Systems and Neuroscience: Vienna Retreat on NeuroIs 2019. , 245-251 (2020).</li><li>Mourra, G. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Addicted to my smartphone: what factors influence the task-switching cost that occurs when using a smartphone while walking. , (2019).</li><li>Schabrun, S. M., van den Hoorn, W., Moorcroft, A., Greenland, C., Hodges, P. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Texting+and+walking:+strategies+for+postural+control+and+implications+for+safety.">Texting and walking: strategies for postural control and implications for safety.</a> PloS One. 9 (1), 84312 (2014).</li></ol>

Os autores não têm nada a revelar.

Uma escolha crítica ao usar o protocolo seria garantir a qualidade dos dados neurofisiológicos. Há uma complicação inerente ao uso de uma ferramenta como o EEG durante o movimento, porque o movimento excessivo pode criar muito ruído nos dados. Portanto, é importante considerar, antes da coleta de dados, como os dados serão preparados para remover o maior número possível de artefatos sem modificar o sinal real. No entanto, ainda é bastante provável que haja taxas mais altas de exclusão de dados porque os part...

An erratum was issued for:&#160;<a href="https://www-jove-com.bdigitaluss.remotexs.co/t/60555/measuring-the-switch-cost-of-smartphone-use-while-walking">Measuring the Switch Cost of Smartphone Use While Walking</a>. An&#160;author&#39;s name&#160;was&#160;updated.
The name was corrected from:
Gabrielle-Na&#239;m&#233; Mourra
to:
Gabrielle Na&#239;m&#233; Mourra

An erratum was issued for:&#160;<a href="https://www-jove-com.bdigitaluss.remotexs.co/t/60555/measuring-the-switch-cost-of-smartphone-use-while-walking">Measuring the Switch Cost of Smartphone Use While Walking</a>. An&#160;author&#39;s name&#160;was&#160;updated.

