The Acceptability and Feasibility of Using Text Messaging to Support the Delivery of Physical Health Care in those Suffering from a Psychotic Disorder: a Review of the Literature

Griffiths, Henry

doi:10.1007/s11126-020-09847-x

Cited by 10 publications

(3 citation statements)

References 34 publications

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“…A key strength of SMS text messaging is its flexibility, allowing a range of content (within the 160-character limit), including psychoeducation, reminders (eg, to exercise or take medication), motivational messages, self-care (eg, stress management tips), and informative links to health-related internet sites [ 51 ]. The simple language format used in texts also increases their accessibility, irrespective of the reading level [ 52 - 55 ]. The ability to send SMS text messages anonymously may help minimize stigma and barriers to accessing health care [ 56 ], which are particularly evident in geographically isolated underserved populations [ 57 ].…”

Section: Introductionmentioning

confidence: 99%

Co-design and Development of EndoSMS, a Supportive Text Message Intervention for Individuals Living With Endometriosis: Mixed Methods Study

Sherman¹,

Pehlivan²,

Singleton³

et al. 2022

JMIR Form Res

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Background Endometriosis, which affects 1 in 10 people assigned female at birth, is a chronic systemic inflammatory disease with a high symptom burden and adverse socioemotional impacts. There is a need for an accessible, cost-effective, and low-burden intervention to support individuals in managing their endometriosis condition. Objective This study aimed to co-design and evaluate the acceptability, readability, and quality of a bank of supportive SMS text messages (EndoSMS) for individuals with endometriosis. Methods In phase 1 of this mixed method design, 17 consumer representatives (individuals with endometriosis) participated across three 3-hour web-based (Zoom, Zoom Video Communications, Inc) focus groups. The transcripts were encoded and analyzed thematically. In phase 2, consumer representatives (n=14) and health care professionals (n=9) quantitatively rated the acceptability, readability, and appropriateness of the developed text messages in a web-based survey. All the participants initially completed a background survey assessing sociodemographic and medical factors. Results Consumer representatives demonstrated diverse sociodemographic characteristics (Mage=33.29), varying in location (metropolitan vs rural or regional), employment, and relationship and educational statuses. Participants reached a consensus regarding the delivery of 4 SMS text messages per week, delivered randomly throughout the week and in one direction (ie, no reply), with customization for the time of day and use of personal names. Seven main areas of unmet need for which participants required assistance were identified, which subsequently became the topic areas for the developed SMS text messages: emotional health, social support, looking after and caring for your body, patient empowerment, interpersonal issues, general endometriosis information, and physical health. Through a web-based survey, 371 co-designed SMS text messages were highly rated by consumers and health care professionals as clear, useful, and appropriate for individuals with endometriosis. Readability indices (Flesch-Kincaid scale) indicated that the SMS text messages were accessible to individuals with a minimum of 7th grade high school education. Conclusions On the basis of the needs and preferences of a diverse consumer representative group, we co-designed EndoSMS, a supportive SMS text message program for individuals with endometriosis. The initial evaluation of the SMS text messages by consumer representatives and health professionals suggested the high acceptability and suitability of the developed SMS text messages. Future studies should further evaluate the acceptability and effectiveness of EndoSMS in a broader population of individuals with endometriosis.

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Section: Introductionmentioning

confidence: 99%

Co-design and Development of EndoSMS, a Supportive Text Message Intervention for Individuals Living With Endometriosis: Mixed Methods Study

Sherman¹,

Pehlivan²,

Singleton³

et al. 2022

JMIR Form Res

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“…The approach relies on analyzing patterns in language that may be indicative of cognitive, psychological, or even some physical health conditions. Here's how it might work for various conditions, like: (1) Mental health disorders [28,29]: (a) changes in the frequency of communication, use of negative words, and shifts in the complexity of language may suggest depression or anxiety [7]; (b) the disorganized thought patterns that emerge in how a person composes messages could be a warning sign of psychotic disorders [30]; or (c) the repeated questions, simpler sentence structures, or a notable decline in the complexity of language could indicate cognitive decline, like Alzheimer's disease and dementia [4];…”

