sexta-feira, 21 de julho de 2017

Desreguladores endócrinos: há motivo para medo?


Nota dos Editores americanos: o texto a seguir é uma discussão editada entre os endocrinologistas Dr. Boris Hansel e Dr. Patrick Fénichel, traduzida do francês.
Dr. Boris Hansel: Existe um assunto que vem levantando muitas questões e acendendo debate e animosidade: os desreguladores endócrinos (também chamados de disruptores endócrinos). É uma questão de saúde pública, mas também um problema ecológico que preocupa a todos.
Reconhecidamente, muitos de nós não sabemos muito sobre desreguladores endócrinos, e é difícil de separar fatos de ficção. Para os médicos, isso traz um problema na prática diária: o que dizemos aos nossos pacientes quando estamos aconselhando, e ao mesmo tempo evitando fobias, quanto aos desreguladores endócrinos? Isso é uma nova mania?
Dr. Patrick Fénichel: Não, eu não acredito que seja uma mania. É um conceito que precisa ser colocado em um contexto histórico adequado.
Tudo começou com os biólogos americanos, que observaram uma redução de fertilidade em certas espécies, micropênis em crocodilos da Flórida, criptorquidismo (testículos não descidos) em panteras na Flórida, e assim por diante. Em cada caso, eles observaram que a anormalidade era causada por um desastre ecológico local provocado por um vazamento químico, o uso de pesticidas na agricultura ou químicos industriais.
Dr. Hansel: Essas observações foram feitas em animais nas décadas de 1950, 1960 e 1970. E quanto aos humanos?
Dr. Fénichel: Clínicos gerais, pediatras e especialistas em reprodução realizaram diversas observações em humanos que foram reunidas e levaram ao conceito de desreguladores endócrinos.
Uma dessas observações foi a infeliz história do dietilestilbestrol (DES) envolvendo meninas que haviam sido expostas in utero a esse estrogênio sintético, prescrito entre as décadas de 1950 e 1970 para milhões de mulheres nos Estados Unidos e Europa para reduzir o risco de aborto. Isso causou cânceres (incluindo câncer vaginal, que é muito raro e grave), anormalidades de ciclo menstrual, e anormalidades uterinas, como útero em forma de T, todas relacionados à exposição a esse estrogênio sintético.
As pessoas têm a impressão de que os desreguladores endócrinos são apenas químicos industriais, mas alguns são na verdade encontrados na natureza.
Dr. Hansel: Ao ler a literatura especializada ou científica, ou mesmo jornais, é difícil chegar a uma definição exata de desregulador endócrino. A definição simples é "uma substância química que interfere no sistema hormonal, aumentando ou bloqueando a produção de hormônios ou bloqueando os efeitos deles". Nós poderíamos ser mais precisos?
Dr. Fénichel: A definição é exatamente essa. É qualquer substância – natural ou sintética – de uma planta (como grãos de soja ou certas toxinas fúngicas) ou um químico usado na indústria ou na agricultura (como um pesticida), que interfere de alguma forma com sistemas de regulação hormonal, perturbando a homeostase. Uma parte importante da definição é a potencial consequência para a prole.

Dr. Hansel: As pessoas têm a impressão de que desreguladores endócrinos são apenas químicos industriais, mas na verdade alguns são encontrados na natureza.
Dr. Fénichel: Certamente. Alguns são encontrados na natureza. Em certas circunstâncias, alguns desses têm efeitos negativos, mas outros podem ter efeitos benéficos.
Um exemplo de desregulador endócrino com efeitos negativos é a genisteína, encontrada na soja. O resveratrol, componente no tanino dos bons vinhos Bordeaux, que dizem ser um agente antioxidante e anticancerígeno, pode ter efeitos benéficos em certas circunstâncias. No entanto, ele interfere no sistema estrogênico e em outros receptores hormonais – sendo um desregulador endócrino.
Dr. Hansel: Então, substâncias de origem vegetal, substâncias sintéticas, químicos, medicamentos e assim por diante podem ser desreguladores endócrinos. Algum outro medicamento, como o DES, tem efeitos prejudiciais potenciais?
Dr. Fénichel: Certos medicamentos usados em endocrinologia, com todos os seus efeitos colaterais negativos, poderiam ser considerados desreguladores endócrinos. Tome, por exemplo, a espironolactona, um diurético bem conhecido. Ele causa ginecomastia. Esse é um excelente exemplo de uma medicação que é um desregulador endócrino.
Dr. Hansel: Você acaba tendo a impressão de que existem desreguladores endócrinos em toda parte – medicamentos, alimentos, pesticidas, etc. É possível fazer alguma classificação? Em outras palavras, em relação a quais níveis de desreguladores endócrinos precisamos estar especialmente vigilantes por nossa saúde?
Dr. Fénichel: A grande questão é encontrar as doses ambientais "limítrofes".
O conceito de desreguladores endócrinos revolucionou a toxicologia. Ele mostrou que a exposição crônica a quantidades muito pequenas de uma dada substância (que é geralmente lipofílica e se acumula no tecido adiposo) pode ser prejudicial, mesmo em pequenas quantidades, durante certos períodos da vida; em particular, durante janelas de susceptibilidade de alto risco, como o desenvolvimento fetal ou o início da infância.
Dr. Hansel: Você está dizendo que não deveríamos simplesmente declarar que vamos proibir um certo nível de exposição ou banir uma substância em particular. Mais que isso, em determinados momentos deveríamos ser especialmente vigilantes quanto ao longo prazo e talvez mais vigilantes em certas populações, como indivíduos com sobrepeso.

Efeitos sexuais e reprodutivos

Dr. Hansel: Vamos analisar alguns exemplos concretos. Nós ouvimos muito sobre o efeito dos desreguladores endócrinos no sistema reprodutor. Isso é um problema real? Chega a ser um problema de saúde pública em termos de fertilidade?
Dr. Fénichel: O efeito não é somente na fertilidade. É preciso avaliar o sistema reprodutor em um sentido amplo. Podemos estender o efeito para a identidade sexual. Pesquisadores chegaram a levantar a possibilidade de uma ligação com o aumento no número de indivíduos transexuais e homossexuais.
Substâncias muito semelhantes a estrogênios são encontradas mesmo em plantas, como soja e genisteína. Em outras palavras, muitos compostos naturais e sintéticos são semelhantes a estrogênios, e podem ter atividade estrogenomimética. Como resultado, vão existir repercussões para o sistema reprodutor.
Uma maior atenção foi dada inicialmente aos meninos. A questão da exposição aos desreguladores endócrinos foi levantada para quatro condições:
  • Criptorquidia, que afeta 2% dos recém-nascidos masculinos;
  • Hipospadia, na qual o meato uretral está na face inferior do pênis ao nascimento;
  • Câncer testicular; e
  • Redução da fertilidade masculina.
Essas quatro condições vêm aumentando em incidência desde a década de 1930. Essas condições foram reproduzidas em animais por meio da exposição das mães a certos desreguladores endócrinos estrogênicos. Meninos nascidos de mães tratadas com DES tiveram uma maior prevalência de criptorquidia, hispopádia e câncer testicular.
Na medicina, um experimento ou estudo nunca prova nada em 100%. É mais como uma reunião de argumentos.
Na história do DES, temos argumentos experimentais e epidemiológicos. Existe uma ligação entre a exposição a alguns desses desreguladores endócrinos estrogenomiméticos e anormalidades no sistema reprodutor masculino e na função reprodutora masculina.

Impacto na obesidade e no diabetes

Dr. Hansel: A segunda área principal é a epidemia de obesidade. Alguns associam os desreguladores endócrinos com a obesidade; especificamente, as consequências metabólicas dele – síndrome metabólica e diabetes. Podemos estabelecer, e com que grau de certeza, uma ligação entre desreguladores endócrinos e doenças metabólicas associadas com o sobrepeso?
Dr. Fénichel: Quanto aos transtornos metabólicos – obesidade (especialmente a obesidade metabolicamente ativa), síndrome metabólica e diabetes tipo 2 – existem três tipos de argumentos.
Exposição acidental. Após a explosão de uma fábrica em Seveso (Itália), em 1975, a população local foi exposta a níveis muito altos de dioxina. Nos anos que se seguiram, a taxa de diabetes foi muito maior do que a da população em geral.
Outro exemplo de exposição ambiental envolve os veteranos que retornaram do Vietnã e que estiveram nos aviões que lançaram bombas com Agente Laranja. Essa substância também contém dioxina. Infelizmente, nós não temos todos os dados do Vietnã, mas é conhecido o fato de que muitos veteranos americanos desenvolveram diabetes, especialmente se estiveram nos aviões que espalharam a substância. Esses eventos podem ser descritos como "agudos".
Estudos epidemiológicos na população em geral. O Nurses' Health Study foi um estudo muito bom no qual enfermeiras americanas foram acompanhadas por 15 anos. As enfermeiras forneceram amostras de sangue e urina, que foram testadas para ftalatos (encontrados em plásticos) e bisfenol A (encontrado em plásticos, resinas, polivinilclorido, e em quase toda parte).
Os níveis nessas enfermeiras eram mais elevados naquelas que desenvolveram diabetes durante o seguimento de 15 anos.[1] Foi um bom estudo prospectivo de correlação, mas não ofereceu nenhuma prova.
Estudos fundamentais. Alguns estudos incríveis[2,3] foram conduzidos na Europa, em particular, por uma equipe espanhola em Alicante, com meu amigo Angel Nadal. Eles mostraram que a exposição in utero a bisfenol A em camundongos promoveu o desenvolvimento de resistência insulínica, transtornos da regulação da glicose e obesidade – não apenas nas mães mas também na prole masculina, possivelmente por conta de hormônios sexuais ou pela ação estrogênica do bisfenol A.
Quando adulta, a prole masculina desenvolveu resistência insulínica, transtornos da regulação da glicose e anormalidades das ilhotas pancreáticas beta. No final, eles tiveram prejuízo da secreção de insulina, resultando em resistência insulínica e transtornos de secreção pancreática, o que levou a um diabetes tipo 2 "experimental".
Nós realmente precisamos ter medo no nosso dia-a-dia quando bebemos em um copo de plástico?
Dr. Hansel: Então estamos falando tanto sobre resistência insulínica quanto sobre alteração na secreção pancreática. Se tomarmos esses exemplos epidemiológicos e estudos experimentais que apontam para uma ligação causal provável, e extrapolarmos para o nosso dia-a-dia, não existe (e esse contrargumento é frequente) uma diferença considerável no nível de exposição a esses desreguladores endócrinos?
Você mencionou alguns acidentes epidemiológicos e alguns estudos experimentais nos quais, acredito eu, doses muito altas de desreguladores endócrinos foram administradas. Nós realmente precisamos ter medo no nosso dia-a-dia quando bebemos em um copo de plástico ou comemos alimentos de uma embalagem que foi aquecida e pode ter liberado desreguladores endócrinos? Essa exposição é semelhante à observada nesses estudos experimentais epidemiológicos?
Dr. Fénichel: Existem duas classes dos desreguladores endócrinos. Um tipo são as substâncias altamente lipofílicas, e que persistem em lençóis freáticos e tecidos adiposos, onde se acumulam. Outro são muito menos persistentes.
Por exemplo, pesticidas são muito persistentes. Se você é exposto a uma quantidade muito pequena de pesticidas, eles vão se acumular em seu tecido adiposo e serão liberados gradualmente.
Um composto como o bisfenol A, que é encontrado em plásticos, não é de todo persistente, mas você está exposto a ele todos os dias. Ele é oxidado no fígado entre duas e três horas, conjugado, e eliminado na urina. Assim, se você foi exposto pela manhã, não estará mais exposto à tarde.

