Background Consisting of dictated free-text documents such as discharge summaries, medical narratives are widely used in medical natural language processing. Relationships between anatomical entities and human body parts are crucial for building medical text mining applications. To achieve this, we establish a mapping system consisting of a Wikipedia-based scoring algorithm and a named entity normalization method (NEN). The mapping system makes full use of information available on Wikipedia, which is a comprehensive Internet medical knowledge base. We also built a new ontology, Tree of Human Body Parts (THBP), from core anatomical parts by referring to anatomical experts and Unified Medical Language Systems (UMLS) to make the mapping system efficacious for clinical treatments.Result The gold standard is derived from 50 discharge summaries from our previous work, in which 2,224 anatomical entities are included. The F1-measure of the baseline system is 70.20%, while our algorithm based on Wikipedia achieves 86.67% with the assistance of NEN.Conclusions We construct a framework to map anatomical entities to THBP ontology using normalization and a scoring algorithm based on Wikipedia. The proposed framework is proven to be much more effective and efficient than the main baseline system. A medical narrative which consists of dictated free-text documents such as discharge summaries is integral to clinical patient records. From databases of free-text medical narratives, a mapping system can be built to find relevant information about the human body parts for clinical and research purposes. Generally, human body parts can be classified according to physiology or body structure . The claOne challenge of designing the mapping system is the diverse forms of an anatomical entities. Named entities related to body parts, organs, and their subparts are defined as anatomical entities . In this“Given this, it was advised that the patient have a colonoscopy to rule out further bleeding”.“The patient underwent a flex sigmoidoscopy on Friday, 11-02”.If we use string-matching, colonoscopy which is a medical test can be mapped to colon because of the common string “colon”. However, “sigmoidoscopy”, which is also a medical test related to colon, cannot be mapped to “colon” since the string “sigmoidoscopy” does not contain the substring “colon”. In Systematized Nomenclature of Medicine - Clinical Term (SNOMED-CT) [For explicit anatomical related entities, there are dictionary-based methods  that can conduct mapping. However, quite a few implicit anatomical related entities cannot be mapped to certain body parts by string-matching methods. The reason is that there is no common substring between the entities and the body parts. For example, the following two sentences come from a discharge summary:OMED-CT) , severalOMED-CT) , indicatIn addition to implicit anatomical related entities, abbreviations, which are common in medical records, are also difficult to retrieve using string-matching. Since the abbreviations of anatomical related entities in medical records are not always the same, it is not easy to build a supporting dictionary to cover all medical abbreviations. For example, “extremity” has abbreviations such as “ext”, “extrem”, “ue” (upper extremity), and “le” (lower extremity). There are too many synonyms in medical texts which are difficult to be recognized using limited dictionaries.The key to solving these issues is to identify the standard form or the related anatomical part for each entity. A named entity normalization (NEN) method is presented in this work to explore the relationships between anatomical related entities and human body parts. The relationships recognized by NEN can locate the original or correlated body parts of implicit anatomical related entities and abbreviations, which makes mapping easier. In the NEN system, extensive information results in a high level of effectiveness . Thus, aAn ontology is an information aggregation made up of standard forms, properties, and relationships of entities . The ontIn this study, we developed a new ontology called THBP using standardized names of human body parts extracted from UMLS with the help of three clinical experts, provided a set of anatomy annotations from 50 discharge summaries, and successfully built a mapping system to map anatomical entities in discharge summaries automatically. In this new ontology, human body parts are organized by their location. Therefore, THBP is more convenient for clinical text mining applications. The main contributions of this study are threefold. 1) We are the first to successfully map anatomical related entities from discharge summaries directly to human body parts. 2) We develop a highly accurate approach to this problem by combining normalization and Wiki-based algorithms. 3) Finally, we organize THBP as an ontology for mapping anatomical named entities, which will be published open source on the Internet.Anatomy is one of the foundations of modern medicine and greatly contributes to medical research as well as clinical diagnoses. Therefore, building a semantic network of anatomical related entities is of great value for both experts and novices . Much efIn clinical practices, medical tests and diagnoses are usually related to the locations of body parts with suffering, an anatomical network based on body positions is more effective and helpful. SNOMED-CT is an ontology that covers a large number of clinical concepts and relations, including anatomical parts, and demonstrates a high efficiency in dealing with several clinical research works , 18. HowNamed entity normalization builds a mapping relationship between named entities in text and ontology. Since standardized vocabularies and terminologies are essential in medical information processing and normalization , severalThe works discussed above apply dictionary-based methods and have achieved considerable progress. Nevertheless, such early investigations simply apply lexical matching, and the accuracy of these systems depends heavily on the quality of both technology and the text. As a result, for dictionary-based approaches, without being assigned with good text and a good dictionary, these methods would not achieve high accuracy, especially recall. Particularly in clinical texts, abbreviations are very common and difficult to be fully included in one dictionary.To address the above-mentioned problems, research interests have recently moved to using NLP and statistical machine learning methods to solve normalization problems , 33. KanOverall, statistical methods have achieved great success in normalizing entities and detecting relationships, particularly for clinical texts. Since our work is built on clinical texts  and we need to identify a huge number of abbreviations, we applied machine learning methods to normalization for anatomical related entities.When building an effective normalization system, employing a large external knowledge base has a positive impact on the performance of the whole system. Wikipedia is selected as the external knowledge base in this study, considering that Wikipedia is regarded as a valuable knowledge base in medical language processing . InformaFacing the difficulty of directly exploiting existing ontologies, we decided to organize a compendious but specialized ontology that is consistent with the structure of established ontologies and compatible with the arrangement of hospital departments. To accomplish this task, entities from the “Body Location or Region” group in UMLS are first extracted. The essential clinical body parts are then chosen according to clinical experts and later be used for the building the THBP. Finally, we get inspiration from FMA and SNOMED-CT and discuss the structure of the new ontology with doctors. These all ensure that the framework of the new ontology is aligned with those ontologies and the arrangement of the departments in a hospital.Anatomical structures of humans can be classified into five levels - systems, organs, tissue, cells, and chemicals  . Since cAnatomical related entities usually have several synonyms and abbreviations in biomedical text. For example, “abdomen”, “peritoneal cavity”, and “abd” all refer to the same body part. For body parts that are called by more than one terms, one alias is treated as the formal name and other aliases including abbreviations and synonyms in the THBP are considered supplemental. For example, “abdomen” is chosen as the formal name for that body part and the others are supplemental .Our annotation dataset comes from the output of our previous work , which uAn easily followed standard guideline is required to ensure consistency and help annotators work independently. The process of building the guideline follows the Delphi method . GuideliTo build the guideline, the annotators first make annotations on 10 discharge summaries separately and then find the differences between these annotations through discussion. Note that these 10 discharge summaries from our previous work are only used for building the annotation guideline and are not included in the experiment annotation dataset. Based on their discussion and further studies of references, each annotator constructs their own list of guidelines. Revisions on annotation are made independently following their own guidelines. Then there is another discussion. The cycle of holding discussions, amending guidelines, and revising annotations is repeated until a convincing agreement is reached. Finally, three sets of mutually independent guidelines are compiled into a unified one.The annotation results of anatomical named entities follow the rules below. Note that “positional words” in the following context refers to prefixes or words that indicating the location of human body parts. We defined some necessary components based on anthropotomy knowledge and then match them with the most relevant concept in UMLS.1. All anatomical named entities are normalized by position instead of function . This is in accordance with the idea of creating an ontology that based on position. The trunk is considered the “stem” of the human body, including organs and tissues that are distributed across the whole body, such as nerves, blood, and bones.2. The mapping results of anatomical named entities are the lowest layers of THBP. For example, the mitral valve is located in the chest and is part of the heart. Considering that the “heart” is a sublayer of the “chest”, the mitral valve is mapped to the heart instead of the chest.3. For anatomical related entities that are exact parts of THBP , the mapping results are their original forms. If the full name of an abbreviation can be found in THBP, the abbreviation is normalized to the full name .4. Abbreviations consisting of several different anatomical locations are mapped to several different parts of THBP. For example, HEENT as an abbreviation of Head–Eye–Ear–Nose–Throat is mapped to “head”, “eye”, “ear”, “nose”, and “throat”.5. The mapping results are in singular form, while the positional prefixes or words in named entities remain the same. For instance, “left extremities” is mapped to “left extremity”.The articles in the experiment set are annotated based on the unified guidelines mentioned above. Two rounds of annotations are carried out on all 50 discharge summaries. In the first round, two annotators (A1 and A2) with biomedical engineering backgrounds annotate the same discharge summary independently. When there are disagreements between their results, the third annotator with a clinical background (A3) acts as referee and makes the final decision. After that, A3 explains the reasons for the judgments and the three annotators discuss the guidelines. During this discussion, guidelines are revised for one last time. In the second round, A1 and A2 annotate 50 discharge summaries according to the final version of guidelines. When A1 and A2 disagrees, A3 makes the final decision. For example, “bronchitis” is an entity that belongs to “neck” and is related to “trachea”, a sub-layer of “neck”. If there is a disagreement between A1 and A2 in this situation, A3 will point out that it should be normalized to the lowest related