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Highlights
- Loneliness is an unpleasant emotional response to perceived isolation. Up to 32% of adults over the age of 55 report feeling lonely.
- Loneliness can have detrimental effects on the overall well-being. These include emotional distress, cognitive decline, cynicism, mistrust, and cardiovascular diseases, to name a few.
- We present a behavior-driven, evidence-based approach that analyzes individual behavioral attributes and recommends personalized intervention strategies.
- The basis for recommendations is an intervention knowledge base curated by behavior scientists and based on scientific evidence.
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Loneliness hits elders the hardest
Loneliness is a state of distress or discomfort resulting when one perceives a gap between their desire for social connection and their actual experience of it.
Up to 32% of adults over 55 report feeling lonely, as against 8.4% of children and 5-7% of middle-aged adults.
When meaningful social connections are perceived as unavailable, loneliness could adversely affect behavior, cognition, and physical health, resulting in chronic loneliness. Lonely persons are more likely to suffer from cognitive decline, progression of Alzheimer’s disease, impairment of executive functioning (reasoning, memory, self-control), heightened sensitivity to social threats, and an attention bias to negative aspects of society. Loneliness can also have detrimental effects on physical health, such as increased vascular resistance, elevated systolic blood pressure, less restorative sleep, and diminished immunity. The cognitive and behavioral influences described above also negatively affect the quality of social interactions.
Behavioral assessment is key to early intervention
Several psychometric assessment scales have been developed to measure loneliness.
Commonly used measures include the 20-item Revised UCLA Loneliness Scale and the 11-item De Jong Gierveld loneliness scale. Measures for social isolation have also been developed, which include the 10-item Lubben Social Network Scale and the Berkman-Syme Social Network Index. Other unobtrusive ways of predicting social isolation have been developed using mobile sensor data, such as Bluetooth encounters, phone interactions, tone of interaction, and location or mobility patterns.
Behavior-driven interventions for loneliness
Behavioral interventions for loneliness are a widely researched area. We observe four major types of interventions:
1) enhancing social support
2) increasing opportunities for social interaction
3) improving social skills
4) addressing maladaptive social cognition
Interventions in types 3 and 4 are likely to yield more benefit as against types 1 and 2 because they are likely to address loneliness more directly by improving the interaction quality, positively influencing the relationship quality.
Cognitive behavior therapy (CBT) for maladaptive social cognition has been effective in reducing loneliness. In this kind of counseling, individuals are trained to identify their automatic negative thoughts and regard them as hypotheses rather than facts. Such CBT approaches have proven to be far more effective when clubbed with social skills training.
Tech enablement will go a long way
Today, the reliance on digital technology to stay connected with loved ones has increased.
However, worryingly, the elderly's perception of technology remains skeptical, and many barriers still exist.
The Human-Computer Interaction (HCI) Community of Research has identified several barriers to smartphone adoption in older adults, some of them being small font size, screen size, less distance between keys, poor visibility or readability, un-labeled and unfamiliar icons, too many menu options and the inability to navigate, drag and drop, and use of the multi-tap functionality, to name a few. Digital solutions must be cognizant of these challenges and employ designs that address them effectively.
We conducted a systematic evaluation of digital solutions in this area and discovered that most of them fall into the following major categories (see Figure 1).
Figure 1: The landscape of digital solutions for loneliness
Pre-empting approaches to address loneliness
The scientific evidence on the efficacy of interventions can help choose the right approach and solution.
We propose a framework that assesses an individual’s behavioral and contextual aspects to recommend an appropriate intervention.
Figure 2: The loneliness intervention framework
- The engage ecosystem: A set of digital solutions and interfaces discussed earlier, the engage ecosystem is based on an assessment of the user’s behaviors, wherein the system chooses an appropriate app or channel to deliver the intervention.
- Behavioral assessments: Richer behavior inferences may be obtained by combining behavioral, contextual, and individual attributes. Behavioral attributes may include aspects such as emotional states, personality traits, activities of daily living, self-reported experiences, physical activity, social interactions, mistrust, and the like. Contextual attributes include location, environment, ambient noise or light, social support, and the size of the social network. Individual attributes such as age, marital status, employment status, ethnicity, financial situation, interests, or hobbies may also prove to be of value in understanding loneliness behaviors. Several techniques may be employed to assess these attributes, such as validated psychometric instruments, single-item measures or probes, game-based behavioral assessments, AI models that discover behavior markers, natural language analysis of chats or conversations, mobile-sensor logs, and speech signal processing, among others.
- Behavioral patterns: While behavioral assessments may generate several attribute measures, these are likely only an observation at a point in time. Since it would not be appropriate to recommend an intervention based on a single observation, looking at recurrent, undesirable patterns in this data would be necessary. Examples of such behavioral patterns are emotional distress observed through repeated negative emotions, social isolation observed through mobility data and ambient noise, social interaction patterns obtained from mobile usage data, and trait loneliness, which is the perception difference between actual and desired social interactions.
- Intervention recommendation: The system embeds within itself a curated knowledgebase of interventions that have shown strong evidence in the field for specific conditions or behavioral patterns. Systematic literature reviews, meta-analysis papers, taxonomies of behavior change techniques, and intervention repositories populated by domain experts could provide the source data for the knowledge base. An example of an evidence-based recommendation would be if a person has been persistently exhibiting cynicism, mistrust, and negativity in social interactions. It could be inferred to be a case of ‘maladaptive social cognition.’ There is evidence that CBT approaches combined with social skills training have been found effective. The system would recommend the user to get a teleconsultation session with a mental health practitioner for CBT and subsequently attend a social skills workshop.
Targeted interventions and their immense potential: Recent advances in sensing, assessments, pattern recognition, and evidence-driven behavioral intervention design and delivery show great potential in creating a scalable and integrated solution to tackle loneliness and social isolation among older adults. As the percentage of seniors is increasing and is expected to reach 1.4 billion by 2030, of which approximately 50% are at risk of social isolation and about one-third will experience loneliness later in life, there is an urgent need to develop scalable, evidence-driven solutions for addressing the growing public health challenge of loneliness among the elderly. A behavior-driven approach grounded in scientific evidence will likely help address this challenge by delivering more personalized and targeted interventions to help seniors better cope with loneliness.
