In the context of the COVID-19 pandemic, social media platforms such as Twitter have been of great importance for users to exchange news, ideas, and perceptions. Researchers from fields such as discourse analysis and the social sciences have resorted to this content to explore public opinion and stance on this topic, and they have tried to gather information through the compilation of large-scale corpora. However, the size of such corpora is both an advantage and a drawback, as simple text retrieval techniques and tools may prove to be impractical or altogether incapable of handling such masses of data. This study provides methodological and practical cues on how to manage the contents of a large-scale social media corpus such as Chen et al. :e19273, 2020) COVID-19 corpus. We compare and evaluate, in terms of efficiency and efficacy, available methods to handle such a large corpus. First, we compare different sample sizes to assess whether it is possible to achieve similar results despite the size difference and evaluate sampling methods following a specific data management approach to storing the original corpus. Second, we examine two keyword extraction methodologies commonly used to obtain a compact representation of the main subject and topics of a text: the traditional method used in corpus linguistics, which compares word frequencies using a reference corpus, and graph-based techniques as developed in Natural Language Processing tasks. The methods and strategies discussed in this study enable valuable quantitative and qualitative analyses of an otherwise intractable mass of social media data. Social media has become one of the main resources for researchers in many fields where public opinion, attitudes, and perceptions are relevant, such as discourse analysis and the social sciences, which seek to study how communication takes place among people in a globalized world. As the use of online platforms has generalized as a means to express and exchange ideas, researchers have turned to this content to explore public opinion and stance on specific social topics, as well as the language they use. This is specifically relevant if we bear in mind the context of the past two years, characterized by one of the worst health crises in contemporary history.The language of the COVID-19 pandemic has been studied from different perspectives and with different operational and cultural contexts in mind. From a linguistic perspective, scholars have made a tremendous effort to gather information in public access repositories, such as the Oxford Covid-19 language hub  its conciseness; (ii) its anonymity, which allows speakers to express their ideas without fear of being identified; and (iii) the nature of the posts, which constitute what is known as user-generated content (UGC), with a number of important idiosyncrasies. In addition, this type of content includes high velocity granular data which includes metadata that allows the analysis of a phenomenon’s evolution over time , simply cannot handle such massive amounts of text, as they do not have text-indexing capabilities (in the case of desktop applications), or do not allow uploading such large amounts of text. Therefore, it is necessary to come up with suitable methodological underpinnings, as well as specific strategies that facilitate managing and exploring such large-scale UGC corpora.Multiple available COVID-19 Twitter datasets have been compiled and made available to the academic community , what sampling strategies can be used in order to make it manageable? In section "Keywords are lexical items that accurately describe the main subject of a text and which are a compact representation of the document under study  corpus and the corpus of reference, which is assumed to have a normal distribution of word frequency. Nevertheless, there are two issues with this definition: (i) it ignores linguistic features such as homography, polysemy, and syntactic relations, and (ii) it leaves out the existence of other multiple ways to identify keywords.Previous definitions of this term, nevertheless, are expressed in terms of the metrics proposed for their identification and extraction; for example, Scott  stated tIn addition to extracting what is special about a certain type of language, keyword identification can serve other more general purposes and reflect what a corpus is about, being thus an alternative or a complement to topic modeling. In fact, the extraction of keywords has become one of the most important tasks not only in text mining, but also in NLP and information retrieval in general  emerged, which explains the sudden leaps in the number of tweets at certain points  that downloads the tweets using Twitter’s streaming API. It takes an average of 12 days-download time for every month in the corpus, as Twitter imposes certain bandwidth limits, and the download process needs to be paused at regular intervals. The corpus is then downloaded as a series of gzipped JSON lines files, where each JSON line is a tweet. These files contain all tweets for all languages, and each tweet contains the full tweet data.The original corpus is distributed as a set of n datum to each tweet indicating its (daily) frequency. During this tweet extraction process we also pre-processed the text to remove hyperlinks and certain characters such as newlines, tabs, and Unicode characters known to cause issues . We also filtered out tweets shorter than 3 words. This procedure allows us to optimize storage and, more importantly, processing time. Figure Therefore, the first step is to extract the tweets themselves from the original files. We used a custom Python script to process the downloaded tweets and extract the English ones, keeping only certain data for each tweet . A key feature of our tweet extraction method is that we avoid saving retweets and repeated tweets and save only one instance of every tweet per day, along with a counter indicating the number of times that such tweet occurs in the day set. Thus, we added a gzip, as this compressed format can be uncompressed on-the-fly while opening them. An example of this output is given in (1):{“text”: “BEWARE! THE CHINA VIRUS HAS LANDED IN US! CDC Confirms First U.S. Case of Deadly China Virus. via @BreitbartNews”, “user”: “trumptrain1111”, “date”: “Tue Jan 21 19:58:49 +0000 2020”, “id”: “1219710963617861632”, “n”: 4}In the original corpus there is one file for each hour of each day. During the extraction process by language, we merged all unique tweets for one day into one file. Therefore, we ended up with as many files as days in the corpus, and each line in the files is one tweet. All files were compressed using The corpus was not lemmatized for any of the tasks described. The reason for this decision is that the TextRank algorithm works by creating graphs of syntactic patterns, which heavily relies on accurate part-of-speech tagging, which in turn requires that the original word forms be present.Specific objective 1: To compare different sampling sizes to decide which one optimally represents the whole corpus for keyword extraction while keeping data to a manageable size using accessibleSpecific objective 2: To compare two different methods of keyword extraction: the reference-corpus method commonly used in corpus linguistics and the graph-based method commonly used in NLP.The general objective of this work is to provide methodological cues and strategies on how to manage and explore the contents of large-scale social media corpora. This general objective encompasses two operational requisites that determine the specific objectives, as detailed below:Here we explore and test the hypothesis that working with smaller, fixed-interval samples is practical and efficient, and that the results obtained for certain tasks, such as keyword extraction, are similar to using bigger samples or even the full corpus. Table gzipped JSONL files , where each JSONL document includes the tweet’s text, date, and its frequency. With this system, we save considerable processing time. Thus, instead of processing the actual number of tweets (many of which are the same text because they are retweeted or copy-pasted), we can simply use the tweet’s frequency as a factor to multiply results by. Table To extract these samples, we regard the corpus as a time series of day intervals. Our sample extraction script takes the desired percentage of the corpus to include in the sample and extracts a daily, randomized, proportional-to-size sample. Proportionality is based on the frequency information that we obtained during the tweet extraction process. Like the full corpus, samples are stored as As shown in Table SketchEngine (SE), which uses a statistics-based approach. We run both methods on two different sample sizes (0.1% sample and 1% sample) to decide whether processing a substantially larger sample is worth the considerable processing overhead.We employ two different methods of keyword extraction, described in section "TextRank, also extracts entities, hashtags, and emojis. It uses the SpaCy  needs to be created for each tweet. In our tests, it took over 5 seconds per 10 tweets, which is obviously impractical. Since document size does not affect the results returned by TextRank  in section "Running spaCy on millions of tweets one by one is an extremely slow task, because one spaCy TextRank returns a large amount of keyword candidates, which were sorted by score, keeping those with a minimum score of 0.010 and a minimum frequency of 1 (within batches). Items from batches were aggregated by averaging scores and adding frequencies. We extracted a maximum of 500 items per day, sorting by score in the case of keywords and by frequency in the other item types , thus keeping items with much higher scores and frequencies than the above-mentioned minimums.SketchEngine.We further aggregated all items by month, splitting keywords into two groups: keywords proper (single words) and keyphrases (n-grams in the range 