Erratum: Measuring the Switch Cost of Smartphone Use While Walking

Como tanto a penetração do smartphone quanto a tendência à multitarefa estão aumentando, é importante entender o impacto que o uso do smartphone ao caminhar tem na atenção. A literatura demonstrou repetidamente que a troca de tarefas vem com um custo1, incluindo o uso de smartphones durante a caminhada. Estudos descobriram que o uso de um smartphone durante a caminhada pode ser perturbador e perigoso 2,3,4. Esses perigos têm sido associados aos prejuízos de atenção de realizar tal tarefa 3,4,5,6,7. Devido à natureza complexa do ambiente pedestre, estudá-lo em um contexto experimental ecologicamente válido pode ser problemático. No entanto, a realização de tais estudos em ambientes pedestres reais pode vir com complicações próprias, porque muitas variáveis estranhas podem entrar em jogo, e há um risco de danos ao participante devido a distrações. É importante ser capaz de estudar tal fenômeno em um ambiente relativamente seguro que permaneça o mais realista possível. Neste artigo, descrevemos uma metodologia de pesquisa que estuda o custo de troca de tarefas de mensagens de texto durante a caminhada, aumentando a validade da tarefa e mitigando os riscos potenciais envolvidos.
Ao usar um smartphone enquanto caminha, os indivíduos são forçados a mudar das tarefas do smartphone para tarefas de caminhada e relacionadas ao meio ambiente. Assim, para estudar tal fenômeno, achamos pertinente enquadrar esse método na literatura sobre multitarefa, especificamente focada no paradigma da troca de tarefas. Para isso, utilizou-se o paradigma de troca de tarefas1, fazendo com que os participantes alternassem entre uma tarefa pré-estímulo e uma tarefa pós-estímulo. Uma das duas tarefas pré-estímulo envolvia multitarefa, enquanto a outra não. Na tarefa pós-estímulo, os participantes tiveram que responder a um estímulo cuja percepção é influenciada pela atenção dividida8. Além disso, estudos experimentais de laboratório que tentam ser o mais ecologicamente válidos possível têm frequentemente utilizado ambientes virtuais de pedestres para entender o impacto atencional do uso de smartphones durante a caminhada 4,9. No entanto, a fim de capturar os mecanismos neurofisiológicos subjacentes, optamos por nos concentrar na reação específica de troca de tarefas a um estímulo para minimizar o número de estímulos aos quais os participantes tiveram que reagir. Desta forma, podemos identificar com mais precisão o custo de troca de tarefas proveniente puramente da mudança de atenção para longe do smartphone e para o estímulo. Com o desenho do nosso estudo, usamos medidas comportamentais (ou seja, custo de troca de tarefas) e dados neurofisiológicos para entender melhor os prejuízos de atenção encontrados durante o uso de smartphones para pedestres.
Durante um experimento de troca de tarefas, os participantes geralmente realizavam pelo menos duas tarefas simples pertencentes a um conjunto de estímulos, com cada tarefa exigindo um conjunto diferente de recursos cognitivos referidos como um "conjunto de tarefas"1. Quando os indivíduos são forçados a alternar entre tarefas, seus recursos mentais precisam se adaptar (ou seja, inibição do conjunto de tarefas anteriores e ativação do conjunto de tarefas atual). Acredita-se que esse processo de "reconfiguração do conjunto de tarefas" seja a causa do custo de alternância de tarefas1. O custo de troca de tarefas é geralmente determinado observando as diferenças no tempo de resposta e / ou na taxa de erro entre as tentativas em que os participantes alternam entre tarefas e aquelas em que não alternam10. Em nosso experimento, tivemos três conjuntos de tarefas: 1) responder a um estímulo de andador pontual-leve; 2) mensagens de texto em um smartphone enquanto caminha; e 3) simplesmente caminhar. Comparamos o custo da troca entre duas condições diferentes: 1) simplesmente andar antes de responder ao estímulo e 2) andar enquanto enviava mensagens de texto antes de responder. Dessa forma, capturamos o custo da multitarefa em um smartphone antes de alternar a tarefa e fomos capazes de compará-lo diretamente com o custo do interruptor não multitarefa de simplesmente caminhar antes do aparecimento do estímulo visual. Como o smartphone usado neste estudo era de uma marca específica, todos os participantes foram examinados antes do experimento para ter certeza de que sabiam como usar corretamente o dispositivo.
Para simular uma experiência realista representativa do contexto do pedestre, optou-se por utilizar uma figura de caminhante ponto-luz como estímulo visual, representando uma forma humana caminhando com um ângulo de desvio de 3,5° para a esquerda ou para a direita do participante. Essa figura é composta por 15 pontos pretos sobre fundo branco, com os pontos representando a cabeça, ombros, quadris, cotovelos, punhos, joelhos e tornozelos de um ser humano (Figura 1). Esse estímulo é baseado no movimento biológico, o que significa que segue o padrão de movimento típico de humanos e animais11. Além disso, esse estímulo é mais do que ecologicamente válido; requer processamento visual complexo e atenção para ser analisada com sucesso12,13. Curiosamente, Thornton et al.8 descobriram que a identificação adequada da direção do andador em forma de ponto é muito impactada pela atenção dividida, tornando-a adequada como uma medida de desempenho ao estudar os custos de troca de tarefas ao multitarefa. Os participantes foram convidados a declarar verbalmente a direção em que a figura estava andando. A aparência do andador era sempre precedida por uma sugestão auditiva que sinalizava sua aparência na tela.
O desempenho na tarefa de andador ponto-luz e os dados neurofisiológicos nos permitiram determinar o impacto atencional de ambas as condições e ajudar a determinar o que as causou. O desempenho foi medido observando as taxas de erro e os tempos de resposta ao determinar a direção da figura do andador ponto-luz. A fim de compreender os mecanismos cognitivos e atencionais subjacentes envolvidos nos comprometimentos de atenção que encontramos com a medida de desempenho, avaliamos os dados neurofisiológicos dos participantes usando o EEG actiCAP com 32 eletrodos. O EEG é uma ferramenta apropriada em termos de precisão temporária, o que é importante ao tentar ver o que causa um desempenho ruim em momentos específicos no tempo (por exemplo, a aparência da figura do andador com luz pontual), embora artefatos possam estar presentes nos dados devido a movimentos. Ao analisar os dados do EEG, dois índices são particularmente relevantes: 1) oscilações alfa; e 2) engajamento cognitivo. Pesquisas descobriram que as oscilações alfa podem representar o controle da memória de trabalho, bem como a inibição ativa de circuitos cerebrais irrelevantes para a tarefa14,15,16,17. Comparando-se as oscilações alfa nos níveis basais com as que ocorrem com a apresentação do estímulo 18,19, obteve-se a razão alfa. Com essa proporção, determinamos as mudanças relacionadas ao evento que poderiam estar subjacentes ao comprometimento da atenção observado ao enviar mensagens de texto durante a caminhada. Com relação ao engajamento cognitivo, Pope et al.20 desenvolveram um índice em que a atividade beta representa aumento da excitação e da atenção, e a atividade alfa e refletem diminuições na excitação e atenção21,22. Esta análise foi feita para determinar se o aumento do engajamento antes do aparecimento do estímulo complicaria a reconfiguração do conjunto de tarefas necessária para responder à figura do andador.
Com a metodologia descrita neste artigo, buscamos apreender os mecanismos subjacentes que afetam o desempenho da troca de tarefas em participantes envolvidos em episódios multitarefas. A condição de andar representa um desempenho de alternador de tarefas não multitarefa que é comparado a um desempenho de comutador de tarefa multitarefa (ou seja, mensagens de texto durante a caminhada). Ao medir os papéis da inibição do conjunto de tarefas e da ativação do conjunto de tarefas, procuramos entender melhor os custos de troca que ocorrem ao enviar mensagens de texto durante a caminhada. É relevante notar que o estudo original foi realizado em um ambiente virtual imersivo23 , mas posteriormente replicado em uma sala experimental (ver Figura 2) com um projetor exibindo a figura do andador em uma tela na frente do participante. Como esse ambiente virtual não está mais disponível, o protocolo foi adaptado ao desenho atual da sala experimental.