Impacto neurológico

Dr. Hansel: Um terceiro tópico geralmente é discutido é quanto as condições que podem estar associadas com desreguladores endócrinos. A preocupação envolve tudo na esfera comportamental e neurológica. Estou falando sobre doença de Parkinson, autismo, síndrome de hiperatividade, e assim por diante.
O que sabemos sobre relação causal?
Dr. Fénichel: A tireoide é especialmente importante para o desenvolvimento do cérebro fetal. Mulheres com hipotireoidismo grave, especialmente no início da gestação, têm filhos com transtornos mentais. O hipotireoidismo é, portanto, muito grave.
Muitos desses desreguladores endócrinos causam alterações na tireoide. Em outras palavras, eles bloqueiam a ação de hormônios tireoidianos em suas células-alvo, incluindo células cerebrais.
PCBs provavelmente têm um papel na alta incidência de transtorno de déficit de atenção e hiperatividade (TDAH), certas formas de autismo e doenças neurodegenerativas.
Vamos analisar os bifenilos policlorados (PCBs), dos quais ouvimos muito a respeito. Os PCBs são encontrados, por exemplo, em níveis extremamente elevados nos peixes da Grã Bretanha à França porque, sendo altamente lipofílicos, acumulam em tecidos gordurosos.
Os PCBs, que foram utilizados como isolantes elétricos por muitos anos e agora estão banidos, ainda estão presentes em lençóis freáticos e no tecido adiposo de animais e humanos. Eles são antagonistas de hormônios tireoidianos. Eles bloqueiam a ação dos hormônios da tireoide em suas células-alvo, e podem bloquear o desenvolvimento do cérebro fetal.
PCBs provavelmente têm um papel na alta incidência de transtorno de déficit de atenção e hiperatividade, certas formas de autismo e doenças neurodegenerativas.
Em um estudo[4] conduzido em Nice, nós medimos os níveis de PCB no sangue de cordão e acompanhamos 50 crianças a cada seis meses por três anos (usando o mesmo psicólogo) para estudar a aquisição de linguagem delas. Descobrimos que, quanto maiores os níveis de PCB no sangue de cordão, mais frequentes foram os transtornos de aquisição de linguagem.
Recomendações práticas
Dr. Hansel: E agora chegamos a algumas recomendações práticas para nossos colegas, para que eles possam dar o aconselhamento correto na prática diária. Sem fazer disso uma obsessão ou fobia, o que poderia acabar causando um estresse no dia-a-dia, quais recomendações deveríamos dar aos pacientes para evitar uma exposição prejudicial a desreguladores endócrinos?
Dr. Fénichel: De forma muito simples, deveríamos dizer o seguinte:
  • O tabagismo é muito danoso para gestantes. O que é menos conhecido é que ele é prejudicial por conta dos desreguladores endócrinos do alcatrão do tabaco. Benzopireno, hidrocarbonetos aromáticos policíclicos e cádmio (um metal encontrado no alcatrão do tabaco) são desreguladores endócrinos. Assim, gestantes devem parar de fumar, e seus parceiros devem parar de fumar também, por conta da exposição passiva.
  • Evitar aquecer alimentos em recipientes plásticos no micro-ondas porque eles contêm bisfenol, que o calor libera para os alimentos, e evitar cobrir a comida com envoltórios plásticos para que ela aqueça mais depressa.
  • Durante a gestação, é melhor ingerir frutas e vegetais orgânicos. Mesmo que você não saiba exatamente de onde são provenientes, você será exposta a menos pesticidas ao comer produtos orgânicos durante esse breve período de tempo.
  • Não pintar o quarto do futuro bebê durante a gestação porque a tinta contém solventes que são desreguladores endócrinos.
  • Obviamente, não utilizar pesticidas ou inseticidas no jardim.
  • Evitar alimentos e bebidas enlatadas. Muitas latas têm um revestimento de plástico para evitar que a comida ou bebida entre em contato com o metal. Esse revestimento plástico contém bisfenol A.
  • Mulheres que planejam engravidar são instruídas a utilizar ácido fólico ou vitamina B9. Apesar dessa recomendação, apenas 5% das mulheres francesas que estão planejando engravidar estão tomando ácido fólico. Por que o ácido fólico é tão importante? Ele bloqueia a metilação de genes, um mecanismo de desregulação endócrina. É um mecanismo epigenético. Então mulheres em idade fértil devem tomar ácido fólico.
  • As mulheres também devem ingerir iodo. Quanto maior a deficiência de iodo de uma pessoa e o hipotireoidismo subclínico, maior a susceptibilidade dela a desreguladores endócrinos.
Dr. Hansel: Fora da gestação, essas recomendações devem ser seguidas o máximo possível diariamente? E se você tivesse de escolher duas delas, quais seriam?
Dr. Fénichel: Além da gestação, o foco deveria ser nas crianças jovens durante o desenvolvimento, e em pacientes com câncer em quimioterapia. Certos desreguladores endócrinos, como o bisfenol, podem interferir com esses medicamentos. Isso não é amplamente conhecido, mas é importante.
Dr. Hansel: Nós podemos assumir que é necessário grande cautela durante o tratamento do câncer e naqueles com alto risco de desenvolver certos cânceres dependentes de hormônio.

segunda-feira, 17 de julho de 2017

Estilo de Vida Minimalista





O Minimalismo que venho estudando, praticando e falando aqui no blog, é na verdade um estilo de vida. Ele anda para o lado oposto do Consumismo.

O que acontece é que desde que nascemos, vivemos bombardeados de anúncios e ofertas de produtos que quase nunca precisamos. São necessidades criadas pelos comerciais que nos deixam inquietos e insatisfeitos. Quem nunca disse “Eu PRECISO desse sapato!” mesmo tendo uns 10 em casa.  Vivemos nesse ambiente sem perceber o quanto isso tudo nos frustra e intoxica.

O que eu percebo e provavelmente você também, é que hoje em dia temos tudo e ao mesmo tempo não estamos felizes com nada. O que chamo de “tudo” seria um lar (próprio, alugado, casa da mãe, sogra…um teto), um emprego, temos tecnologia cada vez mais accessível e barata, uma infinidade de opções de roupas e calçados para todos os gostos e condições financeiras, tratamentos estéticos, mesmo assim falta alguma coisa…aquele vazio existencial nunca foi tão grande.

O minimalismo dá uma sacudida nisso, ele vem te mostrar que seus objetos não podem te dominar. Eles não podem consumir o seu tempo tão precioso, eles não podem tomar a sua casa e seu espaço te deixando sufocado.

Seus objetos estão aqui para te servir, auxiliar nas tarefas diárias, te dar prazer e até mesmo te fazer feliz! É uma delicia abrir um livro novo e passar horas lendo e imaginando os cenários da estória que está sendo contada…mas você precisa de 300 deles encaixotados, mofando, ou enchendo armários e estantes?

[O minimalismo] É uma espécie de curadoria de momentos e coisas, apenas o melhor será parte da sua história.

Foi aí que o minimalismo me pegou, ele não te ensina a se privar de nada, ele te ensina a ficar rodeada apenas do melhor. É uma espécie de curadoria de momentos e coisas, apenas o melhor será parte da sua história.

Pra quem viveu por anos acumulando objetos desnecessários e até tóxicos (por trazerem memórias desagradáveis), o minimalismo vem como uma purificação. Ele te ajuda a se desfazer do que não é benéfico para ficar apenas o que é útil, bonito e te faz feliz.