2-4). PyTextRank does not differentiate between n-gram sizes, but we did this to facilitate comparing results with the other keyword extraction method, which returns two different sets of key items for unigrams and multi-words. Similarly, in the monthly aggregated collections, we kept 1000 single-word and 1000 multiword keywords, as this is the maximum number of keywords offered by SketchEngine, we converted our two corpus samples from JSONL to XML format and uploaded them to the platform. SketchEngine allows users to create subcorpora based on several variables, such as file names and metadata. We created one subcorpus for each month (for each of the two corpora) based on the metadata we embedded in the XML exported files, and extracted the top 1000 keywords and keyphrases (“terms” in their terminology) for each month. All keywords were extracted using the English Web 2020 (enTenTen2020) corpus , per method of keyword extraction, i.e., a total of 96,000 itemsTo quantitatively summarize results, we calculated the intersections of the monthly sets of keywords produced by both methods of keyword extraction across the two samples. Table M = 33.68%, SD = 2.03)M = 22.23%, SD = 2.34). What is more relevant to our objectives is the confirmation that there is very little difference between the shared keywords produced using the 0.1% and 1% samples, as the greatest mean difference found is 2.4% (2020 keyphrases). Thus, our quantitative analysis based on set intersection suggests that very similar results were returned by both samples despite the considerable size difference.As expected, single-word keywords achieve a significantly higher intersection  or highly delexicalized words such as auxiliary verbs . Specifically, there are 22 such items (73.3%) and only 12 actual keywords (26.7%).The list of frequency-ranked keywords provided by SketchEngine list is of better quality as it does not include any function words and all words are related to the coronavirus. However, it prioritizes certain words and phrases that intuitively should be ranked lower. Of course, this is a subjective observation that only makes sense when this list is compared to those generated by TextRank, but even taken in isolation, some obvious flaws stand out: (i) the list focuses excessively on vaccines ; (ii) the items in first and third position refer to a specific variant of Covid, which is in second place; (iii) the fifth-ranked item is a non-English word.The score-ranked TextRank capture more accurately the contents of the corpus, but they are not without issues: while those sorted by score provide a better general view of the pandemic by covering the vaccines, the restrictions and the cases, the keywords sorted by frequency include many names of countries and US states more intensely affected by the pandemic . Conversely, the score-ranked list contains mostly multi-word expressions, as mentioned above; this, however, is simply a side effect of the post-extraction division we made, as the original PyTextRank consists of one score-ranked list of single-word and multi-word keywords.The keywords extracted by TextRank list (in 28th position). In the SketchEngine lists it appears in 97th position (in the score-ranked list) and in 94th position (in the frequency-ranked list).One of the most important Covid-related keywords from a sociological perspective, “social distancing”, appears only in the frequency-ranked In order to tackle specific objective 1, a comparison of keywords extraction methodsSketchEngine score-ranked set is worth further consideration. This is quite probably due to the reference corpus that we used (enTenTen20), as the omicron variant of the SARS-CoV-2 virus came to light in November 2021. Thus, although the term “omicron” only occurs in the November and December 2021 samples, it does so with a high frequency, and thus obtains very high scores in these two months due to the comparatively low frequency it has in the reference corpus. Therefore, this case illustrates one of the main issues of the reference-corpus approach to keyword extraction: the selection of a reference corpus determines the results to a large extent.The fact that “omicron” scores higher than “covid” in the To tackle specific objective 2, we compare the results returned by the two sample sizes in our study. Again, we calculate the intersections for the different sets of keywords and keyphrases, each set being the top 1,000 items ranked by score. Table SketchEngine shows considerably lower intersection percentages than TextRank (10.72% lower on average). As suggested by the very high standard deviation (23.72), this is undoubtedly due to the extremely low intersection percentage of the 2021 keywords sets (29%). Closer examinationSketchEngine as actual keywords and assigned a very high score.Intersection percentages are generally substantial, that is, most of the keywords were captured by both sample sizes across each of the extraction methods and morphosyntactic types (single words and multi-words). M = 67.84%, SD = 17.78), which is similar for single words and multi-words , suggests that using a smaller sample size does not have a considerable impact on results, especially if we bear in mind the above-mentioned issue.The overall average intersection percentage (SketchEngine (96.7% intersection) and three in the case of TextRank (90% intersection). Importantly, all of the keywords in the difference are in the lowest positions in both cases.For practical purposes, however, considering 1,000 keywords is not very realistic, as we usually need much fewer in order to obtain the main topics, terms, and entities of a corpus. Table SketchEngine and TextRank in 2020Finally, in order to compare extraction methods along with sample size in a more detailed way, we generated Venn diagrams with word clouds. Figure SketchEngine are shown in blue, while keywords provided by TextRank are in green. The intersection, representing the keywords that both systems have extracted, is shown in black. Figure The figure shows the results for the 0.1% (left), and those for the 1% sample (right). In both cases the keywords obtained by Wuhan”, “symptoms”, “quarantine”, “infection”). Some of these words were repeated  because the corpus was no lemmatized. We can also find variants of terms that refer to the same concept, such as “covid”, “covid19”, and “covid-19”.As to the keywords shown in Fig. (2)Biden should NOT stop campaigning just because Trump was stupid enough not to follow COVID-19 guidelines and “catch it.”I think Joe However, the intersection goes beyond the main general consequences of the pandemic, as it is possible to find key political figures  in both sample sizes as a consequence of the 2020 United States presidential elections. As shown in example (2), Twitter users talked about the presidential candidates within the COVID-19 context.th 2020, the Health Protection Regulations 2020(3)quarantine because of possible exposure as they prepare for the new school year.A school district in Georgia reported today that 260 employees have tested positive for the coronavirus or are in Although the keyword “quarantine” appears in both intersections, in closer examination we noticed that it stopped being relevant after August 2020. The reason is that most countries went into lockdown between March 2020 and July 2020. In the case of the United Kingdom, for example, on July 4(4)quarantine rule for returning from Spain  really want to go and visit my parents out thereReally wanting the Government to take away the 14 day More examples also show the relevance of this keyword in relation to travel restrictions, since travelers were in many cases forced to quarantine in their destinations, as shown in (4):lockdown”, “test”, or “virus”. In the case of the 0.1% sample, the following keywords were missing: “cdc”, “economy”, “flu”, “gvt”, “lives”, “news”, “restrictions”, and “symptoms”. In spite of these differences, however, the intersection of both sample sizes for 2020 still represents many of the main events associated with the pandemic appropriately and both present generally similar results.In addition, we do find differences between the two sample sizes. For example, the following keywords were not included in the 1% sample, but did appear in the 0.1%: “china”, “india”, “lockdown”, “millions”, “people”, “response”, “test”, and “virus”. Therefore, the 1% sample fails to include relevant terms that are part of the main aspects of the pandemic, such as “SketchEngine returns many wrongly decoded Unicode characters from languages other than English (mostly Hindi) that should not be counted as keywords, because they simply add noise to the extraction method, such as “â”, “à”, “w”, and “ï”. In fact, the bigger the sample, the higher the number of such characters, as can be seen in Fig. TextRank, which only provides actual words that can be considered keywords.As for the keywords extracted by the two different systems, coronavirus pandemic, social distance, coronavirus vaccine) are, once again, intertwined with references to politics . As for the keyphrases obtained by TextRank, these merge references to the pandemic with phrases pointing to social  aspects of the pandemic, such as “first time”, “last week”, “family member”, or “social medium”. TextRank returns actual keyphrases, but also includes some that, at first sight, might be deemed as unimportant . Nevertheless, such proper nouns indicating locations are actually relevant in this corpus, as they refer to some of the cities that struggled the most with the virus. Figures SketchEngine and TextRank for 2021.In the case of the keyphrases, the intersections of both sample sizes show very similar results: some of the main general events and measures related to the pandemic ((5)vaccination, not infection. RT if you agree.The key to immunity is (6)immunity. You can still carry the virus with the shot you just will not have symptoms this is why we are still wearing masksThe shot is required to help your body build The results for the 2021 keywords vary greatly according to the sample size. In both cases, the intersection includes some of the main events, broadly speaking, that took place during 2021 . In the case of the 0.1% sample, however, the number of words that both extraction methods share is much lower if we compare it to the 1%. Thus, the intersection from the 1% sample covers a wider range of keywords that, in a general sense, summarize some of the main events that characterized 2021 from the perspective of the pandemic, many of them referring to the vaccination process . This can be seen in examples (5) and (6) below:(7)Variant is more dangerous than other variants of the virus that causes #COVID19. Get vaccinated as soon as you can.The #DeltaThe 2021 keyword intersection of the 1% sample also reflects the different COVID-19 variants that appeared this year, such as Omicron, Delta, Theta, and Zeta Biden put forward a similarly insufficient covid reliefThis is supposed to change my point how? (9)Johnson is using Covid to accelerate NHS privatisation. Biting the hand that cared for you...Beyond ironic, given (10)Biden is focused solely on helping the American people after Donald Trump did nothing about a deadly pandemic.President As for the key political figures, the intersection of the 0.1% sample only includes Joe Biden (8), whereas that of the 1% also includes Boris Johnson (9) and Donald Trump (10).