<table><tbody><tr><td>The Observer XT</td><td>Noldus</td><td></td><td>Integration and synchronization software: The Noldus Observer XT (Noldus Information Technology) is used to synchronize all behavioral, emotional and cognitive engagement data.</td></tr><tr><td>MediaRecorder</td><td>Noldus</td><td></td><td>Audio and video recording software</td></tr><tr><td>FaceReader</td><td>Noldus</td><td></td><td>Software for automatic analysis of the 6 basic facial expressions</td></tr><tr><td>E-Prime</td><td>Psychology Software Tools, Inc.</td><td></td><td>Software for computerized experiment design, data collection, and analysis</td></tr><tr><td>BrainVision Recorder</td><td>Brain Vision</td><td></td><td>Software used for recording neuro-/electrophysiological signals (EEG in this case)</td></tr><tr><td>Analyzer</td><td></td><td></td><td>EEG signal processing software</td></tr><tr><td>Qualtrics</td><td>Qualtrics</td><td></td><td>Online survey environment</td></tr><tr><td>Tapis Roulant</td><td>ThermoTread GT Office Treadmill</td><td></td><td></td></tr><tr><td>Syncbox</td><td>Noldus</td><td></td><td>Syncbox start the co-registration of EEG and gaze data by sending a Transistor-Transistor Logic (TTL) signal to the EGI amplifier and a keystroke signal to the Tobii Studio v 3.2.</td></tr><tr><td>Move2actiCAP</td><td>Brain Vision</td><td></td><td>Add-on for a digital wireless system for EEG</td></tr><tr><td>iPhone 6s</td><td>Apple</td><td></td><td></td></tr><tr><td>iMessage</td><td>Apple</td><td></td><td></td></tr><tr><td>iPad</td><td>Apple</td><td></td><td></td></tr></tbody></table>