Com ele você minimiza…


  • Compromissos, para que sobre tempo para a família, amigos e para você mesmo.
  • Pessoas tóxicas, que só te trazem mal-estar.
  • Alimentação rica em gordura e açúcar. Você percebe que uma alimentação mais saudável te dá mais disposição e saúde para colocar em prática seus planos.
  • Lixo, por não comprar coisas desnecessárias que serão eventualmente descartadas. Além disso, depois que eu diminuí o consumo de alimentos industrializados a quantidade de embalagens também foi reduzida.
  • Gastos, quando você passa a comprar apenas o necessário, sem desperdício, seu dinheiro até sobra! Sobra uns 50 reais…mas sobra hehe.


Ser minimalista é isso, você passa a ter menos coisas, que vão gerar menos problemas te dando mais tempo e mais liberdade. Essa tem sido uma experiência transformadora!

Fonte: https://minimeminimalismo.wordpress.com/2016/11/28/estilo-de-vida-minimalista/

sexta-feira, 14 de julho de 2017

USP testa estimulação cerebral contra obesidade

A terapia mais recomendada para a obesidade ainda é a mudança do estilo de vida. Mas para muitos, tanto a perda de peso como a sua manutenção são extremamente desafiadoras. Para essas pessoas, surge uma nova esperança: a estimulação transcraniana por corrente contínua, técnica de modulação não invasiva da atividade cerebral.

Essa técnica, que utiliza corrente elétrica contínua e de baixa intensidade, emitida diretamente na área cerebral de interesse por meio de pequenos eletrodos, já vem sendo utilizada em várias partes do mundo para tratar enfermidades neuropsiquiátricas e outras doenças, e agora começa a ser usada para o controle da obesidade.

Pesquisas de curta duração na Europa e Estados Unidos já indicaram a eficácia dessa técnica no controle do desejo de alimentos e do apetite. No Brasil, a Faculdade de Medicina de Ribeirão Preto (FMRP) da USP iniciou no mês de junho pesquisa para análise da efetividade desta técnica no controle da obesidade.

A novidade no Brasil, segundo o professor Miguel Alonso, diretor do laboratório Bariatric and Nutritional Neuroscience, Beth Israel Deaconess Medical Center, Harvard Medical School e parceiro dos pesquisadores brasileiros da FMRP,  é que o estudo será mais amplo e completo. “Será um passo mais adiante do que foi feito até agora, que é entender melhor o potencial da eficácia da estimulação para a perda de peso no contexto clínico da obesidade. Cerca de dez estudos estão sendo feitos em outras partes do mundo, mas todos de curta duração, com número de sessões muito limitado”, enfatiza Alonso.

Alonso visitou a FMRP em junho deste ano e informou que nos Estados Unidos, em um dos estudos já realizados, os voluntários perderam 1% de peso por semana. O que se recomenda para as dietas hipocalóricas é a redução de peso de 0,5 a 1 kg por semana.

Segundo as pesquisadoras responsáveis pelo estudo na FMRP, Priscila Giacomo Fassini e Vivian Marques Miguel Suen, a técnica afeta os mecanismos que ajudam a reduzir o desejo de alimentos e a ingestão alimentar. “Esta técnica melhora os processos cognitivos, que são chave para a autorregulação do comportamento alimentar.”

De fato, dizem, “o progresso dos participantes no estudo da FMRP será avaliado, usando tarefas informatizadas que medem funções executivas do cérebro, relacionadas com o comportamento alimentar”. A especialista Greta Magerowski, do grupo de Alonso, também esteve no Brasil para implementar essas novas metodologias.

Na FMRP, o presente estudo randomizado, duplo-cego e controlado (quando nem pesquisadores nem pacientes sabem quem está tendo o tratamento testado e quem está recebendo apenas placebo), tem como finalidade avaliar os efeitos da estimulação transcraniana por corrente contínua, isoladamente e em combinação com uma dieta hipocalórica no peso corporal. O objetivo é de auxiliar a perda de peso e evitar a recuperação de peso ao longo do tempo. “Esta proposta inovadora de pesquisa clínica exploratória utiliza uma intervenção biomédica que poderia transformar o paradigma para a perda de peso e a manutenção da perda de peso, oferecendo uma nova forma de tratar a obesidade.”

As pesquisadoras lembram que os resultados deste estudo devem levar a estudos maiores a longo prazo para avaliar essa intervenção de neuromodulação não invasiva.

A pesquisa na FMRP incluirá mulheres de 20 a 40 anos com índice de massa corpórea (IMC) entre 30 e 35 kg/m2 e consistirá em uma sessão inicial de estimulação cerebral não invasiva. Na segunda fase, as participantes receberão um total de dez sessões de estimulação cerebral durante o período de duas semanas. Na terceira fase, será iniciada uma dieta hipocalórica individualizada associada a mais duas semanas de estimulação cerebral.

Colaboram com o estudo, o professor Júlio Sérgio Marchini, da FMRP, a pesquisadora Sai Krupa Das, da Tufts University, e os pesquisadores Miguel Alonso e Greta Magerowski, da Harvard Medical School, dos Estados Unidos.

A professora Vivian lembra que a obesidade é um problema global e, no Brasil, está crescendo, enquanto nos Estados Unidos está se mantendo, segundo o professor Alonso. “Necessitamos tratamentos para beneficiar os pacientes para esse grande problema de saúde pública”, lembram.

Mais informações: e-mail priscilafassini@usp.br

Fonte: http://jornal.usp.br/ciencias/ciencias-da-saude/usp-testa-estimulacao-cerebral-contra-obesidade/

Novo marcador indica risco de diabete mesmo com outros exames normais

O escore de lipoproteínas associadas à resistência insulínica (LPIR) é um marcador que pode detectar mais precocemente o risco de desenvolver diabete tipo 2, mesmo em pessoas que possuem peso, glicemia e colesterol normais. O resultado está em pesquisa com participação do clínico geral e cardiologista Paulo Henrique Harada, pós-doutorando do Hospital Universitário (HU) da USP, realizada na Harvard Medical School (Estados Unidos). Esse marcador melhorou a avaliação de risco de diabete tipo 2 em um grupo de 25 mil mulheres, mesmo já se considerando outros marcadores tradicionais.
Os marcadores clássicos do diabete tipo 2 são idade, índice de massa corpórea (IMC), glicemia, HDL colesterol, triglicérides e histórico familiar da doença. “Em conjunto, esses fatores têm um bom desempenho, mas ainda assim com margem de erro significativa”, afirma Harada. O valor agregado desse marcador na detecção do risco de diabete tipo 2 foi testado em 25 mil mulheres ao longo de 20 anos, que fazem parte do Women’s Health Study do Brigham and Women’s Hospital, vinculado à Harvard Medical School.O LPIR é um marcador composto baseado em seis partículas de colesterol (lipoproteínas). Estas são extremamente sensíveis à resistência insulínica, mecanismo ligado ao desenvolvimento de diabete tipo 2. “A partir desses números é feito um escore ponderado que vai de 0 a 100, onde maiores valores indicam maior risco de diabete tipo 2”, explica o médico.
O LPIR esteve associado com o risco de diabete durante esses 20 anos. “Foi possível classificar os pacientes como de alto risco (LPIR acima de 67), que têm 2,2 vezes o risco de desenvolver diabete daqueles de baixo risco (LPIR abaixo de 30)”, destaca o pesquisador. “Mesmo em pessoas que se supunha de risco baixo (IMC, glicose, HDL colesterol e triglicérides normais), a presença de LPIR alto esteve associado a maior risco de diabete.”

Risco

O médico ressalta que o diabete tipo 2 é uma doença que leva anos ou até décadas para se desenvolver. “Nos estágios iniciais, a resistência insulínica é compensada por uma maior atividade do pâncreas, o que não é detectado pelos parâmetros atuais de glicemia”, destaca. “O LPIR pode detectar o risco de desenvolver a doença no início dessa trajetória e alertar o indivíduo com bastante antecedência. Confiar apenas no aumento dos parâmetros de glicemia pode ser enganoso.” Nos pacientes com risco intermediário de desenvolver diabete pelo método tradicional, o uso do marcador reclassificou corretamente esse risco tanto para baixo como para cima. “Baseado em avaliação mais precisa, podemos agir de forma mais direcionada para orientações de dieta e atividade física.”
Segundo o pesquisador, os pré-diabéticos (estágio intermediário antes do diabete) já apresentam maior risco de infarto, derrame, problemas oftalmológicos, neurológicos e renais. O que demonstra as potenciais implicações da detecção de risco e prevenção precoce do diabete tipo 2. Mas faz uma ressalva: “Embora o método seja bastante promissor, ele ainda precisa ser validado por mais estudos para uso nos serviços de saúde”, observa. “Os parâmetros clássicos para a avaliação de risco de desenvolvimento de diabete continuam sendo a referência.”
Embora o método seja bastante promissor, ele ainda precisa ser validado por mais estudos para uso nos serviços de saúde. Parâmetros clássicos continuam referência.”
Esta pesquisa tem como grande força o longo tempo de acompanhamento de um grande número de mulheres. Mas o médico faz uma ressalva: “Esse trabalho envolveu apenas mulheres brancas, seria preciso avaliar o funcionamento do marcador em homens e em grupos de composição étnica mas diversificada”, diz. Apesar disso, os resultados são consistentes com outro grande estudo envolvendo homens/mulheres, brancos/negros/latinos/asiáticos.
Harada faz parte da equipe de pesquisadores do projeto Estudo Longitudinal de Saúde do Adulto (Elsa Brasil) no HU. O médico fez mestrado em saúde pública na TH Chan Harvard School of Public Health e integrou o grupo de pesquisa do Brigham and Women’s Hospital, ligado à Harvard Medical School, com bolsa da Fundação Lemann. As conclusões do trabalho são descritas em artigo da publicação científica Journal of Clinical Lipidology.