(11)Johnson and Johnson COVID-19 vaccine reportedMore blood clot cases following Nevertheless, the “johnson” keyword also refers to the company Johnson & Johnson, supplier of COVID-19 vaccines and whose doses resulted in numerous cases of blood clots and other secondary effects:(12)lockdown again.” There are new variants of every virus every year. If we continue to accept this, we are never escaping this.“There are new variants, we have to Although during 2021 COVID-19 lockdowns were not as equally widespread around the world as in 2020, countries such as Australia still went into lockdown during that year  and “myocarditis” (14).(13)curfew to curb spring break chaos.Miami Beach officials impose Covid (14)myocarditis or pericarditis following COVID vaccination, including 15-year old boy hospitalized after receiving second dose of vaccine.Oregon Health Authority said aware of at least 11 cases of It is also relevant to point out that the keywords provided by SketchEngine also identifies unvaccinated as a keyword. The vaccination process characterized 2021, as it was during that year that the vaccines began to be produced and the population around the world had access to them. As shown in example (15), users often talked about the importance of being vaccinated by referring to the long-time risks of being unvaccinated.(15)unvaccinated people carry it and spread it, and that if you are unvaccinated and get Covid your chances of having long Covid or dying are higher.The willful ignorance here, ignoring the fact that Covid spreads because SketchEngine and TextRank provide more accurate results from the 2021 sample, as they only extract expressions that are related to the pandemic.The intersection of the 2021 keyphrases shows that these revolve around the pandemic with a more specific focus than the keyphrases obtained for 2020: “long covid”, “herd immunity” and the “covid vaccine” appear in both sample sizes. Nevertheless, it must be noted that while references to politics appear in the 0.1% sample , these are not present in the 1% sample. Regarding the keyphrases obtained by each extraction method, both TextRank and SketchEngine in our work, provides interesting results, as the nature of the keywords extracted varies. Frequency-based methods that employ a reference corpus (as exemplified by SketchEngine) have the non-trivial issue derived from the need to select one particular corpus, as words in the focus corpus that have low frequency in the reference corpus will rank high in the keywords list. On the other hand, the keywords extracted by graph-based methods (as exemplified by TextRank) seem to capture more accurately the general contents of the corpus.The results of this study show that keyword extraction is a valuable resource for the exploration of large social media corpora, as it provides a clear pathway and an entry point for the qualitative researcher into an otherwise intractable mass of data. The use of multiple keyword extraction methods, such as TextRank seem to better capture the nature of the corpus, while those provided by SketchEngine are more content-specific and depend on the search parameters employed. Ultimately, these two keyword extraction methods may be appropriate for different aims: graph-based methods seem to be more appropriate to retrieve the most salient topics, events, and entities of a corpus, while statistics-based methods are better at extracting specialized terms. Despite this, the results provided by each method must not be understood as opposed to each other but rather as complementary, as the keywords in the intersection between both systems could be considered to thoroughly represent not only the main events and concepts of the pandemic in a general sense, but also relevant aspects of politics that are also related. There is, however, a higher degree of variability for TextRank according to the year under study: in 2020 the keywords obtained were more COVID-related, while in 2021 these became more general, which is not the case of SketchEngine. The keywords obtained by SketchEngine, on the other hand, provide good insights about the events that took place during the pandemic. Finally, SketchEngine blindly returns high-frequency non-English words, an issue that is not present in TextRank.Thus, the analysis of the keywords per year shows that the results obtained by As for sample sizes, our study suggests that, with very large social media corpora, smaller samples produce comparable results to bigger ones. Thus, when dealing with very large social media corpora, it may be unnecessary to extract larger samples for the sake of statistical representativeness, as smaller samples can also be both representative and relevant for the qualitative researcher, as well as easier to process from a computational perspective.


Persons with behavioral variant frontotemporal dementia (bvFTD) can exhibit apparently antisocial behaviors. An example is their tendency to adopt utilitarian choices in sacrificial moral dilemmas, i.e. harmful actions to promote overall welfare. Moral cognition models interpret such tendencies as deriving from a lack of emotional engagement and selective impairment in prosocial sentiments.We applied a qualitative approach to test those theoretical assumptions and to further explore the emotional experiences and values of people with bvFTD while they contemplate moral scenarios. We conducted semistructured interviews with 14 participants: 7 persons with bvFTD and 7 older healthy controls. Transcripts were coded using ATLAS.ti 5.0.During the moral reasoning task, persons with bvFTD reported more positive emotions than negative and showed significantly less cognitive precision in their moral reasoning compared to controls. Persons with bvFTD also organized their choices predominantly around kindness and altruism, and their responses reflected higher rule compliance. Our study showed that bvFTD persons’ utilitarian responses to moral dilemmas did not arise from an emotionally disengaged or antisocial perspective. Instead, they were underpinned by positive emotionality and prosocial values.These findings enrich current understandings of moral cognition and highlight the importance of incorporating mixed methods approaches in dementia research that take into consideration the viewpoint of cognitively impaired individuals AccordiEmotions play a major role in the majority of our moral decisions, generating significant tilts to our moral compasses. As proposed by the moral emotion theory, for instance, emotions act as salience messengers to help us distinguish right from wrong, point to moral violations, and motivate moral behavior , 9. BecaTheoretical accounts that support the role of emotions in moral reasoning are also found in dual-process models . While tAs emotional disturbances are a cardinal feature of bvFTD, most studies that examine moral reasoning in bvFTD and related neurological conditions contextualize their findings under the prism of dual process models, emphasizing system 1, namely the affective contributions that generate typical moral decisions . For exaEarlier studies indicate that the specific mechanism accounting for impairments in emotionally-based moral behavior is a decreased activation of one’s representations of the state and situation of others , 25. IndHowever, these findings are puzzling both on a conceptual and methodological level. First, utilitarian decisions are thought to reflect care and concern for the good of all sentient beings . Indeed,Additionally, contemporary studies of moral cognition in bvFTD have tended to frame their understanding of moral reasoning by studying moral dilemmas in a way that generates quantitative outputs, i.e., proportions of utilitarian choices. Such an approach, while arguably permitting more objective understandings of moral cognition, disregards the rich information inherent in associated reasoning and psychological processes  that subtend moral choices. In addition, studies rarely collect insights into values and perceptions of rules that could assist in contextualizing morality. This study was designed to bridge these methodological and conceptual gaps by examining moral reasoning qualitatively in healthy older individuals and persons with bvFTD. As such, we examined the psychological processes in moral reasoning through a phenomenological lens, and qualitatively describe morality through the eyes of the individual.2.2.1.This study used a mixed-method design, collecting quantitative  and qualitative data. The