layer “trachea” according to Rule 2 above. The final result, therefore, is “trachea”.To determine whether the annotation results can be used as the gold standard, the inter-annotator agreement is measured by F1-scores. Table According to Table To accomplish the mapping task, we present the result of combining these methods: the string-matching method (baseline system), named entity normalization, and the exploitation of Wikipedia (including Wikipedia distance scoring and frequency scoring). The flowchart is shown in Fig. We use string matching algorithm as our baseline system. Entities are extracted from discharge summaries and directly mapped to our ontology THBP through a string-matching method  denotes the number of strings from the beginning of entry to the anatomical named entity which can be matched by THBP. max(D) represents the distance of last matched anatomical named entity.In this way, the distance is transformed to a cosine similarity ranging from 0 to 1.Algorithm 2: Based on frequency In addition to distance, as stated in [tated in , 60, theF(n) is the frequency of the anatomical named entity which can be matched by THBP. Considering the first appearing anatomical named entity that can be matched by THBP is strongly related to the mapping results, 2.5 is the multiplier to the frequency of the first appearing anatomical related entity. The multiplier 2.5 is determined through cross validation.To verify the validity of our algorithms, 50 anatomical named entities and their Wikipedia entries are randomly extracted as test data, on which two Wiki-based algorithms are employed to obtain mapping results. Table Both algorithms achieve a fairly high level of accuracy, which proves the accuracy of our earlier assumption and the effectiveness of the algorithms. According to the results, both scoring strategies are beneficial to the task.Algorithm 3: Based on distance & frequency After collecting the distance and frequency of each word, we combine the distance score and the frequency score to a final score. Scoring formulas are combined to grade each word so that the correlation is better represented. The formula is as follows: where To determine the importance of the first matched word in the entry, we multiply 2.5 by the frequency of the first named entity showing up in the entry. The constant 2.5 is determined by cross validation. a and b are coefficients of f(D(n)) and f(F(n)) respectively. a=15 and b=1 are selected which contribute to the best results based on cross validation.In the formula, After calculating the scores, we choose the word with the largest score to be the normalization result of the corresponding anatomical named entity.50 discharge summaries from the i2b2 Challenge  are usedTo compare the improvement of each component over the baseline framework, anatomical named entities are mapped by separately adding each method to the baseline framework. Since each component is independent in separate parts of the framework, a group of experiments is carried out to evaluate how the accumulation of methods can influence the final mapping result. Table As Table In accumulation experiments, the final result of the whole mapping system stops increasing at 86.67%, while the distance-based algorithm and the frequency-based algorithm individually rise to 83.55% and 83.52% with the assistance of normalization. This result shows the primary contributions of the normalization system. With normalized entities in entries and explanations, information in Wikipedia can be better used.The increase in the final algorithm  compared to the Baseline is because the problem of synonyms, especially abbreviations, is solved. In Baseline, almost no abbreviations were matched. Even matching the entities directly to SNOMED-CT, 19.82% entities cannot be separately matched and 71.34% of them are abbreviations. In the results of the final algorithm, 53.12% of abbreviations that are not matched to SNOMED-CT are properly matched. These results show the significance of our algorithm especially in solving abbreviation problems.SNOMED-CT was also one of the candidate ontologies for the baseline system. However, since SNOMED-CT is a post-coordinated terminology, it lacks some necessary relationships that are included in our ontology. Experiments also show that the performance of SNOMED-CT is inferior to string matching. Therefore, SNOMED-CT was discarded.Though significant progress is achieved by our mapping system, performance can be further improved. Four possible causes are provided below.The first cause is the abbreviation method. By using several methods, the problem in normalizing abbreviations is alleviated to some extent. However, it is almost impossible to build a comprehensive abbreviation table because abbreviations are countless. Therefore, performance is still limited by abbreviations. Moreover, the same abbreviation may refer to multiple entities, which calls for a disambiguation system to solve the problem.Second, there are special characters in some medical terms which are difficult to process. Both removing them and treating them as space can cause problems. For example, if simply removing special characters, the system will recognize “cad/chf” (coronary artery disease/congestive heart failure) as one entity. In another condition, treating them as space will normalize “hf-cells” (heart failure cells) to “cell”, which is not included in THBP, instead of “heart”. The corresponding anatomical locations cannot be found in either situation.In addition, some anatomical named entities can be mapped to more than one anatomical locations, which causes another error in the mapping system. For example, “renal vein” can be mapped to “vein” and “kidney”. It makes sense in both locations. In our system, it is mapped only to “vein” instead of “kidney”.Finally, some confusing anatomical named entities may appear in discharge summaries. For example, “right lower lobe” can refer to organs such as lung or liver. In annotation, annotators can determine which anatomical location the named entity is mapped to by looking at context. However, the mapping system cannot predict the anatomical location by linking context, which is one of the sources of error.Considering that the abbreviation is a major source of influence to test errors, a more sophisticated abbreviation normalization system is expected to bring significant improvement. As stated above, the same abbreviation might refer to different anatomical related entities, thus introducing errors. A disambiguation system would assist an NEN system to reduce such errors and to find the correct meaning of abbreviations. We could improve our performance in the future research by utilizing a word disambiguation system. In addition, we will also consider using definitions in UMLS Metathesaurus to further improve the performance.The THBP ontology can be used to successfully map and bridge different semantic classes of anatomical named entities and anatomical locations. To complete the mapping objective, a scoring algorithm is presented to make use of information on Wikipedia, while an NEN system is also added to improve performance. The results demonstrate that our methods successfully accomplish this task.


Men and women are increasingly likely to stay sexually active into later life, but research shows that sexual activity and satisfaction decrease with increasing age. Ill health and medical treatments may affect sexual activity but there is little research on why some older people with a health problem affecting their sexual activity are satisfied with their sex life, and others are not.A mixed method study integrating data and analyses from the third British National Survey of Sexual Attitudes and Lifestyles (n = 3343 aged 55–74 years) and from follow-up in-depth interviews with 23 survey participants who reported having a health condition or taking medication affecting their sex life.Overall, 26.9% of men and 17.1% of women aged 55–74 reported having a health condition that affected their sex life. Among this group, women were less likely than men to be sexually active in the previous 6 months (54.3% vs 62.0%) but just as likely to be satisfied with their sex life (41.9% vs 42.1%). In follow-up interviews, participants sometimes struggled to tease out the effects of ill health from those of advancing age. Where effects of ill health were identified, they tended to operate through the inclination and capacity to be sexually active, the practical possibilities for doing so and the limits placed on forms of sexual expression. In close relationships partners worked to establish compensatory mechanisms, but in less close relationships ill health provided an excuse to stop sex or deterred attempts to resolve difficulties. Most fundamentally, ill health may influence whether individuals have a partner with whom to have sex.The data show complex interactions between health, lifestyle and relationship factors that affect sexual activity/satisfaction. When dealing with sexual problems in older people, practitioners need to take account of individual lifestyle, needs and preferences. Attitudes towards, and experience of, sexual activity in later life have changed in recent decades. Many men and women remain sexually active well into later life –5, and tThere is, nevertheless, evidence that sexual expression changes with increasing age. Studies have shown a decline in sexual function with advancing years , 11, 12 The decline in sexual activity and satisfaction can be attributed to various factors including the loss of a long-term sexual partner; deterioration in a continuing relationship; changes in hormonal status; and alteration in physical appearance impacting on self-esteem and response , 6, 16. The list of conditions with the potential to impact on sexual activity and satisfaction is long and includes diabetes, cardiovascular disease, prostate cancer, chronic airways disease, musculo-skeletal disorders and neurological impairment, and some cancers , 17–20. The growing body of literature has been partly stimulated by the advent of phosphodiesterase-5 (PDE-5) inhibitors . With rare exceptions , 23, resThis study had two aims: to explore how older people see their health status as having influenced their sexual activity and satisfaction; and secondly, to further understanding of how they respond and deal with the consequences.Natsal-3 was approved by the Oxford Research Ethics Committee A [Ref: 10/H0604/27] The qualitative data collection was approved by the same committee [Ref: 10/H0604/10].We carried out a mixed method study that integrated data from the third British National Survey of Sexual Attitudes and Lifestyles  with follow up in-depth interviews drawn from a sub-sample of participants aged 55–74 years who reported in the survey having a health condition which affected their sex life in the last year. We describe the prevalence of sexual activity and satisfaction among this group, and draw on in-depth interviews to explore ways in which health can impact on sexual activity and satisfaction.In combining qualitative and quantitative data we sought to exploit synergies between different approaches to examining the same phenomena. The qualitative data were used to illuminate associations found in the survey data and findings from the qualitative research were, in turn, used to shape analysis of the survey data.Natsal-3 is a probability sample survey of men and women aged 16–74 years living in private households in Britain. Overall, 15,162 adults were interviewed, of whom 3,343 were aged between 55–74 years at interview. The response rate for Natsal-3 was 57.7% and the co-operation rate was 65.8%. Full details of Natsal-3 methods have been reported elsewhere , 26.I feel satisfied with my sex life” (in the last year), with ‘agree’ and ‘agree strongly’ responses combined to categorise satisfaction .The two outcomes were ‘sexual activity’, defined as having had vaginal, oral or anal intercourse with one or more opposite-sex or same-sex partners in the past six months, and ‘sexual satisfaction’, measured using a Likert scale probing agreement with the statement, “It’s natural for people to want less sex as they get