Quando a culpa também é sua - Por Nutricionista Carol Sartori



Repost @sartori.carol - Todo profissional da saúde que prescreve um tratamento ilegal ou adota uma conduta antiética está agindo de forma INCORRETA e pode ser penalizado por eventuais danos à saúde do paciente.

Da mesma forma, profissionais que prescrevem medicamentos sem indicação (ex: hormônios para pessoas com valores dentro do padrão de referência, GH e demais hormônios com fins estéticos, sibutramina e orlistate para pacientes não obesos/saudáveis, forxiga, jardiance e insulina para pacientes sem diabetes/obesidade/resistência à insulina, dieta do HCG, etc) ou prometem cura para doenças atualmente incuráveis (autoimunes, diabetes, etc) ou melhora de doenças com base em tratamentos sem comprovação científica (lugol, agua alcalina, detox, etc) estão ERRADOS e podem inclusive ser acusados do crime de estelionato.

Mas hoje o objetivo é falar DE VOCÊ, paciente, que PROCURA esse tipo de coisa SABENDO que não está correto. Nem sempre as pessoas são enganadas por serem ignorantes a respeito. Eu garanto que quem paga 1000,00 reais em uma consulta de um medico que prescreve aquele coquetel enorme (testosterona, oxandrolona, GH, orlistate, forxiga, sibutramina e mais 1848743747448 de cápsulas manipuladas) para melhora ESTÉTICA (com a desculpa de que vai melhorar a saúde) sabe muito bem onde está se metendo e no fundo sabe que não é o caminho correto.

Da mesma forma, se vc segue orientação ou compra consultoria de blogueira, segue modas e acredita em tudo, vc está assumindo o risco! Não é possível que vc não saiba que essas pessoas não têm credibilidade para falar de saúde! Não é possível que vc não saiba identificar a hipocrisia por trás de algumas postagens.

Por isso, repense! Não dê sorte ao azar. Cuide da sua saúde de verdade. Não procure doenças que não existem. Não cultive mais o seu corpo do que a sua mente. Proteja-se dessa necessidade de impor insatisfação que a indústria do corpo perfeito deflagra para movimentar milhões de $ às custas de enganação e prejuízos à saúde. Não queira precisar procurar um médico de verdade para consertar o que o "operador de milagres" estragou! Ame-se!

Louco por doces? Seu fígado pode ser o culpado

Você é uma pessoa muito gulosa e não pode ficar sem comer doces? A culpa disso pode ser de variações de um gene que se ativa no seu fígado.

Segundo o estudo conduzido pelo Novo Nordisk Foundation Center for Basic Metabolic Research, entidade sediada na Universidade de Copenhague, na Dinamarca, "um hormônio chamado FGF21 que é secretado pelo fígado depois de comer doces pode determinar" quem é mais viciado nesses alimentos açucarados.

De acordo com a pesquisa, divulgada na revista científica "Cell Metabolism", quem possui certas variantes ou mutações do gene FGF21 acaba tendo uma chance maior de ser um grande consumidor de doces, como balas, chocolates e sorvetes, em relação a outras pessoas e tendo uma maior dificuldade de resistir à tentação de comer esses alimentos.

Neste estudo, descobriu-se que o gene FGF21 tem um papel importante na regulação da quantidade de doces que uma pessoa tende a comer, ou seja, de um certo modo, ele tem responsabilidade no "controle da gula por doces" de cada um. Os pesquisadores estudaram o DNA e os hábitos alimentares de 6,5 mil dinamarqueses que foram distribuídos em uma "escala de gula" em base na quantidade de doces que consumiam em média.

Os estudiosos também fizeram um teste clínico com uma pequena amostra destes voluntários para realizar a pesquisa. Nele, foi dada uma bebida muito adocicada (com a quantidade de açúcar de duas latas de Coca-Cola) a 51 pessoas, que haviam dito anteriormente que amavam muito ou odiavam comer doces.

Com isso, os pesquisadores fizeram uma medição dos níveis do FGF21 no sangue das pessoas, que estavam em um jejum de 12 horas, logo antes delas beberam o líquido e outras até cinco horas após a ingestão da bebida. A partir das medições chegou-se a conclusão de que as pessoas que não gostavam muito de doces tinham uma quantidade 50% maior do hormônio que os outros participantes.

No entanto, depois que elas consumiram a bebida açucarada, os níveis do FGF21 no sangue seguiram a mesma trajetória que o das outras pessoas até eles ficarem semelhantes. Sendo assim, a conclusão do estudo apontou que quem tem as variações no gene, que foram representadas no teste pelas pessoas com os níveis menores de FGF21 no sangue em jejum, tem uma probabilidade de estar no topo dos consumidores de doces 20% maior em relação aos outros participantes.

Fonte: http://mobile.opovo.com.br/noticias/saude/2017/05/louco-por-doces-seu-figado-pode-ser-o-culpado.html

Celular diminui a cognição?

Um novo estudo publicado no Journal of the Association for Consumer Research indica que apenas a presença de um smartphone já está associada a uma diminuição na capacidade cognitiva.

Segundo dados da publicação, as pessoas interagem com seus smartphones em uma média de 85 vezes por dia, imediatamente após despertar, durante o dia, antes de ir dormir, e mesmo no meio da noite. Estudos recentes tem focado em como a interação do indivíduo com o celular pode interromper o desempenho cognitivo.

No artigo atual, pesquisadores exploraram uma nova situação: quando os smartphones não estão em uso, mas são meramente presentes. Foi testada a hipótese de que a presença de um celular ocupa recursos cognitivos de capacidade limitada, subestimando assim o desempenho cognitivo.

Com base nos resultados de duas experiências, os pesquisadores descobriram que a presença de smartphones está associada a uma redução na capacidade cognitiva. Este desfecho foi mais elevados nos indivíduos com maior dependência do uso de celulares. Segundo os autores, isso pode ter implicações práticas para a tomada de decisões e para o bem-estar dos consumidores.