qualitative analysis included both pre-specified (deductive) and emergent (inductive) research questions , 29. On 2.2.N = 7), i.e., met the clinical diagnostic criteria for probable bvFTD (N = 7), consisting of participants without any type of dementia diagnosis. The healthy control sample was drawn from community-dwelling older adults enrolled in a longitudinal study of healthy brain aging at UCSF. Participants in this cohort were verified as neurologically normal following a multidisciplinary assessment including (i) a neurological examination, (ii) in-person neuropsychological testing, and (iii) and an informant interview. A group of multidisciplinary professionals, including neurologists, neuropsychologists, and nurses established individual diagnoses in both the bvFTD and healthy control groups employing neuroimaging, neurological, neuropsychological, and behavioral assessments.All research participants were recruited at the Memory and Aging Center in the Department of Neurology, UCSF. We utilized purposeful sampling whereby participants were selected explicitly because of an existing bvFTD diagnosis  at the cost of the greatest expected welfare. An example would be parental choices that prioritize one’s own child’s well-being over the well-being of other children. Here, utilitarian judgments reflect a disregard for tight social bonds in consideration of the greater good.Agent-centered permissions dilemmas are composed of two items reflecting choices that assess one’s attitude toward improving others’ welfare at the cost of one’s own interests. An example would be whether to donate or keep the money for one’s personal use. In this case, the utilitarian choice disregards self-interest for the greater good.2.3.2.Personal rights dilemmas are composed of two items concerning choices that substantially affect the interests of other people, and in which the best overall outcome could only be produced by violating an individual’s rights. For instance, whether to push one person into the path of a runaway trolley that would otherwise kill five people. Here, the utilitarian choice is to sacrifice the individual, so that the greatest welfare is produced.Additionally, we collected quantitative performance metrics , with lower scores reflecting higher utilitarian reasoning (max = 28). Additional quantitative scores were calculated for the three moral categories tested: special obligations , agent-centered permissions  personal rights .2.4.2.4.1.A semi-structured interview guide was developed to allow participants to describe their emotions, perceptions, and values as well as their underlying reasoning behind their responses to moral dilemmas see . The int2.4.2.The same interviewer (RA) performed all the interviews. Training for the interviews included work with a medical anthropologist (ABS) and sociologist (TH), reading qualitative interviewing technique books  and cond2.4.3.Recorded interviews were sent for transcription. Each interview was then reviewed in its entirety against the verbatim transcription, with edits  made where necessary. All transcriptions were deidentified. Each transcribed interview received multiple readings to obtain an understanding of each participant’s responses. Deductive codes were established according to the purpose of our study , but the codebook allowed for the identification of additional, inductive codes to note concepts that emerged during data review. Lower-order themes  were used to form the broader scope of higher-order themes . In an iterative process, themes were refined and checked against the transcripts. Cross-evaluation of the themes was conducted with the consultation of the research team. Analysis was performed via ATLAS.ti software , 31.2.4.4.To improve the rigor of the qualitative data analysis process, several validation methods were employed. This included, first, the documentation of the analysis procedure . Second, regular research team meetings were held during the data collection and analysis process, to provide ongoing transparency and cross-validation of themes. Third, to reduce researcher subjectivity bias, we used the technique of triangulation. Five investigators coded the data independently and subsequently compared and discussed the codes until a consensus was reached. Lastly, divergences in theme categorization were discussed until a consensus was established .3.3.1.N = 14) was predominantly white (79%) and consisted of individuals ranging from 52 to 87 years old . Participants’ general cognitive capacity, functional capacity, and mood were evaluated with a screening battery consisting of the Mini-Mental State Examination (MMSE), the Clinical Dementia Rating (CDR), and the Geriatric Depression Scale (GDS) respectively , while the average CDR plus NACC FTLD total score was 1.4, indicating that this sample represented the earliest stages of disease progression, at a “mild dementia” level. Scores on GDS suggested significantly more depressive symptoms in the bvFTD group  than in the healthy controls , though on average the group was below the screening threshold for mild depression on this measure .In terms of moral reasoning, the healthy control  and the bvFTD  groups’ average overall utilitarian scores reflected diverse responses that encompassed both utilitarian and non-utilitarian choices, as both groups scored in the middle range of utilitarian performance. Characteristically, both groups chose more utilitarian responses in the agent-centered permissions moral category, followed by the personal rights and special obligations moral category see . In qual3.2.Participants’ reasoning across moral dilemmas involved emotion elicitation, revealed by answers to the question following each dilemma: “How did you feel when responding to this dilemma?.” Healthy controls and persons with bvFTD were characterized by a different emotional palette, revealing a between-group distinction on the valence of the emotion they reported experiencing while considering their moral responses. Positive emotions, particularly joy were more prevalent when persons with bvFTD contemplated their feelings associated with their reasoning , as opposed to healthy controls, who tended to express more negative emotions, particularly guilt  was accompanied by:I guess I feel empowered to do that. I would feel like, what would be another word? You want one of those feelings words, right? [Interviewer: What do you mean by feeling empowered?] I guess I feel I have the right to make that decision if I had to make that decision. [bvFTD No. 5].This more positively valenced emotional outlook stood in antithesis to the healthy controls, more of whom expressed negative emotions associated with their moral responses, sometimes very strongly. Reflecting on their feelings associated with the dilemma of whether they would sacrifice their nephew to save 6 people, healthy-control participants replied:Yes, well there’s sadness. There’s sadness. There’s some frustration because the kid cannot swim. But it’s primarily sadness for the fact again of the loss of life. [HC No. 6].Horrified. Absolutely completely horrified. Devastated. Horrified. Super upset. Just horror-stricken. Sort of in shock. Probably would not even feel how truly sad I felt for a while until the shock wore off. It was just absolutely horrible. [HC No. 7].When responding to the same dilemma  some healthy controls were even able to tap into the bodily dimension of the expressed emotion, articulating visceral sensations, an occurrence that did not explicitly occur in the bvFTD sample. As one participant put it:My stomach is turning inside out. That’s my feelings. [HC No. 5].In the healthy control group, the majority of participants expressed sadness, frustration, and pride, with three participants expressing guilt, two expressing joy, and one relief as the core emotions accompanying their moral reasoning. Those emotions were often expressed in a more complex, layered manner. Also, self-conscious emotions including guilt and pride were more often expressed by individuals in the healthy control group, while simpler emotions such as joy were less common in this group than in the individuals with bvFTD. This suggests the availability of a more nuanced emotional palette when reflecting on their feelings about their moral responses. Following their response that they would not endure repeated blood donations to prevent a person from dying, one participant described their feelings as follows:Sad and worried about him. And, you know, sort of regretful that I did not feel like I could help him. I feel like a desire to help him and a wish, or her, whoever it is, person. Is it a man? Anyway, whomever it is. A desire to help. And I would feel regretful that I wasn’t choosing to do it. I would feel a loss. A real sadness and regret. Very sad. [HC No. 7].3.3.When contrasting moral reasoning across persons with bvFTD and healthy controls, participants’ responses involved a variety of cognitive processes, which we included as an additional core theme of cognitive elicitation see . The typFour individuals in the bvFTD group engaged in projection or imagination, compared to all seven of the healthy controls, and this cognitive process appeared much less frequently in their responses. Specifically, we defined the cognitive process of projection/imagination as the ability to put oneself into the moral scenario as if one was the main character of the dilemma, more richly taking the perspective of the protagonist or recipient by imagining one is giving or experiencing the harms or benefits personally. Given that this cognitive process was seen in 100% of healthy controls, this seems to be an important element of healthy moral reasoning, perhaps providing a deeper experience of the dilemma through the felt experience of its ramifications.For example, concerning the dilemma of whether they would give the medicine to their own child rather than another child, one participant with bvFTD showed projection