older”; and whether they found it easy or difficult to talk to their regular partner about sex. For those who had been in a relationship for the entire previous year, participants were asked how happy they were in their relationship on a scale ranging from 1 (happy) to 7 (unhappy), with responses 1 and 2 coded as ‘happy’. Additional variables included whether participants had vaginal intercourse, oral sex or genital contact without intercourse in the last six months and satisfaction with the current amount of sexual activity.Variables included in the analysis were age at interview; relationship status; economic status; self-reported health ; experience of longstanding illness or disability; and experience of depressive symptoms in the past two weeks using the Patient Health Questionnaire (PHQ-2). Body mass index (BMI) was calculated from self-reported height and weight, and mobility was assessed by asking about ease of walking up a flight of stairs. Other variables included: whether participants sought help or advice about their sex life in the past year; ever used medication to assist sexual performance; level of agreement with the statement: “The prevalence of having had a health condition or disability, or taken any medication, in the last year which affected sexual activity or enjoyment was estimated among Natsal-3 participants aged 55–74 years. Among the sub-group of participants reporting health conditions, disability or medication affecting their sex life, we estimated sexual activity in the last six months, and satisfaction with current sex life, in relation to selected lifestyle, health-related and relationship factors. After analysing half of the in-depth interviews, statistical analyses were re-run adding variables which emerged as important in participants’ depth accounts, including happiness with the relationship with their (most recent) partner and the repertoire of sexual practices engaged in during the previous year. Regression analysis was used to adjust for age and relationship status.Analyses were carried out using the complex survey functions of Stata (version 14) and were weighted to adjust for the unequal probabilities of selection and for differential non-response. We report prevalence estimates and 95% confidence intervals (CIs) separately for men and women.Participants eligible for the qualitative study were the 388 men and 281 women aged 55–74 years who reported in Natsal-3 having had, in the last year, a health condition or disability, or taken any medication, affecting their sex life. Participants were asked whether they would be willing to be contacted again about taking part in another interview and 80% of the eligible group agreed. A sample was drawn, guided by: the recency with which Natsal-3 interviews had been conducted; the need for roughly equal numbers of men and women; and a geographical spread across Britain (reflecting the quantitative survey). Governing the final sample size was the need to achieve sufficient variation in individual experience to explore the issues of interest and ensure saturation of themes. Letters were sent to participants inviting them to take part in a further interview, followed by a phone call from a researcher to explain the purpose of the interview, check on willingness to take part and arrange interviews.Participants gave signed consent and were provided with an information sheet and a list of agencies from which they could seek advice on topics raised. In-depth interviews with 11 men and 12 women were carried out face-to-face in participants’ homes, recorded with their agreement and transcribed. The sex, age and relationship status of the participants are shown in The topic guide, refined during fieldwork, explored: perceptions of the relationship between health status and sexual activity and enjoyment; how health issues affected sexual activity; the relationship context; and action taken by participants in response to health-related sexual problems.We undertook a thematic analysis drawing on principles of grounded theory  . Key theAmong Natsal-3 participants aged 55–74 years, roughly one in four men (26.9%) and one in six women (17.1%) reported having a health condition, or taking medication, that had affected their sexual activity or satisfaction in the previous 12 months  . The preAmong the sub-group of those participants aged 55–74 years who had a health condition, 62.0% of men and 54.3% of women had been sexually active in the previous six months, and 41.9% of men and 42.1% women reported being satisfied with their sex lives. The proportion reporting recent sexual activity was higher among men and women aged 55–64 years compared with those aged 65–74 years but there were no age-related differences in sexual satisfaction. The proportion reporting recent sexual activity was more than four times as high, and the proportion who were satisfied with their sex lives was nearly twice as high, among those who were cohabiting or in a steady relationship compared with those were not . For men, but not women, a similar association was found for longstanding illness; the adjusted odds for recent sexual activity for men reporting a limiting longstanding illness was 0.29 relative to those with no longstanding illness.Individuals were more likely to report recent sexual activity if they reported no mobility difficulties (men and women); normal weight compared with being obese (men only); having no depressive symptoms (men only); or being employed compared with retired.Sexual activity in the past six months was associated with the use of medication to aid sex (men only); seeking help regarding their sex life (men and women); and finding it easy to talk to their regular partner about sex (women only).By contrast, after adjustment for age and relationship status, satisfaction with sex life showed no significant association with self-reported general health or with any other physical health variables. Satisfaction was most strongly associated with sexual activity in the past 6 months. Adjusted odds for satisfaction with sex life were much higher among both men and women reporting sexual activity in the past 6 months compared with the sexually inactive (with adjusted odds of 3.17 for men and 2.87 for women). The magnitude of the difference was considerably greater for having vaginal intercourse compared with oral sex or genital stimulation, particularly among men. Men and women were also much more likely to be satisfied with their sex life if they felt that the frequency of sex was about right (with adjusted odds of 8.44 for men and 6.38 for women). Experiencing depressive symptoms was significantly associated with lower odds of sexual satisfaction in men, but not women, and those who found it easy to communicate with a partner about sex were more likely to be satisfied. For women (but not men) in a steady or cohabiting relationship, after adjusting for age, satisfaction with their sex life was associated with feeling happy in the relationship.While the associations between age, health and sexual activity observed in the survey data were also evident in the in-depth accounts, many participants found it difficult to separate the effects of declining health from those of increasing age. Ill health was seen as accelerating an inevitable decline in sexual activity with age which made it easier to accept:….as you get older, certain things in life compensate for [sex]. I’ve got companionship and peace and quiet and a nice comfortable home so I won’t be missing it now. (M3)Participants often found it difficult to elaborate on the link between ill health and sexual activity in the in-depth interviews. Establishing cause and effect required them to retrieve information on two aspects of the association: first, specific health conditions to which changes in sexual activity might be attributed, and second, the sequence in which these events had occurred. Both posed challenges. For some, the multiplicity of ailments, and variation in timing of onset and severity, created problems for attribution and recall. Rarely were symptoms of ill-health experienced in isolation from one another, and it was hard for participants to isolate their effect.[…] I wouldn’t have thought it’s down to me colitis because I’ve had that for years and it never had any impact, you know. I mean even after me [blood pressure] tablets and if I’m saying well maybes [the decline in frequency of sex] started then, if it did it was only slightly, it wasn’t a big deal. But likes of now, you know, being with the combination of maybe being older, my TIA [transient ischaemic attack] may have had an effect. But you see I don’t know […] I can’t honestly say, “Oh yeah, it was since then,” definitely, you know, I can’t say that because I think it’s just been gradual, you know. (M8)With regard to timing, it was not always possible to recall the sequence in which health-related events had occurred, particularly where the onset of ill health had been gradual and symptoms intermittent. Similarly, where the onset of ill health occurred simultaneously with life events such as bereavement (M3), it was difficult to disentangle the influences and assert attribution.Despite the uncertainties around the order of events and precise causes of sexual difficulties, most participants were able to describe specific ways in which they felt aspects of their health had affected their sex lives. For some, the health condition impacted directly on the capacity to have sex. Nine of the 11 men  and one woman (M13) (in relation to her partner), saw illness or medication as having led to erectile problems, making penetrative intercourse difficult or impossible to achieve. For two women, conditions that caused sex to be painful, such as cystitis (W8) and severe back pain (W2), had a direct bearing on sexual frequency and enjoyment. As conventionally practised, sexual activity requires a degree of agility, and musculo-skeletal deterioration, accidental damage, or the aftermath of surgical procedures were reported in more than one account as restricting mobility .Medication and procedures aimed at alleviating health conditions were also seen as having had a direct and detrimental effect on sexual enjoyment . Complicated treatment regimens interrupted the spontaneity of sex. One woman described the impact of remedies for her gynaecological problems–a vaginal ring for uterine prolapse and sanitary pads for her weak bladder:By the time you put the [vaginal] ring in and […] put your Tena Lady (pad) on and think “God!” and then you think about it, that would put you off a sex life you must admit (W5).For others, health-related factors were less direct. A husband’s sleep apnoea, for example, or excessive fidgeting because of pain occasioned a move into separate beds, and since sex was associated with being in bed, that too ceased along with the move. Changes to sleeping arrangements, initially intended as temporary, sometimes became permanent. A woman who had recently undergone a hysterectomy asked her husband to move to a separate bed because she was afraid he would knock her stitches and he had never moved back in (W1). Fatigue resulting from ill health also had an impact on sexual activity, as in the case of a participant with diabetes and a thyroid problem (W6).Some participants described their fear of exacerbating an existing health condition. One woman had suffered recurrent bouts of cystitis following a hysterectomy in her early 40s and believed that having sex triggered episodes:Yes, you do, you worry. After you’ve had sex, you think, ‘What will it be like in the morning? Will I be sore, will I be alright, will I pick up another dose [of cystitis]?’ (W8).dying happy’ (M2), chose the former. Another, who had previously experienced a transient ischaemic attack (or mini-stroke), took a different view: ‘I can’t be worrying about that all the time; it might never happen’ (M8).Such fears were more apparent among those who had experienced life-threatening events such as a heart attack or stroke. One man feared that use of PDE-5 inhibitors (such as sildenafil) for his erectile difficulties would prompt another heart attack and, seeing his choice as between living and having no sex life or ‘the last thing you want to think about’ (W4), and where this impacted detrimentally on the relationship, it in turn worsened mood, setting up a vicious spiral. Accounts of depression also shed light on the higher prevalence of sexual activity among employed as opposed to retired participants. Although one man described retirement having resulted in more time to enjoy sex (M1), others described difficulties adjusting to loss of status and routine (M4) and a concomitant decline in mental health which then impacted negatively on sexual activity and satisfaction.As the survey data showed, experience of depression was associated with sexual inactivity. In-depth interviews suggested that, in some cases, depression occurred in the absence of other illness , in others it was attributed to physical illness such as myalgic encephalomyelitis (ME) (M1) or Parkinson’s disease , whilst for others it was seen as a consequence of generally worsening physical health (W5). Feeling depressed was described as dampening sexual desire: ‘In asking whether a health condition affected sexual activity and enjoyment, we assumed the effect would have been adverse, but this was not always the case. A woman in her mid-sixties (W10), for example, felt that the medication she was taking for Parkinson’s disease had reawakened an interest in sex that had waned since the menopause, and her relationship with her husband had become closer as a result. In two other cases, erectile difficulties—experienced by a participant in one, and by the participant’s partner in another—had a positive effect on sexual enjoyment by requiring a more imaginative approach to lovemaking, greater variation in sexual repertoire, and sessions of longer duration .In the survey data, an important determinant of whether participants were sexually active was whether they were living with a partner or in a steady relationship. Several accounts illustrated how health issues impacted fundamentally on the ability to find a partner. For a 59 year-old widow (W11) with a complex range of chronic health problems, her restricted mobility limited opportunities for getting out and meeting people; and for a man with erectile difficulties resulting from surgery (M10), lack of confidence deterred him from seeking a new relationship. These problems were compounded by lack of motivation, anxieties based on previous experiences and feeling too old to consider starting anew.ex-post facto rationalisation, but not having sex seemed to matter less where the relationship was good: ‘I’d rather him a bit more happy and loving than have the sex’ (W12).For men and women with a steady or cohabiting partner, the survey finding that happiness in the relationship was more strongly related to satisfaction with their sex lives than to sexual activity was reflected in the in-depth accounts. Where the relationship was close, men and women described compensatory mechanisms such as an ability to make light of the problem, finding other sources of intimacy, or adjusting to a decline in frequency of sex. These adjustments may have involved some Others, both women , described continuing to have sex despite a health-related decline in their own sexual desire, to please their partner (W1) and to ensure the continued stability of the relationship:Well we still dabble but more on his behalf than mine […] I think, ‘Well, it’s not fair for him to have to go without because I had that operation’ (W1).Where the relationship was less close, health problems either provided an excuse to end sexual activity or else deterred attempts to resolve sexual difficulties. For one woman, the effect of her gynaecological problems on her sex life had been relevant, but the more significant influence was her poor relationship with her husband (W5).Ill health also affected sexual activity and satisfaction via its impact on the quality of the relationship. Declining health sometimes affected the balance of the relationship by leading to new roles and dependencies. Partners were not always able to adjust to a caring role and, conversely, unhealthy individuals could feel unsupported by their partners  creating rifts in the relationship. Sexual problems triggered by ill health impacted negatively on the relationship, for instance where one partner attributed blame even where ill health was known to be a contributing factor (M4).Where sexual activity had ceased because of health issues on the part of one or other partner, the loss of physical intimacy had clear consequences for the relationship:…I haven’t had sex for 18 months and that’s because my husband’s got very bad arthritis and he takes beta-blockers, and because of that he finds it very difficult. [.…] I really do miss the closeness of the cuddles before and the cuddles after. I feel really sorry for my husband, I think he misses it more than I do (W12).In the survey, approximately half of the women and a third of the men who reported a health condition affecting sexual activity, had not had sex in the previous six months. In-depth accounts indicated that when penetrative intercourse became difficult or impossible, it generally signalled an end to sexual activity. This was not necessarily problematic. For some women, health problems provided a welcome respite from sex, though this view was not expressed by any of the men we interviewed.Participants who continued to be sexually active had often made adjustments to the way they had penetrative sex. For the most part, these were limited to changes in positions:P: We don’t have ….different positions and things like that, it’s just one, and that’s it. I mean, usually he likes to stand up because he’s had two knee operations so he can’t bend down. He can’t kneel on them anymore.I: So that affects the position that you can have sex in?P: Yeah, doggy-style as I put it. I know it sounds rude, but you understand what I mean (W1).it doesn’ae go up’ (W7). Their ‘half hour on the bed every afternoon’ meant enjoying kissing, cuddling and petting. In the case of others, the need to find alternatives to their previous repertoires had actually improved their enjoyment. The sexual relationship of a 75 year-old man with a 70 year-old partner which, prior to his illness had been reportedly unsatisfactory, was now flourishing as they had been forced to explore alternatives to penetrative sex as a result of his erectile difficulties (M1).More substantive changes to sexual practices were less commonly mentioned. There were isolated instances in which health issues had forced a re-evaluation of what counted as satisfactory sex. An elderly woman with a raft of health complaints including arthritis and emphysema, and her non-live in partner who had bowel cancer and heart disease, had devised an imaginative routine for the occasions on which, as she said, ‘you get used to what you like and because I can’t have what I like I’m not really bothered’ (W12). One man had bought a vibrator on a weekend away but it had proved unsatisfactory. The accounts of others, however, revealed a more profound distaste for non-penetrative sex:For most participants, though, sex was equated with penetration, and the idea of pursuing alternatives had little appeal. Generally, this seemed to be a matter of habit and familiarity. Men and women–some ruefully–claimed it was ‘too late in the day’ to broaden their repertoire (M3). Over the years, they had adopted practices they found enjoyable and, if these were no longer possible, they had no appetite for substitutes: ‘P: I still cuddle her, we still caress one another occasionally, but no, I think intercourse is important.I: So that’s kind of what sex is for you.P: Oh yes, I think so, yeah, absolutely, I would never dabble with anything but intercourse really, we’ve never gone into oral sex or anything of that nature. Obviously, there’s foreplay, etc, which every couple, I hope, indulge in, but an actual sex act itself has got to be full intercourse as far as I’m concerned (M3).Among survey participants aged 55–74 years with a health condition affecting their sex life, about one in four had sought help or advice about their sex life in the past year (28.6% of men and 20.5% of women. One in three (33.1%) men reported taking medication in the past year to enhance sexual performance (e.g. sildenafil). Asked why they had not sought medical advice in the in-depth interviews, several participants said they did not see this as a priority for their health practitioner. One man felt he needed to first tackle his obesity before seeking help for his sexual problems, another felt he had caused his circulatory problems himself through smoking, and another felt he was making enough demands on his doctor’s time already:… since I’ve had me heart trouble, I’ve said to meself, you know, season ticket in and out ‘t doctor’s. ‘Oh, bloody hell! Not him again!’ you know. This is what it feels like (M2).In two cases, however, it was through the act of seeking help for sexual problems that the underlying health problem had been discovered.little blue pills’) for men. No participants in the qualitative component mentioned any other form of help. The survey data showed that the proportion taking medication in the last year to enhance sexual performance (e.g. sildenafil) (one in three) was higher than the proportion (about one in four) reporting having sought help from health care professionals, suggesting alternative sources of supply. Use of sildenafil was not significantly associated with sexual satisfaction in the survey data and this was confirmed in the in-depth interviews. Several men described having used sildenafil, but with varying degrees of success (M4) and with varying degrees of satisfaction .Where help had been received, it was generally in the form of sildenafil . Since both these groups are likely to be in poorer health, our findings may be less generalizable to individuals with more severe health problems.It is common when identifying weaknesses of studies to point to problems of recall. Yet in the current context–that of informing efforts to help men and women to achieve sex lives that best suit them–difficulties in remembering sequences of life events, illness episodes, and the ups and downs of relationships, are important checks on an over-reliance on memory and can be seen as useful data in their own right.Our results have implications for public health policy and practice. The finding that the majority of older men and women do not seek help for their sexual difficulties has been shown by others , 18, 28.What seems clear is the need for a holistic approach when dealing with sexual problems in older people, taking account of lifestyle factors, needs and preferences in relation to sexual activity. Our finding, and that of other studies, that declining health may be only one of many factors influencing sexual activity and satisfaction in later life , 16 is iGiven the multiplicity of factors influencing their sexual activity and satisfaction, it seems especially important not to over-medicalise treatment options for older men and women. Both our survey and qualitative findings suggest that largely pharmacological remedies offered to older people do not necessarily increase sexual satisfaction.As to what form counselling might take, our data suggest that advice on alternatives to penetrative sex, seen by many as important , 20, 28,Our finding that sexual activity often ceases following major illness is consistent with that of other research , 29.The As others have commented, despite the importance of sexual activity and satisfaction to well-being and quality of life, this aspect of older people’s health has received inadequate public health attention , 30. Our