Fonte: http://www.journals.uchicago.edu/doi/full/10.1086/691462


Our smartphones enable—and encourage—constant connection to information, entertainment, and each other. They put the world at our fingertips, and rarely leave our sides. Although these devices have immense potential to improve welfare, their persistent presence may come at a cognitive cost. In this research, we test the “brain drain” hypothesis that the mere presence of one’s own smartphone may occupy limited-capacity cognitive resources, thereby leaving fewer resources available for other tasks and undercutting cognitive performance. Results from two experiments indicate that even when people are successful at maintaining sustained attention—as when avoiding the temptation to check their phones—the mere presence of these devices reduces available cognitive capacity. Moreover, these cognitive costs are highest for those highest in smartphone dependence. We conclude by discussing the practical implications of this smartphone-induced brain drain for consumer decision-making and consumer welfare.
We all understand the joys of our always-wired world—the connections, the validations, the laughs … the info. … But we are only beginning to get our minds around the costs.
Andrew Sullivan (2016)
The proliferation of smartphones has ushered in an era of unprecedented connectivity. Consumers around the globe are now constantly connected to faraway friends, endless entertainment, and virtually unlimited information. With smartphones in hand, they check the weather from bed, trade stocks—and gossip—while stuck in traffic, browse potential romantic partners between appointments, make online purchases while standing in-store, and live-stream each others’ experiences, in real time, from opposite sides of the globe. Just a decade ago, this state of constant connection would have been inconceivable; today, it is seemingly indispensable.1 Smartphone owners interact with their phones an average of 85 times a day, including immediately upon waking up, just before going to sleep, and even in the middle of the night (Perlow 2012; Andrews et al. 2015; dscout 2016). Ninety-one percent report that they never leave home without their phones (Deutsche Telekom 2012), and 46% say that they couldn’t live without them (Pew Research Center 2015). These revolutionary devices enable on-demand access to friends, family, colleagues, companies, brands, retailers, cat videos, and much more. They represent all that the connected world has to offer, condensed into a device that fits in the palm of one’s hand—and almost never leaves one’s side.
The sharp penetration of smartphones, both across global markets and into consumers’ everyday lives, represents a phenomenon high in “meaning and mattering” (e.g., Kernan 1979; Mick 2006)—one that has the potential to affect the welfare of billions of consumers worldwide. As individuals increasingly turn to smartphone screens for managing and enhancing their daily lives, we must ask how dependence on these devices affects the ability to think and function in the world off-screen. Smartphones promise to create a surplus of resources, productivity, and time (e.g., Turkle 2011; Lee 2016); however, they may also create unexpected deficits. Prior research on the costs and benefits associated with smartphones has focused on how consumers’ interactions with their smartphones can both facilitate and interrupt off-screen performance (e.g., Isikman et al. 2016; Sciandra and Inman 2016). In the present research, we focus on a previously unexplored (but common) situation: when smartphones are not in use, but are merely present.
We propose that the mere presence of one’s own smartphone may induce “brain drain” by occupying limited-capacity cognitive resources for purposes of attentional control. Because the same finite pool of attentional resources supports both attentional control and other cognitive processes, resources recruited to inhibit automatic attention to one’s phone are made unavailable for other tasks, and performance on these tasks will suffer. We differentiate between the orientation and allocation of attention and argue that the mere presence of smartphones may reduce the availability of attentional resources even when consumers are successful at controlling the conscious orientation of attention.
Consumers’ finite capacity for cognitive processing is one of the most fundamental influences on “real world” consumer behavior (e.g., Bettman 1979; Bettman, Johnson, and Payne 1991). Individuals are constantly surrounded by potentially meaningful information; however, their ability to use this information is consistently constrained by cognitive systems that are capable of attending to and processing only a small amount of the information available at any given time (e.g., Craik and Lockhart 1972; Newell and Simon 1972). This capacity limit shapes a wide range of behaviors, from in-the-moment decision-making strategies and performance (e.g., Lane 1982; Lynch and Srull 1982) to long-term goal pursuit and self-regulation (e.g., Hofmann, Strack, and Deutsch 2008; Benjamin, Brown, and Shapiro 2013).
Consumers’ cognitive capabilities—and constraints—are largely determined by the availability of domain-general, limited-capacity attentional resources associated with both working memory and fluid intelligence (e.g., Halford, Cowan, and Andrews 2007; Jaeggi et al. 2008). “Working memory” (WM) refers to the theoretical cognitive system that supports complex cognition by actively selecting, maintaining, and processing information relevant to current tasks and/or goals. “Working memory capacity” (WMC) reflects the availability of attentional resources, which serve the “central executive” function of controlling and regulating cognitive processes across domains (Baddeley and Hitch 1974; Miyake and Shah 1999; Engle 2002; Baddeley 2003). “Fluid intelligence” (Gf) represents the ability to reason and solve novel problems, independent of any contributions from acquired skills and knowledge stored in “crystallized intelligence” (Cattell 1987). Similar to WM, Gf stresses the ability to select, store, and manipulate information in a goal-directed manner. Also similar to WM, Gf is constrained by the availability of attentional resources (e.g., Engle et al. 1999; Halford et al. 2007). Crucially, the limited capacity of these domain-general resources dictates that using attentional resources for one cognitive process or task leaves fewer available for other tasks; in other words, occupying cognitive resources reduces availablecognitive capacity.
Given the chronic mismatch between the abundance of environmental information and the limited ability to process that information, individuals need to be selective in their allocation of attentional resources (e.g., Kahneman 1973; Johnston and Dark 1986). The priority of a stimulus—that is, the likelihood that it will attract attention—is determined by both its physical “salience” (e.g., location, perceptual contrast) and its goal “relevance” (i.e., potential importance for goal-directed behavior) (e.g., Corbetta and Shulman 2002; Fecteau and Munoz 2006). Preferential attention to temporarily relevant stimuli, such as those associated with a current task or decision, is supported by WM; when a goal is active in WM, stimuli relevant to that goal are more likely to attract attention (e.g., Moskowitz 2002; Soto et al. 2005; Vogt et al. 2010). Frequently relevant stimuli, such as those associated with long-term and/or self-relevant goals, may automatically attract attention even when the goals associated with these stimuli are not active in WM (Shiffrin and Schneider 1977; Johnston and Dark 1986); for example, individuals automatically orient to the sounds of their own names in ignored audio channels (Moray 1959), and mothers, more so than nonmothers, automatically attend to infants’ emotional expressions (Thompson-Booth et al. 2014). Automatic attention generally helps individuals make the most of their limited cognitive capacity by directing attention to frequently goal-relevant stimuli without requiring these goals to be constantly kept in mind. However, automatic attention may undermine performance when an environmental stimulus is frequently relevant to an individual’s goals but currently irrelevant to the task at hand; inhibiting automatic attention—keeping attractive but task-irrelevant stimuli from interfering with the contents of consciousness—occupies attentional resources (e.g., Engle 2002).
Smartphones serve as consumers’ personal access points to all the connected world has to offer. We suggest that the increasing integration of these devices into the minutiae of daily life both reflects and creates a sense that they are frequently relevant to their owners’ goals; it lays the foundation for automatic attention. Consistent with this position, research indicates that signals from one’s own phone (but not someone else’s) activate the same involuntary attention system that responds to the sound of one’s own name (Roye, Jacobsen, and Schröger 2007). When these devices are salient in the environment, their status as high-priority (relevant and salient) stimuli suggests that they will exert a gravitational pull on the orientation of attention. And when consumers are engaged in tasks for which their smartphones are task-irrelevant, the ability of these devices to automatically attract attention may undermine performance in two ways (Clapp, Rubens, and Gazzaley 2009; Clapp and Gazzaley 2012). First, smartphones may redirect the orientation of conscious attention away from the focal task and toward thoughts or behaviors associated with one’s phone. Prior research provides ample evidence that individuals spontaneously attend to their phones at inopportune times (e.g., Oulasvirta et al. 2011), and that this digital distraction adversely affects both performance (End et al. 2009) and enjoyment (Isikman et al. 2016). Second, smartphones may redistribute the allocation of attentional resources between engaging with the focal task and inhibiting attention to one’s phone. Because inhibiting automatic attention occupies attentional resources, performance on tasks that rely on these resources may suffer even when consumers do not consciously attend to their phones. We explore this possibility in the current research.
Research on the relationship between mobile devices and cognitive functioning has largely focused on downstream consequences of device-related changes in the orientation of attention. For example, research on mobile device use while driving indicates that interacting with one’s phone while behind the wheel causes performance deficits such as delayed reaction times and inattentional blindness (e.g., Strayer and Johnston 2001; Caird et al. 2008); these deficits mirror those associated with distracting “live” conversations (Recarte and Nunes 2003). Similarly, research in the educational sphere demonstrates that using mobile devices and social media while learning new material reduces comprehension and impairs academic performance (e.g., Froese et al. 2012). However, mobile device use does not affect performance on self-paced tasks, which allow individuals to compensate for device-related distractions by picking up where they left off (e.g., Fox, Rosen, and Crawford 2009; Bowman et al. 2010). Taken together, these findings suggest that many of the cognitive impairments associated with mobile device use may simply represent the general deleterious effects of diverting conscious attention away from a focal task. What may be special about smartphones, however, is the frequency with which they seem to create these diversions; their omnipresence and personal relevance may combine to create a particularly potent draw on the orientation of attention.
A more limited body of work explores the cognitive consequences of smartphone-related distractions in the absence of behavioral interaction (i.e., when consumers consciously think about phone-related stimuli, but do not actually use their phones). Research on the attentional cost of receiving cellphone notifications indicates that awareness of a missed text message or call impairs performance on tasks requiring sustained attention, arguably because unaddressed notifications prompt message-related (and task-unrelated) thoughts (Stothart, Mitchum, and Yehnert 2015). Related research shows that individuals who hear their phones ring while being separated from them report decreased enjoyment of focal tasks as a consequence of increased attention to phone-related thoughts (Isikman et al. 2016). Forced separation from one’s ringing phone can also increase heart rate and anxiety and decrease cognitive performance (Clayton, Leshner, and Almond 2015). To our knowledge, only one prior study has investigated the cognitive effects of the mere presence of a mobile device—one that is not ringing, buzzing, or otherwise actively interfering with a focal task. Thornton et al. (2014, 485–86) found that a visually salient cellphone can impair performance on tasks requiring sustained attention by eliciting awareness of the “broad social and informational network … that one is not part of at the moment.” Together, these investigations of phone-related distractions provide evidence that mobile devices can adversely affect cognitive performance even when consumers are not actively using them. Similar to earlier research on distracted driving and learning while multitasking, however, these studies connect the cognitive costs of smartphones to their (remarkable) ability to attract the conscious orientation of attention. When individuals interact with or think about their phones rather than attend to the task at hand, their performance suffers.
