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Parents and Smartphones: A Healthy Mix?

A new survey out says 64 percent of Americans own a smartphone. Which is interesting because, in a related survey, 100 percent of smartphones say they own an American. —Jimmy Fallon

In the digital age, the way we connect with each other is constantly evolving, especially with the new importance that smartphones play in our lives. And the smartphone revolution is only just beginning.

But what does this current fascination with smartphones mean for some of the most intimate relationships in our lives, including how parents interact with their children? A new research study published in the journal Psychology of Popular Media reveals some intriguing patterns when it comes to parents of young children and how they use their smartphones during the course of an average day.

Conducted by Brandon T. McDaniel of the Parkview Mirro Center for Research and Innovation in Fort Wayne, Indiana, and his co-authors, the study is based on a recent survey of 183 families with young children around the age of three years and examined smartphone use by both parents. Along with looking at phone-related activities, such as texting, calling, using social media, and even mobile gaming, the researchers also examined what the parents themselves thought about their smartphone use and whether it cut into family time.

To read more , check out my new Psychology Today blog post here.

Predicting Alzheimers

Alzheimer’s, or so-called dementia, is one of the types of diseases that affects brain cells and causes memory loss, difficulty in thinking, and forgetfulness. Thus far, there is no effective treatment for AD, but treatment could be helpful in impeding the progression of the disease. Therefore, early AD diagnosis is effective in limiting the disease from progressing to advanced and dangerous stages. Physicians and radiologists face difficulties in diagnosing healthy nerve cells from soft tissue, and it requires substantial expertise and a long time to decipher the MRI images. Thus, artificial intelligence techniques can play a key role in diagnosing MRI images for early detection of AD. In a study published in Connection Science, four proposed systems with different methodologies and materials for tracking the stages of AD development are presented. The first proposed system is to classify a data set using artificial neural networks (ANNs) and feed-forward neural networks (FFNN) based on the features extracted in a hybrid manner by using a combination of Local Binary Pattern (LBP), Discrete Wavelet Transform (DWT), and Gray Level Co-occurrence Matrix (GLCM) algorithms. The second proposed system is to classify the data set using two deep learning models—ResNet-18 and AlexNet—that are pre-trained based on deep feature map extraction. The third proposed system is to diagnose the data set using a hybrid technology between ResNet-18 and AlexNet models to extract feature maps and machine learning (SVM) to classify feature maps. The fourth proposed system diagnoses the data set using ANN and FFNN algorithms based on the hybrid features of ResNet-18 and AlexNet deep learning models and traditional algorithms (LBP, DWT, and GLCM). All the proposed techniques achieved superior results in the diagnosis of MRI images for early detection of AD. The FFNN algorithms based on the hybrid features extracted by ResNet-18 with features extracted using traditional algorithms achieved an accuracy of 99.8%, precision of 99.9%, sensitivity of 99.75%, specificity of 100%, and AUC of 99.94%. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

For the abstract

What Influences Social Networks?

In recent years, social networks have become very popular and an integral part of everyday life. People express their feelings and experiences in this virtual environment and become aware of others’ opinions and interests. Among them, influential users play an important role in disseminating information on social networks. Identifying such influencers is important in designing techniques to increase the speed of information dissemination. Such techniques are applicable in various fields including viral marketing, preventing the dissemination of harmful information, providing specialized recommendations, etc. Various approaches have been used to detect influencers on social networks, mostly based on the individual’s position in the network structure and their interactions. Considering the strengths and weaknesses of the previous methods, a study presented in the journal Applied Artificial Intelligence presents a novel method based on the content of the users’ posts without considering the network structure. This is done using a combination of high-level features extracted from images to measure the individual’s influence. Users’ images are investigated from three aspects: (1) color scheme, (2) advertising nature, (3) images’ semantics. To describe each of these aspects, feature extraction methods were used with acceptable accuracy in recognizing influential users. Finally, to achieve greater efficiency and precision, feature-combination methods have been investigated to provide an integrated classifier. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

For the abstract

Crystal Cube: Forecasting disruptive events.