measuring the switch cost of smartphone use while walking

Este artigo apresenta um protocolo de estudo para medir o custo de troca de tarefas do uso de um smartphone durante a caminhada. Este método envolve fazer com que os participantes andem em uma esteira sob duas condições experimentais: uma condição de controle (ou seja, simplesmente caminhar) e uma condição multitarefa (ou seja, mensagens de texto enquanto caminha). Durante essas condições, os participantes devem alternar entre as tarefas relacionadas à condição experimental e uma tarefa determinante de direção. Esta tarefa de direção é feita com uma figura de caminhante de luz pontual, aparentemente caminhando para a esquerda ou para a direita do participante. O desempenho na tarefa de direção representa os custos de alternância de tarefas do participante. Houve duas medidas de desempenho: 1) identificação correta da direção e 2) tempo de resposta. Os dados de EEG são registrados para medir as oscilações alfa e o engajamento cognitivo que ocorrem durante a troca de tarefas. Este método é limitado em sua validade ecológica: ambientes de pedestres têm muitos estímulos ocorrendo simultaneamente e competindo por atenção. No entanto, esse método é apropriado para identificar os custos de alternância de tarefas. Os dados de EEG permitem o estudo dos mecanismos subjacentes no cérebro que estão relacionados a diferentes custos de troca de tarefas. Este design permite a comparação entre a troca de tarefas ao fazer uma tarefa de cada vez, em comparação com a troca de tarefas ao multitarefa, antes da apresentação do estímulo. Isso permite entender e identificar o impacto comportamental e neurofisiológico dessas duas condições diferentes de troca de tarefas. Além disso, correlacionando os custos de troca de tarefas com a atividade cerebral, podemos aprender mais sobre o que causa esses efeitos comportamentais. Este protocolo é uma base apropriada para estudar o custo de comutação de diferentes usos de smartphones. Diferentes tarefas, questionários e outras medidas podem ser adicionadas a ele para entender os diferentes fatores envolvidos no custo de troca de tarefas do uso do smartphone durante a caminhada.

Este protocolo de estudo foi originalmente conduzido com 54 participantes, cada um respondendo a 88 ensaios de direção. Metade desses ensaios ocorreu quando os participantes estavam simplesmente andando antes da apresentação do estímulo; a outra metade ocorreu quando os participantes estavam enviando mensagens de texto enquanto caminhavam antes da apresentação do estímulo.
Resultados comportamentais O desempenho na direção do andador de luz pontual representa cust...

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Measuring the Switch Cost of Smartphone Use While Walking

Este projeto de estudo mede o custo de troca de tarefas do uso de um smartphone enquanto caminha. Os participantes são submetidos a duas condições experimentais: uma condição de controle (caminhada) e uma condição multitarefa (mensagens de texto durante a caminhada). Os participantes alternam entre essas tarefas e uma tarefa que determina a direção. Dados de EEG, bem como medidas comportamentais são registrados.

Medindo o custo do interruptor do uso do smartphone durante a caminhada

This paper presents a study protocol to measure the task-switching cost of using a smartphone while walking. This method involves having participants walk ...

measuring-the-switch-cost-of-smartphone-use-while-walking

Universidad San Sebastian

Research

JoVE Journal

Behavior

1.8K visualizações.  HEC Montréal. Este projeto de estudo mede o custo de troca de tarefas do uso de um smartphone enquanto caminha. Os participantes são submetidos a duas condições experimentais: uma condição de controle (caminhada) e uma condição multitarefa (mensagens de texto durante a caminhada). Os participantes alternam entre essas tarefas e uma tarefa que determina a direção. Dados de EEG, bem como medidas comportamentais são registrados.

Vídeo: Measuring the Switch Cost of Smartphone Use While Walking

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). 
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% &#177; 7.2% and specificity of 98.96% &#177; 0.6%, while the second prediction set had a sensitivity of 88.35% &#177; 7.80% and specificity of 98.51% &#177; 0.62%. For the third classification set, sensitivity was 84.92% &#177; 6.38% and specificity was 98.17 &#177; 0.62. F1 scores for the first, second and third classification sets were 86.17 &#177; 6.3, 80.19 &#177; 6.36, and 78.42 &#177; 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

Avaliação de um Sistema de Reconhecimento de Atividade Humana baseado em Smartphone em uma Vida Diária Ambiente

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that ...