Along with the exponential growth of number of published literatures, it is more and more difficult for ecologists to get a clear picture of their discipline. Nevertheless, the era of big data has brought us massive information of well documented historical literature and various techniques of data processing, which greatly facilitates the implementation of bibliometric analysis on ecology. Frequency has long been used as the primary metric in keyword analysis to detect ecological hotspots, however, this method could be somewhat biased. In our study, we have suggested a method called PAFit to measure keyword popularity, which considered ecology-related topics in a large temporal dynamical knowledge network, and found out the popularity of ecological topics follows the “rich get richer” and “fit get richer” mechanism. Feasibility of network analysis and its superiority over simply using frequency had been explored and justified, and PAFit was testified by its outstanding performance of prediction on the growth of frequency and degree. In addition, our research also encourages ecologists to consider their domain knowledge in a large dynamical network, and be ready to participate in interdisciplinary collaborations when necessary.As interdisciplinary branches of ecology are developing rapidly in the 21 Relations between biological systems and surrounding environments are of great complexity, numerous disciplines are joining ecology to answer demanding ecological questions and meet the global challenge. This has opened a door for discipline integration, and various branches of ecology had emerged in recent decades, with new theories, methods and technologies [Early in 1994, historian Donald Worster had made an interesting remark in his book, “Ecology achieved intellectual sophistication, academic prominence, and financial security in the postwar years, but also lost much of its coherence. It broke down into a cacophony of subfields, including ecosystematists, populationists, biospherians, theoretical modelers, forest and range managers, agroecologists, toxicologists, limnologists, and biogeographers” [nologies . As the nologies , it is bBut thanks to the era of big data, it is now getting easier and easier for scientists to get mass literature data. Together with the handy tools from automated content analysis, scientists can now carry out bibliometric research and dig deep into the historical ecological literature ,4. In thIn bibliometric studies, keyword analysis, as core content summary of articles, has long been used to identify research focus in ecological disciplines ,7,8,9,10We have doubts about this assumption, for a topic is not only popular for frequently occurring in literatures, but also for it could be widely accepted in public and co-occurred with various other topics in the same article. Previous studies have applied co-word analysis to address this problem ,16,17,18To fill this gap, we first constructed the ecological knowledge network with 247,764 articles from 137 leading ecological journals based on the co-occurrence of author keywords. Then we asked research questions as follows: Is network analysis feasible to detect hotspots in ecology? What are the possible risks when using frequency to measure keyword popularity compared with network-based methods? When the previous questions were answered, we proposed an approach called PAFit, which had been applied successfully in the research of scientific collaboration , to meashttps://www.scopus.com), where we could export at most 2,000 documents per time in csv format efficiently. For the reason that digital archives of historical data were not so complete in the 1900s, we limited our time range to the recent 30 years, namely from 1988 to 2017. Also, only papers with document type of “article” were chosen, and entries containing missing values were excluded in our database. As keywords are not case-sensitive, all the keywords were converted to lower case, and duplicated records were merged. After data cleaning, we finally got a dataset with 247,764 papers from 137 leading ecological journals  and chose journals under the “ecology” category  = 2m/ (n*(n-1)), where n is the total node number and m is the total edge number. In addition, we would display the annual cluster number of ecological knowledge network. Also known as component in social network analysis, cluster is the subgroup in which all nodes are connected, directly or indirectly. By presenting this information, we would like to observe the temporal development of ecological knowledge network during the investigated three decades.We had also used network-level metric density to depict the compactness of the knowledge network. By definition, the density is the proportion of edges in the network to the maximum number of possible edges. As our network is undirected, the density We believed that degree calculated in the constructed knowledge network could be a good competitor against the commonly used metric frequency on the task of measuring keyword popularity, therefore we tried to find the difference in the results yielded by frequency and degree. First, we gathered all the keywords from ecological articles during the recent three decades, and calculated their frequency and degree. Then we ranked the keywords according to both metrics, which generated two different ranking lists. The differences between frequency ranking and degree ranking were calculated so we could find the main distinctions between them. Only top 1,000 keywords in degree ranking list or frequency ranking list were taken into consideration, so that keywords we selected had certain influences in ecology. At last we made two lists, one for keywords with relatively low frequency but high degree, the other for keywords with relatively high frequency but low degree. Geographical names like “france” and “oregon” were excluded and only 20 keywords with largest differences were shown in the lists  is used to measure the popularity of the keywords in the network. Due to the consideration of “rich-get-richer” and “fit-get-richer” phenomenon, PAFit is supposed to be superior to other simple metrics such as frequency and degree. However, this hypothesis should not be self-testifying but supported by facts. Therefore, we design the following experiment to verify our assumption.To perform our experiment, we should answer a vital question in the first place: What is popularity? In the dictionary, popularity is “the quality or state of being popular” (“Popularity.” Merriam-Webster.com), while the definitions of popular include “of or relating to the general public” and “frequently encountered or widely accepted” (“Popular.” Merriam-Webster.com). Therefore, a popular keyword should be related to large number of other keywords and occurring frequently in the ecological journals. These two characters could be well represented by degree and frequency mentioned in the former section.Popularity of keywords should not only be descriptive but also predictive. In other words, when we say a keyword is popular, it has been popular for some time, and this trend will not disappear in the near future. For instance, if we gain the popularity of keywords in a specific time period, we might be able to predict the growth of the keywords in the following years. Therefore, we split our data into two parts, and tried to use the historical keyword popularity to predict the growth of keywords’ frequency and degree in the coming three years. The experiment procedure was designed as follows: 1. Construct the ecological knowledge network with data from 1988 to 2014, and calculate the frequency, degree and PAFit for every keyword appeared in these 27 years; 2. Construct the ecological knowledge network with data from 1988 to 2017, calculate the frequency and degree for every keyword appeared in the total 30 years; 3. Subtract the frequency of 27 years from frequency of 30 years, and we gain the change (or growth) of frequency in the recent three years (namely 2015–2017). The same is done to the keywords’ degree. Note that keywords emerging in the recent three years but not in the previous 27 years would be excluded from our analysis; 4. Fit a simple linear regression model using frequency, degree and PAFit in the former 27 years to predict the growth of frequency and degree in the following 3 years respectively. Compare the results and see if PAFit yields better predictions.2 into unique and common variance of predictors. Though we did not intend to actually implement multiple regression to gain a better prediction of the popularity, this analysis could help us better understand the correlations among the three metrics. For instance, when we used PAFit to measure popularity, we got an adjusted R2, if adding frequency to do multiple regression was not going to rise up overall R2, then PAFit might contain enough power to depict popularity. In another way, when we have the R2 yielded by the frequency alone, and we found that including PAFit could promote the overall R2, then we could conclude that PAFit contains some explanatory power that frequency could not offer. Results of this analysis is showed in discussion. Detailed information about the method could be found in the previous study [This analysis was based on the regression models we got in the former section. Instead of using one metric at a time, we could include all three metrics and run a multiple regression. Obviously, the three metrics we compared are closely related to each other. Therefore, in the task of predicting the frequency growth and degree growth, they would share some explanatory power while each metric has its unique explanatory power. Commonality analysis is capable of decomposing the variance of Rus study . R packaus study  and ‘vegus study  were use−3 to 2.51×10−4 , using it alone might misestimate the keyword popularity, and degree metric yielded based on the knowledge network could provide good supplementary information to fill the gap.2 all larger than 0.75). The frequency metric performed better than degree at predicting the future growth of frequency (R2 = 0.82 > 0.77), while the degree metric surpassed frequency at predicting the future growth of degree (R2 = 0.79 > 0.76). However, both metrics were beat by PAFit, no matter in frequency growth prediction or degree growth prediction (R2 reached 0.89 in both tests).In Ranking the keywords from the total 30 years’ data according to PAFit, we could detect the ecological hotspots in the recent three decades . The topIn our study, we have used three metrics to measure the popularity of ecological topics, namely frequency, degree and PAFit. In essence, the growth of degree is a sufficient but not necessary condition for the growth of frequency. That is to say, when the degree of a keyword rises, the frequency would definitely increase. Nevertheless, the opposite might not be true when the keyword is related to merely several keywords in its subfield. According to our results, some topics in microcosmic ecology could gain a relatively high frequency due to the average short research cycle. That is why degree could be a good supplementary metric to frequency. And when we consider the popularity of keywords in a network, we noticed that the “rich-get-richer” and “fit-get-richer” phenomenon did exist in our temporal network. This was testified by the superior performance of PAFit in predicting the growth of frequency and degree, beating the frequency and degree metrics themselves.α × η, where k is the degree, α is the attachment component and η is the node fitness. When we make α = 1, η = 1, this becomes equivalent to degree. Technically speaking, using degree to measure popularity is a specific case of PAFit, where we make assumptions that node fitness mechanism does not exist and the attachment component equals to 1. This model had been discussed and the pattern was coined as “scale-free feature” in 1999 by Barabási and Albert, and PAFit was a developed model built on this.But take a step backward and we could find that the three metrics discussed in our study are obviously correlated with each other. For one, frequency of a keyword could also be interpreted as how many articles containing a specific ecological topic were published in the investigated period. The more the frequency, the more likely that this ecological topic could be related to other ecological topics. Therefore, there is a statistically strong positive correlation between frequency and degree in most cases. On the other hand, when consider things in a network, degree is actually a component of PAFit. As the equation of PAFit could be displayed as: PAFit = k2 Click here for additional data file.