Disruptive events within a country can have global repercussions, creating a need for the anticipation and planning of these events. Crystal Cube (CC) is a novel approach to forecasting disruptive political events at least one month into the future. The system uses a recurrent neural network and a novel measure of event similarity between past and current events. We also introduce the innovative Thermometer of Irregular Leadership Change (ILC). We present an evaluation of CC in predicting ILC for 167 countries and show promising results in forecasting events one to twelve months in advance. We compare CC results with results using a random forest as well as previous work. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

 

For the abstract

5GSS: A framework for 5G-secure-smart healthcare monitoring

Currently, the main challenges of the frameworks for healthcare monitoring are as follows: minimising latency, especially for delay-sensitive diseases such as sudden heart disease; identifying health situation in a timely and accurate manner when correlating physiological indicators and context information; reducing the risk of exposure because health data are highly private. In response to the above, a new paper published in the journal Connection Science proposes a framework for 5G-secure-smart healthcare monitoring (5GSS) to achieve the following goals: fast and accurate identification of context-aware health situation, blockchain-based secure data sharing, and low-latency services for emergent patients. The framework consists of a data acquisition layer, a diagnosis and security layer (edge cloud), and a health service layer. The proposed framework adopts the following key technologies: a 5G-IPv6 communication network, context-aware health situation identification-based similarity measure, and blockchain-based secure data sharing mechanism. Finally, a prototype system has been implemented to monitor hypertensive heart disease, confirming its effectiveness with respect to a real scenario. Combined with the data of 45 patients, the prototype system can identify health situations with an accuracy of 96.34% at a sensitivity of 92.46% and a specificity of 93.62%, while significantly reducing the latency and improving the data sharing security. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

For the abstract

Internet-delivered therapy for family carers of people with dementia (iACT4CARERS)

A new paper in the International Journal of Qualitative Studies on Health and Well-being examined the impact of internet-delivered, therapist-guided, self-help Acceptance and Commitment Therapy (ACT) for family carers of people with dementia (iACT4CARERS).  A qualitative approach with semi-structured interviews was employed with family carers (N = 23) taking part in a feasibility study of iACT4CARERS. The interviews were audiorecorded, transcribed and analysed using thematic analysis. Results showed that four overarching themes were identified: 1) usefulness and relevance of the content of the sessions, 2) sense of connectedness, 3) the impact of the intervention on participants and 4) acceptability of the online delivery. Positive carer experiences and intervention acceptability were facilitated by learning helpful ACT skills, perceiving the content of the session as relatable to the carers’ needs, feeling connected to other carers and the therapist during the intervention, noticing the benefits of the intervention and the user-friendliness of the online platform. Recommendations for a full-scale trial were identified, such as the inclusion of some “face-to-face” interactions (e.g., via video call) between carers and therapists to facilitate a bidirectional interaction and the provision of an additional aide-memoire to improve the learning experiences. Conclusion: Overall, the intervention was acceptable to the family carers. The proposed recommendations should be considered in a full-scale trial.

For the abstract (open access)

The Rise of Telepsychology

Mental health providers’ usage of telehealth modalities saw an exponential increase in response to the coronavirus disease 2019 (COVID-19) pandemic. Some experts have suggested that it takes considerable time, education, consultation, and/or supervision to develop the new technology-based competencies. Education is required to understand not just the technologies, but the practices (e.g., ethical, legal, safety planning), differences from face-to-face care, and workflows. Given the need for rapid transition due to COVID-19, it is unclear how providers prepared for the integration of telehealth with their traditional practices. As such, the purpose of a new study in Professional Psychology: Research and Practice  was to examine mental health providers’ educational activities prior to and post telehealth implementation. Four hundred and four practicing providers (i.e., psychologists, counselors, therapists, social workers) were assessed via an online researcher-created survey. Results indicated significant increases in telehealth usage following COVID-19, with videoconferencing being the most common modality used. Although approximately 74% of surveyed providers sought telehealth education prior to usage, approximately 26% did not. In supplement of anything learned prior, approximately 61% indicated pursuing additional education post implementation. Taken together, a significant portion of providers can be viewed as having gained information that guided ethical, legal, and safe clinical services. Nevertheless, with consideration of the breadth and duration of the educational activities pursued, many providers would likely still benefit from additional education to foster a more complete understanding of the new telehealth competencies. Details regarding usage and educational activities are discussed. Application of information is outlined. Findings can help provide direction for additional educational efforts for providers. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

