Humans are social creatures; thus, maintaining a healthy and positive social interaction is a necessary component of human existence [1]. Over the past few decades, loneliness has emerged as a global issue, and people rate it as a primary source of unhappiness in their lives [2]. Perlman defined loneliness as when an individual thinks that the quality or quantity of their social relationships is inadequate [3]. Loneliness or social isolation is a common problem that most people experience at some stage in their lives; however, it can be very distressing, especially when it becomes chronic [4]. In other words, loneliness is a distressing emotional state in which a person is dissatisfied with the proximity and pattern of their social interactions and relationships. Thus, loneliness is a fully subjective state of mind; a person can live completely alone and not experience loneliness, whereas another person may experience loneliness despite having a wide social network. Adult loneliness is a growing concern; according to a survey from the United Kingdom, 1 in every 20 adults reports feeling completely alone [5].

Social isolation refers to a person’s lack of social interaction with others at the individual or group level [6]. Social isolation can occur voluntarily or involuntarily and can have a positive or negative effect on an individual based on their mental and physical health [7]. Social isolation and loneliness are overlapping concepts often used interchangeably. Unlike social isolation, loneliness is always involuntary and involves negative emotions [8]. Although these two ideas are closely related, they do not have the same meaning. An individual can feel alone in a crowd, whereas another person can benefit from social isolation while experiencing solitude. Loneliness may occur when an individual is socially isolated for a prolonged period [9]. Loneliness is also a complex phenomenon that varies in intensity and is influenced by a variety of factors and conditions. To explain the multifaceted aspects of loneliness, Sadler and Weiss [10] distinguished between emotional loneliness and social loneliness. Emotional loneliness refers to the absence of close or intimate connections, whereas social loneliness refers to the absence of social networks. For instance, a child who has lost their mother experiences loneliness differently from a child who lacks playmates [11].

Loneliness is influenced by a variety of factors, including age, poor health, physical disability, relationship status, living alone, infertility, low wages, low levels of education, and socioeconomic status [12]. Certain personality traits, such as lower levels of extraversion and higher levels of neuroticism, may also increase the risk of loneliness [13]. According to a study on loneliness over the life span, it peaks in late adolescence, gradually declines during middle age, and then returns in later life [14]. As people age, they often lose social contact because of illness and cognitive decline, leading to loss of social relationships. Gradually, their social networks decline as they grow older, resulting in very few people from whom they receive support [15]. A report published by the Survey of Health, Ageing, and Retirement in Europe found that older adults living alone can experience higher levels of loneliness [16].

Loneliness has a detrimental effect on physical and mental health, increasing an individual’s risk of morbidity and mortality [17]. The effects of loneliness on cardiovascular health have been widely studied [18]. Poor social bonds have been linked to a 29% increase in incident coronary heart disease and a 32% increase in stroke according to a meta-analysis of longitudinal data on 35,925 people [19]. Loneliness can also result in various neuroendocrine problems and a weakened immune system [20]. In addition, loneliness can disrupt normal blood pressure, sleep patterns, and cortisol levels, resulting in serious health problems or even death [21]. Loneliness has been linked to various detrimental mental health outcomes [22], including depression [23], suicidality, less positive mood, poor sleep quality, poor overall physical health, and physiological abnormalities [13].

In addition to their primary function as communication devices, modern smartphones provide a plethora of capabilities [24]. A smartphone can be used as a handheld camera, a navigator, a fitness tracker, and a personal assistant [24]. As smartphones are equipped with a variety of powerful sensors, they have evolved into pervasive tracking systems [25]. Similarly, sensors in new wearable devices, such as smart watches and fitness trackers, have created the possibility of transforming them into a robust health tracking system [26]. Among the many fields that make use of the several capabilities of smartphones and wearables, one is passive sensing, the process of collecting data in the background using the ubiquitous nature of smartphones and wearables without the user’s active engagement. This concept of passive sensing or self-monitoring emerged from studies in the field of ubiquitous computing, where it has been called context-aware computing [27]. Smartphones can collect a vast amount of data regarding a user’s behavioral patterns, which can be modeled into bioindicators of the user’s well-being [28]. Using sensors, such as an accelerometer, a heart rate sensor, or a microphone as well as the GPS or Bluetooth connectivity as a proximity sensor, researchers collect high volumes of data about a user’s everyday life and behavioral patterns, such as social activities, time spent at certain places, daily health data, and a user’s activity log. All these capabilities of smartphones and wearables have made them a promising tool for tracking users’ health and well-being on a ubiquitous and passive level. In addition to smartphones and wearables, various environmental or ambient sensor-based devices have been used to passively gather information about users’ in-home behavioral habits, such as passive and active infrared sensors, pressure mats and tiles, and camera and microphone sensors, particularly for older adults [29].