in the following manner:Because I would like to help people, but I have [unintelligible thoughts] children and I want to be with them. And if it means I’m going to die, I mean … [bvFTD No. 6].I feel that my … I’m about to. I feel that if I chose to sacrifice six people for the life of one, even though I have a relation to that one, is a selfish thing to do. When in fact, those six people have other people that feel for them. So, why take my feelings out of the equation, it’s better to save more than less. [bvFTD No. 7].Healthy controls often embedded such cognitive processes in their moral responses. In response to the same dilemma, one participant responded:One of my rules as a parent is to protect my child. So, if there’s a choice between my child and another child, I’m always going to pick my child. [HC No. 1].In another dilemma, where the participant was asked whether they would save their nephew instead of six strangers, they answered:I mean, I kind of put myself in the place of, like, thinking of it as nephew or even your son, like, also, you know, it’s a family member, you know, you’d have to explain it to their family at some point, so – but you would hope that, like, you know, there’s six other people that lived, you know, that went on to do their lives. So, yeah, again, it’s hard to do it purely on math, but, you know, it’s not that strangers have any less value than my nephew. [HC No. 2].The degree of insight and awareness underlying the participant’s moral responses also differed between the two groups, though this cognitive tendency was seen in fewer participant responses overall (4/14). Only one person with bvFTD expressed metacognitive insight in only one response, while 3 of the healthy controls showed insight on 10 occasions. Healthy controls seemed able to track more accurately their thought and emotional processes accompanying their feelings and reasoning towards moral dilemmas. On the footbridge dilemma, a participant contemplated the reasons for opting to not push a man off the bridge to save the five workers in this way:It’s a tough choice. But I went with the first reaction, to the first emotional reaction that I had. I went with that. Because I could sit here for two or three minutes and change my mind back and forth probably. But I think the real true response came immediately for me. And that was I’m not going to push the guy off the footbridge. [HC No.1].Metacognitive insight was also observed in the bodily realm. In the same dilemma, a participant expressed insight into how their mind and body could function as a moral compass for their moral judgments:Because – as I said because it would be – I would find it very hard to take affirmative action to do something which causes someone to die. It’s much easier – and I think – as I said, I think I would be paralyzed, probably, out of, you know, the god awfulness of the situation to do anything. And there’s just – I just do not think that I could bring myself to go through – to do that. I do not know if it’s a moral dilemma. I think it’s more just my mind and body would probably stop me from doing anything – anything, you know, at all. [HC No. 5].Another category of cognitive elicitation observed in participant’s responses was termed “cognitive imprecision,” to describe participants’ tendency to reconstruct the premise of the dilemma and fail to approach the dilemma according to its internal logic, showing an underlying resistance to the scenario’s structure, and breaking the contractual rules of the posed dilemma. In our sample, the large majority of persons with bvFTD (6 of 7) showed this tendency, compared to only 1 of the healthy controls. Alternating the structure of dilemmas seemed to facilitate decisions and make it easier for participants to respond when the dilemma posed a difficult conflict. For instance, when asked to answer the dilemma that involved pushing a stranger onto the tracks to save five workers, some participants resisted the structure of the dilemma requiring the death of the five workers:Well, even though the way it’s worded sounds pretty positive, the death of the five workers is not absolutely known. Maybe they’ll look up right away and jump. You know, there are possibilities there, but the – the large person that’s right beside me, his – his life is known, and I’d be, you know, sort of the bird in the hand versus the bird in the bush. [bvFTD No.1].I just think it’s wrong to do something bad to make something good happen. I think it would be better, for example, for me to jump out there to save all of them. [bvFTD No. 4].Similarly, another participant, responding to the dilemma about whether they would give a medicine to their own child or another child, when the medicine was explicitly described as ineffective if the dose was shared, remarked:Well, I’d probably try to split it. [bvFTD No.3].An additional category of cognitive elicitation observed in our sample was termed poor elaboration, which was operationalized as providing an inadequate explanation underlying participants’ reasoning despite maximal probing by the interviewer. Persons with bvFTD more often failed to elaborate on their responses . This response style was observed to reflect difficulty providing more in-depth, sophisticated reasoning about their moral choices. For example, when asked to contemplate their feelings about why they would agree to give repeated blood donations to save a person from dying, a participant answered:It’s always good to help people, but you know. [bvFTD No.6].By the same token, another participant when presented with the same dilemma, expressed their feelings with the following short answer:Well, it’s a conflict, again. [bvFTD No.3].3.4.One of the goals of this study was to also contextualize morality in terms of participants’ perception of values and rule compliance. For this reason, after responding to all of the moral dilemmas, participants were asked to openly reflect on the values that they try to live by and their perceptions about rule-breaking. We additionally coded whether participants adhere to philosophical and religious standards, including a frame of reference from which their values seem to emanate.When asked about the most important value they try to live by, we found that some participants associated their moral reasoning with values reflecting suggesting the presence of a prosocial compass – fairness, kindness, honesty, and the greater good. This tendency spanned both study groups fairly evenly, though more statements of “kindness” as a value were seen in persons with bvFTD, while healthy controls were slightly more likely to espouse the value of fairness see . Both grTry to be as helpful as I can. [bvFTD No.3].I guess to love the person you are talking to at the time. [bvFTD No.4].To give love and receive love from other people. [bvFTD No.5].The values of healthy controls, in turn, focused more on honesty and fairness. Asked to express which values they live by, participants gave the following responses:Honesty is one of them. Being true to myself is another one. [HC No1].I think I try to be I want to be fair and treat everyone equally. That’s the most important value to me is that everybody – I do not know if you call that a value in the different types of values, but I guess equality, equal. [HC No2].Do no harm. The golden rule. I mean, however – every different society has a different way of expressing it. But I just think it’s do unto others as they would – you would have them do unto you – or do not do to others what you would not like to have them do to you. Yeah. I mean that – the golden rule is – I would not say it’s at the front of my consciousness all the time. But I absolutely believe in it. [HC No5].Interestingly, while reflecting on their values, more of the persons with bvFTD seemed to base their prosocial compass on philosophical and religious standards than healthy controls see . This obWell, I think the most important value is I tried to depend on the Lord to help me with things. Sometimes I forget, but real closely related to that is I try to do unto others as – as I think I would feel good if they were doing unto me. That’s not necessarily as – as they would treat me but as I would like to have them treat me. But most of them can be related to God. Is that too nebulous? [bvFTD No1].I live by a code, you know, it’s the marine code. It’s great! it makes me feel alive. Serve and protect, the people of the world [bvFTD No2].In terms of rule compliance, a sharp differentiation between healthy controls and persons with bvFTD prevailed. Persons with bvFTD were more rule compliant in their responses, emphasizing the expression of moral facts when dealing with breaking the rules. For example, when asked whether they would break a rule, bvFTD persons were more likely to give broad negations that were lacking in situational nuance, responding:I spent a lot of years as a principal and a teacher and, no, I guess the answer is no. I just would not do it. I would not break a rule in general. [bvFTD No1].No, I would not. no, I would not. Because it’s the wrong thing to do. It’s totally wrong. [bvFTD No2].I do not think you should. [bvFTD No 3].No. It’s not good to break rules. [bvFTD No7].On the contrary, healthy controls seemed more willing to break a rule to achieve a goal, reflecting moral flexibility and a tendency to contextualize their behavior. As three participants stated:Yeah. So, for example, in a lot of times in, like, building processes and things that we are doing now, and a lot of the building municipalities just take forever, and there’s a lot of red tape and a lot of bureaucracy, and I will not break a rule, per se, but I’ll omit steps to get to it, again, if I know the goal is a good one and we had discussions, it’s not my own personal goal, yeah, I would do break a rule. I’m not a rule breaker just to do it. If it’s around safety for the most part I would not, but, yeah, I would do it in instances. [HC No2].If the