We suggest that smartphones may also impair cognitive performance by affecting the allocation of attentional resources, even when consumers successfully resist the urge to multitask, mind-wander, or otherwise (consciously) attend to their phones—that is, when their phones are merely present. Despite the frequency with which individuals use their smartphones, we note that these devices are quite often present but not in use—and that the attractiveness of these high-priority stimuli should predict not just their ability to capture the orientation of attention, but also the cognitive costs associated with inhibiting this automatic attention response.
We propose that the mere presence of one’s smartphone may impose a “brain drain” as limited-capacity attentional resources are recruited to inhibit automatic attention to one’s phone, and are thus unavailable for engaging with the task at hand. Research on controlled versus automatic processing provides evidence that the mere presence of personally relevant stimuli can impair performance on cognitive tasks (e.g., Geller and Shaver 1976; Bargh 1982; Wingenfeld et al. 2006). Importantly, these performance deficits occur without conscious attention to the potentially interfering stimuli and as a function of inhibiting these stimuli from interfering with the contents of consciousness (e.g., Shallice 1972; Lavie et al. 2004). Consistent with this evidence, we posit that the mere presence of consumers’ own smartphones can reduce the availability of attentional resources (i.e., cognitive capacity) even when consumers are successful at controlling the conscious orientation of attention (i.e., resisting overt distraction).
If smartphones undermine cognitive performance by occupying attentional resources, the cognitive consequences of smartphone presence should be sensitive to variation in both the salience and the personal relevance of these devices, which together determine their priority in attracting attention (e.g., Fecteau and Munoz 2006). Prior research suggests that smartphones are chronically salient for many individuals, even when they are located out of sight in one’s pocket or bag (e.g., Deb 2015). However, we expect that increasing the salience of one’s smartphone—for example, by placing it nearby and in the field of vision—will amplify the cognitive costs associated with its presence, as more attentional resources are required to inhibit its influence on the orientation of attention. We also expect that these costs will vary according to the personal relevance of one’s smartphone. We operationalize relevance in terms of “smartphone dependence,” or the extent to which individuals rely on their phones in their everyday lives. We posit that individual differences in dependence on one’s smartphone will moderate the effects of smartphone salience on available cognitive capacity, such that individuals who most depend on their phones will suffer the most from their presence—and benefit the most from their absence.
In two experiments, we test the hypothesis that the mere presence of one’s own smartphone reduces available cognitive capacity. We manipulate smartphone salience by asking participants to place their devices nearby and in sight (high salience, “desk” condition), nearby and out of sight (medium salience, “pocket/bag” condition), or in a separate room (low salience, “other room” condition).2 Our data indicate that the mere presence of one’s smartphone adversely affects two domain-general measures of cognitive capacity—available working memory capacity (WMC) and functional fluid intelligence (Gf)—even when participants are not using their phones and do not report thinking about them (experiment 1). Data from experiment 2 replicate this effect on available cognitive capacity, show no effect on a behavioral measure of sustained attention, and provide evidence that individual differences in consumers’ dependence on their devices moderate the effects of smartphone salience on available WMC.
In experiment 1, we test the proposition that the mere presence of one’s own smartphone reduces available cognitive capacity, as reflected in performance on tests of WMC and Gf. Each of these domain-general cognitive constructs is constrained by the availability of attentional resources, and the moment-to-moment availability of these resources predicts performance on tests of both WMC (Engle, Cantor, and Carullo 1992; Ilkowska and Engle 2010) and Gf (Horn 1972; Mani et al. 2013). If the mere presence of one’s own smartphone taxes the limited-capacity attentional resources that constrain both WMC and Gf, then the salience of this device should predict performance on tasks associated with these constructs. We test this hypothesis in experiment 1.
Method
Participants
Five hundred forty-eight undergraduates (53.3% female; Mage = 21.1 years; SDage = 2.4 years) participated for course credit. Data collection spanned two weeks. Duplicate data from repeat participants were discarded prior to analysis. We applied the same three data selection criteria in experiments 1 and 2; see the appendix for additional detail. In experiment 1, three participants were excluded for indicating they did not own smartphones, eight participants were excluded for failing to follow instructions, and seventeen participants were excluded due to excessive error rates on the OSpan task (less than 85% accuracy; see Unsworth et al. 2005). Our final sample consisted of 520 smartphone users.
Procedure
We manipulated smartphone salience by randomly assigning participants to one of three phone location conditions: desk, pocket/bag, or other room. Participants in the “other room” condition left all of their belongings in the lobby before entering the testing room (as per typical lab protocol). Participants in the “desk” condition left most of their belongings in the lobby but took their phones into the testing room “for use in a later study;” once in the testing room, they were instructed to place their phones face down in a designated location on their desks. Participants in the “pocket/bag” condition carried all of their belongings into the testing room with them and kept their phones wherever they “naturally” would. Of the 174 participants in this condition, 91 (52.3%) reported keeping their phones in their pockets, and 83 (47.7%) reported keeping their phones in their bags; a planned contrast revealed no difference between these groups on our key dependent variable (p = .17), and they were pooled for all subsequent analyses. Participants in all conditions were instructed to “turn your phones completely on silent; this means turn off the ring and vibrate so that your phone won’t make any sounds.”
After participants entered the testing room, they completed two tasks intended to measure available cognitive capacity: the Automated Operation Span task (OSpan; Unsworth et al. 2005) and a 10-item subset of Raven’s Standard Progressive Matrices (RSPM; Raven, Raven, and Court 1998). The OSpan task, a prominent measure of WMC, assesses the ability to keep track of task-relevant information while engaging in complex cognitive tasks. This particular measure was designed to stress the domain-general nature of the attentional resources at the heart of the WM system (Turner and Engle 1989); in each trial set, participants complete a series of math problems (information processing) while simultaneously updating and remembering a randomly generated letter sequence (information maintenance). Performance on the OSpan assesses the domain-general attentional resources “available to the individual on a moment-to-moment basis” (Engle et al. 1992). The RSPM test, a nonverbal measure of Gf, was developed to isolate individuals’ capacity for understanding and solving novel problems (fluid intelligence), independent of any influence of accumulated knowledge or domain-specific skill (crystallized intelligence). In each trial, participants are shown an incomplete pattern matrix and asked to select the element that best completes the pattern. Much like the OSpan task, performance on the RSPM test is sensitive to the current availability of attentional resources (e.g., Mani et al. 2013). Complete details of the tasks and measures used in experiments 1 and 2 are provided in the appendix.
Participants also completed an exploratory test of the “ending-digit drop-off” effect, modeled after the procedure of Bizer and Schindler (2005). In this task, participants are shown a series of products with .99-ending and .00-ending prices and asked to report the quantity they would be able to purchase for $73. Overestimating purchasing power for a .99-ending price relative to a matched .00-ending price (e.g., $3.99 vs. $4.00) constitutes evidence of the drop-off effect. We thought this effect might be more pronounced for those whose phones were made salient. However, we failed to replicate the basic effect and did not find any evidence of ending-digit drop-off in any condition (F(1, 514) = .20, p = .65). See the appendix for detailed analyses and results.
Next, participants completed a questionnaire that included items related to their experiences in the lab and their lay beliefs about the connection between smartphones and performance. These questions assessed how often they thought about their phones during the experiment, to what extent they thought the locations of their phones affected their performance in the lab, how they thought phone location might have affected their performance, and to what extent they believed their phones affected their performance and attention spans more generally; all responses were measured using 7-point Likert scales. Finally, participants answered a series of demographic questions (gender, age, ethnicity, nationality) and provided information about their cellphone make/model and data plan.
Results and Discussion
All analyses in experiment 1 include a “Week” factor to account for variation across research assistants; this factor does not interact with Phone Location in any analysis (all F < 1.27, all p > .28).
Cognitive Capacity
We assessed the effects of smartphone salience on available cognitive capacity using two measures of domain-general cognitive function: OSpan task performance and RSPM test score. Because both tasks rely on limited-capacity attentional resources, both should be sensitive to fluctuations in the availability of these resources.
A multivariate analysis of variance (MANOVA) testing the effects of Phone Location (desk, pocket/bag, other room) on the optimal linear combination of these measures revealed a significant effect of Phone Location on cognitive capacity (Pillai’s Trace = .027, F(4, 1028) = 3.51, p = .007, partial η2 = .014). Paired comparisons revealed that participants in the “other room” condition performed better than those in the “desk” condition (p = .002). Participants in the “pocket/bag” condition did not perform significantly differently from those in either the “desk” (p = .09) or “other room” (p = .11) conditions. However, planned contrasts revealed a significant desk → pocket/bag → other room linear trend (Pillai’s Trace = .023, F(2, 513) = 6.07, p = .002, partial η2 = .023) and no quadratic trend (Pillai’s Trace = .004, F(2, 513) = .96, p = .39), suggesting that as smartphone salience increases, available cognitive capacity decreases.
Follow-up univariate ANOVAs separately testing the effect of Phone Location on OSpan performance and RSPM score were consistent with our focal multivariate analysis. Phone Location significantly affected both OSpan performance (F(2, 514) = 3.74, p = .02, partial η2= .014) and RSPM score (F(2, 514) = 3.96, p = .02, partial η2 = .015). See figure 1 for means, and the appendix for detailed analyses and results.
figure
Figure 1. Experiment 1: effect of randomly assigned phone location condition on available WMC (OSpan Score, panel A) and functional Gf (Correctly Solved Raven’s Matrices, panel B). Participants in the “desk” condition (high salience) displayed the lowest available cognitive capacity; those in the “other room” condition (low salience) displayed the highest available cognitive capacity. Error bars represent standard errors of the means. Asterisks indicate significant differences between conditions, with *p < .05 and **p < .01.
Conscious Thought
A one-way ANOVA on participants’ responses to the question “While completing today’s tasks, how often were you thinking about your cellphone?” (1 = not at all to 7 = constantly/the whole time) revealed no effect of Phone Location on phone-related thoughts (F(2, 514) = .84, p = .43). Notably, the modal self-reported frequency of thinking about one’s phone in each condition was “not at all.” Combined with the significant effect of Phone Location on available cognitive capacity, these results support our proposition that the mere presence of one’s smartphone may impair cognitive functioning even when it does not occupy the contents of consciousness.
Perceived Influence of Smartphone Presence