If technologies are to support aging in place, then it is important to develop fundamental knowledge on what causes stability and changes in the use of technologies by seniors. However, longitudinal studies on the long-term use of technologies that have been accepted into the home  are very scarce. Many factors potentially could influence post-implementation use, including life events, age-related decline, changes in personal goal orientation, and various types of social influences. The aforementioned factors are likely to be interrelated, adding to the complexity. The goal of this study is to better understand changes and stability in the use of technologies by independent-living seniors, by using a dynamical systems theory approach.A longitudinal qualitative field study was conducted involving home visits to 33 community-dwelling seniors in the Netherlands, on three occasions (2012–2014). Interviews were held on technology usage patterns, including reasons for stable, increased, declined and stopped use. Technologies were included if they required electric power in order to function, were intended to be used in or around the home, and could support activities of daily living, personal health or safety, mobility, communication, and physical activity. Thematic analysis was employed, using constant case comparison to better understand dynamics and interplay between factors. In total, 148 technology use patterns by 33 participants were analyzed.A core of six interrelated factors was closely linked to the frequency of technology use: emotional attachment, need compatibility, cues to use, proficiency to use, input of resources, and support. Additionally, disruptive forces  could induce change by affecting these six factors. Furthermore, long-term technology use was in some cases more resilient to disruption than in other cases. Findings were accumulated in a new framework: Dynamics In Technology Use by Seniors (DITUS).Similar to aging, the use of technologies by older people is complex, dynamic and personal. Periods of stability and change both occur naturally. The DITUS framework can aid in understanding stability and instability of technology use, and in developing and implementing sustainable technological solutions for aging in place.The online version of this article (10.1186/s12877-019-1241-9) contains supplementary material, which is available to authorized users. Simultaneously, the number of older persons is growing faster than the number of people in the traditional working ages in many countries, leading to pressure on health systems, and an increasing demand for care, services and technologies to prevent and treat chronic diseases and conditions . A commoal care” . As a coal care” . Still, al care” .More and more, technology is viewed as a potential resource for facilitating or improving aging in place , 6. TechWithin the scientific literature, the emphasis very much lies on why independent-living older adults would start to use technology in the first place  . This alIn aiming to understand changes and stability in frequency of use of technologies over time, Dynamical Systems Theory (DST) can be of use . DST stet1, t2, and t3; 2012–2014). The longitudinal field study led to the publication of two papers: a paper solely focused on how and why technologies are acquired by independent-living older adults [The current study was set up as a prospective longitudinal qualitative field study , involvir adults , and theN = 53, t1). One participant was included per household. Subsequently 18 and 2 participants dropped out . Reasons for drop out were: not interested in continuing (n = 5), deceased (n = 4), somatic health problems (n = 4), cognitive impairment (n = 2), too busy providing informal care for their partner (n = 2), no longer living independently (n = 2), and lost contact (n = 1). For the study reported here, only individuals who participated in t1, t2 and t3 were included (N = 33).After receiving approval for the study from the Ethics Review Board of the Tilburg School of Social and Behavioral Sciences, a purposive sample of independent-living older adults with different health statuses, living arrangements, and levels of technology experience was recruited. Participants were recruited in a medium-sized city in the Netherlands via two home care providers, a senior volunteer organization, a local tablet computer pilot project, a local shopping center, and word of mouth contacts. Criteria for the inclusion of participants were: (1) independently living at home, (2) aged 70 years or older, (3) Dutch nationality, and (4) no cognitive impairment as measured by the Mini-Mental State Examination (MMSE)  using a Pairs of researchers  performed home visits. SP, SA and MR all have a background in psychology, while MN and CvdV both have a background in healthcare. Informed consent was acquired at the start of each first visit. At the end of the first visit, participants were offered a magazine subscription of their choice. Prior to subsequent visits, participants were sent a letter containing information on the research project’s progress and called to schedule a visit at their convenience.t1 (September – December 2012) the aim was to gain an initial understanding of participants’ lives, their perceptions and attitudes towards technologies, and their use of technologies. Three types of data collection were performed: (1) background information on educational level, civil status, living arrangement, level of formal and informal care, chronic conditions, subjective health status, occurrence of life events in the last 12 months, frailty as measured by the Tilburg Frailty Indicator (TFI) [At or (TFI) , and cogor (TFI) . TFI scot2 (May – July 2013) and t3 (March – June 2014) data collection was aimed at understanding why participants’ use of technologies remained stable or changed since t1. First, the same type of background information on participants as in t1 was gathered, and the inventory of technologies in the home was updated. Second, semi-structured interviews were conducted on at least one technology of which the frequency of use was identical to the previous visit, at least one technology of which use had increased, and at least one technology of which use had decreased or stopped entirely. Which technologies were discussed depended on preferences of the participants (who displayed strong feelings towards certain technologies) and on suggestions by the researchers (who aimed to understand the usage of multiple types of technology). During the interviews, we took into account background information that was gathered on each participant and relevant themes which had emerged in previous interviews. We made sure that at least one of the two visiting researchers had visited the participant before. The topic list used was further evolved as data collection progressed. All of the interviews were audiotaped and transcribed verbatim.At In total, 148 technology use patterns by 33 participants were analyzed. Analysis took place during and between all three waves of the data collection and was supported by the use of qualitative data analysis software (Atlas.ti). SP, KL, MN, SA, CvdV, and MR employed realist thematic analysis, which focuses on reporting experiences, meanings and the reality of participants .t1 transcripts, two-thirds of the t2 transcripts, and one-third of the t3 transcripts were first coded independently by two different researchers. The two researchers then discussed their analyses and produced a single coded version of each transcript. Coding was detailed; often multiple codes representing different factors influencing technology use were attached to quotations. During and after each wave, axial coding was conducted. In this pursuit, the open coded transcripts were combined into one Atlas.ti file by SP. This file was used in group sessions in which similar codes were merged, and overarching themes were formed.The thematic analysis entailed three stages: open coding, axial coding, and selective coding. For open coding, we studied transcripts and attached inductive codes to quotations relevant to the research questions. All t1 transcripts, few new codes were added during the analysis of the t2 and the t3 transcripts, which indicated that data saturation was reached with regards to which factors and themes played a role in explaining the frequency of use of technologies. Subsequently, selective coding was used in order to better understand the dynamics and interplay between these factors and themes over time.After the open and axial coding of the t1,t2 and t3. Analysis of the matrixes informed the iterative shaping of a comprehensive framework of our findings. DST was used as a theoretical lens during this process, specifically to further refine our understanding of periods of stable and changing use. In the abovementioned analytical process, insights and findings were regularly discussed with KL, HV, and EW.During selective coding, SP applied constant case comparison , systemat3, in June and July 2015). With participants, we discussed findings that were specific to them, including usage patterns and changes we observed during the study. Out of the 33 participants, 25 participated in this final member check. Reasons for not participating were: personal health problems (n = 3), deceased (n = 3), and lost contact (n = 2). Participants recognized themselves very well in our descriptions of them and their use of technologies.To promote descriptive and interpretative validity , a writtt1, and 77.5 ± 3.9 at t3. The majority of the participants had attainted secondary education (61%), while 27% attainted no or only primary education, and 12% attained a form of higher education. During the study, the proportion of participants that lived alone increased from nearly 64% at t1 to 67% at t2 and t3. A proportion of participants received home care: at t1 this was 58%, at t2 nearly 67%, and at t3 nearly 64%. Looking at subjective health; close to 70% of the participants considered their health good, very good, or excellent at t1 and t2. At t3, this was 61%. Participants’ frailty (TFI) score, was lowest at t2 (3.8 ± 2.4) and highest at t3 (4.6 ± 2.6).t1 (28.1 ± 1.5) and highest at t2 (28.5 ± 1.5).2The sample consisted of 33 participants. Nearly 61% of the participants was female. The average age of participants was 76.1 ± 3.9 at Thematic analysis of technology usage patterns by participants resulted in two major themes: ‘stable use of technologies’ and ‘shifts to other states of use’. In the following paragraph, we present factors that were related to stable use. In the paragraph immediately after, we describe how stable use was disrupted by various forces that induced shifts to increased, decreased or stopped use. In both paragraphs, longitudinal case descriptions are used to illustrate findings. In the descriptions we use fictive names to protect the identity of participants.Analysis of the usage of technologies by the 33 participants showed that the frequency of use of a technology was directly influenced by a combination of six factors: emotional attachment, need compatibility, cues to use, proficiency to use, input of resources and support. Together, these factors formed a system of interrelated components that explained why participants maintained a frequent or less frequent use of certain technologies over time.t1,t2 and t3.Two examples of how this system can operate are displayed in Figs. “You can do all sorts of things with it, my music is on it, the photos I take are on it. It’s a lot of fun” (P20). And: “I feel like I could be getting addicted to it” (P20). Elisabeth also used her computer because it was compatible with her needs, and using the computer reaffirmed this (the second feedback loop): “I: What do you like most about it? P: Just the fact that I am able to send messages and have social contacts. It’s just great! I: You strike me as a social person. P: Yes I definitely am” (P20). Additionally, Elisabeth experienced certain cues that led her to using the computer. In general, we found that in participants’ lives cues to use could entail specific situations, routines and places. In Elisabeth’s case, she regularly received e-mails because she used her computer to send e-mails (the third feedback loop). Additionally, Elisabeth stated that she used her computer because she learned of new applications at the local computer club, and she went to the computer club because she was a user of the computer. Using the computer also made her feel very proficient, and