Section: Introductionmentioning

confidence: 99%

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

Biró,

Cuesta-Vargas,

Szilágyi

2023

Acta Universitatis Sapientiae, Informatica

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The paper examines the potential of artificial intelligence (AI) in parsing text and conducting sentiment analysis to identify early markers of mental health and neurodegenerative disorders. Through the analysis of textual data, we investigate whether AI can provide a noninvasive, continuous, and objective complement to traditional diagnostic practices. Background: the early detection of mental health (such as depression, anxiety, psychotic disorders, Alzheimer’s disease and dementia) and neurodegenerative disorders (like Parkinson’s disease) remains a critical challenge in clinical practice. Traditional diagnostic methods rely on clinical evaluations that may be subjective and episodic. Recent advancements in AI and natural language processing (NLP) have opened new avenues for precognitive health assessments, suggesting that variations in language and expressed sentiments in written text can serve as potential biomarkers for these conditions. Materials and Methods: the research used a dataset comprising various forms of textual data, including anonymized social media interactions, transcripts from patient interviews, and electronic health records. NLP algorithms were deployed to parse the text, and machine learning models were trained to identify language patterns and sentiment changes. The study also incorporated a sentiment analysis to gauge emotional expression, a key component of mental health diagnostics. Results: the AI models were able to identify language use patterns and sentiment shifts that correlated with clinically validated instances of mental health symptoms and neurodegenerative conditions. Notably, the models detected an increased use of negative a ect words, a higher frequency of first-person singular pronouns, and a decrease in future tense in individuals with depression. For neurode-generative conditions, there was a notable decline in language complexity and semantic coherence over time. Conclusions: the implemented pipeline of AI-parsed text and sentiment analysis appears to be a promising tool for the early detection and ongoing monitoring of mental health and neurodegenerative disorders. However, these methods are supplementary and cannot replace the nuanced clinical evaluation process. Future research must refine the AI algorithms to account for linguistic diversity and context, while also addressing ethical considerations regarding data use and privacy. The integration of AI tools in clinical settings necessitates a multidisciplinary approach, ensuring that technological advancements align with patient-centered care and ethical standards.

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“…mHealth technology (e.g. mobile-phone apps), have reduced sedentary behaviour in individuals with mental-health problems; for example, text message prompts increased step-counts in those with severe mental-health problems ( Chen et al, 2017 ), and a literature-review found that motivational/supportive text messages improved physical health in those with a psychotic disorder ( Griffiths, 2020 ). Although, mHealth app research to reduce sedentary behaviour in those with psychosis is limited, apps are effective in increasing active behaviour in the general population ( Conroy et al, 2014 ; Hollis et al, 2015 ; Vandelanotte et al, 2016 ).…”

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confidence: 99%

Acceptability of reducing sedentariness using a mobile-phone application based on ‘if then’ plans for people with psychosis: A focus-group study conducted in North West England, UK

Bailey

Bartlett

Hassan

et al. 2022

Int J Soc Psychiatry

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Objective: To understand the acceptability of (a) reducing sedentary-behaviour in people with psychosis using ‘if-then’ plans and (b) the proposed app content. Design: Qualitative acceptability study. Method: Three structured focus-groups and an interview were conducted with eight participants who had experience of a psychotic episode. They discussed sedentary-behaviour, being more active, critical situations in which they may be tempted to be sedentary and solutions to these (the if-then plans), and a mock-up of the mobile application. The Theoretical Framework of Acceptability (TFA) was used to analyse qualitatively the transcripts. Results: All TFA constructs were coded in each of the transcripts. The idea of reducing sedentary-behaviour was acceptable to people with psychosis, participants knew the importance of being more active, however it is not always their main priority. Likewise, the proposed content of the app was found to be acceptable, with participants already using some of the proposed solutions. Conclusion: This was the first study to use the TFA framework to assess the acceptability of an app that uses critical situations and solutions (‘if-then plans’) to help reduce sedentary behaviour for people with psychosis. In this sample (male, English speaking mainly white people), participants understood the benefits of being more active. However, reducing sedentary-behaviour is not the main priority of this population and being sedentary has benefits when their mental-health is bad.

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The Acceptability and Feasibility of Using Text Messaging to Support the Delivery of Physical Health Care in those Suffering from a Psychotic Disorder: a Review of the Literature

Cited by 10 publications

References 34 publications

Co-design and Development of EndoSMS, a Supportive Text Message Intervention for Individuals Living With Endometriosis: Mixed Methods Study

Co-design and Development of EndoSMS, a Supportive Text Message Intervention for Individuals Living With Endometriosis: Mixed Methods Study

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

Acceptability of reducing sedentariness using a mobile-phone application based on ‘if then’ plans for people with psychosis: A focus-group study conducted in North West England, UK

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