In contrast to systematic reviews and other review-based approaches, scoping reviews are a relatively new tool for searching and summarizing the literature. Systematic reviews address specific questions using predefined methods to assess study quality. Scoping reviews can be used to map new or emerging fields of research and are useful when investigating a broader set of review questions that require the inclusion of studies from a wider range of study methodologies [30-32]. Researchers can conduct a scoping analysis in a recent or neglected research area for four primary reasons: to determine the design and purpose of the study, to determine the necessity of conducting a systematic review, to summarize the research results, and to locate research gaps within the existing literature [33]. This study investigated the broader field of loneliness and social isolation detection through passive sensing as a recent and less explored research area. This review aims to explore the types of passive sensing detection methods available in the literature for loneliness and social isolation, to investigate the target populations of these detection methods, to review how the developed systems were validated, and to find challenges and limitations of these detection systems.

The protocol for this scoping review was developed as per the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) [56] and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) [57]. The databases IEEE Xplore, Scopus, ACM, PubMed, Web of Science, and Embase were searched. Articles published between January 2011 and December 2021 were extracted independently by the authors MMQ and EZ. The search terms and results for each database are presented in Table 1. The search criteria were based on loneliness, social isolation, and detection-related keywords.

Studies were included if they were published in the English language and presented results on identifying loneliness and social isolation using passive sensing technology, such as smartphone apps, fitness trackers, and home sensors. Studies were excluded if they were written in a non-English language, if loneliness or social isolation was not one of the assessed outcomes, or if the detection method was other than passive sensing.

The selection process is illustrated in Figure 1. A total of 40,071 studies were identified using the search terms. After title and abstract screening, 862 full texts were reviewed, of which 29 (3.4%) studies were selected for inclusion in the review, as follows:

Most studies targeted young adults (10/29, 34%), most frequently college students [58-67], or older adults (12/29, 41%) [68-78,86], whereas 17% (5/29) of the studies included a mixed age group [79-84]. The studies sampled between 5 and 364 participants and lasted anywhere from 1 week to 5 years. In all, 3% (1/29) of studies was not longitudinal [61], and 7% (2/29) of other studies did not report the duration [75,81].

In all, 37% (11/29) of studies explicitly stated the participants’ ages [61,62,64,67,68,70,75,76,78,83,84], whereas the other studies (18/29, 62%) mentioned only the age group without providing their age: younger adults [58-60,63,65,66], older adults [69,71-74,77,86], or mixed age groups [79-82]. Of the 11 studies, a total of 4 (36%) of studies focusing on a younger population reported an age range of 18 to 28 years [60-62,64], whereas 6 (55%) studies with a target sample of older adults reported an age range of 53 to 91 years [68,70,75,76,78,83]. Participants in 9% (1/11) of studies ranged in age from 18 to 78 years, representing a mixed group of younger and older adults [84].

With respect to the participants’ gender, a total of 3 studies focusing on a younger population included both male and female participants [60,63,83]. Female participants dominated 3 studies [59,61,67], male participants dominated 2 others [64,66], and 1 study had exclusively female participants [62]. Female dominance has been clearly observed in older population studies. A total of 7 studies concerning older adults reported their gender, and all (7/29, 24%) these studies showed a female predominance, except one. The female ratios in these studies were 60% [70], 81% [72], 74% [68], 54% [74], 89% [75], 44% [78], and 88% [76].

A total of 11 studies mentioned participants’ educational level [59,63-68,72,76,78,83]. The younger population mostly comprised college students [59,63-67]. In all, 2 studies recruited first-year college students [59,63], whereas 4 others targeting a younger population recruited a mix of university students at the graduate and undergraduate levels [61,65-67]. Only 4 studies reported the education levels of the older adults [68,72,76,78], of which 2 (50%) studies included participants who had completed their graduate studies [72,78], and the remaining 2 (50%) studies included participants with varying levels of education, with 46% (12/26) completing a college education in a study [76], 39% (19/50) completing primary level education, and 39% (19/50) having had no formal education in another study [68].