There were no differences between conditions on any measures related to the perceived effects of smartphones on performance (“How much / in what way do you think the position of your cellphone affected your performance on today’s tasks?”; “In general, how much do you think your cellphone usually affects your performance and attention span?”), either in the context of the experiment (all F < 1.58, all p > .21) or in general (F(2, 494) = 2.26, p = .11). Across conditions, a majority of participants indicated that the location of their phones during the experiment did not affect their performance (“not at all”; 75.9%) and “neither helped nor hurt [their] performance” (85.6%). This contrast between perceived influence and actual performance suggests that participants failed to anticipate or acknowledge the cognitive consequences associated with the mere presence of their phones.
Discussion
The results of experiment 1 indicate that the mere presence of participants’ own smartphones impaired their performance on tasks that are sensitive to the availability of limited-capacity attentional resources. In contrast to prior research, participants in our experiment did not interact with or receive notifications from their phones. In addition, self-reported frequency of thoughts about these devices did not differ across conditions. Taken together, these results suggest that the mere presence of one’s smartphone may reduce available cognitive capacity and impair cognitive functioning, even when consumers are successful at remaining focused on the task at hand.
The results of experiment 1 support the proposition that the mere presence of one’s smartphone reduces available cognitive capacity, even when it is not in use. In experiment 2, we replicate the basic design of experiment 1, with the following exceptions. First, we conduct a stronger test of the proposed impairment-without-interruption effect by examining the effects of smartphone salience on both cognitive capacity (WMC) and a behavioral measure of sustained attention. Consistent with both the proposed theoretical framework and participants’ self-reports in experiment 1, we predict that increasing smartphone salience will adversely affect the availability of attentional resources without interrupting sustained attention. Second, one could argue that participants who had access to their phones in experiment 1 surreptitiously checked for notifications, were consciously distracted by unanswerable messages, and displayed impaired performance as a result (as in Clayton et al. 2015; Stothart et al. 2015; Isikman et al. 2016). We did not observe any behavior or this sort, and did not find any differences between conditions in the frequency of phone-related thoughts. In experiment 2, we further address this alternate explanation by randomly assigning participants to either silence their phones (as in experiment 1) or turn them off completely. We predict that the salience of participants’ smartphones will influence available cognitive capacity even when these devices are turned off and will not influence sustained attention even when they are turned on. Third, we test a potential moderator of the effects of smartphone salience on available cognitive capacity: individual differences in the personal relevance of one’s phone, operationalized in terms of “smartphone dependence.” We predict that individuals who are more dependent on their phones will be more affected by their presence.
Method
Participants
Two hundred and ninety-six undergraduates (56.9% female; Mage = 21.3 years; SDage = 2.6 years) participated for course credit. Eleven participants were excluded for reporting that they did not own smartphones, four participants were excluded due to excessive error rates (<85 275="" a="" accuracy="" and="" below="" consisted="" due="" excluded="" extreme="" final="" go="" in="" missing="" o-go="" of="" or="" ospan="" our="" p="" participants.="" participants="" response="" sample="" see="" six="" task="" times="" to="" were="">
Procedure
This experiment followed a 3 (Phone Location: desk, pocket/bag, other room) × 2 (Phone Power: on, off) between-subjects design. Phone Location instructions and randomization procedures were identical to those used in experiment 1, with the exception that participants in the “desk” condition were instructed to place their phones facing up. Of the 91 participants in the “pocket/bag” condition, 68 (74.7%) reported keeping their phones in their pockets, and 23 (25.3%) reported keeping their phones in their bags; as in experiment 1, a planned contrast revealed no difference between these groups on our key dependent variable (p = .55), and they were pooled for all subsequent analyses. Participants were randomly assigned to one of two Phone Power conditions prior to entering the testing room. The instructions for participants in the “power on” condition were identical to those used in experiment 1; we instructed participants in the “power off” condition to completely turn off their devices.
After placing their phones in the proper location and power mode, participants completed our two key dependent measures: the OSpan task and the Cue-Dependent Go/No-Go task (order counterbalanced across participants). In the Go/No-Go task, participants are presented with a series of “go” and “no go” targets, and instructed to respond to “go” targets as quickly as possible without making errors, but to refrain from responding to “no go” targets. In this task, both omission errors (failure to respond to “go” targets) and reaction time (speed of responding to these targets) serve as measures of sustained attention (Bezdjian et al. 2009). After completing both tasks, participants reported the subjective difficulty of each task.
Next, they completed a battery of exploratory questions intended to assess individual differences in use of and connection to one’s smartphone, including a 13-item inventory related to reliance on one's phone (see appendix for all items and analyses). Finally, participants answered a series of demographic questions (gender, age, ethnicity, nationality) and provided information about their cellphone make/model and data plan.
Results and Discussion
All analyses in experiment 2 include a Task Order reflecting our counterbalanced experimental design; this factor does not interact with Phone Location or Phone Location × Phone Power in any analysis (all F < 1.51, all p > .22).
Cognitive Capacity
As in experiment 1, performance on the OSpan task measures the attentional resources available to the individual on a moment-to-moment basis (Engle et al. 1992). A 3 (Phone Location: desk, pocket/bag, other room) × 2 (Phone Power: on, off) between-subjects ANOVA revealed a significant effect of Location on OSpan performance (F(2, 263) = 3.53, p= .03, partial η2 = .026). There was no effect of Power (F(1, 263) = .05, p = .83) or of the Power × Location interaction (F(2, 263) = 1.05, p = .35). Paired comparisons revealed that participants in the “other room” condition performed significantly better on the OSpan task than did those in the “desk” condition (Mdiff = 4.67, p = .008). Participants in the “pocket/bag” condition did not perform significantly differently from those in either the “desk” (Mdiff = 2.30, p = .20) or “other room” (Mdiff = 2.37, p = .17) conditions. See figure 2for means.
figure
Figure 2. Experiment 2: effect of randomly assigned phone location condition on available cognitive capacity (OSpan Score) and sustained attention (Mean Reaction Time, Go/No-Go). Participants in the “desk” condition (high salience) displayed the lowest available cognitive capacity; those in the “other room” condition (low salience) displayed the highest available cognitive capacity. Phone location did not affect sustained attention. Error bars represent standard errors of the means. Asterisks indicate significant differences between conditions, with **p< .01.
Planned contrasts revealed a significant desk → pocket/bag → other room linear trend (F(1, 263) = 7.05, p = .008, partial η2 = .026) and no quadratic trend (F(1, 263) = .001, p = .98). Consistent with experiment 1, this pattern of results indicates that increasing the salience of one’s smartphone impairs OSpan performance, and decreasing the salience of one’s smartphone improves performance. Further, the null effects of Power and the Power × Location interaction suggest that decreases in performance are not related to incoming notifications (or the possibility of receiving notifications), ruling out this alternative explanation of the effects found in experiment 1.
Moderation by Smartphone Dependence
Our framework suggests that the effects of smartphone salience on available cognitive capacity should be moderated by individual differences in dependence on these devices. We tested this prediction by investigating responses to an exploratory 13-item inventory of individual differences in reliance on one’s phone. A principal components factor analysis with Varimax rotation revealed that these items loaded onto two distinct factors, together explaining 52.67% of the variance.3 Factor 1 (Smartphone Dependence; six items) explained 31.02% of the variance and captured our primary concept of interest: the degree of dependence on one’s smartphone (e.g., “I would have trouble getting through a normal day without my cellphone”). Factor 2 (Emotional Attachment; five items) explained 21.65% of the variance and accounted for the emotional aspects of smartphone use (e.g., “Using my cellphone makes me feel happy”). Reliability analyses indicated high reliability for both Smartphone Dependence (α = .89) and Emotional Attachment (α = .79) as distinct factors. See appendix table 1 for all items and factor loadings.
We tested the potential moderating role of Smartphone Dependence in a univariate generalized linear model predicting OSpan performance from all variables included in our original 3 (Phone Location: desk, pocket/bag, other room) × 2 (Phone Power: on, off) ANOVA, mean-centered Smartphone Dependence score, and all independent variable × Smartphone Dependence interaction terms (Baron and Kenny 1986). This analysis revealed a significant Phone Location × Smartphone Dependence interaction (F(2, 247) = 3.25, p = .04, partial η2= .026), indicating that the effects of smartphone salience on OSpan performance are moderated by individual differences in dependence on one’s smartphone. Follow-up analyses probing the conditional effects of Location at the sample mean of the moderator and plus/minus one SD from the mean revealed no effect of Location on OSpan performance at low levels of Smartphone Dependence (−1 SD; p = .28); however, this effect was significant at both mean (p = .05) and high levels (p = .007) of Dependence. See figure 3 for estimated marginal means. Similar results for other measures of smartphone dependence (e.g., number of texts sent per day) are reported in the appendix.
figure
Figure 3. Experiment 2: estimated marginal means representing the effect of phone location on available cognitive capacity (OSpan Score) at low (−1 SD), mean, and high (+1 SD) levels of smartphone dependence. Phone location affects available cognitive capacity at mean and high levels of smartphone dependence, but not at low levels of smartphone dependence. Asterisks indicate significant differences between conditions, with *p < .05 and **p < .01.
Interestingly, a parallel moderation analysis indicated that Emotional Attachment did not moderate the effects of Phone Location on OSpan performance (p = .61). Although we are cautious about making strong claims based on null effects and reiterate that these factors were derived from an exploratory inventory, this disparity between Smartphone Dependence and Emotional Attachment suggests that the effects of smartphone salience on available cognitive capacity may be determined by the extent to which consumers feel they need their phones, as opposed to how much they like them. These results are consistent with the proposition that the effects of smartphone salience on available cognitive capacity stem from the singularly important role these devices play in many consumers’ lives.
Sustained Attention
We analyzed the effects of smartphone salience on two behavioral measures of sustained attention: omission errors and reaction time in the Go/No-Go task. A 3 (Phone Location: desk, pocket/bag, other room) × 2 (Phone Power: on, off) ANOVA revealed no effects of Location, Power, or their interaction on either of these measures (all F < 1.05, all p > .35). See figure 2 for reaction time means, and the appendix for full results.
Perceived Difficulty
Finally, we analyzed perceived task difficulty in order to see if the cognitive consequences of smartphone salience were reflected in participants’ subjective experiences. A 3 (Phone Location: desk, pocket/bag, other room) × 2 (Phone Power: on, off) ANOVA revealed a marginal effect of Location on perceived difficulty for the memory section of the OSpan task (F(1, 256) = 2.38, p = .09, partial η2 = .018). Paired comparisons revealed that participants in the “other room” condition found it significantly easier to remember information in this task relative to participants in the “desk” condition (Mdiff = .49, p = .04) and marginally easier relative to those in the “pocket/bag” condition (Mdiff = .40, p = .09). This pattern of results is consistent with participants’ actual performance on the OSpan task and suggests that the cognitive benefits of escaping the mere presence of one’s phone may be reflected, at least partially, in subjective experience. However, the lay beliefs reported in experiment 1 suggest that even when consumers notice these benefits, they may not attribute them to the presence (or absence) of their phones. There were no differences between conditions on any of the other perceived difficulty or perceived performance measures (all F < 1.82, all p > .16).