her proficiency enabled her to make use of the computer (the fourth and last feedback loop): “It’s good for my self-esteem, the fact that I am able to do it” (P20). In Elisabeth’s case, there were sufficient resources  to be able to use the computer. These resources were invested directly by herself, and indirectly by external sources of support. As external sources of support she mentioned members of the local computer club and her grandchildren, who also helped her when needed.In the case of Elisabeth’s frequent use of her computer Fig. , intervi: “I only have it for when I go driving, in case the car breaks down” (P12). This was different from Elisabeth’s case, where the computer was compatible with more of her needs. In Paul’s case, there were also few cues to use, and proficiency to use was low. Lastly, input of resources and external support were both limited. It is important to note that Paul did maintain a certain (infrequent) level of use. However, as a result of him frequently not taking his mobile phone with him, he was not able to call for help when he experienced a fall outside his home. While he regretted not taking it with him, this incident did not affect Pauls’ mobile phone use.In contrast, looking at Paul’s infrequent use of his mobile phone Fig. , data shI need to know beforehand where I can go, and how to get here. I need to know that” (P28). As a result, she was dependent on a local bus service for people with disabilities, if she wanted to visit a place that was new to her: “Then I need to make use of the special bus service … it requires you to make an advanced reservation... when you want to go back home, you stand there and wait” (P28).Elisabeth’s and Paul’s cases represent two extremes, featuring only favorable or only unfavorable factors influencing technology use. In other less extreme cases, some factors were favorable for technology use, while others were not. An example is displayed in Fig. Longitudinal analysis showed that the use of technologies by the 33 participants was subject to disruptive forces Fig. . These ft1 she used her iron every week. At that time, she rather enjoyed ironing and also ironed clothes of her daughter. At t2, things had changed considerably. She had experienced a fall, and her arthrosis bothered her more than before: “I cannot stand so long on my legs anymore, particularly my left leg” (P13). These changes in her health status had several effects: she could not iron as much as before , she did not enjoy ironing as much as before , and she ironed in less situations (less cues to use). Additionally, Sheila still wanted to keep her clothes tidy but she could not use her iron to meet this need anymore (lower need compatibility). Instead, she used alternatives to ironing, such as hanging and folding her clothes. This also occurred in cases which involved other participants: decreased use of a technology could go together with increased use of alternatives to that technology. Sheila used alternative means because she was forced to, because she could not iron anymore. In contrast, we also saw cases in which participants voluntary decided to make more use of an alternative mean to meet their needs. In Sheila’s case, the result of the abovementioned developments was a notable decline in frequency of use at t2. Frequency of use continued to decline, and at t3 she only used her iron incidentally. At this stage, Sheila still had health issues, although her legs had not gotten worse. It seemed that Sheila had come to terms with hardly using the iron “I am at a point where I do not care anymore about it … No, I just don’t feel like using it” (P13). We saw this more often among participants; it seemed like there was a point in which they had gotten used to the new state of affairs. This also occurred in some cases in which the use of a technology was temporarily decreased due to a life event  or less dramatic events such as getting the flu or temporarily receiving less support. After a while, need compatibility, usage cues and emotional attachment would decline, as participants realized they could very well live with using the technology less. If use was decreased long enough, this could ultimately lead to stopped use . In particular, this was the case when use had become so infrequent that proficiency became severely impeded, as one participant puts it “I cannot work on it (the computer) anymore. That would mean that I would have to learn it all over again” (P2). In other cases, participants primarily stopped using a technology because their needs (or priorities) had changed. For example, a participant who previously had used a home alarm system for security reasons “Now it is not necessary anymore, I am always at home. When I bought it, I used to still go on vacation regularly” (P14). Going back to Sheila’s case: although she hardly ever used her iron, she still kept it in her home. In general, we found that participants had a tendency to hold on to devices that they seldom used or had stopped using completely: “I was born in 1937, I am not used to throwing things away” (P15). When a device did leave the home, this was usually because it had broken down and was replaced, or because a family member expressed interest in using it.Analysis showed that the abovementioned dynamics applied to the wide range of technologies that were used by participants . For example, Fig. “on her own”, and this had motivated her children to help her more. At t2, Elly’s use of her mobile phone had gone from weekly to daily. By that time, Elly felt very proficient in using her mobile phone and was proud of it: “It may sound crazy, but I consider it a victory”(P30). There were many cues for her to text with her mobile phone: “The children all do it. I get a message on my phone, I read it, and quickly send a message back. A quick reply, and I receive another one, and I reply again!” (P30). She was also more emotionally attached to using her phone: “I feel I do not want to miss these messages” (P30). The aforementioned chain of events occurred often in cases in which the use of a multifunctional device (mostly ICT) increased. In these cases, increased use was induced and supported by the social network. When we visited Elly at t3, she still used her mobile phone daily. By that time, she was so familiar with her phone that using it was effortless, and she did not need support anymore. However, Elly had gotten a tablet computer from her children just before t3. In fact, she started to prefer the tablet over her mobile phone when it came to sending text messages: “I still use my phone and using it is easy. But I feel that typing on my tablet is more convenient” (P30), and: “The tablet is new, but it is actually starting to replace my phone” (P30). According to Elly, she felt confident that she could use the tablet because of her positive experiences in learning to use her mobile phone. While Elly’s case is an example of positive developments leading to an increased use state, there could also be negative or less favorable developments that increased use. One example is decreased health leading to the increased use of assistive technologies. Another example is the disappearance of alternatives to a technology. There was a participant (a widower) who had the habit of eating dinner at his son’s house, who was unemployed. The participant described himself as “not the cooking type” (P25). This situation changed when his son and his son’s wife both got a job. He was now forced to cook considerably more, and did this by making much more use of his microwave oven . His microwave oven became essential to him: “I can’t do without it. How else am I supposed to prepare meals?” (P25).In contrast to the abovementioned, an example of increased use is displayed in Fig. The current study sought to explain changes and stability in the use of technologies by independent-living seniors over time. Findings and described concepts are summarized in a new dynamical framework that is presented in Fig. In the literature, there is a lack of longitudinal research on consumers’ use of technologies that have been accepted into the home  , 39. ThiOur findings are more in line with a five-week ethnographic study of experiences of young adults who purchased and used an Apple iPhone for the first time . The autIt is important to note that our findings are aligned with aspects of Rogers’ Diffusion Of Innovations (DOI) theory . Our conSeeing that most older adults will go through cognitive, physical and social changes as they age, one could argue that there is a great need for more longitudinal post-implementation research among this target group. In this paper, we have presented a framework for studying technology use dynamics that can be helpful in this pursuit. The strength of the current framework is that it is dynamical minimalist, meaning it is parsimonious without losing depth of understanding . By formSeveral study limitations need to be noted. First, while our framework allowed us to explain and describe the phenomena in our data, this does not mean that our findings are exhaustive. Older adults may experience other (combinations of) disruptive forces than our participants, and these may affect the core six variables in ways we have not encountered. Furthermore, we have only been able to collect data on technology products that were in the homes of our participants at the time of our study. Since our data collection, new technologies have entered the marketplace. Additional studies in other populations are necessary to determine to what extent our results and framework can be transferred to other contexts and apply to newer technologies.Second, some limitations are related to the application of DST. For example, from DST we know that some variables in a dynamical system may fluctuate more quickly than others . AdditioThird, potential biases of our study need to be discussed. Our findings could be susceptible to recall bias, since the interviews were in part retrospective. Additionally, our findings are affected and possibly biased by our beliefs, values, and assumptions. We addressed this by working in alternating pairs during data collection and analysis, and by critically evaluating the design and findings in group discussions involving all the authors. It is important to note that the current study is focused on understanding the micro-level . Our results highlight the influence of the meso level  on the micro level. However, this is only studied unidirectionally . Additionally, the macro  level is beyond the scope of the current study. As such, multi-level socio-technical research on long-term technology use by older adults may yield crucial additional insights , 55.the ability to adapt and self-manage in the face of social, physical and emotional challenges’ [Recently, it has been argued to define people’s health as ‘llenges’ . This imAging is complex, dynamic and personal, and this is also reflected in the use of technologies by older people. Periods of stability and periods of change both occur naturally. The DITUS framework can aid in understanding stability and instability in long-term technology use, and in developing and implementing sustainable technological solutions for aging in place.Additional file 1:Inteview guide. (DOCX 21 kb)