Participants’ ethnic status varied. A total of 8 studies clearly reported the origin of their participants [59,60,63,65,66,68,75,78], 5 (63%) of which targeted a younger population from diverse ethnic groups [59,60,63,65,66], and 3 (37%) of which targeted older adults [68,75,78]. In the younger population, most participants had an Asian cultural background, whereas 2 studies were from the United States with participants from different backgrounds [59,66], 1 study was from China [65], and 1 was from Singapore [63]. The remainder were White, Hispanic, African American, or Caucasian [60,65]. A study from Singapore examining an older population had 87% Chinese participants [68], whereas another US-based study included 75% White participants [75].

Given that smartphones and wearable fitness trackers are equipped with a variety of physical sensors, they are highly effective instruments for monitoring users’ movements and passively collecting data on their daily life patterns. Of the 29 studies, 18 (62%) used smartphones or wearables as a sensing modality to passively capture data [58-67,71,74,79,80,82-84,87].

Among the various physical sensors used in smartphones or wearable devices, the accelerometer and GPS are the most frequently used sensors for passive data collection. Indeed, 72% (13/18) of the studies that used a smartphone or smart watch as a sensing instrument made use of accelerometer and GPS sensors to gather data on a user’s physical activities [59-63,65-67,74,80-83]. The accelerometer is used because of its low power use and low privacy concerns, whereas GPS is used to monitor the users’ regularly visited locations and the time spent at those locations, which aids in determining their social habits. Another explanation for using GPS in several studies is that it is typically available with all smartphones.

Following this, Bluetooth and microphones are the second most widely used sensors in the identified studies, accounting for 39% (7/18) of studies that used passive sensing via smartphones or wearable devices to acquire user data [59,62,65-67,80]. Bluetooth is typically used as a form of proximity sensor to gather information about a user’s sociability and physical encounters with other users who also have Bluetooth enabled in their devices, whereas a microphone is used to infer sleep and social experiences. In addition, 7% (2/29) of studies used Wi-Fi MAC addresses to collect information about users’ sociability. Moreover, a study collected data from a smartphone light sensor to detect the screen lock or unlock status [82].

Apart from the sensors in smartphones, the studies included data on phone activity, which could also be used as an indicator of loneliness or social isolation. SMS text messages and call logs are the most frequently used; 56% (10/18) of the studies have used these logs to derive user communication activities [59,60,62-65,71,74,76,82]. In addition, 2 experiments integrated the number of times that each smartphone was locked and unlocked [59,82], and 2 studies gathered data on the types of apps that were used more often to ascertain users’ mood and sociability levels [65,82]. To maximize learning opportunities regarding user habits, a study collected contact information, browsing history, and email-related data [64].

Of the 29 studies included here, 9 (31%) solely used ambient and other physical sensors installed at different locations in the home to learn about participants’ in-home mobility patterns and behavior [68-70,72,73,75,77,81,86]. Different types of sensors were used, including passive infrared motion sensors installed on walls to collect motion data, pressure sensors to detect the presence of the participant on a bed or chair, sound sensors to detect social interactions and activities in the household, and door sensors to detect walking patterns and people movements.

Different assessment methods have been used in experiments to validate the passively collected data. Most studies (21/29, 72%) relied on self-reports and questionnaires, which can be administered directly in a clinical setting or remotely using a mobile app. Self-reports or questionnaires were used to gather data on participants’ physical and mental health in some of the included studies (8/29, 27%) that assessed social interactions and well-being [60,67,68,71,80,82-84], depression or anxiety [60,62,65,66,70,77,78], and daily activities [63,67,68,79,80,82]. Numerous scales have been used specifically for the detection of loneliness, including the UCLA loneliness scale [88], the De Jong Gierveld Loneliness Scale [41], and the ESTE-R Loneliness Scale [74]. The Social Interaction Anxiety Scale [89], Depression Anxiety Stress Scale [90], and Positive Affect Negative Affect Schedule [91] have been used to measure depression and anxiety symptoms in individuals. Some studies used self-developed questionnaires, such as the ecological momentary assessment, which were specifically related to the application domain and target populations.

Numerous review studies have examined the use of passive sensing to monitor a variety of mental and physical health and well-being outcomes. Many aspects of passive sensing have been covered in previous review studies, providing researchers with current challenges and suggestions for potential future research. These reviews have focused on different mental and physical health conditions, such as stress [98], mood disorders [99], sleep problems [100], cardiac issues [101], chronic health conditions in older adults [102] and schizophrenia [103]. Compared with previous reviews, this review is the first to focus on the use of passive sensing for loneliness and social isolation detection and to explore the limitations of the passive sensing systems used.