Comportamento

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the Healthy Landscape and Healthy People Lab, National Taiwan University (HLHP-NTU), on smartphones and smartwatches to collect data on users' real-time psychological and physiological responses and environmental information. A flexible and integrated research method was proposed because it can be difficult to measure multi-dimensional aspects of personal data in on-site studies in landscape and outdoor recreation research. An on-site study conducted in 2020 at the National Taiwan University campus was used as an application example. A dataset of 385 participants was used after excluding invalid samples. During the experiment, participants were asked to walk around campus for 30 min when their heart rate and psychological-scale items were measured, together with several environmental metrics. This work aimed to provide a possible solution to help on-site studies track real-time human responses that match ambient factors. Due to the app's flexibility, its use on wearable devices shows excellent potential for multidisciplinary research studies.

The present protocol introduces a non-commercial self-developed application for collecting real-time on-site data, including psychological scales, GPS location, heart rate, and blood-oxygen saturation level, as well as the application's operating procedures. An empirical study conducted in Taiwan in 2020 was used as an application example.

Um aplicativo para emparelhamento com dispositivos vestíveis para monitorar o status de saúde pessoal

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the ...

Ambiente

An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

Keyboard input has played an essential role in human-computer interaction with a vast user base, and the keyboard design has always been one of the fundamental objects of studies on smart devices. With the development of screen technology, more precise data and indicators could be collected by smartphones to in-depth evaluate the keyboard design. The enlargement of the phone screen has led to unsatisfactory input experience and finger pain, especially for one-handed input. The input efficiency and comfort have attracted the attention of researchers and designers, and the curved keyboard with size-adjustable buttons, which roughly accorded with the physiological structure of thumbs, was proposed to optimize the one-handed usage on large-screen smartphones. However, its real effects remained ambiguous. Therefore, this protocol demonstrated a general and summarized method to evaluate the effect of curved QWERTY keyboard design on a 5-inch smartphone through a self-developed software with detailed variables, including objective behavioral data, subjective feedback, and the coordinate data of each touchpoint. There is sufficient existing literature on evaluating virtual keyboards; however, only a few of them systematically summarized and took reflection on the evaluation methods and processes. Therefore, this protocol fills in the gap and presents a process and method of the systematic evaluation of keyboard design with available codes for analysis and visualization.&#160;It needs no additional or expensive equipment&#160;and is easy to conduct and operate. In addition, the protocol also helps to get potential reasons for the disadvantages of the design and enlightens the optimization of designs. In conclusion, this protocol with the open-source resources could not only be an in-class demonstrative experiment to inspire the novice to start their studies but also contributes to improving the user experience and the revenue of input method editor companies.

The presented protocol integrates various evaluation methods and demonstrates a method to evaluate the keyboard design on smartphones. Pairs matched by English characters are proposed as the input material, and the transition time between two keys is used as the dependent variable.

Um método de avaliação e kit de ferramentas para avaliar o design do teclado em smartphones

Keyboard input has played an essential role in human-computer interaction with a vast user base, and the keyboard design has always been one of the ...

Walk with Me Hybrid Virtual/In-Person Walking for Older Adults with Neurodegenerative Disease