Discussion
Consistent with the behavioral and self-report results observed in experiment 1, the results of experiment 2 suggest that the mere presence of consumers’ own smartphones may adversely affect cognitive functioning even when consumers are not consciously attending to them. Experiment 2 also provides evidence that these cognitive costs are moderated by individual differences in dependence on these devices. Ironically, the more consumers depend on their smartphones, the more they seem to suffer from their presence—or, more optimistically, the more they may stand to benefit from their absence.
The proliferation of smartphones represents a profound shift in the relationship between consumers and technology. Across human history, the vast majority of innovations have occupied a defined space in consumers’ lives; they have been constrained by the functions they perform and the locations they inhabit. Smartphones transcend these limitations. They are consumers’ constant companions, offering unprecedented connection to information, entertainment, and each other. They play an integral role in the lives of billions of consumers worldwide and, as a result, have vast potential to influence consumer welfare—both for better and for worse.
The present research identifies a potentially costly side effect of the integration of smartphones into daily life: smartphone-induced “brain drain.” We provide evidence that the mere presence of consumers’ smartphones can adversely affect two measures of cognitive capacity—available working memory capacity and functional fluid intelligence—without interrupting sustained attention or increasing the frequency of phone-related thoughts. Consumers who were engaged with ongoing cognitive tasks were able to keep their phones not just out of their hands, but also out of their (conscious) minds; however, the mere presence of these devices left fewer attentional resources available for engaging with the task at hand.
Further, we find that the effects of smartphone salience on available cognitive capacity are moderated by individual differences in the personal relevance of these devices (operationalized in terms of smartphone dependence); those who depend most on their devices suffer the most from their salience, and benefit the most from their absence. The role of dependence in determining mere presence effects suggests that similar cognitive costs would not be incurred by the presence of just any product, device, or even phone. We submit that few, if any, stimuli are both so personally relevant and so perpetually present as consumers’ own smartphones. However, we leave open the door for our insights to apply more broadly to future connective technologies that may become equally central to consumers’ lives as technology continues to advance.
Our research also offers insight into the tactics that might mitigate “brain drain”—as well as those that might not. For example, we find that the effect of smartphone salience on cognitive capacity is robust to both the visibility of the phone’s screen (face down in experiment 1, face up in experiment 2) and the phone’s power (silent vs. powered off in experiment 2), suggesting that intuitive “fixes” such as placing one’s phone face down or turning it off are likely futile. However, our data suggest at least one simple solution: separation. Although this approach may seem at odds with prior research indicating that being separated from one’s phone undermines performance by increasing anxiety (Cheever et al. 2014; Clayton et al. 2015), we note that participants in those studies were unexpectedly separated from their phones (Cheever et al. 2014) and forced to hear them ring while being unable to answer (Clayton et al. 2015). In contrast, participants in our experiments expected to be separated from their phones (this was the norm in the lab) and were not confronted with unanswerable notifications or calls while separated. We therefore suggest that defined and protected periods of separation, such as these, may allow consumers to perform better not just by reducing interruptions but also by increasing available cognitive capacity.
Our theoretical framework draws on prior research outlining the role of limited-capacity attentional resources in inhibiting responses to high-priority but task-irrelevant stimuli (Shallice 1972; Bargh 1982; Lavie et al. 2004; Clapp et al. 2009). However, our data are equally consistent with an alternate explanation: that these attentional resources are recruited for purposes of hypervigilance, or monitoring high-priority stimuli in the absence of conscious awareness (e.g., Legrain et al. 2011; Jacob, Jacobs, and Silvanto 2015). This interpretation is consistent with the common phenomenon of “phantom vibration syndrome,” or the feeling that one’s phone is vibrating when it actually is not (e.g., Rothberg et al. 2010; Deb 2015). Data suggest that 89% of mobile phone users experience phantom vibrations at least occasionally (Drouin, Kaiser, and Miller 2012), and that this over-responsiveness to innocuous sensations is particularly prevalent in those whose devices are particularly meaningful (e.g., Rothberg et al. 2010). Because the same limited-capacity attentional resources are implicated in both hypervigilance and inhibition, our data cannot distinguish between the two theoretical explanations. In fact, it is plausible that these processes may operate in tandem, as goal-directed attentional control processes both monitor for signals of potentially important information from high-priority stimuli, and (attempt to) prevent these stimuli from interrupting conscious attention until such signals appear.
Implications and Future Directions
Consumers’ limited cognitive resources shape innumerable aspects of their daily lives, from their approaches to decisions (Bettman et al. 1991) to their enjoyment of experiences (Weber et al. 2009). Our data suggest that the mere presence of consumers’ own smartphones may further constrain their already limited cognitive capacity by taxing the attentional resources that reside at the core of both working memory capacity and fluid intelligence. The specific cognitive capacity measures used in our experiments are associated with domain-general capabilities that support fundamental processes such as learning, logical reasoning, abstract thought, problem solving, and creativity (e.g., Cattell 1987; Kane et al. 2004). Because consumers’ smartphones are so frequently present, the mere presence effects observed in our experiments have the potential to influence consumer welfare across a wide range of contexts: when consumers work, shop, take classes, watch movies, dine with friends, attend concerts, play games, receive massages, read books, and more (Isikman et al. 2016). Moreover, results from our pilot study (reported prior to experiment 1) indicate that the majority of consumers typically keep their smartphones nearby and in sight, where smartphone salience is particularly high.
Consumer Choice
Prior research indicates that occupying cognitive resources by increasing cognitive load causes consumers to rely less on analytic and deliberative “system 2” processing, and more on intuitive and heuristic-based “system 1” approaches (Evans 2008). To the extent that both cognitive load and the mere presence of consumers’ smartphones reduce available cognitive capacity, we may expect consumers to be more likely to adopt choice strategies associated with system 1 when their smartphones are present but irrelevant to the choice task. Reliance on system 1 processing could, for example, enhance the appeal of affect-rich choice alternatives (Rottenstreich, Sood, and Brenner 2007), amplify the preference for simple (and possibly inferior) solutions (Drolet, Luce, and Simonson 2009), increase consumers’ willingness to make attribute trade-offs (Drolet and Luce 2004), and heighten susceptibility to anchoring effects (Deck and Jahedi 2015). Building on these connections, future research could explore whether the presence of smartphones accentuates individuals’ preference for options favored by system 1 processing.
Advertising Effectiveness
The availability of cognitive resources also predicts elaboration likelihood (e.g., Petty and Cacioppo 1986) and susceptibility to deceptive advertising (Xie and Boush 2011). Consistent with a potential shift toward reliance on system 1 processing, consumers who view advertising messages in the presence of their smartphones may be less likely to elaborate on advertising messages and more likely to be influenced by heuristics such as likability of the communicator (e.g., Chaiken 1980). Note that the proposed theoretical framework suggests that this may not be the case for advertising delivered via smartphone, because the cognitive costs associated with mere presence should be incurred when consumers’ phones are present but not in use.
Education
Younger adults—92% of whom are smartphone owners—rely heavily on smartphones (Pew Research Center 2016). Given that many of them are in school, the potential detrimental effects of smartphones on their cognitive functioning may have an outsized effect on long-term welfare. As educational institutions increasingly embrace “connected classrooms” (e.g., Petrina 2007), the presence of students’ mobile devices in educational environments may undermine both learning and test performance—particularly when these devices are present but not in use. Future research could focus on how children, adolescents, and young adults are affected by the mere presence of personally relevant technologies in the classroom.
Intentional Disconnection
Although we have primarily focused on the cognitive costs associated with the presence of smartphones, our research is equally relevant to the potential implications of their absence. Discussions of “disconnection” in popular culture reflect increasing consumer interest in intentionally reducing—or at least controlling—the extent to which they interact with their devices (e.g., Perlow 2012; Harmon and Mazmanian 2013). Some consumers are replacing their smartphones with feature phones (i.e., phones lacking the advanced functionality of smartphones; Thomas 2016), others are supplementing their smartphones with stripped-down devices that offer “a short break from connectedness” (http://www.thelightphone.com/), and still others are turning to apps that track, filter, and limit smartphone usage (e.g., https://inthemoment.io/). Our research suggests that these measures may be doubly beneficial for the digitally weary; by redefining the relevance of their devices, these consumers may both reduce digital distraction and increase available cognitive capacity. More broadly, our research contributes to the growing discussion among consumers and marketers alike about the influence of technology on consumers—and consumers on technology—in an increasingly connected world.
One’s smartphone is more than just a phone, a camera, or a collection of apps. It is the one thing that connects everything—the hub of the connected world. The presence of one's smartphone enables on-demand access to information, entertainment, social stimulation, and more. However, our research suggests that these benefits—and the dependence they engender—may come at a cognitive cost.
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Adrian F. Ward () is an assistant professor of marketing in the McCombs School of Business, University of Texas at Austin, 2110 Speedway, Austin, TX 78712. Kristen Duke () is a PhD candidate in marketing at the Rady School of Management, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093. Ayelet Gneezy () is an associate professor of behavioral sciences and marketing at the Rady School of Management, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093. Maarten W. Bos () is a research scientist at Disney Research, 4720 Forbes Avenue, Pittsburgh, PA 15213. The authors thank Jiyoung Lee, Stephanie Schwartz, Yael Horwitz, and the Atkinson Behavioral Lab for research assistance.
1. In 2007, only 4% of American adults owned smartphones (Radwanick 2012). As of January 2017, 77% of American adults—and 92% of those under the age of 35—own smartphones (Pew Research Center 2017). Penetration is similarly high in most Western nations, and even higher in several Middle Eastern and Asian countries. South Korea, for example, has a national smartphone ownership rate of 88%, including 100% of those under 35 (Pew Research Center 2016).
2. A pilot study confirmed that these physical locations predict individuals’ top-of-mind awareness of their smartphones, with a nearby and in sight → nearby and out of sight → not nearby linear trend (F(1, 111) = 14.58, p < .001, partial η2 = .116) and no quadratic trend (p = .996). Interestingly, the majority of respondents (67.5%) indicated that they typically keep their smartphones nearby and in sight, where these devices are most salient. See the appendix for method and detailed analyses.
3. Two items did not clearly load onto either primary factor and were excluded from further analyses (Costello and Osborne 2005).