The targeted population is one of the key dimensions to be considered when designing and developing loneliness detection systems. Most studies (17/29, 58%) included in this scoping review targeted younger populations. It may be relatively easier to generate a sample group of younger people from colleges or universities, as opposed to older adults, who are dispersed across community settings and may not be readily accessible through services, education, or workspaces. Evidence in the literature suggests that isolation is typically higher in late adolescence and after retirement than in the middle age range [14]. Moreover, unemployed individuals in the middle age group are more susceptible to loneliness [104]. An age-related association was observed in the detection methods; loneliness in younger adults was assessed mostly through smartphones [58-60,62,63,65-67], whereas ambient sensors were used more frequently by older adults [68-78,86]. Ambient sensing may be used by older people because they are less likely to use wearables and have higher privacy concerns [105]. Some older adults may not be able to use smartphones because of lack of technological literacy and complex user interfaces [106]. In addition, people with dementia can forget to carry or wear such devices or charge their smartphones in a timely manner, resulting in intermittent or no data collection. Furthermore, there is a strong possibility that some wearable sensors create an unpleasant feeling during prolonged skin connection (eg, electrodes on the skin). As a result, older adults may reject such wearables, particularly at home [107].

Before they are widely available to the public, these loneliness detection systems must undergo extensive validation to ensure that users feel comfortable using and trusting them. The validation of such detection systems is important because it provides relevant input and knowledge about the performance and reliability of the systems to researchers and developers. Two facets of validation of these loneliness detection systems should be discussed: participant selection and technology’s efficacy in assessing social outcomes. With regard to population selection, most of the included studies (17/29, 58%) recruited younger adults and college students via advertisements on student mailing lists or Facebook groups. Moreover, most of the studies recruited first-year college students or undergraduate students from the same college or university, with a random ratio of male and female participants. A study recruited students from a specific class only [58], resulting in a population with very similar sociodemographic backgrounds, daily routines, and study challenges. There is a lack of detail on randomization in student population selection, either in terms of sample collection from more than one college or from younger populations other than college students. A study recruited international students because they were considered at a higher risk of loneliness [64]. In addition, many studies (9/29, 31%) did not include sufficient information on the participants’ socioeconomic status. In the case of older adults, studies recruited participants who resided in sensor-enabled homes created by specific senior citizen welfare organizations. Some studies (6/29, 20%) omitted older adults with physical, sensory, or cognitive impairment, which may have resulted in demographic selection bias because people with disabilities may experience greater loneliness [108,109]. Most studies (9/12, 75%) have focused on older adults who were already living alone to assess their loneliness, which may result in biased findings. Few studies (3/29, 10%) used the Mini-Mental State Examination [110] to screen and exclude older adults with cognitive impairment. In addition, some studies (7/29, 24%) have not mentioned their sample selection or recruitment process for both the younger and older populations; instead, they simply reported the number of individuals from a specific age group.

Most studies (24/29, 82%) have used supervised machine learning techniques to infer loneliness levels from passively acquired data. In supervised machine learning, labeled data are required to train a model, where the gathered ground truth data are used to label the raw sensor data. Ground truth data have been used to compare and validate passively acquired data through smartphones and other passive devices. Inaccuracies in the ground truth evidence result in incorrect model training and an inefficient detection system. Thus, ground truth data were critical in these studies. Ground truth data from assessments, such as loneliness assessment questionnaires, are used to label the data collected through passive sensing streams with the corresponding ground truth state. Most studies (21/29, 72%) have used self-reports and questionnaires, which can then be used for validation purposes with passively collected data. This method has some drawbacks, such as users may not always respond to such questionnaires, or there could be accuracy issues depending on the participants’ current mental and physical state.

Participants’ motives, preferences, and expectations regarding surveillance systems can influence their willingness to use available solutions. Some of those interests could be physiological, such as improving behaviors in daily life, and some could be more technical, such as system scalability and acceptability. Most studies (18/29, 62%) that use smartphone apps to passively acquire user data have used Android-based mobile apps because of their lower privacy restrictions compared with the iPhone iOS operating system when collecting data through various sensors available on smartphones. Another reason might be cost, as Android devices are more affordable and are the most widely used operating system, encouraging more users to use the proposed systems.

Another technological consideration of smartphone-based detection systems is their power use. Owing to continuous data collection and constant use of energy by various sensors, the battery life of smartphones degrades more quickly, which is a major concern for users. Only a few studies (6/29, 20%) have addressed this topic or plan to do so in the future. The approach used to reduce power use is to reduce the sampling rate of sensors when the battery level falls below a certain threshold or to use less beneficial sensors only once a day [48].