This study presents a protocol for virtual and in-person walking groups for older adults with neurodegenerative diseases and addresses the decline in physical activity and social connectivity during the pandemic. Physical activity, specifically moderate-intensity walking, has been shown to have multiple health benefits for older adults. This methodology was created during the COVID-19 pandemic, contributing to lower physical activity levels and social isolation in older adults. Both the in-person and virtual classes utilize technology such as fitness tracking apps and video platforms. Data are presented from two groups of older adults with neurodegenerative disease: people with prodromal Alzheimer's disease and people with Parkinson's disease. Participants in the virtual classes were screened for balance impairments before the walk, and any person deemed at risk for falls was not eligible to participate virtually. As COVID vaccines became available and restrictions were lifted, in-person walking groups became feasible. Staff and caregivers were trained in balance management, roles/responsibilities, and delivering walking cues. Both walks, virtual and in-person, consisted of a warm up, a walk, and a cool down, and posture, gait, and safety cues were given throughout. Measures of the rate of perceived exertion (RPE) and heart rate (HR) were taken pre-warm up, post-warm up, and at 15 min, 30 min, and 45 min time points. Participants also used a walking application (app) on their phones to record the distance walked and the number of steps taken. The study showed a positive correlation between HR and RPE in both groups. The participants in the virtual group rated the walking group favorably in terms of improving their quality of life during a period of social isolation and contributing to physical, mental, and emotional well-being. The methodology shows a safe and feasible way to implement virtual and in-person walking groups for older adults with neurological diseases.

The present protocol aims to inform rehabilitation specialists and fitness instructors in safe, feasible, and evidence-based methods of delivering virtual and in-person walking classes to older adults with neurodegenerative diseases.

Caminhada Híbrida Virtual/Presencial para Idosos com Doença Neurodegenerativa

This study presents a protocol for virtual and in-person walking groups for older adults with neurodegenerative diseases and addresses the decline in ...

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available &#34;apps&#34; that could be used to assess human gait. The primary aim of this manuscript is to test the concurrent validity of kinematic measures recorded by a smartphone application in comparison to a 3D motion capture system in the sagittal plane. The secondary aim was to develop a protocol for clinicians on the set up of the smartphone camera for video movement analysis.
The sagittal plane knee angle was measured during heel strike and toe off events using the smart phone app and a 3D motion-capture system in 32 healthy subjects. Three trials were performed at near (2-m) and far (4-m) smartphone camera distances. The order of the distances was randomized. Regression analysis was performed to estimate the height of the camera based on either the subject&#39;s height or leg length.
Absolute measurement errors were least during toe off (3.12 &#177; 5.44 degrees) compared to heel strike (5.81 &#177; 5.26 degrees). There were significant (p &#60; 0.05) but moderate agreements between the application and 3D motion capture measures of knee angles. There were also no significant (p &#62; 0.05) differences between the absolute measurement errors between the two camera positions. The measurement errors averaged between 3 - 5 degrees during toe off and heel strike events of the gait cycle.
The use of smartphone apps can be a useful tool in the clinic for performing gait or human movement analysis. Further studies are needed to establish the accuracy in measuring movements of the upper extremity and trunk.

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

This manuscript describes the method to test the concurrent validity of kinematic measures recorded by the smartphone application in comparison to a 3D motion capture system in the sagittal plane. This protocol will enable clinicians to set up smartphones for video capture of human movement.

Vídeo Análise do Movimento Usando Smartphones (VIMAS): Um estudo piloto

The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available &#34;apps&#34; that could be used to ...

Medicina

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Wearable sensors, which are often embedded in commercial smartwatches, allow for continuous and non-invasive health measurements and exposure assessments in clinical studies. Nevertheless, the real-life application of these technologies in studies involving a large number of participants for a significant observation period may be hindered by several practical challenges. 
In this study, we present a modified protocol from a previous intervention study for the mitigation of health effects from desert dust storms. The study involved two distinct population groups: asthmatic children aged 6-11 years and elderly patients with atrial fibrillation (AF). Both groups were equipped with a smartwatch for the assessment of physical activity (using a heart rate monitor, pedometer, and accelerometer) and location (using GPS signals to locate individuals in indoor "at home" or outdoor microenvironments). The participants were required to wear the smartwatch equipped with a data collection application on a daily basis, and data were transmitted via a wireless network to a centrally administered data collection platform for the near real-time assessment of compliance. 
Over a period of 26 months, more than 250 children and 50 patients with AF participated in the aforementioned study. The main technical challenges identified included restricting access to standard smartwatch features, such as gaming, internet browser, camera, and audio recording applications, technical issues, such as loss of GPS signal, especially in indoor environments, and the internal smartwatch settings interfering with the data collection application.
The aim of this protocol is to demonstrate how the use of publicly available application lockers and device automation applications made it possible to address most of these challenges in a simple and cost-effective way. In addition, the inclusion of a Wi-Fi received signal strength indicator significantly improved indoor localization and largely minimized GPS signal misclassification. The implementation of these protocols during the roll-out of this intervention study in the spring of 2020 led to significantly improved results in terms of data completeness and data quality.