goal is to help someone, that’s what I was thinking. Yes. Oh, my. But in another situation, if the goal is my goal, you know, I want to win; therefore, I’m going to break a rule, no, I would not do it. But if it’s to help someone, yeah, I would break the rule. [HC No 3].Well, there’s really no need, no reason not to, and as I get older and as you get older, even in good health you start to contemplate your death, and there’s too much that has to happen in the world for me to be satisfied, and I do not like the direction we are going away from what I consider to be necessary. My goals are being more and more ignored and made improbable. And to achieve that goal I would break a law. If I would not get caught. [HC No4].4.This research is one of the few studies to directly reveal the voices of persons with bvFTD by giving them the opportunity to voice their reasoning, feelings, and values when responding to moral dilemmas. In previous studies, bvFTD has been associated with impairment in socioemotional function, which has been presumed to be the primary reason they are more likely to make utilitarian choices in sacrificial moral dilemmas in which they are willing to endorse harm in service of the greater good. However, our study captured a more holistic understanding of their thought processes by examining moral reasoning qualitatively, and by using a more nuanced measure of utilitarianism. This revealed that some of their utilitarian responses could be characterized as prosocial choices where they sacrificed their own self-interest to support the greater good. Persons with bvFTD were more likely than has previously been reported to endorse prosocial values such as kindness and service to the greater good as important personal values contextualizing their moral choices, and they exhibited significant positive emotionality around even difficult moral decisions.“I felt great.”), whereas healthy controls were more likely to tag their moral reflections with negative emotions . Disrupted experience of emotion, particularly in relation to self-conscious moral emotions such as guilt . This observation is in line with previous literature, where this cognitive approach has previously been described in the context of bvFTD as denkfaulheit or “mental laziness,”  . This ne“rules should not be broken”). Healthy controls, in turn, exhibited markedly more rule-breaking tendencies and moral flexibility in their responses . Selective degeneration of neural systems important for mental flexibility could be associated with this increased rule adherence in the participants with bvFTD, and this finding may reflect some mental rigidity  and agent-centered permission and special obligations dilemmas (positive dimension). Importantly, our study challenges existing theories about moral reasoning in bvFTD that recognize utilitarian judgments as stemming from a lack of prosociality , 20, 21.By leveraging a phenomenological approach, we demonstrate how emotional and cognitive processing interact in the service of moral reasoning. Based on our findings, we propose that knowledge of values and rules is preserved in bvFTD, and to some extent is conveyed in participants’ responses to moral dilemmas. One could assume that persons with bvFTD responded in a way that reflected the retention of a moral compass encapsulating both the dimensions of discipline (rule compliance) and prosociality (kindness). It remains of great interest to explore whether, and how, these findings vary across other dementia syndromes, such as Alzheimer’s disease syndrome and semantic variant primary progressive aphasia. Finally, despite not being the focus of our study, significant potential scope exists for examining additional processes involved in moral reasoning, such as empathic concern , faith , and socExploring the practical implications of these findings within the context of dementia care, particularly in therapeutics, is an important area for consideration. One notable application lies in the realm of enriching psychosocial interventions between individuals with bvFTD and those in their social environment, wherein cultivating symbolic meaning for careers assumes a central role . This enOur study has several limitations. Foremost, our study examines the responses of a small and somewhat homogenous sample. Participants’ responses are grounded on cultural norms and ways of life in the United States, and more specifically the West Coast. Thus, different perspectives and views could emerge from other cultural and research contexts. Expanding the sample size and broadening cultural variation would further elucidate the themes noted in this study and perhaps reveal additional factors. Additionally, our sample represents the earliest stages of bvFTD disease progression, whereas participants at a later stage might show different results. Lastly, this study relies on moral vignettes to measure moral reasoning instead of direct observations of real-life behavior. The dilemmas represent the participants’ ideas about hypothetical behaviors rather than observed occurrences. Even though we asked participants to report on their moral reasoning and describe their values, our design did not assess moral behavior in real life. Future studies might achieve greater ecological validity by comparing similar dilemmas to real-world moral behavior. Overall, these results more comprehensively reflect the emotional and cognitive experience of persons with bvFTD by centering their voices, and further highlighting the importance of incorporating qualitative approaches in dementia research.https://memory.ucsf.edu.Individual-level data presented in this article cannot be made available in a public repository because it consists of raw interview data of a sensitive nature and thus is subject to healthcare privacy regulations. Appropriate sharing of group-level or deidentified aspects of individual-level data will be possible upon request to The Committee on Human Research at UCSF approved this study. Before testing, all participants signed informed consent forms, confirming voluntary research participation, and gave permission to use the collected data.RA: conceptualization, writing–original draft preparation, resources, methodology, and writing–review and editing. TH and AS: methodology, investigation, resources, and writing–review and editing. KF and PC: methodology and writing–review and editing. BM and JK: resources and writing review and editing. WC: investigation, resources, and writing–review and editing. KR: conceptualization, resources, supervision, project administration, and writing–review and editing. All authors contributed to the article and approved the submitted version.This work was supported by the National Institutes of Health under grant numbers R01AG029577/RF1AG029577 (PI: Rankin), P01AG019724 (PI: Miller), NIAK01AG059840 (PI Bernstein), and the Larry L. Hillblom foundation under Grant [2014-A-004-NET (PI: Kramer)].The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Little is known about the general adult population’s adoption of digital technology to support healthy lifestyle, especially when they are expected to take greater personal responsibility for managing their health and well-being today. The current qualitative study intended to gain an in-depth understanding of determinants of digital technology adoption for healthy lifestyle among community-dwelling adults in Singapore.A qualitative study design, with thematic framework analysis was applied to develop themes from the data.Semi-structured individual interviews were conducted with participants either face-to-face or online through a videoconferencing platform.14 women and 16 men from the general population who were between the ages of 22 and 71 years.Three major themes were developed: (1) digitally disempowered (2) safety and perceived risks and harm; (3) cultural values and drives. Adoption of technology among the general population is needs-driven, and contingent on individual, technological and other cross-cultural contextual factors.Our findings highlight there is no one solution which fits all individuals, emphasizing the challenges of catering to diverse groups to reduce barriers to adoption of digital technologies for healthy lifestyle. Digital guidance and training, as well as social influences, can motivate technological adoption in the population. However, technical problems as well as data security and privacy concerns should first be adequately addressed. This study provides rich cross-cultural insights and informs policy-making due to its alignment with government public health initiatives to promote healthy lifestyle. War on Diabetes (WoD) campaign   , 15, morracking) . Digitalracking) , 25; andracking) , 26. Othracking) . Indeed,racking)  —not onlracking) ; complimracking) . Anotherracking) . More rech users .While it is encouraging that there is a rapid growth in the number and sophistication of digital innovations for active lifestyles, it is only worthwhile if these are accepted by both the young and old, and used to improve their health outcomes. However, several researchers have found that unlike the younger generation, most older adults may be digitally estranged , 34. OthThe Technology Acceptance Model (TAM) provides a framework for understanding the adoption of technologies . This moTaking into consideration the emerging evidence for digital innovations as one of the promising solutions which potentially allow easy, personalized, and accessible means to improve the well-being of individuals, we felt that it would be meaningful to examine personal experiences surrounding the determinants of digital technology adoption for promoting active and healthy lifestyle behaviors in the general population of Singapore.A qualitative design was undertaken in the study to explore individual experiences with digital technology to promote healthy lifestyle. This study was part of a larger nationwide study that examined the