The purpose of this study was to improve the measurement of participation. Research questions were as follows: (1) What constitutes participation according to adults? (2) Do they mention participation subdomains that are not covered in the Patient-Reported Outcomes Measurement Information System (PROMIS) item bank “Ability to Participate in Social Roles and Activities”?Semi-structured interviews were conducted with 46 adults from the general population. Interviews were thematically analysed using the International Classification of Functioning, Disability and Health (ICF) as conceptual framework. Thereafter, assigned codes were compared to PROMIS item bank.Participants mentioned a variety of participation subdomains that were meaningful to them, such as socializing and employment. All subdomains could be classified into the ICF. The following subdomains were not covered by the PROMIS item bank: acquisition of necessities, education life, economic life, community life, and religion and spirituality. Also a distinction between remunerative (i.e. paid) and non-remunerative (i.e. unpaid) employment, and domestic life was missing. Several ICF sub-codes were not mentioned, such as ceremonies.Many participation subdomains were mentioned to be meaningful. As several of these subdomains are not covered in the PROMIS item bank, it may benefit from extension with new (patient-)reported subdomains of participation. There is increasing awareness that participation is important to an inclusive society, including people with and without disabilities, and their families . Partici®)  Well, it means anyway that I’ll take care of myself again. That you’re part of society.” Several household tasks were mentioned as important aspects of participation, such as shopping and cleaning. The following quote illustrates this:“Yes. And making sure that they [my family] are being cared for and making sure that it continues.” Also several ways of caring for household objects and assisting others were mentioned, among others being a mouthpiece to the neighbourhood, providing informal care, or assisting a child with the care for grandchildren as one participant stated:“I have a daughter who is divorced and she has two children. When she has to work, I am there for her.” Within the ICF chapter “Domestic Life”, all ICF codes (and most sub-codes) were mentioned, i.e. acquisition of necessities d610–d629), household tasks (d630–d649), and caring for household objects and assisting others (d650–d669)  and intimate relationships (d770) as was pointed out by one of the participants:“What I want to add, intimacy and disability are quite important to me. Because initiating a personal relationship with someone also includes intimacy. And that is still underestimated too often. Whether it is only that cuddle, or real sexual intimacy, that is too rarely discussed. And that should be quite a bit more. Also with a disability, a lot is still possible.” With regard to relating with strangers (d730), one participant, for example, mentioned:“But you ensure that you keep enough feeling with society, which already happens by walking down the street, by the way you walk down the street, the way you approach others in stores.” Within the ICF chapter Interpersonal Interactions and Relationships, all ICF codes and most sub-codes were mentioned Table . GeneralWithin the ICF chapter major life areas, most ICF codes and sub-codes were mentioned Table . An exce“Meanwhile I am retired. It took me some time to process it. That sounds quite heavy, but I was a German teacher for 41 years in a secondary school. That was a beautiful job, including all the stress and problems of the job of course. Because you were able to be part, also in a very modest and sometimes less modest way, of young people’s lives aged between twelve and twenty.“Also non-remunerative employment (i.e. unpaid employment) (d855) was mentioned as a meaningful domain of participation, which is illustrated by the following quote:“I would love most of all to do paid work, but well, my body doesn’t allow that. So I fill it in in a different way, by having voluntary work and by being there for friends and acquaintances.” Some participants explicitly mentioned that remunerative employment (d850) has a different meaning to them than non-remunerative employment (d855), which is illustrated by the following quote:“But also my work, and eventually this turns out to be the worst, since I was always busy for other people and so I did also have influence in my job. […] That’s what I miss the most. […] Participating in society… for me it’s hard to find, at least to be a satisfied participant of society when you’re actually out of society because you’ve been declared unfit.” With regard to economic life (d860–d879), basic economic transactions (d860) and economic self-sufficiency (d870) were mentioned. An illustrative quote is the following:“Getting paid is of course actually important for very many people to be able to pay for daily business. And for their house, those kind of things.” With regard to work and employment (d840–d859), most ICF codes were mentioned. An illustrative quote with regard to remunerative employment (i.e. paid employment) (d850) is:“I organize activities for a fellow contact group. All kind of things.” With regard to recreation and leisure (d920), several kinds of leisure activities were mentioned, such as relaxing, making music, going to a party, theatre or museum (d902), engaging in sports (d901), and going on holiday. Also religion and spirituality (d930) emerged to be a meaningful participation domain among participants, which involves going to the church to profess faith as well as doing non-remunerative employment in the church which is illustrated by the following quotes:“I go to church; I’m still active there. Yes, that also provides personal contacts, among others.” “Yes, and I would like to add something about church: I often see in surveys—I filled out more of these things—that religion is always being skipped. It seems like it was not important. While yet, for a lot of people, it is indeed important.” “I’m also a deacon at the Protestant congregation of [name of the city]. That fulfils me with satisfaction and joy, yes, to stand for something with other people.” According to the ICF conceptual framework, political life and citizenship is part of religion and spirituality. Participation in politics (d950), both passive and active, was mentioned by participants as meaningful domain of participation. Illustrative quotes are the following:“I’m interested in political developments, so I actively follow local and worldwide news.” “I’ve been quite active in politics previously. Now, for example, I work in municipal politics.” With regard to Community, Social and Civic Life, all ICF codes, i.e. community life (d910), recreation and leisure (d920), and religion and spirituality (d930), were mentioned as meaningful participation subdomains. Community life involves both participation in formal (d9101) and informal associations (d9100), which were both mentioned by participants as meaningful subdomains of participation, while ceremonies (d902), was not mentioned. An illustrative quote with regard to participation in an informal association is:“Yes. I think that’s actually the most important thing in the participation story, that needs most attention. Yes, That can be done in many ways. It can just be a day out, but it can also be work, education. You name it.” Other participants preferred specific subdomains of participation, such as remunerative employment, above others, such as non-remunerative employment. Additionally, some participants stated that participation in one domain, for example remunerative employment, hindered participation in other subdomains, such as taking care of (grand)children. They felt forced to make choices between several subdomains of participation.Some participants mentioned that it did not matter to them whether they participated in work, study, or in taking care of elders as long as they had at least some form of participation, which is illustrated by the following quote:All items of the PROMIS item bank “Ability to Participate in Social Roles and Activities” could be classified according to the ICF (sub-)codes that were assigned to the quotes of the participants. Eleven of the 50 assigned ICF sub-codes are also covered by the item bank Table . But parThe purpose of this study was to improve measurement of participation. We found that a great variety of subdomains were mentioned as constituting to participation, which could be classified within the ICF conceptual framework. We also found that some subdomains considered important by some adults are currently not covered in the PROMIS v2.0 item bank “Ability to Participate in Social Roles and Activities”.The existence of a variety of subdomains of participation is supported by previous research . The subParticipants of the current study considered remunerative and non-remunerative employment as subdomains of participation that are fundamentally different from each other, which is in line with previous research . It alsoSome subdomains (i.e. acquiring a place to live and economic self-sufficiency) were mentioned by participants as constituting to their participation. However, these are not considered as subdomains of participation by the PROMIS conceptual framework  but can A strength of the current study is its qualitative design, which allowed us to gain insight in the perspective of adults with regard to meaningful subdomains of participation. As our purpose is to improve the measurement of participation in the general population, the perspective of the target group is essential. Through our purposeful sampling strategy, we selected a heterogeneous sample with regard to age, sex, educational level, health problems, and meaning of participation. The advantage of a heterogeneous sample is that it contributes to evidence that the findings are not only applicable to a specific group, but to a broader range of people .However, this study also has limitations. First, as participants were recruited via a national online panel of a patient organization that includes patients and caregivers who have had recent experience(s) with the healthcare system, some selection bias may have taken place. Patients who are active in an online patient panel may specifically include those who have a higher level of participation, which may influence their meaning of participation in comparison with patients who have limited ability to participate. In addition, people who have had recent experience(s) with the healthcare system may in general be older and less healthy. As a result, people with a limited ability to participate and those being young and relatively healthy may not have been included in our study. Therefore, when studying a preliminary version of the measurement instrument on participation in a large and representative sample of the Dutch population, we would recommend to add a question whether all subdomains of participation are covered. Second, there may be a downside of conducting interviews via telephone instead of face-to-face, by missing non-verbal communication cues . HoweverParticipation involves a diversity of subdomains, which varies between individuals and stage of life. A generic measurement instrument should take into account this diversity. The PROMIS item bank “Ability to Participate in Social Roles and Activities” offers a good starting point for the measurement of participation, as it covers items on social life, family, and work/home. However, based on the findings of the current study we recommend to explore the possibilities to extend the PROMIS item bank “Ability to Participate in Social Roles and Activities” with the following subdomains: acquisition of necessities, education life, economic life, community life, and religion and spirituality. In addition, this item bank contains items that ask respondents about the ability to partake in work, but does not distinguish between remunerative employment, non-remunerative employment, and work at home as separate subdomains of participation. As adults valued these types of work constituting differently to their participation, we recommend to consider separating these subdomains in the item bank.While a measurement instrument for participation should take into account the diversity in subdomains of participation, such an instrument should also be relevant to everyone without being too long and time consuming. An advantage of using PROMIS item banks is that they are based on Item Response Theory, and therefore allow for selection of items that are relevant to a specific person or group (e.g. a patient group) . The iteMeasurement of participation using the PROMIS item bank “Ability to Participate in Social Roles and Activities” is also promising for clinical practice. To illustrate, at the University of Rochester Medical Center, three PROMIS instruments  are being completed during every outpatient clinic visit throughout 30 departments and divisions, and individual departments can choose to collect additional data relevant to their patients . ResearcThis study shows that participation involves many subdomains. Not all of these subdomains are currently captured in the PROMIS item bank “Ability to Participate in Social Roles and Activities”, i.e. acquisition of necessities, economic and community life, and religion and spirituality  are missing. Also a clear distinction between paid work, volunteer work, and work at home is missing. Based on these findings, we recommend to explore the possibilities to extend the PROMIS item bank “Ability to Participate in Social Roles and Activities” with these new relevant (patient-)report areas of societal participation.