Commercial smartwatches equipped with wearable sensors are increasingly being used in population studies. However, their utility is often constrained by their limited battery duration, memory capacity, and data quality. This report provides examples of cost-effective solutions to real-life technical challenges encountered during studies involving asthmatic children and elderly cardiac patients.

Configuração de Dispositivos Vestíveis de Consumo para Exposição e Monitoramento da Saúde em Estudos Populacionais

Wearable sensors, which are often embedded in commercial smartwatches, allow for continuous and non-invasive health measurements and exposure assessments ...

Bioengenharia

<meta charset="utf8"></head><body>Aplicativo de smartphone para medir erros de refração e distância interpupilar

Subjective Refraction Test Using a Smartphone for Vision Screening

To improve access to vision care and to enable mass vision screening, a smartphone app has been developed to measure refractive errors. Without needing any external attachment, the app running on a standalone phone can be used by lay personnel to measure subjective refraction. Its validity has been pilot-tested in clinical settings and underserved communities. The app estimates the refractive error by measuring the distances of far points for discerning visual stimuli. Spherical equivalent refraction and astigmatism can be measured using Tumbling E letters and grating patterns, respectively. The purpose of this paper is to describe the measurement protocols for performing subjective refraction using the app. Experimental results with 34 subjects (30 eyes for spherical equivalent and 38 eyes for astigmatism assessment) are presented. Measurement with the app was compared with standard clinical methods. The average absolute error of spherical equivalent refraction was 0.63D, and the average absolute error of astigmatism measurement was 0.28D. In addition, 22 subjects were enrolled to evaluate inter-pupillary distance (IPD) measurement with the app. The average absolute error in IPD measurement with the app was 1.2 mm. The protocol for measuring IPD with the app is also described.

<meta charset="utf8"></head><body>Teste de refração subjetiva usando um smartphone para triagem visual

This paper presents the protocols and clinical validation data for using a smartphone app to subjectively measure refractive error.

Teste de refração subjetiva usando um smartphone para triagem visual

To improve access to vision care and to enable mass vision screening, a smartphone app has been developed to measure refractive errors. Without needing ...

Supply

Supply is a fundamental concept in economics that refers to the quantity of goods and services that producers are willing and able to offer for sale at various prices within a given period.
It represents the relationship between the price of a product and the quantity supplied. Generally as prices rise, producers are typically motivated to supply more goods or services to the market, increasing the quantity supplied. Conversely, when prices fall, producers may reduce the quantity supplied as it becomes less profitable.
Several factors affect supply, including input costs, technology, government policies, the number of suppliers, and future price expectations. Input costs, like raw materials and labor, significantly impact production expenses. Higher input costs tend to decrease supply, while lower input costs can increase it. Technological advancements can enhance production efficiency, leading to an increase in supply. Government policies, such as subsidies, taxes or regulations, shape supply dynamics. The number of suppliers in the market increases or decreases overall supply levels. Additionally, producers&#39; expectations about future prices can influence current supply decisions. If higher price anticipated, producers might reduce current supply to sell more later, and vice versa. All these factors interact to determine how much producers are willing and able to supply at different price levels.
Understanding supply is essential as it helps individuals comprehend how businesses make production decisions and how changes in supply affect market equilibrium.

Abastecimento

Supply is the amount of a good or service that producers are willing and able to sell at a particular price during a specific period.
It depends on ...

Medindo o custo do interruptor do uso do smartphone durante a caminhada

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