knowledge, attitudes, and protective practices toward diabetes among the public in Singapore . The stuWritten informed consent was obtained from all participants, and ethical approval for the study was granted by the relevant institutional review board, the National Healthcare Group Domain Specific Review Board (DSRB ref.: 2019/00926). This study is reported in accordance with the Consolidated criteria for Reporting Qualitative research guidelines .Patients were not involved in the design, recruitment or conduct of the study.Can you please tell me a little bit more about that?,” “Could you give me an example of that?”) were used if necessary. At times, the interviews required a ‘two-way process’ (The study period (from August 2020 to March 2021) coincided with the rapidly developing Covid-19 pandemic situation and therefore, interviews were conducted either in person or via the video conferencing platform Zoom, depending on the participant’s preference. A semi-structured interview guide see  aimed toprocess’ , where iData collection and analysis happened concurrently, allowing emergent themes to inform ongoing data collection. The team decided to end data collection when saturation was assumed to have been reasonably attained with no new themes arising from the data. Data were analyzed first from the English-language interviews before commencing with the other language  interviews. This was to ensure that we had reached thematic saturation with data collection and to simultaneously observe and analyze the other language interviews for the emergence of new themes. A total of 30 interviews were conducted by experienced qualitative researchers from the study team; 20 interviews were in English, while four were in Chinese, and three were conducted in Malay and Tamil respectively. Interviews were audio recorded, and transcribed verbatim by an external provider of transcription services. These were then checked for accuracy by researchers in the study team.a priori issues and emergent data driven themes should guide the development of the analytic framework  first thoroughly read and re-read each transcript, and listened back to the audio-recorded interviews familiarizing themselves with the contents of the transcripts. We found this familiarization process essential in cases where the researcher analyzing the data had not been present during the interview . The teaNext, the researchers independently developed initial themes by further refining codes and adapting, merging and sorting them into a preliminary structure representing themes and subthemes. The researchers then met to discuss and review emerging categories and ideas to construct an initial analytical framework. Themes and subthemes were reviewed multiple times to ensure external heterogeneity and internal homogeneity. On reaching a consensus, a codebook was constructed which described each code, the inclusion and exclusion criteria, and exemplars from the transcripts to assist with reliable code application.We applied this final analytical framework, the codebook, to each transcript using NVivo. We divided the transcripts among the researchers and imported them into NVivo ready for indexing. The semi-structured interviews were the unit of analysis. We then systematically went through each transcript, highlighting each meaningful passage of text and selecting and attaching an appropriate code from the codebook. We then used NVivo to share our indexed transcripts, ensuring that each researcher could access the whole data set for the next stage.Once all the data had been coded using the analytical framework, we reviewed and summarized the data in a matrix for each theme using Microsoft Excel. The matrix comprised of one row per participant and one column per code. We abstracted data from transcripts for each participant and code, summarized it using verbatim words and inserted it into the corresponding cell in the matrix. We also highlighted references to potentially interesting quotations within respective cells in the matrix.The themes for this study were generated from the data set by reviewing the matrix and making connections within and between participant and categories. This process was influenced both by the original research objectives and by new concepts generated inductively from the data. We tried to go beyond descriptions of individual cases toward developing themes which offered possible explanations for what was happening within the data. Ideas were generated, explored and fleshed out through discussions with the lead researcher (MS) on the team. Our participants’ experiences and beliefs have been presented with minimally edited verbatims in the results section below.Thirty individuals from the general public participated in the study, of which 16 were male and 14 were female. The mean age of participants was 44.7 years (SD = 14.7), with ages ranging from 22 to 71 years. Forty percent identified their ethnicity as Chinese, 33.3% as Malay, 20% as Indian and 7% as Others. Majority of the participants reported being married (70%), employed (66.7%), and most had attained secondary level education or higher (86.7%). Our participants reported that using digital technologies affected their health status and lifestyle in some way. These digital technologies promoting active and healthy lifestyle behavior were mobile applications (apps), wearable devices, social media platforms and websites. The most commonly used were mobile health apps, most times associated with a wearable fitness activity tracking device.Most participants also shared that they used one or more types of digital technologies concurrently. Participants from ethnic minority groups  expressed a tendency to use digital technology for weight and nutrition related activities such as weight management, healthy food consumption, and nutrition or calorie information compared to the Chinese majority. While many reported downloading or accessing digital tools of their own volition, more than a third, on the other hand, also reported using digital technology infrequently or not at all after.The theme “digitally disempowered” was used to describe a small but significant group of participants who believed their access and use of digital technology was hampered by a lack of capability or language barriers. This theme also captured those who believed they were hindered by poor technological design and quality to engage with these digital technologies.Most participants were challenged by a lack of, or an inadequate level of specific digital skills to access and use the variety of technological innovations available. In general, participants felt a significant barrier to digital technology use was their age. Among these participants, some shared that they felt the use of digital technology for active and healthy lifestyle required significantly higher levels of digital knowledge and skills which were too complex and demanding for them to learn today.“knowledge wise, you see if you take me at my age, I don’t have that level of scientific knowledge, knowledge to use (technology) or all of these” – Male, 35–39 years.Instead, some felt that there was a lack of guidance and training to acquire these digital skills in order to take advantage of available digital tools.“for those who are new to going online, they still don’t know. It may be an obstacle. Because I totally don’t know; I don’t know how to book or go see. That’s what I mean. Then, if someone teaches, I will know, and it will be easy, like that” – Female, 55–59 years.Interestingly, few older participants were among those who reported a lack of digital skills or training. Some older adults shared how they were capable, and currently engaging with digital technology to engage in active lifestyle behaviors.“sometimes we open to this YouTube right, has exercises; to follow healthy lifestyle kind of exercise. They show you how to do this or do that…we use YouTube a lot” – Female, 65–69 years.Poor technology quality or otherwise ill-suited designs of digital devices and smart instruments were found to hinder the possibilities of use. Some participants felt the current quality of smart wearables was still sub-par and the ill-suited designs or poor affordances meant they were not convenient or led to unappealing personal costs and effort to have them fixed.“My friend wears a smartwatch, but mine is spoiled, so I didn’t change it because it is too difficult…I stopped. Just like that” – Female, 55–59 years.Other participants aired their grievances about how existing digital tools lack innovation and sophistication to circumvent what they felt were cognitively challenging experiences with digital technology promoting active and healthy lifestyle behavior. For instance, having to repeatedly look at digital screens while trying to follow physical exercises or the need for frequent playback to observe the exercises more closely in order to get them right. Many shared these experiences were too cumbersome and off-putting.“I really used it only for a few months now then gave up… it's very difficult because you will need to watch the screen as you do the exercise. For me, it's very difficult” – Female, 35–39 years.Participants also felt that sometimes language was a challenge to using digital technology. Several participants who were not English-literate, felt there was a severe lack of digital tools, particularly mobile apps, in their native languages.“I mean, for us people who only speak or understand Tamil…A lot of us, who only know Tamil, we won’t know a lot of things on because of this you see” – Male, 35–39 years.This theme, “safety and perceived risks and harm” was described by the participants as one of the important determinants for adoption of digital technology for healthy lifestyle. Our participants reported three key subthemes: security and privacy, distrust and discontentment, and perceiving threats to health.Security issues and a lack of trust complicated the adoption of technology particularly among consumers of technology. Many participants expressed safety concerns about the security of personal information shared with various health-promoting digital tools and platforms in order to receive personalized, authentic, and meaningful experiences. Participants felt that their fear and anticipation of consequences regarding misuse of their personal data led to their avoidance or discontinued use of the technology.