For the abstract

Making Chatbots More User-Friendly

Emotionally-responsive chatbots are marketed as agents with which one can form emotional connections. They can also become weak ties in the outer layers of one's acquaintance network and available for social support. A recent study in Computers In Human Behavior described an experiment designed to study the acquaintance process in whgichg randomly assigned 417 participants into three conditions: face-to-face (FTF) chat with a human, online chat with a human, and online chat with a commercially-available, emotionally-responsive chatbot, Replika. After a 20-min getting-acquainted chat, participants reported their affective state and relational evaluations of the chat. Additionally, all chats were recorded and text analyzed using the Linguistic Inquiry and Word Count (LIWC) program. In all conditions, participants reported moderate levels of positive emotions and low levels of negative emotions. Those who chatted FTF with a human reported significantly more negative emotions than those who chatted with a bot. However, those who chatted with a human also reported more homophily with and liking of their chat partner and that their partner was more responsive. Meanwhile, participants had fewest conversational concerns with the chatbot. These findings have implications for future computer-mediated interaction studies: conversations with chatbots appear to have different affordances and effects on chatter enjoyment and conversational concerns in getting-acquainted contexts. These results may help designers improve reception and marketability for chatbots in consumer markets. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

For the abstract

Can Social Network Analysis Be Used to Study Crime?

The use of social network analysis to study groups of offenders engaged in illicit activities such as drug trafficking and terrorism has grown in popularity over the last three decades. Along with such growth, however, researchers have been confronted with a suite of challenges related to the use of data extracted from criminal justice records. IA new study in the journal Social Networks reviews these challenges through a discussion of the extant empirical literature utilizing social network analysis approaches that draw data from the criminal justice system. First, the authors outline and discuss the different types of data used across this literature. Second, the authors chronicle the challenges that have emerged across the field of criminal networks via a comprehensive review of the literature. In particular, the authors draw on the documented experiences of researchers in the field, including our own, and detail “archeological” approaches that future researchers can utilize to adapt and overcome said challenges. The use of criminal justice records can suffer from a number of limitations, mainly with respect to accuracy, validity and reliability. Such data may include errors, both intentional (e.g. aliases, false information) and unintentional (e.g. transcription errors), including missing data. The use of criminal justice records present particular problems with defining the network boundary as the boundary as determined by law enforcement or prosecution agencies may not correspond to the boundary as defined by network members. The authors conclude by offering a number of recommendations for researchers about data collection and preparation when utilizing criminal justice records. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

 

For the abstract

Examining the use of mhealth technology for weight management: A hybrid effectiveness-implementation trial in family medicine.

Overweight and obesity are prevalent and problematic conditions in the United States and worldwide, and effective weight management interventions are underutilized. Efforts to improve weight management practices have focused almost exclusively on changing physician behavior, without considering the larger healthcare context or the reciprocal patient-physician relationship. A new dissertation study explored the possibility of leveraging technology to improve the implementation of weight management clinical practice guidelines and increase patient-physician weight management discussions. 100 patients of five family medicine physicians were randomly assigned to either complete a weight management mobile application (app) prior to their primary care visit (app condition), or to undergo their primary care visit as usual (control condition). Findings suggested that patients largely viewed the app as an acceptable use of their time in the waiting room and they were able to complete the app with moderate fidelity prior to meeting with their physicians. Consistent with hypotheses, patients in the app condition were statistically more likely to initiate discussions about weight status than patients in the control group. However, contrary to hypotheses, these discussions did not occur at a statistically higher rate in the app condition than in the control condition. Overall weight status conversations were low relative to patient need, occurring in 42.7% of total visits, though 82.5% of the total sample was classified as overweight or obese. Also contrary to hypotheses, patients in the app condition did not demonstrate statistically improved BMI at follow-up than the control condition. Strengths, limitations, and directions for future research are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

For the abstract