Apart from energy use, the participants’ primary concern is privacy [48]. Although most of the reports did not mention privacy concerns, few studies (8/29, 28%) indicated that this is one of the key concerns of participants. These studies indicated that they obtained approval from participants for passive data collection or that they obtained privacy and ethical clearance from an organization. Several (6/8, 75%) of these studies used anonymous participant identifiers to store data and transmitted them securely to the server. However, a sizable portion of the studies did not address privacy concerns, which raises questions about their acceptability to participants and the wider public and about the potential harms for this detection method in the case of data and privacy breaches.

This review demonstrates that previous research has not drawn strong distinctions between social isolation and loneliness, with many researchers using both terms interchangeably. According to previous research, loneliness and isolation overlap relatively little, with most lonely times occurring when not alone, for example, when an individual is in a crowd of strangers [67]. Across all self-reports, loneliness seems to be greatest when participants also report being alone at the time and lowest when participants additionally report being with a significant other (partner or friend) [67]. In addition, some experiments have intertwined loneliness with other related concepts. For instance, in a study [111], the authors recorded the number of visits to older adults living alone and did not attempt to explicitly detect loneliness. Thus, it is necessary to distinguish loneliness from other similar terms, most notably, social isolation.

Another significant gap pertains to privacy concerns. Most research has not focused on privacy issues. A few studies (9/29, 31%) discussed their strategies for maintaining device and data privacy, which included anonymization and safe data transmission to data collection servers; however, most of the studies (18/29, 62%) plainly stated that they gathered data with the consent of the users but did not discuss data privacy and security. Most studies (12/18, 66%) collecting participant data passively through smartphones and wearables were based in the United States, China, and Asia, with very few studies conducted in European countries. This may be because the European Union’s General Data Protection Regulations place stringent restrictions on data collection and transfer. It is important to engage potential study participants in the design, development, and validation phases of such systems to ensure that the system satisfies their expectations [112]. This is crucial if the system is to be adopted extensively and sustainably by the target population.

Many smartphone or wearable users prefer tracking systems to provide them with valuable knowledge and feedback about their activities and well-being status [113]. When people receive well-being-based feedback, they are more likely to make positive changes in their lifestyle and behaviors related to physical activity, including well-being, sociability, and mental health [114,115]. However, little is known about the impact of user feedback on behavioral changes related to loneliness. As demonstrated in this scoping review, existing systems are deficient in delivering real-time feedback to participants, which can help participants develop interest and trust in such tracking systems and help them to make attempts to change their lifestyle in response to system recommendations. Experts are examining ways to develop these tracking systems to provide valuable feedback, alerts, and advice to users to improve their current mental health [116].

Most of the studies (25/29, 86%) in this scoping review were pilot or feasibility studies with very small or limited sample sizes. The validation of these detection systems is a crucial component, and the systems should be validated with a subset of the target population that is highly representative over an adequate period to obtain appropriate data to deliver as reliable results as possible. Many studies are of short duration, which may provide an insufficient window for detection and build participants’ trust and acceptability in detection systems. In addition, some of the proposed systems selected population samples from a very specific population type or at a specific period, such as a lockdown during a pandemic crisis [84], which may result in misleading outcomes when applied to other populations or at different periods. Similarly, the population selection methods used were not rigorous, resulting in potential self-selection bias and limited generalizability, particularly with low representations of at-risk groups, such as unemployed people or older adults not in supported housing.

Although we conducted a thorough search of computer science, health, and social science databases using multiple search terms related to loneliness and social isolation detection, some articles could have been overlooked. For example, studies that focused on general mental health or emotion recognition, with loneliness or social isolation serving as a subset of larger research, were not included. Furthermore, because we included only studies conducted in English, there might be several studies on loneliness detection published in other languages that were not included in this scoping analysis.

It is evident that the use of smartphones, wearable smart devices, and ambient sensors to detect loneliness and social isolation in different age groups has increased in the last few years. Compared with more conventional tracking systems, smartphones are simple to use, unobtrusive, familiar, and inexpensive. They also have a variety of sensors that allow the collection of users’ data in real time, without interfering with users’ daily activities. This comprehensive scoping review reveals that smartphones and mobile and ambient sensing systems have the potential to monitor users’ behaviors and daily activities to infer loneliness and social isolation, and it is likely that research interest in this field will grow in the future. However, most existing methods have shortcomings, particularly in privacy preservation and validation across diverse populations, which need to be rigorously addressed in future research. Finally, it is worth noting that researchers need to investigate what motivates people to use such tracking mechanisms and what inspires their trust and long-term adherence if they are to be adopted and implemented within wider populations.