“I'm not very comfortable with the idea of sharing such personal data with technology companies….I think that's really very scary to me. Yeah, taking over our lives. And what we can or cannot do. So yeah.…because they really do steal data from their own (users)” – Male, 20–24 years.Digital technologies associated with physical activity, sleep, mood and weight management are very popular in the general population; however, the quality of the digital technologies and health information propagated on these are hard to assess for making informed health care decisions by users.“These are the biggest negative factors online, I feel, a lot of gimmicks, a lot of scams, a lot of these kind of people that claim to know what they're doing but they don't” – Male, 25–29 years.Experiences of distrust of digital content also emerged from the interviews, as participants shared about how they grappled with the challenge of false, inaccurate, and misleading information in digital technology promoting healthy lifestyle.“So if a company wants to sell its product, it can really buy off a few YouTubers, popular YouTubers and tell them to sell their products. So I think this can really sway a lot of people…Basically, spread a lot of half-truths or misinformation” – Male, 20–24 years.A number of participants felt dissatisfied particularly with mobile health apps. While most have a free version, it tends to be limited in functionality and often inundated with advertising. Participants also shared feeling deceived by ‘premium’ apps and ‘paid’ apps. They shared how it was difficult to find suitable and effective apps to achieve their lifestyle goals.“They are just traps to get you to spend money. So it is very difficult to like figure out which ones are legitimate and which ones are out to get your money… even for a paid app, it doesn’t really guarantee results” – Female, 25–29 years.Some participants felt most of the digital technology for active and healthy lifestyle were primarily targeted at consumers from Western populations. Participants felt that often these digital tools were not always culturally relevant, or worse, causing physical and psychological harm to uninformed users in non-Western populations.“I think relying on it may not be very helpful, or it can actually disadvantage you because your body is definitely very unique and different from others. So your body is probably very different from that of an average Westerner. So with Western companies telling us what we should or should not do or eat, it can be affecting us very differently” – Male, 20–24 years.In addition to the above safety concerns, a substantial number of participants commonly described digital technology as potentially harmful to healthy living. While many acknowledged that digital technology was useful for promoting healthy living, they shared how it can also bring a lot of distractions which may jeopardize their plans to engage in healthy lifestyle behaviors.“I think when people are just stuck on their phones non-stop, it shows that technology is really not helpful for healthy living…a lot of people will end up just watching or using it for so long and it stops us…from doing our exercise” – Female, 60–64 years.However, several participants reported that technology use as a distraction was in fact useful in motivating them to last longer during activities such as exercise. Thus, participants felt digital distractions can be a double-edged sword.“It’s like oh I want to watch just another episode or something on Netflix and then after that, I will go (exercise)…But then because they are distracted by the show, they run a bit more. So it is really like a double-edged sword. It distracts you but it also helps you to do more of your fitness stuff because you are distracted” – Female, 25–29 years.The theme, “Cultural values and drives” was identified as another key determinant of technological adoption. There were three subthemes: Social norms, Peer influences, and Self-directed motivations.Traditionally, healthy lifestyle interventions have been in-person activities conducted individually or in a group. A group of participants held a keen preference for these methods over the use of digital technology, as the latter was not seen to provide an equivalent experience or beneficial one. Unlike the ‘digitally disempowered’ described earlier, whose technology adoption was predominantly hampered by accessibility issues, participants in this social group shared common values to do with the undesirability of digital technology for healthy lifestyle and an avoidance of it. In addition, security and privacy concerns were also rather common in this faction of non-adopters.“the best way, right, is through human to human. That's the best way because like, for my sister, she didn't get proper training on apps. So, it turns out she's not getting slimmer, she's getting bigger”– Male, 30–34 years.Many of the participants shared challenges related to the nature of communication and poor interaction through the digital environment, whereas in-person sessions were thought to reduce potential misunderstandings because they provided opportunities to clarify, ask questions, receive more accurate feedback, and enhance the experience. Additionally, participants felt it was considerably less personalized in the digital environment and had doubts about receiving quality services remotely.“Online thing is not very good. Some people might think, “I'm not sure whether am I doing correct or not though I'm following it,”…the instructor might have a hard time telling them what is the correct posture, what you should be feeling because they are not with them” – Male, 35–39 years.Yet, peer influences surrounding an individual were found to affect participants’ propensity to engage with digital technology for healthy lifestyle. Peer opinions carried significant influence and could affect one’s personal attitudes to various digital innovations, based on the prevailing attitudes within the social network.“Sometimes like our friends they will send us online messages, "Sis, this or that exercise online is very good," so I will just follow from there just like that…” – Female, 65–69 years.Many participants felt that these digital technologies intentionally or inadvertently give us an edge, promoting healthy lifestyle activities. They shared how leveraging technology, such as fitness apps and online coaching platforms, provided them quick access and flexibility to take up one or more workout routines at their convenience, and adapt their fitness goals to suit a variety of fitness levels at any time.“during the circuit breaker, I downloaded a gym exercise app, and I did some gym, some weight training at home for the weight loss. And then recently, I changed to yoga from the same app. And then I did the running app. It was the app that was from couch to 5K (laughter)…and now I'm just continuing…” – Female, 45–49 years.Participants also tended to agree on the importance of personal motivation in order to benefit from digital technology for healthy lifestyle.“I looked at my screen time, and my screen time was three hours on the phone. And I'm like, "Oh my god. What is this? This is such a great waste of time." …. And I said, "One hour out of that time, I could have used it for doing something probably, something useful." So then, yeah, I think it's just finding the motivation is probably the biggest obstacle” – Female, 45–49 years.Digitally disempowered; Safety and perceived risks and harm; and Cultural values and drives) illustrated the key determinants of digital technology adoption as perceived and experienced by an ethnically diverse sample of adults in Singapore. In this discussion, we highlight our key research findings, a local conceptual model on digital technology adoption, discuss the limitations of our study and discuss directions for future research. We discuss our findings on the experiences and perceptions of digital technology through a technology acceptance , beyond perceived social pressure . LikewisThe results of our study revealed that a small but significant proportion of the general population were digitally disempowered and felt they were challenged by the ease of use of digital technology for healthy lifestyle for reasons including digital skills, language limitations and the complexity of digital innovations.According to the TAM, self-efficacy renders positive effects on the perception of usability of technology while lack of knowledge and experience negatively affects ease of use. Similarly, our findings indicate the most obvious personal barrier was issues related to digital skills and capabilities of individuals, particularly those who were among the digitally disempowered. A typical challenge for individuals, regardless of age, was that their current levels of knowledge and skills were inadequate. Other research has also discovered that the problem of a lack of digital skills has broad effects in a general population , 60 in tlingua franca can easily access and utilize such digital technology better, to the detriment of non-English speaking, less digitally-connected individuals. Researchers have highlighted that language difficulties pose significant challenges to the adoption of digital technologies, especially among ethnic minorities who may struggle with weak language skills . The funders had no role in the design of the study, collection and interpretation of data, or preparation of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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