Cognitive-Behavioral Therapy in the Digital Age: Presidential Address

Wilhelm, Sabine; Weingarden, Hilary; Ladis, Ilana; Braddick, Valerie; Shin, Jin Yong; Jacobson, Nicholas C.

doi:10.1016/j.beth.2019.08.001

Cited by 84 publications

(66 citation statements)

References 97 publications

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“…The findings summarized in this section suggest efforts to improve PA engagement may benefit from a focus on fostering more positively valenced incidental affect (and lower levels of negatively valenced affect) as well as potentially generating strategies for provoking "bumps" in positively valenced affect (especially at times across the day where affect may be vulnerable to negative shifts in valence). The proliferation of smartphone technology and methods, such as machine learning, hold significant promise regarding future opportunities for precision medicine in this area (Michie et al, 2017;Jacobson et al, 2019;Wilhelm et al, 2020). For instance, one could envision the development of a just-in-time intervention combining use of EMA and machine learning to anticipate participant behavior based on the micro-temporal associations between affect and PA (Nahum-Shani et al, 2016;Dunton et al, 2019).…”

Section: Future Directionsmentioning

confidence: 99%

Affective Determinants of Physical Activity: A Conceptual Framework and Narrative Review

et al. 2020

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The literature on affective determinants of physical activity (PA) is growing rapidly. The present paper aims to provide greater clarity regarding the definition and distinctions among the various affect-related constructs that have been examined in relation to PA. Affective constructs are organized according to the Affect and Health Behavior Framework (AHBF), including: (1) affective response (e.g., how one feels in response to PA behavior) to PA; (2) incidental affect (e.g., how one feels throughout the day, unrelated to the target behavior); (3) affect processing (e.g., affective associations, implicit attitudes, remembered affect, anticipated affective response, and affective judgments); and (4) affectively charged motivational states (e.g., intrinsic motivation, fear, and hedonic motivation). After defining each category of affective construct, we provide examples of relevant research showing how each construct may relate to PA behavior. We conclude each section with a discussion of future directions for research.

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Section: Future Directionsmentioning

confidence: 99%

Affective Determinants of Physical Activity: A Conceptual Framework and Narrative Review

et al. 2020

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“…Mental health providers should inform clients about the potential limits of confidentiality in email use [12], along with the risks, benefits, and people who may have access to them. supplementation [15], and delivering interventions [16,17]. Clients increasingly expect to be able to contact providers via text messaging [18].…”

Section: Direct Use With Clients Emailmentioning

confidence: 99%

Digital privacy in mental healthcare: current issues and recommendations for technology use

Lustgarten

Garrison

Sinnard

et al. 2020

Current Opinion in Psychology

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Mental healthcare providers increasingly use technology for psychotherapy services. This progress enables professionals to communicate, store information, and rely on digital software and hardware. Emails, text messaging, telepsychology/ telemental health therapy, electronic medical records, cloudbased storage, apps/applications, and assessments are now available within the provision of services. Of those mentioned, some are directly utilized for psychotherapy while others indirectly aid providers. Whereas professionals previously wrote notes locally, technology has empowered providers to work more efficiently with third-party services and solutions. However, the implementation of these advancements in mental healthcare involves consequences to digital privacy and might increase clients' risk of unintended breaches of confidentiality. This manuscript reviews common technologies, considers the vulnerabilities therein, and proposes suggestions to strengthen privacy.

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“…There has been considerable thought from researchers about methods to close this treatment gap, with efforts factoring the quantity of the services provided, the cost of the services, and the tier of care (Shidhaye et al, 2015). Given their cost and scalability, self-care models have been a focus of a great deal of research (Gulliver et al, 2010), with the most recent attention on the promise of digital treatments to meet this need (Wilhelm et al, 2020). Being aware of their symptoms, many persons seek information online as a first step towards care receipt, they often come to mental health screens which can in turn connect them to these potential treatments.…”

Section: Impact Of Online Mental Health Screening Tools On Help-seekimentioning

confidence: 99%

Impact of Online Mental Health Screening Tools on Help-Seeking, Care Receipt, and Suicidal Ideation and Suicidal Intent: Evidence from Internet Search Behavior in a Large U.S. Cohort

Jacobson¹,

Yom‐Tov²,

Lekkas³

et al. 2020

Preprint

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Introduction: Most people with psychiatric illnesses do not receive treatment for almost a decade after disorder onset. Online mental health screens reflect one mechanism designed to shorten this lag in help-seeking, yet there has been limited research on the effectiveness of screening tools in naturalistic settings. Material and methods: We examined a cohort of persons directed to a mental health screening tool via the Bing search engine (n=126,060). We evaluated the impact of tool content on later searches for mental health self-references, self-diagnosis, care seeking, psychoactive medications, suicidal ideation, and suicidal intent. Website characteristics were evaluated by pairs of independent raters to ascertain screen type and content. These included the presence/absence of a suggestive diagnosis, a message on interpretability, as well as referrals to digital treatments, in-person treatments, and crisis services. Results: Using machine learning models, the results suggested that screen content predicted later searches with mental health self-references (AUC =0·73), mental health self-diagnosis (AUC = 0·69), mental health care seeking (AUC = 0·61), psychoactive medications (AUC = 0·55), suicidal ideation (AUC = 0·58), and suicidal intent (AUC = 0·60). Cox-proportional hazards models suggested individuals utilizing tools with in-person care referral were significantly more likely to subsequently search for methods to actively end their life (HR = 1·727, p = 0·007). Discussion: Online screens may influence help-seeking behavior, suicidal ideation, and suicidal intent. Websites with referrals to in-person treatments could put persons at greater risk of active suicidal intent. Further evaluation using large-scale randomized controlled trials is needed.

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Cognitive-Behavioral Therapy in the Digital Age: Presidential Address

Cited by 84 publications

References 97 publications

Affective Determinants of Physical Activity: A Conceptual Framework and Narrative Review

Affective Determinants of Physical Activity: A Conceptual Framework and Narrative Review

Digital privacy in mental healthcare: current issues and recommendations for technology use

Impact of Online Mental Health Screening Tools on Help-Seeking, Care Receipt, and Suicidal Ideation and Suicidal Intent: Evidence from Internet Search Behavior in a Large U.S. Cohort

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